AN EXAMINATION OF FAMILY DYNAMICS, PARENTAL RESPONSIVITY, AND CHILD COMMUNICATION IN FRAGILE X SYNDROME

AN EXAMINATION OF FAMILY DYNAMICS,

PARENTAL RESPONSIVITY, AND CHILD

COMMUNICATION IN FRAGILE X SYNDROME

The three papers that comprise this dissertation sought to examine multiple features of the family

environment in families of young boys with fragile X syndrome (FXS). FXS is the leading inherited

cause of intellectual disability (Crawford et al., 2001) and results from an expansion in the FMR1 gene

located on the X chromosome. Individuals with FXS experience a wide range of developmental

impairments, including delays in language (Abbeduto et al., 2007), symptoms of autism spectrum

disorder (ASD; Abbeduto et al., 2019) and attention deficit hyperactivity disorder (ADHD; Chromik et

al., 2015), heightened levels of anxiety (Cordeiro et al., 2011), and increased rates of challenging

behaviors (Hatton et al., 2002). Biological mothers of children with FXS, who are carriers of either the

FMR1 premutation or full mutation, are genetically predisposed to experiencing elevated levels of mental

health challenges, including depression and anxiety (Bailey et al., 2008). Moreover, given the challenges

associated with raising a child with significant impairments, these mothers are also likely to experience

elevated levels of parenting stress (Abbeduto et al., 2004). Past research also suggests that mothers of

children with FXS may experience reduced marital satisfaction (Baker et al., 2012). Additionally, child

characteristics, including challenging behaviors and symptoms of autism spectrum disorder (ASD), have

been shown to negatively affect parental and couple well-being (e.g., Abbeduto et al., 2004; Baker et al.,

2012; Fielding- Gebhardt et al., 2020; Johnston et al., 2003 Lewis et al., 2006; McCarthy et al., 2006).

Many past studies of parent-child relationships in FXS have demonstrated the importance of responsive

parenting for a range of developmental outcomes in children with FXS (e.g., Brady et al., 2014; Warren

et al., 2017).

However, the majority of past studies have focused on the mother-child relationship or the effects of the

child’s characteristics on aspects of maternal well-being. Therefore, very little is known about fathers in

these families.

The papers that follow describe three studies that build upon past research in families of children

with FXS by examining features of the entire family system, including the mother-father relationship, the

mother-child relationship, and the father-child relationship. Study 1 examined relationships between

maternal and paternal well-being and their associations with couple functioning, as well as the ways in

which characteristics of the child relate to these domains of parent functioning. Study 2 examined how

these parental and couple factors related to parental responsivity in both mother-child and father-child

dyadic interactions with the child. Finally, Study 3 examined the differences between maternal and

paternal behavior during parent-child dyadic interactions and their relationships to child language

performance. Study participants were 23 families of young boys with FXS. All data were collected in the

family home at a distance through video teleconferencing and online questionnaires, which had both cost

and logistical benefits, and also allowed for uninterrupted data collection during the COVID-19

pandemic.

Study 1 was designed to examine relationships among maternal and paternal mental health

challenges and parenting stress as well as couple functioning in families of young boys with FXS. In

addition, Study 1 examined relationships between characteristics of the child and parent and couple well-

being. The results of Study 1 suggest that mothers and fathers in these families experience clinically

significant levels of mental health challenges and elevated rates of parenting stress relative to the general

population. However, the majority of parents reported average to above average levels of couples

satisfaction and dyadic coping, indicating that the mother-father relationship may be a source of strength

or resilience for these parents. Parents of children with higher levels of challenging behaviors experienced

greater levels of mental health challenges and parenting stress, as well as lower levels of both couples

satisfaction and dyadic coping. Moreover, parents of children with higher levels of adaptive behavior

reported less parenting stress and greater couples satisfaction.

Study 2 was designed to examine relationships between parent and couple characteristics (i.e.,

those examined in Study 1) and parent behavior (i.e., responsivity and behavior management) during

mother-child and father-child dyadic interactions in the same families of young boys with FXS. Past

research has demonstrated that parent mental health challenges and stress are associated with lower levels

of responsiveness in interactions with the child (e.g., Sterling et al., 2013; Wheeler et al., 2007).

Additionally, mothers and fathers who have stable and healthy relationships are more likely to have

positive and responsive parent-child relationships and thereby, children with more optimal outcome (e.g.,

Greenlee et al., 2021). The first aim of Study 2 was to examine relationships among maternal

responsivity, paternal responsivity, and parent individual well-being (i.e., mental health challenges and

parenting stress). The second aim of Study 2 was to examine relationships among maternal responsivity,

paternal responsivity, and couple well-being (i.e., couples satisfaction and dyadic coping). The results of

this study indicated that mothers and fathers use similar rates of responsive behaviors with their child, but

that fathers use higher rates of behavior management strategies compared to mothers (i.e., they are more

directive during play interactions). Additionally, parenting stress predicted lower rates of parental

responsivity and higher rates of behavior management, but these effects were only marginally significant.

Couples satisfaction, however, which was highly negatively skewed in this sample of parents, did not

predict parent behavior during dyadic parent-child interactions.

Study 3 was designed to examine relationships between parent behavior and child language

performance in mother-child and father-child dyadic interactions, as well as relationships between child

characteristics and both parent behavior and child language performance. Past research demonstrates that

parental responsivity positively influences language development in neurotypical children (e.g., Landry et

al., 2006) and children with intellectual and developmental disabilities (e.g., Brady et al., 2009; Warren &

Brady, 2007), including FXS (e.g., McDuffie et al., 2018; Warren et al., 2010), ASD (e.g., Haebig et al.,

2013; McDuffie & Yoder, 2010), and Down syndrome (e.g., Yoder & Warren, 2004). However, nearly all

of these studies have focused exclusively on the mother-child relationship, or maternal responsivity.

Therefore, very little is known about how paternal behavior compares to maternal behavior or how

paternal behavior relates to child outcomes. Results of Study 3 indicated that both maternal and paternal

responsivity were positively associated with child language performance, including talkativeness and

lexical diversity. Parental responsivity was not associated with child syntactic complexity, and parental

behavior management was not associated with any of the child language measures. Additionally, mothers

and fathers of older children and children with higher levels of adaptive behavior were found to use

higher rates of responsive behavior.

Across the three studies, we found many similarities between mothers and fathers, both in their

levels of individual and couple well-being, as well as in their behavior during dyadic interactions with

their child. These studies also provide evidence that families of children with FXS would likely benefit

from interventions focused on reducing parent stress, such as Mindfulness Based Stress Reduction

(MBSR; Neece, 2014). Other interventions that are likely to improve family functioning include parent-

implemented interventions focused on decreasing child challenging behaviors (e.g., Hall et al., 2020) as

well as those focused on increasing levels of parental responsivity (e.g., McDuffie et al., 2018). Child and

family-based services that include both mothers and fathers would likely lead to better outcomes for all

family members (Fox et al., 2015; Wang et al., 2006). The data from this dissertation provide a rationale

for future studies investigating family relationships in families of children with FXS and other intellectual

and developmental disabilities, particularly those focused on understanding child developmental

trajectories in the context of the family environment as well as bidirectional relationships between parent

and child functioning.

Participants

Fathers, biological mothers, and male children with FXS between 3;0 and 7;11 years of age were

recruited to participate in the broader study on which the three papers that comprise this dissertation are

based. Families were recruited through (1) a database of previous research participants, (2) the MIND

Institute IDDRC Clinical Translational Core’s research participant registry, (3) a listserv and study

information page facilitated by the National Fragile X Foundation (NFXF), and (4) the NFXF’s

FORWARD Registry and Database. Eligibility criteria were a) the child lived at home with both parents,

b) English was the primary language spoken in the home, and c) the child had no uncorrected sensory or

motor impairments that would limit his ability to participate in the study. Parents were asked to provide

documentation of their child’s diagnosis of FXS as well as the mother’s FMR1 premutation or full

mutation status if available. Medical reports were required to confirm the child’s diagnosis of the FMR1

full mutation, but verbal confirmation was accepted for the mother’s genetic status. The study was

approved by the Institutional Review Board at the University of California, Davis in advance of

recruitment, and both parents provided informed consent electronically via REDCap (Research Electronic

Data Capture; Harris et al., 2019; Harris et al., 2009).

A total of 37 families of male children with FXS between the ages of 3;0 and 7;11 years were

screened for participation in the current study between October 2019 and April 2021. One family did not

meet eligibility criteria, four families declined participation, and seven families were lost to follow-up.

Twenty-five families completed the informed consent process, but two families left the study prior to data

collection due to the COVID-19 pandemic. Therefore, the current study includes a total of 69 participants:

23 fathers (22 biological fathers and one stepfather), 23 biological mothers, and 23 male children with

FXS. Participant characteristics are presented in Table 1. A majority of the participants identified as white

and not Hispanic or Latinx. Twenty mothers were carriers of the FMR1 premutation, two were carriers of

the FMR1 full mutation, and one had not been tested. A majority of both mothers and fathers in the study

had at least a bachelor’s degree and parent-reported household income indicated that most families were

relatively well-resourced. All families resided in North America, with 13 U.S. states and two Canadian

provinces represented. Data were collected between December 2019 and July 2021; therefore, the

majority of families were tested during the COVID-19 pandemic. Only two families completed their

participation in the study prior to the first community-diagnosed case in California on February 23, 2020.

Procedures

Participation in the study involved multiple calls with an examiner via video teleconferencing, the

completion of online questionnaires, and an interview. Mothers and fathers separately engaged in play-

based dyadic interactions with their child on separate days of the study. Mothers and fathers also

independently completed multiple questionnaires about their individual well-being, couple functioning,

and child behavior. One parent completed an interview about the child.

Skype for Business and Zoom were the preferred teleconferencing platforms for securely

connecting with the participating families. These platforms were approved by UC Davis Health IT and

Research Compliance. With both Skype for Business and Zoom, calls were hosted through the examiner’s

UC Davis account and participants joined the calls as a guest. Using these procedures, the examiner was

able to securely record each call and notified the participants when the recording was being started.

All questionnaire data were collected and managed using REDCap electronic data capture tools

hosted at the University of California, Davis (Harris et al., 2019; Harris et al., 2009). REDCap is a secure,

web-based software platform designed to support data capture for research studies, providing 1) an

intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export

procedures; 3) automated export procedures for seamless data downloads to common statistical packages;

and 4) procedures for data integration and interoperability with external sources.

Additionally, a unique study SharePoint page was created for each family so that they could

easily connect to the study teleconferencing calls and access their REDCap questionnaires. Prior to data

collection, each family participated in a training call with the lead researcher. The purpose of this initial

call was to orient the family to the study procedures and technology that would be used for the duration

of the project. See Figure 1 for additional details regarding study design and procedures.

Table 1

Family Demographic Characteristics

Individual Characteristics Child Mother Father

Age (years)

Mean (SD)

Range

5.68 (1.45)

3.07 – 7.90

38.28 (6.00)

25.15 – 50.43

40.16 (5.86)

27.79 – 51.46

Race (n, %)

White

Asian

Mixed/Multiracial

20 (87%)

2 (9%)

1 (4%)

21 (91%)

2 (9%)

0 (0%)

20 (87%)

3 (13%)

0 (0%)

Ethnicity (n, %)

Not Hispanic/Latinx

Hispanic/Latinx

20 (87%)

3 (13%)

20 (87%)

3 (13%)

19 (83%)

4 (17%)

Parent Characteristics Mother Father

Education (n, %)

Some high school

High school/GED

Some college/technical school

Associate’s/technical degree

Bachelor’s degree

Master’s/other advanced degree

0 (0%)

1 (4%)

3 (13%)

2 (9%)

8 (35%)

9 (39%)

1 (4%)

2 (9%)

9 (39%)

7 (30%)

Employment (n, %)

Not currently employed

Part-time

Full-time

9 (39%)

7 (30%)

4 (17%)

0 (0%)

19 (83%)

Previous or current psychiatric

diagnosis1

7 (30%) 3 (13%)

Family Characteristics

Annual household income (n, %)

Under $50,000

$50,001 - $100,000

$100,001 - $150,000

$150,001 - $250,000

Unknown

1 (4%)

8 (35%)

5 (22%)

7 (30%)

2 (9%)

Additional siblings in family (n, %)2

3 (13%)

13 (56%)

[4]

5 (22%)

[4]

2 (9%)

Note. The individual percentage values are rounded and may not total 100%. 1According to parent report.

2Number of additional siblings in family with a disability noted in brackets.

Measures

Individual-level Parent Measures

Parents independently completed two questionnaires via REDCap to assess their mental health

challenges and views on parenting.

Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1994) – The SCL-90-R is a 90-item

scale that measures mental health symptoms along the following dimensions: Somatization, Obsessive-

Compulsive, Interpersonal Sensitivity, Depression, Anxiety, Hostility, Phobic Anxiety, Paranoid Ideation,

Psychoticism, and additional symptoms, yielding a Positive Symptom Total, a Positive Symptom Distress

Index, and a Global Severity Index. Lower scores indicate lower levels of mental health challenges. T-

scores from each dimension and the Global Severity Index (representing overall mental health challenges)

were used in analyses. The scale takes approximately 15 minutes to complete.

Figure 1

Study Design

Note. Days 2 and 3 were counter-balanced across families.

Parenting Stress Index—Fourth Edition, Short Form (PSI-4-SF; Abidin, 2012) – The PSI-4-

SF is a 36-item scale that measures parenting stress in the domains of anxiety, mood, relationships,

attachment, and family mental health and functioning. Like the 120-item PSI, the short form provides

scores on the following subscales: (1) Parental Distress, (2) Parent-Child Dysfunctional Interaction, and

(3) Difficult Child. Lower scores on these subscales indicate lower levels of parenting stress. T-scores and

percentiles from these dimensions as well as the Total Stress score were used in analyses. This

questionnaire takes approximately 10 minutes to complete.

Couple-level Parent Measures

Parents independently completed two questionnaires via REDCap to assess aspects of their

couple functioning.

Couples Satisfaction Index (CSI-32; Funk & Rogge, 2007) – The CSI-32 is a 32-item scale

that measures satisfaction in the couple’s relationship, with higher total scores indicating higher

satisfaction. Item-response theory was used to develop the CSI-32 from a set of 180 relationship

satisfaction items administered to over 5,000 individuals. Compared to previous measures of relationship

satisfaction, the CSI-32 demonstrates higher precision and has strong internal consistency and construct

validity. Scores on the CSI-32 range from 0 to 161. Scores below 104.5 indicate notable relationship

dissatisfaction. This scale takes approximately 10 minutes to complete.

Dyadic Coping Inventory (DCI; Bodenmann, 2008; Ledermann et al., 2010) – The DCI is a

37-item scale that measures perceived communication and coping that occurs in relationships when one

or both partners are experiencing stress. In this measure, dyadic coping is assessed as a multidimensional

construct that includes the following components: supportive, delegated, negative, and joint (common)

coping. The DCI helps to assess an individual’s perceptions about both the quality and quantity of the

partner’s support in the dyadic relationship. Scores on the DCI range from 35 to 175. Scores below 111

indicate below average dyadic coping, whereas scores above 145 indicate above average coping. This

scale takes approximately 10 minutes to complete.

Child Measures

Parents independently completed two questionnaires via REDCap to assess child challenging

behavior and symptoms of ASD. Additionally, one parent completed an interview to assess the child’s

level of adaptive functioning.

Aberrant Behavior Checklist, 2nd Edition (ABC-2; Aman & Singh, 2017) – The ABC-2 is a

58-item scale developed to assess challenging behaviors of individuals with developmental disabilities in

several domains. For this study, subscale scoring based on the revised FXS-specific factor structure from

Sansone et al. (2012) was used. The following factors are included in the FXS-specific subscale scoring:

Irritability, Socially Unresponsive/Lethargic, Stereotypy, Hyperactivity, Inappropriate Speech, and Social

Avoidance. Total raw scores for the FXS-specific factor structure scoring range from 0 to 165 and were

used in the present analyses (Sansone et al. (2012) omitted three items from the original ABC due to weak

loadings in the exploratory factor analysis). This checklist takes approximately 10 minutes to complete.

Social Responsiveness Scale, 2nd Edition (SRS-2; Constantino & Gruber, 2012) – The SRS-2

is a 65-item scale used to assess social impairments commonly associated with ASD. Mothers and fathers

independently completed either the Preschool (2½ - 4½ years) or School-Aged (4 - 18 years) form

depending on their child’s chronological age. On the SRS-2, the following subscales are included in

addition to a total score: Social Awareness, Social Cognition, Social Communication, Social Motivation,

and Restricted Interests and Repetitive Behavior. DSM-5 compatible subscale scores include a Social

Communication and Interaction (SCI) score and a Restricted Interests and Repetitive Behavior (RRB)

score. SCI, RRB, and Total T-scores were used in analyses. The scale takes approximately 15 minutes to

complete.

Vineland Adaptive Behavior Scales, 3rd Edition (Vineland-3; Sparrow, Cicchetti, &

Saulnier, 2016) – The Vineland-3 measures adaptive behavior across the following domains:

Communication, Socialization, Daily Living Skills, Motor Skills, and Maladaptive Behavior. For this

study, only the Communication, Socialization, and Daily Living Skills domains were administered. The

Vineland-3 was administered as an interview by a trained examiner using Q-Global, a web-based

platform for online administration. The child’s primary caregiver (as reported by the parents) was

interviewed over the phone or via a secure teleconferencing platform (i.e., Skype for Business or Zoom).

The Vineland-3 is a norm-based instrument with a mean standard score of 100 and a standard deviation

15. The Adaptive Behavior Composite score as well as the Communication, Daily Living Skills, and

Socialization domain standard scores were reported and used in analyses. The Vineland-3 interview takes

approximately one to two hours to complete.

The SCL-90-R, PSI-4-SF, CSI-32, DCI, ABC-2, and SRS-2 are traditionally paper-and-pencil

measures. They were modified so that they could be completed in packages (i.e., individual parent

measures, couple measures, child measures) as online surveys via REDCap during different days of the

study (see Figure 1).

Dyadic Interactions

Each family who participated in the study was loaned a set of developmentally appropriate toys,

including a puzzle, DUPLO blocks, a garbage truck, a farm set, and a breakfast food set. On different

days of the study, mothers and fathers were instructed to play with their child as they usually would for

12 minutes. Families were told that they could also include any toys of their own in the play interaction

if they desired. The play interactions were recorded using secure teleconferencing. During the play

interaction, the examiner turned off their camera and muted their microphone. Immediately after the

sample, the examiner asked the parent whether the child’s behavior during the interaction was typical in

comparison to their usual interactions to ensure that a representative sample was collected.

Transcription

Video recordings of the dyadic play-based interactions were transcribed by trained research

assistants using SALT (Systematic Analysis of Language Transcripts; Miller & Iglesias, 2008). SALT is

a software program that standardizes the process of transcribing and analyzing language samples. The

dyadic samples were transcribed according to the procedures described in Abbeduto et al. (2020). In these

procedures, a primary transcriber completes a first draft of a transcript which is then reviewed and edited

by a second transcriber. Following this, the primary transcriber finalizes the transcript based on the

second transcriber’s feedback. These procedures yield average interrater transcript reliability of

approximately 90% or above in language samples of participants with FXS (Abbeduto et al., 2020; Kover

et al., 2012; Nelson et al., 2018).

Measures of parent and child language can be automatically generated from finalized SALT

transcripts, including total number of utterances or conversational turns (i.e., a measure of talkativeness),

number of different words (NDW; a measure of lexical diversity), and mean length of utterance in

morphemes (MLUm; a measure of syntactic complexity). These outcomes were generated from

transcripts of the mother-child and father-child interactions.

Coding of Parental Behavior

After the video recordings of the dyadic interactions were transcribed, the transcripts were coded

along with the videos for the presence of various parental behaviors utilizing a coding scheme adapted

from Warren and colleagues (e.g., Sterling et al., 2013; Warren et al., 2010). When the parent had

multiple utterances in succession, only the final utterance prior to either a three second pause or a child

communication act was coded. See Table 2 for definitions and examples of the parental behavior codes.

Composite scores for parental responsivity and behavior management were based on frequency counts of

the observed behaviors within each category. Proportion scores for these variables were also calculated as

the total composite score in each category divided by the total number of parent utterances in the

transcript. For example, a parent who had 144 responsive utterances, 17 behavior management

utterances, and 227 total utterances would have a responsivity proportion of 144/227 = 0.63 and a

behavior management proportion of 17/227 = 0.07.

Coding reliability. Four undergraduate research assistants were trained through group consensus

coding to utilize the adapted coding scheme. Each transcript was independently coded by two research

assistants. Following independent coding, transcripts were compared, and disagreements resolved via

consensus coding. Inter-observer agreement of total scores for parental responsivity and behavior

management codes was based on a random sampling of approximately 20% of the sessions. Two-way

random intra-class correlation coefficients (ICCs) were .994 for the parental responsivity composite and

.937 for the behavior management composite. Additionally, ICCs for the subcategories of parental

responsivity and behavior management ranged from .809 to .996. The only categories with ICCs below

.850 were low frequency parent behaviors (i.e., recodes, redirects, and zaps), all of which had mean

frequencies below two occurrences per dyadic interaction.

The three papers that comprise this dissertation utilized data from the broader study described

above. Brief summaries of these procedures and measures will be included in the papers that follow.

Table 2

Parental Behavior Codes and Definitions

Category and Behavior Definition Examples

Parental Responsivity

Comments All comments that maintain the

child’s focus of attention, relate to

the child’s actions and interests at

the time, or are in response to

something the child is doing or

saying

Talking about what the child or parent

can see, hear, smell, taste, or touch:

“That’s bumpy.”

Praise in reaction to something the

child has done: “Good job!”

Requests for verbal

compliance

Parent questions or statements

intended to elicit a verbal

response from the child that relate

to the child’s focus of attention

All questions that require a verbal

response from the child: “What color

is the truck?”

Parent asks the child to say something:

“Can you say, ‘choo-choo?’”

Recodes Verbal interpretation of the

child’s communication act that

extends the form of the child’s

utterance

Parent reproduces a content word in a

reasonable interpretation of the child’s

verbal act: Child says, “Ball.” Parent

says, “That’s a big blue ball!”

Behavior Management

Request for behavioral

compliance

Parent questions or statements

intended to elicit a behavioral

response from the child

Look/See statements that are followed

by a directive: “Look, put it on top like

this.”

Let/Let’s statements intended to get

child to do something: “Let’s put these

animals in the barn.”

Redirects Parent directs the child to engage

with something that is outside of

the child’s current focus of

attention

The child is playing with a toy and the

parent instructs or asks the child to do

something different: “Let’s put away

the food and do the puzzle now.”

Zaps Parent directives that limit,

restrict, or discipline the child’s

behavior in some way

Examples of verbal restrictions:

“Be careful!”

“Don’t do that!”

References

Abbeduto, L., Berry-Kravis, E., Sterling, A., Sherman, S., Edgin, J. O., McDuffie, A., ... & Thurman, A.

J. (2020). Expressive language sampling as a source of outcome measures for treatment studies in

fragile X syndrome: Feasibility, practice effects, test-retest reliability, and construct

validity. Journal of Neurodevelopmental Disorders, 12(1), 1-23.

Abbeduto, L., Brady, N., & Kover, S. T. (2007). Language development and fragile X syndrome:

Profiles, syndrome‐specificity, and within‐syndrome differences. Mental Retardation and

Developmental Disabilities Research Reviews, 13(1), 36-46.

Abbeduto, L., Seltzer, M. M., Shattuck, P., Krauss, M. W., Orsmond, G., & Murphy, M. M.

(2004). Psychological well-being and coping in mothers of youths with autism, down

syndrome, or fragile X syndrome. American Journal on Mental Retardation, 109(3), 237-

254.

Abbeduto, L., Thurman, A. J., McDuffie, A., Klusek, J., Feigles, R. T., Brown, W. T., ... & Roberts, J. E.

(2019). ASD comorbidity in fragile X syndrome: Symptom profile and predictors of symptom

severity in adolescent and young adult males. Journal of Autism and Developmental

Disorders, 49(3), 960-977.

Abidin, R. R. (2012). Parenting Stress Index, Fourth Edition. Lutz, FL: Psychological Assessment

Resources, Inc.

Aman M. G., & Singh, N. N. (2017). Aberrant Behavior Checklist, Second Edition. East Aurora, NY:

Slosson.

Bailey, D. B., Raspa, M., Olmsted, M., & Holiday, D. B. (2008). Co‐occurring conditions associated with

FMR1 gene variations: findings from a national parent survey. American Journal of Medical

Genetics Part A, 146(16), 2060-2069.

Baker, J. K., Seltzer, M. M., & Greenberg, J. S. (2012). Behaviour problems, maternal internalising

symptoms and family relations in families of adolescents and adults with fragile X

syndrome. Journal of Intellectual Disability Research, 56(10), 984-995.

Bodenmann, G. (2008). Dyadisches Coping Inventar: Test manual [Dyadic Coping Inventory: Test

manual]. Bern, Switzerland: Huber.

Brady, N., Warren, S. F., Fleming, K., Keller, J., & Sterling, A. (2014). Effect of sustained maternal

responsivity on later vocabulary development in children with fragile X syndrome. Journal of

Speech, Language, and Hearing Research, 57(1), 212-226.

Brady, N., Warren, S. F., & Sterling, A. (2009). Interventions aimed at improving child language by

improving maternal responsivity. International Review of Research in Mental Retardation, 37,

333-357.

Constantino, J. N., & Gruber, C. P. (2012). Social Responsiveness Scale, Second Edition. Torrance, CA:

Western Psychological Services.

Chromik, L. C., Quintin, E. M., Lepage, J. F., Hustyi, K. M., Lightbody, A. A., & Reiss, A. L. (2015).

The influence of hyperactivity, impulsivity, and attention problems on social functioning in

adolescents and young adults with fragile X syndrome. Journal of Attention Disorders, 1-8.

Cordeiro, L., Ballinger, E., Hagerman, R., & Hessl, D. (2011). Clinical assessment of DSM-IV anxiety

disorders in fragile X syndrome: Prevalence and characterization. Journal of Neurodevelopmental

Disorders, 3(1), 57-67.

Crawford, D. C., Acuña, J. M., & Sherman, S. L. (2001). FMR1 and the fragile X syndrome: Human

genome epidemiology review. Genetics in Medicine, 3(5), 359-371.

Derogatis, L. (1994). Symptom Checklist-90-Revised. San Antonio, TX: Pearson.

Fielding-Gebhardt, H., Warren, S. F., & Brady, N. C. (2020). Child challenging behavior

influences maternal mental health and relationship quality over time in fragile X syndrome.

Journal of Autism and Developmental Disorders, 50(3), 779-797.

Fox, G. L., Nordquist, V. M., Billen, R. M., & Savoca, E. F. (2015). Father involvement and early

intervention: Effects of empowerment and father role identity. Family Relations, 64(4), 461-475.

Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing precision of

measurement for relationship satisfaction with the Couples Satisfaction Index. Journal of Family

Psychology, 21(4), 572.

Greenlee, J. L., Piro‐Gambetti, B., Putney, J., Papp, L. M., & Hartley, S. L. (2021). Marital satisfaction,

parenting styles, and child outcomes in families of autistic children. Family Process.

Haebig, E., McDuffie, A., & Ellis Weismer, S. (2013). Brief report: Parent verbal responsiveness and

language development in toddlers on the autism spectrum. Journal of Autism and Developmental

Disorders, 43(9), 2218-2227.

Hall, S. S., Monlux, K. D., Rodriguez, A. B., Jo, B., & Pollard, J. S. (2020). Telehealth-enabled

behavioral treatment for problem behaviors in boys with fragile X syndrome: a randomized

controlled trial. Journal of Neurodevelopmental Disorders, 12(1), 1-15.

Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., ... & REDCap Consortium.

(2019). The REDCap consortium: Building an international community of software platform

partners. Journal of Biomedical Informatics, 95, 103208.

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic

data capture (REDCap)—A metadata-driven methodology and workflow process for providing

translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381.

Hatton, D. D., Hooper, S. R., Bailey, D. B., Skinner, M. L., Sullivan, K. M., & Wheeler, A. (2002).

Problem behavior in boys with fragile X syndrome. American Journal of Medical

Genetics, 108(2), 105-116.

Johnston, C., Hessl, D., Blasey, C., Eliez, S., Erba, H., Dyer-Friedman, J., ... & Reiss, A. L. (2003).

Factors associated with parenting stress in mothers of children with fragile X syndrome. Journal

of Developmental & Behavioral Pediatrics, 24(4), 267-275.

Kover, S. T., McDuffie, A., Abbeduto, L., & Brown, W. T. (2012). Effects of sampling context on

spontaneous expressive language in males with fragile X syndrome or Down syndrome. Journal

of Speech, Language, and Hearing Research, 55(4), 1022-1038.

Landry, S. H., Smith, K. E., & Swank, P. R. (2006). Responsive parenting: Establishing early foundations

for social, communication, and independent problem-solving skills. Developmental

Psychology, 42(4), 627.

Ledermann, T., Bodenmann, G., Gagliardi, S., Charvoz, L., Verardi, S., Rossier, J., … & Iafrate, R.

(2010). Psychometrics of the Dyadic Coping Inventory in three language groups. Swiss Journal of

Psychology/Schweizerische Zeitschrift f=r Psychologie/Revue Suisse de Psychologie, 69(4), 201-

212.

Lewis, P., Abbeduto, L., Murphy, M., Richmond, E., Giles, N., Bruno, L., ... & Orsmond, G. (2006).

Psychological well‐being of mothers of youth with fragile X syndrome: Syndrome specificity and

within‐syndrome variability. Journal of Intellectual Disability Research, 50(12), 894-904.

McCarthy, A., Cuskelly, M., van Kraayenoord, C. E., & Cohen, J. (2006). Predictors of stress in mothers

and fathers of children with fragile X syndrome. Research in Developmental Disabilities, 27(6),

688-704.

McDuffie, A., Banasik, A., Bullard, L., Nelson, S., Feigles, R. T., Hagerman, R., & Abbeduto, L. (2018).

Distance delivery of a spoken language intervention for school-aged and adolescent boys with

fragile X syndrome. Developmental Neurorehabilitation, 21(1), 48-63.

McDuffie, A., & Yoder, P. (2010). Types of parent verbal responsiveness that predict language in young

children with autism spectrum disorder. Journal of Speech, Language, and Hearing

Research, 53(4), 1026-1039.

Miller, J., & Iglesias, A. (2008). Systematic Analysis of Language Transcripts (SALT), English &

Spanish (Version 9) [Computer software]. Madison: University of Wisconsin—Madison,

Waisman Center. Language Analysis Laboratory.

Nelson, S., McDuffie, A., Banasik, A., Feigles, R. T., Thurman, A. J., & Abbeduto, L. (2018). Inferential

language use by school-aged boys with fragile X syndrome: Effects of a parent-implemented

spoken language intervention. Journal of Communication Disorders, 72, 64-76.

Sansone, S. M., Widaman, K. F., Hall, S. S., Reiss, A. L., Lightbody, A., Kaufmann, W. E., ... & Hessl,

D. (2012). Psychometric study of the Aberrant Behavior Checklist in fragile X syndrome and

implications for targeted treatment. Journal of Autism and Developmental Disorders, 42(7),

1377-1392.

Sparrow, S., Cicchetti, D., & Saulnier, C. (2016). Vineland-3: Vineland Adaptive Behavior Scales, Third

Edition. Minneapolis, MN: Pearson.

Sterling, A., Warren, S. F., Brady, N., & Fleming, K. (2013). Influences on maternal responsivity in

mothers of children with fragile X syndrome. American Journal on Intellectual and

Developmental Disabilities, 118(4), 310-326.

Warren, S. F., & Brady, N. C. (2007). The role of maternal responsivity in the development of children

with intellectual disabilities. Mental Retardation and Developmental Disabilities Research

Reviews, 13(4), 330-338.

Warren, S. F., Brady, N., Sterling, A., Fleming, K., & Marquis, J. (2010). Maternal responsivity predicts

language development in young children with fragile X syndrome. American Journal on

Intellectual and Developmental Disabilities, 115(1), 54-75.

Warren, S. F., Brady, N., Fleming, K. K., & Hahn, L. J. (2017). The longitudinal effects of parenting on

adaptive behavior in children with fragile X syndrome. Journal of Autism and Developmental

Disorders, 47(3), 768-784.

Wang, M., Summers, J. A., Little, T., Turnbull, A., Poston, D., & Mannan, H. (2006). Perspectives of

fathers and mothers of children in early intervention programmes in assessing family quality of

life. Journal of Intellectual Disability Research, 50(12), 977-988.

Wheeler, A., Hatton, D., Reichardt, A., & Bailey, D. (2007). Correlates of maternal behaviours in mothers

of children with fragile X syndrome. Journal of Intellectual Disability Research, 51(6), 447-462.

Yoder, P. J., & Warren, S. F. (2004). Early predictors of language in children with and without Down

syndrome. American Journal on Mental Retardation, 109(4), 285-300.

Study 1: An Examination of Mental Health Challenges, Parenting Stress, and Features of the Couple

Relationship in Parents of Children with Fragile X Syndrome

Abstract

Individuals with fragile X syndrome (FXS) have significant delays in cognition and language,

as well as symptoms of autism spectrum disorder, anxiety, and challenging behaviors including

hyperactivity and aggression. Biological mothers of children with FXS, who are themselves FMR1

premutation or full mutation carriers, are at elevated risk for mental health challenges in addition to

experiencing the stress associated with parenting a child with significant disabilities. However, little is

known about fathers in these families, including the ways in which parental well-being influences the

mother-father relationship and the impact of child characteristics on paternal and couple functioning. The

current study examined features of, and relationships between, parental well-being, couple well-being,

and child functioning in 23 families of young boys with FXS. Results suggest that both mothers and

fathers in these families experience clinically significant levels of mental health challenges and elevated

rates of parenting stress relative to the general population. Findings also indicate that the mother-father

relationship may be a source of strength that potentially buffers against some of the daily stressors faced

by these families. Additionally, parents who reported less parenting stress also had higher couples

satisfaction and dyadic coping. Finally, parents of children with less severe challenging behaviors

exhibited fewer mental health challenges, less parenting stress, and higher levels of both couples

satisfaction and dyadic coping, and parents of children with higher levels of adaptive behavior also

reported less parenting stress and higher couples satisfaction. Overall, this study provides evidence that

families of children with FXS need access to services that not only target improvements in the child’s

functioning, but also ameliorate the parents’ levels of stress. Family-based services that include both

mothers and fathers would lead to better outcomes for all family members.

Keywords: fragile X syndrome, mental health, parenting stress, couple relationships, parent-child

relationships

Children with fragile X syndrome (FXS), the leading inherited cause of intellectual disability (ID;

Crawford et al., 2001), demonstrate delays in multiple domains of spoken language (Abbeduto et al.,

2007). In addition, these children also present with increased rates of challenging behaviors, symptoms of

autism spectrum disorder (ASD), inattention, hyperarousal, and anxiety (Hagerman et al., 2017). Mothers

of children with FXS, who transmit the full mutation of the FMR1 gene which causes FXS to their

children, are themselves either carriers of the FMR1 premutation or the full mutation. Both the FMR1

premutation and the full mutation are associated with a multitude of physical, mental health, and

cognitive challenges (Hagerman et al., 2018; Hartley, Seltzer, et al., 2011), which when compounded with

the characteristics of the child with FXS, could negatively impact mother-child interactions and thus,

child development. In addition, both mothers and fathers of children with FXS are likely to experience

heightened levels of parenting stress due to characteristics of their children (McCarthy et al., 2006), which

may impede their ability to engage with their child in the sustained and productive interactions needed to

facilitate the child’s development. Elevated parenting stress, which is likely given the challenging

behaviors of individuals with FXS, is also likely to have a negative impact on the marital relationship for

parents of children with FXS, which may further exacerbate the relationship between parents and their

children (Peltz et al., 2018).

Unfortunately, the majority of past studies on parenting in FXS have focused exclusively on the

mother-child dyad. In doing so, these studies have neglected to consider the role that fathers play in the

development of the child or how features of the broader family environment may influence maternal or

paternal behavior and child outcomes. The current study was designed to examine the broader family

environment in families of young children with FXS, including maternal and paternal well-being, features

of the mother-father relationship, and relationships between child characteristics and parent and couple

well-being. A better understanding of parent and couple well-being in families of children with FXS, as

well as the ways in which child characteristics influence these domains, will provide the foundation for

developing interventions and services focused on improving outcomes for all family members.

The Impact of FMR1 Mutation Phenotypes on the Family System

FXS is an X-linked disorder that results from an expansion of a cytosine-guanine-guanine (CGG)

sequence in the promoter region of the FMR1 gene, located at Xq27.3, from the typical 35 or so repeats to

greater than 200 repeats (Oostra & Willemsen, 2003). Individuals with more than 200 CGG repeats have

the full mutation, whereas individuals with 55 to 200 CGG repeats are considered premutation carriers.

The full mutation typically leads to hypermethylation and transcriptional silencing of FMR1, causing a

deficiency in, or absence of, the gene’s associated protein, FMRP (fragile X mental retardation 1 protein),

which is critical for early brain development, including synaptic protein synthesis and plasticity as well

as experience-dependent learning (Bhakar et al., 2012; Hagerman et al., 2017; Hagerman et al., 2005). In

contrast, the premutation typically involves elevated levels of FMR1 mRNA, which leads to RNA

toxicity. RNA toxicity is associated with reduced neuronal function, oxidative stress, chronic DNA

damage repair changes, and ultimately the development of fragile X-associated tremor/ataxia syndrome

(FXTAS; Hagerman et al., 2018; Wheeler et al., 2014) and other co-occurring physical and behavioral

health challenges (described subsequently).

Because it is inherited, the presence of FXS in a family has far-reaching intergenerational effects,

offering a unique opportunity to investigate the ways in which multiple family subsystems influence

child outcomes. Nearly all males with FXS have ID (Hessl et al., 2009), and many also experience a

variety of other conditions, including hyperactivity, attention problems, anxiety, symptoms of ASD,

aggressive and self-injurious behaviors, and abnormal sensory processing (Bailey et al., 2008; Raspa et

al., 2018).

Language is also significantly impaired in individuals with FXS, with some domains affected to an even

greater extent than would be expected based upon their level of cognitive functioning (Finestack &

Abbeduto, 2010; Kover & Abbeduto, 2010; Kover et al., 2012). The combination of cognitive and

psychiatric impairments in boys with FXS reduce the likelihood that they will be able to engage in

successful and productive interactions critical for the development of cognitive, language, and social

skills (Abbeduto et al., 2007).

The biological mothers of children with FXS are most often carriers of the FMR1 premutation,

although some also have the full mutation which causes FXS. Full mutation mothers are at an increased

risk for experiencing mental health challenges, including anxiety and depression, as well as social

deficits, including avoidance and withdrawal (Bailey et al., 2008; Franke et al., 1998; Freund et al., 1993;

Hagerman et al., 2018; Hartley, Seltzer, et al., 2011; Keysor & Mazzocco, 2002; Wheeler et al., 2014).

Women with the FMR1 premutation may also experience deficits in executive functioning, memory, and

language (Klusek et al., 2021; Sterling, Mailick, et al., 2013; Wheeler et al., 2014). Moreover, cognitive

functioning is variable in women with FXS, ranging from severe impairment to above average, with most

of these women demonstrating IQs in the range of average to slightly below average intelligence

(Bartholomay et al., 2019). However, even some with average-range intelligence can have a learning

disability and/or deficits in executive functioning and attention (Hall & Berry-Kravis, 2018). These

cognitive phenotypic features of premutation and full mutation mothers are significant given that low

maternal IQ is a risk factor for poorer child outcomes (Hooper et al., 1998; Sterling, Warren, et al., 2013).

Unfortunately, the mental health conditions that are experienced by both premutation and full

mutation mothers of children with FXS can also be exacerbated by the stress they are likely to experience

as a result of raising a child with significant challenges and impairments (Abbeduto et al., 2004;

Hagerman & Hagerman, 2004). Furthermore, maternal depression and anxiety are associated with

disrupted marital cohesion and decreased couples satisfaction (Baker et al., 2012; Essex et al., 2003). Of

course, disruptions or problems in the marital relationship also affect paternal well-being, which may in

turn negatively influence the quality of the relationship between father and child (England & Sim, 2009).

Overall, poor parental and marital functioning have been repeatedly shown to contribute to negative child

outcomes in neurotypical children (Hanington et al., 2012; Masarik & Conger, 2017; Peterson & Zill,

1986).

Many previous studies have found that child characteristics, partner characteristics, and features

of the marital relationship differentially affect mothers and fathers of children with disabilities (e.g.,

Bristol et al., 1988; Dabrowska & Pisula, 2010; Hartley et al., 2016; Hastings, 2003). For example, in

families including children with ASD, fathers are likely to be negatively affected by the child’s

challenging behaviors to an even greater extent than are mothers (Davis & Carter, 2008). The same may

be true in families affected by FXS given the symptom overlap between FXS and ASD (Abbeduto et al.,

2014). Moreover, maternal anxiety and depression—as well as the mother’s parenting stress—are also

likely to take a significant toll on fathers in these families (Hartley, Seltzer, Hong, et al., 2012), which

could spill over and negatively affect the father-child relationship. McCarthy et al. (2006) found that both

mothers and fathers of children with FXS reported high levels of stress, but that the predictors of stress

differed between mothers and fathers with the strongest predictor of maternal stress being marital

satisfaction and the strongest predictor of paternal stress being the child’s level of adaptive skills.

Very little else is known about fathers of children with FXS given that the majority of past studies

have focused on the mother-child dyad. However, including both mothers and fathers in behavioral

therapies and health care services positively contributes to a child’s success, especially for young children

(Fox et al., 2015; Wang et al., 2006). In order to maximize treatment gains for children with FXS and to

improve well-being for the entire family system, researchers and clinicians need to develop a greater

understanding of the challenges faced by families affected by FXS. This understanding will inform

services and interventions for these families.

Current study

The current study was designed to examine multiple features of the family environment,

including maternal and paternal mental health, stress associated with parenting, aspects of couple

functioning, and relationships between child characteristics and these parental domains. The first aim was

to examine mental health challenges and parenting stress in biological mothers of children with FXS,

who are carriers of the FMR1 premutation or the full mutation. We hypothesized that these mothers,

compared to the general population, would report elevated levels of mental health challenges and

parenting stress (e.g., Hagerman et al., 2018; Hartley, Seltzer, et al., 2011; Wheeler et al., 2007). The

second aim was to examine mental health challenges and parenting stress in fathers of children with FXS

and compare paternal and maternal mental health challenges and parenting stress. We hypothesized that

fathers whose

partners reported experiencing elevated levels of mental health challenges and parenting stress would

themselves report elevated levels of mental health challenges and parenting stress compared to the general

population based on past findings in families of children with ASD, Down syndrome, and FXS (Hartley,

Seltzer, Head, & Abbeduto, 2012). The third aim was to examine relationships between aspects of the

couple relationship (i.e., couples satisfaction and dyadic coping) and mothers’ and fathers’ mental health

challenges and parenting stress. We hypothesized that couples satisfaction and dyadic coping would be

negatively related to mental health challenges and parenting stress for both mothers and fathers (Kersh et

al., 2006). Finally, the fourth aim was to examine relationships between child characteristics (i.e.,

challenging behaviors, ASD symptoms, and adaptive behavior) and parental individual well-being (i.e.,

mental health challenges and parenting stress) and couple well-being (i.e., couples satisfaction and dyadic

coping). We hypothesized that children with higher levels of behavior problems and ASD symptoms, as

well as lower levels of adaptive behavior, would have parents who endorsed lower levels of individual

well-being (Abbeduto et al., 2004) and couple well-being (Baker et al., 2012) with fathers being affected

by child characteristics to a greater extent than mothers (McCarthy et al., 2006).

Method

Procedures and Measures

The data for the current study were collected as part of a larger study investigating family

relationships and parenting in families of children with FXS. Participants included 23 families of male

children with FXS between the ages of 3;0 and 7;11 years, yielding a total of 69 participants including 23

fathers (22 biological fathers and one stepfather), 23 biological mothers, and 23 male children with FXS.

See pages 5 – 6 for additional details on the participants in the current study.

In order to address the aims stated above, mothers and fathers independently completed multiple

questionnaires via REDCap (Harris et al., 2019; Harris et al., 2009) and one parent completed an

interview about the child. Parents completed questionnaires pertaining to: (a) their individual well-being,

including the Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1994) and the Parenting Stress

Index – 4th edition, Short Form (PSI-4-SF; Abidin, 2012); (b) couple functioning, including the Couples

Satisfaction Index (CSI-32; Funk & Rogge, 2007) and the Dyadic Coping Inventory (DCI; Bodenmann,

2008; Ledermann et al., 2010); and (c) child functioning and behavior, including the Aberrant Behavior

Checklist, 2nd edition (ABC-2; Aman & Singh, 2017) and the Social Responsiveness Scale, 2nd edition

SRS-2; Constantino & Gruber, 2012). One parent also completed the Vineland Adaptive Behavior Scales,

3rd edition (Vineland-3; Sparrow, Cicchetti, & Saulnier, 2016) as an interview to assess the child’s

adaptive behavior.

The SCL-90-R measures mental health symptoms along multiple dimensions yielding a Positive

Symptom Total, a Positive Symptom Distress Index, and a Global Severity Index (GSI). T-scores for each

dimension and the GSI were reported; the GSI T-score was used in analyses. The PSI-4-SF measures

parenting stress, providing subscale scores for Parental Distress, Parent-Child Dysfunctional Interaction,

and Difficult Child. T-scores and percentiles from these dimensions as well as the Total Stress score were

reported. The Total Stress T-score was used in analyses. The CSI-32 measures couples satisfaction, with

higher scores indicating higher levels of relationship satisfaction. Total raw scores were reported and used

in analyses. The DCI measures perceived communication and coping that occurs in relationships when

one or both partners are experiencing stress. Higher scores indicate higher levels of dyadic coping. Total

raw scores from this measure were reported and used in analyses. The ABC-2 measures challenging

behaviors of individuals with developmental disabilities in multiple domains. Raw scores from the FXS-

specific subscale scoring (Sansone et al., 2012) were reported along with total raw scores; total raw scores

were used in analyses. The SRS-2 measures social impairments commonly associated with ASD,

providing DSM-5 compatible subscale scores for Social Communication and Interaction (SCI) and

Restricted Interests and Repetitive Behavior (RRB), as well as a Total T-score. SCI, RRB, and Total T-

scores were reported; Total T-scores were used in analyses. The Vineland-3 measures adaptive behavior

across multiple domains. For the current study, the Adaptive Behavior Composite score as well as the

Communication, Daily Living Skills, and Socialization domain standard scores were reported and used in

analyses. See pages 7 – 10 for additional details regarding the measures used in the current study.

Analysis Plan

All variables were visually inspected to check for model assumptions of normality and

homoscedasticity of the residuals. Tests for skewness and kurtosis were also examined. Transformations

and nonparametric alternatives were considered for any data that did not meet parametric assumptions.

To address the first and second aims, descriptive summaries of mothers’ and fathers’ mental

health challenges and parenting stress (the outcomes variables) were reported and compared to levels

reported in the general population. Then, interspousal correlations were calculated to determine the

degree of correspondence between mothers’ and fathers’ ratings of mental health challenges and

parenting stress. Comparisons of mothers’ and fathers’ mean scores on the SCL-90-R and PSI-4-SF were

also reported.

To address the third aim, descriptive summaries of the outcome variables (i.e., couples

satisfaction and dyadic coping) were reported and mean scores for mothers and fathers were compared.

Interspousal correlations were then calculated to determine the degree of correspondence between

mothers’ and fathers’ ratings of couples satisfaction and dyadic coping. Comparisons of mothers’ and

fathers’ mean scores on the CSI-32 and DCI were also reported. To address the fourth aim, descriptive

summaries of the predictor variables (i.e., challenging behaviors, ASD symptoms, and adaptive behavior)

and interspousal correlations were reported. Comparisons of mothers’ and fathers’ mean scores on the

ABC-2 and SRS-2 were also reported. Additionally, given that data collected from couples are considered

to be non-independent observations (e.g., Hartley, Barker, et al., 2011), a multilevel modeling (MLM)

approach, also known as hierarchical linear modeling (HLM), was used for Aims 3 and 4 (Raudenbush &

Bryk, 2002). In this approach, the data from each partner is nested within a group that has an N of 2

(Campbell & Kashy, 2002). Effect coding was used for parent sex such that Male = 1 and Female = -1.

Continuous predictors were centered to their respective grand means.

Visual inspection of the variables and tests for skewness and kurtosis indicated that the CSI-32

scores for mothers and fathers were negatively skewed. A cubic transformation of the variable reduced

the negative skewness and was used to examine correlations between the CSI-32 and the other measures.

To avoid difficulty in interpreting the cubic transformation of the CSI-32 variable in a multilevel model, a

new categorical variable was created that reduced the significant negative skewness of the CSI-32 scores

(confirmed using the Shapiro-Wilk test of normality). For this new variable, ranges of the CSI-32 score

were given a value of 1-8 (e.g., scores ≤ 49 had a value of 1, scores from 50 to 99 had a value of 2,

scores from 100-109 had a value of 3).

Intraclass correlation coefficients (ICCs) were then calculated to estimate the proportion of the

total variation in the dependent variables that exists between versus within couples for Aims 3 and 4. The

dependent variables for Aim 3 included couples satisfaction and dyadic coping (total raw scores from the

CSI-32 and DCI, respectively). The dependent variables for Aim 4 included couples satisfaction and

dyadic coping, as well as mental health challenges (SCL-90-R GSI T-score) and parenting stress (PSI-4-

SF Total Stress T-score). Next, multilevel models were specified to examine the outcomes for Aims 3 and

4. For Aim 3, separate models for couples satisfaction and dyadic coping were conducted. The strong

and significant association between the variables for parenting stress and mental health challenges did

not allow for them both to be included in the models for Aim 3; the parenting stress measure was chosen

as it was more strongly associated with both couples satisfaction and dyadic coping than the measure of

mental health challenges for both mothers and fathers.

As an example, the model for couples satisfaction (𝐶𝑆) was specified as follows, with parenting

stress (𝑃𝑆) and parent sex (𝑠𝑒𝑥) set as predictors at Level 1. Covariates included parent age (𝑎𝑔𝑒) and

parent education (𝑒𝑑𝑢). In this example, random effects were not included at Level 2 for parenting

stress, parent sex, parent age, or parent education; therefore, the effects of these predictors on the outcome

(𝐶𝑆) are fixed. However, a family-level random effect for the intercept was included at Level 2:

Level 1:

Level 2:

𝐶𝑆𝑖𝑗

= 𝛽0𝑗 + 𝛽1𝑗(𝑃𝑆𝑖𝑗) + 𝛽2𝑗(𝑠𝑒𝑥𝑖𝑗) + 𝛽3𝑗(𝑎𝑔𝑒𝑖𝑗)

+ 𝛽4𝑗(𝑒𝑑𝑢𝑖𝑗) + 𝑒𝑖𝑗

𝛽0𝑗 = 𝛾00 + 𝜇0𝑗

𝛽1𝑗 = 𝛾10

𝛽2𝑗 = 𝛾20

𝛽3𝑗 = 𝛾30

�

Composite:

𝐶𝑆𝑖𝑗 = [𝛾00 + 𝛾10(𝑃𝑆𝑖𝑗) + 𝛾20(𝑠𝑒𝑥𝑖𝑗)+ 𝛾30(𝑎𝑔𝑒𝑖𝑗)+ 𝛾40(𝑒𝑑𝑢𝑖𝑗)] + [𝜇0𝑗 + 𝑒𝑖𝑗]

For Aim 4, separate models for mental health challenges, parenting stress, couples

satisfaction, and dyadic coping were specified. Interactions between parent sex and child variables

were included to examine the differential effects of child characteristics on mothers and fathers.

Results

Aims 1 and 2

Aim 1 was to examine mental health challenges and parenting stress in biological mothers of

children with FXS, who are carriers of the FMR1 premutation or the full mutation. Aim 2 was to examine

mental health challenges and parenting stress in fathers of children with FXS and to compare maternal

and paternal mental health challenges and parenting stress. Table 1 displays descriptive statistics for the

SCL-90-R dimension scores, the measure of mental health challenges, for both mothers and fathers.

Paired samples t-tests and Wilcoxon signed-ranks tests (when appropriate) confirmed that there were no

statistically significant differences between mothers’ and fathers’ standardized scores on the SCL-90-R.

Table 1 also displays information regarding the number of parents who met the instrument’s

cutoff for clinical significance on the SCL-90-R dimensions. Overall, a T-score of 63 or above

(equivalent to the 90th percentile) on the Global Severity Index, or two or more scores of 63 or above on

any dimension, suggest clinically significant levels of mental health challenges. According to these

criteria, 10 out of 23 (43%) mothers and 10 out of 23 (43%) fathers in the sample reported clinically

significant levels of mental health challenges. Six of these mothers and fathers were from the same

family. The rates of clinically significant mental health challenges in this sample are higher than what is

reported in the general population for both males and females. Specifically, recent estimates suggest that

approximately 24.5% of women in the United States suffer from any mental illness compared to

approximately 16.3% of men (SAMHSA, 2019).

Table 1

Mother-Father Comparisons of SCL-90-R Dimension Scores

Mean T-score (SD)

Range n (%) with T-score  63 Interspousal

Correlations

SCL-90-R Dimension Mothers Fathers Moth

ers

Fathers



(p-value)

Somatization 50.70 (9.24)

35 – 70

53.57 (12.43)

37 – 77

3 (13%) 4

(17

-0.22

(0.320)

Obsessive-Compulsive 58.52 (10.57)

37 – 78

59.39 (12.23)

39 – 80

9 (39%) 9

(39

0.14

(0.516)

Interpersonal Sensitivity 57.35 (11.82)

39 – 80

58.04 (13.61)

41 – 80

10 (43%) 9

(39

0.03

(0.892)

Depression 57.57 (11.15)

34 – 75

59.22 (13.17)

38 – 80

8 (35%) 9

(39

-0.03

(0.877)

Anxiety 52.52 (10.92)

37 – 73

51.65 (11.94)

40 – 73

4 (17%) 5

(22

0.11

(0.626)

Hostility 57.30 (8.74)

40 – 74

56.35 (12.46)

41 – 80

6 (26%) 7

(30

-0.15

(0.485)

Phobic Anxiety 52.44 (10.76)

44 – 77

52.13 (9.12)

47 – 71

4 (17%) 5

(22

0.29

(0.186)

Paranoid Ideation 51.57 (10.48)

41 – 72

55.22 (14.61)

41 – 80

3 (13%) 7

(30

0.25

(0.245)

Psychoticism 52.83 (10.56)

44 – 80

51.74 (11.95)

44 – 80

6 (26%) 5

(22

0.22

(0.314)

Global Severity Index 56.52 (11.09)

30 – 79

56.78 (14.24)

34 – 80

6 (26%) 8

(35

0.02

(0.912)

Table 2 displays descriptive statistics for the PSI-4-SF domain scores, the measure of parenting

stress, for both mothers and fathers. Much like the SCL-90-R, paired samples t-tests and Wilcoxon

signed-ranks tests (when appropriate) confirmed that there were no statistically significant differences

between mothers’ and fathers’ standardized scores on the PSI-4-SF. Furthermore, on the PSI-4-SF, scores

that fall between the 16th and 84th percentiles are considered within the normal range, scores between the

85th and 89th percentiles are considered high, and scores at the 90th percentile and above are within the

clinically significant range. Table 3 shows the number of mothers and fathers who reported scores within

each of these ranges on the PSI-4-SF domains. Notably, a majority of mothers and fathers reported

normal levels of parenting stress on the Parental Distress and Parent-Child Dysfunctional Interaction

domains. However, a fairly large proportion of both mothers (43%) and fathers (30%) reported clinically

significant levels of parenting stress in the Difficult Child domain.

Table 2

Mother-Father Comparisons of PSI-4-SF Domain Scores

Mothers Fathers

Mean (SD)

Range

Mean (SD)

Range

Interspousal

Corr

elati

PSI-4-SF

Domain

T-Score Percentile T-Score Percentile



(p-value)

Parental

Distress

54.09 (12.41)

34 – 79

61.39 (30.57)

3 – 99

50.74 (10.30)

34 – 72

54.30 (28.22)

3 – 99

0.21

(0.348)

P-C Dysfunctional

Interaction

55.30 (8.55)

41 – 76

68.78 (19.97)

24 – 99

55.35 (9.19)

40 – 81

68.39 (18.55)

19 – 99

0.04

(0.849)

Difficult Child 59.91 (9.34)

42 – 80

78.09 (19.51)

26 – 99

57.70 (10.56)

35 – 77

72.26 (24.89)

6 – 99

0.30

(0.170)

Total Stress 57.04 (10.24)

41 – 81

69.96 (23.21)

19 – 99

54.87 (9.73)

35 – 79

66.09 (23.28)

4 – 99

0.23

(0.288)

To determine the degree of correspondence between mothers’ and fathers’ ratings of mental

health challenges and parenting stress, interspousal correlations were calculated. Table 1 and Table 2 also

display interspousal correlations for the SCL-90-R dimensions and PSI-4-SF domains, respectively.

Given that some of the scores for these two measures were not normally distributed, Spearman’s rank-

order correlations were reported instead of Pearson’s correlations. Surprisingly, no significant

correlations were found between mothers’ and fathers’ scores across these measures despite the overlap

within families in clinically significant cases on the SCL-90-R and the fact that there were no significant

differences in the mean scores between mothers and fathers on either of the measures.

Table 3

PSI-4-SF Domain Percentile Scores

Percentile Range

n, (%)

Normal (16 – 84) High (85 – 89) Clinically Significant

(90+)

PSI-4-SF

Domain

Mothers Fathers Mothers Fathers Mothers Fathers

Parental

Distress

(74%)

(91%)

(4%)

(0%)

(22%)

(9%)

P-C Dysfunctional

Interaction

(78%)

(87%)

(4%)

(17%)

(9%)

Difficult Child 12

(52%)

(65%)

(4%)

(43%)

(30%)

Total Stress 17

(74%)

(78%)

(0%)

(9%)

(26%)

(13%)

Note. The individual percentage values are rounded and may not total 100%. Parents whose percentile

scores were below 16 were included in the Normal category.

Aim 3

Aim 3 was to examine relationships between aspects of the couple relationship (i.e., couples

satisfaction and dyadic coping) and mothers’ and fathers’ mental health challenges and parenting stress.

The ICC for couples satisfaction indicated that 76.6% of the variation was due to between-couples factors

whereas 23.4% was due to within-couple factors. For dyadic coping, 42.1% of the variation was due to

between-couples factors whereas 57.9% was due to within-couple factors. Table 4 displays descriptive

statistics for mothers’ and fathers’ CSI and DCI scores as well as interspousal correlations, which indicate

the degree of correspondence between their ratings of couples satisfaction and dyadic coping.

Unlike the measures of mental health challenges and parenting stress, interspousal correlations

indicated that there were significant correspondences between mothers’ and fathers’ scores on both the

CSI-32 and DCI, with mean scores indicating average levels of couples satisfaction and dyadic coping

for both mothers and fathers. Additionally, paired samples t-tests confirmed that there were no

statistically significant differences between mothers’ and fathers’ scores on the CSI-32 or the DCI.

Overall, only six mothers (26%) and four fathers (17%) reported notable relationship dissatisfaction on

the CSI-32.

Additionally, on the DCI, five mothers and five fathers (22%) reported below average levels of dyadic

coping, 13 mothers (57%) and 14 fathers (61%) reported average levels of dyadic coping, and five

mothers (22%) and four fathers (17%) reported above average levels of dyadic coping.

Table 4

Mother-Father Comparisons of Couple Relationship Measures

Mean (SD)

Range

(p-value)

Variable Mothers Fathers Interspousal

Correlation

Couples Satisfaction1123.74 (30.28)

33 – 155

124.57 (30.82)

43 – 158

0.76***

(0.0001)

Dyadic Coping 130.78 (18.74)

98 – 167

125.83 (16.26)

100 – 156

0.44*

(0.038)

Note. 1CSI-32 raw score underwent cubic transformation to reduce negative skewness.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Couples Satisfaction

Table 5 presents the results of the MLM analysis for couples satisfaction. As expected, there was

a significant main effect of parenting stress on couples satisfaction. Across all couples, when the other

predictors were at their mean values, parents with greater than average parenting stress experienced less

couples satisfaction than parents with less than average parenting stress. There were no significant main

effects of parent sex, parent age, or parent education on couples satisfaction.

Prediction of Dyadic Coping

Table 5 also presents the results of the MLM analysis for dyadic coping. As predicted, there was

a significant main effect of parenting stress on dyadic coping. Across all couples, when the other

predictors were at their mean values, parents with greater than average parenting stress experienced less

dyadic coping than parents with less than average parenting stress. There was also a main effect of parent

education on dyadic coping. Across all couples, when the other predictors were at their mean values,

parents with greater than average levels of education experienced less dyadic coping than parents with

less than average levels of education. There was no significant main effect of parent sex on dyadic

coping, but there was a marginally significant main effect of parent age on dyadic coping (p = 0.083),

suggesting that across all couples, when the other predictors were at their mean values, older parents

experienced less dyadic coping than younger parents.

Table 5

Multilevel Model Results for Aim 3

Couples Satisfaction Dyadic

Coping

Fixed Effects

Intercept 5.30***

(0.36)

128.30***

(2.32)

Parenting Stress -0.07*

(0.03)

-0.94***

(0.23)

Parent Sex 0.01

(0.20)

-2.16

(1.99)

Parent Age -0.10

(0.06)

-0.71~

(0.41)

Parent Education -0.21

(0.19)

-3.47*

(1.61)

Random Effects

Residual (σ2)

1.72 169.75

Intercept (σ2 )

2.10 39.30

Goodness-of-fit

AIC 205.33 376.16

BIC 218.13 388.96

Note. Standard errors in parentheses.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Aim 4

Aim 4 was to examine the contributions of child challenging behaviors, ASD symptoms, and

adaptive behavior to parental individual well-being (i.e., mental health challenges and parenting stress)

and couple well-being (i.e., couples satisfaction and dyadic coping). The ICC for mental health

challenges indicated that 3.1% of the variation was due to between-couples factors whereas 96.9% was

due to

within-couple factors. For parenting stress, 27.0% of the variation was due to between-couples factors

whereas 73.0% was due to within-couple factors. Visual inspection of the variables and tests for kurtosis

and skewness indicated that several of the ABC-2 subscale scores and one of the SRS-2 subscale scores

were not normally distributed. Tables 6 and 7 display descriptive statistics for parents’ ABC-2 and SRS-2

scores, respectively, as well as interspousal correlations, which indicate the degree of correspondence

between their ratings of child challenging behaviors and ASD symptoms on these measures. Given that

some of the scores for these two measures were not normally distributed, Spearman’s rank-order

correlations were reported instead of Pearson’s correlations.

Interspousal correlations indicated that there were significant correspondences between mothers’

and fathers’ scores on the ABC-2 for the Hyperactivity and Inappropriate Speech subscales on the ABC-

2, but not for the other four subscales. On the SRS-2, there were significant correspondences between

mothers’ and fathers’ scores on the SCI and RRB subscale T-scores as well as the Total T-score.

Additionally, paired samples t-tests and Wilcoxon signed-ranks tests (when appropriate) confirmed that

there were no significant differences between mothers’ and fathers’ subscale and total scores on these

measures except for the ABC-2 Hyperactivity subscale, t(22) = 2.11, p = 0.046, and the SRS-2 RRB

subscale, Z = 2.27, p = 0.023. For both of these subscales, mothers endorsed higher scores than fathers.

Table 6

Mother-Father Comparisons of ABC-2 Subscale Raw Scores

Mean (SD)

Range



(p-value)

ABC-2 Subscale Mothers Fathers Interspousal

Correlation

Irritability 18.26 (10.62)

1 – 38

17.44 (11.54)

1 – 47

0.36

(0.096)

Socially Unresponsive / Lethargic 4.52 (5.86)

0 – 23

5.17 (4.65)

0 – 20

0.23

(0.299)

Stereotypy 6.09 (2.78)

1 – 12

4.96 (3.76)

0 – 13

0.24

(0.268)

Hyperactivity 14.04 (8.07)

2 – 27

11.00 (5.42)

1 – 24

0.54**

(0.007)

Inappropriate Speech 4.48 (3.85)

0 – 11

3.17 (2.29)

0 – 8

0.48*

(0.021)

Social Avoidance 1.39 (2.61)

0 – 9

1.52 (2.39)

0 – 9

0.33

(0.123)

Total Score 49.39 (27.71)

12 – 98

43.87 (24.70)

10 – 103

0.36

(0.094)

Note. * p < 0.05, ** p < 0.01, *** p < 0.001.

Furthermore, according to the SRS-2 Total Score guidelines, scores can be classified as within

normal limits (T-scores ≤ 59), in the mild range (T-scores = 60 to 65), in the moderate range (T-scores =

66 to 75), or in the severe range (T-scores ≥ 76). Table 8 displays the number of mothers and fathers

who reported scores within each of these ranges on the SRS-2. Additionally, Table 9 displays descriptive

statistics for the Vineland-3 scores, and Tables 10 and 11 display correlations between the measures of

couple (i.e., CSI-32 and DCI total raw scores), individual (i.e., SCL-90-R Global Severity Index and PSI-

4-SF Total Stress T-scores), and child (ABC-2 total raw score and SRS-2 Total T-Score) functioning for

mothers and fathers, respectively.

Table 7

Mother-Father Comparisons of SRS-2 Subscale T-Scores

Mean (SD)

Range



(p-value)

SRS-2 Subscale Mothers Fathers Interspousal

Correlation

Social Communication and

Interaction

69.00 (11.00)

49 – 90

66.83 (9.53)

49 – 85

0.56**

(0.005)

Restricted Interests and Repetitive

Behavior

75.48 (11.63)

57 – 92

69.17 (10.35)

54 – 90

0.41*

(0.049)

Total Score 70.87 (10.96)

51 – 91

67.57 (9.40)

51 – 87

0.52*

(0.011)

Note. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 8

Mother-Father Comparison of SRS-2 Total Score Severity Range

n, (%)

Range Mothers Fathers

Within normal limits 3 (13%) 5 (22%)

Mild 4 (17%) 4 (17%)

Moderate 9 (39%) 8 (35%)

Severe 7 (30%) 6 (26%)

Note. The individual percentage values are rounded and may not total 100%.

Table 9

Vineland-3 Scores

Vineland-3 Domain Mean (SD) Range

Communication 62.65 (18.31) 20 – 96

Daily Living Skills 69.13 (10.09) 52 – 93

Socialization 72.52 (14.51) 40 – 104

Adaptive Behavior Composite 67.70 (11.87) 39 – 89

The significant correlations between the ABC-2 and SRS-2 scores for both mothers and fathers

(see Tables 10 and 11) indicated that they should not be included together in the MLMs for Aim 4. There

was also a significant correlation between mothers’ SRS-2 scores and the Vineland-3 Adaptive Behavior

Composite (r = -0.72, p = 0.001) and a marginally significant correlation between fathers’ SRS-2 scores

and the Vineland-3 Adaptive Behavior Composite (r = -0.36, p = 0.085). Therefore, the ABC-2 Total

Score, Vineland-3 Adaptive Behavior Composite, child age, and parent sex were included as predictors in

the MLMs for Aim 4, as well as an interaction between the ABC-2 Total Score and parent sex. Table 12

presents the results of the MLM analyses for mental health challenges, parenting stress, couples

satisfaction, and dyadic coping.

Table 10

Pearson Correlations for Maternal Variables

Variable 1. 2. 3. 4. 5. 6.

1. SCL-90-R11.00

2. PSI-4-SF20.73***

(0.0001)

1.00

3. CSI-323-0.29

(0.181)

-0.44*

(0.037)

1.00

4. DCI4-0.27

(0.212)

-0.53*

(0.010)

0.81***

(0.0001)

1.00

5. ABC-250.65**

(0.001)

0.64**

(0.001)

-0.55**

(0.006)

-0.54**

(0.007)

1.00

6. SRS-260.29

(0.188)

0.32

(0.139)

-0.50*

(0.016)

-0.35

(0.098)

0.47*

(0.026)

1.00

Note. 1Global Severity Index T-score used for SCL-90-R. 2Total Stress T-score used for PSI-4-SF. 3CSI-

32 raw score underwent cubic transformation to reduce negative skewness. 4DCI raw score. 5ABC-2 Total

raw score. 6SRS-2 Total T-score.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Table 11

Pearson Correlations for Paternal Variables

Variable 1. 2. 3. 4. 5. 6.

1. SCL-90-R11.00

2. PSI-4-SF20.64**

(0.001)

1.00

3. CSI-323-0.25

(0.243)

-0.68***

(0.0004)

1.00

4. DCI4-0.40

(0.058)

-0.47*

(0.022)

0.52*

(0.011)

1.00

5. ABC-250.37

(0.082)

0.59**

(0.003)

-0.46*

(0.028)

-0.39

(0.063)

1.00

6. SRS-260.55**

(0.007)

0.62**

(0.001)

-0.54**

(0.008)

-0.27

(0.216)

0.72***

(0.0001)

1.00

Note. 1Global Severity Index T-score used for SCL-90-R. 2Total Stress T-score used for PSI-4-SF. 3CSI-

32 raw score underwent cubic transformation to reduce negative skewness. 4DCI raw score. 5ABC-2 Total

raw score. 6SRS-2 Total T-score.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Parent Mental Health Challenges

As expected, there was a significant main effect of child challenging behaviors on parental mental

health challenges. Across all couples, when the other predictors were at their mean values, parents of

children with greater than average levels of challenging behaviors experienced more mental health

challenges than parents of children with less than average levels of challenging behaviors. However, there

were no significant main effects of child adaptive behavior, child age, or parent sex on parent mental

health challenges, nor was there a significant interaction between child challenging behaviors and parent

sex in predicting mental health challenges. Model diagnostics suggested that a linear regression model

would be sufficient for predicting mental health challenges. The results of a linear regression model were

similar to the results of the multilevel model.

Prediction of Parenting Stress

As expected, there was a significant main effect of child challenging behaviors on parenting

stress. Across all couples, when the other predictors were at their mean values, parents of children with

greater than average levels of challenging behaviors experienced more parenting stress than parents of

children with less than average levels of challenging behaviors. There was also a significant main effect

of child adaptive behavior on parenting stress, suggesting that across all couples, when the other

predictors were at their mean values, parents of children with greater than average levels of adaptive

behavior experienced less parenting stress than parents of children with less than average levels of

adaptive behavior. There were no significant main effects of child age or parent sex on parenting stress,

nor was there a significant interaction between child challenging behaviors and parent sex in predicting

parenting stress. Model diagnostics suggested that a linear regression model would also be sufficient for

predicting parenting stress. The results of a linear regression model were similar to the results of the

multilevel model.

Table 12

Multilevel Model Results for Aim 4

Mental Health

Challenges Parenting Stress Couples

Satisfaction Dyadic Coping

Fixed Effects

Intercept 56.58***

(1.73)

55.91***

(1.16)

5.29***

(0.32)

128.51***

(2.68)

Challenging Behavior 0.23**

(0.07)

0.21***

(0.05)

-0.03**

(0.01)

-0.27**

(0.10)

Adaptive Behavior 0.001

(0.15)

-0.21*

(0.10)

0.07*

(0.03)

0.21

(0.25)

Child Age -0.50

(1.28)

-0.89

(0.86)

-0.34

(0.23)

0.55

(2.04)

Parent Sex 0.77

(1.72)

-0.51

(1.16)

-0.13

(0.18)

-3.22~

(1.90)

Challenging Behavior

x Parent Sex

-0.03

(0.06)

-0.02

(0.05)

-0.01

(0.01)

0.08

(0.08)

Random Effects

Residual (σ2)

134.34 61.60 1.42 163.09

Intercept (σ2 )

0.89 1.29e-17 1.63 94.18

Goodness-of-fit

AIC 367.19 335.75 207.95 389.43

BIC 381.82 350.38 222.58 404.06

Note. Standard errors in parentheses.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Couples Satisfaction

As expected, there was a significant main effect of child challenging behaviors on couples

satisfaction. Across all couples, when the other predictors were at their mean values, parents of children

with greater than average levels of challenging behaviors experienced less couples satisfaction than

parents of children with less than average levels of challenging behaviors. There was also a significant

main effect of child adaptive behavior on couples satisfaction. Across all couples, when the other

predictors were at their mean values, parents of children with greater than average levels of adaptive

behavior experienced more couples satisfaction than parents of children with less than average levels of

adaptive behavior. There were no significant main effects of child age or parent sex on couples

satisfaction, nor was there a significant interaction between child challenging behaviors and parent sex in

predicting couples satisfaction.

Prediction of Dyadic Coping

As expected, there was a significant main effect of child challenging behaviors on dyadic coping.

Across all couples, when the other predictors were at their mean values, parents of children with greater

than average levels of challenging behaviors experienced lower levels of dyadic coping than parents of

children with less than average levels of challenging behaviors. There was also a marginally significant

main effect of parent sex on dyadic coping (p = 0.091). In reference to the overall mean, when the other

predictors were at their mean values, fathers reported lower levels of dyadic coping compared to mothers.

There were no significant main effects of child adaptive behavior or child age on dyadic coping, nor was

there a significant interaction between child challenging behaviors and parent sex in predicting dyadic

coping.

Discussion

The current study was designed to examine aspects of the broader family environment in families

of young children with FXS, including maternal and paternal well-being, features of the mother-father

relationship, and relationships between child characteristics and parent and couple functioning. Findings

suggest that both mothers and fathers of young children with FXS are at risk for experiencing significant

mental health challenges and parenting stress. Unfortunately, nearly half of all mothers and fathers

reported clinically significant levels of mental health challenges on the SCL-90-R. These rates are

notably higher compared to what is observed in the general population for both males and females

(SAMHSA, 2019). An abundance of past research has established that women with the FMR1

premutation experience mental health problems independent of the stress associated with parenting a

child or multiple children with significant challenges (Hagerman et al., 2018). The results of the current

study confirm that fathers in these families are also experiencing substantial mental health problems and

that both parents may be in need of greater support and services.

Additionally, 43% of mothers and 30% of fathers reported clinically significant levels of

parenting stress in the Difficult Child domain on the PSI-4-SF. Parents reported greater stress in the

Difficult Child domain compared to the other domains (i.e., Parental Distress and Parent-Child

Dysfunctional Interaction), suggesting that their perceptions of the child’s behavior were contributing

more to their stress than their adjustment to parenting or their relationship with their child. This profile of

parenting stress is consistent with past research on mothers of children with FXS (Johnston et al., 2003;

Wheeler et al., 2007). Interestingly, there were no significant correlations between parents’ scores on the

SCL-90-R or the PSI-4-SF. The lack of associations between parents’ scores on these measures

potentially suggests that within families, one parent may be compensating for or supporting a partner who

is struggling with mental health or parenting stress, potentially buffering against negative effects on the

child. However, on the SCL-90-R, there was overlap for six families such that both the mother and father

reported clinically significant levels of mental health challenges. Therefore, developing a better

understanding of these dynamics within families is an area for future research. Additionally, an important

consideration regarding these findings is that the majority of these data were collected during the COVID-

19 pandemic, which may have contributed to parents’ mental health challenges and stress related to

parenting, especially given that services for children with developmental disabilities were severely

interrupted during this time (Abbeduto, 2020; Chan & Fung, 2021; Constantino et al., 2020; Manning et

al., 2020; White et al., 2021).

Despite experiencing challenges with mental health and parenting stress, most mothers and

fathers reported moderate to high levels of couples satisfaction and dyadic coping, with very few parents

reporting notable relationship dissatisfaction, and a majority of parents reporting dyadic coping in the

average or above average range. In these families, higher levels of couples satisfaction and dyadic coping

may be protective against the daily stressors that the parents are experiencing (McCarthy et al., 2006).

Importantly, features of the mother-father relationship are likely to affect the mother-child and father-

child relationships. Specifically, parents with higher levels of couples satisfaction and dyadic coping may

be more likely to engage in positive and responsive interactions with their children (e.g., Belsky, 1981;

Belsky, 1984; Kouros et al., 2014; Peltz et al., 2018). However, one limitation of these results is the

possibility of selection bias such that only relatively satisfied couples were willing to participate in the

current study.

Mothers and fathers also reported independently on their child’s challenging behaviors and ASD

symptoms. Interspousal correlations indicated high degrees of correspondence between mothers’ and

fathers’ scores on the SRS-2, but not the ABC-2. On average, mothers and fathers reported moderate

levels of challenging behaviors that were similar to the ABC scores reported in Sansone et al. (2012).

With regard to ASD symptoms, a majority of both mothers and fathers reported scores that fell within

the moderate to severe range, indicating that many of the children in this sample were demonstrating

deficiencies in reciprocal social behavior that may interfere with everyday social interactions. Although

significant correspondences were found between mothers’ and fathers’ scores on the SRS-2, mothers on

average reported higher levels of behaviors in the RRB subscale compared to fathers. Mothers may be

more likely to observe these behaviors, especially given that 16 of the 23 families in the study reported

that the mother spent more time with the child compared to the father. This difference in time spent with

the child may also influence the lack of correspondences between mothers’ and fathers’ scores on the

ABC-2. Fathers may be observing fewer of the child’s challenging behaviors when the mother is the

child’s primary caregiver.

There were also some interesting differences in the correlations between maternal and paternal

measures. For mothers (but not fathers), there were strong and significant correlations between the ABC-

2 and the other measures of individual, couple, and child functioning (i.e., the SCL-90-R GSI score, the

PSI-4-SF Total Stress T-Score, the CSI-32 raw score, the DCI raw score, and the SRS-2 Total T-score).

However, for fathers (but not mothers), the SRS-2 was strongly correlated with every measure except the

DCI. This finding may be due to differences in parental experiences of challenging behaviors and ASD

symptoms; that is, mothers may be experiencing and managing more challenging behaviors compared to

fathers, and fathers may be more concerned about or influenced by the child’s ASD symptoms compared

to mothers. In particular, paternal parenting stress was associated with the child’s ASD symptoms,

whereas maternal parenting stress was not. However, consistent with past research, parenting stress for

both mothers and fathers was related to child challenging behaviors (e.g., Johnston et al., 2003; McCarthy

et al., 2006). Future studies should investigate the similarities and differences between mothers and

fathers further to determine how parents’ impressions of the child’s behavior influence their well-being.

Additionally, parenting stress was found to associate with both couples satisfaction and dyadic

coping, with no significant differences found between mothers and fathers. Child challenging behavior

was also found to associate with parental mental health challenges, parenting stress, couples satisfaction,

and dyadic coping. Surprisingly, no significant differences were found between mothers and fathers

across these analyses, including any differences between mothers and fathers based on child challenging

behaviors. Perhaps future investigations with larger sample sizes would find differences between parents.

Child adaptive behavior was also found to associate with couples satisfaction and parenting stress. These

findings emphasize the importance of early intervention for children with FXS focused not only on

communication and socialization skills, but also daily living skills that promote independence.

Interventions focused on reducing parenting stress in these families could also have a positive

impact on parents’ individual well-being and the mother-father relationship. One potential intervention

that could be beneficial for parents of children with FXS is Mindfulness-Based Stress Reduction (MBSR).

MBSR is an established and empirically supported stress-reduction intervention that has been shown to

reduce parental stress, depressive symptoms, and parent-reported child behavior problems in families of

children with developmental disabilities (e.g., Chan & Neece, 2018; Neece et al., 2019). Another study of

MBSR for parents of children with developmental disabilities also found improvements in child social

skills that were mediated by parent-child relational factors (i.e., attachment and discipline practices;

Lewallen & Neece, 2015). Interestingly, a recent study of mothers of children with FXS found that trait

mindfulness, acceptance, and mindful parenting were associated with lower levels of anxiety, depression,

and stress (Wheeler et al., 2018), providing additional evidence that mindfulness interventions may be

beneficial for these families. Based on the findings of the current study, reductions in parental stress are

likely to benefit parental individual well-being and couple well-being with anticipated benefits for the

parent-child relationship as well.

Parent-implemented interventions focused on teaching parents strategies for managing child

challenging behaviors and engaging in responsive interactions may also benefit parental and couple well-

being in families of children with FXS. A recent study by Hall et al. (2020) examined the effects of

functional communication training (FCT) delivered via telehealth on problem behaviors in young boys

with FXS. Children with FXS often engage in problem behaviors that serve different communicative

functions, including gaining access to attention or a highly preferred item or escaping a demanding task or

situation. The focus of FCT is to ensure that these problem behaviors are no longer reinforced by the

caregiver while simultaneously teaching the child alternative and appropriate ways to communicate their

preferences and needs. The FCT intervention conducted by Hall and colleagues (2020) led to significant

reductions in child problem behaviors as well as decreased levels of parenting stress, likely benefiting the

entire family system.

The recent FCT study, along with other parent-implemented intervention studies conducted in the

past several years with families of children with FXS (e.g., McDuffie et al., 2018; Thurman et al., 2020),

support the use of telehealth as an effective service delivery model for families of children with FXS. The

use of telehealth-enabled interventions in this relatively rare population also allows families from rural

and/or underserved communities to participate in research studies and receive services that may otherwise

not be available to them (Abbeduto, 2020; Abbeduto et al. 2019; Hall et al., 2020). Telehealth also offers

more flexibility compared to in-person services and is more cost-effective (Abbeduto, 2020; McDuffie et

al., 2016). Therefore, telehealth methods for conducting assessments with and delivering interventions to

families affected by FXS are likely to be utilized more frequently in the post-pandemic world.

Limitations and Future Directions

There are some notable limitations to this study, including the relatively small sample size and

the lack of diversity in the sample. FXS research studies focused on parent-child relationships tend to

have small samples and the majority of the sample is typically families who identify as white, highly

educated, and have household incomes in the middle to high range. Therefore, future studies should

attempt to reduce barriers to participation in research for FXS families from underrepresented groups.

These barriers include age of diagnosis, lack of information about research opportunities, time

commitment for participation in research, and low household income (Chechi et al., 2014; Visootsak et

al., 2011). Future research should identify ways to reduce these barriers and extend outreach to groups

underrepresented in FXS research.

One notable strength of the study is the inclusion of both mothers and fathers as independent

informants given that the majority of past research in FXS has focused on the mother-child dyad. Fathers

have been historically underrepresented in research on child and adolescent development, both in the

general population and in families that include children with disabilities, despite the fact that fathers have

a unique and independent role in parenting compared to mothers and may differentially affect the child’s

development (Cabrera et al., 2018; Fabiano & Caserta, 2018; Phares et al., 2005). For decades, scholars

have recognized the importance of the father’s role in the family and made suggestions for future research

that involve conceptualizing the family as a complex and dynamic system (Cabrera et al., 2014; Cabrera

et al., 2000; Phares, 1992), but very little progress has been made in this regard, particularly as it

concerns families that include a child with a developmental disability (Braunstein et al., 2013; Hartley,

Seltzer, Head, & Abbeduto, 2012). Future studies in these families should continue to include fathers, and

also

consider differences between two-parent families and single-parent families. These approaches will

increase understanding of the family as a complex and dynamic system that differentially influences the

development of each family member.

Additionally, parents provided the measures of child ASD symptoms and challenging behaviors

as opposed to these behaviors being rated by an independent informant. Future studies should

incorporate multiple distinct assessments of both parent and child functioning to ensure accurate

measurement within various domains of behavioral and psychological functioning. Furthermore,

biological markers of stress were not collected nor were any measures of IQ. Future studies could benefit

from including these variables. Another limitation was the focus on concurrent associations as opposed

to longitudinal ones.

Future studies should examine relationships between parent and child functioning over time to develop a

better understanding of how these relationships fluctuate as the child develops. Finally, given that the

majority of families participated in the study during the COVID-19 pandemic, the data reported in the

current study may not reflect typical family functioning in families of children with FXS.

Conclusions

The findings from the current study indicate the importance of considering the entire family

system in families affected by FXS. Both mothers and fathers are in need of greater support to reduce

their mental health challenges and parenting stress, which would likely benefit not only parental well-

being, but also the mother-father relationship and the relationships between each parent and the child.

These results also provide evidence that child challenging behaviors and limited adaptive functioning

influence the mother-father relationship as well as individual parent functioning. Early intervention for

children with FXS, parent-implemented interventions focused on managing challenging behaviors, and

parent interventions focused on reducing stress are likely to benefit these families. Future studies should

continue to investigate the complex dynamics between mothers, fathers, and children in families affected

by FXS.

References

Abbeduto, L. (2020). Presidential Address, 2020—Using Technology to Deliver Services and Supports in

Homes, Neighborhoods, and Communities: Evidence and Promise. Intellectual and

Developmental Disabilities, 58(6), 525-531.

Abbeduto, L., Brady, N., & Kover, S. T. (2007). Language development and fragile X syndrome:

Profiles, syndrome‐specificity, and within‐syndrome differences. Mental Retardation and

Developmental Disabilities Research Reviews, 13(1), 36-46.

Abbeduto, L., Bullard, L., Nelson, S., Banasik, A., Tempero Feigles, R., Nguyen, V., & Elhasan, S.

(2019). Using technology to bring treatments to families affected by autism and related

neurodevelopmental disorders in their communities. In E. Grigorenko, Y. Shtyrov, & P.

McCardle (Eds.) All about language: Science, theory, and practice. Baltimore, MD: Paul H.

Brookes.

Abbeduto, L., McDuffie, A., & Thurman, A. J. (2014). The fragile X syndrome–autism comorbidity:

what do we really know? Frontiers in Genetics, 5, 355.

Abbeduto, L., Seltzer, M. M., Shattuck, P., Krauss, M. W., Orsmond, G., & Murphy, M. M.

(2004). Psychological well-being and coping in mothers of youths with autism, down

syndrome, or fragile X syndrome. American Journal on Mental Retardation, 109(3), 237-

254.

Abidin, R. R. (2012). Parenting Stress Index, Fourth Edition. Lutz, FL: Psychological Assessment

Resources, Inc.

Aman, M. G., & Singh, N. N. (2017). Aberrant Behavior Checklist, Second Edition. East Aurora, NY:

Slosson.

Bailey, D. B., Raspa, M., Olmsted, M., & Holiday, D. B. (2008). Co‐occurring conditions associated with

FMR1 gene variations: findings from a national parent survey. American Journal of Medical

Genetics Part A, 146(16), 2060-2069.

Baker, J. K., Seltzer, M. M., & Greenberg, J. S. (2012). Behaviour problems, maternal internalising

symptoms and family relations in families of adolescents and adults with fragile X

syndrome. Journal of Intellectual Disability Research, 56(10), 984-995.

Bartholomay, K. L., Lee, C. H., Bruno, J. L., Lightbody, A. A., & Reiss, A. L. (2019). Closing the gender

gap in fragile S syndrome: Review on females with FXS and preliminary research findings. Brain

Sciences, 9(1), 11.

Belsky, J. (1981). Early human experience: A family perspective. Developmental Psychology, 17(1), 3-

23.

Belsky, J. (1984). The determinants of parenting: A process model. Child Development, 83-96.

Bhakar, A. L., Dölen, G., & Bear, M. F. (2012). The pathophysiology of fragile X (and what it teaches us

about synapses). Annual Review of Neuroscience, 35, 417-443.

Bodenmann, G. (2008). Dyadisches Coping Inventar: Test manual [Dyadic Coping Inventory: Test

manual]. Bern, Switzerland: Huber.

Braunstein, V. L., Peniston, N., Perelman, A., & Cassano, M. C. (2013). The inclusion of fathers in

investigations of autistic spectrum disorders. Research in Autism Spectrum Disorders, 7(7), 858-

865.

Bristol, M. M., Gallagher, J. J., & Schopler, E. (1988). Mothers and fathers of young developmentally

disabled and nondisabled boys: Adaptation and spousal support. Developmental

Psychology, 24(3), 441.

Cabrera, N. J., Fitzgerald, H. E., Bradley, R. H., & Roggman, L. (2014). The ecology of father‐child

relationships: An expanded model. Journal of Family Theory & Review, 6(4), 336-354.

Cabrera, N., Tamis‐LeMonda, C. S., Bradley, R. H., Hofferth, S., & Lamb, M. E. (2000). Fatherhood in

the twenty‐first century. Child Development, 71(1), 127-136.

Cabrera, N. J., Volling, B. L., & Barr, R. (2018). Fathers Are Parents, Too! Widening the Lens on

Parenting for Children's Development. Child Development Perspectives, 12(3), 152-157.

Campbell, L., & Kashy, D. A. (2002). Estimating actor, partner, and interaction effects for dyadic data

using PROC MIXED and HLM: A user–friendly guide. Personal Relationships, 9(3), 327-342.

Chan, R. C., & Fung, S. C. (2021). Elevated Levels of COVID-19-Related Stress and Mental Health

Problems Among Parents of Children with Developmental Disorders During the

Pandemic. Journal of Autism and Developmental Disorders, 1-12.

Chan, N., & Neece, C. L. (2018). Parenting stress and emotion dysregulation among children

with developmental delays: The role of parenting behaviors. Journal of Child and Family

Studies, 27(12), 4071-4082.

Chechi, T., Siyahian, S., Thairu, L., Hagerman, R., & Lozano, R. (2014). Participation of

underrepresented minority children in clinical trials for Fragile X syndrome and other

neurodevelopmental disorders. Intractable & rare diseases research, 3(4), 147-152.

Constantino, J. N., & Gruber, C. P. (2012). Social Responsiveness Scale, Second Edition. Torrance, CA:

Western Psychological Services.

Constantino, J. N., Sahin, M., Piven, J., Rodgers, R., & Tschida, J. (2020). The impact of COVID-19 on

individuals with intellectual and developmental disabilities: Clinical and scientific

priorities. American Journal of Psychiatry, 177(11), 1091-1093.

Crawford, D. C., Acuña, J. M., & Sherman, S. L. (2001). FMR1 and the fragile X syndrome: Human

genome epidemiology review. Genetics in Medicine, 3(5), 359-371.

Dabrowska, A., & Pisula, E. (2010). Parenting stress and coping styles in mothers and fathers of

pre‐ school children with autism and Down syndrome. Journal of Intellectual Disability

Research, 54(3), 266-280.

Davis, N. O., & Carter, A. S. (2008). Parenting stress in mothers and fathers of toddlers with autism

spectrum disorders: Associations with child characteristics. Journal of Autism and Developmental

Disorders, 38(7), 1278.

Derogatis, L. (1994). Symptom Checklist-90-Revised. San Antonio, TX: Pearson.

England, M. J. E., & Sim, L. J. (2009). Depression in parents, parenting, and children: Opportunities to

improve identification, treatment, and prevention. Washington, D.C.: National Academies Press.

Essex, M. J., Klein, M. H., Cho, E., & Kraemer, H. C. (2003). Exposure to maternal depression and

marital conflict: Gender differences in children's later mental health symptoms. Journal of the

American Academy of Child & Adolescent Psychiatry, 42(6), 728-737.

Fabiano, G. A., & Caserta, A. (2018). Future directions in father inclusion, engagement, retention, and

positive outcomes in child and adolescent research. Journal of Clinical Child & Adolescent

Psychology, 47(5), 847-862.

Finestack, L. H., & Abbeduto, L. (2010). Expressive language profiles of verbally expressive adolescents

and young adults with Down syndrome or fragile X syndrome. Journal of Speech, Language, and

Hearing Research, 53(5), 1334-1348.

Fox, G. L., Nordquist, V. M., Billen, R. M., & Savoca, E. F. (2015). Father involvement and early

intervention: Effects of empowerment and father role identity. Family Relations, 64(4), 461-475.

Franke, P., Leboyer, M., Gänsicke, M., Weiffenbach, O., Biancalana, V., Cornillet-Lefebre, P., ... &

Hautzinger, M. (1998). Genotype–phenotype relationship in female carriers of the premutation

and full mutation of FMR-1. Psychiatry Research, 80(2), 113-127.

Freund, L. S., Reiss, A. L., & Abrams, M. T. (1993). Psychiatric disorders associated with fragile X in the

young female. Pediatrics, 91(2), 321-329.

Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing precision of

measurement for relationship satisfaction with the Couples Satisfaction Index. Journal of Family

Psychology, 21(4), 572.

Hagerman, P. J., & Hagerman, R. J. (2004). The fragile-X premutation: A maturing perspective. The

American Journal of Human Genetics, 74(5), 805-816.

Hagerman, R. J., Berry-Kravis, E., Hazlett, H. C., Bailey Jr, D. B., Moine, H., Kooy, R. F., ... &

Hagerman, P. J. (2017). Fragile X syndrome. Nature Reviews Disease Primers, 3, 17065.

Hagerman, R. J., Ono, M. Y., & Hagerman, P. J. (2005). Recent advances in fragile X: A model for

autism and neurodegeneration. Current Opinion in Psychiatry, 18(5), 490-496.

Hagerman, R. J., Protic, D. D., Rajaratnam, A., Salcedo-Arellano, M. J., Aydin, E. Y., & Schneider, A.

(2018). Fragile X-Associated Neuropsychiatric Disorders (FXAND). Frontiers in Psychiatry, 9,

1-9.

Hall, D. A., & Berry-Kravis, E. (2018). Fragile X syndrome and fragile X-associated tremor ataxia

syndrome. In D. H. Geschwind, H. L. Paulson, & C. Klein (Eds.), Handbook of clinical

neurology (Vol. 147, pp. 377-391). Amsterdam, The Netherlands: Elsevier.

Hall, S. S., Monlux, K. D., Rodriguez, A. B., Jo, B., & Pollard, J. S. (2020). Telehealth-enabled

behavioral treatment for problem behaviors in boys with fragile X syndrome: a randomized

controlled trial. Journal of Neurodevelopmental Disorders, 12(1), 1-15.

Hanington, L., Heron, J., Stein, A., & Ramchandani, P. (2012). Parental depression and child outcomes–

Is marital conflict the missing link? Child: Care, Health and Development, 38(4), 520-529.

Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., ... & REDCap Consortium.

(2019). The REDCap consortium: Building an international community of software platform

partners. Journal of Biomedical Informatics, 95, 103208.

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic

data capture (REDCap)—A metadata-driven methodology and workflow process for providing

translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381.

Hartley, S. L., Barker, E. T., Seltzer, M. M., Greenberg, J. S., & Floyd, F. J. (2011). Marital satisfaction

and parenting experiences of mothers and fathers of adolescents and adults with autism. American

Journal of Intellectual and Developmental Disabilities, 116(1), 81-95.

Hartley, S. L., Seltzer, M. M., Head, L., & Abbeduto, L. (2012). Psychological well‐being in fathers of

adolescents and young adults with Down Syndrome, Fragile X syndrome, and autism. Family

Relations, 61(2), 327-342.

Hartley, S. L., Seltzer, M. M., Hong, J., Greenberg, J. S., Smith, L., Almeida, D., ... & Abbeduto, L.

(2012). Cortisol response to behavior problems in FMR1 premutation mothers of adolescents and

adults with fragile X syndrome: A diathesis-stress model. International Journal of Behavioral

Development, 36(1), 53-61.

Hartley, S. L., Seltzer, M. M., Raspa, M., Olmstead, M., Bishop, E., & Bailey Jr, D. B. (2011). Exploring

the adult life of men and women with fragile X syndrome: results from a national

survey. American Journal of Intellectual and Developmental Disabilities, 116(1), 16-35.

Hartley, S. L., Papp, L. M., Blumenstock, S. M., Floyd, F., & Goetz, G. L. (2016). The effect of daily

challenges in children with autism on parents’ couple problem-solving interactions. Journal of

Family Psychology, 30(6), 732.

Hastings, R. P. (2003). Child behaviour problems and partner mental health as correlates of stress in

mothers and fathers of children with autism. Journal of Intellectual Disability Research, 47(4‐5),

231-237.

Hessl, D., Nguyen, D. V., Green, C., Chavez, A., Tassone, F., Hagerman, R. J.

& Hall, S. (2009). A

solution to limitations of cognitive testing in children with intellectual disabilities: The case of

fragile X syndrome. Journal of Neurodevelopmental Disorders, 1(1), 33-45.

Hooper, S. R., Burchinal, M. R., Roberts, J. E., Zeisel, S., & Neebe, E. C. (1998). Social and family risk

factors for infant development at one year: An application of the cumulative risk model. Journal

of Applied Developmental Psychology, 19(1), 85-96.

Johnston, C., Hessl, D., Blasey, C., Eliez, S., Erba, H., Dyer-Friedman, J.,

& Reiss, A. L. (2003).

Factors associated with parenting stress in mothers of children with fragile X syndrome. Journal

of Developmental & Behavioral Pediatrics, 24(4), 267-275.

Kersh, J., Hedvat, T. T., Hauser‐Cram, P., & Warfield, M. E. (2006). The contribution of marital

quality to the well‐being of parents of children with developmental disabilities. Journal of

Intellectual Disability Research, 50(12), 883-893.

Keysor, C. S., & Mazzocco, M. M. (2002). A developmental approach to understanding fragile X

syndrome in females. Microscopy Research and Technique, 57(3), 179-186.

Klusek, J., Thurman, A. J., & Abbeduto, L. (2021). Maternal Pragmatic Language Difficulties in the

FMR1 Premutation and the Broad Autism Phenotype: Associations with Individual and Family

Outcomes. Journal of Autism and Developmental Disorders, 1-17.

Kouros, C. D., Papp, L. M., Goeke-Morey, M. C., & Cummings, E. M. (2014). Spillover between marital

quality and parent–child relationship quality: Parental depressive symptoms as

moderators. Journal of Family Psychology, 28(3), 315-325.

Kover, S. T., & Abbeduto, L. (2010). Expressive language in male adolescents with fragile X syndrome

with and without comorbid autism. Journal of Intellectual Disability Research, 54(3), 246-265.

Kover, S. T., McDuffie, A., Abbeduto, L., & Brown, W. T. (2012). Effects of sampling context on

spontaneous expressive language in males with fragile X syndrome or Down syndrome. Journal

of Speech, Language, and Hearing Research, 55(4), 1022-1038.

Ledermann, T., Bodenmann, G., Gagliardi, S., Charvoz, L., Verardi, S., Rossier, J., … & Iafrate, R.

(2010). Psychometrics of the Dyadic Coping Inventory in three language groups. Swiss Journal of

Psychology/Schweizerische Zeitschrift f=r Psychologie/Revue Suisse de Psychologie, 69(4), 201-

212.

Lewallen, A. C., & Neece, C. L. (2015). Improved social skills in children with developmental delays

after parent participation in MBSR: The role of parent–child relational factors. Journal of Child

and Family Studies, 24(10), 3117-3129.

Manning, J., Billian, J., Matson, J., Allen, C., & Soares, N. (2020). Perceptions of families of individuals

with Autism Spectrum Disorder during the COVID-19 crisis. Journal of Autism and

Developmental Disorders, 1-9.

Masarik, A. S., & Conger, R. D. (2017). Stress and child development: A review of the family stress

model. Current Opinion in Psychology, 13, 85-90.

McCarthy, A., Cuskelly, M., van Kraayenoord, C. E., & Cohen, J. (2006). Predictors of stress in mothers

and fathers of children with fragile X syndrome. Research in Developmental Disabilities, 27(6),

688-704.

McDuffie, A., Banasik, A., Bullard, L., Nelson, S., Feigles, R. T., Hagerman, R., & Abbeduto, L. (2018).

Distance delivery of a spoken language intervention for school-aged and adolescent boys with

fragile X syndrome. Developmental Neurorehabilitation, 21(1), 48-63.

McDuffie, A., Oakes, A., Machalicek, W., Ma, M., Bullard, L., Nelson, S., & Abbeduto, L. (2016). Early

language intervention using distance video-teleconferencing: a pilot study of young boys with

fragile X syndrome Neece, C. L., Chan, N., Klein, K., Roberts, L., & Fenning, R. M. (2019).

Mindfulness-based stress reduction for parents of children with developmental delays:

Understanding the experiences of Latino families. Mindfulness, 10(6), 1017-1030.

Neece, C. L., Chan, N., Klein, K., Roberts, L., & Fenning, R. M. (2019). Mindfulness-based stress

reduction for parents of children with developmental delays: understanding the experiences of

Latino families. Mindfulness, 10(6), 1017-1030.

Oostra, B. A., & Willemsen, R. (2003). A fragile X balance: FMR1 expression levels. Human Molecular

Genetics, 12, 249–257.

Peltz, J. S., Rogge, R. D., & Sturge-Apple, M. L. (2018). Transactions within the family: Coparenting

mediates associations between parents’ relationship satisfaction and the parent–child

relationship. Journal of Family Psychology, 32(5), 553-564.

Peterson, J. L., & Zill, N. (1986). Marital disruption, parent-child relationships, and behavior problems in

children. Journal of Marriage and the Family, 295-307.

Phares, V. (1992). Where's poppa? The relative lack of attention to the role of fathers in child and

adolescent psychopathology. American Psychologist, 47(5), 656.

Phares, V., Fields, S., Kamboukos, D., & Lopez, E. (2005). Still looking for poppa. American

Psychologist, 60, 735–736.

Raspa, M., Wylie, A., Wheeler, A. C., Kolacz, J., Edwards, A., Heilman, K., & Porges, S. W. (2018).

Sensory difficulties in children with an FMR1 premutation. Frontiers in Genetics, 9, 351.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis

methods (Vol. 1). Thousand Oaks, CA: Sage.

Sansone, S. M., Widaman, K. F., Hall, S. S., Reiss, A. L., Lightbody, A., Kaufmann, W. E., ... & Hessl,

D. (2012). Psychometric study of the Aberrant Behavior Checklist in fragile X syndrome and

implications for targeted treatment. Journal of Autism and Developmental Disorders, 42(7),

1377-1392.

Sparrow, S., Cicchetti, D., & Saulnier, C. (2016). Vineland-3: Vineland Adaptive Behavior Scales, Third

Edition. Minneapolis, MN: Pearson.

Sterling, A. M., Mailick, M., Greenberg, J., Warren, S. F., & Brady, N. (2013). Language dysfluencies in

females with the FMR1 premutation. Brain and Cognition, 82(1), 84-89.

Sterling, A., Warren, S. F., Brady, N., & Fleming, K. (2013). Influences on maternal responsivity in

mothers of children with fragile X syndrome. American Journal on Intellectual and

Developmental Disabilities, 118(4), 310-326.

Substance Abuse and Mental Health Services Administration. (2020). Key substance use and mental

health indicators in the United States: Results from the 2019 National Survey on Drug Use and

Health (HHS Publication No. PEP20-07-01-001, NSDUH Series H-55). Rockville, MD: Center

for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services

Administration. Retrieved from https://www.samhsa.gov/data/

Thurman, A. J., Potter, L. A., Kim, K., Tassone, F., Banasik, A., Potter, S. N., ... & Abbeduto, L. (2020).

Controlled trial of lovastatin combined with an open-label treatment of a parent-implemented

language intervention in youth with fragile X syndrome. Journal of Neurodevelopmental

Disorders, 12(1), 1-17.

Visootsak, J., Charen, K., Rohr, J., Allen, E., & Sherman, S. (2012). Diagnosis of fragile X syndrome: a

qualitative study of African American families. Journal of genetic counseling, 21(6), 845-853.

Wang, M., Summers, J. A., Little, T., Turnbull, A., Poston, D., & Mannan, H. (2006). Perspectives of

fathers and mothers of children in early intervention programmes in assessing family quality of

life. Journal of Intellectual Disability Research, 50(12), 977-988.

Wheeler, A. C., Bailey Jr, D. B., Berry-Kravis, E., Greenberg, J., Losh, M., Mailick, M., ... & Hagerman,

R. (2014). Associated features in females with an FMR1 premutation. Journal of

Neurodevelopmental Disorders, 6(1), 30.

Wheeler, A., Hatton, D., Reichardt, A., & Bailey, D. (2007). Correlates of maternal behaviours in mothers

of children with fragile X syndrome. Journal of Intellectual Disability Research, 51(6), 447-462.

Wheeler, A. C., Miller, S., Wylie, A., & Edwards, A. (2018). Mindfulness and acceptance as potential

protective factors for mothers of children with Fragile X syndrome. Frontiers in Public Health, 6,

316.

White, L. C., Law, J. K., Daniels, A. M., Toroney, J., Vernoia, B., Xiao, S., ... & Chung, W. K.

(2021). Brief report: impact of COVID-19 on individuals with ASD and their caregivers: a

perspective from the SPARK cohort. Journal of Autism and Developmental Disorders, 1-8.

Study 2: The Influence of Parent and Couple Characteristics on Parental Responsivity During

Parent-Child Interactions in Families of Children with Fragile X Syndrome

Abstract

Parents of children with fragile X syndrome (FXS) experience elevated levels of parenting stress

due to the challenges associated with raising a child with significant impairments. Moreover, biological

mothers of children with FXS are at an increased genetic risk for experiencing mental health challenges,

including anxiety and depression. Parental mental health challenges and stress are often associated with

reduced marital cohesion and satisfaction, which is likely to spill over and negatively affect the parent-

child relationship for both mothers and fathers. The current study was designed to examine relationships

among characteristics of parents and couples and parent behavior (i.e., responsivity and behavior

management) during mother-child and father-child dyadic interactions in 23 families of young boys with

FXS. Results indicate that mothers and fathers used similar rates of responsive behaviors, but that fathers

used higher rates of behavior management strategies compared to mothers. Parenting stress predicted

lower rates of parental responsivity and higher rates of behavior management, but these effects were only

marginally significant. Couples satisfaction was not found to associate with either parental responsivity

or behavior management, despite the significant relationship between parenting stress and couples

satisfaction. Overall, this study provides evidence that reducing parenting stress may lead to more

responsive parent-child interactions, and equally so for both mothers and fathers. Therefore, family-based

services for families of children with FXS should include interventions that specifically target the

elevated rates of parenting stress in these families.

Keywords: fragile X syndrome, parenting stress, couples satisfaction, parental responsivity,

behavior management

Many of the conditions and symptoms associated with fragile X syndrome (FXS), including

intellectual disability (ID), autism spectrum disorder (ASD), anxiety, and challenging behaviors, can

make it difficult for parents to engage in the sustained, productive, and responsive interactions with their

children that support optimal development (e.g., McDuffie et al., 2018). Mothers of children with FXS

may also have greater difficulty compared to mothers of neurotypical children or children with other

developmental disabilities maintaining high levels of responsivity given the physical and mental health

challenges associated with being a carrier of the FMR1 premutation (Hagerman et al., 2018). Therefore, it

is important to identify the ways in which mothers of children with FXS can support their child’s

language learning from an early age.

Fathers have been historically underrepresented in developmental research, including research on

FXS, despite the fact that they play a unique and complementary role in parenting compared to mothers

(Cabrera et al., 2018). Past research has shown that high levels of father involvement are associated with

positive cognitive, social, and behavioral outcomes in neurotypical children (Keown et al., 2018; Wilson

& Prior, 2011). In contrast, paternal depression has been linked to less time spent interacting with the

child and engaging in supportive activities such as shared book reading, with negative downstream effects

on child language (Paulson et al., 2009), as well as negative social and behavioral outcomes (Gross et al.,

2008). Unfortunately, fathers of children with FXS also experience elevated levels of parenting stress

(McCarthy et al., 2006), which reduces the likelihood for highly positive and responsive father-child

interactions (Ward & Lee, 2020).

Additionally, dysfunction or problems in the marital relationship may negatively affect the

parent-child relationship for both mothers and fathers (England & Sim, 2009). Family systems theory

(Cox & Paley, 1997) emphasizes the potential value in expanding the focus beyond the dyad to consider

the ways in which broader family processes and relationships might influence both dyadic and individual

development and vice versa. Consequently, features of dyadic subsystems in the family (i.e., the mother-

father dyad, mother-child dyad, and father-child dyad) need to be examined in families of children with

FXS in order to gain a better understanding of how the family environment influences dyadic and

individual functioning across multiple domains. For example, according to the “spillover” hypothesis,

mothers and fathers who have stable and healthy relationships are more likely to have positive and

responsive parent-child relationships and thereby, children with more optimal outcomes (e.g., Belsky,

1981; Belsky, 1984; Greenlee et al., 2021; Kouros et al., 2014; Peltz et al., 2018). The current study

examined the ways in which features of mothers and fathers, as well as features of the mother-father

relationship, influence parents’ use of responsive behaviors during dyadic interactions with their child.

Parental Responsivity

Parental responsivity has been defined in past FXS research as, “a healthy, growth-producing

relationship consisting of such caregiver characteristics as warmth, nurturance, stability, predictability,

and contingent responsiveness” (Spiker et al., 2002, p. 37). Parental responsivity is typically coded from

video observations of a parent and child interacting in one or more contexts, including at home or in a

clinic setting, at either a molecular or molar level (Warren & Brady, 2007). These contexts include shared

book reading, preparing and eating a snack together, free play, and various daily living activities.

Molecular analyses of responsivity entail the coding of parental behaviors on a behavior-by-behavior

basis, whereas molar responsivity entails the use of a rating scale to capture the rater’s global impressions

of parental affect and parenting style. Molecular-level parental responsivity includes behaviors such as

commenting on the child’s focus of attention and asking questions that encourage the child to participate

in the interaction. The more frequent use of these parent behaviors is related to positive cognitive,

language, and social outcomes for neurotypical children (Landry et al., 2006; Landry et al., 2001; Rowe,

2012; Tamis-LeMonda et al., 2001; Tamis-LeMonda et al., 2014), as well as for children with

developmental disabilities, including Down syndrome (DS), ASD, and FXS (McDuffie & Yoder, 2010;

Sterling & Warren, 2014; Warren et al., 2010; Yoder & Warren, 1998, 1999, 2001, 2004). The current

study examined molecular-level responsivity in both mother-child and father-child play-based

interactions in families of children with FXS.

To date, only a small body of work has examined the effects of parental responsivity on child

language in FXS as well as factors that contribute to variability in responsivity. In the last decade, Warren

and colleagues have published a number of studies on a cohort of 55 mother-child dyads. These studies

have demonstrated that both early and sustained maternal responsivity are critically important for child

outcomes across a variety of domains (Brady et al., 2020; Brady et al., 2014; Warren, et al., 2017;

Warren et al., 2010). Specifically, maternal responsivity predicted receptive and expressive language

outcomes in early childhood (Warren et al. 2010), middle childhood (Brady et al., 2014), and adolescence

(Brady et al., 2020) in youth with FXS. Moreover, maternal responsivity predicted adaptive behavior

outcomes in middle childhood in the domains of Communication, Socialization, and Daily Living Skills

(Warren et al., 2017). This research has also demonstrated that wide range of both maternal and child

factors are associated with variability in maternal behavior, including maternal education and IQ,

maternal depression, child developmental level, and rate of child communication (Sterling et al., 2013).

Studies have also shown that mothers of children with FXS are able to successfully adapt to

their children in supportive and responsive ways despite the phenotypic features associated with both the

child’s FXS and the mother’s status as a carrier of the FMR1 premutation (Sterling et al., 2012; Sterling

& Warren, 2018; Sterling et al., 2013). Additionally, recent studies of parent-implemented language

interventions by McDuffie, Abbeduto, and colleagues found that parents are able to successfully

implement targeted responsive strategies that they are taught to use and that there are associated gains in

child engagement and language as a result (Bullard et al., 2017; McDuffie et al., 2018; McDuffie,

Machalicek, et al., 2016; McDuffie, Oakes, et al., 2016; Nelson et al., 2018; Oakes et al., 2015; Thurman

et al., 2020). Interestingly, across these studies, there was a total of 61 mother-child dyads and only one

father-child dyad.

Fathers of Children with Fragile X Syndrome

Little is known about the ways in which fathers of children with FXS influence the child’s

development or the impact of the child on the father, including how the child affects the father’s well-

being and behavior (Riley et al., 2017). Past research has shown that high quality paternal involvement is

associated with improved outcomes for neurotypical children above and beyond the outcomes associated

with high quality maternal involvement (Flippin & Watson, 2015). High levels of father involvement

have also been shown to be positively associated with marital satisfaction, parental competence, and

closeness to neurotypical children in parents (Ehrenberg et al., 2001). In contrast, low levels of paternal

involvement, potentially caused by stress or depression, are related to psychological and emotional

dysfunction in neurotypical children, as well as decreased rates of cognitive and language development

(Kane & Garber, 2004; Paulson et al., 2009; Wanless et al., 2008). Evidence also suggests that father-

child interactions support the neurotypical child’s ability to regulate their emotions and arousal (Bocknek

et al., 2017; Feldman, 2003). The father’s role in shaping child outcomes warrants further attention in

families of children with FXS.

Unfortunately, more often than not, the father’s role in the family has been ignored from both an

empirical and a societal standpoint, leading to an exclusive focus and, as a result, an increased burden on

the mother such that her role, either positive or negative, in influencing child outcomes is more likely to

be overstated (Wilson & Prior, 2011). Understanding more about the role of fathers in shaping the

development of children with FXS is important for several reasons, including fathers’ increasing role in

caregiving responsibilities in recent decades and the benefit of having both mothers and fathers involved

in the child’s therapies and interventions (Fox et al., 2015; Wang et al., 2006). Father involvement in the

child’s interventions, including parent-implemented interventions that target paternal behavior, could lead

to increased parental competence and decreased stress, as well as improved coparenting and higher

mother-father relationship quality (Bronte-Tinkew et al., 2007; Flippin & Watson, 2015).

Factors Related to Variability in Parental Responsivity

There are multiple parental and family-related factors that contribute to variability in parental

responsivity, yet the majority of research, whether involving children with FXS, other developmental

disorders, or neurotypical development, has focused exclusively on the mother-child dyad. Past studies of

families of children with FXS have shown that maternal depressive symptoms and stress, as well as

maternal IQ and education, relate to variability in maternal responsivity (Sterling et al., 2013; Wheeler et

al., 2007). Additionally, a study of mother-child dyads in families of children with FXS found

associations between maternal physiological arousal (measured through salivary cortisol) and maternal

responsivity toward the child with FXS (Robinson et al., 2016). Moreover, in families of young children

with DS, parental depression was found to be associated with lower levels of expressive language

development (D’Souza et al., 2020), with studies of neurotypical children linking parental depression to

reduced parental responsivity and associated negative effects on child outcomes (e.g., Justice et al., 2019;

Paulson et al., 2009). Another study of children with ID of heterogenous etiology found that maternal

anxiety and depression were negatively related to child language outcomes, and that maternal

responsiveness and paternal teaching were positively related to child cognitive and language outcomes

(Vilaseca et al., 2019). Aspects of the mother-father relationship, including couples satisfaction, also

influence each parents’ behavior and relationship with their child (e.g., Fink et al., 2020; Pratt et al., 1992;

Peltz et al., 2018; Stroud et al., 2011), but these associations have not yet been explored in families of

children with FXS.

Current study

The current study was designed to examine relationships among parental and couple

characteristics and parental responsivity in mother-child and father-child dyadic interactions. The first

aim was to examine relationships among maternal responsivity, paternal responsivity, and parent

individual well-being (i.e., mental health challenges and parenting stress). We hypothesized that mothers

and fathers who endorsed higher levels of individual well-being (i.e., lower levels of mental health

challenges and parenting stress) would demonstrate higher rates of parental responsivity in the dyadic

parent-child interactions (Sterling et al., 2013). The second aim was to examine relationships among

maternal responsivity, paternal responsivity, and couple well-being (i.e., couples satisfaction and dyadic

coping). We hypothesized that mothers and fathers who endorsed higher levels of couples satisfaction and

dyadic coping would demonstrate higher rates of parental responsivity in the dyadic parent-child

interactions (Pratt et al., 1992).

Method

Procedures and Measures

The data for the current study were collected as part of a larger study investigating multiple

aspects of family relationships and parent and child behavior in families of children with FXS.

Participants included 23 families of male children with FXS between the ages of 3;0 and 7;11 years, with

a total of 69 participants including 23 fathers (22 biological fathers and one stepfather), 23 biological

mothers, and 23 male children with FXS. See pages 5 – 6 for additional details on the participants in the

current study.

Parents independently completed multiple questionnaires via REDCap (Harris et al., 2019; Harris

et al., 2009), including the Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1994), the Parenting

Stress Index – Fourth Edition, Short Form (PSI-4-SF; Abidin, 2012), the Couples Satisfaction Index (CSI-

32; Funk & Rogge, 2007), and the Dyadic Coping Inventory (DCI; Bodenmann, 2008; Ledermann et al.,

2010). The SCL-90-R measures mental health symptoms along multiple dimensions yielding a Positive

Symptom Total, a Positive Symptom Distress Index, and a Global Severity Index (GSI). The GSI T-score

was used in analyses. The PSI-4-SF measures parenting stress, providing subscale scores for Parental

Distress, Parent-Child Dysfunctional Interaction, and Difficult Child. The Total Stress T-score was used

in analyses. The CSI-32 measures couples satisfaction, with higher scores indicating higher levels of

relationship satisfaction. Total raw scores were used in analyses. The DCI measures perceived

communication and coping that occurs in relationships when one or both partners are experiencing stress.

Higher scores indicate higher levels of dyadic coping. Total raw scores from this measure were used in

analyses. See pages 7 – 10 for additional details regarding the measures used in the current study.

Mothers and fathers also separately engaged in a 12-minute dyadic play-based interaction with

their child. The parent-child interactions served as the language samples for the current study. These

interactions were recorded on different days of the study using secure video-based teleconferencing

software (i.e., Skype for Business or Zoom). The interactions were transcribed using SALT (Systematic

Analysis of Language Transcripts; Miller & Iglesias, 2008) and coded for the presence of responsive

parental behaviors using a coding scheme adapted from Warren and colleagues (e.g., Warren et al., 2010).

See page 11 – 13 for additional details regarding transcription and coding, including definitions and

examples of the specific codes used in the current study.

Analysis Plan

All variables were visually inspected to check for model assumptions of normality and

homoscedasticity of the residuals. Tests for skewness and kurtosis were also examined. Transformations

and nonparametric alternatives were considered for any data that did not meet parametric assumptions.

Descriptive summaries of the primary outcome variables for Aims 1 and 2 (i.e., parental responsivity and

behavior management coded from mothers’ and fathers’ respective parent-child dyadic interactions)

were reported. Composite scores for parental responsivity and behavior management were based on

frequency counts of the observed behaviors within each category. Proportion scores for these variables

were also calculated as the total composite score in each category divided by the total number of parent

utterances in the transcript. For example, a parent who had 144 responsive utterances, 17 behavior

management utterances, and 227 total utterances would have a responsivity proportion of 144/227 = 0.63

and a behavior management proportion of 17/227 = 0.07.

Interspousal correlations were also reported to determine the degree of correspondence between

the mothers’ and fathers’ responsive behaviors in the dyadic interactions. Then, intraclass correlation

coefficients (ICCs) were calculated to estimate the proportion of the total variation in the measures of

parental responsivity and behavior management that exists between versus within couples. Finally, a

multilevel modeling (MLM) approach was used to manage the non-independence of the data collected

from couples within families (Raudenbush & Bryk, 2002). In this approach, the data from each partner

are nested within a dyad (Campbell & Kashy, 2002). Effect coding was used for parent sex (i.e., Male =

1 and Female = -1), and continuous predictors were grand-mean centered.

For Aim 1, separate models for parental responsivity and behavior management were specified.

Given the substantial range in the data for parental responsivity and behavior management, and to better

account for the quality of the parents’ language as opposed to the quantity, proportion scores for the

dependent variables were used instead of frequency scores. The strong and significant association

between the variables for parenting stress and mental health challenges did not allow for them both to be

included as predictors in the models for Aim 1; the parenting stress measure (PSI-4-SF Total T-score)

was chosen because it was associated with parental responsivity for mothers, whereas the measure of

mental health challenges was not associated with either parental responsivity or behavior management for

mothers or fathers. Other predictors in the models for Aim 1 included parent sex and parent education.

As with Aim 1, separate models for parental responsivity and behavior management were

specified for Aim 2, again using proportion scores for the dependent variables instead of frequency

scores. The strong and significant association between the couples satisfaction or dyadic coping variables

did not allow for them both to be included in the models for Aim 2; the couples satisfaction measure

(CSI-32 Total raw score) was chosen as the primary predictor for Aim 2 as there was a marginally

significant association between the CSI-32 and parental responsivity across all parents. Parent sex and

parent education were also included as predictors in the models for Aim 2.

Results

Aim 1 was designed to examine relationships among maternal responsivity, paternal responsivity,

and parent individual well-being (i.e., mental health challenges and parenting stress). Aim 2 was to

examine relationships among maternal responsivity, paternal responsivity, and couple well-being (i.e.,

couples satisfaction and dyadic coping).

Table 1 displays descriptive statistics for the parental responsivity and behavioral management

variables that were coded from the mother-child and father-child dyadic play-based interactions. For both

parental responsivity and behavior management, frequency counts and proportions are reported. Paired

samples t-tests and Wilcoxon signed-ranks tests (when appropriate) confirmed that there were no

statistically significant differences between mothers’ and fathers’ use of responsive behaviors or behavior

management strategies except for frequency of comments (t(22) = 2.77, p = 0.011) and the behavior

management proportion score (Z = -2.01, p = 0.044). Mothers used comments more frequently than

fathers, and fathers had a higher proportion score for behavior management compared to mothers.

Additionally, the differences between mothers’ and fathers’ frequency of redirects (Z = -1.69, p = 0.092)

and zaps (Z = -1.78, p = 0.075) approached significance, with fathers using redirects and zaps more

frequently than mothers.

Table 1

Mother-Father Comparisons of Parental Behavior During Dyadic Parent-Child Interactions

Mean (SD)

Range



(p-value)

Mothers Fathers Interspousal

Correlation

Parental Responsivity

Frequency of Comments 52.22 (21.69)

20 – 90

42.13 (22.44)

8 – 84

0.71***

(0.0001)

Frequency of Requests for Verbal

Compliance

39.17 (22.92)

5 – 96

38.52 (20.21)

3 – 72

0.68***

(0.0004)

Frequency of Recodes 2.00 (2.00)

0 – 5

1.83 (2.55)

0 – 10

0.32

(0.135)

Total Parental Responsivity

(Frequency)

93.39 (42.91)

26 – 176

82.47 (39.58)

11 – 149

0.81***

(0.00001)

Total Parental Responsivity

(Proportion of Total Utterances)

0.33 (0.14)

0.07 – 0.61

0.35 (0.15)

0.13 – 0.60

0.69***

(0.0003)

Behavior Management

Frequency of Requests for Behavioral

Compliance

12.57 (12.25)

1 – 51

14.04 (11.59)

2 – 55

0.29

(0.177)

Frequency of Redirects 1.13 (1.42)

0 – 5

1.96 (1.87)

0 – 6

-0.05

(0.835)

Frequency of Zaps 0.74 (1.21)

0 – 5

1.57 (2.76)

0 – 13

0.03

(0.884)

Total Behavior Management

(Frequency)

14.43 (12.41)

3 – 53

17.57 (12.78)

4 – 58

0.34

(0.116)

Total Behavior Management

(Proportion of Total Utterances)

0.05 (0.04)

0.01 – 0.14

0.07 (0.04)

0.02 – 0.19

0.29

(0.177)

Note. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 1 also displays interspousal correlations; given that some of the scores on these measures

were not normally distributed, Spearman’s rank-order correlations were reported instead of Pearson’s

correlations. Interspousal correlations indicated that there were significant correspondences between

mothers’ and fathers’ frequencies of comments and requests for verbal compliance, but not recodes. There

were also significant correspondences between mothers’ and fathers’ responsivity frequency and

proportion totals. No significant correspondences were found between mothers’ and fathers’ use of the

individual behavior management strategies or the total frequency or proportion scores for behavior

management.

Tables 2 and 3 displays correlations between measures of parent individual well-being (i.e., SCL-

90-R Global Severity Index and PSI-4-SF Total Stress T-scores), couple well-being (i.e., CSI-32 and DCI

total raw scores), and parent behavior (i.e., responsivity and behavior management) for mothers and

fathers, respectively. Table 4 displays correlations between these parent variables combined across

mothers and fathers. Table 2 shows that there was a significant correlation between parenting stress and

responsivity for mothers as well as a marginally significant correlation between dyadic coping and

parental responsivity; these relationships were not found for fathers. However, for parents overall, there

was a significant correlation between parenting stress and responsivity (Table 4). Table 4 also shows that

there was a marginally significant correlation between couples satisfaction and responsivity.

Table 2

Spearman Correlations for Maternal Variables

Variable 1. 2. 3. 4. 5. 6.

1. Responsivity 1.00

2. Behavior

Management

-0.16

(0.471)

1.00

3. SCL-90-R1-0.24

(0.277)

0.32

(0.138)

1.00

4. PSI-4-SF2-0.45*

(0.030)

0.27

(0.207)

0.77***

(0.00001)

1.00

5. CSI-3230.12

(0.571)

-0.24

(0.261)

-0.32

(0.138)

-0.49*

(0.018)

1.00

6. DCI40.35~

(0.097)

-0.20

(0.356)

-0.21

(0.325)

-0.48*

(0.021)

0.78***

(0.00001)

1.00

Note. Proportion scores used for Responsivity and Behavior Management variables. 1SCL-90-R Global

Severity Index T-score. 2PSI-4-SF Total Stress T-score. 3CSI-32 raw score. 4DCI raw score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 3

Spearman Correlations for Paternal Variables

Variable 1. 2. 3. 4. 5. 6.

1. Responsivity 1.00

2. Behavior

Management

-0.25

(0.250)

1.00

3. SCL-90-R1-0.08

(0.724)

0.25

(0.252)

1.00

4. PSI-4-SF2-0.33

(0.125)

0.23

(0.300)

0.54**

(0.008)

1.00

5. CSI-3230.33

(0.126)

-0.22

(0.315)

-0.17

(0.447)

-0.68***

(0.0004)

1.00

6. DCI4-0.01

(0.968)

-0.04

(0.868)

-0.40~

(0.061)

-0.48*

(0.021)

0.48*

(0.019)

1.00

Note. Proportion scores used for Responsivity and Behavior Management variables. 1SCL-90-R Global

Severity Index T-score. 2PSI-4-SF Total Stress T-score. 3CSI-32 raw score. 4DCI raw score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 4

Spearman Correlations for Combined Parental Variables

Variable SCL-90-R1PSI-4-SF2CSI-323DCI4Resp BM

Responsivity -0.15

(0.310)

-0.41**

(0.004)

0.25~

(0.090)

0.16

(0.291)

1.00

Behavior

Management

0.24

(0.111)

0.17

(0.271)

-0.20

(0.188)

-0.12

(0.443)

-0.17

(0.250)

1.00

Note. Proportion scores used for Responsivity and Behavior Management variables. 1SCL-90-R Global

Severity Index T-score. 2PSI-4-SF Total Stress T-score. 3CSI-32 raw score. 4DCI raw score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Aim 1 Models – Influence of Parent Well-Being on Parent Behavior

The ICC for parental responsivity indicated that 68.2% of the variation was due to between-

couples factors, whereas 31.8% was due to within-couple factors. For behavior management, 14.4% of

the variation was due to between-couples factors, whereas 85.6% was due to within-couple factors. Table

5 presents the results of the MLM analyses for Aim 1. The behavior management variable was log-

transformed to reduce positive skew.

Table 5

Multilevel Model Results for Aim 1

Parental Responsivity Behavior Management

Fixed Effects

Intercept 0.34***

(0.02)

-3.04***

(0.12)

Parenting Stress -0.003~

(0.002)

0.02~

(0.01)

Parent Sex 0.01

(0.20)

0.24**

(0.08)

Parent Education -0.01

(0.01)

0.09

(0.07)

Random Effects

Residual (σ2)

0.01 0.29

Intercept (σ2 )

0.01 0.18

Goodness-of-fit

AIC -25.01 116.12

BIC -14.04 127.09

Note. Standard errors in parentheses.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Parental Responsivity

There was a marginally significant effect of parenting stress on parental responsivity (p = 0.074).

Across all couples, when the other predictors were at their mean values, parents with greater than average

parenting stress used a smaller proportion of responsive behaviors during the dyadic parent-child

interactions. There were no significant main effects of parent sex or education on parental responsivity.

Prediction of Behavior Management

There was a marginally significant effect of parenting stress on behavior management (p =

0.053). Across all couples, when the other predictors were at their mean values, parents with greater than

average parenting stress used a greater proportion of behavior management strategies during the dyadic

parent-child interactions. There was also a significant main effect of parent sex on behavior management.

In reference to the overall mean, when the other predictors were at their mean values, fathers used a

greater proportion of behavior management strategies during the parent-child interactions than mothers.

There was no significant main effect of parent education on behavior management.

Aim 2 Models – Influence of Couple Well-Being on Parent Behavior

Table 6 presents the results of the MLM analyses for Aim 2. As with Aim 1, the behavior

management variable was log-transformed to reduce positive skew.

Table 6

Multilevel Model Results for Aim 2

Parental Responsivity Behavior Management

Fixed Effects

Intercept 0.34***

(0.02)

-3.04***

(0.12)

Couples Satisfaction -0.0002

(0.001)

0.002

(0.003)

Parent Sex 0.01

(0.01)

0.23**

(0.09)

Parent Education -0.01

(0.01)

0.05

(0.08)

Random Effects

Residual (σ2)

0.02 0.34

Intercept (σ2 )

0.01 0.15

Goodness-of-fit

AIC -20.34 121.42

BIC -9.37 132.40

Note. Standard errors in parentheses.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Parental Responsivity

Unexpectedly, there were no significant main effects of couples satisfaction, parent sex, or parent

education on parental responsivity.

Prediction of Behavior Management

Again, as with Aim 1, there was a significant main effect of parent sex on behavior management,

suggesting that in reference to the overall mean, when the other predictors were at their mean value,

fathers used a greater proportion of behavior management strategies during the parent-child interactions

than mothers. However, there were no significant main effects of couples satisfaction or parent education

on behavior management.

Discussion

The current study was designed to examine relationships between parental and couple

characteristics and parent behavior during parent-child interactions. Findings suggest that mothers and

fathers within families use similar levels of responsive strategies with their child. Specifically,

comparisons of mothers’ and fathers’ behavior coded from the parent-child dyadic interactions

demonstrated that there were significant correspondences between parents’ use of responsive behaviors

(i.e., comments, requests for verbal compliance, and recodes), with the exception of recodes. Total

parental responsivity (measured both in frequency and in the proportion of the parents’ total utterances)

was also related for mothers and fathers. Mothers, however, used significantly more comments compared

to fathers during the parent-child interactions. Maternal and paternal use of requests for verbal

compliance, a parent question or statement that serves to elicit a spoken response from the child, were not

significantly different, nor were there significant differences between mothers’ and fathers’ use of

recodes, which are parent interpretations of the child’s communication that extend the form of the child’s

act. Recodes were used very infrequently compared to comments and requests for verbal compliance.

This finding may be due to the fact that when parents produced multiple utterances in succession, only

the final utterance prior to either a three second pause or a child communication act was coded, consistent

with past research on maternal responsivity in FXS (e.g., Brady et al., 2014; Warren et al., 2010; Wheeler

et al., 2007). Therefore, the coding of parental behavior, as well as parents’ use of recodes in particular,

are likely to be affected by the child’s language level.

No correspondences between mothers’ and fathers’ use of behavior management strategies (i.e.,

requests for verbal compliance, redirects, and zaps) were found in the current study. Additionally, there

were marginally significant differences between parental use of both redirects and zaps, with fathers using

these behaviors more frequently than mothers. Overall, fathers also used a higher proportion of behavior

management strategies. However, for all parents, these behaviors were fairly low incidence, especially in

comparison to responsive behaviors, which is consistent with past research on maternal behavior during

mother-child interactions in families of children with FXS (e.g., Wheeler et al., 2007). Moreover,

redirects and zaps occurred very infrequently (redirects: maternal M = 1.13 versus paternal M = 1.96;

zaps: maternal M = 0.74 versus paternal M = 1.57). Requests for behavioral compliance, which were the

most common behavioral management strategy used, tended to center around suggestions regarding play.

For example, in one interaction, a father said to his child, “Let’s see how tall we can build this tower.” In

another interaction, a mother said to her child, “Pour it into my dish here.” These types of parent

utterances encouraged meaningful and interactive play and engagement in the interaction. Future studies

focused on other developmental outcomes in these families should investigate these parent behaviors

further, as well as differences between maternal and paternal use of behavior management strategies.

There were some interesting differences in the correlations between parental responsivity and

mothers’ versus fathers’ measures of individual and couple functioning. Specifically, parental

responsivity was related to parenting stress for mothers, but not for fathers. This finding may be related to

the fact that more mothers compared to fathers reported clinically significant levels of stress on the PSI-4-

SF. There was also a marginally significant relationship between dyadic coping and parental responsivity

for mothers but not fathers. More research is needed to understand potential differences between mothers

and fathers in the relationships between individual well-being and couple well-being, both in families of

children with neurodevelopmental disabilities as well in families of neurotypical children.

Moreover, when the data for mothers and fathers were combined, there was a significant

relationship between parenting stress and parental responsivity and a marginally significant relationship

between couples satisfaction and parental responsivity. Interestingly, no significant relationships were

found between behavior management and the measures of parent and couple functioning, which suggests

that parental directive behaviors may be influenced by other factors or not related to individual or couple

well-being, possibly including factors related to the child’s behavior or developmental level.

In the multilevel models for Aim 1, parenting stress was found to associate with both parental

responsivity and behavior management; however, the main effects were only marginally significant in

each of these models. Wheeler et al. (2007) found that maternal stress was a significant predictor of the

total number of maternal behaviors (i.e., maintaining and directing behaviors) exhibited during mother-

child interactions, demonstrating that mothers with higher levels of stress engaged in fewer interactions

with their child. Sterling et al. (2013), however, did not find maternal stress to be a significant predictor

of maternal responsivity when child developmental level, maternal depressive symptoms, and maternal

IQ were also included in the model as predictors. Wheeler et al. (2007) used the Total Stress score from

the PSI to predict maternal responsivity (as was done in the current study), whereas Sterling et al. (2013)

used the Parental Distress subscale score of the PSI. The difference in findings between Wheeler et al.

(2007) and the current study may be related to the fact that 52% of mothers in the study by Wheeler et al.

(2007) reported experiencing clinically significant levels of parenting stress compared to approximately

only 20% of parents in the current study.

Additionally, in contrast to Sterling et al. (2013), parental education did not significantly predict

either parental responsivity or behavior management in the present study. However, this finding was

likely due to the limited variability in education status in the current sample, given that the majority of

parents in the study had either a bachelor’s degree, master’s degree, or other advanced degree. Future

studies with more diverse samples could investigate the contributions of parental education on parent

behavior in these families, or measure parent IQ and examine IQ as a predictor of parent behavior.

Parent sex was not found to associate with parental responsivity in the multilevel model for Aim

1; however, there was a significant main effect of parent sex on behavior management in the analyses for

both Aims 1 and 2. In particular, fathers used a higher proportion of behavior management strategies

compared to mothers during the parent-child interactions. Couples satisfaction did not predict either

parental responsivity or behavior management in the models for Aim 2 and couples satisfaction was only

marginally significantly correlated with parental responsivity (Table 4). However, couples satisfaction in

this sample was negatively skewed, with a majority of parents reporting moderate to high levels of

satisfaction; thus, a lack of variability in couples satisfaction could explain the lack of a correlation with

responsivity. Parents who were satisfied with their relationships may have been more likely to participate

in the current study compared to dissatisfied couples. Therefore, future studies should examine these

relationships further in larger samples given past studies that demonstrate the influence of parent and

couple functioning on parent behavior in both dyadic and triadic (i.e., mother-father-child) interactions

(e.g., Kouros et al., 2014; Pratt et al., 1992; Stroud et al., 2011).

Limitations and Future Directions

One significant limitation of the current study is the small sample size, which may have

influenced the power to detect significant main effects of both parenting stress and couples satisfaction

on parenting behavior. Moreover, the sample lacked diversity in terms of race/ethnicity—with the

majority of the participants identifying as White and not Hispanic or Latinx—and parent education, with

approximately 70% of both mothers and fathers having a bachelor’s degree, master’s degree, or other

advanced degree.

Another limitation is the use of only one sampling context for the parent-child interactions. Past

studies have gathered and analyzed data from multiple parent-child interaction contexts, including free

play, unstructured naturalistic activities (e.g., folding laundry, putting dishes away), book reading, and

making and eating a snack together (e.g., Brady et al., 2014; Warren et al. 2010). In these studies,

parental responsivity was coded from a total of approximately 25 minutes of parent-child interactions

across these contexts. The use of additional contexts or longer interactions could have led to more

variability in the data within and between families, which may have increased the ability to detect

significant relationships between individual and couple functioning and parent behavior in the current

study.

The use of frequencies versus proportions to analyze the effects of parental input on child

outcomes in past research is varied (Rowe & Snow, 2020), with several studies reporting is frequencies

(e.g., Landry et al, 2001; McDuffie & Yoder, 2010) and several other studies reporting proportions (e.g.,

Ambrose et al., 2015; Bornstein et al., 1992; Lorang et al., 2020). Importantly, mothers in the current

study provided more linguistic input compared to fathers (i.e., had a greater total number of utterances

overall; maternal M = 288.52, SD = 62.22 versus paternal M = 240.30, SD = 82.97), which is consistent

with past research on parental input in families of neurotypical children (Davidson & Snow, 1996;

Pancsofar & Vernon-Feagans, 2006; Shapiro et al., 2021). Mothers also used significantly more

comments than fathers (maternal M = 52.22, SD = 21.69 versus paternal M = 42.13, SD = 22.44), but the

difference in the proportion of comments used by mothers compared to fathers was not significantly

different (maternal M = 0.185, SD = 0.07 versus paternal M = 0.178, SD = 0.08), nor was the difference in

the overall proportion of responsive behaviors used (maternal M = 0.33, SD = 0.14 versus paternal M =

0.35, SD = 0.15). The current study sought to examine differences in the quality of maternal and paternal

input as opposed to the quantity, and therefore used proportions instead of frequencies. However, future

studies could examine multiple metrics of parental responsivity (e.g., frequency, proportion of total

utterances, rate per minute) to better understand the effects of potential differences in maternal versus

paternal input on various outcomes.

Moreover, although a great deal of past research has demonstrated that children develop

language through interactions with their parents, the majority of this work conducted in families of

children with developmental disabilities has neglected the fact that these interactions are dynamic and

often involve more than one communicative partner at a time. That is, children spend a substantial

amount of time engaging in dyadic interactions with their mothers and fathers as well as in triadic

mother-father-child interactions (McHale & Fivaz-Depeursinge, 1999). The triad is comprised of

multiple family subsystems (i.e., the mother-child dyad, father-child dyad, and mother-father dyad) and

thereby is a more complex and diverse environment than any of the individual dyadic partnerships

(Lindsey & Caldera, 2006; Stoneman & Brody, 1981). Therefore, determining the variability in child and

parent behavior that arises as a function of whether the interaction is dyadic or triadic, as well as the

individual and dyadic-level variables that contribute to that variability, are other areas for future research

in families affected by FXS.

Additionally, the coding procedures used in the current study were such that certain responsive

parental behaviors may have been missed if the parents’ utterance was not followed by a child utterance

or a three second pause. For example, parent recodes were very low frequency. However, parents may

have been recoding the child’s communication more frequently than the data in the current study reflect,

but immediately saying something else following the recode, thereby interfering with that behavior being

coded. Indeed, this could be true for all types of parent behavior coded during these interactions. Future

studies should investigate the various types of parentally responsive behavior further with different

coding methods, as well as how the use of different types of behavior may change over time as the child

develops.

Another important consideration regarding the findings of the current study is that nearly all of

these data were collected during the COVID-19 pandemic. In addition to widespread concerns about the

coronavirus, parents may have been experiencing elevated levels of stress during this time due to loss of

employment, social isolation, additional caregiving responsibilities, and the increased burden of

managing the child’s educational and therapeutic programs (Chan & Fung, 2021; Neece et al., 2020).

Despite the relatively high levels of parental stress reported in the current study, the majority of parents in

the sample reported average to above average levels of couples satisfaction. In fact, only approximately

20% of parents reported notable relationship dissatisfaction, which likely limited our ability to detect a

significant effect of couples satisfaction on parental responsivity. Future studies should continue to

examine relationships between parent and couple functioning and parent behavior in larger and more

diverse samples.

Conclusions

The findings from the current study provide preliminary evidence that parenting stress may

influence the responsive behavior of both mothers and fathers during parent-child interactions in families

of children with FXS. Although only a marginally significant main effect of parenting stress on parental

responsivity was detected, these findings still suggest that families would likely benefit from

interventions aimed at reducing parent stress (e.g., Neece, 2014; Neece et al., 2019), especially given the

prevalence of elevated parenting stress in parents of children with FXS (e.g., Johnston et al., 2003;

McCarthy et al., 2006). Future studies should continue to investigate individual and couple functioning

for both mothers and fathers of children with FXS as well as the influence of these characteristics on

parent behavior.

References

Abidin, R. R. (2012). Parenting Stress Index, Fourth Edition. Lutz, FL: Psychological Assessment

Resources, Inc.

Ambrose, S. E., Walker, E. A., Unflat-Berry, L. M., Oleson, J. J., & Moeller, M. P. (2015). Quantity and

quality of caregivers’ linguistic input to 18-month and 3-year-old children who are hard of

hearing. Ear and hearing, 36(0 1), 48S.

Belsky, J. (1981). Early human experience: A family perspective. Developmental Psychology, 17(1), 3-

23.

Belsky, J. (1984). The determinants of parenting: A process model. Child Development, 83-96.

Bocknek, E. L., Dayton, C., Raveau, H. A., Richardson, P., Brophy-Herb, H. E., & Fitzgerald, H. E.

(2017). Routine active playtime with fathers is associated with self-regulation in early

childhood. Merrill-Palmer Quarterly, 63(1), 105-134.

Bodenmann, G. (2008). Dyadisches Coping Inventar: Test manual [Dyadic Coping Inventory: Test

manual]. Bern, Switzerland: Huber.

Bornstein, M. H., Tamis‐LeMonda, C. S., Tal, J., Ludemann, P., Toda, S., Rahn, C. W., ... & Vardi, D.

(1992). Maternal responsiveness to infants in three societies: The United States, France, and

Japan. Child Development, 63(4), 808-821.

Brady, N. C., Fleming, K., Bredin-Oja, S. L., Fielding-Gebhardt, H., & Warren, S. F. (2020). Language

development from early childhood to adolescence in youths with fragile X syndrome. Journal of

Speech, Language, and Hearing Research, 63(11), 3727-3742.

Brady, N., Warren, S. F., Fleming, K., Keller, J., & Sterling, A. (2014). Effect of sustained maternal

responsivity on later vocabulary development in children with fragile X syndrome. Journal of

Speech, Language, and Hearing Research, 57(1), 212-226.

Bronte-Tinkew, J., Moore, K. A., Matthews, G., & Carrano, J. (2007). Symptoms of major depression in a

sample of fathers of infants: Sociodemographic correlates and links to father

involvement. Journal of Family Issues, 28(1), 61-99.

Bullard, L., McDuffie, A., & Abbeduto, L. (2017). Distance delivery of a parent-implemented language

intervention for young boys with fragile X syndrome. Autism & Developmental Language

Impairments, 2, 2396941517728690.

Cabrera, N. J., Volling, B. L., & Barr, R. (2018). Fathers Are Parents, Too! Widening the Lens on

Parenting for Children's Development. Child Development Perspectives, 12(3), 152-157.

Campbell, L., & Kashy, D. A. (2002). Estimating actor, partner, and interaction effects for dyadic data

using PROC MIXED and HLM: A user–friendly guide. Personal Relationships, 9(3), 327-342.

Chan, R. C., & Fung, S. C. (2021). Elevated Levels of COVID-19-Related Stress and Mental Health

Problems Among Parents of Children with Developmental Disorders During the

Pandemic. Journal of Autism and Developmental Disorders, 1-12.

Cox, M. J., & Paley, B. (1997). Families as systems. Annual Review of Psychology, 48(1), 243-267.

Davidson, R. G., & Snow, C. E. (1996). Five-year-olds' interactions with fathers versus mothers. First

Language, 16(47), 223-242.

Derogatis, L. (1994). Symptom Checklist-90-Revised. San Antonio, TX: Pearson.

D’Souza, H., Lathan, A., Karmiloff-Smith, A., & Mareschal, D. (2020). Down syndrome and parental

depression: A double hit on early expressive language development. Research in Developmental

Disabilities, 100, 103613.

Ehrenberg, M. F., Gearing‐Small, M., Hunter, M. A., & Small, B. J. (2001). Childcare task division and

shared parenting attitudes in dual‐earner families with young children. Family Relations, 50(2),

143-153.

England, M. J. E., & Sim, L. J. (2009). Depression in parents, parenting, and children: Opportunities to

improve identification, treatment, and prevention. Washington, D.C.: National Academies Press.

Feldman, R. (2003). Infant–mother and infant–father synchrony: The coregulation of positive

arousal. Infant Mental Health Journal: Official Publication of The World Association for Infant

Mental Health, 24(1), 1-23.

Fink, E., Browne, W. V., Kirk, I., & Hughes, C. (2020). Couple relationship quality and the infant home

language environment: Gender-specific findings. Journal of Family Psychology, 34(2), 155.

Flippin, M., & Watson, L. R. (2015). Fathers' and mothers' verbal responsiveness and the language skills

of young children with autism spectrum disorder. American Journal of Speech-Language

Pathology, 24(3), 400-410.

Fox, G. L., Nordquist, V. M., Billen, R. M., & Savoca, E. F. (2015). Father involvement and early

intervention: Effects of empowerment and father role identity. Family Relations, 64(4), 461-475.

Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing precision of

measurement for relationship satisfaction with the Couples Satisfaction Index. Journal of Family

Psychology, 21(4), 572.

Greenlee, J. L., Piro‐Gambetti, B., Putney, J., Papp, L. M., & Hartley, S. L. (2021). Marital satisfaction,

parenting styles, and child outcomes in families of autistic children. Family Process.

Gross, H. E., Shaw, D. S., Moilanen, K. L., Dishion, T. J., & Wilson, M. N. (2008). Reciprocal models of

child behavior and depressive symptoms in mothers and fathers in a sample of children at risk for

early conduct problems. Journal of Family Psychology, 22(5), 742.

Hagerman, R. J., Protic, D. D., Rajaratnam, A., Salcedo-Arellano, M. J., Aydin, E. Y., & Schneider, A.

(2018). Fragile X-Associated Neuropsychiatric Disorders (FXAND). Frontiers in Psychiatry, 9,

1-9.

Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., ... & REDCap Consortium.

(2019). The REDCap consortium: Building an international community of software platform

partners. Journal of Biomedical Informatics, 95, 103208.

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic

data capture (REDCap)—A metadata-driven methodology and workflow process for providing

translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381.

Johnston, C., Hessl, D., Blasey, C., Eliez, S., Erba, H., Dyer-Friedman, J., ... & Reiss, A. L. (2003).

Factors associated with parenting stress in mothers of children with fragile X syndrome. Journal

of Developmental & Behavioral Pediatrics, 24(4), 267-275.

Justice, L. M., Jiang, H., Purtell, K. M., Schmeer, K., Boone, K., Bates, R., & Salsberry, P. J. (2019).

Conditions of poverty, parent–child interactions, and toddlers’ early language skills in low-

income families. Maternal and Child Health Journal, 23(7), 971-978.

Kane, P., & Garber, J. (2004). The relations among depression in fathers, children's psychopathology, and

father–child conflict: A meta-analysis. Clinical Psychology Review, 24(3), 339-360.

Keown, L. J., Franke, N., & Kaur, R. (2018). The role of fathers in supporting children’s development.

In Handbook of parenting and child development across the lifespan (pp. 121-142). Springer,

Cham.

Kouros, C. D., Papp, L. M., Goeke-Morey, M. C., & Cummings, E. M. (2014). Spillover between marital

quality and parent–child relationship quality: Parental depressive symptoms as

moderators. Journal of Family Psychology, 28(3), 315-325.

Landry, S. H., Smith, K. E., & Swank, P. R. (2006). Responsive parenting: Establishing early foundations

for social, communication, and independent problem-solving skills. Developmental

Psychology, 42(4), 627.

Landry, S. H., Smith, K. E., Swank, P. R., Assel, M. A., & Vellet, S. (2001). Does early responsive

parenting have a special importance for children's development or is consistency across early

childhood necessary? Developmental Psychology, 37(3), 387-403.

Ledermann, T., Bodenmann, G., Gagliardi, S., Charvoz, L., Verardi, S., Rossier, J., … & Iafrate, R.

(2010). Psychometrics of the Dyadic Coping Inventory in three language groups. Swiss Journal of

Psychology/Schweizerische Zeitschrift f=r Psychologie/Revue Suisse de Psychologie, 69(4), 201-

212.

Lindsey, E. W., & Caldera, Y. M. (2006). Mother–father–child triadic interaction and mother–child

dyadic interaction: Gender differences within and between contexts. Sex Roles, 55(7-8), 511-521.

Lorang, E., Hartley, S., & Sterling, A. (2020). Physiological arousal and observed behaviour in parent–

child interactions involving young children with Down syndrome. Journal of Intellectual

Disability Research, 64(6), 426-433.

McCarthy, A., Cuskelly, M., van Kraayenoord, C. E., & Cohen, J. (2006). Predictors of stress in mothers

and fathers of children with fragile X syndrome. Research in Developmental Disabilities, 27(6),

688-704.

McDuffie, A., Banasik, A., Bullard, L., Nelson, S., Feigles, R. T., Hagerman, R., & Abbeduto, L. (2018).

Distance delivery of a spoken language intervention for school-aged and adolescent boys with

fragile X syndrome. Developmental Neurorehabilitation, 21(1), 48-63.

McDuffie, A., Machalicek, W., Bullard, L., Nelson, S., Mello, M., Tempero-Feigles, R., ... & Abbeduto,

L. (2016). A spoken-language intervention for school-aged boys with fragile X

syndrome. American Journal on Intellectual and Developmental Disabilities, 121(3), 236-265.

McDuffie, A., Oakes, A., Machalicek, W., Ma, M., Bullard, L., Nelson, S., & Abbeduto, L. (2016). Early

language intervention using distance video-teleconferencing: a pilot study of young boys with

fragile X syndrome and their mothers. American Journal of Speech-Language Pathology, 25(1),

46-66.

McDuffie, A., & Yoder, P. (2010). Types of parent verbal responsiveness that predict language in young

children with autism spectrum disorder. Journal of Speech, Language, and Hearing

Research, 53(4), 1026-1039.

McHale, J. P., & Fivaz-Depeursinge, E. (1999). Understanding triadic and family group interactions

during infancy and toddlerhood. Clinical Child and Family Psychology Review, 2(2), 107-127.

Miller, J., & Iglesias, A. (2008). Systematic Analysis of Language Transcripts (SALT), English &

Spanish (Version 9) [Computer software]. Madison: University of Wisconsin—Madison,

Waisman Center. Language Analysis Laboratory.

Neece, C. L. (2014). Mindfulness‐based stress reduction for parents of young children with

developmental delays: Implications for parental mental health and child behavior

problems. Journal of Applied Research in Intellectual Disabilities, 27(2), 174-186.

Neece, C. L., Chan, N., Klein, K., Roberts, L., & Fenning, R. M. (2019). Mindfulness-based stress

reduction for parents of children with developmental delays: understanding the experiences of

Latino families. Mindfulness, 10(6), 1017-1030.

Neece, C., McIntyre, L. L., & Fenning, R. (2020). Examining the impact of COVID‐19 in ethnically

diverse families with young children with intellectual and developmental disabilities. Journal of

Intellectual Disability Research, 64(10), 739-749.

Nelson, S., McDuffie, A., Banasik, A., Feigles, R. T., Thurman, A. J., & Abbeduto, L. (2018). Inferential

language use by school-aged boys with fragile X syndrome: Effects of a parent-implemented

spoken language intervention. Journal of Communication Disorders, 72, 64-76.

Oakes, A., Ma, M., McDuffie, A., Machalicek, W., & Abbeduto, L. (2015). Providing a parent-

implemented language intervention to a young male with fragile X syndrome: Brief

report. Developmental Neurorehabilitation, 18(1), 65-68.

Pancsofar, N., & Vernon-Feagans, L. (2006). Mother and father language input to young

children: Contributions to later language development. Journal of Applied Developmental

Psychology, 27(6), 571-587.

Paulson, J. F., Keefe, H. A., & Leiferman, J. A. (2009). Early parental depression and child language

development. Journal of Child Psychology and Psychiatry, 50(3), 254-262.

Pratt, M. W., Kerig, P. K., Cowan, P. A., & Cowan, C. P. (1992). Family worlds: Couple satisfaction,

parenting style, and mothers' and fathers' speech to young children. Merrill-Palmer Quarterly,

38(2), 245-262.

Peltz, J. S., Rogge, R. D., & Sturge-Apple, M. L. (2018). Transactions within the family: Coparenting

mediates associations between parents’ relationship satisfaction and the parent–child

relationship. Journal of Family Psychology, 32(5), 553-564.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis

methods (Vol. 1). Thousand Oaks, CA: Sage.

Riley, C., Mailick, M., Berry-Kravis, E., & Bolen, J. (2017). The future of fragile X syndrome: CDC

stakeholder meeting summary. Pediatrics, 139(Supplement 3), S147-S152.

Robinson, A. N., Roberts, J. E., Brady, N. C., McQuillin, S. D., & Warren, S. F. (2016). Physiological

correlates of maternal responsivity in mothers of preschoolers with fragile X syndrome. American

Journal on Intellectual and Developmental Disabilities, 121(2), 111-120.

Rowe, M. L. (2008). Child-directed speech: Relation to socioeconomic status, knowledge of child

development and child vocabulary skill. Journal of Child Language, 35(1), 185-205.

Rowe, M. L., & Snow, C. E. (2020). Analyzing input quality along three dimensions: Interactive,

linguistic, and conceptual. Journal of Child Language, 47(1), 5-21.

Shapiro, N. T., Hippe, D. S., & Ramírez, N. F. (2021). How Chatty Are Daddies? An Exploratory Study

of Infants' Language Environments. Journal of Speech, Language, and Hearing Research, 1-11.

Spiker, D., Boyce, G., & Boyce, L. (2002). Parent-child interactions when young children have

disabilities. International Review of Research in Mental Retardation, 25, 35-70.

Sterling, A., Barnum, L., Skinner, D., Warren, S. F., & Fleming, K. (2012). Parenting young children

with and without fragile X syndrome. American Journal on Intellectual and Developmental

Disabilities, 117(3), 194-206.

Sterling, A., & Warren, S. F. (2014). Maternal responsivity in mothers of young children with Down

syndrome. Developmental Neurorehabilitation, 17(5), 306-317.

Sterling, A., & Warren, S. F. (2018). Parenting of children with Down syndrome compared to fragile X

syndrome. Developmental Neurorehabilitation, 21(1), 64-67.

Sterling, A., Warren, S. F., Brady, N., & Fleming, K. (2013). Influences on maternal responsivity in

mothers of children with fragile X syndrome. American Journal on Intellectual and

Developmental Disabilities, 118(4), 310-326.

Stoneman, Z., & Brody, G. H. (1981). Two's company, three makes a difference: An examination of

mothers' and fathers' speech to their young children. Child Development, 52, 705-707.

Stroud, C. B., Durbin, C. E., Wilson, S., & Mendelsohn, K. A. (2011). Spillover to triadic and dyadic

systems in families with young children. Journal of Family Psychology, 25(6), 919-930.

Tamis-LeMonda, C. S., Bornstein, M. H., & Baumwell, L. (2001). Maternal responsiveness and children's

achievement of language milestones. Child Development, 72(3), 748-767.

Tamis-LeMonda, C. S., Kuchirko, Y., & Song, L. (2014). Why is infant language learning facilitated by

parental responsiveness? Current Directions in Psychological Science, 23(2), 121-126.

Thurman, A. J., Potter, L. A., Kim, K., Tassone, F., Banasik, A., Potter, S. N., ... & Abbeduto, L. (2020).

Controlled trial of lovastatin combined with an open-label treatment of a parent-implemented

language intervention in youth with fragile X syndrome. Journal of Neurodevelopmental

Disorders, 12(1), 1-17.

Vilaseca, R., Rivero, M., Bersabé, R. M., Cantero, M. J., Navarro-Pardo, E., Valls-Vidal, C., & Ferrer, F.

(2019). Demographic and parental factors associated with developmental outcomes in children

with intellectual disabilities. Frontiers in Psychology, 10, 872.

Wang, M., Summers, J. A., Little, T., Turnbull, A., Poston, D., & Mannan, H. (2006). Perspectives of

fathers and mothers of children in early intervention programmes in assessing family quality of

life. Journal of Intellectual Disability Research, 50(12), 977-988.

Wanless, S. B., Rosenkoetter, S. E., & McClelland, M. M. (2008). Paternal depression and infant

cognitive development: implications for research and intervention. Infants & Young

Children, 21(2), 134-141.

Ward, K. P., & Lee, S. J. (2020). Mothers’ and fathers’ parenting stress, responsiveness, and child

wellbeing among low-income families. Children and Youth Services Review, 116, 105218.

Warren, S. F., & Brady, N. C. (2007). The role of maternal responsivity in the development of children

with intellectual disabilities. Mental Retardation and Developmental Disabilities Research

Reviews, 13(4), 330-338.

Warren, S. F., Brady, N., Fleming, K. K., & Hahn, L. J. (2017). The longitudinal effects of parenting on

adaptive behavior in children with fragile X syndrome. Journal of Autism and Developmental

Disorders, 47(3), 768-784.

Warren, S. F., Brady, N., Sterling, A., Fleming, K., & Marquis, J. (2010). Maternal responsivity predicts

language development in young children with fragile X syndrome. American Journal on

Intellectual and Developmental Disabilities, 115(1), 54-75.

Wheeler, A., Hatton, D., Reichardt, A., & Bailey, D. (2007). Correlates of maternal behaviours in mothers

of children with fragile X syndrome. Journal of Intellectual Disability Research, 51(6), 447-462.

Wilson, K. R., & Prior, M. R. (2011). Father involvement and child well‐being. Journal of Paediatrics

and Child Health, 47(7), 405-407.

Yoder, P. J., & Warren, S. F. (1998). Maternal responsivity predicts the prelinguistic communication

intervention that facilitates generalized intentional communication. Journal of Speech, Language,

and Hearing Research, 41(5), 1207-1219.

Yoder, P. J., & Warren, S. F. (1999). Maternal responsivity mediates the relationship between

prelinguistic intentional communication and later language. Journal of Early Intervention, 22(2),

126-136.

Yoder, P. J., & Warren, S. F. (2001). Relative treatment effects of two prelinguistic communication

interventions on language development in toddlers with developmental delays vary by maternal

characteristics. Journal of Speech, Language, and Hearing Research, 44(1), 224-237.

Yoder, P. J., & Warren, S. F. (2004). Early predictors of language in children with and without Down

syndrome. American Journal on Mental Retardation, 109(4), 285-300.

Study 3: Parental Responsivity and Child Communication During Mother-Child and Father-Child

Interactions in Fragile X Syndrome

Abstract

Past research shows that parentally responsive behavior positively influences language

development in both neurotypical children and children with intellectual and developmental disabilities,

including those with fragile X syndrome (FXS), but the majority of these studies have focused

exclusively on the mother-child relationship. Therefore, very little is known about how paternal behavior

compares to maternal behavior or how paternal behavior relates to child outcomes. The current study

examined relationships between parent behavior (i.e., responsivity and behavior management) and child

language performance in both mother-child and father-child interactions, as well as relationships

between child characteristics and both parent behavior and child language. Participants were 23 families

of young boys with FXS between the ages of 3 and 7 years. Results indicated that mothers and fathers

used similar rates of responsive behaviors during parent-child interactions, and that parental responsivity

was positively associated with child language performance, including talkativeness and lexical diversity.

Parental behavior, however, was not associated with child syntactic complexity. Moreover, findings also

indicated that older children and children with higher levels of adaptive behavior had parents who used

higher rates of responsive behaviors. Additionally, fathers used higher rates of behavior management

strategies compared to mothers, and this type of parental behavior was not associated with child

language performance. Overall, this study provides evidence that interventions focused on increasing

parental responsiveness would be beneficial for these families and that these interventions should be

delivered early given the association between responsivity and child age. The similarities in parental

behavior across mother-child and father-child interactions suggests that including both parents in the

intervention would likely lead to better outcomes for families.

Keywords: fragile X syndrome, parental responsivity, child language, behavior management

Individuals with fragile X syndrome (FXS) experience significant delays in multiple domains of

language (Abbeduto et al., 2007). Language is one of the most important developmental domains to

understand and target for intervention in these individuals given its role in a range of adaptive outcomes,

including social relationships and academic success (Abbeduto & Hagerman, 1997). Children develop

language through interactions with their caregivers, whose behavior changes over time to match the

developmental level of the child (Brady et al., 2009). In recent years, research on maternal responsivity in

FXS suggests that both early and sustained responsivity are critically important for child outcomes across

a variety of domains (e.g., Brady et al., 2020; Brady et al., 2014; Warren et al., 2017; Warren et al.,

2010). Moreover, recent parent-implemented language interventions for children with FXS between the

ages of two and 17 years have shown that parents are able to learn and implement targeted responsive

strategies and that there are associated gains in child engagement and language (e.g., McDuffie et al.,

2018; McDuffie, Machalicek, et al., 2016; McDuffie, Oakes, et al., 2016; Thurman et al., 2020).

However, the majority of past research on the role of responsive parenting in FXS, and on parent-

implemented language interventions, has focused exclusively on the mother-child dyad. The goals of the

current study are (1) to examine relationships between parental responsivity and child language

performance in young boys with FXS; (2) to examine relationships between characteristics of the child

and both parental responsivity and child language performance; and (3) to expand the focus beyond the

mother-child dyad to include examinations of these relationships in father-child dyads.

Behavioral Phenotype of FMR1-Associated Conditions

Fragile X syndrome (FXS), an X-linked disorder, is the leading inherited cause of intellectual

disability (ID; Crawford et al., 2001). FXS results from the expansion of a cytosine-guanine-guanine

(CGG) trinucleotide sequence in the FMR1 gene to greater than 200 repeats, which is defined as the full

mutation (Oostra & Willemsen, 2003). Because FXS is X-linked, males tend to be affected more often

and more severely than females. Specifically, it is estimated that approximately 1 in 7,143 males are

affected by the full mutation compared to only approximately 1 in 11,111 females (Hunter et al., 2014).

Additionally, most males with FXS have IQ scores in the range of ID (i.e., typically < 70), whereas only

25-50% of females with FXS have scores that meet criteria for ID (Hessl et al., 2009; Wright-Talamante

et al., 1996).

Other phenotypic characteristics of FXS, all of which are likely to interfere with language

learning and social interaction, include hyperactivity (Baumgardner et al., 1995), deficits in executive

functioning (Loesch et al., 2003), anxiety and social withdrawal (Cordeiro et al., 2011), and aggression

(Hessl et al., 2008). Males with FXS also frequently display symptoms of ASD, with as many as 50-60%

of males receiving a diagnosis of ASD when diagnostic tools designed to measure symptoms of ASD in

the general population are used (Abbeduto et al., 2019). Moreover, individuals with FXS seem to

experience more significant impairment in social functioning compared to individuals with other genetic

neurodevelopmental disorders, perhaps in part because of the increased rates of hyperactivity,

impulsivity, and inattention observed in FXS (Chromik et al., 2015).

Only females can transmit the FMR1 full mutation to the next generation. Biological mothers of

children with FXS are most often FMR1 premutation carriers, although some may have the full mutation.

FMR1 premutation carriers have between 55 and 200 CGG repeats and present with a unique phenotype

that is shaped by genetic and environmental factors (Mailick et al., 2018; Seltzer et al., 2012). The FMR1

premutation can result in two established disorders: fragile X-associated primary ovarian insufficiency

(FXPOI) and fragile X-associated tremor ataxia syndrome (FXTAS), a late-onset neurogenerative disease

that affects both male and female premutation carriers (Visootsak et al., 2014). Females with the FMR1

premutation are at risk by virtue of their biology for a range of psychiatric, medical, and cognitive

differences compared to the general population (Hagerman et al., 2018). For example, female premutation

carriers are more likely to experience mood or anxiety disorders compared to the general population

(Bourgeois et al., 2011). These women are also more likely to experience medical problems, including

migraines, fibromyalgia, neuropathy, and vestibular difficulties compared to females without the

premutation, as well as deficits in executive functioning, attention, working memory, arithmetic, and

various aspects of language (Wheeler et al., 2014). Mothers of children with FXS are also likely to

experience high levels of parenting stress (Hartley et al., 2012) and mental health challenges (Abbeduto et

al., 2004) due at least in part to the significant challenges associated with parenting a child with a

disability. These challenges include high levels of child challenging behaviors, low levels of adaptive

behaviors, an increased financial burden, and social isolation (Minnes et al.,Weiss, 2015; Tint & Weiss,

2015).

The cumulative effects of the factors affecting female carriers may constrain the development of

a warm and responsive mother-child relationship with subsequent negative impacts on optimal child

development (Lovejoy et al., 2000; Warren & Brady, 2007). These challenges faced by families affected

by FXS are also likely to contribute to reduced marital satisfaction and family cohesion (Baker et al.,

2012), which could further negatively affect both the mother-child relationship and the father-child

relationship and thereby the child’s development across multiple domains.

Language Learning and Responsive Parenting

Children learn language by engaging in back-and-forth interactions with more advanced

communicative partners, such as their parents or other adult caregivers (e.g., Bruner, 1975; Ford et al.,

2020; Golinkoff et al., 2018; Sameroff & Fiese, 2000). According to the social-interactionist approach to

language development, as children become more advanced communicators, adults respond by adjusting

their behaviors to match the child’s developmental level (Warren & Brady, 2007). These modifications in

reaction to the child’s developmental level are considered to be examples of responsivity (Brady et al.,

2009). For example, mothers often use a slower rate of speech, exaggerated prosody, and more simplified

language when talking to infants and very young children compared to their talk to older children and

adults (i.e., infant/child-directed speech; Ma et al., 2011; Newman et al., 2016).

As the child becomes more communicative and socially engaged, responsive parents modify their

behavior and adapt to their child’s developing abilities and interests by maintaining their child’s focus of

attention and following the lead of the child through behaviors such as commenting and recasting on the

child’s current activities and interests (Brady et al., 2009; Tamis-LeMonda et al., 2014). In typical

development, the degree of maternal responsiveness has been found to be predictive of the timing of early

language milestones, including first imitations, first words, attainment of first 50 expressive words, first

combinations, and first use of language to talk about the past (Tamis-LeMonda et al., 2001). Moreover,

consistent, or sustained, responsiveness over time has been shown to be important for cognitive and social

development throughout early childhood (Landry et al., 2001).

Parental responsivity has also been found to have positive effects on various developmental

domains, including language development, in populations with neurodevelopmental disabilities. For

example, Yoder and Warren (1998) found that parental responsivity was predictive of the display of

intentional communication by young children with developmental disabilities of various etiologies.

Maternal responsivity was also found to have a positive influence on the relationship between children’s

intentional communication and later language development (Yoder & Warren, 1999), as well as an effect

on children’s receptive and expressive language six and twelve months after participation in two different

prelinguistic communication interventions (Yoder & Warren, 2001). Moreover, McDuffie & Yoder

(2010) found that certain types of parental verbal responsiveness predicted early vocabulary acquisition in

young children with ASD; specifically, parents’ use of follow-in comments, follow-in directives, and

expansions of child communication acts. In another study, Sterling and Warren (2014) found that mothers

of children with Down syndrome (DS) were able to employ a highly responsive and interactive style of

parenting that was facilitative of the child’s linguistic development, particularly for those children in the

sample who were older and more communicative. That is, the extent to which mothers are able to

successfully adapt to their children’s linguistic growth by increasing their use of facilitative behaviors that

match their child’s current levels of functioning and need, the more positive the children’s outcomes.

Maternal Responsivity and Child Outcomes in FXS

Over the past decade, Warren, Brady, Sterling, and colleagues have investigated longitudinal

relationships between maternal responsivity and child outcomes in a sample of 55 mother-child dyads.

Warren et al. (2010) examined the effects of maternal responsivity (i.e., gesture use, requests for verbal

compliance, comments, and recodes) on language development across three years in young children with

FXS. They found that maternal responsivity predicted both proximal and distal levels of receptive and

expressive language at 36 months, even after controlling for ASD symptoms and nonverbal

developmental level. Similarly, in another study of these mother-child dyads, Brady et al. (2014) found

that sustained responsivity measured across four years predicted later receptive and expressive vocabulary

development up to nine years of age. In a recent study, Brady et al. (2020) found that maternal

responsivity continued to be important for language development during adolescence in this sample.

Specifically, maternal commenting was related to growth in child rate of different words produced in

conversation samples as well as receptive vocabulary as measured by a standardized test. However,

maternal commenting was not related to growth in either expressive vocabulary (as measured by a

standardized test) or expressive syntax, which was consistent with previous findings in this sample

(Komesidou et al., 2017). These findings suggest that maternal responsivity in FXS is not only important

in early childhood (Warren et al., 2010) and middle childhood (Brady et al., 2014), but that sustaining

responsivity has a positive influence on language development even throughout adolescence and,

therefore, could be a potential target for intervention even beyond the early years of development for this

population.

The effects of maternal responsivity in FXS extend beyond language development. In another

study of their mother-child dyads, Warren et al. (2017) examined the relationship between maternal

responsivity and adaptive behavior as measured by the Vineland Adaptive Behavior Scales (Sparrow et

al., 1984, 2005). Overall, they found that sustained maternal responsivity was found to have a significant

and positive impact on growth in child Communication scores, even after controlling for ASD symptoms

and developmental level. In addition, maternal responsivity predicted trajectories of Socialization and

Daily Living Skills, but to a lesser extent than Communication skills. Perhaps the most interesting

finding from this study was that roughly half of the children showed declines in adaptive behavior (i.e.,

decreases in raw scores over time); yet those participants who had mothers who were more responsive

declined less than those who had mothers who were less responsive. This finding was most evident in the

Communication domain, suggesting the importance of responsive parenting for the development of

adaptive communication skills during middle childhood. The results of these studies highlight the

importance of maternal responsivity for child language and adaptive functioning outcomes in FXS. The

current study was designed to replicate some of these past findings in a new sample of mother-child

dyads, and to extend the investigation to also include father-child dyads.

Theoretical Framework

Both the transactional model (Sameroff & Chandler, 1975) and family systems theory (Cox &

Paley, 1997) explain how responsive parenting can influence child development across multiple domains,

including language. These models also provide an explanatory framework for the social-interactionist

approach to language learning (e.g., Brady et al., 2009; Chapman, 2000; Warren & Brady, 2007). Family

systems theory focuses on the importance of the family as an ecological system in which an individual

develops such that “any individual family member is inextricably embedded in the larger family system

and can never be fully understood independent of the context of that system” (Cox & Paley, 1997, p.

246). The transactional model suggests that the development of a child results from the bidirectional

effects between the child and the environment, such that experiences in the environment are not

considered independent of the child. From birth onward, a child’s relationship with their parents affects

socioemotional, behavioral, and cognitive outcomes. According to Laursen and Collins (2009),

“bidirectional models imply that parent behaviors are both the cause and the consequence of child

behaviors” (p. 11). Features of the parent, including parental physical and mental health, parenting

practices, and parental perceptions, as well as features of the child, including cognitive level,

temperament, and the ability to self-regulate, make for transactional interactions such that the parent and

child are an interdependent unit (e.g., Belsky, 1984; Neece et al., 2012; Sameroff & Mackenzie, 2003;

Sameroff, 2009). Thus, early onset conditions (e.g., genetic disorders) lead to later outcomes (e.g.,

impairments vs. growth in language) through bidirectional transactions between the child and their

environment (e.g., interactions with a parent). The current study examined relationships between parent

behavior, child behavior, and child characteristics in both mother-child and father-child interactions.

Current study

The current study was designed to examine relationships between parental responsivity and child

language, as well as the ways in which child characteristics affect parent behavior and relate to language

100

performance, in both mother-child and father-child dyads. The first aim was to examine relationships

among maternal responsivity, paternal responsivity, and child language in parent-child dyadic

interactions, as well as relationships between child characteristics (i.e., challenging behaviors, ASD

symptoms, and adaptive behavior) and child language. We hypothesized that higher rates of parental

responsivity in the dyadic interactions would be positively associated with better child language

performance (e.g., Warren & Brady, 2007; Warren et al., 2010). The second aim was to examine

relationships between child characteristics and parental responsivity. We hypothesized that higher levels

of child adaptive behavior as well as fewer challenging behaviors and ASD symptoms would be

associated with higher rates of parental responsivity (Warren et al., 2010; Brady et al., 2014).

Method

Procedures and Measures

The data for the current study were collected as part of a larger study investigating relationships

within families and parent and child behavior in families affected by FXS. Participants included 23

families of male children with FXS between the ages of 3;0 and 7;11 years, with a total of 69 participants

including 23 fathers (22 biological fathers and one stepfather), 23 biological mothers, and 23 male

children with FXS. See pages 5 – 6 for additional details on the participants in the current study.

In order to address the aims of this study, mothers and fathers independently completed two

questionnaires via REDCap (Harris et al., 2019; Harris et al., 2009) to assess child characteristics,

including the Aberrant Behavior Checklist-Community, 2nd edition (ABC-2; Aman & Singh, 2017) and

the Social Responsiveness Scale, 2nd edition (SRS-2; Constantino & Gruber, 2012). One parent also

completed the Vineland Adaptive Behavior Scales, 3rd edition (Vineland-3; Sparrow, Cicchetti, &

Saulnier, 2016) as an interview to assess the child’s adaptive behavior.

The ABC-2 measures challenging behaviors of individuals with developmental disabilities in

several domains. Total raw scores from the FXS-specific subscale scoring (Sansone et al., 2012) were

used in analyses. The SRS-2 measures social impairments commonly associated with ASD, providing

DSM-5 compatible subscale scores for Social Communication and Interaction (SCI) and Restricted

101

Interests and Repetitive Behavior (RRB), as well as a Total T-score. Total T-scores from the SRS-2 were

used in analyses. The Vineland-3 measures adaptive behavior across multiple domains. For the current

study, the Adaptive Behavior Composite score as well as the Communication, Daily Living Skills, and

Socialization domain standard scores were used in analyses.

Mothers and fathers also separately engaged in 12-minute play-based interactions with their child.

The parent-child interactions served as the language samples for the current study. These interactions

were transcribed using SALT (Systematic Analysis of Language Transcripts; Miller & Iglesias, 2008)

and coded for parental behavior using a coding scheme adapted from Warren and colleagues (e.g.,

Warren et al., 2010; see page 13 for definitions and examples of the specific codes used in the current

study).

Measures of parent and child language were also obtained from the SALT transcripts, including total

number of utterances (a measure of talkativeness), number of different words (NDW; a measure of lexical

diversity), and mean length of utterance in morphemes (MLUm; a measure of syntactic complexity). See

pages 9 – 13 for additional details regarding the measures and procedures used in the current study.

Analysis Plan

All variables were visually inspected to check for model assumptions of normality and

homoscedasticity of the residuals. Tests for skewness and kurtosis were also examined. Transformations

and nonparametric alternatives were considered for any data that did not meet parametric assumptions.

Descriptive summaries of the parent behavior and parent and child language measures from the dyadic

interactions were reported. Interspousal correlations were calculated to determine the degree of

correspondence between parent behavior and parent and child language measures in the mother-child and

father-child interactions. Comparisons of means for parent behavior and parent and child language

measures from the mother-child and father-child interactions were also reported. Then, correlations

between the dependent and independent variables for the study aims were reported to investigate

relationships between the variables, examine potential differences between these relationships for

mothers and fathers, and assess for multicollinearity between the independent variables for Aims 1 and 2.

102

Correlations between (1) maternal ratings of the child and mother-child interaction variables, (2) paternal

103

ratings of the child and father-child interaction variables, and (3) combined parental ratings of the child

and parent-child interaction variables were reported. The combined parental ratings were based on means

of the variables (as opposed to sums of the variables within families).

The dependent variables for Aim 1 included the following child language measures obtained

from the SALT transcripts of the dyadic play-based language samples: total number of utterances, NDW,

and MLUm. The independent variables for Aim 1 were proportion scores for parental responsivity and

behavior management from the mother-child and father-child dyadic interactions, as well as child

characteristics embodied in the ABC-2 Total raw scores, SRS-2 Total T-scores, the Vineland-3 domain

and Adaptive Behavior Composite scores, and child age. For Aim 2, the primary outcome variables were

proportion scores for parental responsivity and behavior management from the mother-child and father-

child dyadic interactions and the independent variables were child characteristics (i.e., those listed above

for Aim 1). For Aims 1 and 2, models were specified using a multilevel modeling (MLM) approach

(Raudenbush & Byrk, 2002), given the non-independence of the data collected from different parent-child

dyads within families. In this approach, the data from each dyad is nested within a group that has an N of

2 (Campbell & Kashy, 2002). Effect coding was used for parent sex (i.e., Male = 1 and Female = -1), and

continuous predictors were centered to their respective grand means.

For Aim 1, intraclass correlation coefficients (ICCs) were calculated to estimate the proportion of

the total variation in the child language measures (i.e., total number of utterances, NDW, and MLUm)

that exists between versus within families. Then, separate models for child total number of utterances,

child NDW, and child MLUm were specified to investigate the contributions of parent behavior and child

characteristics to child language performance. There were strong and significant associations between the

variables for child ASD symptoms, child challenging behavior, and child adaptive behavior; however,

there was not a significant association between the measures of child challenging behavior and child

adaptive behavior. Therefore, in addition to child age, the ABC-2 Total raw score and the Vineland-3

Adaptive Behavior Composite score were used in the models for Aim 1 to predict child language

performance. Parent sex was also included as a predictor in the models for Aim 1 to examine whether

104

there were differences in the child language performance between the mother-child and father-child

interactions.

For Aim 2, ICCs were calculated to estimate the proportion of the total variation in the measures

of parental responsivity and behavior management that exists between versus within couples. Then

MLMs were specified to investigate the contributions of child characteristics to parental responsivity and

behavior management. The same child variables that were used in Aim 1 were used in Aim 2 to predict

parent behavior. Parent sex was also included as a predictor to examine whether there were differences in

parent behavior between the mother-child and father-child interactions.

Results

Aim 1 was designed to examine the relationships among maternal responsivity, paternal

responsivity, and child language performance in parent-child dyadic interactions, as well as relationships

between child characteristics and child language performance. Aim 2 was designed to examine

relationships between child characteristics and parental responsivity. Table 1 displays descriptive

statistics for the parent behavior variables as well as the parent and child language variables that were

coded or obtained from the transcripts of the mother-child and father-child dyadic play-based

interactions. Paired samples t-tests and Wilcoxon signed-ranks tests (when appropriate) confirmed that

there were no statistically significant differences between child language performance, mothers’ and

fathers’ language, or mothers’ and fathers’ use of responsivity or behavior management strategies except

for parent talkativeness (i.e., total number of utterances; t(22) = 3.22, p = 0.004), parent lexical diversity

(i.e., NDW; t(22) = 4.29, p = 0.0003), and the behavior management proportion score (Z = -2.01, p =

0.031). Mothers had a greater number of total utterances and different words compared to fathers, and

fathers had a higher proportion score for behavior management strategies compared to mothers.

Table 1 also displays interspousal correlations; given that some of these variables were not

normally distributed, Spearman’s rank-order correlations were reported instead of Pearson’s correlations.

Interspousal correlations indicated that there were significant correspondences between the measures of

mothers’ and fathers’ language as well as their responsivity frequency and proportion totals. There were

105

also significant correspondences between the measures of child language in the mother-child and father-

child interactions. No significant correspondences were found between mothers’ and fathers’ use of the

individual behavior management strategies or the total frequency or proportion scores for behavior

management.

Table 1

Comparisons of Parental Behavior and Child Language During Dyadic Parent-Child Interactions

Mean (SD)

Range



(p-value)

Mother-Child

Interaction

Father-Child

Interaction

Interspousal

Correlation

Parent Language

Total Utterances 288.52 (62.22)

150 – 405

240.30 (82.97)

85 – 450

0.49*

(0.017)

Number of Different

Words

211.48 (46.71)

126 – 307

171.57 (49.46)

74 – 271

0.55**

(0.007)

Mean Length of Utterance -

Morphemes

3.36 (0.51)

2.21 – 4.53

3.41 (0.50)

2.43 – 4.25

0.42*

(0.047)

Total Parental Responsivity

(Frequency)

93.39 (42.91)

26 – 176

82.48 (39.58)

11 – 149

0.81***

(0.00001)

Total Parental Responsivity

(Proportion of Total Utterances)

0.33 (0.14)

0.07 – 0.61

0.35 (0.15)

0.13 – 0.60

0.69***

(0.0003)

Total Behavior Management

(Frequency)

14.43 (12.41)

5 – 53

17.57 (12.78)

4 – 58

0.34

(0.116)

Total Behavior Management

(Proportion of Total Utterances)

0.05 (0.04)

0.01 – 0.14

0.07 (0.04)

0.02 – 0.19

0.29

(0.177)

Child Language

Total Utterances 132.17 (87.29)

6 – 307

138.13 (89.34)

1 – 303

0.81***

(0.00001)

Number of Different

Words

68.13 (48.04)

1 – 154

71.09 (55.61)

1 – 178

0.88***

(0.00001)

Mean Length of Utterance -

Morphemes

1.70 (0.55)

1.00 – 2.75

1.82 (0.77)

1.00 – 3.88

0.90***

(0.00001)

Note. ~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Tables 2 and 3 displays correlations between measures of parent behavior, child language, and

child characteristics for mothers and fathers, respectively. Table 4 displays correlations between these

106

variables combined across parents. Table 2 shows that there were significant positive correlations

between maternal responsivity and the following variables: child age, child total utterances, child NDW,

child MLUm, and the Vineland-3 Daily Living Skills and Socialization domains as well as the Vineland-

3 Adaptive Behavior Composite score. There were no significant correlations between maternal behavior

management and child language or child characteristics with the exception of a significant correlation

between maternal behavior management and the Vineland-3 Socialization domain score.

Table 2

Spearman Correlations for Maternal Ratings of the Child and Mother-Child Interaction Variables

(N = 23)

Variable 1. 2. 3. 4. 5. 6. 7. 8.

1. Responsivity 1.00

2. Behavior

Management

-0.16

(0.471)

1.00

3. Child Age 0.42*

(0.0495)

0.06

(0.799)

1.00

4. Child Total

Utterances

0.81***

(0.00001)

0.18

(0.399)

0.47*

(0.025)

1.00

5. Child NDW10.84***

(0.00001)

-0.01

(0.993)

0.47*

(0.023)

0.94***

(0.00001)

1.00

6. Child

MLUm2

0.68***

(0.0004)

-0.10

(0.666)

0.47*

(0.025)

0.75***

(0.00001)

0.89***

(0.00001)

1.00

7. Challenging

Behavior3

-0.03~

(0.089)

0.37~

(0.080)

0.09

(0.687)

0.23

(0.290)

0.16

(0.468)

0.19

(0.393)

1.00

8. ASD

Symptoms4

-0.27

(0.209)

0.40~

(0.059)

0.36~

(0.093)

-0.05

(0.805)

-0.20

(0.369)

-0.17

(0.450)

0.47*

(0.023)

1.00

9. Commun-

ication5

0.41~

(0.054)

-0.21

(0.343)

-0.25

(0.257)

0.39~

(0.070)

0.44*

(0.036)

0.45*

(0.031)

-0.10

(0.653)

-0.63

(0.001)

10. Daily

Living Skills6

0.43*

(0.043)

-0.27

(0.209)

-0.17

(0.438)

0.45*

(0.031)

0.52*

(0.011)

0.40~

(0.059)

-0.24

(0.273)

-0.65

(0.0007)

11. Social-

ization7

0.48*

(0.021)

-0.43*

(0.042)

-0.19

(0.378)

0.34

(0.118)

0.43*

(0.041)

0.39~

(0.066)

-0.36~

(0.094)

-0.83

(0.00001)

12. Vineland

ABC8

0.46*

(0.026)

-0.21

(0.326)

-0.25

(0.259)

0.45*

(0.032)

0.50*

(0.015)

0.43*

(0.039)

-0.22

(0.315)

-0.78

(0.00001)

Note. Proportion scores used for Responsivity and Behavior Management variables. 1NDW = number of

different words. 2MLUm = mean length of utterance in morphemes. 3ABC-2 Total raw score. 4SRS-2

Total T-Score. 5Vineland-3 Communication domain standard score. 6Vineland-3 Daily Living Skills

domain standard score. 7Vineland-3 Socialization domain standard score. 8Vineland-3 Adaptive Behavior

Composite score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

107

Table 3

Spearman Correlations for Paternal Ratings of the Child and Father-Child Interaction Variables

(N = 23)

Variable 1. 2. 3. 4. 5. 6. 7. 8.

1. Responsivity 1.00

2. Behavior

Management

-0.25

(0.250)

1.00

3. Child Age 0.27

(0.213)

0.29

(0.173)

1.00

4. Child Total

Utterances

0.83***

(0.00001)

0.05

(0.809)

0.33

(0.130)

1.00

5. Child NDW10.81***

(0.00001)

0.04

(0.849)

0.40~

(0.061)

0.94***

(0.00001)

1.00

6. Child

MLUm2

0.76***

(0.00001)

-0.07

(0.757)

0.46*

(0.025)

0.85***

(0.00001)

0.93***

(0.00001)

1.00

7. Challenging

Behavior3

-0.32

(0.136)

-0.20

(0.356)

-0.28

(0.198)

-0.47*

(0.024)

-0.46*

(0.026)

-0.43*

(0.040)

1.00

8. ASD

Symptoms4

-0.47*

(0.023)

0.11

(0.632)

-0.10

(0.635)

-0.47*

(0.023)

-0.42*

(0.046)

-0.45*

(0.032)

0.66***

(0.0007)

1.00

9. Commun-

ication5

0.62**

(0.002)

-0.26

(0.236)

-0.25

(0.257)

0.52*

(0.011)

0.57**

(0.005)

0.54**

(0.007)

-0.07

(0.748)

-0.27

(0.211)

10. Daily

Living Skills6

0.61**

(0.002)

-0.14

(0.536)

-0.17

(0.438)

0.55**

(0.007)

0.59**

(0.003)

0.47*

(0.025)

-0.21

(0.342)

-0.23

(0.290)

11. Social-

ization7

0.60**

(0.003)

-0.343

(0.109)

-0.19

(0.378)

0.48*

(0.021)

0.49*

(0.019)

0.48*

(0.022)

-0.28

(0.203)

-0.43*

(0.038)

12. Vineland

ABC8

0.68**

(0.001)

-0.28

(0.200)

-0.25

(0.259)

0.59**

(0.003)

0.61**

(0.002)

0.53**

(0.009)

-0.227

(0.297)

-0.40~

(0.058)

Note. Proportion scores used for Responsivity and Behavior Management variables. 1NDW = number of

different words. 2MLUm = mean length of utterance in morphemes. 3ABC-2 Total raw score. 4SRS-2

Total T-Score. 5Vineland-3 Communication domain standard score. 6Vineland-3 Daily Living Skills

domain standard score. 7Vineland-3 Socialization domain standard score. 8Vineland-3 Adaptive Behavior

Composite score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 3 shows that there were significant correlations between paternal responsivity and the

following variables: child total utterances, child NDW, child MLUm, and child ASD symptoms, as well

as all Vineland-3 domain (i.e., Communication, Daily Living Skills, and Socialization) scores and the

Vineland-3 Adaptive Behavior Composite score. Additionally, there were no significant correlations

between paternal behavior management and any of the measures of child language or child

characteristics.

Table 4

Spearman Correlations for Combined Parental Ratings and Parent-Child Interaction Variables (N = 46)

Variable 1. 2. 3. 4. 5. 6. 7. 8.

1. Responsivity 1.00

2. Behavior

Management

-0.17

(0.250)

1.00

3. Child Age 0.34*

(0.019)

0.17

(0.271)

1.00

4. Child Total

Utterances

0.82***

(0.00001)

0.14

(0.356)

0.40**

(0.006)

1.00

5. Child NDW10.83***

(0.00001)

0.03

(0.821)

0.43**

(0.002)

0.93***

(0.00001)

1.00

6. Child MLUm20.73***

(0.00001)

-0.06

(0.703)

0.48***

(0.0007)

0.79***

(0.00001)

0.91***

(0.00001)

1.00

7. Challenging

Behavior3

-0.19

(0.218)

0.04

(0.797)

-0.08

(0.610)

-0.14

(0.368)

-0.15

(0.314)

-0.13

(0.404)

1.00

8. ASD Symptoms4-0.39**

(0.007)

0.16

(0.277)

0.12

(0.419)

-0.28

(0.063)

-0.30*

(0.046)

-0.30*

(0.044)

0.58***

(0.00001)

1.00

9. Communication50.50***

(0.0004)

-0.21

(0.170)

-0.25

0.100

0.44**

(0.002)

0.51***

(0.0003)

0.49***

(0.0006)

-0.10

(0.499)

-0.45**

(0.002)

10. Daily Living Skills60.51***

(0.0003)

-0.18

(0.239)

-0.17

(0.258)

0.49***

(0.0005)

0.57***

(0.00001)

0.43**

(0.003)

-0.229

(0.126)

-0.44**

(0.002)

11. Socialization70.53***

(0.0002)

-0.35*

(0.016)

-0.19

(0.199)

0.41**

(0.004)

0.46**

(0.001)

0.42**

(0.004)

-0.33*

(0.027)

-0.64***

(0.00001)

12. Vineland ABC80.56***

(0.00001)

-0.21

(0.167)

-0.25

(0.100)

0.52***

(0.0002)

0.56***

(0.00001)

0.47**

(0.0009)

-0.24

(0.115)

-0.59***

(0.00001)

Note. Proportion scores used for Responsivity and Behavior Management variables. 1NDW = number of different words. 2MLUm = mean length

of utterance in morphemes. 3ABC-2 Total raw score. 4SRS-2 Total T-Score. 5Vineland-3 Communication domain standard score. 6Vineland-3

Daily Living Skills domain standard score. 7Vineland-3 Socialization domain standard score. 8Vineland-3 Adaptive Behavior Composite score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

102

103

In the mother-child interactions, there were significant correlations between child age and the

three measures of child language. However, in the father-child interactions, child age was associated with

child MLUm, but not child total utterances or NDW. Additionally, in the father-child interactions, the

child language measures were associated with paternal ratings of the child’s challenging behavior and

ASD symptoms as well as the three Vineland-3 domain scores and the Adaptive Behavior Composite

score. In the mother-child interactions, the child language measures were not associated with maternal

ratings of the child’s challenging behavior or ASD symptoms. However, child talkativeness was

associated with the Vineland-3 Daily Living Skills domain and Adaptive Behavior Composite score,

child lexical diversity was associated with all Vineland-3 domain scores and the Adaptive Behavior

Composite score, and child syntactic complexity was associated with the Vineland-3 Communication

domain score and the Adaptive Behavior Composite score.

Table 4 shows that when the maternal and paternal variables are analyzed together, parental

responsivity was associated with child age, ASD symptoms, adaptive behavior, and all child language

measures, and parental behavior management was only associated with the Vineland-3 Socialization

domain. Child age was associated with all child language measures, but not with child challenging

behaviors, ASD symptoms, or adaptive behavior. All child language measures were associated with

adaptive behavior, and child lexical diversity and syntactic complexity were associated with ASD

symptoms.

Aim 1 Models – Influence of Parent Behavior on Child Language

The ICC for child talkativeness indicated that 88.1% of the variation was due to between-dyad

factors, whereas 11.9% was due to within-dyad factors. For child lexical diversity, 93.2% of the variation

was due to between-dyad factors, whereas 6.8% was due to within-dyad factors. For child syntactic

complexity, 81.1% of the variation was due to between-dyad factors, whereas 18.9% was due to within-

dyad factors. Table 5 presents the results of the MLM analyses for Aim 1. The lexical diversity and

syntactic complexity variables were square-root-transformed. Both of these transformations were done to

reduce positive skew.

104

Table 5

Multilevel Model Results for Aim 1

Child Talkativeness

(Total Utterances)

Child Lexical

Diversity (NDW)

Child Syntactic

Complexity (MLUm)

Fixed Effects

Intercept 135.15***

(9.79)

7.49***

(0.40)

1.30***

(0.03)

Parental

Responsivity

276.81***

(57.89)

8.21***

(1.98)

0.31

(0.22)

Challenging

Behavior

0.19

(0.24)

-0.0004

(0.01)

-0.0001

(0.001)

Adaptive Behavior 2.51*

(0.98)

0.16***

(0.04)

0.01***

(0.003)

Child Age 20.60**

(7.67)

1.07**

(0.31)

0.09***

(0.02)

Parent Sex 0.47

(3.82)

-0.06

(0.13)

0.01

(0.02)

Random Effects

Residual (σ2)

632.

0.68 0.01

Intercept (σ2 )

1890.

3.42 0.01

Goodness-of-fit

AIC 454.

190.41 2.19

BIC 469.

205.04 16.83

Note. Standard errors in parentheses.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Child Talkativeness – Total Number of Utterances

As expected, there was a significant main effect of parental responsivity on child talkativeness.

Across all dyads, when the other predictors were at their mean values, children whose parents displayed

greater than average rates of responsive behaviors were more talkative compared to children whose

parents displayed less than average rates of responsive behaviors. There was also a significant main effect

of adaptive behavior. Across all dyads, when the other predictors were at their mean values, children with

above average levels of adaptive behavior were more talkative compared to children with below average

levels of adaptive behavior. Additionally, there was a main effect of child age, suggesting that across all

dyads, when the other predictors were at their mean values, older children were more talkative than

105

younger children. There were no significant main effects of either challenging behavior or parent sex on

child talkativeness.

Prediction of Child Lexical Diversity – Number of Different Words

As with child talkativeness, there were significant main effects of parental responsivity, child

adaptive behavior, and child age on child lexical diversity, but no significant main effects of either

challenging behavior or parent sex on child lexical diversity. As such, across all dyads, when the other

predictors were at their mean values, children whose parents displayed greater than average rates of

responsive behaviors had higher levels of lexical diversity compared to children whose parents displayed

lower than average rates of responsive behaviors. Additionally, across all dyads, when the other

predictors were at their mean values, children with above average levels of adaptive behavior had higher

levels of lexical diversity compared to children with below average levels of adaptive behavior. Finally,

across all dyads, when the other predictors were at their mean values, older children had higher levels of

lexical diversity than younger children.

Prediction of Child Syntactic Complexity – Mean Length of Utterance in Morphemes

Unlike the other child language measures, there was not a significant main effect of parental

responsivity on syntactic complexity. There were, however, significant main effects of both child adaptive

behavior and child age on syntactic complexity. Across all dyads, when the other predictors were at their

mean values, children with above average levels of adaptive behavior had higher levels of syntactic

complexity compared to children with below average levels of adaptive behavior. Additionally, across all

dyads, when the other predictors were at their mean values, older children had higher levels of syntactic

complexity than younger children. As with the other child language measures, there were no significant

main effects of challenging behavior or parent sex on child syntactic complexity.

Behavior Management as a Predictor of Child Language

There was not a significant main effect of behavior management on child talkativeness, lexical

diversity, or syntactic complexity. These models also included challenging behavior, adaptive behavior,

child age, and parent sex as predictors of the child language measures.

106

Aim 2 Models – Influence of Child Characteristics on Parental Behavior

The ICC for parental responsivity indicated that 68.2% of the variation was due to between-

couples factors whereas 31.8% was due to within-couple factors. For behavior management, 14.4% of the

variation was due to between-couples factors whereas 85.6% was due to within-couple factors. Table 6

presents the results of the MLM analyses for Aim 2. The behavior management variable was log-

transformed to reduce positive skew.

Table 6

Multilevel Model Results for Aim 2

Parental Responsivity Behavior Management

Fixed Effects

Intercept 0.34***

(0.02)

-3.04***

(0.10)

Challenging Behavior -0.0001

(0.0006)

0.001

(0.004)

Adaptive Behavior10.008***

(0.002)

-0.02*

(0.01)

Child Age 0.05***

(0.01)

0.02

(0.08)

Parent Sex 0.01

(0.01)

0.25**

(0.09)

Random Effects

Residual (σ2)

0.007 0.35

Intercept (σ2 )

0.004 0.08

Goodness-of-fit

AIC -27.92 125.52

BIC -15.12 138.32

Note. Standard errors in parentheses. 1The Vineland-3 Socialization domain standard score was used to

predict behavior management instead of the Vineland-3 Adaptive Behavior Composite score.

~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Prediction of Parental Responsivity

As expected, there was a significant main effect of child adaptive behavior on parental

responsivity. Across all dyads, when the other predictors were at their mean values, children with above

average levels of adaptive behavior had parents who used higher rates of responsive behaviors compared

to children with below average levels of adaptive behavior. There was also a significant main effect of

107

child age on parental responsivity. Across all dyads, when the other predictors were at their mean values,

parents of older children demonstrated higher rates of parental responsivity than parents of younger

children. There were no significant main effects of child challenging behavior or parent sex on parental

responsivity.

Prediction of Behavior Management

There was a significant main effect of child adaptive behavior (i.e., socialization) on behavior

management. Across all dyads, when the other predictors were at their mean values, children with above

average levels of adaptive behavior in the Socialization domain had parents who used behavior

management strategies proportionally less often compared to children with below average levels of

adaptive behavior in the Socialization domain. There was also a significant main effect of parent sex on

behavior management. In reference to the overall mean, when the other predictors were at their mean

values, fathers demonstrated higher rates of behavior management strategies compared to mothers. There

were, however, no significant main effects of challenging behavior or child age on parent behavior

management.

Discussion

The current study was designed to examine relationships between parental responsivity and child

language in mother-child and father-child dyadic interactions, as well as the ways in which child

characteristics relate to both child language performance and parental behavior. The findings suggest that

parental responsivity supports child language performance, with no discernable differences between child

language performance in the mother-child versus the father-child interactions. There were also significant

correspondences within families between mothers’ and fathers’ overall language use and responsiveness

with the child, which is consistent with past research in families of neurotypical children (Tamis-

LeMonda et al., 2012). This is the first time this association has been reported in families of children with

FXS. Moreover, despite the significant correspondences between mothers’ and fathers’ language use,

mothers were more talkative (i.e., had a higher total number of utterances) and demonstrated greater

lexical diversity (i.e., had a higher total number of different words) compared to fathers. This finding is

108

also consistent with past research that shows that mothers tend to provide more language input to their

neurotypical children compared to fathers (Davidson & Snow, 1996; Pancsofar & Vernon-Feagans, 2006;

Shapiro et al., 2021), but this is the first time that differences between the frequency of maternal and

paternal input during dyadic parent-child interactions have been reported in families of children with

FXS. Importantly, these differences in overall maternal and paternal input did not lead to any significant

differences in child language performance. However, because only concurrent associations were

examined in the current study, future investigations should continue to examine the potential differences

between parents’ overall language use, as well as any differences in responsivity and behavior

management, to determine whether or how they influence the child’s language development. Differential

effects of parent input on child outcomes may emerge over time in longitudinal studies.

Some interesting differences emerged in the correlations between parental responsivity and child

characteristics for mother compared to fathers. For example, child ASD symptoms (which were

independently rated by both mothers and fathers) were negatively related to parental responsivity for

fathers but not for mothers, with fathers using a greater proportion of responsive behaviors with children

who had fewer symptoms of ASD. Furthermore, child adaptive behavior in the Communication domain

was positively related to parental responsivity for fathers but not for mothers, with fathers using a greater

proportion of responsive behaviors with children who had higher levels of communication. Interestingly,

child ASD symptoms were also negatively associated with all child language measures in the father-child

interactions but not the mother-child interactions. These findings suggest that fathers may have more

difficulty compared to mothers engaging in responsive behaviors with children who have greater levels of

social impairment and lower levels of communication skills.

In contrast, child age was positively related to parental responsivity for mothers but not for

fathers, with mothers using a greater proportion of responsive behaviors with older children. Past research

in FXS has demonstrated a positive association between maternal responsivity and child rate of

communication (Sterling et al., 2013), and child age in the current study was positively associated with

child talkativeness, lexical diversity, and syntactic complexity in mother-child interactions. However, in

109

the father-child interactions, child age was associated only with child syntactic complexity and not with

the other two language measures (i.e., talkativeness and lexical diversity). These findings suggest that

mothers may be modifying their input to the child to a greater extent than fathers based on the child’s age

and developmental level. Future studies should investigate the differential contributions of child

characteristics to parent behavior and child language in mother-child compared to father-child interactions

to develop a better understanding of these potentially transactional relationships. Future studies should

also consider how the relationships between these variables change over time.

In the multilevel models for Aim 1, parental responsivity was found to associate with child

talkativeness and lexical diversity, but not syntactic complexity. Past research on parental responsivity in

FXS has repeatedly failed to find an association between parental input and child syntax, and this has

been true both for studies of naturalistic interactions (e.g., Brady et al., 2020; Komesidou et al., 2017) as

well as studies of parent-implemented language interventions (e.g., McDuffie et al. 2018). For example,

Komesidou et al. (2017), who examined the longitudinal trajectory of expressive syntax over three years

in children with FXS, found significant syntactic growth over time, but maternal responsivity did not

predict syntactic outcomes. The authors suggested that perhaps more specific parental behaviors might

contribute to growth of syntax and that their measure of maternal responsivity was potentially not

specific enough. Additionally, certain responsive behaviors, such as requests for verbal compliance (e.g.,

questions such as, “What color is the truck?” or intonation prompts such as, “They are driving to the

.”) may only result in one- or two-word responses from the child (McDuffie et al., 2018). Parent use

of other responsive behaviors, such as commenting on the child’s focus of attention or recasting child

communication acts, may not lead to observable or significant changes in the child’s syntactic

complexity, especially for young children. Future research is needed to investigate whether other parental

behaviors may promote syntactic skills in children with FXS.

Moreover, in the current study, parent behavior management did not predict any of the child

language measures, demonstrating the importance of certain kinds of parental input (e.g., comments and

requests for verbal compliance compared to requests for behavioral compliance) in shaping child

110

language development (e.g., McDuffie & Yoder, 2010). Past studies in families of children with FXS

have repeatedly demonstrated that maternal responsivity predicts child language performance (Brady et

al., 2020, Brady et al., 2014; Warren et al., 2017; Warren et al., 2010). Importantly, the findings of the

current study demonstrate that paternal responsivity is important for child language performance as well,

especially given that no differences were found in the child language measures between the mother-child

and father-child interactions. Future studies should investigate how maternal and paternal behaviors

change over time as the child develops, and whether significant differences emerge between mothers and

fathers that could differentially impact the child’s communication. In particular, the role of paternal

responsivity on child language performance during the school-age and adolescent years has not yet been

explored in families of children with FXS.

In the multilevel models for Aim 2, there was no significant main effect of child challenging

behavior on either parental responsivity or behavior management. However, in the current study, children

were generally very compliant during the dyadic play-based interactions and parents reported that

challenging behaviors were more likely to occur during other interactions, particularly when demands

were being placed on the child or there were unexpected changes in the child’s routine. Parents’ use of

responsive behaviors may decrease during interactions when the child is demonstrating higher levels of

challenging behaviors. Additionally, a more proximal measure of child challenging behavior (e.g., ratings

of the child’s behavior during an interaction) compared to a more distal measure (e.g., ABC-2 Total

scores) may be more likely to relate to parent behavior. Therefore, future studies interested in

investigating these associations should include additional interaction contexts as well as additional

measures of the child’s behavior.

Child adaptive behavior was also a significant predictor of parental responsivity such that

children with higher levels of adaptive behavior had parents who were more responsive. This finding is

similar to past research that found that the child’s developmental level (as measured by the Mullen

Scales of Early Learning; Mullen 1995) strongly influenced maternal responsivity (Sterling et al., 2013).

Moreover, child age was also a significant predictor of parental responsivity, but not behavior

111

management, with parents of older children demonstrating higher rates of responsivity. Sterling and

Warren (2014) also found a positive association between child age and maternal responsivity in families

of children with DS. Interestingly, adaptive behavior in the Socialization domain was a significant

predictor of parental behavior management, with parents of children with lower levels of adaptive

behavior in this domain implementing higher rates of behavior management. Parents of children with

lower levels of social functioning may be more likely to use certain directives during interactions with

their child to teach and encourage appropriate play. Moreover, even though were no significant

differences in parental responsivity based on parent sex, there were differences in behavior management

such that fathers used a greater proportion of behavior management strategies compared to mothers.

Future studies should examine how parental behavior management changes over time, whether fathers

continue to use higher rates of behavior management compared to mothers, and how parental behavior

management influences child developmental outcomes.

Overall, these findings suggest that parents of young children with FXS could benefit from

interventions focused on increasing levels of responsive behaviors, especially given the association

between child age and parental responsivity. Undoubtedly, parents of children who are more

communicative will have an easier time implementing responsive behaviors, but responsive behaviors

also serve to increase child engagement and participation in an interaction, thereby leading to

improvements in the child’s development. In the past decade, McDuffie, Abbeduto, and colleagues have

published multiple studies examining the effects of parent-implemented language interventions on parent

and child outcomes in families of children with FXS (Bullard et al., 2017; McDuffie et al., 2018;

McDuffie, Machalicek, et al., 2016; McDuffie, Oakes, et al., 2016; Nelson et al., 2018; Oakes et al.,

2015; Thurman et al., 2020). These interventions were designed to teach parents to use strategies that

support their child’s language development. In one study that included young boys with FXS (between

the ages of two and six years) and their mothers, mothers increased their use of responsive strategies,

including comments and prompts for child communication (e.g., requests for verbal compliance).

Moreover, the

112

children in this study showed increases in their prompted communication acts (McDuffie, Oakes, et al.,

2016).

Other studies of a parent-implemented language intervention for school-age children and

adolescents with FXS have also shown improvements in parent use of responsive strategies and child

language performance (McDuffie et al., 2018; McDuffie, Machalicek, et al. 2016; Nelson et al., 2018;

Thurman et al., 2020). In these studies, the parent-child interaction context was shared storytelling using

wordless picture books and parents were taught to: (a) model developmentally appropriate story-related

vocabulary and grammar, (b) expand (i.e., recode) child communication acts, (c) ask wh-questions, and

(d) use intonation prompts (i.e., fill-in-the-blank statements). Parents were able to successfully learn and

implement these strategies independently over the course of the intervention and there were associated

improvements in child participation and language. Collectively, these studies demonstrate that parental

responsiveness is important for child outcomes in FXS from early childhood through late adolescence and

that parents are able to successfully implement targeted strategies to children who vary widely in both age

and developmental level.

Limitations and Future Directions

There are some notable limitations to this study. First, the measures of child language are from

the same interactions being used to ascertain levels of parental responsivity. Future studies should

incorporate additional external or distal measures of child language. Moreover, including additional

parent-child interaction contexts, such as shared book reading and unstructured naturalistic activities (e.g.,

getting ready for school, eating dinner), would potentially provide more representative information about

the nature of the parent-child relationship and the ways in which parental behavior influences child

behavior and communication throughout the day in various settings. Another limitation is that the current

findings describe concurrent associations and not longitudinal ones. Future studies should examine

changes in these bidirectional parent-child associations over time to see how parents modify their

behavior to adapt to the child’s development

113

Conclusions

The findings from the current study demonstrate that both maternal and paternal responsivity are

positively associated with child language performance for young boys with FXS. Interestingly, there were

no significant differences within families between mothers’ and fathers’ use of responsive behaviors.

Future studies should investigate whether there are differences in maternal and paternal behavior in

dyadic compared to triadic (i.e., mother-father-child) interactions in these families. This study also

provides preliminary evidence that certain child characteristics (e.g., ASD symptoms) may differentially

affect maternal versus paternal responsivity, which warrants further investigation. Finally, the

associations between both child age and adaptive functioning with parental responsivity support the use of

parent-implemented language interventions in families of children with FXS to increase parents’ use of

responsive strategies that target improvements in child communication.

114

References

Abbeduto, L., Brady, N., & Kover, S. T. (2007). Language development and fragile X syndrome:

Profiles, syndrome‐specificity, and within‐syndrome differences. Mental Retardation and

Developmental Disabilities Research Reviews, 13(1), 36-46.

Abbeduto, L., & Hagerman, R. J. (1997). Language and communication in fragile X syndrome. Mental

Retardation and Developmental Disabilities Research Reviews, 3(4), 313-322.

Abbeduto, L., Seltzer, M. M., Shattuck, P., Krauss, M. W., Orsmond, G., & Murphy, M. M.

(2004). Psychological well-being and coping in mothers of youths with autism, down

syndrome, or fragile X syndrome. American Journal on Mental Retardation, 109(3), 237-

254.

Abbeduto, L., Thurman, A. J., McDuffie, A., Klusek, J., Feigles, R. T., Brown, W. T., ... & Roberts, J. E.

(2019). ASD comorbidity in fragile X syndrome: Symptom profile and predictors of symptom

severity in adolescent and young adult males. Journal of Autism and Developmental

Disorders, 49(3), 960-977.

Aman M. G., & Singh, N. N. (2017). Aberrant Behavior Checklist, Second Edition. East Aurora, NY:

Slosson.

Baker, J. K., Seltzer, M. M., & Greenberg, J. S. (2012). Behaviour problems, maternal internalising

symptoms and family relations in families of adolescents and adults with fragile X

syndrome. Journal of Intellectual Disability Research, 56(10), 984-995.

Baumgardner, T. L., Reiss, A. L., Freund, L. S., & Abrams, M. T. (1995). Specification of the

neurobehavioral phenotype in males with fragile X syndrome. Pediatrics, 95(5), 744-752.

Belsky, J. (1984). The determinants of parenting: A process model. Child Development, 83-96.

Bourgeois, J. A., Seritan, A. L., Casillas, E. M., Hessl, D., Schneider, A., Yang, Y., ... & Hagerman, R. J.

(2011). Lifetime prevalence of mood and anxiety disorders in fragile X premutation carriers. The

Journal of Clinical Psychiatry, 72(2), 175-182.

115

Brady, N. C., Fleming, K., Bredin-Oja, S. L., Fielding-Gebhardt, H., & Warren, S. F. (2020). Language

development from early childhood to adolescence in youths with fragile X syndrome. Journal of

Speech, Language, and Hearing Research, 63(11), 3727-3742.

Brady, N., Warren, S. F., Fleming, K., Keller, J., & Sterling, A. (2014). Effect of sustained maternal

responsivity on later vocabulary development in children with fragile X syndrome. Journal of

Speech, Language, and Hearing Research, 57(1), 212-226.

Brady, N., Warren, S. F., & Sterling, A. (2009). Interventions aimed at improving child language by

improving maternal responsivity. International Review of Research in Mental Retardation, 37,

333-357.

Bruner, J. S. (1975). The ontogenesis of speech acts. Journal of Child Language, 2(1), 1-19.

Bullard, L., McDuffie, A., & Abbeduto, L. (2017). Distance delivery of a parent-implemented language

intervention for young boys with fragile X syndrome. Autism & Developmental Language

Impairments, 2, 2396941517728690.

Chapman, R. S. (2000). Children's language learning: An interactionist perspective. The Journal of Child

Psychology and Psychiatry and Allied Disciplines, 41(1), 33-54.

Chromik, L. C., Quintin, E. M., Lepage, J. F., Hustyi, K. M., Lightbody, A. A., & Reiss, A. L. (2015).

The influence of hyperactivity, impulsivity, and attention problems on social functioning in

adolescents and young adults with fragile X syndrome. Journal of Attention Disorders, 1-8.

Constantino, J. N., & Gruber, C. P. (2012). Social Responsiveness Scale, Second Edition. Torrance, CA:

Western Psychological Services.

Cordeiro, L., Ballinger, E., Hagerman, R., & Hessl, D. (2011). Clinical assessment of DSM-IV anxiety

disorders in fragile X syndrome: Prevalence and characterization. Journal of Neurodevelopmental

Disorders, 3(1), 57-67.

Cox, M. J., & Paley, B. (1997). Families as systems. Annual Review of Psychology, 48(1), 243-267.

Crawford, D. C., Acuña, J. M., & Sherman, S. L. (2001). FMR1 and the fragile X syndrome: Human

genome epidemiology review. Genetics in Medicine, 3(5), 359-371.

116

Davidson, R. G., & Snow, C. E. (1996). Five-year-olds' interactions with fathers versus mothers. First

Language, 16(47), 223-242.

Ford, A. L., Elmquist, M., Merbler, A. M., Kriese, A., Will, K. K., & McConnell, S. R. (2020). Toward

an ecobehavioral model of early language development. Early Childhood Research Quarterly, 50,

246-258.

Golinkoff, R. M., Hoff, E., Rowe, M. L., Tamis‐LeMonda, C. S., & Hirsh‐Pasek, K. (2018). Language

Matters: Denying the Existence of the 30‐Million‐Word Gap Has Serious Consequences. Child

Development.

Hagerman, R. J., Protic, D. D., Rajaratnam, A., Salcedo-Arellano, M. J., Aydin, E. Y., & Schneider, A.

(2018). Fragile X-Associated Neuropsychiatric Disorders (FXAND). Frontiers in Psychiatry, 9,

1-9.

Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., ... & REDCap Consortium.

(2019). The REDCap consortium: Building an international community of software platform

partners. Journal of Biomedical Informatics, 95, 103208.

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic

data capture (REDCap)—A metadata-driven methodology and workflow process for providing

translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381.

Hartley, S. L., Seltzer, M. M., Hong, J., Greenberg, J. S., Smith, L., Almeida, D., ... & Abbeduto, L.

(2012). Cortisol response to behavior problems in FMR1 premutation mothers of adolescents and

adults with fragile X syndrome: A diathesis-stress model. International Journal of Behavioral

Development, 36(1), 53-61.

Hessl, D., Nguyen, D. V., Green, C., Chavez, A., Tassone, F., Hagerman, R. J.....& Hall, S. (2009). A

solution to limitations of cognitive testing in children with intellectual disabilities: The case of

fragile X syndrome. Journal of Neurodevelopmental Disorders, 1(1), 33-45.

Hessl, D., Tassone, F., Cordeiro, L., Koldewyn, K., McCormick, C., Green, C.,.....& Hagerman, R. J.

(2008). Brief report: Aggression and stereotypic behavior in males with fragile X syndrome—

117

Moderating secondary genes in a “single gene” disorder. Journal of Autism and Developmental

Disorders, 38(1), 184-189.

Hunter, J., Rivero‐Arias, O., Angelov, A., Kim, E., Fotheringham, I., & Leal, J. (2014). Epidemiology of

fragile X syndrome: A systematic review and meta‐analysis. American Journal of Medical

Genetics Part A, 164(7), 1648-1658.

Komesidou, R., Brady, N. C., Fleming, K., Esplund, A., & Warren, S. F. (2017). Growth of expressive

syntax in children with fragile X syndrome. Journal of Speech, Language, and Hearing

Research, 60(2), 422-434.

Landry, S. H., Smith, K. E., Swank, P. R., Assel, M. A., & Vellet, S. (2001). Does early responsive

parenting have a special importance for children's development or is consistency across early

childhood necessary? Developmental Psychology, 37(3), 387-403.

Laursen, B., & Collins, W. A. (2009). Parent–child relationships during adolescence. In R. M. Lerner &

L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., Vol. 1, pp. 3– 42). Hoboken,

NJ: John Wiley and Sons, Inc.

Loesch, D. Z., Bui, Q. M., Grigsby, J., Butler, E., Epstein, J., Huggins, R. M., ... & Hagerman, R. J.

(2003). Effect of the fragile X status categories and the fragile X mental retardation protein levels

on executive functioning in males and females with fragile X. Neuropsychology, 17(4), 646-657.

Lovejoy, M. C., Graczyk, P. A., O'Hare, E., & Neuman, G. (2000). Maternal depression and parenting

behavior: A meta-analytic review. Clinical Psychology Review, 20(5), 561-592.

Ma, W., Golinkoff, R. M., Houston, D. M., & Hirsh-Pasek, K. (2011). Word learning in infant-and adult-

directed speech. Language Learning and Development, 7(3), 185-201.

Mailick, M. R., Movaghar, A., Hong, J., Greenberg, J. S., DaWalt, L. S., Zhou, L., ... & Berry-Kravis, E.

(2018). Health profiles of mosaic versus non-mosaic FMR1 premutation carrier mothers of

children with fragile X syndrome. Frontiers in Genetics, 9(173), 1-10.

118

McDuffie, A., Banasik, A., Bullard, L., Nelson, S., Feigles, R. T., Hagerman, R., & Abbeduto, L. (2018).

Distance delivery of a spoken language intervention for school-aged and adolescent boys with

fragile X syndrome. Developmental Neurorehabilitation, 21(1), 48-63.

McDuffie, A., Machalicek, W., Bullard, L., Nelson, S., Mello, M., Tempero-Feigles, R., ... & Abbeduto,

L. (2016). A spoken-language intervention for school-aged boys with fragile X

syndrome. American Journal on Intellectual and Developmental Disabilities, 121(3), 236-265.

McDuffie, A., Oakes, A., Machalicek, W., Ma, M., Bullard, L., Nelson, S., & Abbeduto, L. (2016).

Early language intervention using distance video-teleconferencing: a pilot study of young boys

with fragile X syndrome and their mothers. American Journal of Speech-Language Pathology,

25(1), 46-66.

McDuffie, A., & Yoder, P. (2010). Types of parent verbal responsiveness that predict language in young

children with autism spectrum disorder. Journal of Speech, Language, and Hearing

Research, 53(4), 1026-1039.

Miller, J., & Iglesias, A. (2008). Systematic Analysis of Language Transcripts (SALT), English &

Spanish (Version 9) [Computer software]. Madison: University of Wisconsin—Madison,

Waisman Center. Language Analysis Laboratory.

Minnes, P., Perry, A., & Weiss, J. A. (2015). Predictors of distress and well‐being in parents of young

children with developmental delays and disabilities: The importance of parent

perceptions. Journal of Intellectual Disability Research, 59(6), 551-560.

Neece, C. L., Green, S. A., & Baker, B. L. (2012). Parenting stress and child behavior problems: A

transactional relationship across time. American Journal on Intellectual and Developmental

Disabilities, 117(1), 48-66.

Nelson, S., McDuffie, A., Banasik, A., Feigles, R. T., Thurman, A. J., & Abbeduto, L. (2018). Inferential

language use by school-aged boys with fragile X syndrome: Effects of a parent-implemented

spoken language intervention. Journal of Communication Disorders, 72, 64-76.

119

Newman, R. S., Rowe, M. L., & Ratner, N. B. (2016). Input and uptake at 7 months predicts toddler

vocabulary: the role of child-directed speech and infant processing skills in language

development. Journal of Child Language, 43(5), 1158-1173.

Oakes, A., Ma, M., McDuffie, A., Machalicek, W., & Abbeduto, L. (2015). Providing a parent-

implemented language intervention to a young male with fragile X syndrome: Brief

report. Developmental Neurorehabilitation, 18(1), 65-68.

Oostra, B. A., & Willemsen, R. (2003). A fragile X balance: FMR1 expression levels. Human Molecular

Genetics, 12, 249–257.

Pancsofar, N., & Vernon-Feagans, L. (2006). Mother and father language input to young children:

Contributions to later language development. Journal of Applied Developmental

Psychology, 27(6), 571-587.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis

methods (Vol. 1). Thousand Oaks, CA: Sage.

Sameroff, A. (2009). The transactional model. In A. Sameroff (Ed.), The transactional model of

development: How children and contexts shape each other (pp. 3-21). Washington, DC:

American Psychological Association.

Sameroff, A. J., & Chandler, M. J. (1975). Reproductive risk and the continuum of caretaking casualty. In

F. D. Horowitz, M. Hetherington, S. Scarr-Salapatek, and G. Siegal (Eds.), Review of child

development research (Vol. 4, pp. 187–244). Chicago: University of Chicago Press.

Sameroff, A.J., & Fiese, B.H. (2000). Transactional regulation: The developmental ecology of early

intervention. In J.P. Shonkoff, & S.J. Meisels (Eds.), Handbook of early childhood intervention,

(2nd Edition, pp. 135-159), New York: Cambridge University Press.

Sameroff, A. J., & Mackenzie, M. J. (2003). Research strategies for capturing transactional models of

development: The limits of the possible. Development and Psychopathology, 15(3), 613-640.

Sansone, S. M., Widaman, K. F., Hall, S. S., Reiss, A. L., Lightbody, A., Kaufmann, W. E., ... & Hessl,

D. (2012). Psychometric study of the Aberrant Behavior Checklist in fragile X syndrome and

120

implications for targeted treatment. Journal of Autism and Developmental Disorders, 42(7),

1377-1392.

Seltzer, M. M., Barker, E. T., Greenberg, J. S., Hong, J., Coe, C., & Almeida, D. (2012). Differential

sensitivity to life stress in FMR1 premutation carrier mothers of children with fragile X

syndrome. Health Psychology, 31(5), 612-622.

Shapiro, N. T., Hippe, D. S., & Ramírez, N. F. (2021). How Chatty Are Daddies? An Exploratory Study

of Infants' Language Environments. Journal of Speech, Language, and Hearing Research, 1-11.

Sparrow, S. S., Balla, D. A., & Cicchetti, D. (1984). Vineland Adaptive Behavior Scales. Circle Pines,

MN: American Guidance Service.

Sparrow, S. S., Cicchetti, D., & Balla, D. A. (2005). Vineland-II: Vineland Adaptive Behavior Scales, 2nd

Edition. Minneapolis, MN: Pearson.

Sparrow, S., Cicchetti, D., & Saulnier, C. (2016). Vineland-3: Vineland Adaptive Behavior Scales, Third

Edition. Minneapolis, MN: Pearson.

Sterling, A., & Warren, S. F. (2014). Maternal responsivity in mothers of young children with Down

syndrome. Developmental Neurorehabilitation, 17(5), 306-317.

Sterling, A., Warren, S. F., Brady, N., & Fleming, K. (2013). Influences on maternal responsivity in

mothers of children with fragile X syndrome. American Journal on Intellectual and

Developmental Disabilities, 118(4), 310-326.

Tamis-LeMonda, C. S., Baumwell, L., & Cristofaro, T. (2012). Parent–child conversations during

play. First Language, 32(4), 413-438.

Tamis-LeMonda, C. S., Bornstein, M. H., & Baumwell, L. (2001). Maternal responsiveness and children's

achievement of language milestones. Child Development, 72(3), 748-767.

Tamis-LeMonda, C. S., Kuchirko, Y., & Song, L. (2014). Why is infant language learning facilitated by

parental responsiveness? Current Directions in Psychological Science, 23(2), 121-126.

Thurman, A. J., Potter, L. A., Kim, K., Tassone, F., Banasik, A., Potter, S. N., ... & Abbeduto, L. (2020).

Controlled trial of lovastatin combined with an open-label treatment of a parent-implemented

121

language intervention in youth with fragile X syndrome. Journal of Neurodevelopmental

Disorders, 12(1), 1-17.

Tint, A., & Weiss, J. A. (2016). Family wellbeing of individuals with autism spectrum disorder: A

scoping review. Autism, 20(3), 262-275.

Visootsak, J., Hipp, H., Clark, H., Berry-Kravis, E., Anderson, T., & Laney, D. (2014). Climbing the

branches of a family tree: Diagnosis of fragile X syndrome. The Journal of Pediatrics, 164(6),

1292-1295.

Warren, S. F., & Brady, N. C. (2007). The role of maternal responsivity in the development of children

with intellectual disabilities. Mental Retardation and Developmental Disabilities Research

Reviews, 13(4), 330-338.

Warren, S. F., Brady, N., Fleming, K. K., & Hahn, L. J. (2017). The longitudinal effects of parenting on

adaptive behavior in children with fragile X syndrome. Journal of Autism and Developmental

Disorders, 47(3), 768-784.

Warren, S. F., Brady, N., Sterling, A., Fleming, K., & Marquis, J. (2010). Maternal responsivity predicts

language development in young children with fragile X syndrome. American Journal on

Intellectual and Developmental Disabilities, 115(1), 54-75.

Wheeler, A. C., Bailey Jr, D. B., Berry-Kravis, E., Greenberg, J., Losh, M., Mailick, M., ... & Hagerman,

R. (2014). Associated features in females with an FMR1 premutation. Journal of

Neurodevelopmental Disorders, 6(1), 30.

Wright-Talamante, C., Cheema, A., Riddle, J. E., Luckey, D. W., Taylor, A. K., & Hagerman, R. J.

(1996). A controlled study of longitudinal IQ changes in females and males with fragile X

syndrome. American Journal of Medical Genetics, 64(2), 350-355.

Yoder, P. J., & Warren, S. F. (1998). Maternal responsivity predicts the prelinguistic communication

intervention that facilitates generalized intentional communication. Journal of Speech, Language,

and Hearing Research, 41(5), 1207-1219.

122

Yoder, P. J., & Warren, S. F. (1999). Maternal responsivity mediates the relationship between

prelinguistic intentional communication and later language. Journal of Early Intervention, 22(2),

126-136.

Yoder, P. J., & Warren, S. F. (2001). Relative treatment effects of two prelinguistic communication

interventions on language development in toddlers with developmental delays vary by maternal

characteristics. Journal of Speech, Language, and Hearing Research, 44(1), 224-237.

AN EXAMINATION OF FAMILY DYNAMICS, PARENTAL RESPONSIVITY, AND CHILD COMMUNICATION IN FRAGILE X SYNDROME

Students also viewed