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BMI influences relationships among health factors

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for adults with persistent pain who use prescription opioids

Teresa Bigand, PhD, MSN, RNa,b,*, Ruth Bindler, PhD, RNa, Lois James, PhDa, Kenneth Daratha, PhDb, Marian Wilson, PhD, MPH, RNa

aCollege of Nursing, Washington State University, Spokane, WA bNursing Professional Development, Providence Health Care, Spokane, WA

rresponding author: Teresa Bigand, Sa ail address: Teresa.bigand@providenc 554/$ -see front matter � 2020 Elsevier //doi.org/10.1016/j.outlook.2020.03.005

A B S T R A C T

Background: Long-term use of prescription opioids for pain results in negative health outcomes. Overweight and pain are related, and adults with either condi- tion commonly report poor sleep quality, high levels of depression, low levels of self-efficacy, and high pain interference and intensity. Insufficient research exists regarding howweight may influence pain outcomes in the context of com- mon symptoms. Purpose: To investigate how bodymass index (BMI) influences relationships between health factors and pain outcomes among adults with pain prescribed opioids. Methods: The sample included 226 adults. Linear regression models tested rela- tionships among variables and outcomes of pain intensity and pain interference. Findings: BMI significantly strengthened relationships between health factors and pain interference but not pain intensity. Discussion: Adults with persistent pain suffer worsened pain interference in the context of increased weight status. Nurses should consider addressing BMI as part of a holistic pain management care plan. Cite this article: Bigand, T., Bindler, R., James, L., Daratha, K., & Wilson, M. (2020, July/August). BMI influ-

ences relationships among health factors for adults with persistent pain who use prescription opioids.

Nurs Outlook, 68(4), 440�448. https://doi.org/10.1016/j.outlook.2020.03.005.

A R T I C L E I N F O

Article history: Received 30 September 2019 Received in revised form 13 March 2020 Accepted 21 March 2020 Available online May 10, 2020.

Keywords:

BMI Chronic pain Opioid use Overweight Pain management Symptommanagement

cred Heart Medical Center, Professional Development, 101 W 8th Ave Spokane, WA 99203. e.org (T. Bigand). Inc. All rights reserved.

Background

Persistent pain, or pain lasting most days for the prior three months or more (Treede et al., 2015), affects nearly 100 million adults in the United States (Nahin, 2015). Persistent pain may be measured by outcomes of pain intensity or severity at a given time, and pain interference, or the degree to which pain intrudes upon emotional and physical functioning (Younger,

McCue, & Mackey, 2009). Pain interference and pain intensity are correlated, yet are often measured together to capture a holistic sense of the pain experi- ence (Fayers et al., 2011; Mun, Davis, Campbell, Finan, & Tennen, 2019). Commonly, pain treatment includes long-term use of prescription opioid medications, defined as opioid use longer than 90 days (Yang et al., 2015). Long-term use of opioids is related to negative health outcomes such as opioid use disorder, overdose (Drug Enforcement Administration [DEA], 2018), poor

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breathing during sleep (Filiatrault et al., 2016), negative metabolic consequences (Cassidy, Trenell, & Anderson, 2017), and poorer pain ratings (Krebs et al., 2018). In order to curb negative consequences, national efforts are underway to reduce the number and length of opi- oid prescription. Alternative and adjunctive treatments are needed that can improve pain intensity and pain interference among adults with persistent pain. Adults with overweight body mass index (BMI � 25)

are more likely to report a persistent pain condition than adults with recommended weight status (Bigand, Bindler, & Daratha, 2018). Increased BMI among adults with persistent pain may worsen pain intensity (Chou et al., 2016) and pain interference (Allen, Da Grande, Abernathy, & Currow, 2016). Additionally, weight loss among adults with both persistent pain and overweight has been associated with significant improvements in pain intensity (Vincent, Adams, Vincent, & Hurley, 2013) and pain interference (Messier et al., 2013). There- fore, holistic nursing interventions that consider weight a modifiable factor in adults prescribed opioids for per- sistent pain could offer viable alternative or adjunctive treatments of the pain experience. People with persistent pain experience multiple dis-

tressing symptoms such as depression and poor sleep quality regardless of weight status. Research has well- documented that depression and sleep quality are sig- nificant predictors of persistent pain (Mundal, Gra

� we,

Bjørngaard, Linaker, & Fors, 2014) and poor pain out- comes (Campbell et al., 2013; Pinheiro et al., 2016). Research also supports that adults with overweight status have higher levels of depression (De Wit et al., 2010) and sleep disturbance (Cappuccio et al., 2008) compared to those with recommended weight status. Less is known about the impact of weight status on pain outcomes in the context of co-occurring symp- toms such as depression or poor sleep quality. More knowledge is needed about how BMI may influence relationships between pain-related symptoms of depression and poor sleep quality and pain outcomes of intensity and interference. A nursing theoretical framework well-suited to

understanding complex, multidimensional disease states such as persistent pain or overweight is the Individual and Family Self-Management Theory (IFSMT) (Ryan & Sawin, 2009). The IFSMT posits that disease management is driven by personal/familial health knowledge and beliefs and physical and emo- tional risk or protective factors. Using this theory and evidence from the literature review, authors identified risk factors of depression and sleep quality as barriers to pain management, and outcomes as patient- reported pain intensity and interference ratings. Fur- thermore, the IFSMT guided authors to investigate self-efficacy as a part of an individual’s belief of ability to self-manage pain outcomes. Self-efficacy is a well- established psychological construct that can assist one’s ability to manage a chronic condition (Stephens et al., 2016). Self-efficacy has been linked to adaptive functioning for adults with persistent pain (Jackson,

Wang, Wang, Fan, 2014), including those who are pre- scribed opioids (Wilson, Roll, Corbett, & Barbosa- Leiker, 2015) and adults with overweight (Knerr et al., 2016). However, how weight status influences relation- ships between risk factors of sleep quality, depression, and self-efficacy and outcomes of pain intensity/inter- ference among adults with prescription opioids remains unclear. The main objective of the study was to address

knowledge gaps regarding how common health factors in adults with persistent pain using prescription opioid medications might be influenced by weight status. We hypothesized that higher BMI would relate to more negatively-rated health factors in the sample. Findings from this study could offer evidence for nurses to address overweight status more thoroughly as one strategy to improve pain self-management outcomes. The targeted population was adults with a painful con- dition who were prescribed opioids for pain or opioid use disorder. Chronic pain is estimated to occur in more than half of adults with opioid use disorder (Hser et al., 2017). Presence of persistent pain is the most commonly cited reason for initiation of illicit opioid use, yet among adults with persistent pain, opioid use disorder is often undiagnosed (DEA, 2018). Thus, this study sought to explore the impact of BMI on known relationships between health factors and pain out- comes among adults with persistent pain taking long- term opioid medications for either opioid use disorder or persistent pain.

Methods

The current cross-sectional study analyzed survey data from a subset of 300 adults prescribed opioid medications completed between September 2016 and June 2017. In the original study, a total of 150 partici- pants were recruited from three persistent pain (PP) management clinics, and 150 adults were recruited from two methadone treatment clinics for opioid use disorder (OUD) in metropolitan cities in the Pacific Northwest. To be included in the original study, partic- ipants had to be greater than 18 years of age, taking a prescribed opioid medication for either PP or OUD, able to provide verbal informed consent, and able to read English and understand the study surveys. For the present analysis, participants with a PP diagnosis along with documented height and weight data were selected (N = 226).

Ethical Considerations

The Institutional Review Board at the principal inves- tigator’s academic facility determined the current analysis was exempt from federal regulations for human research and the secondary analysis was per- formed.

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Measures

Weight Status Weight status was represented by BMI, defined as weight (kg)/height (m2), as reported by participants. The measure of BMI is highly correlated with other measures of weight status among adults with reports of pain (Heuch, Heuch, Hagen, & Zwart, 2015). Previous large cross-sectional studies exploring relationships between PP and overweight status among adults have used self-report height and weight data to approxi- mate BMI values (Stone & Broderick, 2012). Addition- ally, a pilot study found strong correlations between self-reported and objectively measured height and weight data (r = 0.99, n = 11) among adults with PP con- ditions (Bigand, James, &Wilson, 2019). A BMI of �25 is considered overweight and �30 is defined as obese (Hales, Carroll, Fryar, & Ogden, 2017).

Sleep Quality Sleep-related symptoms were measured with the Pittsburg Sleep Quality Index (PSQI) component one score which measures perceived sleep quality in the past month on a scale of zero to three; higher numbers indicate poorer sleep quality (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). The question asks, “In the past month, how would you rate your overall sleep quality?” Choices range from: “very good,” “fairly good,” “fairly bad” and “very bad.” The PSQI demon- strates high convergent validity with other valid and reliable sleep measures among adults with low back pain (Mollayeva et al., 2016) and is comprised of seven component scores (Buysse et al., 1989). Component one was chosen to represent sleep quality in the sam- ple related to a large number of missing answers pro- hibiting calculation of the total score (n = 59 missing). Cronbach’s alphas were calculated to determine the strongest relationship between each component score and the total PSQI score. Component one was most strongly related (r = 0.75, p < 0.05) and substitution of this measure with total score reduced missing cases to three total.

Depressive Symptoms Depressive symptoms were measured using the Patient-Health Questionnaire eight-question short form, derived from the Diagnostic and Statistical Man- ual for Mental Health Disorders (DSM-V) clinical diag- nosis of depression (Choi, Williams, & Gatchel, 2014). Raw scores range from 0 to 24; a higher score indicates greater presence of depressive symptoms. For instance, a score of 10 is rated as moderate depressive symptoms. The PHQ-8 has been tested among popula- tions of adults with pain diagnoses and has demon- strated internal reliability with Cronbach’s alpha between 0.86 and 0.92 (Choi et al., 2014).

Self-efficacy for SymptomManagement Self-efficacy was measured using the Patient-Reported Outcomes Information System (PROMIS) self-efficacy

subscale for symptom management. PROMIS scales were developed by the National Institutes of Health in an effort to standardize research scales used to mea- sure patient health factors (U.S. Department of Health and Human Services). The PROMIS self-efficacy scale for symptom management contains a total of four questions. The raw score of this subscale ranges between 4 and 20, with a higher score indicating better levels of self-efficacy; the raw score is then converted to a t score for generalizable interpretation (NIH, 2015c). A t score of 60 or greater indicates one standard deviation higher self-efficacy than the average popula- tion, and a t score of 40 or less reflects one standard deviation less self-efficacy than average (NIH, 2015c). The scale has high internal consistency (r > 0.70) when administered to adults with PP (Gruber-Baldini, Velozo, Romero, & Shulma, 2017).

Pain Interference Pain interference was measured using the PROMIS pain interference subscale, an eight-question scale with high test�retest reliability (Cronbach’s alpha = 0.84) when tested among adults with PP (Broderick, Schneider, Jun- ghaenel, Schwartz, & Stone, 2013). The raw score of the PROMIS subscale ranges between 8 and 40, where increasing scores indicate higher levels of pain interfer- ence. The raw score can be estimated if at least 50% of the questions are answered (NIH, 2015b). Once raw scores have been tabulated, they are then converted to t scores, interpreted by being above or below the mean of the general population (t score of 50).

Pain Intensity Pain intensity was measured by the pain intensity PROMIS subscale 3-question short form which is scored in the same way as pain interference, except that raw scores cannot be estimated if questions were skipped. The raw score of this subscale ranges between 3 and 15, where a higher number indicates higher levels of pain intensity (NIH, 2015a). The raw score is converted to a t score interpreted in the same way as described in PROMIS scales above. This sub- scale showed high test�retest reliability when admin- istered to adults with PP (Broderick et al., 2013).

Data Analysis

Frequency and descriptive statistics were run first to detect missing data for each variable and characterize the sample. These data are summarized as means (standard deviations) and frequencies (percentages). Next, Pearson’s correlation coefficients were calculated to examine the bivariate relationships among all varia- bles included in the regression models. Linear regres- sions using stepwise methods were conducted to explore multivariable relationships among health fac- tors (independent variables: self-efficacy, sleep quality, depressive symptoms, and BMI) and pain outcomes (dependent variables: pain interference and pain inten- sity). In the first step of each regression model, all

Table 1 – Demographic Characteristics of the Sam- ple (N = 226)

Variable N (%)

Gender Male 75 (33) Female 150 (66) Prefer not to say 1 (1)

Recruitment center PP 144 (64)

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independent variables except BMI were added to test relationships with dependent variables. Age and gender were selected a priori as covariates and placed in a sec- ond step since both are key nonmodifiable factors among adults with PP (Stone & Broderick, 2012). BMI was then added into regression models as a final step. Data were analyzed using version 25 of IBM SPSS soft- ware. All significance testing was two-sided to remain conservative and avoid a Type I error (alpha = 0.05).

OUD 82 (36) BMI

Recommended weight 65 (29) Overweight 72 (32) Obese 89 (39)

Occupational status Full-time 27 (12) Part-time 14 (6) Unemployed/disabled 116 (51) Retired/homemaker/other 64 (28) Prefer not to say 5 (2)

Income < $20,000 121 (54) $20,000�$39,000 35 (16) $40,000�$79,000 28 (12) >$80,000 17 (7) Prefer not to say 25 (11)

Mental health diagnosis None reported 109 (48) Past or current reported 101 (45) Prefer not to say 16 (7)

Ethnicity American Indian/Alaska Native 16 (7) Asian 1 (1) Black or African American 5 (2) White 181 (80) Multiracial 17 (7) Other 2 (1) Prefer not to say 4 (2)

Hispanic No 204 (90) Yes 13 (6) Prefer not to say 9 (4)

Education Less than High school 26 (12) High school or GED 54 (24) Some college/associate/technical school 113 (50) Bachelor’s degree 19 (8) Graduate degree 10 (4) Prefer not to say 4 (2)

Marital Status Married/living with partner 82 (36) Widowed 13 (6) Divorced/separated 76 (34) Never married/other 44 (19) Prefer not to say 11 (5)

Table 2 – Mean Health Variable Scores

Variable Average (SD) Missing Cases

Depressive symptoms 10.9 § 6.05 2 Sleep quality 1.5 § 1.3 3 Self-efficacy 44.5 § 7.7 4 BMI 30.8 § 8.6 0 Pain intensity 58.2 § 6.5 17 Pain interference 66.9 § 6.8 0

Findings

Description of Sample

Of 300 cases available from the primary study, 233 adults self-reported a PP condition and were consid- ered for inclusion in the present study. After list-wise removal of cases without height or weight data, 226 participants were available for the analysis. In the sample, mean participant age was 48.55 § 14.7 years and ranged from 19 years to 85 years. The top five reported pain conditions were, respectively: back pain, arthritis, neck pain, nerve pain, and migraines; this was consistent for adults recruited from both PP and OUD treatment centers. Prior work with this sample assessed for demographic differences between adults prescribed opioids for PP (n = 142) and those for OUD (n = 84) which found comparable demographics aside from average age as older for PP compared to OUD (Big- and et al., 2019). Participants reported taking the cur- rently prescribed opioid medication for an average of greater than 6 years (SD = 5.9), ranging from less than 1 year to 29 years, meeting criteria for long-term opioid use. Further demographic characteristics of the sam- ple are summarized in Table 1. Frequency statistics revealed that only six partici-

pants reported an underweight BMI (n = 2.6%). Exclu- sion and inclusion of these cases made no change to results, thus the decision was made to include these cases in analysis. Average scores on variables of inter- est are displayed in Table 2 below.

Health Factor Relationships

Pearson’s correlation coefficients were calculated for all health variables and are reported in Table 3. Pain interference was significantly correlated with all varia- bles. Pain intensity demonstrated similar bivariate relationships to those of pain interference yet was not significantly correlated with BMI.

Pain Interference Model

Linear regression analyses revealed significant models explaining pain interference in the presence of tested health factors and covariates (p < .01). The inclusion of BMI in model 2 significantly increased explanation of pain interference by 2.1% compared to model 1

Table 3 – Bivariate Correlations among all Health Variables

Depressive Symptoms Pain Intensity Pain Interference BMI Self-efficacy Poor Sleep Quality

Depressive symptoms 1 0.250** 0.307** 0.004 �0.569** 0.507** Pain intensity 1 0.582** 0.093 �0.153* 0.255** Pain interference 1 0.163* �0.305** 0.359** BMI 1 �0.012 0.122 Self-efficacy 1 �0.349** Poor sleep quality 1

* p � .05. ** p � .01.

Table 4 – BMI Influences Relationships between Health Factors and Pain Interference

Model Adjusted R2 R2 Change F

1 0.16 - 14.47** 2 0.18 0.02* 5.46**

* p � .05. ** p � .01.

Table 5 – Final Model Statistics for Pain Outcomes

Final Model Pain Interference Pain Intensity

Sleep Quality (b) 1.27** 0.77* Depression (b) 0.24** 0.22* Self-efficacy (b) �0.15* �0.02 BMI (b) 0.13* 0.06 Age (b) 0.09** 0.01 Gender (b) 0.06 0.18** F 5.56** 4.1** R2 24% 11% Adjusted R2 21% 9%

b, unstandardized regression coefficient.

* p � .05. ** p � .01.

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without BMI (Table 4). Sleep quality was weighted as the strongest variable explaining pain interference. Table 5 below summarizes relationships among varia- bles in final, fully adjusted models for both pain inter- ference and pain intensity.

Pain Intensity Model

Final regression models examining pain intensity as the outcome achieved statistical significance (Table 5). In these models, the addition of BMI was not found to significantly influence or moderate the variance in pain intensity compared to models without BMI (p >

.05). Depressive symptoms (B = 0.22, t216 = 2.47, p <

.05) and sleep quality (B = 0.77, t216 = 3.73, p < .05) were the only two health factors independently related to pain intensity; sleep quality was more strongly weighted (Table 5).

Discussion

In the sample, 31% met criteria for overweight and nearly 40% self-reported an obese BMI, similar to national overweight and obesity levels (Hales et al., 2017). Our primary hypothesis that BMI would signifi- cantly influence pain interference was supported, while the significant influence of BMI on pain intensity was not supported (Figure 1). Pain intensity and pain interference were positively related in our sample con- sistent with previous studies in this population (Fayer et al., 2011; Mun et al., 2019). BMI significantly and pos- itively related to pain interference when accounting for age and gender, and all tested health variables independently predicted pain interference. The find- ings show that BMI influences pain interference. How- ever, when compared to other factors that are important to pain self-management, BMI may be less influential. Factors weighted to relate to pain interfer- ence most strongly were sleep, depressive symptoms, and self-efficacy, respectively. Poor sleep quality emerged as the most influential

variable for both pain interference and pain intensity in this study. Therefore, disturbed sleep could be a sig- nificant pain-related symptom for nurses to screen for and address within the context of an integrative treat- ment plan. Disrupted sleep has been found to be related to long-term opioid use (Filiatrault et al., 2016), presence of depressive symptoms (Campbell et al., 2013), and overweight status among adults with PP (Mundal et al., 2014). Thus, it is possible that the com- plex interplay between symptoms of poor sleep, depressive symptoms, and overweight status may relate to poor pain outcomes among adults with PP. Depressive symptoms and self-efficacy served as the

second and third most influential variables in pain interference models, respectively, suggesting that both may be pertinent health factors to focus on for pain treatment strategies. Evidence supports that offering cognitive behavioral therapies to treat depres- sive symptoms can also improve sleep, pain (Kwekke- boom et al., 2018), mood and weight loss (Somers et al., 2012), and self-efficacy (Turner et al., 2016) among adults with pain. Nurses are trained to identify and treat such health factors and thus could be well-pre- pared to partner with interdisciplinary health care

Figure 1 –How BMI relates to health factors and pain outcomes in the sample.

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providers to advocate for appropriate evidence-based treatment of symptoms like depression or poor sleep. Nurses can also follow national guidelines to refer patients to pain self-management programs that are designed to boost pain self-efficacy (The Interagency Pain Research Coordinating Committee, 2016). Weight status was the fourth most influential vari-

able on pain interference, suggesting that weight man- agement may offer an adjunctive method to positively impact pain. Prior evidence suggests that interdisci- plinary approaches to create tailored, safe exercise routines for patients with overweight and PPmay facil- itate weight loss and improve pain outcomes (Vincent et al., 2013). Strategies to address nutrition in adults with PP and overweight have also contributed to improved weight loss and pain outcomes (Ramsden et al., 2015). However, effective weight loss among adults with PP and overweight may be challenged in the context of co-occurring physical and emotional symptoms (Messier et al., 2013). Clearly, an integrative pain management approach involving nursing exper- tise can yield comprehensive health benefits for the high symptom burden in this population. BMI did not significantly change the relationships

between health factors and pain intensity in the current sample, although both depressive symptoms and sleep quality did independently explain variance in pain inten- sity. Some studies have also been unable to support a relationship between pain intensity and BMI among adults with a self-reported PP condition (Fowler-Brown, Wee, Marcantonio, Ngo, & Leveille, 2013). It may be that the measure of pain intensity is not as robust as pain interference when considering psychosocial impacts on both pain and overweight status. Additionally, reduction in pain interference has been proposed as a more favor- able clinical outcome than pain intensity to measure among adults with PP who are recovering from OUD (Centers for Substance Abuse Treatment, 2012) as well as for those who do not have diagnosed OUD (Dworkin et al., 2005). Targeting pain interference may be more beneficial to improve emotional and physical pain- related symptoms (Somers et al., 2012) as opposed to relieving subjective reports of pain intensity. However,

because pain intensity and pain interference are highly correlated in this sample, a finding consistent in literature among adults with PP (Mun et al., 2019), improvements in pain interferencemay also improve pain intensity. The IFSMT (Ryan & Sawin, 2009) encourages integra-

tive strategies to promote health and disease manage- ment. Nurses are well-situated to drive integrative management of complex pain symptoms. For instance, nursing factors such as educating patients on drug side effects have been found to significantly influence patient-reported pain levels (Van Hecke et al., 2016). In addition, the National Institute of Nurs- ing Research has called for nurses to head efforts for implementing precision health models to offer patient-centered self-management strategies derived from individual factors such as lifestyle, environment, and even biomarkers (Hickey et al., 2019). However, many adults with PP and overweight status report that health care providers do not adequately address health symptoms outside of pain (Janke, Ramirez, Haltzman, Fritz, & Kozak, 2016), suggesting gaps exist that prohibit adoption of such advanced symptom management models. To our knowledge, our study was the first to investi-

gate relationships between weight status and symp- toms that impact the pain experience among adults with PP who are prescribed opioids including those with OUD. This is a clinically important population because evidence suggests that long-term use of opioids may contribute to or exacerbate health status. In our sample, adults met criteria for long-term opioid use, yet overall self-reported symptom burden was noted to be high. This finding may suggest that adults with PP with long-term prescription opioid use may have unmet symptom burden needs. Further research is necessary to determine appropriate adjunctive and alternative pain symptommanagement.

Strengths and Limitations

Our recruitment from two populations prescribed opioids, OUD and PP, may have resulted in a heteroge- neous population thus limiting generalizability. Yet, PP is

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prevalent among adults diagnosed with OUD and many adults report that a PP condition precipitated OUD (Hser et al., 2017). Additionally, prior work with this sample revealed fairly homogenous levels of variables of interest among both adults with OUD and PP (Bigand et al., 2019). Identification of the current state of relationships among health factors and pain in a population of adults with PP using prescription opioids is a first step in understanding how to treat underlying symptoms with targeted treat- ments. Additionally, given the limited racial and ethnic diversity within our sample we caution against broad generalizability of our study findings. The parent study was cross-sectional which cannot imply causation since factors are not tested over time. However, the aim of the current study was to explore relationships between vari- ables for future hypothesis testing. The use of self-report data to measure factors poten-

tially allowed for response biases whereby people with worse health are more likely to respond. Yet, self-report measures are considered the gold standard when assessing subjective health outcomes (Dworkin et al., 2005) and use of tools with high validity and reliability in the target population increases confidence in the scien- tific accuracy of findings. The use of BMI as a measure of weight status has also been critiqued (Rothman, 2008). However, among adults with chronic pain, BMI was found to be highly correlated with other anthropometric measures such as waist-hip ratio and body fat mass and was independently associated with pain outcomes (Heuch et al., 2015), and self-reported BMI was found to be highly correlated with researcher-measured height and weight among women with osteoarthritis (Skeie, Mode, Henningsen, & Borc, 2015). Thus, the measure was used with confidence in the current study.

Conclusions

The current study found that depressive symptoms, sleep quality, self-efficacy for symptom management, and BMI were all significantly associated with pain interference. Furthermore, weight status significantly strengthened relationships between health factors and pain interference, suggesting that having overweight may worsen the pain experience. Sleep quality may be important to assess in this population when consider- ing treatment alternatives and adjuncts to opioid medi- cations to improve pain interference or intensity outcomes. Future studies should validate findings on a larger scale with more diverse populations and deter- mine whether advantages exist in targeting specific symptoms or treating a cluster of symptoms simulta- neously given the reciprocal relationships between sleep, pain, weight, and mood. Patients may benefit from receiving nursing support to use an integrative pain management approach. Achieving recommended weight may benefit adults with PP who report symp- toms known to exacerbate pain.

Acknowledgment

Research efforts for this work were supported in part by Washington State University Grand Challenges Seed Grant funding (grant no. WSU128515).

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