Advanced Pharmacology: Metformin; Theoretical and nursing: Pregnancy and STD; Capstone project (Due 26 and 48 hours)

Technology-Based Interventions to Reduce Sexually Transmitted Infections and Unintended Pregnancy Among Youth

Laura Widman, PhD1, Jacqueline Nesi, MA2, Kristyn Kamke, MS3, Sophia Choukas- Bradley, PhD4, and J. L. Stewart, BFA5

1North Carolina State University, Department of Psychology, Campus Box 6740, Raleigh, NC 27695

2University of North Carolina at Chapel Hill, Department of Psychology and Neuroscience, Campus Box 3270, Chapel Hill, NC 27599

3North Carolina State University, Department of Psychology, Campus Box 6740, Raleigh, NC 27695

4University of Pittsburgh, Department of Psychology, 3413 Sennott Square, 210 S. Bouquet St, Pittsburgh, PA 15260

5North Carolina State University, Department of Psychology, Campus Box 6740, Raleigh, NC 27695

Abstract

Purpose—Technology-based interventions to promote sexual health have proliferated in recent

years, yet their efficacy among youth has not been meta-analyzed. This study synthesizes the

literature on technology-based sexual health interventions among youth.

Methods—Studies were included if they: 1) sampled youth ages 13-24; 2) utilized technology-

based platforms; 3) measured condom use or abstinence as outcomes; 4) evaluated program effects

with experimental or quasi-experimental designs; and 5) were published in English.

Results—16 studies with 11,525 youth were synthesized. There was a significant weighted mean

effect of technology-based interventions on condom use (d=.23, 95% CI [0.12, 0.34], p<.001) and

abstinence (d=.21, 95% CI [0.02, 0.40], p=.027). Effects did not differ by age, gender, country,

intervention dose, interactivity, or program tailoring. However, effects were stronger when

assessed with short-term (1-5 months) compared to longer-term (greater than 6 months) follow-

ups. Compared to control programs, technology-based interventions were also more effective in

Corresponding Author: [email protected]; 919-513-2546.

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Implications and Contributions: This meta-analysis demonstrates that technology-based interventions can effectively promote sexual health among youth. Compiling data from 16 studies, technology-based interventions were shown to improve condom use, abstinence, safer-sex knowledge, and safer-sex attitudes and norms. Effects were robust across many factors (e.g., age, gender, dose) but were stronger with shorter-term follow-up.

HHS Public Access Author manuscript J Adolesc Health. Author manuscript; available in PMC 2019 June 01.

Published in final edited form as: J Adolesc Health. 2018 June ; 62(6): 651–660. doi:10.1016/j.jadohealth.2018.02.007.

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increasing sexual health knowledge (d=.40, p<.001) and safer sex norms (d=.15, p=.022) and

attitudes (d=.12, p=.016).

Conclusions—After 15 years of research on youth-focused technology-based interventions, this

meta-analysis demonstrates their promise to improve safer sex behavior and cognitions. Future

work should adapt interventions to extend their protective effects over time.

Keywords

adolescent sexual health; sex education; technology-based interventions; eHealth; mHealth; digital health; sexually transmitted diseases; HIV prevention

Introduction

The risk of unplanned pregnancy and sexually transmitted infections (STIs), including HIV,

is high among youth. Youth ages 13-24 comprise nearly half of the 20 million new STI cases

and more than 20% of new HIV diagnoses each year in the United States [1, 2]. Worldwide,

adolescents and young adults account for 45% of all new HIV infections [3]. Rates of

unintended pregnancy are also elevated among youth, with girls ages 15-19 having higher

rates of unintended pregnancy than girls and women in any other age group [4, 5]. Further,

complications from childbirth are the second leading cause of death among 15-19 year old

girls worldwide [5].

Efforts to provide youth with sexuality education to reduce HIV/STIs and unintended

pregnancy have been underway for decades.[6, 7]. Only recently, however, has a movement

emerged toward utilizing technology-based platforms for sexual health program delivery [8–

10]. Technology-based interventions have been alternatively referred to as eHealth, mHealth,

digital media, or new media interventions, and typically utilize computers, smart phones,

text messaging, and/or other web-based platforms. As digital tools have become increasingly

accessible and sophisticated in their functionalities, many researchers have heralded the

promise of technology-based interventions to improve sexual health [8–10]. Compared to

traditional interventions, new media approaches may allow for broad reach at relatively low

costs, improved fidelity during intervention delivery, greater privacy and comfort for teens

learning about sensitive topics, and increased capacity for individually tailoring prevention

messages [11, 12]. Additionally, such tools may provide fruitful means for engaging today’s

digitally native youth, among whom the ubiquitous use of technology plays a central role in

key developmental tasks [13, 14].

The promise of new media tools has prompted a recent proliferation of technology-based

interventions targeting sexual health among youth. While recent systematic reviews have

highlighted potential impacts and limitations of these interventions [15–17], little is known

regarding their combined efficacy. Determining whether and through what mechanisms

technology-based interventions promote youth sexual health is critical, as such knowledge

can inform future interventions and justify the allocation of resources to their development.

To date, there have been only three published meta-analyses across adolescent and adult

populations that provide insights into the efficacy of technology-based interventions for

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sexual health. The first focused on interventions to reduce HIV infection and included 12

randomized controlled trials published between 2002-2008 with participants of any age [18].

This study found a small but significant overall increase in condom use among participants

who completed a technology-based intervention compared to those who were randomized to

a control group, with an effect size that was comparable to similar in-person interventions (d across studies = .26). These effects were stronger when interventions included individualized

tailoring (i.e., materials matched to the needs of specific participants) and had a greater

number of sessions. The second meta-analysis focused exclusively on programs employing

computer-based administration, finding that such interventions were effective in improving

sexual health knowledge, sexual self-efficacy, safer sex intentions, and safer sexual behavior

among adolescents and adults [19]. The third meta-analysis identified significant effects of

new media interventions for increasing condom use and STI testing within non-clinical

populations [20]. Importantly, this meta-analysis found that interventions produced the

largest effect sizes when they targeted female adolescents [20].

Despite these preliminary findings, at least three critical gaps inhibit our ability to draw

conclusions about the overall efficacy of technology-based sexual health programs for youth.

First, there are currently no meta-analyses focused exclusively on youth, even though youth

are at heightened risk for unplanned pregnancies and STIs [3, 4, 21, 22] and are also some of

the most frequent users of new media technologies [13, 23]. Second, no meta-analyses have

included abstinence as a behavioral outcome. Abstinence can be a developmentally-

appropriate objective of comprehensive sexuality education—particularly in programs

targeting early adolescent populations. Third, prior meta-analyses and systematic reviews of

technology-based sexual health programs have only included studies published through 2014

[15, 16, 18–20, 24]. Given the rapidly changing landscape of technology and the many new

interventions introduced each year, an updated review of the literature is warranted.

Thus, the goal of this meta-analysis is to synthesize the growing literature on technology-

based sexual health interventions among youth ages 13-24 and to determine their overall

efficacy on two key behavioral outcomes: condom use and abstinence. Additionally, we will

examine a number of secondary outcomes identified as important components of safer

sexual decision-making within health behavior theories [25] that have guided many

intervention efforts in this area. These outcomes include safer sex attitudes, norms, self-

efficacy, behavioral intentions, and sexual health knowledge. Finally, we will examine

whether characteristics related to the intervention recipients (age, gender, country) or the

intervention design (delivery method, use of tailoring, program interactivity, follow-up

duration) moderate the effectiveness of technology-based interventions on sexual health

outcomes.

Methods

Search Strategy

We conducted a comprehensive search of Medline, PsycINFO, and Communication Source databases to extract relevant studies published through May 2017. We used the following

combination of key words, with asterisks used as “wild cards” to find any variations:

(adolescen* or teen* or youth or middle school or high school) AND (sexual health or safe*

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sex or sex* education or sexually transmitted disease or sexually transmitted infection or

STD or STI or HIV or AIDS or pregnancy or reproductive health or condom* or contracept* or protected sex or unprotected sex) AND (intervention or program or education or trial) AND (technology or internet or web-base* or computer-base* or online or social media or

social network* or SNS or eHealth or mHealth or electronic health or mobile health or

texting or text messag* or digital media or new media or gaming or SMS or mobile phone or

cell phone or phone app* or Facebook or Twitter or Instagram or instant message or web 2.0 or media 2.0). Additional studies of potential relevance were located by examining prior

reviews and meta-analyses [15–20, 24]. We also examined the reference lists of all included

articles to search for additional studies. This search produced an initial 1,932 scientific

articles.

Selection Criteria

Studies were included if they met the following criteria: 1) focused on youth between the

ages of 13-24 (i.e., mean sample age 13-24 and no participant older than 29); 2) utilized

technology as the primary mode of delivering an HIV/STI or pregnancy prevention

intervention (studies that utilized technology but also included extensive in-person

components were excluded [26, 27]); 3) included a behavioral outcome measure of either

condom use/unprotected sex (referred to as “condom use” in this paper; the effect sizes for

unprotected sex were recoded so that the direction of effect always indicated greater

protection) or abstinence/delayed intercourse (referred to as “abstinence”); 4) evaluated

program effects with an experimental or quasi-experimental design; 5) were published in

English; and 6) provided sufficient statistics to calculate effect sizes. These selection criteria

resulted in a final sample of 16 articles (see Supplemental Figure 1). In total, we calculated

16 independent effect sizes for condom use, 8 for abstinence, 6 for safer sex attitudes, 5 for

norms, 5 for self-efficacy, 5 for behavioral intentions, and 9 for sexual health knowledge.

Data Extraction

Two of the authors (K.K. and J.L.S.) independently coded the primary studies. The

following data were abstracted: 1) demographic and sample characteristics; 2) intervention

characteristics (e.g., type of technology used, number of sessions, program interactivity); 3)

methodological characteristics (e.g., length of follow-up, type of comparison group); and 4)

type of outcome measurement(s). The mean percentage agreement across all coding

categories was 88%. Discrepancies between coders were resolved through discussion with

the first author (L.W.). Additionally, three of the authors (L.W., K.K., and J.L.S.) rated the

16 studies for quality and risk of bias using the Cochrane Collaboration’s tool for assessing

risk of bias (see Supplemental Table 1).

Calculation of Effect Sizes

The standardized mean difference, d, was used as the indicator of effect size (i.e., the

treatment group and control group means divided by the pooled standard deviation).

According to Cohen [28], effect size d can be interpreted as small (.20), medium (.50), or

large (.80). When ds were reported in an article, they were directly extracted. If ds were not

reported, other statistics that could be converted to ds (e.g., summary statistics, odds ratios)

were calculated using Comprehensive Meta-Analysis V2.0 [29]. When no statistics in the

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study could be converted to a d, study authors were contacted and appropriate data were

requested. When more than one follow-up time point was reported, effect sizes were

calculated based on data from the longest follow-up for which data were available. To ensure

the consistency and interpretability of effect sizes, higher values always indicate the

technology-based intervention group performed better than the control group.

We used random effects meta-analytic procedures for the primary analyses across all

independent effect sizes; this procedure allowed for the possibility of differing variances

across studies [30]. The Q statistic and I2 were used to examine whether significant

heterogeneity existed among effect sizes. Effect sizes for hypothesized moderators were

calculated along with their 95% confidence intervals, and those effect sizes were compared

using the Qb statistic. For these analyses, mixed effects models were utilized to allow for the

possibility of differing variances across subgroups. These models employ random effects

assumptions, while stratifying the effect sizes by fixed factors such as age and study dose

[30]. Analyses were conducted using Comprehensive Meta-Analysis V2.0 [29].

Results

Study Characteristics

Table 1 provides a summary of the studies included in this meta-analysis, including sample

characteristics and moderator variables. A total of 11,525 participants (mean age=18.42)

were enrolled across 16 technology-based interventions for youth. The majority of studies

were conducted in the United States (k=11). Many studies used combined samples of boys

and girls (k=9); however, a few studies analyzed data from boys (k=3) and girls (k=4)

independently.

The delivery of program content varied widely across different digital media technologies.

Specifically, 5 programs were delivered exclusively via computer programs; 2 were

delivered exclusively through internet websites; 1 was delivered exclusively through texting;

1 was delivered exclusively through social media; and nearly half of programs (7 of 16) were

delivered with more than one method, such as an Internet website with email follow-up [31–

33] or a combination of text messaging and email delivery ([34]). It was common for

interventions to be interactive (i.e., accept input from the user as the program progressed;

k=12) and to utilize tailoring (i.e., include program components that were matched

specifically to the user based on user characteristics; k=10). The dose of intervention varied

widely across studies, with k=5 programs including 1-2 sessions and k=8 programs

including 7 or more sessions.

Primary Outcomes

Condom Use—While individual effect sizes ranged from d=−.07 to .67, the weighted

mean effect size for condom use across these studies was d=0.23 (95% CI [0.12, 0.34];

p<0.001), indicating that technology-based interventions have a small but significant

protective effect on condom use behavior among youth (see Figure 1). To examine whether

publication bias may have inflated the effect size of the interventions on condom use, a fail-

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safe N was calculated. This fail-safe N was 92, suggesting 92 non-significant studies would

need to exist in order to reduce the effect size to a trivial level (p>.05).

Statistical testing indicated marginal heterogeneity among studies with regard to the condom

use outcome (Q=24.27, p=.061, I2=38.19). Thus, we examined the potential impact of

several moderating variables. As shown in Table 2, there was only one marginally significant

difference based on the duration of follow-up assessment, with shorter follow-ups (less than

6 months) producing stronger effects (d=0.32, p<.001) than programs with follow-ups of

longer duration (6 months or more; d=0.14, p=.093). No significant differences were found

by participant age, gender, study dose, program interactivity, program tailoring, or whether

the study was conducted in the U.S.

Abstinence—Individual study effect sizes for abstinence/delayed sex ranged from d=.01

to .65, with an overall weighted mean effect size for abstinence across studies of d=.21 (95%

CI [0.02-0.40]; p=.027). This indicates that technology-based interventions have significant

protective effects on abstinence over time (see Figure 1). To examine whether publication

bias may have inflated the effect size of the interventions on abstinence, a fail-safe N was

calculated. This fail-safe N was 18, suggesting 18 non-significant studies would need to

exist in order to reduce the effect size to a trivial level (p>.05).

Statistical testing indicated significant heterogeneity among studies with regard to the

abstinence outcome (Q=16.93, p=.018, I2=58.65); thus, we examined the potential impact of

several moderating variables. As shown in Table 3, the only significant moderator was the

duration of follow-up. Similar to the effects on condom use, intervention effects on

abstinence were significantly stronger for short-term follow-ups (i.e., less than 6 months; d=.

45, p<.001) than those with long-term follow-ups (d=.07, p=.321). No significant differences

were found by gender, age, intervention dose, tailoring, or country of study. Of note, all 8

studies that included abstinence as a primary outcome were interactive interventions; thus,

we could not examine the effects of interactive versus static programs for abstinence.

Secondary Outcomes

We examined 5 secondary outcomes, including safer sex attitudes, social norms for safer

sexual activity, self-efficacy, behavioral intentions to practice safer sex, and sexual health

knowledge. As shown in Table 4, compared to control programs, technology-based

interventions were efficacious in increasing sexual health knowledge (d=.40, p<.001), social

norms for safer sexual activity (d=.15, p=.022), and safer sex attitudes (d=.12, p=.016)

among youth. Compared to controls, there were no significant differences in the effect of

technology-based interventions on safer sex intentions or perceived self-efficacy to engage

in safer sexual behavior.

Data Quality

The majority of study designs were rigorous (14 RCTs) and there was limited evidence of

attrition bias, with retention rates across studies generally exceeding 80%. However, some

uncertainty was noted in whether studies had utilized best practices for concealing the

allocation of participants into study conditions. For 8 studies, it was also unclear if selective

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reporting of outcomes could have been an issue since these studies had not been

preregistered. However, overall data quality across studies was deemed to be high, and most

studies were found to have a low risk of bias (see Supplemental Table 1).

Discussion

Results of the current meta-analysis, which synthesize nearly 15 years of research on the

development and evaluation of youth-focused technology-based interventions, highlight the

great promise of these approaches to improve safer sex behaviors among youth. Pooling data

from 16 studies with over 11,000 adolescents, this meta-analysis found a significant positive

effect of technology-based sexual health interventions for improving two key sexual

behaviors among youth: increasing consistent condom use (effect size d=.23) and delaying

sexual activity (effect size d=.21). Across studies, technology-based programs also led to

increases in sexual health knowledge, safer sex attitudes, and positive norms surrounding

safer sexual activity. Importantly, these effect sizes are comparable or even exceed the

effects of in-person interventions, with generally small to moderate intervention effects

noted for in-person programs (effect size d=.13 for condom use and d=.11 for abstinence in

a meta-analysis by Johnson et al [6]).

Technology-based interventions offer many possible benefits over traditional, face-to-face

interventions [11, 35, 36]. Most notably, these programs can be administered with high

fidelity without extensive facilitator training. This may result in cost-effective programs that

are capable of reaching a greater number of youth than are in-person programs. These

approaches can also increase youths’ openness to learning by providing a safe, controlled,

and familiar environment to receive sexual health knowledge and skills. This may be

especially salient for sexual minority youth who, in traditional face-to-face interventions

(e.g., school-based), may receive sexual education that is stigmatizing, inaccurate, or

irrelevant to their specific needs [37]. Further, technology-based programs offer ample

opportunities for customizability, interactivity, and individual tailoring [11, 35, 36].

Technology-based interventions also have the potential to reach adolescents in resource-

limited areas as worldwide access to mobile phones and internet technologies is rapidly

increasing among young people, even in low- and middle-income countries and rural areas

[23, 38].

Results suggest that the positive effects of technology-based interventions on sexual health

may be robust across a number of different factors. Several potential moderating factors did

not impact the efficacy of technology-based programs for youth, including the age and

gender of participants and the intervention interactivity, tailoring, and dose. For example,

while intervention dose varied from a single 15-minute session [39] to a full 12 months of

content delivery [31, 34], intervention dose did not influence effect sizes for condom use or

abstinence across studies. This finding differs from a previous systematic review of in-

person sexual health programs, which found that programs with higher duration and

intensity produced the greatest treatment effects [40]. However, our findings are consistent

with a more recent analysis of new media interventions [20], which found that the program

duration did not influence the size of effects on condom use. Indeed, the effects of

technology-based program dose on youth sexual health outcomes may be complex. On the

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one hand, the lack of association between intervention dose and outcomes could simply

reflect the challenges of differentiating between the dose intended and the dose received within some technology-based programs [41]. On the other hand, this finding may reflect the

growing potency of technology-based interventions. As technology has become increasingly

available, interactive, and customizable in recent years, these interventions have clearly

shown effectiveness, even at small doses. Future work could investigate whether such

intervention impacts vary by how actively youth engage with technology on an everyday

basis.

One crucial insight gleaned from this meta-analysis is that the effects of current technology-

based programs tend to decrease over time. Specifically, for both condom use and

abstinence, stronger effects were found in the short-term (i.e., 1-5 month follow-up)

compared to studies that evaluated intervention effects over 6 months or more. Adolescent

sexual behavior is complex, and it is perhaps not surprising that program effects appear to

diminish over time. The lack of long-term sustainability is a problem that has also plagued

many programs delivered to youth in-person [42]. The knowledge and skills adolescents

learn in specific programs may fail to adequately prepare them for long-term changes in

their dating and sexual relationships and their evolving sexual interests and desires. Given

the initial promise of technology-based programs, an exciting extension of this work would

be to evaluate longer-term, adaptive interventions utilizing Sequential Multiple Assignment

Randomized Trial (SMART) designs [43]. These designs will allow investigators to examine

the impact of added program components (e.g., booster sessions) over time to target the

specific youth for whom treatment effects are diminishing. In fact, SMART designs may be

particularly amenable to technology-based interventions, given unprecedented opportunities

to automate assignment to intervention options (i.e., tailoring) based on participants’

responses at critical decision points [44].

Limitations and Future Directions for Intervention Efforts

A number of important limitations in the studies included in this meta-analysis are worthy of

future research attention. First, it is worth noting that there was substantial variation across

studies in the way that the outcome measures were defined. For example, for both condom

use and abstinence, some investigators focused exclusively on acts of vaginal sex [45–47] or

anal sex [48], whereas others included acts of vaginal, anal, and oral sex in their definitions

[41] or referred only to “sex” or “intercourse” without a definition [32, 39]. Additionally, the

timeline for measuring these behaviors differed. For example, some studies measured

condom use at last sex [45, 47], whereas others measured the frequency of condom use over

a specified period of time [49, 50]. Likewise, for the outcome measure of abstinence/delayed

sex, some studies focused on whether youth had ever initiated sexual activity [41, 47]

whereas others focused on abstaining from sexual activity over a specified period of time

[49]. Similar variation was observed in the definitions of the secondary outcomes under

investigation. It is clear that the sexual health intervention literature lacks a “gold standard”

for measuring sexual risk behavior and related attitudes and cognitions. This lack of

consistency in measurement has been observed in other meta-analyses examining sexual risk

behavior [6, 18, 51–53]. The measurement variation across studies could be obscuring our

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ability to detect the most precise estimates of intervention success, including which

behaviors are most effectively addressed by technology-based interventions.

Second, only one of the included studies measured a biological outcome (i.e., Chlamydia

infection [49]) and no programs assessed intervention effects beyond one year. These issues

raise important questions about the long-term clinical impacts of these programs; however,

they are not unique to technology-based programs. In fact, a recent systematic review of all

school-based (i.e., in-person) sexual health programs among youth in the U.S. [54] found

only seven interventions that measured HIV/STI incidence or HIV/STI testing as outcomes.

Further, among the 98 in-person interventions reviewed by Johnson et al. [6], the average

outcome assessment occurred at 13 weeks. Future efforts to evaluate technology-based

programs would be enhanced by data on HIV/STI testing and/or diagnosis among program

recipients, as well as long-term follow-up to measure the longitudinal impacts of these

programs.

Another potentially fruitful avenue for efforts to improve adolescent sexual health outcomes

will be to identify the specific, replicable behavioral change techniques that are utilized in

technology-based interventions to enhance behavior change, as is increasingly being done

for in-person interventions [55]. It also will be critical for researchers to identify common

design elements that are specific to technology-based interventions going forward (for

examples, see [36, 56]). As technology rapidly evolves, recognizing common design features

across programs (e.g., tailoring, online social support and communication, automatic

feedback) will be essential for comparing interventions delivered across a range of

technological platforms. In this meta-analysis, we tested several possible technological

design features that could serve as moderators of intervention effectiveness, such as whether

the program was interactive and whether personal tailoring was utilized. However, there

were too few studies to examine further design elements that may increase program impact,

such as self-monitoring or personalized reminders [36]. These remain interesting avenues for

future work as new technology-based programs continue to emerge [57, 58]

As previously discussed, technology-based interventions may be a beneficial avenue for

delivering non-stigmatizing, accurate, and relevant sexual health education to sexual

minority youth. However, with the exception of one intervention targeting young men who

have sex with men [48], no study included in this meta-analysis specifically reported

tailoring intervention content to be inclusive of this population. Sexual minority youth

experience disproportionate rates of sexual risk [59], and face a number of stressors that may

impact their overall sexual health. Researchers should more explicitly consider the unique

needs of sexual minority youth in the development and evaluation of sexual health

programming, in addition to providing needed services at various levels of societal and

individual intervention [60]. Technology-based approaches may be particularly valuable

within this population, as sexual minority youth are more likely than heterosexual youth to

use the internet to look for sexual health information [61].

Future studies also should consider the potential moderating role of other important

demographic factors, including race/ethnicity, gender, and socio-economic status, given that

these group comparisons were not possible in the current study. Sexual health disparities

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among racial and ethnic minorities have been well documented, with rates of reported STIs

higher among some groups (e.g., Black and Hispanic individuals), compared to White

individuals [62]. Although a “digital divide” remains in terms of access and use of

technology in the United States [63], recent statistics suggest that rates of smartphone access

are comparable among Hispanic and White adolescents, and that Black youth are more likely to have smartphones compared to other racial/ethnic groups [13]. Even among low

income youth (i.e., household income less than $30,000), 91% of adolescents report that

they access the Internet via a mobile device [13]. Thus, sexual health interventions delivered

via technology may hold particular promise for reaching these at-risk groups.

Finally, there is a need for greater attention to the comparative effects of content delivered

in-person versus through technology. Only one study in our meta-analysis compared a

technology-based intervention to an in-person control [64]. However, the structure and

quality of technology-based interventions may differ from in-person treatments in important

ways, including the possibility for anonymous communication (e.g., discussion boards), the

provision of unique forms of social support and skills practice (e.g., using avatars or

simulated person-to-person communication), and possibilities for different types of self-

monitoring [36]. Additionally, with advancements in technology and incorporation of

tailored, interactive features (e.g., “ask the expert”), technology-based interventions may

offer the opportunity for personalized, rich communication and support from intervention

personnel that once was limited to in-person interventions [36, 65]. However, interventions

delivered in-person may increase the perceived credibility of information sources and

provide a sense of accountability or connection [65]. Thus, although this meta-analysis

suggests that technology-based interventions are effective compared to no-treatment or

attention-matched controls, their relative efficacy in relation to traditional, in-person sexual

health interventions remains a question for future work.

Summary and Implications

After nearly 15 years of research on youth-focused technology-based interventions, this

meta-analysis demonstrates the great promise of these approaches to improve the safer sex

behavior and cognitions of youth. Pooling data from 16 studies with over 11,000 adolescents

and young adults, we found that technology-based interventions were more effective than

control programs at improving condom use, abstinence, sexual health knowledge, safer-sex

attitudes, and safer-sex norms. However, effects were stronger in studies with shorter-term

follow-ups compared to follow-ups of 6 months or more. Future work should focus on

intervention adaptations and supplements that may extend their protective effects over time.

Future work should also move beyond program development and into the realm of

implementation science to ensure these programs are broadly disseminated for maximum

impact [66].

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

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Acknowledgments

This research was supported in part by the following National Institutes of Health grants: R00 HD075654 and T32 MH018269. This work was also supported in part by a National Science Foundation Graduate Research Fellowship DGE-1144081. The content, interpretations, and conclusions of this study are those of the authors and do not necessarily reflect the views of the National Institutes of Health or the National Science Foundation.

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Figure 1. Forest Plots for Primary Outcomes

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ow Y

es Y

es 3

C on

do m

U se

; A tti

tu de

s; N

or m

s

M us

ta ns

ki e

t a l [

48 ]

10 2

se xu

al ly

a ct

iv e

yo un

g ad

ul t m

en f

ro m

U .S

.; ra

ce /

et hn

ic ity

: 2 5%

W hi

te , 1

3% B

la ck

, 4 6%

H is

pa ni

c; s

ex ua

l or

ie nt

at io

n: 1

00 %

m en

w ho

h av

e se

x w

ith m

en ; r

ec ru

ite d

fr om

H IV

te st

in g

ce nt

er ; i

nt er

ve nt

io n

vi a

w eb

si te

a nd

v id

eo

ga m

e

21 .3

( 18

-2 4)

M od

Y es

Y es

3 C

on do

m U

se ; A

tti tu

de s;

N or

m s;

Se

lf -E

ff ic

ac y;

I nt

en tio

ns ;

K no

w le

dg e

N or

to n

et a

l [ 46

] 19

8 se

xu al

ly -a

ct iv

e co

lle ge

s tu

de nt

s fr

om U

.S .;

70 %

w om

en ;

ra ce

/e th

ni ci

ty : 8

5% w

hi te

; s ex

ua l o

ri en

ta tio

n: 1

00 %

he

te ro

se xu

al ; r

ec ru

ite d

fr om

p sy

ch ol

og y

st ud

en t p

ar tic

ip an

t po

ol ; i

nt er

ve nt

io n

vi a

co m

pu te

r D

V D

18 .6

( nr

) L

ow N

o N

o 2

C on

do m

U se

J Adolesc Health. Author manuscript; available in PMC 2019 June 01.

A uthor M

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A uthor M

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Widman et al. Page 18

A ut

ho rs

Sa m

pl e

an d

St ud

y D

es cr

ip ti

on A

ge M

( ra

ng e)

D os

e In

te ra

ct iv

e Ta

ilo ri

ng F

ol lo

w -U

p (m

on th

s) O

ut co

m e(

s)

Pe sk

in e

t a l [

41 ]

15 71

a do

le sc

en ts

f ro

m U

.S .;

59 %

f em

al e;

r ac

e/ et

hn ic

ity : 1

7%

B la

ck , 7

4% H

is pa

ni c;

r ec

ru ite

d fr

om u

rb an

m id

dl e

sc ho

ol s

in

Te xa

s; in

te rv

en tio

n vi

a co

m pu

te r

14 .3

( nr

) H

ig h

Y es

Y es

12 C

on do

m U

se ; A

bs tin

en ce

; A

tti tu

de s;

S el

f- E

ff ic

ac y;

In

te nt

io ns

; K no

w le

dg e

R ob

er to

e t a

l [ 47

] 32

6 10

th g

ra de

a do

le sc

en ts

f ro

m U

.S .;

56 %

f em

al e;

r ac

e/ et

hn ic

ity : 9

7% W

hi te

; r ec

ru ite

d fr

om r

ur al

h ig

h sc

ho ol

s;

in te

rv en

tio n

vi a

co m

pu te

r

15 .6

( nr

) H

ig h

Y es

Y es

2. 5

C on

do m

U se

; A bs

tin en

ce ;

A tti

tu de

s; N

or m

s; S

el f-

E ff

ic ac

y;

K no

w le

dg e

Su ff

ol et

to e

t a l [

45 ]

52 s

ex ua

lly -a

ct iv

e yo

un g

ad ul

t w om

en f

ro m

U .S

.; ra

ce /

et hn

ic ity

: 6 5%

B la

ck , 6

% H

is pa

ni c;

s ex

ua l o

ri en

ta tio

n: 1

00 %

he

te ro

se xu

al ; r

ec ru

ite d

fr om

e m

er ge

nc y

ro om

s; in

te rv

en tio

n vi

a te

xt m

es sa

ge s

21 .4

( 18

-2 5)

H ig

h Y

es Y

es 3

C on

do m

U se

; A bs

tin en

ce

Y ba

rr a

et a

l [ 69

] 36

6 se

xu al

ly -a

ct iv

e ad

ol es

ce nt

s fr

om U

ga nd

a; 1

6% f

em al

e;

re cr

ui te

d fr

om h

ig h

sc ho

ol s;

in te

rv en

tio n

vi a

w eb

si te

16 .1

(1 3-

19 )

M od

Y es

Y es

6 C

on do

m U

se ; A

bs tin

en ce

N ot

e. D

os e

= H

ig h

(7 o

r m

or e

se ss

io ns

), M

od er

at e

(3 -6

s es

si on

s) , L

ow (

1- 2

se ss

io ns

). n

r = n

ot r

ep or

te d.

S ex

ua l o

ri en

ta tio

n is

in cl

ud ed

in th

e sa

m pl

e de

sc ri

pt io

n w

he ne

ve r

it w

as r

ep or

te d

in th

e te

xt ; m

an y

st ud

ie s

di d

no t r

ep or

t t he

s ex

ua l o

ri en

ta tio

n of

th ei

r pa

rt ic

ip an

ts .

J Adolesc Health. Author manuscript; available in PMC 2019 June 01.

A uthor M

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Widman et al. Page 19

Ta b

le 2

W ei

gh te

d M

ea n

E ff

ec t S

iz es

f or

C on

do m

U se

b y

C at

eg or

ic al

M od

er at

or V

ar ia

bl es

B et

w ee

n G

ro up

s

V ar

ia bl

e k

d 95

% C

I p

Q B

p

G en

de r

2. 95

ns

Fe

m al

e on

ly 4

.3 6

[0 .1

9, 0

.5 3]

< .0

01

M

al e

on ly

3 .2

0 [−

0. 14

, 0 .5

3] .2

46

M

ix ed

g en

de r

9 .1

7 [0

.0 3,

0 .3

1] .0

16

A ge

0. 33

ns

Sa

m pl

e m

ea n

ag e

≤ 18

9 .2

0 [0

.0 8,

0 .3

1] .0

01

Sa

m pl

e m

ea n

ag e

> 1

8 7

.2 7

[0 .0

3, 0

.5 1]

.0 25

In te

rv en

tio n

D os

e 0.

18 ns

L

ow (

1- 2

se ss

io ns

) 5

.3 4

[0 .1

5, 0

.5 2]

< .0

01

M

od er

at e

(3 -6

s es

si on

s) 4

.2 2

[0 .0

3, 0

.4 1]

.0 25

H

ig h

(7 +

s es

si on

s) 7

.1 7

[− 0.

01 , 0

.3 5]

.0 70

In te

ra ct

iv ity

0. 00

ns

In

te ra

ct iv

e 1

1 .2

3 [0

.0 9,

0 .3

8] .0

02

St

at ic

5 .2

3 [0

.0 6,

0 .4

1] .0

02

Ta ilo

ri ng

0. 07

ns

U

se d

ta ilo

ri ng

9 .2

4 [0

.0 9,

0 .3

9] .0

02

D

id n

ot u

se ta

ilo ri

ng 7

.2 1

[0 .0

4, 0

.3 9]

.0 18

C ou

nt ry

0. 02

ns

U

.S .

11 .2

2 [0

.0 9,

0 .3

5] .0

01

N

on -U

.S .

5 .2

4 [−

0. 02

, 0 .5

0] .0

69

Fo llo

w -U

p 2.

73 .0

99

L

es s

th an

6 m

on th

s 9

.3 2

[0 .1

8, 0

.4 7]

< .0

01

6

m on

th s

or m

or e

7 .1

4 [−

0. 02

, 0 .3

1] .0

93

N ot

e. N

= s

am pl

e si

ze ; k

= n

um be

r of

s tu

di es

; d =

w ei

gh te

d m

ea n

ef fe

ct s

iz e;

C I

= c

on fi

de nc

e in

te rv

al ; n

s =

n ot

s ig

ni fi

ca nt

. M ix

ed e

ff ec

ts m

od el

s ar

e pr

es en

te d

fo r

m od

er at

or a

na ly

se s.

J Adolesc Health. Author manuscript; available in PMC 2019 June 01.

A uthor M

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uthor M anuscript

A uthor M

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Widman et al. Page 20

Ta b

le 3

W ei

gh te

d M

ea n

E ff

ec t S

iz es

f or

A bs

tin en

ce b

y C

at eg

or ic

al M

od er

at or

V ar

ia bl

es

B et

w ee

n G

ro up

s

V ar

ia bl

e k

d 95

% C

I p

Q B

p

G en

de r

2. 86

ns

Fe

m al

e on

ly 2

.1 1

[− 0.

11 , 0

.3 2]

.3 18

M

al e

on ly

2 .0

1 [−

0. 27

, 0 .2

9] .9

25

M

ix ed

g en

de r

4 .3

6 [0

.0 5,

0 .6

7] .0

21

A ge

0. 26

ns

Sa

m pl

e m

ea n

ag e

≤ 18

7 .2

3 [0

.0 2,

0 .4

3] .0

30

Sa

m pl

e m

ea n

ag e

> 1

8 1

.0 5

[− 0.

61 , 0

.7 0]

.8 88

In te

rv en

tio n

D os

e 0.

06 ns

L

ow (

1- 2

se ss

io ns

) 0

– –

–

M

od er

at e

(3 -6

s es

si on

s) 3

.1 8

[− 0.

01 , 0

.3 8]

.0 61

H

ig h

(7 +

s es

si on

s) 5

.2 3

[− 0.

08 , 0

.5 4]

.1 50

In te

ra ct

iv ity

– –

In

te ra

ct iv

e 8

.2 1

[0 .0

2, 0

.4 0]

.0 27

St

at ic

0 –

– –

Ta ilo

ri ng

0. 01

ns

U

se d

ta ilo

ri ng

7 .2

1 [−

0. 02

, 0 .4

4] .0

69

D

id n

ot u

se ta

ilo ri

ng 1

.2 3

[− 0.

05 , 0

.5 0]

.1 04

C ou

nt ry

0. 10

ns

U

.S .

6 .2

2 [−

0. 01

, 0 .4

6] .0

64

N

on -U

.S .

2 .1

6 [−

0. 16

, 0 .4

8] .3

33

Fo llo

w -u

p 7.

00 .0

08

L

es s

th an

6 m

on th

s 4

.4 5

[0 .2

1, 0

.6 9]

< .0

01

M

or e

th an

6 m

on th

s 4

.0 7

[− 0.

07 , 0

.2 2]

.3 21

N ot

e. N

= s

am pl

e si

ze ; k

= n

um be

r of

s tu

di es

; r =

w ei

gh te

d m

ea n

ef fe

ct s

iz e;

C I

= c

on fi

de nc

e in

te rv

al ; n

s =

n ot

s ig

ni fi

ca nt

. M ix

ed e

ff ec

ts m

od el

s ar

e pr

es en

te d

fo r

m od

er at

or a

na ly

se s.

J Adolesc Health. Author manuscript; available in PMC 2019 June 01.

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Widman et al. Page 21

Table 4

Effect Sizes for Safer Sex Related Supplemental Outcomes

Variable k d 95% CI p

Safer Sex Attitudes 6 .12 [−0.02, 0.23] .016

Safer Sex Norms 5 .15 [0.02, 0.29] .022

Safer Sex Self-Efficacy 5 .07 [−0.05, 0.19] .249

Safer Sex Intentions 5 .06 [−0.02, 0.14] .158

Sexual Health Knowledge 9 .40 [0.25, 0.55] <.001

Note. Random effects models are presented. k = number of studies; d = weighted mean effect size; CI = confidence interval.

J Adolesc Health. Author manuscript; available in PMC 2019 June 01.