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Health Communication

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Are Social Media Interventions for Health Behavior Change Efficacious among Populations with Health Disparities?: A Meta-Analytic Review

Rhyan N. Vereen, Rachel Kurtzman & Seth M. Noar

To cite this article: Rhyan N. Vereen, Rachel Kurtzman & Seth M. Noar (2023) Are Social Media Interventions for Health Behavior Change Efficacious among Populations with Health Disparities?: A Meta-Analytic Review, Health Communication, 38:1, 133-140, DOI: 10.1080/10410236.2021.1937830

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Are Social Media Interventions for Health Behavior Change Efficacious among Populations with Health Disparities?: A Meta-Analytic Review Rhyan N. Vereen a, Rachel Kurtzman b,c, and Seth M. Noar a,c

aHussman School of Journalism and Media, University of North Carolina; bGillings School of Global Public Health, University of North Carolina; cLineberger Comprehensive Cancer Center, University of North Carolina

ABSTRACT While prior reviews have identified positive effects of social media interventions for health behavior change generally, it is unclear whether these effects persist in traditionally underrepresented populations that are at disproportionate risk of disease. The current meta-analysis examined the effectiveness of social media interventions for health behavior change among populations with health disparities. We analyzed 17 studies with a cumulative N = 3,561. Social media interventions had a significant moderate-sized effect on behavior change among populations with health disparities (d = 0.303, 95% CI: 0.156, 0.460, p < .001), and there was significant heterogeneity across the studies (Q = 64.48, p < .001, I2 = 75.19). Exploratory moderator analyses revealed larger effects in studies with smaller sample size (p < .05) and those using additional intervention channels, including e-mail and telephone (p < .05). Findings suggest that social media interventions may be a promising intervention tool for stimulating behavior change among populations with health disparities, but several gaps remain in the literature. Public health professionals and other health communicators should further explore ways to increase both the reach and impact of social media interventions among populations with health disparities.

Despite decades of efforts to decrease health disparities, a dispro- portionate burden of disease persists among particular socio- demographic groups in the United States (Office of Minority Health, 2019). To decrease the burden of these disparities, scholars must identify innovative ways to reach and intervene with these populations. One increasingly prevalent intervention channel is social media. Social media allows individuals to produce and share content with other users, who can then interact with both the content and other users (Anderson, 2007). In health research, social media provides an additional platform for lay individuals and public health professionals to interact by sharing content, some of which they may have created (Moorhead et al., 2013). Some reviews have determined social media interventions have positive effects on behavior change; however, the majority of studies included in these reviews have consisted of general population samples (Guo & Bian, 2019). Therefore, the effect of social media interventions among traditionally underrepresented populations remains unclear. This meta-analysis seeks to determine the overall effect and potential moderators of the effect of social media interventions on health behavior change among populations with health disparities.

Social media interventions for health behavior change

Conventional media generally has a single producer and many viewers. However, Web 2.0 allows many users to create, share, and interact with online content and other users (Chou et al., 2013). One of the most commonly used components of Web 2.0 is social media. Contrary to traditional costly and time-

intensive public health interventions, social media interven- tions offer a unique cost-effective way to reach large numbers of people (Guo & Bian, 2019; Yang, 2017). Social media can also be used to build networks of support, provide tailored information, increase accessibility, and provide a platform to discuss sensitive topics (Moorhead et al., 2013). Given its reach, social media may also aid public health professionals in reach- ing and creating change among large numbers of individuals, particularly those who have traditionally been underrepre- sented in health research (Abroms, 2019).

To date, a number of studies have examined the effects of social media interventions on behavior change. A recent review of reviews concluded that while social media interventions were not promising for mental health and disease management outcomes, they had positive effects on modifiable physical health behaviors (e.g., diet, physical activity). This review, however, noted hetero- geneity in the intervention methodologies and acknowledged the need for meta-analyses (as opposed to systematic reviews) on the topic (Guo & Bian, 2019). Findings from reviews that did use meta- analytic methods have provided support for the positive effect of social media interventions on health behavior change, with effect sizes of d = 0.16 (Yang, 2017) and g = 0.24 (Laranjo et al., 2015). While these were relatively small effect sizes, an effect of this magnitude among a large population is likely to be meaningful. Both meta-analytic reviews acknowledged the heterogeneity of studies in their reviews and noted the important contribution social media may make in expanding access to and impact of public health interventions.

CONTACT Rhyan N. Vereen [email protected] Hussman School of Journalism and Media, University of North Carolina, 371 Carroll Hall (CB 3365), Chapel Hill, NC 27599-3365, USA

Supplemental data for this article can be accessed on the publisher’s website.

HEALTH COMMUNICATION 2023, VOL. 38, NO. 1, 133–140 https://doi.org/10.1080/10410236.2021.1937830

© 2021 Taylor & Francis Group, LLC

Having acknowledged the heterogeneity of studies included in prior reviews, we must also acknowledge the homogeneity of the study populations. Many of the studies included in these reviews recruited from general population samples, as opposed to focusing on underrepresented populations that authors have often stated social media could aid in reaching (Guo & Bian, 2019; Laranjo et al., 2015; Yang, 2017). Therefore, the effect of social media interventions among underrepresented popula- tions remains unclear.

Populations with health disparities

The National Institute on Minority Health and Health Disparities (NIMHD) defines what they refer to as “health disparity populations” as those that have higher rates of disease incidence, prevalence, morbidity, and mortality than the gen- eral population (Alvidrez et al., 2019). This includes racial and ethnic minorities, socioeconomically disadvantaged indivi- duals, sexual and gender minorities, and rural populations. The reasons why disparity populations have higher rates of disease are complex, and include social determinants of health, such as discrimination, access to quality health care, education, housing, and government policies (Thornton et al., 2016). Public health officials have attempted to address such dispa- rities with a wide range of interventions.

Some public health interventions have prompted structural and system level changes to mitigate the impact of these deter- minants, while others have focused on individual level impacts using digital channels. While behavioral and communication interventions cannot completely eliminate health disparities, they do play a significant role in reducing health disparities. To date, however, there has been limited inclusion of disparity populations in digital health interventions, despite knowledge that there could be reduced efficacy of interventions among these groups.

Some studies may have focused on general population sam- ples due to challenges, such as financial and time restraints (Konkel, 2015). These barriers, however, are noted advantages of social media interventions. Yet, relatively few studies have targeted populations with health disparities nor presented dis- aggregated findings to aid in understanding whether these inter- ventions are effective among such populations. Furthermore, findings from a 2016 review seeking to determine the effect of social media interventions to promote health equity were sug- gestive, but inconclusive (Welch et al., 2016). Therefore, the field currently lacks an up-to-date review to determine the effect of social media interventions for health behavior change among populations with health disparities.

Understanding potential moderators of efficacious social media interventions

Given the high heterogeneity reported in previous meta-analytic reviews of social media interventions (Laranjo et al., 2015; Yang, 2017), it is likely that moderators exist. One potential set of moderators are the intervention channels themselves. Social media components are often embedded in larger, complex inter- ventions, and the inability to parse out the effect of social media- alone is a limitation (Guo & Bian, 2019; Laranjo et al., 2015). Our

meta-analysis seeks to identify the presence of other intervention channels (i.e., telephone, text message, in-person) and explore the effects of interventions when these other channels are pre- sent. Similar to Yang (2017), we also explored potential differ- ences by health topic and methodological characteristics. Given findings from previous reviews and literature noting the positive impact of targeted interventions on health disparities (Williams & Purdie-Vaughns, 2016), the purpose of this study was to examine whether social media interventions had a positive effect on health behavior change among populations with health dis- parities, and explore whether that effect was moderated by intervention channel and study characteristics.

Methods

Search strategy

A comprehensive search for published and gray literature was conducted in PubMed, PsycINFO, CINAHL, Communication & Mass Media Complete (CMMC) and Embase databases in March of 2020. Search strings included terms for combinations of existing social media platforms (e.g., Facebook, Twitter) or social media components (e.g., interactive discussion board, forums), populations with health disparities (e.g., Black, rural), and preventive health behaviors (e.g., physical activity, smok- ing cessation; see Supplemental Materials for search terms). Titles and abstracts were reviewed for relevance, followed by review of full text. When a relevant abstract did not have an accessible full text manuscript (e.g., conference abstract), the author was emailed and a full text manuscript was requested.

To be included, studies had to report recruitment of partici- pants from targeted populations with health disparities. NIH- designated “health disparity populations” include “Blacks/ African Americans, Hispanics/Latinos, American Indians/ Alaska Natives, Asian Americans, Native Hawaiians and other Pacific Islanders, socioeconomically disadvantaged populations, underserved rural populations, and sexual and gender minori- ties”. As this definition reflects domestic health status and cul- tures, only studies conducted in the US were included. Eligible studies needed to be a randomized control trial (RCT) that assessed the effectiveness of an intervention in comparison to a control condition. The intervention needed to be conducted via an existing social media platform (ex. Facebook, Twitter) or social media feature (i.e., discussion boards, forums). Given that social media was technically established during the implementa- tion of Web 2.0 around 2004, literature prior to 2004 was not included in the search (see Figure 1). Studies were excluded if they did not use social media components in the intervention (ex. used social media to recruit participants only), used a study design other than an RCT, or were an RCT design but presented study findings other than intervention efficacy (e.g., protocol papers and feasibility study findings). We also excluded mediated interventions, where the intervention sought to change behavior in an individual other than the intervention participant (e.g., an intervention provided to a caregiver to change the behavior of their child). We further excluded studies with a nonphysical health outcome such as mental health (i.e. cyber- bullying, depression) or an upstream behavioral outcome (i.e., intention, attitudes).

134 R. N. VEREEN ET AL.

After full-text review, some overlap in study samples across published studies was found. For articles that reported pilot data findings for a larger study already included in the review, the pilot study was excluded and data from the larger study was used for analyses. For articles that used the same data source, data from the article with the most recently published efficacy findings was used for analyses.

Coding study characteristics

Participant characteristics, as well as the targeted behavioral outcome, study characteristics, methodology characteristics, and intervention channels were coded. Participant characteris- tics included details about the targeted sample population. Behavioral outcomes were categorized as obesity prevention (i. e., physical activity or diet), sexual health (i.e., condom use or HIV prevention), or smoking cessation. Study characteristics included whether the intervention was guided by theory and the social media platform used (existing platform or one created for the study). Methodology characteristics were the control group treatment (alternative intervention or none/waitlist con- trol) and the study sample size at the first follow up. Sample size

was dichotomized as less than 100 participants or 100 or more participants. All interventions included the use of social media features, but were coded for the use of additional intervention channels, including in-person, telephone, text message, and/or email channels.

Studies were coded by two independent reviewers. Once eligibility criteria were identified, the protocol was discussed and revised as needed. Both reviewers identified relevant abstracts, and then coded corresponding full text articles for inclusion (or exclusion). Characteristics of the full text were coded by the same independent reviewers. Reviewers met to come to an agreement on coded characteristics (mean percent agreement = 88.7%; mean Krippendorf’s Alpha α = 0.76). Any disagreements were resolved through discussion with the third reviewer.

Effect size extraction and calculation

The effect of interventions was measured using the standardized mean difference statistic, d. Effect sizes were calculated using the data reported in the articles (i.e., means and standard deviations or standard errors, proportions; Lipsey & Wilson, 2001). If the data

Figure 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram showing the study screening process (RCT = randomized control trial).

HEALTH COMMUNICATION 135

reported in the article was not sufficient to calculate an effect size, we requested additional data from the authors.

For studies with multiple time points, the effect size from first follow up was used for analyses. For consistency, the out- comes that were reported most routinely across the different studies were used as the outcome for analyses. For example, multiple studies reported mean weight, but few reported waist circumferences. Therefore, mean weight was used for analyses across these studies. Positive effect sizes were used to denote cases where participants in the social media intervention achieved more optimal behavioral change (e.g., decrease in body mass index, higher proportion of testing kits returned) than the control group, whereas negative effect sizes represent cases where the control outperformed the intervention.

Meta-analytic approach

Effect sizes were weighted by their inverse variance and com- bined using random effects analytic methods (Lipsey & Wilson, 2001). Heterogeneity among studies was assessed using the Q statistic and I2, and exploratory moderator analyses were conducted using mixed effects models. The Qb statistic was calculated to determine whether effect sizes significantly dif- fered among moderating variables. Analyses were conducted using Comprehensive Meta-Analysis Software Version 2.

Results

Study characteristics

A total of k = 17 studies with a with a cumulative N = 3,561 and a range of 16 to 1,092 participants were included in the meta- analysis (Table 1). Studies targeted racial/ethnic minority groups (k = 10), sexual or gender minorities (k = 5), socioeconomically disadvantaged populations (k = 4), and/or rural populations (k = 2). The majority of the interventions focused on obesity (k = 10). Other behavioral outcomes included sexual health (k = 5) and smoking cessation (k = 2). Recruitment of participants in the studies consisted of combinations of digital (e.g., existing social media platforms, Craigslist, targeted demographic web- pages) and in-person (e.g., flyers, community events, clinical set- tings) efforts. All interventions conducted on an existing platform (k = 8) used Facebook. One of these studies (Trude et al., 2019) also incorporated Instagram. The first follow-up assessment across studies ranged from 4 weeks to about 12 months.

Effectiveness of social media interventions

Social media interventions had a significant moderate-sized effect on behavior change among populations with health dis- parities (d = 0.303, 95% confidence interval [CI]: 0.156, 0.460, p < .001; Figure 2). Additional analyses examined the possibi- lity of publication bias. Orwin’s method (Orwin, 1983) deter- mined that 56 studies with non-significant findings would be needed to reduce the overall effect to a trivial effect size of d = 0.05. Duval and Tweedie’s Trim and Fill method (Duval & Tweedie, 2000) imputed 5 effect sizes and calculated an unbiased effect size of d = 0.179 (95% CI: 0.020, 0.338). Findings suggest some publication bias in this literature, but

also reveal the significant effect of interventions remains even after taking that bias into account.

Exploratory moderator analyses

There was significant heterogeneity across the 17 studies (Q = 64.48, p < .001, I2 = 75.19). Therefore, moderator analyses were conducted to explore potential moderators of the weighted mean effect. Regarding methodological characteris- tics, studies with fewer than 100 participants exhibited a larger effect size (d = 0.630) than those with 100 or more participants (d = 0.200; Qb = 7.629, df = 1, p < .05; Table 2). Effect sizes did not significantly differ by the use of guiding theory, social media platform, or type of control group.

Regarding intervention channels, interventions that included additional channels beyond the use of social media alone (d = 0.476) had larger effects on behavior change compared to those that did not (d = 0.119). More specifically, interventions that included telephone (d = 0.728) or e-mail (d = .639) channels had larger effects on behavior change than those not including telephone (d = 0.245) or e-mail (d = 0.235) channels.

Discussion

The purpose of this meta-analysis was to examine the efficacy of social media interventions among populations with health dis- parities. We synthesized the effects of 17 studies with 3,561 participants primarily from disparity populations. Our results revealed that social media interventions are effective for behavior change among these underserved populations. Furthermore, exploratory analyses suggested moderation of this effect by methodological characteristics and intervention channels.

While prior meta-analyses have reported relatively small effects of social media interventions [g = 0.24 (Laranjo et al., 2015) and d = 0.16 (Yang, 2017)], we observed a moderate effect size of d = 0.303. Thus, our findings suggest the effects of social media interventions on behavior change among populations with health disparities is comparable to, and possibly slightly higher than, the effect observed in general population samples. Why might this be the case? Populations with health disparities are affected by a unique combination of determinants, and these targeted inter- ventions may allow for the creation of culturally relevant inter- vention content aiding in achieving desired outcomes (Purnell et al., 2016; Williams & Purdie-Vaughns, 2016). For example, the studies included in the current meta-analysis that had the highest effect sizes reported creating culturally appropriate content for an inner city African American sample (Carter et al., 2011), tailored messaging for blue-collar workers (Choi et al., 2014), and cultu- rally relevant content for African American women (Joseph et al., 2015). Health professionals should continue to explore the use of targeted social media interventions to improve behaviors and decrease health disparities in the US.

We also found heterogeneity among intervention effects and explored the potential for moderating influences on effect size. Given the modest number of studies in our meta-analysis, these findings should be interpreted with caution. Still, our analyses suggested the effect of the interventions was moder- ated by methodology characteristics and intervention channels, but not by other study characteristics. Specifically, studies with

136 R. N. VEREEN ET AL.

samples of less than 100 participants had larger effects than studies with relatively larger sample sizes. This finding is sup- ported by previous literature that has found larger sample sizes to yield smaller effect sizes (Schafer & Schwarz, 2019). In fact, in their analysis of 302 meta-analyses of behavioral and psy- chological interventions, Lipsey and Wilson (1993) attributed an almost identical finding to the possibility of differing treat- ments, methods, and measures in smaller versus larger studies, any or all of which can impact effect size.

Prior reviews have sought to understand the individual role of social media in the efficacy of interventions. Due to the imbedded nature of social media into broader interventions, we were unable to determine the counterfactual effect of social media not being present at all. However, we explored if the presence of other intervention channels acted as potential moderators, finding that additional intervention channels (in addition to social media) may increase intervention effects. Telephone and e-mail channels were found to be the most beneficial additions, though these results should be interpreted with caution as few studies included these channels. It is possible telephone and e-mail were particularly influential because they were more accessible by participants than other channels, such as in-person meetings and text mes- sages. The use of indirect communication channels has been

shown to improve access to care for rural communities (Bardach et al., 2020; Nelson, 2017) and e-mail (accessed via the internet) may be more accessible than text messages (accessed via paid data access) among socioeconomically disadvantaged popu- lations who may not be able to consistently pay for a data plan (Alcaraz et al., 2018). If replicated in future work these findings would suggest the inclusion of additional channels aids in the efficacy of social media interventions, and consistent access to channels should be taken into consideration when designing and implementing such interventions.

We explored a number of other moderators but failed to find any other differences. Notably, the effect of interventions did not significantly differ when they were informed by a guiding theory. Previous literature points to the inconsistent application of the- ory in health communication studies using social media (Guo & Bian, 2019; Huo & Turner, 2019). There may also be a need for new or expanded theories, as existing theories may not be sufficient in guiding the design of social media interventions. The most commonly reported guiding theory (k = 7) was the social cognitive theory. While this theory may help guide the creation of intervention content, it lacks specific constructs that account for the influence of characteristics unique to social media such as engagement metrics and variability in content

Table 1. Characteristics of studies included in the meta-analysis.

Study Targeted sample N Behavioral

outcome Measure Follow-up

assessment d

Bender et al. (2017) Filipino Americans with Type 2 Diabetes and BMI >23 kg/m2 (for Asians)

45 Obesity prevention

Body weight (kg) 3 months 0.519

Bull et al. (2012) African American and Latino youth 1092 Sexual health Condom use during last sexual behavior

2 months 0.283

Carter et al. (2011) African Americans with Type 2 Diabetes 47 Obesity prevention

Mean BMI 9 months 0.764

Choi et al. (2014) Current smokers working in a blue-collar job (i.e., operating engineers)

145 Smoking cessation

7-day smoking abstinence 30 days 0.798

Hageman et al. (2017) Women aged 40–69 living in a rural area 175 Obesity prevention

Mean body weight (kg) 6 months −0.098

Herring et al. (2016) Overweight or obese pregnant African American women

56 Obesity prevention

Mean body weight gain (kg) 12 weeks 0.554

Hightow-Weidman et al. (2019)

Young Black men who have sex with men 404 Sexual health Condom use during last anal intercourse

3 months 0.052

Horvath et al. (2013) HIV+ gay or bisexual men 110 Sexual health Percentage of ART taken in past 30 days

8 weeks 0.150

Joseph et al. (2015) African American woman who were in- sufficiently active (<150 min/week of moderate-intensity physical activity)

29 Obesity prevention

Total MVPA 8 weeks 0.625

Marcus et al. (2016) Hispanic or Latina women who had insufficient physical activity (defined as participating in MVPA less than 60 minutes per week)

205 Obesity prevention

Increase in MVPA 6 months 0.530

O’Brien et al. (2016) Overweight or obese women aged 55 or older living in a rural area

24 Obesity prevention

Fruit/ vegetable servings per day

4 weeks −0.047

Phelan et al. (2017) Postpartum women from 12 clinics participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Program with a BMI more than 25 or a BMI from 22 to 24.9 but exceeding pre- pregnancy weight by 4.5 kg or more

367 Obesity prevention

Mean change in body weight (kg)

6 months 0.379

Pullen et al. (2008) Overweight women aged 50–69 living in a rural community

16 Obesity prevention

Mean body weight (lbs) 3 months 0.819

Trude et al. (2019) Adult caregivers living in thirty low-income, predominantly African-American neighborhood zones in Baltimore, with low access to healthy foods

527 Obesity prevention

Fruit/ vegetable servings per day

7 months −0.180

Vogel et al. (2019) Smokers aged 18–25 who identified as sexual and/or gender minorities

165 Smoking cessation

7-day smoking abstinence 3 months 0.023

Washington et al. (2017) Black men who have sex with men aged 18–30 42 Sexual health HIV testing 6 weeks 1.073 Young et al. (2013) African American and Latino men who have sex with

men 112 Sexual health Returned HIV test kit 12 weeks 0.143

Note: We generally use the term “Black” to refer to black populations throughout this review, but use the term “African American” when studies specifically used that term to refer to their target populations. MVPA = Moderate-to-vigorous activity; BMI = Body mass index

HEALTH COMMUNICATION 137

source. Future research should identify or develop theories that aid in understanding the mechanisms behind effective behavior change interventions using social media.

Despite the meaningful findings from this study, several limitations should be considered. Most notably, this literature is in a nascent stage, and the meta-analysis included a limited number of studies. This points to a key gap in the literature – i.e., the disproportionate lack of social media interventions targeting populations with health disparities. Due to the limited number of studies and the heterogeneity of those studies, findings must be interpreted with caution. For example, there is heterogeneity within and between disparity populations themselves (e.g., racial/ethnic minorities, socioeconomic status, rural, sexual and gender minorities), and there currently are not enough studies to examine the impact of interventions by disparity population. Also, the overrepresentation of Black samples within our sample of studies highlight the dearth of related studies among other populations experiencing health disparities. Furthermore, the intersectionality of the characteristics of populations experien- cing health disparities make it difficult to focus findings on a single aspect of a sample (e.g., race, class, sexual orientation). Despite these challenges, identifying effects is important to understand the role of social media interventions in behavior change among these important populations.

Similarly, the variability in targeted behaviors (e.g., physical activity, diet, sexual health) that have been studied to date also bring additional heterogeneity. In addition, social media and its features have evolved throughout the 13 year timespan when the included studies were published, and social media will likely con- tinue to evolve in the future. These limitations lead to a recommendation for a meta-analytic update some years from now, when more studies exist and additional evidence can be brought to bear on questions of overall effects on behavior, effects

by health topic, audience (e.g., specific disparity populations), plat- form, and active ingredients of interventions that may moderate intervention efficacy. Finally, while we used a well-accepted approach to meta-analysis, this approach does not include statis- tical corrections that are advocated by some other approaches to meta-analysis (Hunter & Schmidt, 2004). Although, such correc- tions typically serve to increase the observed effect, suggesting that if we had used such an approach, it would only serve to bolster the effects and overall conclusions of our meta-analysis.

Conclusion

To our knowledge, this is the first meta-analysis to examine the efficacy of social media interventions among populations with health disparities. Findings suggested social media interventions were moderately effective. However, relatively few studies eval- uated the efficacy of social media interventions among popula- tions with health disparities. Though much remains to be learned about social media interventions, their efficacy, and moderators of impact, our findings suggest they have the poten- tial to produce impact on behavior change among populations with health disparities. Future research should be more inclusive

Study name Std diff in means and 95% CI

Bender Bull Carter Choi Hageman Herring Hightow-Weidman Horvath Joseph Marcus O'Brien Phelan Pullen Trude Vogel Washington Young

-2.00 -1.00 0.00 1.00 2.00 Favors Control Favors Intervention

Figure 2. Forest plot of effect sizes and 95% confidence intervals showing the impact of social media interventions on behavior change.

Table 2. Potential moderators of the impact of social media interventions on behavior change.

k d 95% CI p-value Qb Qb p-value

Behavioral outcome 0.413 0.814 Obesity prevention 10 0.326 0.068, 0.584 0.013 Sexual health 5 0.236 0.035, 0.437 0.022 Smoking cessation 2 0.407 -0.353, 1.167 0.294

Study characteristics Used a guiding theory

1.210 0.271

No 4 0.454 0.151,0.758 0.003 Yes 13 0.253 0.063, 0.443 0.009 Social media platform type

0.105 0.745

Existing platform 8 0.279 0.035, 0.523 0.025 Created for study 9 0.334 0.113, 0.555 0.003

Methodology characteristics Control group 0.000 0.998 None or waitlist control

6 0.309 -0.009, 0.627 0.059

Alternative intervention

11 0.309 0.122, 0.496 0.001

Sample size 7.629 0.006 Less than 100 7 0.630 0.379, 0.881 0.000 100 or more 10 0.200 0.028, 0.373 0.023

Intervention channels Social media only 5.121 0.024 No 10 0.476 0.204, 0.749 0.001 Yes 7 0.119 -0.029, 0.266 0.116 In-person 0.607 0.436 No 15 0.290 0.115, 0.465 0.001 Yes 2 0.394 0.199, 0.590 0.000 Telephone 8.431 0.004 No 15 0.245 0.090, 0.401 0.002 Yes 2 0.728 0.442, 1.014 0.000 Text message 0.213 0.645 No 12 0.334 0.153, 0.515 0.000 Yes 5 0.244 -0.092, 0.580 0.155 Email 5.836 0.016 No 14 0.235 0.074, 0.396 0.004 Yes 3 0.639 0.353, 0.924 0.000

Note: k = number of studies; d= standardized mean difference (effect size).

138 R. N. VEREEN ET AL.

of these populations in digital interventions and seek to identify intervention effects and moderators among distinct disparity populations and health behaviors.

ORCID

Rhyan N. Vereen http://orcid.org/0000-0003-0519-0053 Rachel Kurtzman http://orcid.org/0000-0003-2511-2001 Seth M. Noar http://orcid.org/0000-0002-3453-5391

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