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. Author manuscript; available in PMC: 2025 May 6.
Published in final edited form as: J Clin Child Adolesc Psychol. 2023 Nov 6;54(5):541–554. doi: 10.1080/15374416.2023.2272935

Serving the Underserved? Uptake, Effectiveness, and Acceptability of Digital SSIs for Rural American Adolescents

Erica Szkody 1, Ya-Wen Chang 1, Jessica L Schleider 1,2
PMCID: PMC11070444  NIHMSID: NIHMS1939843  PMID: 37931065

Abstract

Objective:

Rural teens are less likely to access care for depression than urban teens. Evidence-based digital single-session interventions (SSIs), offered via social media advertisements, may be well-suited to narrowing this gap in treatment access and increasing access to support for adolescents living in rural areas. We evaluated the viability of using social media-based advertisements to equitably recruit adolescents living in rural areas with elevated depression symptoms to digital SSIs; we sought to characterize and assess whether SSI completion rates and acceptability differed for adolescents living in rural versus more urban areas, across three intervention conditions (two active, evidence-based SSIs; one placebo control); and we tested whether digital SSIs differentially reduced depressive symptoms.

Method:

We used pre-intervention and three-month follow up data from 13- to 16-year-old adolescents (N = 2,322; 88% female; 55% non-Hispanic White) within a web-based randomized control trial of three free, digital SSIs (ClinicalTrials.gov identifier: NCT04634903) collected eight months into the COVID-19 pandemic in the United States.

Results:

Digital SSIs reached adolescents at population-congruent rates; however, social media ads resulted in relative underrepresentation of youths from rural areas who hold minoritized racial/ethnic identities. Adolescents living in rural areas also completed digital SSIs at similar rates to their urban peers, found SSIs equivalently as acceptable, and reported comparable depression symptom reductions as youth living in urban areas.

Conclusion:

Digital SSIs and their dissemination through social media may offer a promising means of narrowing the gap between access to evidence-based mental health support between adolescents living in rural and urban areas; however, targeted efforts are warranted to reach racially minoritized youths in rural U.S. counties.

Keywords: Rural, Urban, Adolescents, Depression, Single Session Interventions

The rates of depressive symptoms among adolescents have dramatically increased in the last 10 years. A recent report by the CDC found that 1 in 3 high school students felt persistent feelings of sadness or hopelessness in 2019 (CDC, 2019). However, of those adolescents with mental health problems, less than half will seek services, and of those who do seek services, many will drop out before completing treatment (De Haan et al., 2013). Furthermore, mental health disparities among adolescents are even more pronounced in rural communities where adolescents have a higher rate of mental health problems in comparison to their peers within urban settings (Substance Abuse and Mental Health Services Administration, 2016). Compounding relatively high rates of depressive symptoms, many adolescents within rural communities face significant geographic, cultural, and economic barriers to accessing mental health care. For example, adolescents living in rural areas face a dearth of mental health facilities and mental health professionals in their local regions, as well as poverty, stigma, and a lack of resources targeted for rural populations (Andrilla et al., 2018; CDC, 2020; Kepley & Streeter, 2018). Due to these barriers, even adolescents living in rural areas who do access care are likely to end treatment earlier than their peers living in urban areas (Seidler et al., 2020).

Improving Access to Care Through Social Media Recruitment

As a result of these barriers to traditional in-person mental health treatment, many adolescents living in rural areas seek information and support for their mental health online (Pretorius et al., 2019). Digital mental health interventions (DMHIs) hold great potential to bridge gaps between mental healthcare need and access to care among adolescents living in rural areas (Naslund et al., 2019). DMHIs have been found to be effective in reducing depression severity in adolescents with outcomes comparable to face-to-face psychotherapy (Lehtimaki et al., 2021; Moshe et al., 2021). DMHIs also have built-in accessibility advantages over in-person treatment, given census data showing that, despite the disparities in access to reliable broadband internet between those living in urban versus rural areas, > 90% of U.S. households have at least one type of computer or smartphone device and over 75% of households have access to reliable internet connections (Martin & Suitland, 2021; Population Reference Bureau, 2021). During the coronavirus-19 pandemic (COVID-19), adolescents across the United States turned to social media to connect with their peers, attend school, and cope with depression, loneliness, and other mental health concerns (Marciano et al., 2022). As a result, their rates of social media use increased from 8% to 38% (Statista, 2021). Thus, social media is an ideal location for recruiting adolescents living in rural areas and improving the uptake of DMHI. Indeed, 97% of adolescents continue to use social media on a daily basis (Anderson & Jiang, 2018; Curtis et al., 2019) and several studies have been successful in recruiting adolescents via social media platforms to complete DMHI (e.g., Dobias, et al., 2022). Social media may be a promising avenue for recruiting adolescents living in rural areas.

In a 2022 study by the Pew Research Center, approximately 95% of rural teens ages 13 to 17 used YouTube, 67% used TikTok, and 58% used Instagram regularly, which is fairly consistent with the use of these platforms among urban teens (i.e., 95% of urban teens use YouTube, 71% use TikTok, and 70% use Instagram regularly; Vogels et al, 2022). Unfortunately, we have yet to uncover whether uptake of these digital interventions social media recruitment differ across geographic rurality which limits our ability to assess whether these recruiting strategies, and whether providing digital interventions through social media, are efficacious in bridging the gap in access to care for adolescents living in rural areas. Thus, in the current study we examined whether uptake and completion of digital SSIs differed across adolescent geographic rurality, in the context of a randomized trial.

The Promise of Digital SSIs for Depression in Rural Adolescents

However, it is notable that the potential accessibility of digital mental health interventions does not always translate into utilization. Many DMHIs demand very high levels of engagement (i.e., require hours, weeks, or months of consistent use), despite the fact that continued engagement in DMHIs tends to be low (Borghouts et al., 2021). Indeed, users prefer brief self-guided interventions over those that require high levels of engagement (Borghouts et al., 2021). To better fit real-world patterns of accessing digital mental health support, recent research has begun to explore the effectiveness and acceptability of intentionally briefer digital interventions. Studies have shown that the length of a given mental health treatment does not necessarily result in significantly different treatment outcomes—that is, longer interventions are not necessarily more effective (e.g., Ost & Ollendick, 2017; Schleider et al., 2021; Weisz et al., 2017).

As such, DMHIs that are designed for brevity and completability (e.g., digital single session interventions or digital SSIs), including those that minimize unrealistic demands on user initiative and motivation, might optimize DMHIs’ promise to reduce treatment access gaps for adolescents living in rural areas with unmet mental health needs. Accordingly, digital single session interventions (SSI) may provide an innovative solution to bridging the gap in access to mental health care for adolescents living in rural areas of the country. Online interventions, and digital SSIs, are well-positioned to overcome several of the barriers adolescents encounter when attempting to access mental health care. Digital SSIs are brief (eliminating challenges linked to early treatment dropout), can often be completed in the privacy of the home, and are cost effective (e.g., Wasil et al., 2021). Digital SSIs are also effective: A recent meta-analysis found that SSIs can significantly reduce youth mental health problems, including depression, (Schleider et al., 2017; Schleider et al., 2020a), with no differences in effects emerging for self-guided (e.g., online) versus therapist-delivered SSIs. Moreover, studies have suggested that SSIs can be disseminated to U.S. adolescents through online social media networks, raising the possibility that digital SSIs may overcome adolescents living in rural areas barriers to seeking support (Cohen et al, 2023; Dobias et al., 2022; Schleider et al., 2022).

Furthermore, evidence on digital SSIs for mental health suggests that adolescents in rural areas may benefit specifically from certain evidence-based digital SSIs. Digital SSIs have been found to be effective at reducing depressive severity across diverse cultural contexts, from the United States to India to Kenya (e.g., Osborn et al., 2020; Schleider et al., 2019a; Schleider et al., 2022; Wasil et al., 2020). Results from recent randomized control trials of digital SSIs for depression suggest that these evidence-based interventions (i.e., one that taught skills of behavioral activation, another that taught that personality traits and symptoms can change over time; motivational interviewing; Jans et al., 2023; Schleider et al., 2022, Schleider & Weisz, 2018), compared to an active control condition are effective in lowering depression symptoms among adolescents at 3 and 6 month follow up. However, we cannot take for granted that digital SSIs are equally acceptable and effective for rural vs urban teens, given many disparities in effectiveness and acceptability of interventions for other underserved populations (e.g., sexual/gender minority youth, racial/ethnic minority youth; Schueller at al., 2019). Indeed, several studies have already examined disparities in the effectiveness and acceptability of digital SSIs for sexual/gender minority youth and racial minorities but no similar evaluations have been conducted with adolescents living in rural areas (McDanal et al., 2022a; McDanal et al., 2022b). Studies have yet to examine the uptake or completion of digital SSIs among adolescents living in rural areas recruited through social media or online platforms, nor have they examined whether the effectiveness and acceptability of such interventions differs across youth living in rural versus urban areas of the United States. In other words, the degree to which digital SSIs have actually helped mitigate treatment gaps for adolescents living in rural areas when disseminated via social media platforms remains unexamined. While social media recruitment may help to bridge the gap in access to care, it is vital to assure that adolescents living in rural areas experience evidence-based digital SSIs as effective and acceptable. Thus, leveraging data from a recent -nationwide trial, which recruited 2,452 U.S. adolescents via Instagram to test digital, evidence-based, SSIs for depression, we evaluated whether adolescents in rural areas accessed digital SSIs at nationally-representative rates, as well as whether SSI acceptability and SSI effects on depressive symptoms differed for rural versus urban adolescents.

Study Aims.

The present study had three interrelated Aims. First, we evaluated the viability of using social media-based advertisements and posts to recruit adolescents living in rural areas with elevated depression symptoms into a randomized trial testing digital SSIs. Specifically, we characterized the extent to which Instagram-based recruitment approach yielded nationally representative samples of youth living in more rural and more urban U.S. counties, in the context of a previously-completed clinical trial (Schleider et al., 2022). As per recent estimates, 14% of the United States adolescents live in rural counties (Semega et al., 2020); thus, we predicted that 14–20% of adolescents’ participants in this trial lived in relatively rural (versus relatively urban) counties at the time of participation.

Second, given previously-observed disparities in access to and completion of depression treatment among youth living in urban versus rural areas of the United States (CDC, 2020; Kepley & Streeter, 2018), we sought to characterize and assess whether completion rates and program acceptability ratings differed for adolescents living in more rural versus more urban counties, among youth in any of the randomized trial’s three intervention conditions (two active single-session interventions; one placebo control program). We categorized SSI completion rates in two ways: First, as a binary variable based on whether participants did or did not complete the full intervention, and second, across three “levels” of SSI completion, drawing on categories established in prior research characterizing digital SSI completion rates (did not complete the intervention, completed some of the intervention, and completed the entire intervention; Cohen & Schleider, 2022; Sung et al., 2021).

International studies have found that DMHI are often just as acceptable and effective among rural youth as they are with urban youth (e.g., Vallury et al., 2015) but no evaluations have been conducted in the United States comparing these factors with rural populations. As a third exploratory Aim, given limited research to date on whether depression interventions might differently benefit rural versus urban adolescents among those who do access care, we tested whether digital SSIs differentially reduced depressive symptoms among rural versus urban teens. Specifically, we tested whether participant-level rurality moderated the effects of the digital SSIs, compared to a placebo control, on adolescent depression symptoms across the trial’s three-month follow-up period. Overall, results promise to clarify whether social media-delivered SSIs can equitably support rural and urban adolescents experiencing depression, or whether population-specific dissemination and intervention design approaches might be warranted to meet the needs of rural teens.

Method

Recruitment

We used baseline (pre-intervention) and three-month follow up data from 13- to 16-year-old adolescents (N = 2,452) within a web-based randomized control trial of three free, digital single-session mental health interventions (ClinicalTrials.gov identified: NCT04634903) collected eight months into the COVID-19 pandemic (i.e., baseline data was collected between November 19 and December 6, 2020 and follow-up surveys were completed by March 2021) in the United States (Schleider et al., 2022). Adolescents were passively recruited from across the U.S. via social media ads on Instagram as part of a large study examining the effectiveness of scalable, single-session interventions for adolescent depression in the context of the COVID-19 pandemic (Schleider et al., 2022). Advertisements include a colorful cartoon photo asking for participants for a two-part, confidential psychological study to be done completely digital, compensation was listed at $10 “for each time”. Instagram ads linked to an online survey platform, Qualtrics, through which participants had access to the survey components and a randomized SSI (see our pre-registration materials on Open Science Framework at https://osf.io/5dt7q/ for examples of the social media ads). The first page of the Qualtrics linked page included study details and participants were screened for the following eligibility criteria: (1) being 13–16 years old at the time of enrollment; (2) comfort reading and writing in English; (3) internet and computer, laptop, or smartphone access; and (4) endorsement of elevated depressive symptoms, per a score of >= 2 on the Patient Health Questionnaire-2. Eligible participants then provided assent before participating further. Parent permission was not required (as waived by the university review board) to protect adolescents’ confidentiality and minimize access barriers (see Schleider, 2023 and Smith et al., 2021 for further discussion of the ethics surrounding waivers of parental consent in online adolescent mental health research). Additional inclusion criteria for the present study included reporting a valid U.S. zip code. Based on these criteria, the overall sample size for the present study was 2,322 adolescents across the United States.

Procedure

Eligible participants were invited to complete pre-SSI questionnaires, one of three 20–30-minute SSIs (i.e., adolescents were randomly assigned to one of the three SSIs using the Qualtrics-embedded randomizer; both adolescents and researchers were masked to the condition assignment prior to data analysis), and post-SSI questionnaires within a one-hour session. Three months later, participants were invited to complete a 10-minute follow-up questionnaire via Qualtrics, which included a subset of measures from the baseline data collection. Participants were compensated $10 for completing the baseline survey and an additional $10 for completing the follow up survey. After data collection at three-month follow-up was completed, all adolescent participants received access to all three SSIs, along with a resource list of mental health-related hotlines, text lines and online psychoeducation resources (all accessible with or without parent involvement). Participants also received contact information for the research team if they had any questions or desired additional support.

Conditions

Control Intervention

Supportive Therapy SSI (ST-SSI).

Sharing Feelings Intervention (Schleider & Weisz, 2018): The web-based supportive therapy (ST-SSI) intervention was designed to serve as an active (placebo) control condition. The intervention was delivered entirely via Qualtrics, was self-administered by participants, and takes approximately 20 minutes to complete. It was structurally similar to the other SSIs but was designed to mimic supportive therapy (ST). The goals of the ST intervention are to encourage participants to identify and express feelings to close others. The intervention does not teach or emphasize specific skills or beliefs. In previous clinical trials, ST has resulted in significantly fewer reductions in youth internalizing problems compared to cognitive-behavioral and growth mindset digital single-session interventions (Schleider & Weisz, 2018; Schleider et al., 2022). The ST-SSI was designed to control for nonspecific aspects of intervention, including engagement in a computer program. It included the same number of reading and writing activities as the other SSIs; it also mirrored the web-based growth mindset intervention as closely as possible, including vignettes written by older college students who describe times when they benefited from sharing their feelings with friends or family. See all materials for this intervention here: https://osf.io/u4axs/.

Active Interventions

Behavioral Activation SSI (BA-SSI).

“The ABC Project” (Schleider et al., 2019a): The BA-SSI included 5 elements: (1) An introduction to the program’s rationale: that engaging in value-based activities that build pleasure and accomplishment can combat sad mood and low self-esteem; (2) Psychoeducation about depression, including how behavior shapes feelings and thoughts; (3) A life values assessment, where youth identify key areas (family relationships, friendships, school, or hobbies) from which they draw (or once drew) enjoyment and meaning; (4) Creation of an activity hierarchy, where youth identify (from pre-generated lists) and personalize (in guided exercises) 3 activities to target for change; and (5) An exercise in which youths write about benefits that might result from engaging in each activity; an obstacle that might keep them from doing the activities; and a strategy for overcoming identified obstacles. See all materials for this intervention here: https://osf.io/ch2tg/.

Growth Mindset SSI (GM-SSI).

“Project Personality” (Schleider & Weisz, 2016; Schleider & Weisz., 2018). The 30-minute, self-administered youth program includes: An introduction to the brain and a lesson on neuroplasticity; Testimonials from older youths who describe their views that traits are malleable, due to the brain’s plasticity; Further stories by older youths, describing times when they used “growth mindsets” to persevere during social and emotional setbacks; Study summaries noting how/why personality can change; And an exercise in which youths write notes to younger students, using scientific information to explain people’s capacity for change. See all materials for this intervention here: https://osf.io/a9uv2/.

Measures

Demographic Variables

Participants were asked to report demographic information including age, sex assigned at birth, gender identity, race and ethnicity, and zip code.

Rural vs. Urban Location

We measured rurality using the nine-tiers of the 2013 Rural-Urban Continuum Codes (RUCC). This scheme allowed for the examination of urbanicity, and rurality based on population density and centrality to a metro area. Participants were asked to indicate their current residence zip code. These zip codes were transformed into U.S. counties and then assigned a number based on the RUCC from 1 (Urban area with a population of over 1 million) to 9 (Non-Urban area completely rural or less than 2,500 urban population, not adjacent to a metro area). Rurality was also further operationalized as a binary variable (i.e., the 9-point scale was dummy coded into an urban-rural split with a score of 1–3 being Urban and 4–9 being Rural for the purpose of analysis for Aim 1 according to the USDA guidelines for rurality; Cromartie & Bucholtz, 2008) and used as a continuous variable (on a 1–9 scale) to test Aims 2 and 3.

Perceived Socioeconomic and Social Status

Immediately pre-intervention, participants were asked to rate their perceived socioeconomic and social status using the two items from the MacArthur Scale of Subjective Social Status-Youth Version (Goodman et al., 2001). Respondents indicated where they see themselves on a ladder with 10 rungs (range: 1 to 10 for both items, where 1 = families with most money/education/jobs and youth with highest respect/grades/social standing; 10 = families with least money/education/jobs and youth with lowest respect/grades/social standing). The measure has shown good reliability and validity among youth populations (see Goodman et al., 2001). In this study, we included perceived socioeconomic status in all models of interest to control for any confounding influence, as previous studies have found that socioeconomic and social status are highly correlated with access to previous mental health care (Cummings, 2014).

Program Acceptability

The Program Feedback Scale (PFS; Schleider et al., 2019b; Schleider et al., 2020b) was collected immediately post-intervention. The PFS asked youth to rate agreement with 7 statements indicating perceived acceptability of an SSI (e.g., “I enjoyed the program”) on a 5-point Likert scale (1 = “really disagree”; 5 = “totally agree”). The PFS also assessed youths’ open-response feedback on each SSI. A score of 3.5/5 or above on the item-level mean score of PFS is interpreted as an “acceptable” rating on that item. The total score was dummy coded as low acceptability (score of < 3.5/5) = 0 and high acceptability (score of 3.5/5 or above) =1. The PFS has been utilized in multiple randomized control trials with adolescents (e.g., Schleider et al., 2020) and has been validated against qualitative responses regarding program acceptability in other studies (McDanal et al., 2021). In the current study, the Cronbach alpha for program acceptability was .80 post intervention.

Completion Rate

Completion Rate was assessed as a categorical variable, using completion categories utilized in prior research on digital single-session interventions (Sung et al., 2021; Cohen & Schleider, 2022). Specifically, participants were characterized as either Full Completers, Post- Activity Non-Completers, or Pre-Activity Non-Completers. Full Completers were defined as participants who reached the end of the full digital SSI content, as evidenced by submitting a response to the final activity in the program. An “activity was defined as an interactive program element that requires the participant to expend effort to complete. Examples include selecting an answer to a question, writing a sentence in response to a prompt, or creating an action plan. To be considered a follow-up completer, a participant must have made it through all the questionnaires in the follow-up surveys (i.e., they must reach the final “page” of the follow-up questionnaires though it is acceptable for the participant to leave the answer blank after seeing it). Post-Activity Non-Completers were defined as participants who dropped out after completing at least one activity. Pre-Activity Non-Completers were defined as participants who dropped out before completing any activities. In this study, we categorized participants in two ways. First, we categorized participants using a binary variable based on whether they did not complete the intervention vs. whether they completed the entire intervention. Second, we categorized participants on the three levels defined above including Full Completers, Post- Activity Non-Completers, or Pre-Activity Non-Completers.

Adolescent depressive symptom severity

Depressive symptom severity was assessed using the Children’s Depression Inventory (CDI) 2 - Child form; Kovacs, 2014). Depressive symptom severity was assessed pre-SSI and at the three-month follow up. The CDI-2 is a reliable, valid measure of youth depression severity, normed for youth age and sex and yielding raw and T scores. The Cronbach alpha for the current study was 0.78 at baseline and 0.86 at three-month follow up.

Data Analyses

The RStudio Statistical Program was used for analyses (Allaire, 2012). The original study (Schleider et al., 2022) tested all assumptions necessary to interpret analyses conducted for the present project (i.e., independence, linearity, multicollinearity, homoscedasticity, and normality of residuals; see https://osf.io/kumdv). All data cleaning was conducted in line with the original pre-registration and thus, no additional data cleaning was completed for the current analyses. Missing data for depression symptoms at 3-month follow-up was imputed using the expectation-maximization and bootstrapping algorithm implemented with Amelia II in R in the base study (see https://osf.io/kumdv and Schleider et al., 2022, for details).

To test whether recruitment to digital interventions via social media recruitment differed significantly between rural versus urban location, we examined the distribution of participants by rurality in the full trial sample (i.e., where they fell on the 9-point rurality scale from 1–3 as urban and 4–9 as rural according to USDA guidelines for rurality; Cromartie & Bucholtz, 2008). We then graphed a map of the United States to qualitatively illustrate the locations of all participants in the study, color-coding participant markers as a function of location type (more rural, per a 4–9 rurality score, versus more urban, per a 1–3 rurality score). Frequency analyses were used to examine whether the rurality of the recruited sample approximately matched that of the national population (i.e., whether the percentage of adolescents with a rurality score of 4–9 approximately matched the 14% of adolescents in the United States living in rural areas of the country).

To test whether rurality was associated with completion and acceptability rates across interventions, we ran one logistic regression and one linear regression, with rurality (on a 9-point scale), the intervention condition (i.e., assessed as a three-level categorical variable comparing each active intervention vs. the control), and potential covariates as predictors of SSI completion rates and acceptability (i.e., our dependent variables). We assessed theoretically relevant covariates were associated with the outcomes of interest (i.e., acceptability, completion rate) to determine whether to include them in the final regression models by examining an ANOVA and correlations between potential covariates (i.e., age, race/ethnicity, gender, subjective social status) and outcome variables. Candidate covariates that showed a significant association with either outcome of interest was added to the relevant regression models.

Across all models, a p-values of less than .05 indicated a significant effect of the predictor variable (i.e., rurality) on the dependent variable of interest. Lastly, we dummy-coded rurality into a binary variable (i.e., urban area vs. rural area) to explore the differences in completion rates across SSIs. In addition to the two regressions, we used a Pearson chi square to assess for differences in completion rates (i.e., pre-activity completion, post-activity completion, and full completion) between individuals from urban areas and rural areas across SSIs. To correct for multiple tests, the False Discovery Rate (FDR) correction, specifically the Benjamini-Hochberg (BH) procedure (Benjamin & Hochberg, 1995), was applied to all p-values obtained from all analyses (i.e., the regression analyses and chi-square test) to reduce the potential for false-positive results (Type I errors). The BH procedure adjusts the significance level of each individual test, considering the total number of tests being performed, while controlling the expected number of false discoveries among the rejected hypotheses. The FDR-adjusted p-value from all analyses was considered significant if the FDR-adjusted p-value is less than .05.

To test whether digital SSIs differentially reduced depressive symptoms among rural versus urban teens Aim 3 was examined using a moderation analysis, with rurality (assessed continuously), type of intervention (across three levels), demographic covariates, and the interaction of rurality X intervention condition (with depression scores at baseline as a covariate) as predictors of depressive symptom severity from baseline to 3-month follow-up. We determined which demographic covariates to include in our final models by examining associations between potential covariates (i.e., age, race/ethnicity, gender, subjective social status) and pre-intervention depressive symptom levels (assessed continuously). Variables that demonstrated significant associations with pre-intervention levels of depressive symptoms were included as covariates in regressions. A p-value of less than .05 indicated a significant effect of the predictor variables (e.g., participant-level rurality) on our dependent variable of depressive symptoms.

Results

Data Manipulation

For all participants with a valid zip code, we transformed the zip code into county level data to procure a RUCC code. Codes for participants with a zip code that corresponded to more than one county, were averaged, and rounded up to produce one single RUCC score (on a 1–9 scale). Out of the total sample, only 232 (10%) participants required an averaged RUCC score.

Sample Characteristics

Table 1 presents the characteristics of the 2,322 adolescent participants who were both randomized to an SSI or the control condition and provided a valid U.S. zip code that we used for quantifying the urbanicity vs. rurality of their location. Overall, no significant differences were found between the rural and urban groups in terms of age, biological sex, and randomization into an intervention group (see Table 1). Compared to urban adolescents, those living in rural areas of the country endorsed significantly lower perceived socioeconomic status, (see Table 1). Similarly, a chi-square test found that adolescents living in rural areas were less racially and ethnic diverse (p < .001) than their urban counterparts and identified as following: 83.94% White, 0.73% American Indian/Alaska Native, 0.36% Asian, 2.19% Black/African American, 0% Native Hawaiian/Other Pacific Islander, 12.04% Multiracial, 0.36% Other, and 0.36% Prefer not to say. No group differences were found in intervention conditions, indicating successful randomization. See Table 1 for more demographic information.

Table 1.

Sample Characteristics

Urbanicity
(N = 2032)		 Rurality
(N = 290)
Variable Name n (%) n (%) p-value
Race < .001
White 1127 (64.33%) 230 (83.94%)
American Indian/Alaska Native 18 (1.03%) 2 (0.73%)
Asian 194 (11.07%) 1 (0.36%)
Black or African American 162 (9.25%) 6 (2.19%)
Native Hawaiian or Pacific Islander 10 (0.57%) 0 (0.00%)
Multiracial 192 (10.96%) 33 (12.04%)
Other 28 (1.60%) 1 (0.36%)
Prefer Not to Answer 21 (1.20%) 1 (0.36%)
Biological Sex .652
Female 1789 (88.04%) 256 (88.28%)
Male 212 (10.43%) 28 (9.66%)
Other 31 (1.53%) 6 (2.07%)
Subjective Social Status 1 5.61 (1.71) 6.03 (1.57) < .001
Intervention Group .427
Support Therapy SSI2 672 (33.07%) 107 (36.90%)
Growth Mindset SSI3 672 (33.07%) 89 (30.69%)
Behavioral Activation SSI4 688 (33.86%) 94 (32.41%)
Completion Rate .254
Full Completers 1698 (83.56%) 234 (80.69%)
Non-Completers 334 (16.44%) 56 (19.31%)
Variable Name M (SD) M (SD) p-value
Age 15.14 (0.94) 15.12 (0.94) .741
Program Acceptability 5 4.29 (0.50) 4.28 (0.47) .889
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Note.

1

MacArthur Scale of Subjective Social Status-Youth Version

2

Sharing Feelings Intervention

3

“The ABC Project”

4

“Project Personality”

5

Program Feedback Scale

Aim 1: Intervention Uptake (Recruitment) in Rural Youth

Among our sample, 290 (12.5%) adolescents indicated a residential zip code in a rural area and 2032 (87.5%) indicated a residential zip code in an urban area. Consistent with our prediction that adolescents living in rural areas recruitment patterns (i.e., adolescents living in rural areas would opt into the study testing digital SSIs) would reflect national population proportions of adolescents living in rural and urban areas, the percentage of participants living in rural areas among our sample was numerically comparable with the national population in rural and urban areas (i.e., approximately 14% of adolescents in the U.S.; Semega et al., 2020). Participants from rural and urban areas appeared reasonably distributed across the map; rural participants were represented in 40 states and urban participants were represented in 43 states. For the geographical distribution of adolescent participants, see Figure 1.

Figure 1.

Figure 1

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Geographic Representation of Sample Rurality

Note. Participants who indicated a zip code in Hawaii (n = 7) or Alaska (n = 4) are not pictured.

Aim 2: Intervention Completion Rates and Acceptability across Rural and Urban Youth

Across participants in more rural versus more urban areas, participants’ race and ethnicity [F(7, 271) = 17.72, p < 0.001] and subjective social status [F(1, 77) = 26.89, p < 0.001] were significantly different (i.e., participants with a minoritized racial or ethnic identity, as well as those who reported lower subjective social status, were less likely to live in a rural area; see Table 2). Participant age did not significantly differ by rurality [F(1, 3) = 0.30, p = 0.58] and sex assigned at birth [F(2, 4) = 0.90, p = 0.41]. Therefore, race (i.e., as a multi-level categorical variable) and subjective social status were entered as control variables in all analyses. Among adolescents who were randomized to one of the three study conditions, 234/290 (80.69%) adolescents living in rural areas and 1698/2032 (83.56%) adolescents living in urban areas fully completed their assigned activity (either an active single-session intervention, in two conditions, or a placebo program, in the third condition). A logistic regression was performed to ascertain the effects of rurality on condition completion rates, when controlling for race, subjective social status, and the intervention condition. After controlling for these variables, rurality was not significantly associated with completion rates across all conditions (B = - 0.06, p = 0.13, OR =.943, 97.5% CI [0.88, 1.02]; after FDR correction). Likewise, when not controlling for race/ethnicity and subjective social status, rurality was not significantly associated with completion rates across all conditions (B = - 0.07, p = 0.05, OR = .932, 97.5% CI [0.87, 1.00]).

Table 2.

Moderation Analysis:

Variable Name B SE p-value
Key Predictors
Intervention condition [GM] X Rurality 0.01 0.02 0.69
Intervention condition [BA] X Rurality 0.03 0.02 0.08
Covariates
Intervention condition [GM] − 0.15 0.04 < 0.001
Intervention condition [BA] − 0.10 0.04 0.02
Rurality − 0.02 0.01 0.14
Depression at Baseline 0.62 0.03 < 0.001
Race/ethnicity
 White 0.17 0.11 0.10
 Asian 0.11 0.11 0.32
 Black or African American 0.09 0.11 0.40
 Native Hawaiian or Pacific Islander − 0.04 0.17 0.80
 Multiracial 0.11 0.11 0.30
 Other 0.15 0.13 0.25
 Prefer Not to Answer − 0.08 0.14 0.59
Subjective Social Status1 0.01 0.01 0.07
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Note.

1

MacArthur Scale of Subjective Social Status-Youth Version; Binary covariates of the intervention were referenced against the Sharing Feelings Condition, racial binary covariates were referenced against Native American Indian/Alaska Native groups; GM = Growth Mindset Intervention, BA = Behavioral Activation Intervention.

Both rural and urban adolescents who completed one of the three conditions reported that the program they were randomly assigned to was acceptable, on average, as indicated by the item-level mean scores being above 3.5/5 of the Program Feedback Scale (PFS) (see Table 1 for statistics). A logistic regression was performed to ascertain the possible effect of rurality on SSI acceptability rating, when controlling for race, subjective social status, and the intervention condition. After controlling these variables, rurality was not significantly associated with acceptability (B = 0.13, p = 0.10, OR = 1.14, 97.5% CI [0.98, 1.35]). Similarly, when not controlling race/ethnicity and subjective social status, rurality was not a significant predictor of SSI acceptability (B = 0.07, p = 0.34, OR = 1.07, 97.5% CI [0.94, 1.25]). Lastly, results from the Pearson chi square (i.e., to assess for differences in completion rates (i.e., pre-activity completion, post-activity completion, and full completion) between individuals from urban areas and rural areas across SSIs) demonstrated no significant differences between rural and urban participant completion rates for any condition (X2(2, N = 2,322) = 1.86, p = 0.39).

Aim 3: SSI Effects on Depression Symptoms in Rural versus Urban Youth

Similar to our previous analyses, race and subjective social status were added as control variables for all regression analyses to account for the significant differences across geography. Furthermore, in our correlation results, we found that the CDI score at baseline was a significant covariate (r = 0.52, p < 0.001) and was entered as a control variable in the regression analyses for Aim 3. A moderation regression was used to examine whether these covariates, the intervention condition, rurality, and the interaction between rurality and intervention condition (i.e., growth mindset intervention X rurality, behavioral activation intervention X rurality) significantly predicted depression severity at the three month follow up (i.e., whether or not rurality moderated the association between baseline scores on depression scores at follow-up). Results indicated that rurality did not significantly moderate the association between intervention condition that depression symptom reductions from baseline to follow-up (for the GM-SSI X Rurality interaction term, B = 0.01, p = 0.69; for the BA-SSI X Rurality interaction term, B = 0.02, p = 0.08; See Table 2 for more information.). Further, depression symptoms among participants who completed the intervention condition (i.e., in comparison to the control condition) decreased between baseline and follow up regardless of participants rurality (BA-SSI: B = - 0.15, p < 0.001; GM-SSI: B = - 0.10, p = .02). All regression analyses remained consistent with and without covariates in the model.

Discussion

Previous studies have found digital SSIs to be effective and acceptable for use in reducing hopelessness, depression, and anxiety among teens recruited through social media (e.g., Dobias et al., 2022; Schleider & Weisz, 2017). In the current study, we sought to extend the literature on digital SSIs by exploring whether adolescents living in rural areas were accessing and completing digital SSIs when recruited through paid social media ad recruitment techniques. Further, we examined whether adolescents living in rural areas benefitted from the completion of these SSIs as much as their counterparts living in urban areas. Overall, we found that adolescents living in rural areas were represented within social media recruitment, completed digital SSIs at a similar rate as their urban peers, found these digital SSIs just as acceptable, and experienced similar benefits as urban youth (when controlling for race/ethnicity and socioeconomic status).

Dissemination of Digital SSIs through Social Media

Consistent with Aim 1, we found consistent rates of engagement from adolescents living in rural areas in comparison to the United States national population rates such that our study was able to engage adolescents at a nationally representative rate (i.e., 12.5% of our participants were from rural areas vs. the 14% of adolescents living in rural areas in the United States). Further, through a visual examination of the mapping of participant locations, we were able to see that social media-based recruitment and subsequent engagement in our study reached participants all over the country. Unfortunately, we did not have access to data on the rates of viewing the social media advertisements by rurality (social media companies generally do not provide this information to researchers). Thus, we cannot conclude that the literal reach of social media-based recruitment was comparable across groups, just that our approach resulted in nationally representative uptake. Partnership with social media companies and more expansive data-sharing between industry and academic partners will be necessary to bridging this gap and increasing our knowledge of whether SM ads for mental health support tools are disseminated equitably to youth from different parts of the country.

Similarly, while this study suggests that social media-based recruitment holds promise as a dissemination strategy for digital SSIs for adolescents living in rural areas, further research is needed to determine whether equitable dissemination exists across different social media platforms to refine recruitment approaches even further to reduce inequities in treatment access. For example, recent research has shown that adolescents living in rural areas are less likely to use the social media platform Instagram (i.e., the platform used for recruitment in our study) in general in comparison to urban adolescents, and that adolescents overall may be more likely to engage more with other social media platforms such as TikTok or YouTube (Vogels et al., 2022). Further research will be required to assess whether video-based recruitment on such platforms may be more efficient or effective in reaching and engaging adolescents living in rural areas. Furthermore, in the future, direct partnerships with social media platform will be essential to testing the effectiveness of recruitment through social media as currently there are guardrails in place against recruiting for research studies on many video-based platforms.

Differences in Racial/Ethnic Identity and Subjective Social Status

We did find significant differences in the racial/ethnic identities and subjective social status between rural and urban participants. Specifically, we found that participants from rural areas were less likely to identify with a minoritized racial or ethnic group and to identify as having a lower subjective social status compared to urban youth. These results are inconsistent with national estimates. Studies have found that approximately one third of rural adolescents identify with a minoritized identity (Johnson & Lichter, 2022) and in our study 16.06% identified with a minoritized identity. Youth with minoritized identities are just as likely to use social media and report using social media at the same rate as their non-Hispanic White peers (Vogels et al, 2022). These results suggest that social media ads about mental health interventions may need to be specifically tailored to improve uptake among minoritized youth. Culturally tailored marketing suggests tailoring the advertisements according to a framework in line with the cultural values of the target population (e.g., Miller et al., 2023; Simenec et al., 2023). An example of tailor of these advertisements may be to identify that the intervention was designed with these youth in mind directly in the advertisement, offer the intervention in more than one language, change the colors and photos of the ads to be more appealing (as decided by a panel of minoritized youth advisers). Moreover, more research needs to be done to understand whether social media platforms provide similar advertisements across racial/ethnic users, whether these users are equally interested in mental health interventions, and how to improve uptake of these SSIs in other ways to facilitate the representation of these populations in SSI research.

Further in our sample, adolescents living in rural areas, on average, reported a higher subjective social status in comparison to adolescents living in urban areas. This is also inconsistent with nationally collected data which suggests that individuals living in rural areas are more likely to witness or be the victim of a crime, often live in low-income households, and rely on government assistance programs for food or medical care (Ferdinand et al., 2015; U.S. Department of Health and Human Services; 2015). This suggests that social media-based recruitment may be targeting specific demographic groups within rural communities (e.g., affluent communities), or that our ads may not have reached subpopulations of adolescents living in rural areas. More research is needed that identifies whether social media recruitment, or specific social media platforms or targeted advertisements and materials, are more effective or beneficial for recruiting adolescents from rural areas who hold racially and ethnically minoritized identities.

Acceptability, Completion, and Effectiveness

In Aim 2, which characterized the acceptable and completion rates of the digital SSIs between participants living in rural versus urban areas, we found no significant differences between geographic groups when controlling for race/ethnicity and socioeconomic status. Further, we found no significant differences in the effectiveness of the digital SSIs between participants from rural and urban areas. These results are promising in comparison with previous literature about the completion rates and acceptability of psychotherapy interventions. Studies have found that individuals in rural areas are more likely to report a distrust of psychotherapy, psychotherapists, and a desire for more culturally sensitive materials (Substance Abuse and Mental Health Services Administration, 2016). Moreover, our lack of significant differences in completion rates contrasts with literature which shows that participants living in rural areas are more likely to attend less overall therapy sessions and complete less therapy overall in comparison with urban populations (Cully et al., 2010). Similarly, the literature suggests that while individuals living in rural areas may benefit equally from psychotherapy interventions, they often face a distance effect in which individuals who live further from treatment locations benefit less from the psychotherapy intervention than those who are closer to the treatment location; an effect that is not present in urban populations (e.g., Wong et al., 2019). Thankfully, our digital SSIs overcome these distance effects by providing interventions online, wherever and whenever users prefer to complete them, resulting in equal intervention effectiveness across populations. However, in our study, we must take caution with these results.

One reason to take caution with our results is that we paid participants if they completed the study. Studies have shown that paying participants for their time greatly influences whether they complete the intervention and may have resulted in biased completion rates (Cohen & Schleider, 2022). Second, we mainly utilized quantitative data for gauging acceptability which may not have captured all aspects of participants feelings or experiences of the interventions. Future research is encouraged to use focus groups to receive much more nuanced feedback for how to reach adolescents living in rural areas or to improve uptake further (e.g., tailoring to rural youth needs and preferences, adapting content to match rural teen experiences and values). Moreover, future research is encouraged to continue to examine the ways in which digital SSIs can be further adapted to meet the specific needs of adolescents living in rural areas and further improve the effectiveness of these mental health supports in areas where youth need them most.

Further Limitations and Strengths

A major limitation of the study resided in the use of zip code data to determine adolescent geographical rurality. For the current study, we utilized the RUCC scoring of rurality which is a county-based code system. Unfortunately, in this study we did not collect county level data and some adolescents indicated a zip code which covered multiple possible RUCC coded geographical areas. Thus, we ended up using an average RUCC score for those participants which may not have accurately captured their rurality. However, only 10% of the total sample (n = 232) required an averaged RUCC score, whereas the vast majority of the sample was accurately coded with the one RUCC score. Therefore, we believe that our results are representative of participants’ rurality in the current study. Fortunately, the large overall sample size of the study, the examination of results across multiple interventions, and the robustness of our results across outcomes, stand as strengths in the current study. In the future, researchers are encouraged to reexamine rurality through multiple lens and measurements to assess whether county level rurality or distance from access to care may influence the outcomes of SSIs or the uptake of SSIs through social media. Furthermore, the study was conducted primarily during and after the COVID-19 pandemic, during which adolescent internet and technology use rose along with mental health concerns (Marciano et al., 2022). The sharp increase in rate of technology use and mental health concerns may have also impacted the acceptability and uptake of the digital SSI through social media. As researchers continue to improve DMHI, researchers are encouraged to work with policy makers to acknowledge and capitalize on these potential avenues for dissemination of evidence based mental health care.

Conclusion

Youth in rural areas experience barriers to accessing mental health care in their region. Digital SSIs and their dissemination through social media, have been found to be efficacious in helping to bridge the gap between access to care for adolescents living in rural areas and their need for effective care strategies. Further, these interventions may serve as a stop gap measure for helping adolescents develop skills or learn tools to manage their mental health concerns until they are able to access mental health care with a professional. However, targeted efforts are warranted to reach racially minoritized youths in rural U.S. counties.

Funding:

JLS has received funding from the National Institute of Health Office of the Director (DP5OD028123), National Institute of Mental Health (R43MH128075), the Upswing Fund for Adolescent Mental Health, the National Science Foundation (2141710), Health Research and Services Association (U3NHP45406–01-00), the Society for Clinical Child and Adolescent Psychology, HopeLab, and the Klingenstein Third Generation Foundation. Preparation of this article was supported in part by the Implementation Research Institute (IRI), at the George Warren Brown School of Social Work, Washington University in St. Louis; through an award from the National Institute of Mental Health (R25MH080916; JLS is an IRI Fellow).

Footnotes

Competing Interests: JLS serves on the Scientific Advisory Board for Walden Wise and the Clinical Advisory Board for Koko; receives consulting fees from Kooth, LLC and Woebot Health; is Co-Founder and Co-Director of Single Session Support Solutions; and receives book royalties from New Harbinger, Oxford University Press, and Little Brown Book Group.

CRediT statement: E.S.: Conceptualization, Methodology, Writing - original draft, and Writing - review & editing. Y.-W.C.: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, and Writing - review & editing. J.L.S.: Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Supervision, and Writing - review & editing.

Data availability statement:

The study was pre-registered, and the data stored on the Open Science Framework at https://osf.io/5dt7q/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The study was pre-registered, and the data stored on the Open Science Framework at https://osf.io/5dt7q/.

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