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. Author manuscript; available in PMC: 2025 Jul 1.
Published in final edited form as: Int J Eat Disord. 2024 Mar 6;57(7):1518–1531. doi: 10.1002/eat.24186

Eating disorder symptoms and comorbid mental health risk among teens recruited to a digital intervention research study via two online approaches

Erin Kasson 1, Hannah S Szlyk 1, Xiao Li 1, Anna Constantino-Pettit 1, Arielle C Smith 5, Melissa M Vázquez 1, Denise E Wilfley 2, C Barr Taylor 3,4,†, Ellen E Fitzsimmons-Craft 1, Patricia Cavazos-Rehg 1
PMCID: PMC11262972  NIHMSID: NIHMS1972423  PMID: 38445416

Abstract

Introduction:

It is crucial to identify and evaluate feasible, proactive ways to reach teens with eating disorders (EDs) who may not otherwise have access to screening or treatment. This study aimed to explore the feasibility of recruiting teens with EDs to a digital intervention study via social media and a publicly available online ED screen, and to compare the characteristics of teens recruited by each approach in an exploratory fashion.

Method:

Teens aged 14–17 years old who screened positive for a clinical/subclinical ED or at risk for an ED and who were not currently in ED treatment completed a baseline survey to assess current ED symptoms, mental health comorbidities, and barriers to treatment. Bivariate analyses were conducted to examine differences between participants recruited via social media and those recruited after completion of a widely available online EDs screen (i.e., National Eating Disorders Association [NEDA] screen).

Results:

Recruitment of teens with EDs using the two online approaches was found to be feasible, with 934 screens completed and a total of 134 teens enrolled over 6 months: 77% (n = 103) via social media 23% (n = 31) via the NEDA screen. Mean age of participants (N=134) was 16 years old, with 49% (n=66) identifying as non-White, and 70% (n=94) identifying as a gender and/or sexual minority. Teens from NEDA reported higher ED psychopathology scores (medium effect size) and more frequent self-induced vomiting and driven exercise (small effect sizes). Teens from NEDA also endorsed more barriers to treatment, including not feeling ready for treatment and not knowing where to find a counselor or other resources (small effect sizes).

Discussion:

Online recruitment approaches in this study reached a large number of teens with interest in a digital intervention to support ED recovery, demonstrating feasibility of these outreach methods. Both approaches reached teens with similar demographic characteristics, however teens recruited from NEDA reported higher ED symptom severity and barriers to treatment. Findings suggest that proactive assessment and intervention methods should be developed and tailored to meet the needs of each of these groups.

Introduction

Eating disorders (EDs) are serious mental health conditions that can result in severe health consequences (ANAD, 2021; NEDA, 2021), but only one in five individuals with these problems ever receive treatment (Kazdin et al., 2017; Weissman & Rosselli, 2017). Adolescence and young adulthood is often the period of onset for ED symptoms (Javaras et al., 2015; Stice et al., 2013), with youth experiencing myriad risk factors including body dissatisfaction, self-esteem, and negative affect (e.g., symptoms of anxiety and depression) (Suarez-Albor et al., 2022). Early engagement with teens and identification of symptoms are critical, as this can decrease the need for more intensive treatment later on as well as increase the likelihood of full recovery (Hornberger et al., 2021; Koreshe et al., 2023). However, teens with EDs face many barriers to receiving early intervention, including feelings of stigma and shame, perceptions of lack of severity of illness, low motivation for treatment, and difficulties accessing ED-specific treatment, for example due to high costs or lack of access to trained providers (Ali et al., 2017; Mills et al., 2023; Nicula et al., 2022). Many adolescents also do not want to disclose symptoms of an ED to parents, posing an additional barrier to treatment (Cavazos-Rehg et al., 2020; Hamilton et al., 2022; Kasson et al., 2021). The problem of identification is even worse amongst those with EDs with binge/purge presentations, with this group experiencing longer delays from the onset of symptoms to receiving ED-specific treatment compared to those with anorexia nervosa (AN) (Linville et al., 2010). Therefore, innovative methods of outreach are needed to connect teens both to research studies evaluating the efficacy of digital ED interventions, as well as to established ED interventions and resources to support recovery.

Self-screening is an important, accessible strategy that may help to promote identification of ED symptoms among teens, and ultimately, connect them to resources for care. One such self-screening tool is hosted by the National Eating Disorders Association (NEDA), the leading non-profit organization supporting individuals with EDs in the U.S. The NEDA online screening tool reaches about 200,000 respondents per year (NEDA, 2019) including about 25% under 18 years of age. The NEDA screen provides individuals with information about their symptoms and refers them to resources. Recent research has also utilized this screening tool to connect individuals who screen positive for EDs with online support and intervention (Fitzsimmons-Craft et al., 2022; Shah et al., 2022).

In addition to online self-screening methods, social media can also be leveraged for online recruitment of individuals with EDs (Cavazos-Rehg et al., 2020; Jiotsa et al., 2021; Linardon & Messer, 2019), including teens (Cavazos-Rehg et al., 2020; Kasson et al., 2Z021). With 97% of teens reporting use of at least one social media platform daily (Anderson & Jiang, 2018), social media provides an opportunity to identify teens with EDs where they are at and when they may be engaging with harmful thin-ideal content online (Schueller et al., 2019). Leveraging social media for recruitment may also provide the opportunity to reach teens with limited ability to access resources or social support. For example, minoritized teens and other youth may experience additional challenges accessing treatment, given fears surrounding disclosure, discrimination, and previous negative experiences with healthcare systems (McGlynn et al., 2020).

While many studies report use of online methods to reach individuals with EDs to provide digital interventions (Albano et al., 2023; Grammer et al., 2023; Nitsch et al., 2016; Shroff et al., 2023; Tregarthen et al., 2019), few direct comparisons between groups reached by various recruitment strategies have been conducted. One exception is a study by Vollert et al. (2020) outlining recruitment for a digital intervention study, which found that social media-based recruitment strategies engaged participants who were significantly more likely to exhibit ED behaviors and attitudes at baseline, when compared with participants recruited via face-to-face methods (Vollert et al., 2020), a finding possibly due to perceived anonymity and reduced stigma/shame related to disclosing ED symptoms online versus in person. Therefore, more research is needed to determine if ED symptoms, comorbid mental health symptoms, and backgrounds (including gender identity, sexual orientation, race, and ethnicity) among teens vary based on recruitment methodology. Characterizing the profiles of youth with EDs who engage in different media-driven recruitment strategies could inform research and dissemination efforts.

Teens in this study were recruited for a randomized controlled trial evaluating the preliminary efficacy of a digital intervention to support teens in ED recovery. This study had two aims: 1) to explore the feasibility of recruiting teens with EDs to a digital intervention study via social media and an online NEDA self-screen, defined as reaching 150 eligible teens over a 6 month period and 2) to compare symptoms and characteristics (including ED chronicity, mental health comorbidities, barriers to treatment, social support, access to basic needs, and demographic characteristics) of teens with EDs by recruitment approach. This study was exploratory in nature and no hypotheses were specified a-priori.

Method

Study participants were recruited using two online strategies concurrently. Recruitment Strategy 1- Social Media advertisements: Social media advertisements on Instagram and Facebook were created to reach teens aged 14–17 who may have body image, eating, or mental health concerns. We set our advertisement campaign to target teens (i.e., selected age target option of 13–17-year-olds) in the US, and these advertisements showed up directly in an individual’s home page feed as they scrolled, like other advertisements on social media. We did not use any body image, eating, or mental health-related targeting options based on platform guidelines limiting behavior and interest targeting among individuals under 18 years of age. These advertisements were images, animated images, or videos displaying study information, and teens who clicked on advertisements were routed to complete our study screener. Recruitment Strategy 2- NEDA online screen: NEDA’s existing widely available online screen was also leveraged to reach eligible teens with body image and eating concerns. If a teen navigated to the NEDA website to complete the NEDA online self-screener, and their responses indicated they were eligible for our study, they were shown our study information on the end-of-screen resources page and were able to click on our study link to complete our study screen if interested. See Appendix A for two social media advertisement examples and the NEDA end-of-screen message used in this study.

Previous studies on the feasibility of different recruitment approaches have reported recruitment rates, i.e., the ability to recruit a certain number of eligible individuals over a specified timeframe, as a way to evaluate the practicality and feasibility of different methods of outreach (Brøgger-Mikkelsen et al., 2020; Bowden et al. 2009; Orsmond & Cohn, 2015). To evaluate our recruitment approach, feasibility in the current study was defined as our team’s ability to connect with 150 teens who met inclusion criteria for our digital intervention study using our two online recruitment methods over 6 months, a timeframe comparable to other digital intervention studies among teens (Ali et al., 2022; Salem et al., 2023; Smith, Grohmann, & Trivedi, 2023). Both recruitment approaches were used for approximately 6 months in total. Recruitment through social media was started briefly in August 2021 for 1 week and paused, and- both recruitment approaches were used simultaneously from December 2021 to May 2022. Study eligibility criteria included the following: 1) 14–17 years old, 2) US resident, 3) English speaking, 4) Not currently engaged in ED treatment, 5) Screen positive for a Clinical or Subclinical ED (e.g., engagement in binge eating, self-induced vomiting, and/or laxative use 6 or more times in the past 3 months), or at High Risk for an ED (e.g., endorsing some ED behaviors such as binge eating and compensatory behaviors) as well as meeting at least one of the following criteria: (1) very afraid or terrified of gaining 3 pounds; (2) weight is more important than most, but not all, things in life or weight is the most important thing in life; (3) Weight Concerns Scale score exceeding 46 based on the Stanford-Washington Eating Disorder (SWED) screen (Graham et al., 2019). Participants who screened positive for anorexia nervosa were excluded as these teens may require additional medical monitoring that the mHealth tool used in this trial was unable to provide. During the screening phase, the research team verified study eligibility and completed data monitoring steps on all screens (see Appendix B), and those determined eligible were provided a personalized link to complete their baseline survey.

From both recruitment approaches, we were able to recruit a total of 191 teens who were eligible for the study. Of them, we identified 34 teens who did not complete over 50% of the baseline survey, 10 teens who scored at High Risk for an ED on the SWED but reported no ED behaviors (i.e., no binge eating or compensatory behaviors), and 13 teens who reported a history of ED treatment, and these participants were excluded from the following analysis (N total = 134). Teens who reported prior engagement with ED treatment were excluded because this group may experience barriers to treatment reengagement that are different from barriers to treatment uptake experienced among teens who had never engaged with ED treatment. All participants who completed their baseline survey were enrolled into the randomized controlled trial and were provided an invitation link to access an mHealth intervention (preliminary effectiveness to be evaluated in future work) (National Library of Medicine [NLM]: NCT04636840).

All surveys were completed in Qualtrics. The Washington University Institutional Review Board granted waiver of parental consent for this study due to the minimal risk involved (IRB # 202103099).

Measures

Eating Disorder Symptoms.

To determine a participant’s risk of having an ED, the Stanford-Washington Eating Disorder Screen (SWED) was used (Graham et al., 2019), which screened participants for EDs according to DSM-5 criteria and sorted them into possible diagnostic and risk categories (i.e., anorexia nervosa [AN], bulimia nervosa [BN], subclinical BN, binge-eating disorder [BED], subclinical BED, purging disorder, unspecified feeding or eating disorder [UFED], high risk for an ED, and no ED). The SWED assessment tool has been used to screen for EDs in those 13+ years (USPSTF, 2022; Graham et al., 2019). Those who were eligible for the study based on SWED screening responses were then divided, using the results, into two ED risk groups. These groups were clinical or subclinical ED (i.e., BN, BED, and purging disorder, subclinical BN, subclinical BED, and UFED), and High Risk for ED with behaviors (i.e., binging, compensatory behaviors).

Participants also completed the 28-item Eating Disorder Examination-Questionnaire (EDE-Q) (Fairburn & Beglin, 2008). The EDE-Q measures ED psychopathology using a global score and 4 subscales (i.e., restraint, eating concern, weight concern, and shape concern) rated on a 7-point scale ranging from 0 to 6, as well frequency of ED behaviors over the past 28 days. The EDE-Q has demonstrated validity for use with teens (Rand-Giovannetti et al., 2020; Wilksch et al., 2020) as well as gender diverse populations (Arikawa et al., 2021; Nagata, et al., 2020).

ED Chronicity.

Participants reported on the chronicity of their ED symptoms by responding to the question, “You indicated that you have been experiencing some concerns related to your eating, shape, or weight. When did you first start experiencing these symptoms?” The responses were dichotomized as 3 years or less vs. over 3 years. These response options were adapted from previous literature (i.e., 5 years changed to 3 years) (Grammer et al., 2021; Wonderlich et al., 2012) to account for the young age of our sample.

Mental Health Comorbidities.

Lifetime suicide attempt was assessed with the question: “Have you EVER, in your WHOLE LIFE, tried to kill yourself or made a suicide attempt?” to which teens responded either yes or no (Arnold et al., 2023; Patel et al., 2021).

Participants’ depression symptoms were assessed using the Patient Health Questionnaire (PHQ-8). This questionnaire includes 8 items with scores ranging from 0 to 24 (the 9th item assessing suicidal ideation was removed consistent with other remote and digital health studies among teens) (Kulikov et al., 2023). Scores greater than 14 indicate moderately severe/severe depression (Kroenke et al., 2009).

Anxiety symptoms were assessed using the 41-item Screen for Child Anxiety Related Disorders (SCARED), with scores ranging from 0–82 (Birmaher et al., 1999). For each item, a total of 3 options were provided, including not true or hardly ever true (0), somewhat true or sometimes true (1), and very true or often true (2). Overall, a total score of 25 or above and 30 or above was used to define the presence of moderate or severe anxiety disorder, respectively. Additionally, the SCARED includes 5 subscales assessing panic disorder, generalized anxiety disorder, separation anxiety disorder, social anxiety, and significant school avoidance. All subscales were dichotomized as binary variables based on the recommended scoring (Birmaher et al., 1997), either yes or no.

Perceived social support.

Perceived social support was measured by The Multidimensional Scale of Perceived Social Support (MSPSS), a 12-item scale designed to measure an individual’s perception of support from three dimensions: family, friends, and significant others (Zimet et al., 1988). In the present study, the significant other subscale was excluded because our study sample consisted of teens. The MSPSS has been demonstrated to have good internal and test-retest reliability and validity (i.e., internal reliability: 0.88) in both clinical and research settings (Laksmita et al., 2020; Wongpakaran et al., 2011; Zimet et al., 1988). Each item was rated on a 7-point scale ranging from very strongly disagree to very strongly agree. The possible total score, ranging from 1 to 7, was calculated by summing across all 12 items then dividing by 12. Higher scores on this scale indicate greater perceived social support (Zimet et al., 1988).

Barriers for ED treatment.

All participants were asked to answer 11 questions (yes/no) about barriers to accessing ED treatment, including: I do not want to tell my parents about my eating concerns; Treatment is too expensive; I am worried treatment would make me emotionally uncomfortable; I don’t want my friends to know about my eating concerns; I do not want to tell a healthcare professional about my eating concerns; I am too busy for treatment; I have no transportation to treatment; I don’t know how to find a counselor, or other resources; I feel that I am not ready for treatment; I am not interested in treatment; Other. These questions have been used to assess barriers to ED treatment in previous studies, including studies among teens with EDs (Cachelin & Striegel-Moore, 2006; Kasson et al., 2021).

Demographic characteristics.

Participants also provided demographic information, including age, race, ethnicity, gender identity, and sexual orientation. For gender identity, participants had the option to choose from man, woman, trans man, trans woman, genderqueer/gender nonconforming, or prefer to self-identify. For sexual orientation, participants had the option to choose from heterosexual, lesbian, gay, bisexual, pansexual, asexual, prefer to self-identify, questioning, or other. Throughout the paper, we will refer to those participants who endorsed gender identities outside man/woman and sexual orientations outside heterosexual as sexual and/or gender minorities (SGM). Ability to access basic needs was queried by a question “In the past month, how hard has it been for you to acquire items you need such as food, housing, clothing, hygiene products, and medical care?”. The responses were dichotomized as “very hard/ hard/somewhat hard” vs “not very hard”.

Statistical Data Analysis

Given that this was an exploratory study, quantitative data on all variables were first analyzed descriptively through medians (and interquartile ranges) and counts (and proportions) as a whole and by social media vs. NEDA recruited participants, respectively. Either CHISQ tests or Wilcoxon Rank Sum Tests were performed to examine the difference between social media vs. NEDA recruited teens, accounting for non-normal distribution of data. Effect sizes, Cohen’s d or Number Needed to Treat (NNT) were reported, depending on the type of variable (Cohen, 1988, Laupacis, Sackett, & Roberts, 1988). To be consistent with previous research, conventional effect sizes cutoffs were applied [e.g., small (Cohen’s d = 0.2/NNT: 7) vs. medium (Cohen’s d = 0.5/NNT = 5) vs. large (Cohen’s d = 0.8/NNT = 3)] (Lakens, 2013; McGough & Faraone, 2009). Additionally, bivariate analyses were conducted based on the status of baseline survey completion among participants who passed screening (N = 191). All analyses were performed using SAS Version 9.4. All the statistical analyses were two-sided, and P value < .05 were considered statistically significant.

Results

Recruitment advertisements were shown to 503,019 individuals on social media, and our study information was shown to 5,218 teens at the end of the NEDA screen. A total of 934 study screens were completed (816/934 from social media, 118/934 from NEDA). While the total number of screens from social media was higher, the proportion of individuals who viewed our study information on NEDA’s website and clicked to complete a study screen was higher than the proportion of individuals who viewed an ad on social media and clicked to complete a study screen (2.26%, 118/5,218 from NEDA versus 0.16%, 816/503,019 from social media). Screens from social media were less likely to be eligible and required considerably more data monitoring (see Appendix B), and 17.4% (142/816) teens from social media were eligible after this process relative to 41.5% (49/118) of teens in the NEDA sample after similar data monitoring procedures (NNT= 4.15, slightly less than a medium effect size). Overall, 191 teens were eligible for our digital intervention study after data monitoring (142/191 from social media, 49/191 from NEDA), surpassing our goal of reaching 150 eligible participants in 6 months and indicating the feasibility of these online recruitment approaches based on our pre-specified criteria for this study. A total of 134 teens were included in the current sample for analysis after all relevant post-study exclusions (103/134 from social media, 31/134 from NEDA). Although social media yielded more participants in total toward the sample for analysis, recruitment through NEDA was more efficient with a higher proportion of teens who completed screens from this method being included in our final sample (26.2%, 31/118 from NEDA versus 12.6%, 103/816 from social media) (NNT= 7.29, small effect size). Please see Appendix C for a consort flow diagram and details on participants included in each stage of recruitment, screening, and data monitoring procedures.

As shown in Table 1, the median age of the study sample (n = 134), recruited from social media (n = 103) and NEDA (n = 31), was 16 years old. Most of the participants were female (assigned at birth 91.0%) and self-identified as sexual and/or gender minority (70.1%; 69.9% from social media vs. 71.0% from NEDA; see Supplementary Table 1). Over half of the sample were from underrepresented racial or ethnic groups (non-White and/or Hispanic, 53.7%). No differences were observed on demographic characteristics by recruitment approach. The results from bivariate analysis between those who were invited to complete their survey but did not (n=34) versus those who completed their survey (n = 157) found that participants who identified as a sexual or gender minority were more likely to complete their survey (large effect size, see Supplementary Table 2).

Table 1.

Demographic characteristics of the participant sample (N = 134).

Overall (N = 134)
N (%)
Age (Median, IQR) 16 (15, 17)
Race
 White 68 (51.5)
 Asian/PI 18 (13.6)
 Black/African American 17 (12.9)
 Native American 1 (0.8)
 Others1 28 (21.2)
Ethnicity
 Hispanic/Latino 29 (21.6)
 Non-Hispanic 105 (78.4)
Assigned sex at birth
 Female 122 (91.0)
 Male 11 (8.2)
 Intersex 1 (0.8)
SGM2
 No 40 (29.9)
 Yes 94 (70.1)
Basic support past 30 days
 Not very hard 110 (82.1)
 Somewhat hard/Hard/Very hard 24 (17.9)
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NA, Not Applicable; IQR, Interquartile Range.

1

The category of others include other races (not listed above), multiple races, and unknown or do not wish to disclose.

2

SGM indicates gender minority and/or sexual minority youths.

Note: Differences in sociodemographic characteristics by recruitment approach (social media vs. NEDA) were examined. No statistically significant differences were found.

Results from examining ED risk/diagnostic categories and ED symptom severity by recruitment approach are presented in Table 2. Based on the SWED screener, two thirds (66.4%) of our participants screened positive for a clinical or subclinical ED. Groups recruited through social media and NEDA did not differ in the number of participants with a potential ED diagnosis (see Supplementary Table 3). Participants recruited from NEDA had a higher EDE-Q global score relative to participants recruited through social media, with the difference observed having slightly less than a medium effect size. Similarly, differences were observed on mean frequencies of ED behaviors reported over the past 28 days, including self-induced vomiting and driven exercise, with those recruited from NEDA endorsing higher frequencies of those behaviors (although effect sizes were small).

Table 2.

Eating disorder characteristics by recruitment status (N = 134).

Overall (N = 134) Social Media (N = 103) NEDA (N = 31)
N (%) ꭔ2/ z P Effect size
EDEQ (range 0–6; Median, IQR)
 Global EDEQ 3.85 (2.99, 4.58) 3.48 (2.80, 4.28) 4.59 (3.90, 5.16) 4.78 <.001 0.41
ED behaviors1 (Median, IQR)
 Binge eating 6 (3, 15) 5 (3, 12) 10 (4, 20) 1.91 .06 0.16
 Self-induced vomiting 0 (0, 1) 0 (0, 0) 0 (0, 4) 2.15 .03 0.19
 Laxative use 0 (0, 0) 0 (0, 0) 0 (0, 0) 0.18 .85 0.02
 Excessive exercise 3 (0, 10) 3 (0, 10) 4 (1, 14) 1.98 .05 0.17
Screening result by SWED 2.02
 Clinical/subclinical ED 89 (66.4) 67 (65.0) 22 (71.0) 0.37 .54 16.6†
 High risk ED 45 (33.6) 36 (35.0) 9 (29.0)
ED Chronicity - More than 3 years
 No 76 (56.7) 59 (57.3) 17 (54.8) 0.06 .81 40.0†
 Yes 58 (43.3) 44 (42.7) 14 (45.2)
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NA, Not Applicable; IQR, Interquartile Range.

1

ED behaviors were assessed during the past 28-day.

2

At screening, all participants were assessed by the Stanford-Washington University Eating Disorder Screen (SWED 2.0).

†

For continuous variables, the Cohen’s d was used to report effect size. For categorical variables, the Number Needed to Treat (NNT) was used to report as effect size.

Psychiatric comorbidities were common (see Table 3), with nearly all participants endorsing symptoms of at least one anxiety disorder (95.5%) and moderate or severe depression (83.3%). However, no differences were observed on mental health comorbidities by recruitment approach. A majority of participants reported high or moderate levels of social support (89.2%). Teens from each recruitment approach did not differ on overall level of perceived social support reported, but teens recruited from social media reported more social support from friends compared to teens recruited from NEDA. The between group difference was slightly less than a medium effect size.

Table 3.

Mental health comorbidities among the participant sample (N = 134).

N (%)
Lifetime Suicide Attempt
 No 89 (66.4)
 Yes 45 (33.6)
Depression
 No depression 22 (16.7)
 Moderate depression or above 110 (83.3)
 Missing 2
Anxiety disorder1
 No 17 (12.7)
 Moderate 6 (4.5)
 Severe 111 (82.8)
Any symptoms of anxiety
 No 6 (4.5)
 Yes 128 (95.5)
 Panic Disorder2
 No 26 (19.4)
 Yes 108 (80.4)
 Generalized Anxiety Disorder3
 No 14 (10.5)
 Yes 120 (89.5)
 Separation Anxiety4
 No 62 (46.3)
 Yes 72 (53.7)
 Social Anxiety Disorder5
 No 45 (33.6)
 Yes 89 (66.4)
 School Avoidance6
 No 35 (26.1)
 Yes 99 (73.9)
Perceived social support (MSPSS)
 Total score 4.75 (4.00, 5.83)
 Family (Median(IQR)) 4.25 (2.75, 5.00)
 Friends (Median(IQR)) 5.00 (4.00, 6.00)
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NA, Not Applicable; IQR, Interquartile Range.

1

The score of anxiety disorder was calculated by the Screen For Child Anxiety Related Disorders (SCARED). The cutoffs of 25 and 30 (out of 82) were used to define no (0–24), moderate (25–29), and severe (30 or above).

2

The cutoff of 7 (out of 26) was used to define the severity of the subscale of panic disorder.

3

The cutoff of 9 (out of 18) was used to define the severity of the subscale of generalized anxiety disorder.

4

The cutoff of 5 (out of 16) was used to define the severity of the subscale of separation anxiety.

5

The cutoff of 8 (out of 14) was used to define the severity of the subscale of social anxiety disorder.

6

The cutoff of 3 (out of 8) was used to define the severity of the subscale of school avoidance.

Note: Differences in mental health characteristics and social support by recruitment approach were examined. No differences were observed, with only one exception in perceived social support from friends (p = .03, effect size = 0.44).

Barriers to ED treatment by recruitment approach are shown in Table 4. The median number of barriers reported by teens across the sample was five, however, teens recruited from NEDA were more likely to endorse more barriers to ED treatment (Median (IQR): Social media: 5 (3, 6) vs. NEDA: 5 (4, 7)), while the effect size was small. The most prevalently endorsed barriers among the sample included not wanting to tell parents about ED symptoms (81.3%), concerns that treatment is too expensive (60.5%) and worries that treatment would result in emotional discomfort (54.5%). Compared to teens recruited from social media, teens from NEDA were more likely to endorse feeling not ready for treatment and not knowing where to find a counselor or other resources, while the effect sizes were small. Endorsement of other barriers did not differ by recruitment approach.

Table 4.

Barriers to treatment by recruitment status (N = 134).

Overall (N = 134) Social Media (N = 103) NEDA (N = 31)
N (%) ꭔ2/ z P Effect size
Total number of barrier endorsed (Median (IQR)) 5 (3, 6) 5 (3, 6) 5 (4, 7) 2.3 .02 0.13
Barriers to treatment, yes
 I do not want to tell my parents about my eating concerns 109 (81.3) 84 (81.6) 25 (80.7) 0.01 .99 111.11
 Treatment is too expensive 81 (60.5) 60 (58.3) 21 (67.7) 0.90 .34 10.64
 I am worried treatment would make me emotionally uncomfortable 73 (54.5) 52 (50.4) 21 (67.7) 2.86 .09 5.78
 I don’t want my friends to know about my eating concerns 72 (53.7) 51 (49.5) 21 (67.7) 3.18 .07 5.49
 I do not want to tell a healthcare professional about my eating concerns 70 (52.2) 52 (50.4) 18 (58.1) 0.55 .54 12.99
 I am too busy for treatment 60 (44.8) 43 (41.8) 17 (54.8) 1.65 .22 7.69
 I have no transportation to treatment 46 (34.3) 36 (35.0) 10 (32.3) 0.08 .83 37.04
 I don’t know how to find a counselor, or other resources 45 (33.6) 28 (27.2) 17 (54.8) 8.17 .001 3.62
 I feel that I am not ready for treatment 39 (29.1) 25 (24.3) 14 (45.2) 5.04 .02 4.78
 I am not interested in treatment 26 (19.4) 19 (18.5) 7 (22.6) 0.26 .61 24.39
 Other 9 (6.7) 7 (6.8) 2 (6.5) 0.01 .94 333.33
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Bold indicates statistically significant.

Discussion

This exploratory study examined the use of two different online methods (i.e., recruitment through advertisements on social media and recruitment through a publicly available online EDs screen offered on the NEDA website) to reach teens with or at high risk for an ED to participate in a randomized controlled trial testing the effectiveness of a digital self-help app. While recruitment through social media had substantial reach, more of these individuals were ineligible and this approach required closer data monitoring and validation than recruitment through NEDA, as fraudulent actors and bots have become increasingly problematic for online survey studies (Bybee et al., 2022). A higher percentage of teens recruited through NEDA were eligible and enrolled into the study (26.2% from NEDA versus 12.6% from social media; small effect size). Use of both recruitment approaches simultaneously to recruit teens for a digital health intervention study was found to be feasible, as we reached our specified recruitment goal for our study in under 6 months. Our findings contribute substantially to the literature about teens with EDs, not only demonstrating the potential of both online approaches to reach diverse teens with EDs and comorbid mental health symptoms, but also signaling the potential barriers to treatment among this group that may require innovative outreach and intervention approaches to address. Both online recruitment methods reached teens with similar demographic characteristics, however participants recruited through NEDA reported higher ED symptom severity (medium effect size). Both approaches also reached teens reporting similarly high rates of anxiety and depression, suggesting the feasibility of these outreach approaches to reach teens who may be at high risk or experience additional barriers to ED recovery. Use of such online recruitment methods, whether individually or simultaneously, is crucial to provide options for accessible, tailored ED support such as digital health interventions.

Participants recruited through NEDA were more likely to have greater overall ED psychopathology (medium effect size) and to more frequently engage in self-induced vomiting and/or excessive exercise behaviors (small effect sizes) than participants recruited through social media. However, despite this higher relative ED symptom severity, teens recruited through the NEDA online screening were also more likely than those recruited through social media to endorse not feeling ready for treatment (45% vs 24%) and not knowing where to find resources (55% vs 27%). We hypothesize that this may be related to the self-screening process and reflective of more severe ED symptomatology among teens recruited through NEDA. For example, seeking out the NEDA website for ED self-screening may constitute more active help-seeking than clicking on an advertisement that shows up passively on one’s social media account. But this engagement in or seeking of self-screening could also indicate an interest in symptom checking and symptom comparison, consistent with previous findings that those with higher symptoms may be more likely to self-select to complete screening (Vollert et al., 2020). Further, disordered eating behaviors may be used to self-soothe or cope with distress, be a part of one’s identity, and carry stigma and shame, especially among teens who may have not yet disclosed these symptoms to others. In this way, teens from NEDA may have reported being less ready for help (or recovery), despite their greater symptom severity. Nevertheless, given that teens signed up to participate in an intervention even though they were unsure about their readiness for treatment, recruiting through the NEDA screen may have the potential to bring awareness of internet-based options to those who may not feel ready for in-person treatment or who are not aware of how to find an in-person counselor but would still like help for their symptoms. To note, the participants recruited from NEDA who participated in this study had to go through several steps (e.g., going to website, taking screen, clicking on our study info, taking our study screen), and therefore represent a select and specific sample of all those who took the NEDA screen. More research can be done to explore why recruitment via the NEDA screen may reach populations that feel less ready for treatment and to further explore the feasibility of online recruitment methods and interventions as a means of encouraging treatment readiness among teens with EDs (Shah et al., 2022).

While teens recruited from NEDA reported higher ED symptom severity, in this study, social media also was able to reach teens with greater ED impairment as compared to conventional in-person recruitment approaches (Vollert et al., 2020). In our sample, participants recruited through social media reported an average score of 69.90 on the Weight Concern Scale (WCS) at screening, higher than the mean score of 32.4 reported by a sample of 2,739 teens recruited directly from high schools (Bauer et al., 2020). Additionally, we found elevated rates for comorbid depression and anxiety across both social media and NEDA-recruited teens including high rates of lifetime suicide attempts (41.7% of social media recruits and 37.4% of NEDA recruits). Prior research has found that suicidal behavior is significantly more common among individuals with BED or BN than in the general population (Smith et al., 2018). Epidemiologic data on the true prevalence of suicide attempts and other behaviors among teens with EDs are lacking (Fennig & Hadas, 2010; Zerwas et al., 2015), but literature does reflect that around 30% of adults with BED or BN have a lifetime history of suicide attempts (Udo et al., 2019). Additionally, most adolescents from both groups endorsed anxiety symptoms (95%), and a large proportion met clinical cut-offs for not only generalized anxiety disorder but also panic disorder, consistent with previous literature estimating anxiety disorders among roughly 50.1% to 71% of individuals with EDs (Franko & Keel, 2006). Relatedly, teens in this sample also reported fears that treatment would make them emotionally uncomfortable (54%), and this fear of emotional discomfort has been reported in past studies as a barrier to ED treatment uptake or engagement (Brown & Levinson, 2022; Pallister & Waller, 2008). Given that we were able to recruit a sample with such high mental health symptoms, future research should examine anxiety and depression specifically as barriers to ED treatment and explore the use of digital tools to engage and retain those with elevated mental health symptoms in supportive interventions.

Relatedly, most adolescents from both groups identified as SGM (72%), a population that reports higher rates of suicidal behaviors, and mental health disorders (Guz et al., 2021), including EDs (Hartman-Munick et al., 2021; Nagata, Ganson, et al., 2020), compared to their non-SGM peers. Recruitment was not targeted at SGM youth which underscores the potential for online approaches to improve ED treatment access among SGM teens by reducing barriers related to in-person screening and care, including existing stereotypes that EDs occur primarily among white, cisgender females that may results in lower rates of screening and care among SGM youth (Hartman-Munick et al., 2021). These stereotypes affect SGM individuals broadly, with previous studies outlining less access to treatment among SGM individuals relative to their non-SGM peers (Fish et al., 2020; Hsieh & Ruther, 2017; Jackson et al., 2016; Jennings et al., 2019). The Trevor Project recently reported that 60% of LGBTQ+ youth who wanted mental health care in the past year were unable to access it (The Trevor Project, 2022). Our study’s waiver of parental consent enabled teens to access online support for their ED symptoms without disclosing their symptoms to a parent or healthcare provider. This access was important, as the most frequently endorsed barriers among teens in our sample were fears of disclosing symptoms to their parents (81%), to friends (53%), or to healthcare providers (52%). Parental consent and disclosure of ED symptoms to others is a significant barrier for teens to access ED treatment (Ali et al., 2017; Ali et al., 2020; Cavazos-Rehg et al., 2020; Kasson et al., 2021). Digital support may have been appealing particularly to SGM teens in that it reduces the need to disclose identities and ED symptoms to others, highlighting the role of interventions and recruitment methods that do not require or create pressure to disclose to others as a means of accessing resources and support (Cavazos-Rehg et al., 2020).

In addition to emotional barriers and barriers related to disclosure, participants reported accessibility-related barriers such as financial cost (60%), lack of transportation (34%), and not knowing where to find resources (34%), barriers that have been reported in previous literature (Ali et al., 2017; Hamilton et al., 2022), further indicating a need for continued research into online outreach and digital interventions that can serve to attenuate these barriers.

Our findings need to be considered in light of several potential limitations. The study was conducted from August 2021-December 2022, i.e., shortly after restrictions due to the COVID-19 pandemic were being lifted. As mentioned previously, these circumstances and the experiences of teens may have affected the extent of mental health symptomatology and access to resources endorsed by the study participants. Two, it is possible that the online self-screening tools were vulnerable to fraudulent data; however, we took several precautions comparable with other online studies (Bybee et al., 2022; Heffner et al., 2021) to verify the validity of the data, including using data quality monitoring procedures outlined in Appendix B. Three, while we are able to note how many individuals viewed and clicked on our social media advertisements, we are unable to know which participants clicked on which advertisements to better understand the language and imagery that may have engaged certain demographic groups. Differences in language and imagery used in the social media advertisements and NEDA ad may have contributed to the differences in participant characteristics found between these two groups. Finally, the relatively small sample of this study may have impacted our ability to identify any additionally meaningful differences among the sub-groups presented here.

This study evaluated the feasibility of two online recruitment approaches in reaching teens with EDs, and reports preliminary success with recruitment of this population through both social media and NEDA’s online screen. Our findings characterized differences in ED symptoms among teens recruited using both methods, and highlight the racial, ethnic, and gender diversity represented among this population. The results emphasize the importance of novel recruitment strategies to engage teens with EDs in digital intervention studies, as well as considerations for mental health comorbidities that may impact uptake or engagement with ED treatment among teens.

Supplementary Material

1

Funding

This study was funded by the National Institutes on Drug Abuse, grant number K02 DA043657, and the National Institute of Mental Health, grant numbers R34 MH119170 and K08 MH120341.

Appendix A. Example Social Media Advertisement (top two images) and End of NEDA Screen Message (bottom image)

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Appendix B. Data Monitoring Procedures

  1. Reviewed within screen validity
    1. Repeat IP Addresses
    2. Repeat Location
      1. Verify Location with zip code provided
    3. Repeat email or phone numbers
    4. Age provided matches with birth year provided
    5. Suspicious Timing- Similar screen responses in quick succession, or too regular of completion cadence (i.e., every 2 minutes)
    6. Other patterns of suspicious responding (i.e. same response order, same demographics in string of screens)
  2. Verification emails for entries with suspicious data or patterns
    1. Verification email sent to ensure a real email address with same name provided in screen
    2. Ask secondary questions to cross-check with screen data (e.g., state, phone)
  3. Cross-validated screen data with survey data
    1. Check that email aligns
    2. Check that demographics align
    3. Check that zip code and phone align
    4. Suspicious Completion Rates- Check time of completion against prospective time to complete as tested by research team
    5. Check that open-ended responses are sensical (i.e., not strings of words populated by bots)
    6. Other patterns of suspicious responding (e.g., same response option for entire measure)

Appendix C. Participants Eligible during Each Stage of Recruitment, Screening, and Survey Completion

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Footnotes

COI

Patricia A. Cavazos-Rehg is a consultant for Rissana, LLC, PredictView, and Woebot.

Ellen Fitzsimmons-Craft receives royalties from UpToDate, is on the Clinical Advisory Board for Beanbag Health, and is a paid consultant for Kooth.

Craig Barr Taylor was a paid consultant for Google Mental Health and was an unpaid member of the SilverCloud, HelloBetter Scientific Advisory Boards and the Koko.Ai Community Advisory Board.

Hannah S. Szlyk is a paid consultant for Google Health.

Data Sharing

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

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

Supplementary Materials

1
NIHMS1972423-supplement-1.pdf (138.1KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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