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. Author manuscript; available in PMC: 2023 Sep 18.
Published in final edited form as: J Behav Cogn Ther. 2023 May 23;33(2):97–117. doi: 10.1016/j.jbct.2023.05.002

Reach and uptake of digital mental health interventions based on cognitive-behavioral therapy for college students: A systematic review

Laura D’Adamo a,b, Layna Paraboschi a, Anne Claire Grammer a, Molly Fennig a, Andrea K Graham c, Lauren H Yaeger d, Michelle G Newman e, Denise E Wilfley a, C Barr Taylor f,g, Daniel Eisenberg h, Ellen E Fitzsimmons-Craft a,*
PMCID: PMC10506850  NIHMSID: NIHMS1926352  PMID: 37724304

Abstract

Background:

Mental health problems are increasing in prevalence among college students, yet few students receive treatment due to barriers such as insufficient resources in college counseling centers. Digital mental health interventions (DMHIs) have potential to overcome barriers and offer accessible, evidence-based care to college students. However, to evaluate the true public health impact of evidence-based DMHIs, it is important to assess the reach and uptake rates of DMHIs on college campuses.

Objectives:

We conducted a systematic review to examine the reach (i.e., % of invited students who express interest) and uptake (i.e., % of enrolled participants who initiate an intervention) of DMHIs based on cognitive-behavioral therapy (CBT) for college students.

Methods:

Eight databases were searched. Inclusion criteria included: (1) college population; (2) experimental design; (3) CBT-based intervention; (4) intervention targeting specific mental health conditions; and (5) digital intervention. Reach and uptake rates were calculated from data reported. A systematic narrative review framework was used to synthesize results.

Results:

Of 10,315 articles screened, 90 were included. Seventeen studies (19%) reported sufficient data to calculate reach; 35 studies (39%) reported uptake rates. Of studies that reported reach or uptake, most evaluated unguided (n = 20) or guided (n = 16) self-help programs. Measurement methods varied widely. Overall reach was low, whereas uptake was high among enrolled participants.

Discussion:

Despite evidence that improving reach and uptake can increase the public health impact of DMHIs, most studies did not report on either outcome. Suggested practices to improve these outcomes, and their reporting, are discussed.

Keywords: Cognitive-behavioral therapy, College mental health, Digital interventions, mHealth, Digital mental health, Implementation science

Introduction

Mental health disorders are highly prevalent among college students, with 31% screening positive for at least one past-year clinical disorder (Auerbach et al., 2018). In the U.S., the prevalence of mental health disorders among college students has sharply increased over the past decade (Duffy, Twenge, & Joiner, 2019; Oswalt et al., 2020), and rates of depression, alcohol use disorder, eating disorders, and comorbidity further increased among this population during the pandemic (Kim et al., 2022; Romano, Lipson, Beccia, Quatromoni, & Murgueitio, 2022). Given that onset for most lifetime cases of mental health disorders occurs before age 24 (Kessler et al., 2005), it is a public health priority to focus prevention and treatment efforts on the college student population. However, only about one third of college students with mental health disorders receive any treatment, despite evidence that mental health care utilization among college students has significantly increased over the past decade (Lipson, Lattie, & Eisenberg, 2019; Xiao et al., 2017). This persisting treatment gap may be due to the growing number of college attendees, the increasing prevalence of psychopathology, and insufficient resources in college counseling centers to meet the growing demand (Mowbray et al., 2006; Xiao et al., 2017). In addition, college students report financial concerns, stigma, embarrassment, and lack of information about how to access services as barriers to receiving care (Ebert et al., 2019; Wang et al., 2020). Thus, it is critical to implement and evaluate novel solutions to expand access to mental health services on college campuses and reach students in need.

Digital mental health interventions (DMHIs; i.e., interventions delivered via smartphones, computers, or other digital devices) are promising solutions for addressing the mental health treatment gap among college students and reducing burden on college service delivery systems (Lipson et al., 2019). DMHIs are highly accessible, often inexpensive, and able to reach large subsets of populations (Montagni et al., 2020; Newman et al., 2011a, 2011b). They also have potential to minimize stigma and personalize treatment, which may promote sustained engagement (Conley, Durlak, Shapiro, Kirsch, & Zahniser, 2016). DMHIs may particularly appeal to college students, as this age group readily uses web-based tools to access mental health information (Montagni et al., 2020). Indeed, college students report being open to using online mental health services (Dunbar, Sontag-Padilla, Kase, Seelam, & Stein, 2018) and perceive DMHI tools as accessible (Irish et al., 2021) and reasonable alternatives or complements to face-to-face therapy (Dederichs, Weber, Pischke, Angerer, & Apolinario-Hagen, 2021). Cognitive-behavioral therapy (CBT), a leading treatment for numerous mental health conditions (Carpenter et al., 2018; Hofmann, Asnaani, Vonk, Sawyer, & Fang, 2012), is the most commonly offered form of DMHI for college students (Lattie et al., 2019). Due to its structured nature, CBT is easily translatable to a digital format (Newman, Consoli, & Taylor, 1997). Internet-based CBT has demonstrated effectiveness at reducing symptoms of depression (Day, McGrath, & Wojtowicz, 2013; Harrer et al., 2018; Newman et al., 2021; Newman, Szkodny, Llera, & Przeworski, 2011a; Rackoff et al., 2022), anxiety (Day et al., 2013; LaFreniere & Newman, 2016, 2023; Mackinnon, Griffiths, & Christensen, 2008; Newman et al., 2021; Newman, Przeworski, Consoli, & Taylor, 2014; Newman et al., 2011a), stress (Harrer et al., 2018; Newman, Jacobson, Rackoff, Jones Bell, & Taylor, 2021; Rackoff et al., 2022), eating disorders (Fitzsimmons-Craft et al., 2022; Fitzsimmons-Craft et al., 2020; Linardon, Shatte, Rosato, & Fuller-Tyszkiewicz, 2022), insomnia (Morris et al., 2016), alcohol, drug use, and smoking (Newman, Szkodny, Llera, & Przeworski, 2011b), and somatic symptoms (Hennemann et al., 2022) in college students. Additionally, several studies have documented high usability and acceptability of internet-based CBT among college students (Currie, McGrath, & Day, 2010; Newman et al., 2021; Nitsch et al., 2016).

Despite the established effectiveness and acceptability of DMHIs, their public health potential may be limited if students in need of care are not reached or do not utilize treatment tools (Fleming et al., 2018). Although research in this area is limited, some prior evidence indicates that use and engagement with DMHIs among college students is low (Melcher, Camacho, Lagan, & Torous, 2022). For population-level impact, increasing reach and utilization may be more effective than increasing efficacy or effectiveness of an intervention. Using a mathematical model to simulate the effects of increasing reach/uptake or effectiveness of interventions for eating disorders, Moessner and Bauer (2017) found that an increase of services utilization would reduce the number of clinical cases by a greater proportion than an equal increase of efficacy (Moessner & Bauer, 2017). Thus, increasing reach and uptake of DMHIs is of the utmost importance. Reach has been defined as the proportion of a target population who makes initial contact with a service (e.g., by completing a screen or interest survey) and is subsequently offered a relevant intervention (Taylor et al., 2020). Uptake, often referred to as adoption, has been defined as initiating an intervention (e.g., downloading, registering, or logging in; Fleming et al., 2018; Proctor et al., 2011).

No prior review has explored the rates of reach and uptake of CBT-based DMHIs in college populations (i.e., students at community colleges and/or universities), limiting our understanding of DMHIs’ potential to reduce prevalence of mental health disorders in college populations. Thus, this study aimed to report the reach and uptake of CBT-based DMHIs for college students to date.

Method

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, Altman, & The, 2009).

Operationalization of reach and uptake

Reach was operationalized as the proportion of students who completed an initial screen or interest survey of those invited to partake in a DMHI (Taylor et al., 2020). Previous research has used this operationalization to assess reach of DMHIs on college campuses (Fitzsimmons-Craft et al., 2019). If reach was not explicitly reported, the proportion of students reached was calculated using reported data. If studies described the pool from which they recruited participants (e.g., all undergraduate students at a particular university) without specifying the size of the pool in the article, we conducted external searches for university enrollment data to approximate the recruitment pool size and calculate the proportion of students who were reached.

Uptake was operationalized as the proportion of study participants who were given access to the DMHI and who initiated the DMHI (Taylor et al., 2020). Intervention initiation typically refers to log-ins, downloads, or completion of the first session (Lattie et al., 2019). For studies that only reported on program completion, uptake was calculated such that those who completed the program were counted as having initiated it. As such, for those studies, this percentage indicated a conservative estimate of uptake, given that engagement is a challenge in DMHIs and that most individuals who start these programs do not complete them (Andrews et al., 2018; Fleming et al., 2018; Lattie et al., 2019).

Literature search and study selection

A medical librarian (LHY) searched the literature for records including the concepts of college students, mental health conditions, digital, mobile, and technology interventions, outcomes, uptake, and reach. The librarian created search strategies using a combination of keywords and controlled vocabulary in Embase.com 1947-, Ovid Medline 1946-, Scopus 1823-, Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Database of Systematic Reviews (CDSR), APA PsycINFO 1800 s-, the Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus) 1937-, and Clinicaltrials.gov 1997-. These databases were chosen to cover a wide range of literature in biomedical sciences, nursing, allied health, behavioral and social sciences, and psychology.

All search strategies were completed August 23, 2022, with no added limits and a total of 16,686 results were found. Duplicate records (n = 6,331) were deleted after using the de-duplication processes described in ‘‘Deduplication of database search results for systematic reviews in EndNote,” (Bramer, Giustini, de Jonge, Holland, & Bekhuis, 2016), and an additional 40 duplicates were found using covidence.org, resulting in a total of 10,315 unique citations included in the project library. Fully reproducible search strategies for each database can be found in Appendix A.

Inclusion and exclusion criteria

Identified articles were screened based on the following inclusion criteria: (1) college population; (2) studies evaluating efficacy/effectiveness or implementation of an intervention; (3) intervention described as based on CBT or which included cognitive-behavioral skills (e.g., cognitive restructuring, behavioral activation); (4) intervention targeting ≥ one mental health conditions (e.g., depression) as either a primary or secondary outcome; and (5) use of digital technology (e.g., apps, text messages, ecological momentary interventions) for intervention delivery, either as a standalone treatment or in conjunction with face-toface treatment. Included articles met all five inclusion criteria. Because this review sought to examine digital interventions with potential for overcoming barriers to traditional mental health care, studies that solely evaluated teletherapy (i.e., traditional psychotherapy delivered via videoconference or phone) without an added digital component (e.g., a self-guided app used to augment traditional therapy) were not included. To evaluate the reach and uptake of a broad range of digital interventions offered to college students, included studies were not required to be randomized controlled trials. Identified studies were excluded based on the following exclusion criteria: (1) lack of original data (e.g., trial protocol, commentary, review); (2) intervention solely aimed to improve general psychological functioning or well-being rather than symptoms of specific mental health problems; (3) not a college sample; (4) intervention did not include CBT skills or principles.

Data extraction and synthesis

Identified articles were uploaded into Covidence systematic review software. Article titles and abstracts were independently screened for relevance by the first four authors (LD, LP, AG, and MF). Two independent study teams (LD and LP, AG and MF) conducted full text reviews. Disagreements were resolved between the two independent study teams. The first two authors (LD and LP) each reviewed the full texts of included studies to confirm agreement. The following information was extracted from the articles: year of publication, country, sample characteristics (e.g., sample size, demographics, mental health conditions and symptom severity), type of digital intervention, and data on reach or uptake of the intervention.

Results

Article selection

A total of 10,315 articles identified in the search following the removal of duplicates were screened, of which 9,824 were excluded from full-text review due to lack of relevance to the study. Of the 491 full texts assessed for eligibility, 401 were excluded for the following reasons: study protocol or unpublished manuscript (n = 139), wrong intervention (n = 143), wrong study design (n = 54), wrong outcomes (n = 21), no digital intervention component (n = 21), wrong study population (n = 12), not in English (n = 6), or not original research (n = 5). Thus, 90 articles met eligibility criteria and were included in the review. See Fig. 1 for a PRISMA flow chart of the article search and selection process.

Fig. 1.

Fig. 1

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Flow chart of study search and selection.

Study characteristics

Of the 90 studies included in the review, 41 (45.5%) reported sufficient data to calculate reach and/or uptake of DMHIs (17 reported on reach; 35 reported on uptake). A list of studies that did not report sufficient data on reach or uptake can be found in Appendix B.

Study characteristics for the 41 articles that reported reach and/or uptake are presented in Table 1. The most common study designs were randomized controlled trials (n = 25; Day et al., 2013; Fitzsimmons-Craft et al., 2019; Fitzsimmons-Craft et al., 2020; Freeman et al., 2017; Grieve et al., 2022; Hanano, Rith-Najarian, Boyd, & Chavira, 2022; Harrer et al., 2018; Kählke et al., 2019; Kass et al., 2014; Leeman et al., 2016; Lintvedt et al., 2013; Low et al., 2006; Melnyk et al., 2015; Mullin et al., 2015; Musiat et al., 2014; Papini, Jacquart, Zaizar, Telch, & Smits, 2022; Patterson, Peynenburg, & Hadjistavropoulos, 2022; Rackoff et al., 2022; Richards et al., 2016; Rozental et al., 2018; Sakata et al., 2022; Salamanca-Sanabria et al., 2020; Sánchez-Ortiz et al., 2011; Spanhel et al., 2022; Suffoletto et al., 2021), followed by non-randomized experimental studies (n = 16; Benton, Heesacker, Snowden, & Lee, 2016; Broglia, Millings, & Barkham, 2019; Charbonnier et al., 2022; Crosby & Witte, 2021; Dear et al., 2019; Fitzsimmons-Craft et al., 2019; Gabrielli et al., 2021; Lattie, Cohen, Winquist, & Mohr, 2020; LeBlanc, Talbot, Fournier, Titov, & Dear, 2022; Palacios et al., 2018; Rith-Najarian, Chorpita, Gong-Guy, Hammons, & Chavira, 2022; Robinson & Serfaty, 2001; Ruehlman & Karoly, 2021; Santucci et al., 2014; Trockel, Manber, Chang, Thurston, & Taylor, 2011; Zabinski, Calfas, Gehrman, Wilfley, & Sallis, 2001) and implementation studies (Fitzsimmons-Craft et al., 2019; Santucci et al., 2014); 12 studies were pilot studies (Broglia et al., 2019; Charbonnier et al., 2022; Crosby & Witte, 2021; Lattie et al., 2019; Melnyk et al., 2015; Rith-Najarian et al., 2022; Robinson & Serfaty, 2001; Ruehlman & Karoly, 2021; Santucci et al., 2014; Spanhel et al., 2022; Suffoletto et al., 2021; Zabinski et al., 2001). DMHIs evaluated were unguided online self-help programs (n = 20; Charbonnier et al., 2022; Crosby & Witte, 2021; Freeman et al., 2017; Grieve et al., 2022; Hanano et al., 2022; Kählke et al., 2019; Lattie et al., 2020; Leeman et al., 2016; Lintvedt et al., 2013; Melnyk et al., 2015; Musiat et al., 2014; Papini et al., 2022; Patterson et al., 2022; Rackoff et al., 2022; Rith-Najarian et al., 2022; Rozental et al., 2018; Ruehlman & Karoly, 2021; Sakata et al., 2022; Santucci et al., 2014; Spanhel et al., 2022), guided online self-help programs (n = 16; Benton et al., 2016; Broglia et al., 2019; Day et al., 2013; Dear et al., 2019; Fitzsimmons-Craft et al., 2019; Fitzsimmons-Craft et al., 2019; Fitzsimmons-Craft et al., 2020; Harrer et al., 2018; LeBlanc et al., 2022; Low et al., 2006; Mullin et al., 2015; Palacios et al., 2018; Richards et al., 2016; Salamanca-Sanabria et al., 2020; Sánchez-Ortiz et al., 2011; Zabinski et al., 2001), chat/text/email-based interventions (n = 4; Gabrielli et al., 2021; Robinson & Serfaty, 2001; Suffoletto et al., 2021; Trockel et al., 2011), and a comparison between guided and unguided versions of a program (n = 1; Kass et al., 2014). Thirteen studies recruited students who met criteria for clinical mental health problems or had elevated symptoms (Broglia et al., 2019; Fitzsimmons-Craft et al., 2020; Freeman et al., 2017; Harrer et al., 2018; Kählke et al., 2019; Lintvedt et al., 2013; Richards et al., 2016; Robinson & Serfaty, 2001; Rozental et al., 2018; Ruehlman & Karoly, 2021; Sánchez-Ortiz et al., 2011; Santucci et al., 2014; Suffoletto et al., 2021); 16 studies recruited students with ≥ subclinical symptoms or who were at-risk for developing a clinical disorder (Benton et al., 2016; Crosby & Witte, 2021; Day et al., 2013; Dear et al., 2019; Grieve et al., 2022; Kass et al., 2014; LeBlanc et al., 2022; Leeman et al., 2016; Mullin et al., 2015; Palacios et al., 2018; Papini et al., 2022; Patterson et al., 2022; Rackoff et al., 2022; Sakata et al., 2022; Salamanca-Sanabria et al., 2020; Zabinski et al., 2001). 3 studies recruited students at various risk levels (Fitzsimmons-Craft et al., 2019; Lattie et al., 2020; Musiat et al., 2014); and 9 studies did not have inclusion cutoffs based on severity of mental health concerns (Charbonnier et al., 2022; Fitzsimmons-Craft et al., 2019; Gabrielli et al., 2021; Hanano et al., 2022; Low et al., 2006; Melnyk et al., 2015; Rith-Najarian et al., 2022; Spanhel et al., 2022; Trockel et al., 2011). An overview of study characteristics of included studies without reach and uptake results can be found in Appendix C.

Table 1.

Study characteristics of studies that reported sufficient data to calculate reach and/or uptake of DMHIs.

Author (year, country, design) Sample size and target population Sample characteristics, % female, % white, M age (SD) DMHI evaluated Recruitment strategies used Reach Reach calculation Uptake Uptake definition
Rith-Najarian et al. (2022), US, non-ran-domized pilot) 651 students*; universal prevention 80% female, 28% white, 22.9 (5.5) Unguided online self-help prevention program Email, social media, printed materials, referrals through counseling center 3.8% 1,655 enrolled out of 43,552 of the entire student body 72.7% Read at least one module
Sánchez-Ortiz et al. (2011), UK, RCT 76 students* with clinical BN or EDNOS Gender NR, race NR, 23.9 (5.9); Guided online self-help program Email and mailed letters to students 0.4% a 262 responded out of 68,380 total students who were sent email or mail invitations 78.9% Completed at least one session
(Melnyk et al., 2015), US, pilot RCT 121 first-year undergraduates; universal intervention 86.4% female, 80.9% white, age NR Unguided online self-help program All 13 sections of a required online survey course at three colleges 16.9% 121 consented out of 713 students taking the course who were eligible 98.8% Completed all sessions1
Lattie et al. (2020), US, non-randomized pilot) 20 students* with higher and lower depression or anxiety 85% female; 52% white; 24.19 (6.03) App with personalized feedback Flyers, social media, mass emails, student organizations and campus offices ≥ 0.3%a 30 completed screening out of about 10,000 students invited 100% Downloaded the app
Dear et al. (2019), Australia, non-randomized experimental 1081 students* with stress, anxiety, low mood or depressive symptoms 68% female, race NR, 23.0 (6.10) Guided online self-help program Referrals from counseling center or self-referrals 81.5% 1,081 elected to participate out of 1326 students offered the intervention 100% Read at least the first lesson
Ruehlman and Karoly (2021), US, non-ran-domized pilot 52 undergraduates with clinical depression Intervention: 57% female, 46% white, 19.14 (1.69); Control: 58% female, 66% white, 19.71 (1.99) Brief unguided online self-help program All 1007 undergraduates enrolled in introductory psychology classes 45.9% 68 consented out of 148 students selected at random from a pool of 289 students who were eligible 83%. Completed any part of the program
Rackoff et al. (2022), US, RCT 585 students* with moderate or higher stress Intervention: 71.76% female, 71.1% non-minority racial/ethnic status; Control: 76.41% female, 65.1% non-minority racial/ethnic status; 93% of sample between 18–24 Unguided online self-help program Email with the screen from university administrators, online research participation bulletin, Reddit forums for students at various colleges 4.6% a 2,607 students accessed the screen out of 56,939 total students who received an invite to access the screen and participate in the study 52.5% Accessed the program
Lintvedt et al. (2013), Norway, RCT 163 students* with elevated psychological distress; targeted depression prevention 76.7% female; race NR; 28.2 (7.4) Unguided online self-help program Mailed recruitment screening survey and consent form to whole student body 9.8% 1,025 completed the screen survey out of approximately 10,000 students at the two universities 74.4% Using at least some of the CBT content provided
Robinson and Serfaty (2001), UK, non-randomized pilot) 23 students* with clinical probable BN, BED, or EDNOS NR Guided email-delivered self-help program who Campus-wide email inviting students considered themselves to be suffering from bulimia nervosa 0.3% a 52 responded to an email sent to all 15,000 students 83% Completed at least one session diary
Fitzsimmons-Craft et al., 2019, US, RCT 4,894 students* low risk, high risk, subclinical, and clinical probable EDs 87.4% female, 61.5% white, 22.28 Guided online self-help program Email, social media, printed materials, campus-wide and student group presentations, referral through peer-educator/counselor 1.9% An average of 1.9% of the undergraduate female student body on each campus took the screen across all 28 universities NR
Fitzsimmons-Craft et al., 2019, US, non-randomized experimental 2,454 students*; universal screen and intervention for various risk levels 82.4% female; 78.1% white, 22.89 (6.59) Online screen for various guided online self-help programs Emails to all students, student subgroups, faculty, and student organizations; ads on university webpages and publications; social media; flyers; campus presentations; referrals from counselors 2.5% An average of 2.5% of the undergraduate student body on each campus took the screen over 3 years 43.9% StayingFit (low risk), 50.5% SB-Classic (high risk), 47.8% SB-ED (clinical or subclinical ED) Enrolled in their assigned intervention
Leeman et al. (2016), US, RCT 208 undergraduates with ≥ 1 past month heavy drinking day 62.5% female, 60.1% white; 19.85 (1.43) Unguided online self-help program with personalized feedback Emails to all enrolled students 59.3% 411 students took the screen out of 693 students offered the screen 84.4% Accessed the intervention
Charbonnier et al. (2022), France, non-randomized pilot) 114 students*; universal intervention Intervention: 84.8% female, race NR, 20.06 (3.09); Control: 88.2% female, race NR, 22.76 (8.01) Unguided online self-help program Emails to all enrolled students 7.5% b 347 expressed interest out of 4,627 students invited and enrolled at the university NR
Musiat et al. (2014), UK, RCT 1,047 students* at high and low risk for depression, anxiety, SUDs, and EDs; targeted prevention 70.5% female, 47.9% white, 21.8 (4.2) Unguided online self-help program Emails to all students 1.2% a 1,141 registered out of 95,000 students who were sent the recruitment email NR
Trockel et al. (2011), US, non-randomized experimental 48 first-year undergraduates; universal intervention 48.8% female, 50% white, 79.2% > 18 years Unguided email-delivered self-help program All students 18 + in 2 first-year student residence halls 51.0% 125 enrolled out of the 245 students in either of the residence halls NR
Benton et al. (2016), US, non-randomized experimental) 1,241 students* with moderate anxiety TAO: 72% female, 73% white, 21.72 (4.46); Comparison: 63% female, 68% white, 21.61 (4.63) Guided online self-help program plus app Referrals from counseling center 93.3%a 97 enrolled out of 104 counseling center clients who were asked to participate NR
Rackoff et al. (2022), US, RCT 585 students* with moderate or higher stress Intervention: 71.76% female, 71.1% non-minority racial/ethnic status; Control: 76.41% female, 65.1% non-minority racial/ethnic status; 93% of sample between 18–24 Unguided online self-help program Email with the screen from university administrators, online research participation bulletin, Reddit forums for students at various colleges 4.6% a 2,607 students accessed the screen out of 56,939 total students who received an invite to access the screen and participate in the study 52.5% Accessed the program
Lintvedt et al. (2013), Norway, RCT 163 students* with elevated psychological distress; targeted depression prevention 76.7% female; race NR; 28.2 (7.4) Unguided online self-help program Mailed recruitment screening survey and consent form to whole student body 9.8% 1,025 completed the screen survey out of approximately 10,000 students at the two universities 74.4% Using at least some of the CBT content provided
Robinson and Serfaty (2001), UK, non-randomized pilot) 23 students* with clinical probable BN, BED, or EDNOS NR Guided email-delivered self-help program Campus-wide email inviting students who considered themselves to be suffering from bulimia nervosa 0.3% a 52 responded to an email sent to all 15,000 students 83% Completed at least one session diary
Fitzsimmons-Craft et al. (2019), US, RCT 4,894 students* low risk, high risk, subclinical, and clinical probable EDs 87.4% female, 61.5% white, 22.28 Guided online self-help program Email, social media, printed materials, campus-wide and student group presentations, referral through peer-educator/counselor 1.9% An average of 1.9% of the undergraduate female student body on each campus took the screen across all 28 universities NR
Fitzsimmons-Craft et al., 2019, US, non-randomized experimental 2,454 students*; universal screen and intervention for various risk levels 82.4% female; 78.1% white, 22.89 (6.59) Online screen for various guided online self-help programs Emails to all students, student subgroups, faculty, and student organizations; ads on university webpages and publications; social media; flyers; campus presentations; referrals from counselors 2.5% An average of 2.5% of the undergraduate student body on each campus took the screen over 3 years 43.9% StayingFit (low risk), 50.5% SB-Classic (high risk), 47.8% SB-ED (clinical or subclinical ED) Enrolled in their assigned intervention
Leeman et al. (2016), RCT US, 208 undergraduates with ≥ 1 past month heavy drinking day 62.5% female, 60.1% white; 19.85 (1.43) Unguided online self-help program with personalized feedback Emails to all enrolled students 59.3% 411 students took the screen out of 693 students offered the screen 84.4% Accessed the intervention
Charbonnier et al. (2022), France, non-randomized pilot) 114 students*; universal intervention Intervention: 84.8% female, race NR, 20.06 (3.09); Control: 88.2% female, race NR, 22.76 (8.01) Unguided online self-help program Emails to all enrolled students 7.5% b 347 expressed interest out of 4,627 students invited and enrolled at the university NR
Musiat et al. (2014), UK, RCT 1,047 students* at high and low risk for depression, anxiety, SUDs, and EDs; targeted prevention 70.5% female, 47.9% white, 21.8 (4.2) Unguided online self-help program Emails to all students 1.2% a 1,141 registered out of 95,000 students who were sent the recruitment email NR
Trockel et al. (2011), US, non-randomized experimental 48 first-year undergraduates; universal intervention 48.8% female, 50% white, 79.2% > 18 years Unguided email-delivered self-help program All students 18 + in 2 first-year student residence halls 51.0% 125 enrolled out of the 245 students in either of the residence halls NR
Benton et al. (2016), US, non-randomized experimental) 1,241 students* with moderate anxiety TAO: 72% female, 73% white, 21.72 (4.46); Comparison: 63% female, 68% white, 21.61 (4.63) Guided online self-help program plus app Referrals from counseling center 93.3%a 97 enrolled out of 104 counseling center clients who were asked to participate NR
Suffoletto et al. (2021), US, pilot RCT 52 undergraduates with clinical mental health disorders or recent mental health care 85% female, 91% white, 18.7 (0.45) Texting-based intervention Referrals through one primary care clinic and one mental health clinic 74.5% a 73 took the screen out of 98 youths who were referred NR
Broglia et al. (2019), UK, non-randomized pilot 38 students* with clinical anxiety or depression Intervention: 50% female, race NR, 23.0 (4.11)
TAU: 67% female, race NR, 21.0 (3.24)		 Guided app-based self-help program Recruited university students seeking counseling NR 100% All participants at least began the intervention
Rozental et al. (2018), Sweden, RCT) 92 students* with severe procrastination 73.9% female, race NR, 29.2 (7.7) Unguided online self-help program Printed materials at the student health centers of 5 universities and on the clinics’ websites NR 86.1% Completed at least one module
Zabinski et al. (2001), US, non-randomized pilot) 4 undergraduates with high body image concerns 100% female, 50% white, 19.4 Guided online chat room Recruited from an introductory psychology course NR 100% Gave feedback on the intervention2
Crosby and Witte (2021), US, non-randomized pilot 38 students* with ≥ subclinical insomnia and a lifetime history of suicidal ideation 84% female, 90% white, 19.24 (SD = 1.55) Single session online intervention SONA human subjects research pool NR 100% Completed the program
Hanano et al. (2022), US, RCT 947 undergraduates; universal intervention 77% female, 31.6% white, 23.01 (5.56) Unguided online self-help program Department-wide emails, flyers, social media, and announcements in psychology courses NR 78.8%. Started at least one activity
Gabrielli et al. (2021), Italy, non-randomized experimental 71 first-year undergraduates; universal intervention 68% female; race NR; 20.6 (2.4) Chatbot Invitation to all students in a human interaction course NR 67.8% Completed the first session
Richards et al. (2016), UK, RCT 137 students* with clinical generalized anxiety symptoms 77% female; race NR; 23.82 (7.05) Guided online self-help program University-wide email NR 74.1% Completed the first module
Santucci et al. (2014), US, non-randomized pilot 43 students* with elevated symptoms of anxiety and/or depression 69.8% female, 76.7% white, 22.9 (4.2) Unguided online self-help program Referral through counseling center NR Reminder condition: 85.7%; No reminder condition: 87.0% Completed the first session
Day et al. (2013), Canada, RCT 66 students* with mild to moderate anxiety, depression or stress 89.3% female, race NR, 23.55 (4.98) Guided online self-help program Emails, advertisements in a campus newspaper, printed materials NR 49.3% Completed the first module3
Palacios et al. (2018), US, non-randomized experimental 102 students* with mild depression, anxiety, or stress 73.5% female; 79.4% hite; 54.9% between help program 18–21 Guided online self-flyers, and website Staff referrals, advertised across 3 student centers NR 100% Opted into the study and engaged in chosen program
Patterson et al. (2022), Canada, RCT 146 undergraduates with mild anxiety or depression symptoms 78.1% female; 29.5% White; 23.32 (5.49) Unguided online self-help booster session following a guided program Referrals from providers, email NR 32.2% Accessed the first lesson
Fitzsimmons-Craft et al. (2020), US, RCT 690 undergraduates with binge-purge EDs at threshold and subthreshold levels Intervention: 100% female, 61% white, 21.63 (4.19);Control: 100% female, 58.7% white, 22.76 (5.52) Guided online self-help program Email, flyers, presentations, social media, and counseling or health center staff referrals NR 83% Started the first session
Sakata et al. (2022), Japan, RCT 1093 students* with subthreshold depression 58% female, race NR, 21.6 (3.03) Unguided app-based self-help program Printed materials, posts on part-time job websites for university students NR 93% Completed the first module
Grieve et al. (2022), Australia, RCT 41 undergraduates with problems with perfectionism 89% female, race NR, 24.74 (8.36) Unguided online self-help program School of Psychology Research Participation System and printed materials NR 83% Completed the first module
Kählke et al. (2019), Germany, RCT 200 undergraduates with clinical SAD 62% female, race NR, 26.70 (6.34) Unguided online self-help program Emails to all enrolled students at several German, Austrian, and Swiss universities NR 96% Completed the first session
Kass etal. (2014), US, RCT 151 undergraduates at high risk for an ED Intervention: 100% female, 62.2% white, 21.0 (2.0); Control: 100% female, 58.4% white, 21.0 (2.1) Guided vs. unguided online self-help program Printed materials, email advertisements from university student groups, referrals from campus health centers NR 49.7% Logged into the program at least once
Spanhel et al. (2022), Germany, pilot RCT 82 students*; universal intervention 49.4% female, race NR, 26.8 (4.4) Unguided online self-help program Social media, mailing lists for international offices at various universities, flyers NR 75.6% Completed the first module
Papini et al. (2022), US, RCT 159 undergraduates with high anxiety sensitivity 73.6% female, 58.4% white, 19.27 (1.42) Unguided online self-help program University research subject pool open to undergraduates enrolled in the introductory psychology course NR 100% Completed all modules
Salamanca-Sanabria et al. (2020), Colombia, RCT 214 students* with mild to moderate depressive symptoms 71% female, race NR, 22.15 (4.7) Culturally adapted online guided self-help program Email sent to all students at a university and to undergraduate students studying medicine, psychology, and education at another university NR 80% Began the first module
Freeman etal. (2017), UK, RCT 3755 students* with insomnia 71% female, 82% white, 24.6 (7.6) Unguided online self-help program Email within universities, advertisements on websites, posters NR 68.9% Logged on for at least one treatment session
Harrer et al. (2018), Europe4, RCT 138 students* with elevated levels of perceived stress 74.7% female, race NR, 24.1 (4.1) Guided online and app-based self-help program University press reports, student counseling services, and social media platforms NR 61% Downloaded and logged into the app at least once
Harrer et al. (2018), Europe4, RCT 138 students* with elevated levels of perceived stress 74.7% female, race NR, 24.1 (4.1) Guided online and app-based self-help program University press reports, student counseling services, and social media platforms NR 61% Downloaded and logged into the app at least once
LeBlanc et al. (2022), Canada, non-randomized experimental 25 students* with at least mild levels of depression or anxiety 80% female, race NR, 20.28 (2.0) Guided online self-help program Advertisements across various media, undergraduate courses NR 100% Read at least the first lesson
Low et al. (2006), US, RCT 72 undergraduates; universal prevention 100% female, 91.6% white, age NR Guided online self-help prevention program with discussion group Email announcements within the university NR 90.3% Logged on to the program
Mullin et al. (2015), Australia, RCT 63 students* with symptoms of anxiety or depression 63.3% female, race NR, 28.6 (10.0) Guided online self-help program Social and print media and via the student counseling service NR 96% Started the first lesson
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Note.

*

Denotes undergraduate and graduate students.

a

Denotes that reach was calculated manually using data reported in the study.

b

Denotes that reach was calculated manually using external data.

1

This study stated that all participants except one completed all intervention sessions; thus, all but one were counted as having initiated the intervention.

2

Given that all participants gave feedback on the intervention, all participants were counted as having initiated the intervention.

3

Students in this study were required to complete the first intervention module to be eligible to continue.

4

German-speaking universities in Europe. Abbreviations: SUD = substance use disorder; ED = eating disorder; BN = bulimia nervosa; BED = binge eating disorder; EDNOS = eating disorder not otherwise specified (DSM-IV); SAD = social anxiety disorder; SB = Student Bodies program; NR = not reported.

Reach of DMHIs

Seventeen studies (18.9%) reported sufficient data to calculate approximate reach. Of these, 11 studies invited all students or large pools of students to participate using a variety of methods (Charbonnier et al., 2022; Fitzsimmons-Craft et al., 2019; Fitzsimmons-Craft et al., 2019; Lattie et al., 2020; Leeman et al., 2016; Lintvedt et al., 2013; Musiat et al., 2014; Rackoff et al., 2022; Rith-Najarian et al., 2022; Robinson & Serfaty, 2001; Sánchez-Ortiz et al., 2011); 3 studies referred students who presented to counseling centers or specified clinics and met inclusion criteria to participate in DMHI studies (Benton et al., 2016; Dear et al., 2019; Suffoletto et al., 2021); and 3 studies invited all students within a course or residence hall to participate (Melnyk et al., 2015; Ruehlman & Karoly, 2021; Trockel et al., 2011).

Among studies that reported sufficient data to calculate reach, reach was relatively low on average (26.2%) and variable (range: 0.3% to 93.3%). Studies that referred students presenting to college counseling centers or clinics to participate in DMHIs generally reached a high proportion of invited students, with an average rate of 83.1% of referred students completing an initial screen or interest survey. Studies that recruited students enrolled in specific courses or from residence halls yielded moderate reach, with an average of 38.0% of invited students reached. Programs that invited entire college campuses or very large pools of students to participate generally had low reach, with an average of 8.3% of invited students completing an initial screen or interest survey.

Uptake of DMHIs

Thirty-five studies (38.9% of included studies) reported rates of initial DMHI uptake. Uptake was high on average (79.1%) and variable across studies (range: 32.2% to 100%). Varied metrics of uptake were used across studies. Fifteen studies reported rates of having ever logged into the program, accessed the program, or initiated the intervention (Broglia et al., 2019; Fitzsimmons-Craft et al., 2020; Freeman et al., 2017; Hanano et al., 2022; Harrer et al., 2018; Kass et al., 2014; LeBlanc et al., 2022; Leeman et al., 2016; Lintvedt et al., 2013; Low et al., 2006; Mullin et al., 2015; Patterson et al., 2022; Rackoff et al., 2022; Ruehlman & Karoly, 2021; Salamanca-Sanabria et al., 2020); 12 studies reported rates of completion of ≥ one intervention module or session (Day et al., 2013; Dear et al., 2019; Gabrielli et al., 2021; Grieve et al., 2022; Kählke et al., 2019; Richards et al., 2016; Rith-Najarian et al., 2022; Rozental et al., 2018; Sakata et al., 2022; Sánchez-Ortiz et al., 2011; Santucci et al., 2014; Spanhel et al., 2022); 4 studies did not directly report uptake, but noted that all participants (or all but one) completed all intervention sessions (Crosby & Witte, 2021; Melnyk et al., 2015; Papini et al., 2022; Zabinski et al., 2001); 2 studies reported rates of enrolling in an assigned intervention (Fitzsimmons-Craft et al., 2019; Palacios et al., 2018); 1 study reported rates of downloading the app (Lattie et al., 2020); and 1 study reported rates of having submitted ≥ one self-monitoring diary (Robinson & Serfaty, 2001). Studies targeting students with elevated/clinical mental health symptoms had high uptake rates, with an average rate of 81.9%. Studies with universal enrollment (i.e., no minimum threshold of mental health symptoms for entry) had slightly more variable uptake rates (average: 69.6%).

Discussion

This review aimed to report the reach and uptake of CBT-based DMHIs for college students. Of the 90 studies included, 81% did not provide sufficient data to calculate reach, 61% did not report rates of uptake, and 53% did not report either outcome. Table 2 outlines key terms, findings, and recommendations to guide future research on reach and uptake of DMHIs.

Table 2.

Key terms, findings, and recommendations.

Key Terms
Treatment gap Proportion of individuals who receive mental health treatment relative to those in need of mental health treatment
Reach Proportion of a target population who makes initial contact with a mental health service (e.g., complete a screen or interest survey)
Uptake Proportion of individuals offered a mental health service who initiate it (e.g. log-in, download an app, or complete the first session)
Key Findings
 • Most DMHI studies did not report rates of reach or uptake or sufficiently describe strategies used to achieve these outcomes
 • Variable metrics of reach and uptake were used across studies
 • Studies that used targeted recruitment methods (e.g., counseling center referrals) reached fewer students overall relative to universal recruitment methods, but enrolled greater proportions of students invited to use DMHIs
 • Uptake was greatest in DMHI studies that targeted students with elevated or clinical mental health symptoms
Recommendations for Future Research
 • Routinely report rates of reach and uptake and specific methods used to maximize these outcomes
 • Explore the influences of various recruitment and referral methods on reach and uptake rates
 • Conduct DMHI research under real-world conditions
 • Prioritize research focused on implementation of evidence-based DMHIs on college campuses, leveraging collaborations with campus stakeholders and policymakers
 • Engage students’ needs, preferences, and barriers to mental health treatment to inform development and implementation of DMHIs in the college setting
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The average proportion of students reached was low. DMHIs that targeted students with elevated symptoms through referrals at college counseling centers reached a large proportion of students invited. This suggests that the referral process may be a successful pipeline to DMHIs for students who present to college counseling centers. However, Dear et al. (2019) noted that despite yielding interest from 81.5% of referred students, only 10% of students who presented to the counseling center were referred, suggesting that the majority did not receive the opportunity to use a DMHI. Moreover, given that most students with symptoms do not present to counseling centers, other methods of targeted identification and intervention tools for at-risk students are needed. For example, routine symptom screening to identify high-risk students, as well as public messaging aimed to help students recognize symptoms and seek help, may engage more students with symptoms in treatment (Lee, Jeong, & Kim, 2021). Of note, counseling center therapists may have gauged students’ interest in participating in DMHIs before referring them, which may have inflated the number of referred students who enrolled. Evaluating the impact of universal programs (i.e., those that invite all students to participate) is also an important endeavor. Universal programs may reach students who may not otherwise engage with healthcare delivery systems. For instance, universal DMHIs included in this review screened a substantially greater number of students overall relative to studies using targeted methods. Pairing universal screening with tailored interventions based on risk level is a promising approach for increasing reach and assigning interventions that will best serve students’ needs (Fitzsimmons-Craft et al., 2019). Nevertheless, universal DMHIs in this review only reached a mean of 8% of invited students, and this average was inflated by the uniquely high reach observed in Leeman et al. (2016) study. Excluding that study, universal DMHIs reached approximately 3% of invited students, highlighting a need to better understand how to engage students across the clinical risk continuum. Potential strategies for extending the reach of universal screening and intervention programs include creating advertisements and outreach strategies in collaboration with campus stakeholders in order to increase awareness of programs on college campuses (Fitzsimmons-Craft et al., 2019; Jones et al., 2014).

Most studies failed to report rates of reach or sufficient details about the methods they used to achieve reach. To make progress, it is critical for future studies to report the proportion of students reached by DMHIs and specific strategies used. To increase reach, we encourage the evaluation of DMHIs under real-world conditions and efforts to support implementation of DMHIs on college campuses. Several frameworks and recommendations for designing digital interventions for sustainable implementation have been developed, which can be adapted for the college setting (Glasgow, Phillips, & Sanchez, 2014; Graham & Fitzsimmons-Craft, 2022; Greenhalgh et al., 2017; Mohr, Lyon, Lattie, Reddy, & Schueller, 2017). Supporting implementation of DMHIs requires collaboration between campus policymakers and stakeholders to garner and allocate resources for DMHI deployment, increase student awareness and use of DMHIs, and evaluate ongoing satisfaction (Jones et al., 2014). Yet, research has reported that most students are not aware of available DMHIs on their campuses (Topooco et al., 2022). Indeed, few studies in this review (e.g., Fitzsimmons-Craft et al., 2019) worked with relevant stakeholders to implement DMHIs, indicating an important future direction.

Despite low reach, studies observed high uptake of DMHIs among enrolled students on average. This may suggest that students who enroll in and initiate DMHIs are highly motivated for treatment or more open to the use of DMHIs. Uptake was generally higher in studies that targeted students with elevated symptoms, which aligns with research that has found greater intention to use DMHIs among those with greater symptom severity (Ryan, Shochet, & Stallman, 2014). Although uptake was generally high, there was considerable variability (32% to 100%), in line with prior DMHI reviews (Fleming et al., 2018; Lattie et al., 2019). Variable uptake could be partly due to use of different metrics (e.g., downloading an app vs. completing the first module). Of note, most studies did not report on reasons participants declined to participate, which is an important future direction (Fleming et al., 2018). High uptake rates may be partly explained by the context of research studies, which often provide incentives for participation. One pilot implementation study included in this review (Santucci et al., 2014) found comparable DMHI effectiveness to that observed in randomized trials but noted that DMHI completion under real-world conditions was low. Additionally, we counted program completers as having started interventions in studies that reported completion but not uptake. These estimates may not accurately represent uptake, given that most individuals who begin DMHIs do not complete them. Finally, some studies may have excluded participants who did not begin the intervention, which could have further inflated the uptake rates.

Given the low rates of reporting uptake, future research should prioritize consistently reporting uptake of DMHIs and strategies used to maximize uptake. To increase both uptake and sustained engagement, employing usercentered design to engage college students (Lattie et al., 2019; Mohr, Riper, & Schueller, 2018) may bridge the current gap between students’ interest in and actual use of DMHIs (Dunbar et al., 2018). For instance, students report stress and cost as barriers to mental health app use and particularly welcome DMHIs that include human support (Melcher et al., 2022; Topooco et al., 2022). Concerns about data privacy, user interface, credibility, and personalization have also been documented (Melcher et al., 2022). Leveraging these barriers and preferences in the design stage can inform the inclusion of relevant intervention content (Graham, Lattie, & Mohr, 2019). We also recommend that future research employ study designs that allow for measurement of reach and uptake in addition to efficacy/effectiveness, which will allow for further investigation of factors that influence reach and uptake of DMHIs. Finally, colleges should allocate resources to repeated campus-wide screening, which could improve reach and uptake of DMHIs and provide more reliable data on their impacts on the prevalence of mental health disorders among students.

This review had strengths and limitations. A strength was the rigorous methodological approach employed for study selection, which included the involvement of a medical librarian and screening of over 10,000 articles. A limitation was that data on effectiveness of DMHIs were not reviewed. Although effectiveness data have been assessed in a prior review (Lattie et al., 2019), future research should examine effectiveness alongside contextual implementation outcomes to comprehensively understand DMHIs’ impact. Another limitation was that DMHIs were restricted to those based on CBT. Finally, we manually calculated reach using available data for studies that did not report reach statistics, which may not have been as accurate as studies that provided reach data. Nonetheless, this approach allowed us to provide the best available estimates of reach and highlights the need for future research to consistently report this outcome. Future studies should explore reach and utilization of other empirically supported treatments for college students. Shifting focus to these outcomes within DMHI research may have meaningful impacts on college mental health.

Supplementary Material

supplement 1
supplement 2
supplement 3

Acknowledgments

Funding

This work was supported by NIH grants T32 HL130357, F31 HL158000, K08 MH120341, and K01 DK116925.

Footnotes

Financial Disclosures

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

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jbct.2023.05.002.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Conflicts of Interest

No other authors have financial disclosures.

Availability of Data, Code and Other Materials

The data will be made available by reasonable request to the corresponding author.

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Associated Data

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

Supplementary Materials

supplement 1
NIHMS1926352-supplement-supplement_1.docx (16.2KB, docx)
supplement 2
NIHMS1926352-supplement-supplement_2.xlsx (19.2KB, xlsx)
supplement 3
NIHMS1926352-supplement-supplement_3.docx (13.4KB, docx)

Data Availability Statement

The data will be made available by reasonable request to the corresponding author.

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