CBT-based Mental Health App Treatment Outcomes for Heterosexual and LGBTQ+ College Students With Depression

Vion He

The percent of college students in the United States suffering from depression has increased, rising from 25% in 2007 to 30% in 2017 (Lipson et al., 2019). Among college students, LGBTQ+ individuals experience even higher levels of depression than their heterosexual peers,  suggesting that they may be more in need of mental health services (Backhaus et al., 2019; Dunbar et al., 2017). Despite their increased need for mental health services, LGBTQ+ young people often encounter unique barriers that prevent them from accessing or benefiting from such services, including concerns about cost and confidentiality (Brown et al., 2015; Sanchez et al., 2009), fear of harassment (Brown et al., 2015), fear of letting their family members know about their gender or sexual orientation (Williams & Chapman, 2011), and skepticism about the inclusivity of services (Williams & Chapman, 2011).

Recent studies suggest that some of these barriers might be addressed via mental health mobile applications, interventions delivered through smartphones to promote users’ mental health (Rozbroj et al., 2015). Such health tools consist of psychoeducation and therapeutic exercises designed for different psychological disorders, such as depression (Hollis et al., 2016). Although these applications have not been specifically tested on LGBTQ+ students, their efficacy in reducing depressive symptoms in other populations is supported by research (e.g., Firth et al., 2017; Mohr et al., 2017). Compared to in-person services, mobile mental health apps are less expensive, easier to use, and often self-directed, which provide individuals with more control over their treatment (Rozbroj et al., 2015). Among the many types of mental health apps, those using cognitive-behavioral therapy (CBT) are the most common (Porras-Segovia et al., 2020). CBT is a type of psychotherapy based on the premise that mental illness stems from maladaptive cognitive and behavioral patterns, and it treats psychological problems by changing these patterns (Beck, 1970; Ellis, 1962). There is a large body of literature supporting the efficacy of CBT in treating depression because it identifies negative beliefs that lead to depression and replaces them with positive ways of thinking (Charkhandeh et al., 2016; Driessen & Hollon, 2010; Hofmann et al., 2012). When administered through mobile apps, CBT treats depression by guiding people to identify and challenge their negative thinking patterns, track thoughts and feelings, and engage in mood-improving exercises (Stawarz et al., 2018). Users complete homework activities, practice different coping skills, and work towards greater wellbeing (Stawarz et al., 2018). Many CBT-based apps have yielded positive results on reducing depressive symptoms, especially among the college student population (Broglia et al., 2019; Bruehlman-Senecal et al., 2020; McCloud et al., 2020).

Despite the encouraging results of CBT-based apps, little is known about whether LGBTQ+ college students will benefit from them as much as their heterosexual peers. As sexual minorities, LGBTQ+ people often experience unique stressors related to their sexual identity, such as coming out, discrimination, and stigma, which have been associated with higher levels of depression (Meyer, 2003). As such, depression apps designed for the general population might not be inclusive enough to effectively address these LGBTQ-related stressors. Specifically, previous research has found that the majority of digital interventions tend to assume users to be heterosexual (e.g., use pictures of heterosexual relationships to describe marriage; Rozbroj et al., 2014). By adopting these assumptions, they overlook that LGBTQ+ people are experiencing unique stress because of their identity, thereby failing to address relevant topics (e.g., coming out) in the intervention (Rozbroj et al., 2014). This exclusion can lead to inappropriate treatment of LGBTQ+ youth, which contributes to a poor experience and feelings of alienation and distrust of services (Rozbroj et al., 2014). As a result, LGBTQ+ youth may delay seeking help in the future, which allows symptoms to worsen and leads to poorer health outcomes (Elliott et al., 2015; Kilicaslan & Petrakis, 2019). In short, if CBT-based health apps fail to account for LGBTQ+ experiences and stressors, they may not be efficacious, and even be detrimental, for LGBTQ+ people with mental health problems. Nonetheless, if CBT-based apps are found to be effective for LGBTQ+ youth, they have the potential to facilitate easier access to mental health care for this population (Rozbroj et al., 2015). Because LGBTQ+ youth already have a tendency to seek mental health information and support online, they may be more receptive to and benefit from the apps (Lucassen et al., 2018; Rozbroj et al., 2015). In order to assess these concerns, this study proposes to answer the following question: How do CBT-based mental health apps affect depression treatment outcomes in LGBTQ+ college students compared with heterosexual students?

Proposed Method

Participants

The study will recruit 180 U.S. college students diagnosed with depression, evenly distributed across racial and ethnic backgrounds. Half of them will be individuals who identify as heterosexual, and the other half will identify as LGBTQ+. To be eligible, students must be at least 18 years old, have a smartphone or a tablet with internet access, and exhibit at least a mild level of depression, as measured by the 9-item Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001). Individuals who are using SSRI(s) at the time of recruitment will be excluded, as the effects of antidepressants on depression may interfere with the study outcomes. Individuals with suicidal ideations within the month prior to recruitment will also be excluded, due to participant safety and ethical considerations. Instead, they will be directly referred to professional psychological services.

Procedure

As a first step, participants will complete a demographic survey and a PHQ-9 assessment (Kroenke et al., 2001) to determine their depression level. Those who score 5 or higher, indicating at least a mild level of depression, will be randomly assigned to one of the two groups: (a) doing activities on the SuperBetter app or (b) doing activities on the MoodKit app. The participants will further be separated into heterosexual and LGBTQ+ students, resulting in four groups total. Both apps have demonstrated a significantly positive impact on alleviating depression in randomized controlled trials in a largely heterosexual sample (Bakker et al., 2018; Roepke et al., 2015). Because the two apps have different emphases when approaching depression, including both in this study can help compare results and distinguish between features that contribute to the efficacy of the intervention. SuperBetter is a game-based app that helps build resilience and improve mental health by helping individuals build self-esteem and self-acceptance through activities such as “hugging yourself” and “writing down things you feel grateful for” (Roepke et al., 2015). Participants assigned to this group will be instructed to complete a To-Do List every day with seven activities of their choice. MoodKit is an app that focuses on mood self-management. It contains four main tools: mood-improving activities, a thought checker, a mood tracker, and a journal (Dahne et al., 2019). Participants assigned to this group will be instructed to record their mood and complete at least three mood-improving activities every day and use the journal and the thought checker as needed. After two months, all participants will be assessed again for depression. Researchers will also assess their depression levels three months after the intervention to see if there is any lasting effect.

Measures

Demographic form. A demographic form will be administered to collect information on participants’ age, gender, sexual identity, race, academic standing, use of professional mental health services (e.g., therapy or life coaching) and antidepressants, suicide ideations, Internet access, use of electronic devices, and technical difficulties if any.

Depression. Depression will be assessed using the PHQ-9 (Kroenke & Spitzer, 2001), a self-report scale that measures depression and its severity. The PHQ-9 consists of nine items on a four-point Likert Scale, eight of which measure the frequency of symptoms from not at all to nearly every day, and one of which measures the extent of difficulty these symptoms cause in everyday life, from not difficult at all to extremely difficult. The PHQ-9 has been shown to be a reliable depression assessment tool with a Cronbach’s alpha of approximately 0.86 (Kroenke et al., 2001), and validity has been established, especially in the college student population (Keum et al., 2018; Kroenke & Spitzer, 2001).

Data Analysis Plan

A repeated measures analysis of co-variance (ANCOVA) will be conducted to help determine if there are significant differences in treatment outcomes between the four groups, using the mean differences in PHQ-9 scores before the intervention, immediately after, and three months later, controlling for covariates (e.g., the use of mental health services). If there are differences, post-hoc tests will be used to determine which group yields the best result.

Discussion

The proposed study seeks to examine whether there are differences in treatment outcomes of CBT-based mental health apps on depression in LGBTQ+ and heterosexual students. While mental health apps have the potential to improve access to mental health care for LGBTQ+ youth, not much is known about whether they are effective in addressing the needs of this population (Schueller et al., 2019). This study might contribute to a theoretical understanding of whether sexual identity (identifying as a LGBTQ+ or not) moderates the apps’ effects on depression.

  Given the potential of mobile mental health in increasing LGBTQ+ youth’s service utilization rate (Rozbroj et al., 2015; Schueller et al., 2019), this study might enhance our understanding of the impact of these apps on treating depression in LGBTQ+ college students. It will help elucidate whether there is a need to improve such apps or develop more apps, to better meet their needs. Future research might examine and compare the impact of other types of mental health apps to determine the optimal treatment approach, as well as expand the measures of depression beyond self-report measures, so as to avoid the results may be subject to response bias.  Future research might also examine and compare the impact of other types of mental health apps to determine the optimal treatment approach, as well as expand the measures of depression beyond self-report measures, so as to avoid response bias. Moreover, the outcomes of this study can be especially helpful in the context of the current COVID-19 pandemic. As LGBTQ+ youth are experiencing a heavier mental health burden and increased demand for services due to limited resources and support available, CBT-based apps may have potential to serve this population in the aftermath of the pandemic (Moore et al., 2021; Salerno et al., 2020).

References

Backhaus, I., Lipson, S. K., Fisher, L. B., Kawachi, I., & Pedrelli, P. (2019). Sexual assault, sense of belonging, depression and suicidality among LGBQ and heterosexual college students. Journal of American College Health, 69(4), 404–412.

Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2018). A randomized controlled trial of three smartphone apps for enhancing public mental health. Behaviour Research and Therapy, 109, 75-83.

Beck, A. T. (1970). Cognitive therapy: Nature and relation to behavior therapy. Behavior Therapy, 1(2), 184–200.

Broglia, E., Millings, A., & Barkham, M. (2019). Counseling with guided use of a mobile well-being app for students experiencing anxiety or depression: Clinical outcomes of a feasibility trial embedded in a student counseling service. JMIR mHealth and uHealth, 7(8), e14318.

Brown, A., Rice, S. M., Rickwood, D. J., & Parker, A. G. (2016). Systematic review of barriers and facilitators to accessing and engaging with mental health care among at‐risk young people. Asia‐Pacific Psychiatry, 8(1), 3-22.

Bruehlman-Senecal, E., Hook, C. J., Pfeifer, J. H., FitzGerald, C., Davis, B., Delucchi, K. L., Haritatos, J., & Ramo, D.E. (2020). Smartphone app to address loneliness among college students: Pilot randomized controlled trial. JMIR Mental Health, 7(10), e21496.

Charkhandeh, M., Talib, M. A., & Hunt, C. J. (2016). The clinical effectiveness of cognitive behavior therapy and an alternative medicine approach in reducing symptoms of depression in adolescents. Psychiatry Research, 239, 325-330.

Dahne, J., Lejuez, C. W., Diaz, V. A., Player, M. S., Kustanowitz, J., Felton, J. W., & Carpenter, M. J. (2019). Pilot Randomized Trial of a Self-Help Behavioral Activation Mobile App for Utilization in Primary Care. Behavior therapy, 50(4), 817–827.

Driessen, E., & Hollon, S. D. (2010). Cognitive behavioral therapy for mood disorders: Efficacy, moderators and mediators. Psychiatric Clinics of North America, 33(3), 537–555.

Dunbar, M. S., Sontag-Padilla, L., Ramchand, R., Seelam, R., & Stein, B. D. (2017). Mental health service utilization among lesbian, gay, bisexual, and questioning or queer college students. Journal of Adolescent Health, 61(3), 294-301.

Elliott, M. N., Kanouse, D. E., Burkhart, Q., Abel, G. A., Lyratzopoulos, G., Beckett, M. K., Schuster, M. A., & Roland, M. (2015). Sexual minorities in England have poorer health and worse health care experiences: A national survey. Journal of General Internal Medicine, 30(1), 9-16.

Ellis, A. (1962). Reason and emotion in psychotherapy. Lyle Stuart.

Hofmann, S. G., Asnaani, A., Vonk, I. J. J., Sawyer, A. T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427-440.

Hollis, C., Falconer, C. J., Martin, J. L., Whittington, C., Stockton, S., Glazebrook, C., & Davies, E. B. (2016). Annual research review: Digital health interventions for children and young people with mental health problems-a systematic and meta-review. Journal of Child Psychology and Psychiatry, 58(4), 474-503.

Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287-298. 

Keum, B. T., Miller, M. J., & Inkelas, K. K. (2018). Testing the factor structure and measurement invariance of the PHQ-9 across racially diverse U.S. college students. Psychological Assessment, 30(8), 1096–1106.

Kilicaslan, J., & Petrakis, M. (2019). Heteronormative models of health-care delivery: Investigating staff knowledge and confidence to meet the needs of LGBTIQ+ people. Social Work in Health Care, 58(6), 612-632.

Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32(9), 509–515.

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613.

Lipson, S. K., Lattie, E. G., & Eisenberg, D. (2019). Increased rates of mental health service utilization by US college students: 10-year population-level trends (2007–2017). Psychiatric Services, 70(1), 60-63.

Lucassen, M., Samra, R., Iacovides, I., Fleming, T., Shepherd, M., Stasiak, K., & Wallace, L. (2018). How LGBT young people use the internet in relation to their mental health and envisage the use of e-therapy: Exploratory study. JMIR Serious Games, 6(4), e11249.

McCloud, T., Jones, R., Lewis, G., Bell, V., & Tsakanikos, E. (2020). Effectiveness of a mobile app intervention for anxiety and depression symptoms in university students: Randomized controlled trial. JMIR mHealth and uHealth, 8(7), e15418.

Meyer, I. H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5), 674-697.

Mohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., Caccamo, L., Stiles-Shields, C., Schueller, S. M. (2017). IntelliCare: An eclectic, skills-based app suite for the treatment of depression and anxiety. Journal of Medical Internet Research, 19(1), e10.

Moore, S. E., Wierenga, K. L., Prince, D. M., Gillani, B., & Mintz, L. J. (2021). Disproportionate impact of the COVID-19 pandemic on perceived social support, mental health and somatic symptoms in sexual and gender minority populations. Journal of Homosexuality, 68(4), 577-591.

Porras-Segovia, A., Díaz-Oliván, I., Gutiérrez-Rojas, L., Dunne, H., Moreno, M., & Baca-García, E. (2020). Apps for depression: Are they ready to work?. Current Psychiatry Reports, 22(3), 1-9.

Roepke, A. M., Jaffee, S. R., Riffle, O. M., McGonigal, J., Broome, R., & Maxwell, B. (2015). Randomized controlled trial of SuperBetter, a smartphone-based/internet-based self-help tool to reduce depressive symptoms. Games for Health Journal, 4(3), 235-246.

Rozbroj, T., Lyons, A., Pitts, M., Mitchell, A., & Christensen, H. (2014). Assessing the applicability of e-therapies for depression, anxiety, and other mood disorders among lesbians and gay men: Analysis of 24 web-and mobile phone-based self-help interventions. Journal of Medical Internet Research, 16(7), e166.

Rozbroj, T., Lyons, A., Pitts, M., Mitchell, A., & Christensen, H. (2015). Improving self-help e-therapy for depression and anxiety among sexual minorities: An analysis of focus groups with lesbians and gay men. Journal of Medical Internet Research, 17(3), e66.

Salerno, J. P., Williams, N. D., & Gattamorta, K. A. (2020). LGBTQ populations: Psychologically vulnerable communities in the COVID-19 pandemic. Psychological Trauma: Theory, Research, Practice, and Policy, 12(S1), S239–S242.

Sanchez, N. F., Sanchez, J. P., & Danoff, A. (2009). Health care utilization, barriers to care, and hormone usage among male-to-female transgender persons in New York City. American Journal of Public Health, 99(4), 713-719.

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6(3), 243-255.

Stawarz, K., Preist, C., Tallon, D., Wiles, N., & Coyle, D. (2018). User experience of cognitive behavioral therapy apps for depression: An analysis of app functionality and user reviews. Journal of Medical Internet Research, 20(6), e10120.

Williams, K. A., & Chapman, M. V. (2011). Comparing health and mental health needs, service use, and barriers to services among sexual minority youths and their peers. Health & social work, 36(3), 197-206.