The Appa Health App for Youth Mental Health: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Demand for adolescent mental health services has surged in the aftermath of the COVID-19 pandemic, and traditional models of care entailing in-person services with licensed mental health providers are inadequate to meet demand. However, research has shown that with proper training and supervision mentors can work with youth with mental health challenges like depression and anxiety and can even support the use of evidence-based strategies like cognitive behavioral therapy (CBT). In our increasingly connected world, youth mentors can meet with young people on a web-based platform at their convenience, reducing barriers to care. Moreover, the internet has made evidence-based CBT skills for addressing depression and anxiety more accessible than ever. As such, when trained and supervised by licensed clinicians, mentors are an untapped resource to support youth with mental health challenges. OBJECTIVE: The objective of this study was to develop and assess the feasibility and acceptability of Appa Health (Appa), an evidence-based mental health mentoring program for youth experiencing symptoms of depression and anxiety. This paper describes the development, pilot testing process, and preliminary quantitative and qualitative outcomes of Appa's 12-week smartphone app program which combines web-based near-peer mentorship with short-form TikTok-style videos teaching CBT skills created by licensed mental health professionals who are also social media influencers. METHODS: The development and testing processes were executed through collaboration with key stakeholders, including young people and clinical and research advisory boards. In the pilot study, young people were assessed for symptoms of depression or anxiety using standard self-report clinical measures: the Patient Health Questionnaire-8 and the Generalized Anxiety Disorder-7 scales. Teenagers endorsing symptoms of depression or anxiety (n=14) were paired with a mentor (n=10) based on preferred characteristics such as gender, race or ethnicity, and lesbian, gay, bisexual, transgender, queer (LGBTQ) status. Quantitative survey data about the teenagers' characteristics, mental health, and feasibility and acceptability were combined with qualitative data assessing youth perspectives on the program, their mentors, and the CBT content. RESULTS: Participants reported finding Appa helpful, with 100% (n=14) of teenagers expressing that they felt better after the 12-week program. Over 85% (n=12) said they would strongly recommend the program to a friend. The teenagers were engaged, video chatting with mentors consistently over the 12 weeks. Metrics of anxiety and depressive symptoms reduced consistently from week 1 to week 12, supporting qualitative data suggesting that mentoring combined with CBT strategies has the potential to positively impact youth mental health and warrants further study. CONCLUSIONS: Appa Health is a novel smartphone app aiming to improve the well-being of youth and reduce anxiety and depressive symptoms through web-based mentoring and engaging CBT video content. This formative research sets the stage for a large-scale randomized controlled trial recently funded by the National Institutes of Health Small Business Innovation Research program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".