Acceptability and Utility of a Smartphone App to Support Adolescent Mental Health (BeMe): Program Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Adolescents face unprecedented mental health challenges, and technology has the opportunity to facilitate access and support digitally connected generations. The combination of digital tools and live human connection may hold particular promise for resonating with and flexibly supporting young people's mental health. OBJECTIVE: This study aimed to describe the BeMe app-based platform to support adolescents' mental health and well-being and to examine app engagement, usability, and satisfaction. METHODS: Adolescents in the United States, aged 13 to 20 years, were recruited via the web and enrolled between September 1 and October 31, 2022. App engagement, feature use, clinical functioning, and satisfaction with BeMe were examined for 30 days. BeMe provides content based on cognitive behavioral therapy, dialectical behavior therapy, motivational interviewing, and positive psychology; interactive activities; live text-based coaching; links to clinical services; and crisis support tools (digital and live). RESULTS: The average age of the sample (N=13,421) was 15.04 (SD 1.7) years, and 56.72% (7612/13,421) identified with she/her pronouns. For the subsample that completed the in-app assessments, the mean scores indicated concern for depression (8-item Patient Health Questionnaire mean 15.68/20, SD 5.9; n=239), anxiety (7-item Generalized Anxiety Disorder Questionnaire mean 13.37/17, SD 5.0; n=791), and poor well-being (World Health Organization-Five Well-being Index mean 30.15/100, SD 16.1; n=1923). Overall, the adolescents engaged with BeMe for an average of 2.38 (SD 2.7) days in 7.94 (SD 24.1) sessions and completed 11.26 (SD 19.8) activities. Most adolescents engaged with BeMe's content (12,270/13,421, 91.42%), mood ratings (13,094/13,421, 97.56%), and interactive skills (10,098/13,421, 75.24%), and almost one-fifth of the adolescents engaged with coaching (2539/13,421, 18.92%), clinical resources (2411/13,421, 17.96%), and crisis support resources (2499/13,421, 18.62%). Overall app engagement (total activities) was highest among female and gender-neutral adolescents compared with male adolescents (all P<.001) and was highest among younger adolescents (aged 13-14 years) compared with all other ages (all P<.001). Satisfaction ratings were generally high for content (eg, 158/176, 89.8% rated as helpful and 1044/1139, 91.66% improved coping self-efficacy), activities (5362/8468, 63.32% helpful and 4408/6072, 72.6% useful in coping with big feelings), and coaching (747/894, 83.6% helpful and 747/894, 83.6% improved coping self-efficacy). Engagement (total activities completed) predicted the likelihood of app satisfaction (P<.001). CONCLUSIONS: Many adolescents downloaded the BeMe app and completed multiple sessions and activities. Engagement with BeMe was higher among female and younger adolescents. Ratings of BeMe's content, activities, and coaching were very positive for cognitive precursors aimed at reducing depression and anxiety and improving well-being. The findings will inform future app development to promote more sustained engagement, and future evaluations will assess the effects of BeMe on changes in mental health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".