Usability of a Culturally Informed mHealth Intervention for Symptoms of Anxiety and Depression: Feedback From Young Sexual Minority Men
Bibliographic record
Abstract
BACKGROUND: To date, we are aware of no interventions for anxiety and depression developed as mobile phone apps and tailored to young sexual minority men, a group especially at risk of anxiety and depression. We developed TODAY!, a culturally informed mobile phone intervention for young men who are attracted to men and who have clinically significant symptoms of anxiety or depression. The core of the intervention consists of daily psychoeducation informed by transdiagnostic cognitive behavioral therapy (CBT) and a set of tools to facilitate putting these concepts into action, with regular mood ratings that result in tailored feedback (eg, tips for current distress and visualizations of mood by context). OBJECTIVE: The aim of this study was to conduct usability testing to understand how young sexual minority men interact with the app, to inform later stages of intervention development. METHODS: Participants (n=9) were young sexual minority men aged 18-20 years (Mean=19.00, standard deviation [SD]=0.71; 44% black, 44% white, and 11.1% Latino), who endorsed at least mild depression and anxiety symptoms. Participants were recruited via flyers, emails to college lesbian, gay, bisexual, and transgender (LGBT) organizations, Web-based advertisements, another researcher's database of sexual minority youth interested in research participation, and word of mouth. During recorded interviews, participants were asked to think out loud while interacting with the TODAY! app on a mobile phone or with paper prototypes. Feedback identified from these recordings and from associated field notes were subjected to thematic analysis using a general inductive approach. To aid interpretation of results, methods and results are reported according to the consolidated criteria for reporting qualitative research (COREQ). RESULTS: Thematic analysis of usability feedback revealed a theme of general positive feedback, as well as six recurring themes that informed continued development: (1) functionality (eg, highlight new material when available), (2) personalization (eg, more tailored feedback), (3) presentation (eg, keep content brief), (4) aesthetics (eg, use brighter colors), (5) LGBT or youth content (eg, add content about coming out), and (6) barriers to use (eg, perceiving psychoeducation as homework). CONCLUSIONS: Feedback from usability testing was vital to understanding what young sexual minority men desire from a mobile phone intervention for symptoms of anxiety and depression and was used to inform the ongoing development of such an intervention.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".