Acceptability of an mHealth App for Youth With Substance Use and Mental Health Needs: Iterative, Mixed Methods Design
Bibliographic record
Abstract
BACKGROUND: Treating substance use disorders (SUDs) during adolescence can prevent adult addiction and improve youth outcomes. However, it can be challenging to keep adolescents with SUDs engaged in ongoing services, thus limiting potential benefits. Developmentally appropriate tools are needed to improve treatment engagement during and between sessions for youth with SUDs and mental health disorders. Mobile health apps may augment or replace psychotherapy components; however, few have been developed specifically for youth with SUDs following user-guided design principles, which may limit their appropriateness and utility. Formative research on acceptability to intended end users is needed before the efficacy of such tools can be examined. OBJECTIVE: This study involves user-centered, iterative development and initial user testing of a web-based app for adolescents with SUDs and mental health concerns. METHODS: Adolescents aged 14 to 17 years with past-year involvement in outpatient psychotherapy and behavioral health clinicians with adolescent SUD treatment caseloads were recruited. Across 2 assessment phases, 40 participants (alpha: 10 youths and 10 clinicians; beta: 10 youths and 10 clinicians) viewed an app demonstration and completed semistructured interviews and questionnaires about app content and functionality. RESULTS: Participants expressed positive impressions of the app and its potential utility in augmenting outpatient therapy for youth with SUDs and mental health concerns. Noted strengths included valuable educational content, useful embedded resources, and a variety of activities. Adolescents and clinicians favored the app over conventional (paper-and-pencil) modalities, citing convenience and familiarity. The app was found to be user-friendly and likely to improve treatment engagement. Adolescents suggested the inclusion of privacy settings, and clinicians recommended more detailed instructions and simplified language. CONCLUSIONS: The novel app developed here appears to be a promising, acceptable, and highly scalable resource to support adolescents with SUDs and mental health concerns. Future studies should test the efficacy of such apps in enhancing adolescent behavioral health treatment engagement and outcomes.
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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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".