Youth and Provider Perspectives on Behavior-Tracking Mobile Apps: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Mobile health apps stand as one possible means of improving evidence-based mental health interventions for youth. However, a better understanding of youth and provider perspectives is necessary to support widespread implementation. OBJECTIVE: The objective of this research was to explore both youth and provider perspectives on using mobile apps to enhance evidence-based clinical care, with an emphasis on gathering perspectives on behavior-tracking apps. METHODS: Inductive qualitative analysis was conducted on data obtained from semistructured interviews held with 10 youths who received psychotherapy and 12 mental health care providers who conducted therapy with youths aged 13-26 years. Interviews were independently coded by multiple coders and consensus meetings were held to establish reliability. RESULTS: During the interviews, the youths and providers broadly agreed on the benefits of behavior tracking and believed that tracking via app could be more enjoyable and accessible. Providers and youths also shared similar concerns that negative emotions and user burden could limit app usage. Participants also suggested potential app features that, if implemented, would help meet the clinical needs of providers and support long-term use among youth. Such features included having a pleasant user interface, reminders for clients, and graphical output of data to clients and providers. CONCLUSIONS: Youths and providers explained that the integration of mobile health into psychotherapy has the potential to make treatment, particularly behavior tracking, easy and more accessible. However, both groups had concerns about the increased burden that could be placed on the clients and providers.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".