Development and Usability Testing of an mHealth Tool for Trauma-Informed Prevention of Substance Use, HIV Acquisition, and Risky Sexual Behaviors Among Adolescents: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Youth who experience traumatic events are at a substantially higher risk of engaging in substance use and sexual risk behaviors and problems (eg, HIV acquisition) than their non-trauma-exposed counterparts. Evidence-based substance use and risky sexual behavior prevention may reduce the risk of these outcomes. Trauma-focused mental health treatment provides a window of opportunity for the implementation of such preventive work with these youth. However, overburdened clinicians face challenges in adding prevention content while implementing evidence-based treatments. Mobile health (mHealth) tools can help reduce this burden in delivering prevention curricula. Trauma-Informed Prevention for Substance Use and Risky Sexual Behavior (TIPS) is an mHealth app that was developed to aid trauma-focused cognitive behavioral therapy (TF-CBT) clinicians in the implementation of an evidence-based risk behavior prevention curriculum. OBJECTIVE: The goal of this paper is to describe the rationale for and development of the TIPS app and present the results of a mixed methods approach for the initial evaluation of its usability. METHODS: Participants included clinicians (n=11), adolescents (n=11), and caregivers (n=10) who completed qualitative interviews and an adapted version of the Website Analysis and Measurement Inventory. RESULTS: In total, 4 overarching themes emerged from the participants' answers to the qualitative interview questions, demonstrating a generally positive response to the app. The themes were (1) strength of app content, (2) suggestions about app content, (3) esthetics and usability, and (4) benefits to the patient and session implementation. Clinicians, adolescents, and caregivers all agreed that the content was very relevant to adolescents and used examples and language that adolescents could relate to. All 3 groups also discussed that the content was comprehensive and addressed issues often faced by adolescents. All 3 groups of users made suggestions about the esthetics, which mostly comprised suggestions to change the font, color, or pictures within the app. Of all the groups, adolescents were most positive about the esthetics and usability of the app. Results from the Website Analysis and Measurement Inventory further illustrated the users' favorable reaction to the TIPS app, with 100% (11/11) of clinicians, 100% (10/10) of caregivers, and most adolescents (7/11, 64%) selecting strongly agree or somewhat agree to the following statement: "This app has much that is of interest to me." Adolescents generally found the app easier to use than did caregivers and clinicians. CONCLUSIONS: The TIPS app shows promise as an mHealth tool for TF-CBT clinicians to integrate evidence-based substance use, risky sexual behavior, and HIV prevention during treatment. Future research, including a randomized controlled trial comparing TF-CBT implementation with and without the inclusion of the app, is necessary to evaluate the feasibility and efficacy of the app in reducing the risk of substance use and risky sexual behavior among trauma-exposed adolescents. TRIAL REGISTRATION: ClinicalTrials.gov NCT03710720; https://clinicaltrials.gov/study/NCT03710720.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".