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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».