Usage and Weekly Attrition in a Smartphone-Based Health Behavior Intervention for Adolescents: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The majority of adolescents own smartphones, although only 8% of them use health apps. Attrition rates from adolescent mobile health (mHealth) interventions for treating mental health problems such as anxiety and depression are an issue with a high degree of variation. Attrition in mHealth interventions targeting adolescent populations is frequently presented in a two-point fashion, from initiation of the intervention to the end of treatment, lacking more time-specific information on usage and times of attrition. Self-efficacy could provide an avenue to lower attrition rates, although a better understanding of the relationship between mental health factors and time-specific attrition rates is needed. OBJECTIVE: The aims of this study were to obtain time-specific attrition rates among adolescents in an mHealth intervention, and to describe the intervention's usage and feasibility in relation to adolescent self-efficacy levels, and emotional and physical health. METHODS: A single-center randomized controlled public school pilot trial was undertaken with 41 adolescents. Outcome measures were assessed at baseline and after 6 weeks, while in-app activity and attrition rates were continually assessed throughout the intervention period. The primary outcome was attrition based on time and type of in-app health behavior usage, and feasibility of the mHealth app. Secondary outcome measures were self-efficacy levels, depressive and anxiety symptoms, as well as standardized BMI and sleep. Analyses of group mean variances with adjusted α levels through Bonferroni corrections were used to assess main outcome effects. RESULTS: =0.43, P<.001). The number of average weekly in-app health exercises completed decreased significantly from the first week of the intervention (mean 55.25, SD 10.96) to the next week (mean 13.63, SD 2.94). However, usage increased by 22% between week 2 and the last week of the intervention (mean 16.69, SD 8.37). Usability measures revealed satisfactory scores (mean 78.09, SD 9.82) without gender differences (P=.85). Self-reported daily physical activity increased by 19.61% in the intervention group but dropped by 26.21% among controls. Self-efficacy levels increased by 8.23% in the invention arm compared to a 3.03% decrease in the control group. CONCLUSIONS: This pilot study demonstrated the feasibility and usability of an mHealth intervention among adolescent participants. Indications were toward beneficial effects on physical and mental health that warrant further research. Focus on time-specific attrition measures alongside daily times of usage and ways to increase participants' self-efficacy levels appear to be a promising avenue for research on mHealth interventions for adolescent populations with the aim to ultimately lower attrition rates.
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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 | 0,007 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».