A Refined Mobile Health Intervention (SMARTFAMILY2.0) to Promote Physical Activity and Healthy Eating in a Family Setting: Randomized Controlled Trial
Notice bibliographique
Résumé
Background Many mobile health (mHealth) apps focus on promoting physical activity (PA) and healthy eating (HE). However, there is limited empirical evidence regarding their effectiveness in initiating and sustaining behavior change, particularly among children and adolescents. Considering that behavior is influenced by social contexts, it is essential to take core settings like family dynamics into account when designing mHealth apps. Objective The purpose of this study was to further develop and refine the SMARTFAMILY (SF) app targeting PA and HE in a collective family-based setting by enhancing design and usability, as well as by adding gamification aspects, health literacy, and just-in-time adaptive interventions to the first version of the app. Methods The SF2.0 app, based on behavior change theories and techniques, was developed, implemented, and evaluated. The app was used in a collective family setting, with family members using it individually and cooperatively. In a cluster-randomized controlled trial, the intervention group (IG) used the app for 3 consecutive weeks, while the control group (CG) received no treatment. Primary outcomes included PA measured through self-reports and accelerometry, as well as self-reported fruit and vegetable intake (FVI) for HE. Secondary outcomes included intrinsic motivation, behavior-specific self-efficacy, and the Family Health Climate. A follow-up assessment (T2) was conducted 4 weeks after the postmeasurement (T1) to assess intervention effects. Multilevel analyses were performed in R (R Foundation for Statistical Computing), considering the hierarchical structure of individuals (level 1) within families (level 2). Results Overall, 55 families (28 CG, 105/209; 27 IG, 104/209 participants) were recruited for the study. In total, 3 families (3 CG, n=12) chose to drop out of the study due to personal reasons before T0. Overall, no evidence for meaningful and statistically significant increases in PA was observed in favor of the IG of our physically active sample. However, the app elucidated positive effects in favor of the IG for FVI diary (T0-T1; P=.03), joint PA (T0-T1; P=.02 and T0-T2; P<.001), and joint family meals (T0-T1; P=.004). Conclusions The SF2.0 trial evaluated an mHealth intervention designed to promote PA and HE within families. Despite incorporating a theoretical foundation, several behavior change techniques based on family life, and gamification and just-in-time adaptive intervention features, the intervention did not significantly increase PA levels among physically active participants. FVI, joint PA, and joint meals were improved within the IG. Previous studies on digital health interventions have produced mixed results, and family-based mHealth interventions remain rare, with limited focus on whole-family behavior and randomized controlled trials. To enhance intervention effectiveness, future app development could consider incorporating even more advanced features and should focus on inactive participants. Further research is needed to better understand intervention engagement and tailor mHealth approaches for primary prevention efforts. Trial Registration German Clinical Trials Register DRKS00010415; https://www.drks.de/search/en/trial/DRKS00010415 International Registered Report Identifier (IRRID) RR2-10.2196/20534
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,011 | 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 ».