Effectiveness of a Hybrid Community-Based Heart-Healthy Lifestyle Intervention: Three-Arm Randomized Controlled Trial Integrating mHealth and Motivational Interviewing
Notice bibliographique
Résumé
BACKGROUND: Limited empirical evidence exists on the effectiveness of a hybrid approach to heart-healthy lifestyle interventions that integrates mobile health (mHealth) technology with face-to-face counseling. Moreover, its superiority over exclusive mHealth use in promoting heart-healthy behavioral outcomes within a community setting remains unclear. OBJECTIVE: This study aims to evaluate the effectiveness of a hybrid community-based approach to heart-healthy lifestyle intervention incorporating a mobile app and motivational interviewing among community-dwelling adults without a history of cardiovascular disease. METHODS: We conducted a 3-arm, parallel-group, randomized controlled trial with assessments at baseline and after 12 weeks. A total of 75 participants, each presenting at least 1 component of metabolic syndrome and no history of cardiovascular disease, were randomly assigned to 1 of 3 groups: hybrid (n=25), mobile (n=25), or control (n=25). Participants were recruited through an online platform. The hybrid group underwent a 12-week hybrid intervention combining a mobile app (ie, "My HeartHELP") and face-to-face motivational interviewing led by a nursing researcher. The mobile group used only the mobile app, while the control group received written material on general heart health. The intervention was facilitated by 3 trained nursing researchers. The primary outcome was a composite score of "heart-healthy behaviors," while secondary outcomes included scores for heart-healthy "information," "self-efficacy," "motivation," and cardiovascular parameters. The trial was conducted in 2 rounds from October 2022 to May 2023. An intention-to-treat analysis was performed. RESULTS: =3.90, P=.03) following the 12-week intervention. Particularly, the hybrid group-unlike the mobile group-showed significantly greater improvement in dietary behavior, a subscale of heart-healthy behavior, compared with the control group, and demonstrated significantly greater improvements in interest or enjoyment, a core subscale of intrinsic motivation, than the mobile and control groups. CONCLUSIONS: The hybrid community-based heart-healthy lifestyle intervention-integrating a mobile app and motivational interviewing-demonstrated overall effectiveness comparable to the mobile app alone, while yielding greater improvements in dietary behavior and core intrinsic motivation. These findings highlight the potential of mHealth apps as practical, stand-alone tools to promote cardiovascular health, particularly in community settings with limited access to in-person professional support. However, incorporating motivational interviewing may further enhance internalized motivation and complex behavior changes over time. Health professionals can therefore adopt mHealth either independently or in combination with motivational interviewing. Future studies should optimize integration strategies to enhance effectiveness and evaluate the long-term sustainability of such hybrid approaches. TRIAL REGISTRATION: ISRCTN Registry ISRCTN83643383; https://www.isrctn.com/ISRCTN83643383. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1161/circ.147.suppl_1.P147.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 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) | 0,007 | 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 ».