Theory-Based Mobile App Intervention to Promote Healthy Salt Intake Among Adults: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: Nowadays, mobile health technology has been increasingly used for treatment and prevention at all levels of health care. Associating this technology with the promotion of healthy salt consumption-both cost-effective and cost-saving public health strategies-can reduce this risk factor that contributes significantly to the increase of noncommunicable diseases worldwide. Objective: We aim to assess the usability and the efficacy of a mobile app intervention-"Sal na Medida" app-on the promotion of a healthy salt intake among adults, based on the Behavior Change Wheel framework. Additionally, to investigate if intention, self-efficacy, and habit variables mediated the effect of intervention on salt intake behavior. Methods: An experimental, randomized, and longitudinal study that evaluated the intervention effect with assessments at baseline, postintervention (1 mo), and at follow-up (2 mo). Sociodemographic and clinical data were collected from participants recruited at primary health care centers. The behavior of salt intake and per capita salt consumption were the primary outcomes. Psychosocial variables of intention, self-efficacy, and habit were assessed as possible mediators. Usability was evaluated after 1 month and 2 months of using the app. Results: Eighty-six participants were randomized in the intervention group (IG; n=43) or control group (CG; n=43). Most of the participants were female (IG: n=36, 84% and CG: n=37, 86%). Usability of app intervention scored 77.8 points (on a scale of 0 to 100) among IG participants. There was a significant reduction in salt intake in IG according to the variables of per capita salt and the behavior of salt intake. Furthermore, at the end of the follow-up, individuals in IG were 63% more likely to have a lower salt intake than those in the CG. The regression analysis showed an increase in intention and the perception of self-efficacy, and a more pronounced reduction in the habit of using more than 3 g of salt/day in preparing meals in IG when compared to CG. Habit and self-efficacy were identified as mediators of the intervention's effect. Conclusions: The theory-based mobile app intervention for reducing salt intake has shown promise both in terms of usability and efficacy among adults. Conducting further studies to assess its potential for implementation on a larger scale would be valuable for determining its real-world impact and feasibility.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, pas un consensus.
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