A Persuasive and Social mHealth Application for Physical Activity: A Usability and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Advances in smartphones and the wide usage of social networking systems offer opportunities for the development of innovative interventions to promote physical activity. To that end, we developed a persuasive and social mHealth application designed to monitor and motivate users to walk more every day. OBJECTIVE: The objectives of this project were to conduct a focused review on the fundamental characteristics of mHealth for physical activity promotion, to develop an mHealth application that meets such characteristics, and to conduct a feasibility study to deploy the application in everyday life. METHODS: This project started as an analytical study to review the fundamental characteristics of the technologies used in physical activity monitoring and promotion. Then, it was followed by a technical development of the application. Next, a 4 week deployment was conducted where participants used the application as part of their daily life. A think-aloud method and in-depth semistructured interviews were conducted following the deployment. A qualitative description method was used to thematically analyze the interviews. Feasibility measures included, adherence to the program, user-system interactions, motivation to use, and experience with physical activity and online social interactions. RESULTS: There were seven fundamental characteristics of physical activity monitoring and promotion that were identified, which were then used as a foundation to develop the application. There were fourteen participants that enrolled in the application evaluation. The age range was from 24 to 45; body mass index ranged from 18.5 to 42.98, with 4 of the subjects falling into the category "obese". Half of them were experienced with smartphones, and all were familiar with a social network system. There were thirteen participants that completed the study; one was excluded. Overall, participants gave high scores to almost all of the usability factors examined, with averages of 4.52 out of a 5.00 maximum. Over 29 days, participants used the application for a total of 119,380 minutes (average=7.57 hours/day/participant; SD 1.56). CONCLUSIONS: Based on the fundamental characteristics, the application was successfully developed. The usability results suggest that the system is usable and user satisfaction was high. Deploying the application was shown to be feasible for the promotion of daily physical activity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».