Maintaining Physical Activity Level Through Team-Based Walking With a Mobile Health Intervention: Cross-Sectional Observational Study
Notice bibliographique
Résumé
BACKGROUND: The health conditions of Japanese salespersons may be adversely affected by their lifestyle. Face-to-face or on-site health interventions are not convenient for salespersons because of their tendency for out-of-office sales. Previous studies showed that mobile health (mHealth) interventions (compared to usual practice) have great potential to promote physical activity. For Japanese salespersons, mHealth can offer additional convenience to change their physical activity habits because they can access the mHealth contents anytime and anywhere. However, the specific elements that are most important to maintain physical activity levels using an mHealth approach remain unclear. OBJECTIVE: We aimed to identify elements that account for both a high average physical activity level and can help to prevent a decrease in physical activity during a 9-week intervention period. METHODS: Salespersons were recruited from 11 Japanese companies. A team-based walking intervention was held from October to December 2018 (for a total of 9 weeks), during which the walking step data were recorded by smartphone apps. Average walking steps of each participant during the intervention and the difference in walking steps between the initial and the final week were respectively used as dependent variables. The effects of team characteristics (ie, frequency of communication with team members and team size) and behavioral characteristics (ie, number of days with recorded steps on the apps) on the average walking steps, and the difference in walking steps between the initial and the final week were estimated using multiple and multilevel regression analyses. RESULTS: Of the 416 participants, walking step data of 203 participants who completed postintervention assessments were included in the analyses. Multiple regression analysis of the average walking steps showed that the number of days with recorded steps was positively correlated with the log-transformed average walking steps (β=.01, P<.001). Multilevel analysis of the average walking steps considering the company level estimated that the intraclass correlation coefficient was 37%. This means that belonging to the same company largely affected an individual's average walking steps. Multiple regression analysis of the difference in walking steps showed that communication with team members once or twice a week correlated with preventing a decrease in walking steps from the initial to the final week (β=1539.4, P=.03), and being on a larger team correlated with a decrease in walking steps from the initial to the final week (β=-328.4, P=.01). CONCLUSIONS: This study showed that the elements accounting for high average walking steps and those preventing the decrease in walking steps from the initial to the final week differed. Behavioral characteristics correlated positively with average walking steps. Team characteristics (ie, regular communication and a smaller team size) significantly correlated with preventing a decrease in walking steps.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 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 ».