A Mobile Health Team Challenge to Promote Stepping and Stair Climbing Activities: Exploratory Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) approaches are growing in popularity as a means of addressing low levels of physical activity (PA). OBJECTIVE: This study aimed to determine the validity of wearables in measuring step count and floor count per day and assess the feasibility and effects of a 6-week team challenge intervention delivered through smartphone apps. METHODS: Staff and students from a public university were recruited between 2015 and 2016. In phase 1, everyone wore a Fitbit tracker (Charge or Charge HR) and an ActiGraph for 7 days to compare daily step count estimated by the two devices under free-living conditions. They were also asked to climb 4 bouts of floors in an indoor stairwell to measure floor count which was compared against direct observation. In phase 2, participants were allocated to either a control or intervention group and received a Fitbit tracker synced to the Fitbit app. Furthermore, the intervention group participants were randomized to 4 teams and competed in 6 weekly (Monday to Friday) real-time challenges. A valid day was defined as having 1500 steps or more per day. The outcomes were as follows: (1) adherence to wearing the Fitbit (ie, number of days in which all participants in each group were classified as valid users aggregated across the entire study period), (2) mean proportion of valid participants over the study period, and (3) the effects of the intervention on step count and floor count determined using multiple linear regression models and generalized estimating equations (GEEs) for longitudinal data analysis. RESULTS: In phase 1, 32 of 40 eligible participants provided valid step count data, whereas all 40 participants provided valid floor count data. The Fitbit trackers demonstrated high correlations (step count: Spearman ρ=0.89; P<.001; floor count: Spearman ρ=0.98; P<.001). The trackers overestimated step count (median absolute error: 17%) but accurately estimated floor count. In phase 2, 20 participants each were allocated to an intervention or control group. Overall, 24 participants provided complete covariates and valid PA data for analyses. Multiple linear regressions revealed that the average daily steps was 15.9% higher for the intervention group (95% CI -8.9 to 47.6; P=.21) during the final two intervention weeks; the average daily floors climbed was 39.4% higher (95% CI 2.4 to 89.7; P=.04). GEE results indicated no significant interaction effects between groups and the intervention week for weekly step count, whereas a significant effect (P<.001) was observed for weekly floor count. CONCLUSIONS: The consumer wearables used in this study provided acceptable validity in estimating stepping and stair climbing activities, and the mHealth-based team challenge interventions were feasible. Compared with the control group, the participants in the intervention group climbed more stairs, so this can be introduced as an additional PA promotion target in the context of mHealth strategies. Methodologically rigorous studies are warranted to further strengthen this study's findings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».