Effect of Wearable Activity Trackers and Social Media Use on Day-Level Physical Activity Motivation and Behaviours
Notice bibliographique
Résumé
Background. Physical activity (PA) is integral to maintaining good health yet physical inactivity remains a concern. Wearable activity trackers (WATs) have grown in popularity and research recognizes their potential impact on motivation and PA behaviours, specifically when combined with additional intervention strategies. Research has also shown positive associations between health-related social media use and PA. While both WATs and social media are potentially effective tools for behaviour change, research in this field has focused on between-person associations. Currently, less is known about within-person associations between WAT use and daily PA as well as potential interaction effects with daily health-related social media use. Objectives. 1) Examine differences in day-level situational motivation for PA between WAT users and non-users, 2) Examine within-person associations of day-level situational motivation for PA with same-day health-related social media use, 3) Examine differences in day-level PA intensity, duration, and moderate-to-vigorous physical activity (MVPA) in WAT users and non-users, and 4) Examine within-person associations of daily PA with same-day health-related social media use. Methods. English-speaking Canadian adults (≥ 18 years) were recruited. Eligible participants completed a baseline survey assessing social media use, WAT status and demographic information. Participants then completed up to three (3) daily surveys assessing daily situational motivation for PA, daily social media use and self-reported PA behaviours for 14 days. Multi-level modelling was conducted. Results. 328 participants were included. Mean age of participants was 27.2 (9.1) years, 67% (n=220) of participants were female and 71.3% (n=234) of participants identified as WAT users. WAT use was associated with greater intrinsic and identified situational motivation before engaging in daily PA. Daily health-related social media use was not found to be associated with greater autonomous situational motivation. The only significant interaction effect for WAT use and health-related social media was found for external regulation (b=0.23, SE 0.11, p = .03). WAT use was not associated with greater daily PA; however, daily social media use was significantly associated with PA intensity (b=0.29, SE 0.10, p < .01) and MVPA (b=3.38, SE 1.52, p = .026). No significant interaction effects were observed between health-related social media and WAT use for any PA outcome. Conclusions. Greater autonomous (intrinsic motivation, identified regulation) situational motivation for PA in WAT users did not translate to increases in PA behaviours. While daily social media use had no association with daily motivation for PA, results showed a significant association between health-related social media use and PA intensity as well as with MVPA. Alone, WATs and health-related social media use may influence situational motivation for PA and behaviours but no additional benefits on motivation or PA were observed when used in combination. Although WATs should be not discounted as an effective tool, health-related social media platforms could exert a more direct influence on actual PA engagement and is a potential positive addition to PA interventions. Future research should continue to examine the type and timing of health-related social media use to have an optimal effect on PA behaviours.
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».