Selection of and Response to Physical Activity–Based Social Comparisons in a Digital Environment: Series of Daily Assessment Studies
Notice bibliographique
Résumé
BACKGROUND: Innovative approaches are needed to understand barriers to and facilitators of physical activity among insufficiently active adults. Although social comparison processes (ie, self-evaluations relative to others) are often used to motivate physical activity in digital environments, user preferences and responses to comparison information are poorly understood. OBJECTIVE: We used an iterative approach to better understand users' selection of comparison targets, how they interacted with their selected targets, and how they responded to these targets. METHODS: Across 3 studies, different samples of insufficiently active college students used the Fitbit system (Fitbit LLC) to track their steps per day as well as a separate, adaptive web platform each day for 7 to 9 days (N=112). The adaptive platform was designed with different layouts for each study; each allowed participants to select their preferred comparison target from various sets of options, view the desired amount of information about their selected target, and rate their physical activity motivation before and after viewing information about their selected target. Targets were presented as achieving physical activity at various levels below and above their own, which were accessed via the Fitbit system each day. We examined the types of comparison target selections, time spent viewing and number of elements viewed for each type of target, and day-level associations between comparison selections and physical activity outcomes (motivation and behavior). RESULTS: Study 1 (n=5) demonstrated that the new web platform could be used as intended and that participants' interactions with the platform (ie, the type of target selected, the time spent viewing the selected target's profile, and the number of profile elements viewed) varied across the days. Studies 2 (n=53) and 3 (n=54) replicated these findings; in both studies, age was positively associated with time spent viewing the selected target's profile and the number of profile elements viewed. Across all studies, upward targets (who had more steps per day than the participant) were selected more often than downward targets (who had fewer steps per day than the participant), although only a subset of either type of target selection was associated with benefits for physical activity motivation or behavior. CONCLUSIONS: Capturing physical activity-based social comparison preferences is feasible in an adaptive digital environment, and day-to-day differences in preferences for social comparison targets are associated with day-to-day changes in physical activity motivation and behavior. Findings show that participants only sometimes focus on the comparison opportunities that support their physical activity motivation or behavior, which helps explain previous, equivocal findings regarding the benefits of physical activity-based comparisons. Additional investigation of day-level determinants of comparison selections and responses is needed to fully understand how best to harness comparison processes in digital tools to promote physical activity.
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 | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,001 | 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 ».