The Feasibility of Using Instagram Data to Predict Exercise Identity and Physical Activity Levels: Cross-sectional Observational Study
Notice bibliographique
Résumé
BACKGROUND: Exercise identity is an important predictor for regular physical activity (PA). There is a lack of research on the potential mechanisms or antecedents of identity development. Theories of exercise identity have proposed that investment, commitment and self-referential (eg, I am an exerciser) statements, and social activation (comparison, support) may be crucial to identity development. Social media may be a potential mechanism to shape identity. OBJECTIVE: The objectives of this study were to (1) explore whether participants were willing to share their Instagram data with researchers to predict their lifestyle behaviors; (2) examine whether PA-related Instagram uses (ie, the percentage of PA-related Instagram posts, fitness-related followings, and the number of likes received on PA-related posts) were positively associated with exercise identity; and (3) evaluate whether exercise identity mediates the relationship between PA-related Instagram use and weekly PA minutes. METHODS: Participants (18-30 years old) were asked to complete a questionnaire to evaluate their current levels of exercise identity and PA. Participants' Instagram data for the past 12 months before the completion of the questionnaire were extracted and analyzed with their permission. Instagram posts related to PA in the 12 months before their assessment, the number of likes received for each PA-related post, and verified fitness- or PA-related followings by the participants were extracted and analyzed. Pearson correlation analyses were used to evaluate the relationship among exercise identity, PA, and Instagram uses. We conducted mediation analyses using the PROCESS macro modeling tool to examine whether exercise identity mediated the relationship between Instagram use variables and PA. Descriptive statistical analyses were used to compare the number of willing participants versus those who were not willing to share their Instagram data. RESULTS: Of the 76 participants recruited to participate, 54% (n=41) shared their Instagram data. The percentage of PA-related Instagram posts (r=0.38; P=.01) and fitness-related Instagram followings (r=0.39; P=.01) were significantly associated with exercise identity. The average number of "likes" received (r=0.05, P=.75) was not significantly associated with exercise identity. Exercise identity significantly influenced the relationship between Instagram usage metrics (ie, the percentage of PA-related Instagram posts [P=.01] and verified fitness-related Instagram accounts [P=.01]) and PA level. Exercise identity did not significantly influence the relationship between the average number of "likes" received for the PA-related Instagram posts and PA level. CONCLUSIONS: Our results suggest that an increase in PA-related Instagram posts and fitness-related followings were associated with a greater sense of exercise identity. Higher exercise identity led to higher PA levels. Exercise identity significantly influenced the relationship between PA-related Instagram posts (P=.01) and fitness-related followings on PA levels (P=.01). These results suggest that Instagram may influence a person's exercise identity and PA levels. Future intervention studies are warranted.
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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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,001 |
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 ».