Engagement With Web-Based Fitness Videos on YouTube and Instagram During the COVID-19 Pandemic: Longitudinal Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND The COVID-19 pandemic has drastically changed the physical activity (PA) landscape through the closures of gymnasiums, schools, and many outdoor spaces. Physical distancing guidelines have also reduced opportunity for PA. The popularity of free web-based home fitness videos on video hosting platforms (eg, YouTube and Instagram) has spiked during the pandemic. Many web-based fitness videos offer a convenient, accessible, and cost-effective means of engaging in PA through regularly posted videos or discrete programs. Notably, traditional PA programs often suffer from poor adherence and high dropout rates, despite many advantages over web-based workout programs (eg, equipment, feedback, and in-person engagement). Thus, notwithstanding clear advantages of these web-based fitness videos, their ability to maintain long-term engagement and adherence is unknown. OBJECTIVE We explored patterns of engagement (ie, views, likes, and comments) for channels posting daily or program-based web-based fitness videos since the declaration of COVID-19 as a pandemic, over 4 months. Our secondary objective was to examine potential moderators of engagement metrics. METHODS An environmental scan was used to identify eligible channels. Eligible channels were (1) freely available on YouTube or Instagram and (2) posted daily or weekday series workouts or offered quarantine-specific workout programs. Searches for eligible channels were conducted on June 1 and 4, 2020. Engagement metrics of views, likes, and comments were then collected from channels’ videos posted between March 11 and June 26 or 30, 2020, inclusive, on June 26 or July 8, 2020. A series of multilevel modeling analyses were conducted to examine longitudinal changes in each of the 3 outcome variables. RESULTS Ten channels were deemed eligible and included in analyses; 6 posted regularly, while the other 4 posted discrete workout programs. Multilevel models revealed that both views and likes significantly decreased across days. Visually, channels display the sharpest drop in engagement within the first week. Linear change estimate indicates that the number of views initially declined by 24,700 per day (95% CI –44,400 to –11,300, P=.01) on average across all the channels. Channels with more subscribers declined in their views, likes, and comments at a significantly higher rate than those with fewer subscribers (P≤.04). The day of the week a video is posted, “virality,” and content of a video appear to influence engagement. Integrating behavior change techniques and posting new and varied videos often may help garner further engagement with these videos. Future research should examine common elements of videos, which drive engagement. CONCLUSIONS Despite raw engagement metrics, each channel demonstrated peak engagement with the initial video followed by decreased engagement with subsequent videos. As many countries maintain restrictions on traditional PA facilities owing to the COVID-19 pandemic, determining methods to improve engagement and adherence with web-based fitness videos becomes increasingly important.
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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,003 |
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 ».