Physical Activity Misinformation on Social Media: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Social media is a prominent way in which health information is spread. The accuracy and credibility of such sources range widely, with misleading statements, misreported results of studies, and a lack of references causing health misinformation to become a growing problem. However, previous research on health misinformation related to topics including vaccines, nutrition, and cancer has excluded physical activity despite it being highly searched for and discussed online. OBJECTIVE: This systematic review was designed to synthesize the existing literature focused on physical activity misinformation on social media in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. METHODS: Keyword searches were conducted in PubMed, the Cochrane Library, Web of Science, and Scopus databases for records published between January 2016 and May 2025. This search strategy yielded 9039 articles. Titles and abstracts were screened by independent reviewers, resulting in 168 (1.86%) articles selected for full-text review. After further review, 33 (19.6%) articles met the inclusion criteria and were used in the final synthesis. RESULTS: For the 33 studies selected, topics included physical rehabilitation and therapeutic exercise recommendations (n=15, 45%), general physical activity and messaging (n=6, 18%), exercising with a specific condition (n=4, 12%), women's health (n=3, 9%), weight loss (n=2, 6%), exercise testing (n=1, 3%), "immune boosting exercise" (n=1, 3%), and workplace sitting versus standing guidelines (n=1, 3%). The social media platforms YouTube (n=13, 39%), TikTok (n=7, 21%), Facebook (n=2, 6%), Instagram (n=1, 3%), and Pinterest (n=1, 3%) were studied, whereas other articles (n=9, 27%) analyzed content that had not explicitly been posted to social media but could be shared widely online. In total, 4 (12%) studies reported research that proactively engaged participants, and the remaining 29 (88%) studies analyzed readily available online content, including social media, news articles, websites, and blogs. Furthermore, 27 (82%) studies reported at least 1 measure of misinformation prevalence, whereas 21 (64%) reported a metric of reach, and 6 (18%) studies reported a measure of misinformation spread. CONCLUSIONS: Our findings indicate that research on social media physical activity misinformation spans a diverse array of physical activity topics, with YouTube being the most studied platform due to its widespread use and ease of content evaluation. This review also highlights the prevalence of low-quality information across various platforms and a lack of longitudinal investigations. Our review underscores the need for multifaceted research approaches and suggests several strategies to combat misinformation, including improved messaging, high-quality information dissemination by institutions, detailed debunking efforts, and raising awareness about misinformation. Future research should focus on understanding the spread of physical activity misinformation across platforms and its impact, especially on vulnerable populations. TRIAL REGISTRATION: PROSPERO CRD42022316101; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022316101.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».