Notice bibliographique
Résumé
The study by Hagstromer et al. (2) in this issue of Medicine & Science in Sports & Exercise® is a recent example of the growing number of large-scale applications of accelerometers to measure physical activity. Since the late 1990s, publications of studies with accelerometer measurements have transitioned from describing data collected on tens of subjects to hundreds and now describe results from thousands of participants. The earliest large applications focused on youths. Examples include reports from the European Youth Heart Study (1), the Trial of Activity for Adolescent Girls (6), and the Avon Longitudinal Study of Parents and Children (4). Objective assessments of physical activity on large samples of adults and population-based samples are rarer, but more are coming soon. Objective data on physical activity have been collected on thousands of individuals from nationally representative samples in the United States (3), and are currently being collected in Canada (9). Objective data from population samples provide a new perspective and raise new questions. Hagstromer et al. (2) note that the amount of physical activity determined with an accelerometer was dramatically lower than that based on self-reports. Concern about the accuracy of self-reports is not new (7). However, the validity studies summarized by Sallis and Saelens (7) were relatively small. Accumulating information from large studies with accelerometers allows comparison with population estimates based on self-reports. Whereas self-report data tend to estimate approximately 23-44% European population adherence (8) to the current physical activity recommendations (5), accelerometer data suggest the figure may be less than 5% if sustained bouts of activity are necessary to meet the criterion (2). If self-report data dramatically overestimate levels of activity and accelerometer data reflect something closer to behavioral reality, physical activity researchers may need to revisit some foundations of current recommendations. The multiple benefits of physical activity are well established, but the epidemiological relationships and the resulting physical activity recommendations (5) rely heavily on self-reports. The observed differences between self-report and objective data raise questions about recommended duration, intensity, and need for bouts of physical activity. If overreporting is due to inflated estimates of activity duration, might it be possible that less than 30 min of moderate activity, as measured by accelerometer, conveys health effects associated with a self-report of 30 or more minutes? If intensity is misclassified by reporting lower intensity activity as moderate or greater intensity, might activity of less than 3.0 METs convey benefits that have been associated with reports of moderate intensity activity? Although focusing on bouts may aid recall of activity, are objectively measured bouts of 8-10 min required for health benefits? Insights into these questions may be possible from the cross-sectional population data now available (3), but real progress will depend upon integration of objective measures in prospective studies. Recent applications of accelerometers in population studies suggest that this progress is feasible. Richard P. Troiano National Cancer Institute Bethesda, MD
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».