A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis
Notice bibliographique
Résumé
Background: Compositional data analysis (CoDA) techniques are well suited for examining associations between 24-h movement behaviors (i.e., sleep, sedentary behavior, physical activity) and indicators of health given they recognize these behaviors are co-dependent, representing relative parts that make up a whole day. Accordingly, CoDA techniques have seen increased adoption in the past decade, however, heterogeneity in research reporting practices may hinder efforts to synthesize and quantify these relationships via meta-analysis. This systematic review described reporting practices in studies that used CoDA techniques to investigate associations between 24-h movement behaviors and indicators of health. Methods: A systematic search of eight databases was conducted, in addition to supplementary searches (e.g., forward/backward citations, expert consultation). Observational studies that used CoDA techniques involving log-ratio transformation of behavioral data to examine associations between time-based estimates of 24-h movement behaviors and indicators of health were included. Reporting practices were extracted and classified into seven areas: (1) methodological justification, (2) behavioral measurement and data handling strategies, (3) composition construction, (4) analytic plan, (5) composition-specific descriptive statistics, (6) model results, and (7) auxiliary information. Study quality and risk of bias were assessed by the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies. Results: 102 studies met our inclusion criteria. Reporting practices varied considerably across areas, with most achieving high standards in methodological justification, but inconsistent reporting across all other domains. Some items were reported in all studies (e.g., how many parts the daily composition was partitioned into), whereas others seldom reported (e.g., definition of a day: midnight-to-midnight versus wake-to-wake). Study quality and risk of bias was fair in most studies (85%). Conclusions: Current studies generally demonstrate inconsistent reporting practices. Consistent, clear and detailed reporting practices are evidently needed moving forward as the field of time-use epidemiology aims to accurately capture and analyze movement behavior data in relation to health outcomes, facilitate comparisons across studies, and inform public health interventions and policy decisions. Achieving consensus regarding reporting recommendations is a key next step. Supplementary Information: The online version contains supplementary material available at 10.1186/s44167-024-00062-8.
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 | 0,021 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».