Towards uniform accelerometry analysis: a standardization methodology to minimize measurement bias due to systematic accelerometer wear-time variation.
Notice bibliographique
Résumé
Accelerometers are predominantly used to objectively measure the entire range of activity intensities - sedentary behaviour (SED), light physical activity (LPA) and moderate to vigorous physical activity (MVPA). However, studies consistently report results without accounting for systematic accelerometer wear-time variation (within and between participants), jeopardizing the validity of these results. This study describes the development of a standardization methodology to understand and minimize measurement bias due to wear-time variation. Accelerometry is generally conducted over seven consecutive days, with participants' data being commonly considered 'valid' only if wear-time is at least 10 hours/day. However, even within 'valid' data, there could be systematic wear-time variation. To explore this variation, accelerometer data of Smart Cities, Healthy Kids study (www.smartcitieshealthykids.com) were analyzed descriptively and with repeated measures multivariate analysis of variance (MANOVA). Subsequently, a standardization method was developed, where case-specific observed wear-time is controlled to an analyst specified time period. Next, case-specific accelerometer data are interpolated to this controlled wear-time to produce standardized variables. To understand discrepancies owing to wear-time variation, all analyses were conducted pre- and post-standardization. Descriptive analyses revealed systematic wear-time variation, both between and within participants. Pre- and post-standardized descriptive analyses of SED, LPA and MVPA revealed a persistent and often significant trend of wear-time's influence on activity. SED was consistently higher on weekdays before standardization; however, this trend was reversed post-standardization. Even though MVPA was significantly higher on weekdays both pre- and post-standardization, the magnitude of this difference decreased post-standardization. Multivariable analyses with standardized SED, LPA and MVPA as outcome variables yielded more stable results with narrower confidence intervals and smaller standard errors. Standardization of accelerometer data is effective in not only minimizing measurement bias due to systematic wear-time variation, but also to provide a uniform platform to compare results within and between populations and studies. Key pointsSystematic variation in accelerometer wear-time both, within and between participants results in measurement bias.Standardization of data after controlling for wear-time produces stable outcome variables.Descriptive and multivariate analyses conducted with standardized outcome variables minimize measurement bias.
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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 | 0,167 | 0,219 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
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 ».