Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods
Notice bibliographique
Résumé
Despite advances in methods to objectively monitor physical activity and sedentary time, much of recently funded health and behavioral research examining physical activity as an exposure or outcome relies on self-report as the principal method of data collection. Development of new instruments to assess physical activity has been an on-going research pursuit. A number of resources are available that direct researchers and practitioners to collections of instruments (some are listed in the appendix of this supplement), but users can be overwhelmed by the array of choices available. Instruments vary in how they operationalize a broad range of concepts and constructs, and there is limited concrete guidance for selecting an instrument for any particular research need. From 19891 until now,2 documented advice for selecting a self-report instrument has tended to remind users that it is important to define the physical activity construct of interest, and that the dimensions of physical activity most often assessed are type, intensity, frequency, and duration. If total physical activity or energy expenditure is of interest, then activity in all life domains (eg, home, work, leisure, transportation) should be queried. Usually there is also an acknowledgement of the potential for seasonality to influence physical activity assessment. Beyond this, there are few recommendations for best practices in self-report assessment of physical activity, let alone sedentary behaviors. Even as the number of instruments available has increased during the last 25 years, there persists a gap in understanding how to optimally assess physical activity by self-report. A knowledge gap often implies a gap in communication. A search of the literature will yield a great number of publications where a self-report instrument has been correlated against a reference measure to indicate a level of validity. However, experience in developing, refining, and applying self-report measures has not often been captured systematically, and lessons learned in the process of measurement science generally have not been leveraged to advance applied health research. Disparate approaches to physical activity and sedentary behavior measurement cause a bottleneck in assimilating the body of science to formulate recommendations for public health.3 In July 2010, a conference was held to explore the major challenges and opportunities for self-report methods. The objective of the conference was to create a collection of information that would encourage novice investigators to develop basic skills for measuring physical activity and sedentary behavior by self-report, and allow experienced investigators to expand and refine their repertoire of appropriate physical activity and sedentary behavior measurement techniques. Funding for the conference was provided by the U.S. National Cancer Institute, the U.S. Centers for Disease Control and Prevention, the U.S. National Institutes of Health Office of Disease Prevention, the National Collaborative on Childhood Obesity Research, and the American College of Sports Medicine. The U.S. National Cancer Institute funded the publication of this supplement. Dr. Barbara Ainsworth and I served as conference co-chairs, and the conference was organized by a planning committee that included Drs. Catherine Alfano, Elva Arredondo, Steven Hooker, Janet Fulton, Louise Mâsse, James Morrow, Lanay Mudd, Kelley Pettee Gabriel, Ashley Smith, Barbara Sternfeld, and Gregory Welk. The content of the conference was divided into two parts: a pre-workshop webinar and a two-day workshop. The purpose of the pre-workshop webinar was to provide practical guidance about the conceptualization of physical activity constructs, the selection and adaptation of self-report instruments, and the evaluation of instrument validity. The pre-workshop webinar was open to the broader research and practice communities, and was attended by over 600 online participants. Archived presentations are available at www.nccor.org. Briefly, the 6 presentations were
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».