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Enregistrement W2030942933 · doi:10.1097/00005768-200106001-00009

Chair summary and comments

2001· article· en· W2030942933 sur OpenAlexaffabout
H. Arthur Quinney

Notice bibliographique

RevueMedicine & Science in Sports & Exercise · 2001
Typearticle
Langueen
DomaineMedicine
ThématiquePhysical Activity and Health
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésEnergy expenditureTerminologyPhysical activitySports medicineIntensity (physics)Energy (signal processing)Exercise intensityStandardizationComputer sciencePhysical therapyMedicineStatisticsMathematics

Résumé

récupéré en direct d'OpenAlex

The purpose of Howley’s paper was to clarify the various terms associated with physical activity and exercise and provide guidelines for consistent interpretation of exercise intensity across various types of exercise. This paper was available to all authors contributing to the symposium and provided a common base of definitions and terminology. Of particular importance in this paper is Table 1, Classification of Physical Activity Intensity, in which intensity descriptors are linked to cut-off points for endurance and resistance exercise. The utilization of these descriptors and cut-offs will be helpful as we attempt to gain a level of standardization in defining exercise intensity. The LaMonte and Ainsworth paper was focused on methods used to quantify physical activity and energy expenditure and identified potential measurement-related limitations to evaluating the dose-response of physical activity for health outcomes. An important concept that was emphasized in this paper was the distinction between physical activity and energy expenditure and the need to clearly differentiate between them. Physical activity is a behavior that results in energy expenditure. Energy expenditure reflects the volume of physical activity and combines the factors of intensity, frequency, and duration of exercise. This paper also highlighted the many methodological limitations that are present in attempting to measure physical activity in a field setting. Assessing and using energy expenditure in health outcome studies is common because it is believed to provide a better prediction of health outcomes than physical activity. One limitation of using energy expenditure, however, is the inability to break out intensity, frequency, or duration of physical activity required to specifically describe the dose. Even with this limitation, the use of the doubly labeled water method of energy expenditure assessment appears to be the gold standard by which other field measures of energy expenditure and physical activity should be validated (2). These authors also call for the development of an integrated physiological and motion detection system to more objectively measure movement in free-living conditions. This methodology could potentially resolve the long-standing problem associated with accurate field assessment of physical activity. The third paper in this group by Blair, Cheng, and Holder focused on the question of whether physical activity or physical fitness is more important in defining health outcomes. These authors report that most studies show an inverse dose-response gradient across physical activity categories for most health outcomes, but it is not possible to accurately quantify a general dose-response gradient. They also report that all studies included in their review show an inverse gradient across fitness categories for various health outcomes and that the gradient for fitness is steeper than that for physical activity. When the outcome measure studied is functional limitations (a critical outcome measure for older adults), there is an inverse gradient for both physical activity and fitness with a steeper gradient for fitness. The authors believe that the stronger dose-response gradient for fitness is due to the increased objectivity with which fitness is measured. The misclassification of individuals is significantly higher in studies in which field measures of physical activity are used than for studies using fitness measures. This conclusion supports the contention of LaMonte and Ainsworth that attention must be directed toward the development of more accurate field measures for physical activity. Blair and colleagues also make a clear case for maintaining the public health message on promoting physical activity as opposed to physical fitness. Roy Shephard addressed the issue of absolute and relative intensity of physical activity in a dose-response context. Shephard made the case for an absolute threshold of approximately 6 METs for over-all health benefits in young adults with a session duration of 30 min and a dose-response gradient beyond this threshold. He also concluded that low or moderate relative intensity of aerobic activity (40–60% V̇O2max or 40–50% V̇O2R) is an appropriate minimal recommendation for population health. This recommendation is particularly important when consideration is given to the advantage of promoting moderate intensity physical activity in a sedentary population. The existence of an absolute threshold for physical activity in order to achieve health benefits is attractive for purposes of public health messaging but would seem to contradict the wide variation of chronic adaptation to exercise that has been demonstrated by Bouchard and colleagues (1,4) and Leon et al. (3). Address for correspondence: H. Arthur Quinney, Ph.D., Office of the Vice-President (Academic) and Provost, University Hall, University of Alberta, Edmonton, AB Canada T6G 2 J9; E-mail: [email protected]

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,033
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,407
Score d'incertitude au seuil0,846

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,033
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,001
Communication savante0,0060,004
Science ouverte0,0030,003
Intégrité de la recherche0,0060,007
Charge utile insuffisante (le modèle a refusé de juger)0,4070,275

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,331
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2001
Routes d'admission2
Résumé présentoui

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