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Enregistrement W2139638593 · doi:10.1542/peds.105.6.e83

Determinants of Adolescent Physical Activity and Inactivity Patterns

2000· article· en· W2139638593 sur OpenAlexaff
Penny Gordon‐Larsen, Robert G. McMurray, Barry M. Popkin

Notice bibliographique

RevuePEDIATRICS · 2000
Typearticle
Langueen
DomaineMedicine
ThématiqueObesity, Physical Activity, Diet
Établissements canadiensCanadian Society for Exercise Physiology
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
Mots-clésRecreationResidenceMedicineSocioeconomic statusPhysical activityPopulationGerontologyEnvironmental healthDemographyPhysical therapy

Résumé

récupéré en direct d'OpenAlex

Objectives. Despite recognition of the important influence of environmental determinants on physical activity patterns, minimal empirical research has been done to assess the impact of environmental/contextual determinants of physical activity. This article aims to investigate environmental and sociodemographic determinants of physical activity and inactivity patterns among subpopulations of US adolescents. We define environmental determinants as modifiable factors in the physical environment that impose a direct influence on the opportunity to engage in physical activity. The present research examines environmental and sociodemographic determinants of physical activity and inactivity with the implication that these findings can point toward societal-level intervention strategies for increasing physical activity and decreasing inactivity among adolescents. Study Design and Methodology. The study population consists of nationally representative data from the 1996 National Longitudinal Study of Adolescent Health on 17 766 US adolescents enrolled in US middle and high schools (including 3933 non-Hispanic blacks, 3148 Hispanics, and 1337 Asians). Hours/week of inactivity (TV/video viewing and video/computer games) and times/week of moderate to vigorous physical activity were collected by questionnaire. Outcome variables were moderate to vigorous physical activity and inactivity, which were broken into categories (physical activity: 0–2 times/week, 3–4 times/week, and ≥5 times/week; inactivity: 0–10 hours/week, 11–24 hours/week, and ≥25 hours/week). Sociodemographic and environmental correlates of physical activity and inactivity were used as exposure and control variables and included sex, age, urban residence, participation in school physical education program, use of community recreation center, total reported incidents of serious crime in neighborhood, socioeconomic status, ethnicity, generation of residence in the United States, presence of mother/father in household, pregnancy status, work status, in-school status, region, and month of interview. Logistic regression models of high versus low and medium physical activity and inactivity were used to investigate sex and ethnic interactions in relation to environmental and sociodemographic factors to examine evidence for the potential impact of physical education and recreation programs and sociodemographic factors on physical activity and inactivity patterns. Results. Moderate to vigorous physical activity was lower and inactivity higher for non-Hispanic black and Hispanic adolescents. Participation in school physical education programs was considerably low for these adolescents and decreased with age. Participation in daily school physical education (PE) program classes (adjusted odds ratio [AOR]: 2.21; confidence interval [CI]: 1.82–2.68) and use of a community recreation center (AOR: 1.75; CI: 1.56–1.96) were associated with an increased likelihood of engaging in high level moderate to vigorous physical activity. Maternal education was inversely associated with high inactivity patterns; for example, having a mother with a graduate or professional degree was associated with an AOR of .61 (CI: .48-.76) for high inactivity. High family income was associated with increased moderate to vigorous physical activity (AOR: 1.43; CI: 1.22–1.67) and decreased inactivity (AOR: .70; CI: .59–.82). High neighborhood serious crime level was associated with a decreased likelihood of falling in the highest category of moderate to vigorous physical activity (AOR: .77; CI: .66–.91). Conclusions. These results show important associations between modifiable environmental factors, such as participation in school PE and community recreation programs, with activity patterns of adolescents. Despite the marked and significant impact of participation in school PE programs on physical activity patterns of US adolescents, few adolescents participated in such school PE programs; only 21.3% of all adolescents participated in 1 or more days per week of PE in their schools. In addition to the more readily modifiable factors, high crime level was significantly associated with a decrease in weekly moderate to vigorous physical activity. The key modifiable factors that had an impact on physical activity did not affect inactivity. Thus, it is clear that physical activity and inactivity were associated with very different determinants. Although physical activity was most associated with environmental factors, inactivity was most associated with sociodemographic factors. The data presented here confirm what researchers and pediatricians have known intuitively; however, these relationships have not been tested empirically, nor have they been studied in any nationally representative survey of US school-aged children. These findings show that patterns in inactivity cannot be explained using the environmental factors studied here and, thus, it is clearly important that researchers search for other environmental determinants likely to impact inactivity. National-level strategies must include attention to school PE and community recreation programs, particularly for segments of the US population without access to resources and opportunities that allow participation in physical activity. Research to measure and explore the effects of other environmental determinants of activity and to ascertain whether there are any environmental determinants of inactivity are important future research directions. physical education, community recreation center, US adolescents, crime, socioeconomic factors, National Longitudinal Study of Adolescent Health.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,287
Écart entre enseignants0,271 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations957
Publié2000
Routes d'admission1
Résumé présentoui

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