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

Determinants of Adolescent Physical Activity and Inactivity Patterns

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

Bibliographic record

VenuePEDIATRICS · 2000
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsCanadian Society for Exercise Physiology
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsRecreationResidenceMedicineSocioeconomic statusPhysical activityPopulationGerontologyEnvironmental healthDemographyPhysical therapy

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.287
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations957
Published2000
Admission routes1
Has abstractyes

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