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Record W2159624953 · doi:10.1186/1479-5868-10-34

Patterns of neighborhood environment attributes related to physical activity across 11 countries: a latent class analysis

2013· article· en· W2159624953 on OpenAlexaffabout
Marc A. Adams, Ding Ding, James F. Sallis, Heather R. Bowles, Barbara E. Ainsworth, Patrick Bergman, Fiona Bull, Harriette Carr, Cora L. Craig, Ilse De Bourdeaudhuij, Luis Fernando Gómez, María Hagströmer, Lena Klasson-Heggebø, Shigeru Inoue, Johan Lefevre, Duncan J. Macfarlane, Sandra Mahecha Matsudo, Victor Keihan Rodrigues Matsudo, Grant McLean, Norio Murase, Michael Sjöstróm, Heidi Tomten, Vida Volbekienė, Adrian Bauman

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsCanadian Fitness and Lifestyle Research Institute
FundersNational Heart, Lung, and Blood InstituteAmerican Heart Association
KeywordsRecreationLatent class modelPhysical activityBuilt environmentLevel designGeographyEnvironmental healthGuidelinePublic transportDemographyActive livingMedicinePsychologyGerontologyTransport engineeringStatisticsComputer scienceSociologyPhysical therapyMathematicsEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Neighborhood environment studies of physical activity (PA) have been mainly single-country focused. The International Prevalence Study (IPS) presented a rare opportunity to examine neighborhood features across countries. The purpose of this analysis was to: 1) detect international neighborhood typologies based on participants' response patterns to an environment survey and 2) to estimate associations between neighborhood environment patterns and PA. METHODS: A Latent Class Analysis (LCA) was conducted on pooled IPS adults (N=11,541) aged 18 to 64 years old (mean=37.5±12.8 yrs; 55.6% women) from 11 countries including Belgium, Brazil, Canada, Colombia, Hong Kong, Japan, Lithuania, New Zealand, Norway, Sweden, and the U.S. This subset used the Physical Activity Neighborhood Environment Survey (PANES) that briefly assessed 7 attributes within 10-15 minutes walk of participants' residences, including residential density, access to shops/services, recreational facilities, public transit facilities, presence of sidewalks and bike paths, and personal safety. LCA derived meaningful subgroups from participants' response patterns to PANES items, and participants were assigned to neighborhood types. The validated short-form International Physical Activity Questionnaire (IPAQ) measured likelihood of meeting the 150 minutes/week PA guideline. To validate derived classes, meeting the guideline either by walking or total PA was regressed on neighborhood types using a weighted generalized linear regression model, adjusting for gender, age and country. RESULTS: A 5-subgroup solution fitted the dataset and was interpretable. Neighborhood types were labeled, "Overall Activity Supportive (52% of sample)", "High Walkable and Unsafe with Few Recreation Facilities (16%)", "Safe with Active Transport Facilities (12%)", "Transit and Shops Dense with Few Amenities (15%)", and "Safe but Activity Unsupportive (5%)". Country representation differed by type (e.g., U.S. disproportionally represented "Safe but Activity Unsupportive"). Compared to the Safe but Activity Unsupportive, two types showed greater odds of meeting PA guideline for walking outcome (High Walkable and Unsafe with Few Recreation Facilities, OR=2.26 (95% CI 1.18-4.31); Overall Activity Supportive, OR=1.90 (95% CI 1.13-3.21). Significant but smaller odds ratios were also found for total PA. CONCLUSIONS: Meaningful neighborhood patterns generalized across countries and explained practical differences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.349
Teacher spread0.321 · 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 teacher head, 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".

Quick stats

Citations91
Published2013
Admission routes2
Has abstractyes

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