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Record W4386788753 · doi:10.1016/j.envint.2023.108184

Does the built environment influence location- and trip-based sedentary behaviors? Evidence from a GPS-based activity space approach of neighborhood effects on older adults

2023· article· en· W4386788753 on OpenAlexafffund
Camille Perchoux, Ruben Brondeel, Sylvain Klein, Olivier Klein, Benoît Thierry, Yan Kestens, Basile Chaix, Philippe Gerber

Bibliographic record

VenueEnvironment International · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsResidenceGeographyGlobal Positioning SystemBuilt environmentSedentary behaviorDemographicsDemographyPhysical activityEnvironmental healthMedicinePhysical therapyEngineeringTelecommunications

Abstract

fetched live from OpenAlex

OBJECTIVE: Evidence on the influence of built environments on sedentary behaviors remains unclear and is often contradictory. The main limitations encompass the use of self-reported proxies of sedentary time (ST), the scarce consideration of the plurality of sedentary behaviors, and environmental exposures limited to the residential neighborhood. We investigated the relationships between GPS-based activity space measures of environmental exposures and accelerometer-based ST measured in total, at the place of residence, at all locations, and during trips. METHODS: This study is part of the CURHA project, based on 471 older adults residing in Luxembourg, who wore a GPS receiver and a tri-axial accelerometer during 7 days. Daily ST was computed in total, at the residence, at all locations and during trips. Environmental exposures included exposure to green spaces, walking, biking, and motorized transportation infrastructures. Associations between environments and ST were examined using linear and negative binomial mixed models, adjusted for demographics, self-rated health, residential self-selection, weather conditions and wear time. RESULTS: Participants accumulated, on average, 8 h and 14 min of ST per day excluding sleep time. ST spent at locations accounted for 83 % of the total ST. ST spent at the residence accounted for 87 % of the location-based ST and 71 % of the total ST. Trip-based ST represents 13 % of total ST, and 4 % remained unclassified. Higher street connectivity was negatively associated with total ST, while the density of parking areas correlated positively with total and location-based ST. Stronger associations were observed for sedentary bouts (uninterrupted ST over 20 and 30 min). CONCLUSION: Improving street connectivity and controlling the construction of new parking, while avoiding the spatial segregation of populations with limited access to public transport, may contribute to limit ST. Such urban planning interventions may be especially efficient in limiting the harmful uninterrupted bouts of ST among older adults.

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)0.007
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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".

Quick stats

Citations15
Published2023
Admission routes2
Has abstractyes

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