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Exposure to useable green space and physical activity during active travel: A longitudinal GPS and accelerometer study before and after retirement

2024· article· en· W4403503279 on OpenAlexaff
Sanna Pasanen, Jaana I. Halonen, Kristin Suorsa, Tuija Leskinen, Carlos Gonzales‐Inca, Yan Kestens, Benoît Thierry, Jaana Pentti, Jussi Vahtera, Sari Stenholm

Bibliographic record

VenueHealth & Place · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversité de Montréal
FundersVarsinais-Suomen SairaanhoitopiiriJuho Vainion SäätiöOpetus- ja KulttuuriministeriöTurun Yliopistollinen KeskussairaalaAcademy of Finland
KeywordsGlobal Positioning SystemAccelerometerPhysical activitySpace (punctuation)GeodesyAeronauticsGeographyComputer scienceBusinessPsychologyTransport engineeringPhysical medicine and rehabilitationMedicineEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Green spaces may serve as population level interventions encouraging active travel. We examined the associations between exposure to useable green space (CORINE Land Cover categories) and physical activity during active travel (GPS and accelerometer) among late middle-aged participants from the Finnish Retirement and Aging study (n = 102). Greater proportion of useable green space was associated with higher physical activity during active travel on days off (+11 min/day per 1 SD increase in exposure) and on retirement days (+12 min/day), but not on workdays. Thus, it appears that in leisure time, people prefer to engage into active travel in green spaces. • Active travel contributes to physical activity among late middle-aged adults. • Transition to retirement can modify active travel behavior. • GPS and accelerometer data collected before and after retirement were used. • Active travel in green space associated with physical activity on non-working days. • People seem to prefer to engage into active travel in green spaces in leisure time.

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 score0.107
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.038
GPT teacher head0.349
Teacher spread0.311 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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