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Helsingin yliopiston avoin julkaisuarkisto

The urban physical environment and leisure-time physical activity in early midlife : a FinnTwin12 study

Pysyvä linkkihttp://hdl.handle.net/10138/599217

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DOI (Helda)

URN

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Kustantaja

MDPI AG

Julkaisun tyyppi

Conference article

Vertaisarvioinnin tila

Non peer reviewed

Käyttöoikeustieto

cc_by

Tiivistelmä

Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34–40) from the FinnTwin12 cohort, residing in five major Finnish cities in 2020. We curated 145 urban physical exposures based on residential addresses and measured three outcomes: total leisure-time physical activity (total LTPA) and two sub-domains: leisure-time physical activity without commuting activity (LTPA) and commuting activity. K-prototypes clustering identified three urban clusters: “original city center,” “new city center,” and “suburban,” each with distinct environmental patterns. Regression models showed that participants in the “suburban” cluster had lower levels of total LTPA and LTPA compared to those in the “original city center” cluster, while we found null findings for commuting activity. Then, repeated regression models with a p-value threshold of 0.01 were used to initially select candidates. eXtreme Gradient Boosting models identified greenspaces and road characteristics as the top important factors influencing total LTPA, while pocket park and greenness were ranked as the top important factors influencing LTPA. The relationships were non-linear. There were thresholds for the count and size of pocket parks within 800 m walking distance and the modified soil adjusted vegetation index, determining whether they positively or negatively predict LTPA. Our findings suggested that the urban environment in Finnish cities was associated with leisure-time physical activity, which revealed new residential pattern and identified key exposures of road, pocket park, and greenness with non-linear effect, that can guide future policies.

Sivumäärä

11

ISBN

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Lisätietoja julkaisun saavutettavuudesta

Asiasana (yso)

Avainsana

Behavior, Exercise, Machine learning, Urbanization, Statistics and probability, SDG 3 - Good Health and Well-being

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