Association of public physical activity facilities and participation in community programs with leisure-time physical activity: does the association differ according to educational level and income?

Werneck, André O.; Oyeyemi, Adewale L.; Araújo, Raphael H. O.; Barboza, Luciana L.; Szwarcwald, Célia Landman; Silva, Danilo

doi:10.1186/s12889-022-12593-3

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Regarding the places to practice PA, it was observed that those who know public leisure spaces such as parks and squares tend to engage in PA regularly. Previous studies showed similar results, even in different population [32,33]. Interestingly, this association may be intertwined with other factors, such as economic and social factors.…”

Section: Discussionsupporting

confidence: 68%

Correlates of Physical Activity in Brazilian Older Adults: The National Health Survey 2019

Silva

Melo

Pereira

et al. 2023

IJERPH

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Engagement in physical activity (PA) depends on intrapersonal, interpersonal/cultural, organizational, physical environment and political factors. Considering that it is important to understand this phenomenon in different populational contexts, this study aimed to investigate the factors related to engagement in PA according to sociodemographic aspects, eating habits, self-rated health, activities of daily living, noncommunicable diseases, mental health and public policies in Brazilian older people. This study had a cross-sectional design and used data from the Brazilian National Health Survey, 2019. Sample size was composed of 22,726 participants, aged 60 years or older, of both sexes, and all the data were collected by interview/questionnaire. According to the adjusted logistic regression, males were more active than females (OR = 1.59 (95% CI 1.40–1.80)), and those living in northern and northeastern Brazil were more likely to be inactive when compared to the southeastern region. Moreover, those with a higher educational level and income (OR = 1.36 (1.06–1.73) and OR = 1.60 (1.22–2.11)); with healthy eating habits (OR = 1.05 (1.03–1.06)); with positive self-rated health (OR = 2.67 (95% CI 1.51–4.71)); with better functional autonomy (OR = 1.22 (1.17–1.27)); and who reported that there was some public place (square, park, closed street, beach) to go for a walk, exercise or practice sport close to their home were more likely to be active (OR = 1.49 (1.34–1.67)). Sociodemographic factors, healthy eating habits, positive self-rated health, higher functioning in activities of daily living and living close to places where PA is practiced were associated with regular engagement in PA (i.e., ≥150 min/week).

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Section: Discussionsupporting

confidence: 68%

Correlates of Physical Activity in Brazilian Older Adults: The National Health Survey 2019

Silva

Melo

Pereira

et al. 2023

IJERPH

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“…Rovniak et al [90] identi ed an 'Active Leisure' cluster, showing recreational facility availability boosts leisure-time PA. This is supported by Werneck et al [91], who found that the presence of public PA facilities near a household was associated with higher leisure-time PA among all quintiles of income and educational level. Burton et al [57] linked active lifestyles with social support, fewer activity barriers, and health issues among higher-income participants.…”

Section: Jayasinghe Et Almentioning

confidence: 55%

Social Environment Related to Opportunities for Physical Activity within Different types of Built Environments: A Scoping Review

Høyer-Kruse,

Schmidt,

Hansen

et al. 2024

Preprint

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Background The association between social and built environments plays a crucial role in influencing physical activity levels. However, a thorough understanding of their combined impact remains unclear. This scoping review seeks to clarify the interplay between social environments and opportunities for physical activity within different built environments, with a particular focus on the implications of socioeconomic status and urban planning on physical activity participation. Methods We conducted a systematic literature search across several databases to identify studies exploring the associations between social factors, built environment characteristics, and physical activity levels. The inclusion criteria were studies published in English between 2000 and 2022, encompassing urban, suburban, and rural contexts. Thematic analysis was employed to categorise studies based on the specific aspects of the built environment they investigated (walkability, cycling infrastructure, parks and open spaces, and sports facilities) and the social determinants they examined. Results A total of 72 studies were included in the review, illustrating a multifaceted relationship between access to physical activity opportunities and social determinants such as socioeconomic status, community engagement, and urban design. The findings highlighted that individuals from higher socioeconomic backgrounds typically enjoyed better access to a broad range of physical activity infrastructures than those from lower socioeconomic backgrounds, who encountered greater access barriers. Effective urban planning was identified as crucial in providing expanded physical activity opportunities, notably through more pedestrian-friendly environments, comprehensive cycling infrastructure, and accessible green spaces and sports facilities. Conclusions This review emphasises the significant impact of socioeconomic status and urban planning on access to physical activity opportunities. This underscores the necessity for urban planning policies to adopt an inclusive approach, considering the varied needs of different population groups to ensure equitable access to physical activity resources. Such strategies are crucial for public health initiatives aimed at enhancing physical activity levels across diverse community sectors, offering a potential avenue to alleviate health disparities associated with inactivity.

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“…Previous studies have also discovered that there are significant age differences in leisure activity participation [2,43], and the participation rate of leisure activities decreases with age [44]. Furthermore, educational level is also found to be linked to leisure activities [45,46]. Thus, the educational level should also be considered in relation to leisure time.…”

Section: Micro-influencing Factors Of Leisure Timementioning

confidence: 95%

Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP

Wang

Jiang

2023

Mathematics

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Leisure time is crucial for personal development and leisure consumption. Accurate prediction of leisure time and analysis of its influencing factors creates a benefit by increasing personal leisure time. We predict leisure time and analyze its key influencing factors according to survey data of Beijing residents’ time allocation in 2011, 2016, and 2021, with an effective sample size of 3356. A Light Gradient Boosting Machine (LightGBM) model is utilized to classify and predict leisure time, and the SHapley Additive exPlanation (SHAP) approach is utilized to conduct feature importance analysis and influence mechanism analysis of influencing factors from four perspectives: time allocation, demographics, occupation, and family characteristics. The results verify that LightGBM effectively predicts personal leisure time, with the test set’s accuracy, recall, and F1 values being 0.85 and the AUC value reaching 0.91. The results of SHAP highlight that work/study time within the system is the main constraint on leisure time. Demographic factors, such as gender and age, are also of great significance for leisure time. Occupational and family heterogeneity exist in leisure time as well. The results contribute to the government improving the public holiday system, companies designing personalized leisure products for users with different leisure characteristics, and residents understanding and independently increasing their leisure time.

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Association of public physical activity facilities and participation in community programs with leisure-time physical activity: does the association differ according to educational level and income?

Cited by 8 publications

References 27 publications

Correlates of Physical Activity in Brazilian Older Adults: The National Health Survey 2019

Correlates of Physical Activity in Brazilian Older Adults: The National Health Survey 2019

Social Environment Related to Opportunities for Physical Activity within Different types of Built Environments: A Scoping Review

Leisure Time Prediction and Influencing Factors Analysis Based on LightGBM and SHAP

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