Selected article for: "activity change and logistic regression"

Author: Mohanty, S.; Sahoo, J.; Epari, V.; Shankar, G. G.; Panigrahi, S. K.
Title: Prevalence, Patterns and Predictors of physical activity in Urban Population of Bhubaneswar smart city, India
  • Cord-id: k5vaq2gl
  • Document date: 2020_11_30
  • ID: k5vaq2gl
    Snippet: Background: Insufficient physical activity is considered as one of the leading risk factors for global mortality and morbidity. WHO has recommended a cumulative engagement of minimum 150 minutes per week of moderate physical activity. Maintaining physical activity throughout life is considered an important public health objective. However, there is a global trend towards engaging in sedentary behaviours. There is no previous work on the physical activity pattern in Eastern India, nor any study t
    Document: Background: Insufficient physical activity is considered as one of the leading risk factors for global mortality and morbidity. WHO has recommended a cumulative engagement of minimum 150 minutes per week of moderate physical activity. Maintaining physical activity throughout life is considered an important public health objective. However, there is a global trend towards engaging in sedentary behaviours. There is no previous work on the physical activity pattern in Eastern India, nor any study to find predictors in terms of non-communicable disease causation. Objectives: To find out the prevalence and patterns of physical activity in urban population. To investigate the relationship of non-communicable disease with physical inactivity. Materials and methods: The study was community based cross-sectional survey and systematic random sampling method was used for arriving at a total sample of 1203 subjects from the 30 clusters selected out of 67 wards in the city. Socio-demographic and health profile were collected along with physical activity using IPAQ score, and stage of change for physical activity behaviour using Prochaska and DiClemente's model. IBM SPSS 20.0 was used for analysis. Logistic regression analyses were used to compute adjusted odds ratios for each variable. Causality of chronic disease due to physical inactivity was tested through inverse probability of treatment weighting (IPTW) using marginal structural model (MSM). Statistical significance were tested at p=0.05. Results: A total of 1221 subjects were included in the study, and a total 1125 cases were included for analysis after data cleaning. Mean age of the study participants was 35.25 (SD 10.72) years. 71.9% of the respondents were found physically inactive, 15.9% practised 'yogasana'. General caste, presence of chronic disease, being in a static stage of change and a yogassana practitioner were all factors influencing physical activity positively. Physically inactive individuals were also found to have 1.54 times higher odds of having chronic disease than those who are physically active. Conclusion: Prevalence of physical activity among the smart city was found to be low. Physical activity was found be a causative factor for chronic diseases among the population.

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