BACKGROUND: Physical inactivity and sedentary behaviors have been linked with impaired health outcomes. Establishing the physical inactivity profiles of a given population is needed to establish program targets and to contribute to international monitoring efforts. We report the prevalence of, and explore sociodemographical and built environment factors associated with physical inactivity in 4 resource-limited settings in Peru: rural Puno, urban Puno, Pampas de San Juan de Miraflores (urban), and Tumbes (semiurban). METHODS: Cross-sectional analysis of the CRONICAS Cohort Study's baseline assessment. Outcomes of interest were physical inactivity of leisure time (<600 MET-min/week) and transport-related physical activity (not reporting walking or cycling trips) domains of the IPAQ, as well as watching TV, as a proxy of sedentarism (≥2 hours per day). Exposures included demographic factors and perceptions about neighborhood's safety. Associations were explored using Poisson regression models with robust standard errors. Prevalence ratios (PR) and 95% confidence intervals (95% CI) are presented. RESULTS: Data from 3593 individuals were included: 48.5% males, mean age 55.1 (SD: 12.7) years. Physical inactivity was present at rates of 93.7% (95% CI 93.0%-94.5%) and 9.3% (95% CI 8.3%-10.2%) within the leisure time and transport domains, respectively. In addition, 41.7% (95% CI 40.1%-43.3%) of participants reported watching TV for more than 2 hours per day. Rates varied according to study settings (P < .001). In multivariable analysis, being from rural settings was associated with 3% higher prevalence of leisure time physical inactivity relative to highly urban Lima. The pattern was different for transport-related physical inactivity: both Puno sites had around 75% to 50% lower prevalence of physical inactivity. Too much traffic was associated with higher levels of transport-related physical inactivity (PR = 1.24; 95% CI 1.01-1.54). CONCLUSION: Our study showed high levels of inactivity and marked contrasting patterns by rural/urban sites. These findings highlight the need to generate synergies to expand nationwide physical activity surveillance systems.
BACKGROUND: Physical inactivity and sedentary behaviors have been linked with impaired health outcomes. Establishing the physical inactivity profiles of a given population is needed to establish program targets and to contribute to international monitoring efforts. We report the prevalence of, and explore sociodemographical and built environment factors associated with physical inactivity in 4 resource-limited settings in Peru: rural Puno, urban Puno, Pampas de San Juan de Miraflores (urban), and Tumbes (semiurban). METHODS: Cross-sectional analysis of the CRONICAS Cohort Study's baseline assessment. Outcomes of interest were physical inactivity of leisure time (<600 MET-min/week) and transport-related physical activity (not reporting walking or cycling trips) domains of the IPAQ, as well as watching TV, as a proxy of sedentarism (≥2 hours per day). Exposures included demographic factors and perceptions about neighborhood's safety. Associations were explored using Poisson regression models with robust standard errors. Prevalence ratios (PR) and 95% confidence intervals (95% CI) are presented. RESULTS: Data from 3593 individuals were included: 48.5% males, mean age 55.1 (SD: 12.7) years. Physical inactivity was present at rates of 93.7% (95% CI 93.0%-94.5%) and 9.3% (95% CI 8.3%-10.2%) within the leisure time and transport domains, respectively. In addition, 41.7% (95% CI 40.1%-43.3%) of participants reported watching TV for more than 2 hours per day. Rates varied according to study settings (P < .001). In multivariable analysis, being from rural settings was associated with 3% higher prevalence of leisure time physical inactivity relative to highly urban Lima. The pattern was different for transport-related physical inactivity: both Puno sites had around 75% to 50% lower prevalence of physical inactivity. Too much traffic was associated with higher levels of transport-related physical inactivity (PR = 1.24; 95% CI 1.01-1.54). CONCLUSION: Our study showed high levels of inactivity and marked contrasting patterns by rural/urban sites. These findings highlight the need to generate synergies to expand nationwide physical activity surveillance systems.
Authors: M S Burroughs Peña; E J Velazquez; J D Rivera; F Alenezi; C Wong; M Grigsby; V G Davila-Roman; R H Gilman; J J Miranda; W Checkley Journal: Indoor Air Date: 2017-01-16 Impact factor: 5.770
Authors: Antonio Bernabe-Ortiz; Rodrigo M Carrillo-Larco; Robert H Gilman; Liam Smeeth; William Checkley; J Jaime Miranda Journal: Ann Epidemiol Date: 2021-12-16 Impact factor: 3.797
Authors: J Smith Torres-Roman; Diego Urrunaga-Pastor; Jose L Avilez; Luis M Helguero-Santin; German Malaga Journal: BMC Public Health Date: 2018-03-14 Impact factor: 3.295
Authors: Rodrigo M Carrillo-Larco; J Jaime Miranda; Robert H Gilman; William Checkley; Liam Smeeth; Antonio Bernabé-Ortiz Journal: J Epidemiol Community Health Date: 2018-02-22 Impact factor: 3.710