Patterns and Determinants of Physical Inactivity in Rural and Urban Areas in Peru: a Population-Based Study
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Journal of Physical Activity and Health, 2016, 13, 654 -662 http://dx.doi.org/10.1123/jpah.2015-0424 © 2016 Human Kinetics, Inc. ORIGINAL RESEARCH Patterns and Determinants of Physical Inactivity in Rural and Urban Areas in Peru: A Population-Based Study J. Jaime Miranda, Rodrigo M. Carrillo-Larco, Robert H Gilman, Jose L. Avilez, Liam Smeeth, William Checkley, Antonio Bernabe-Ortiz, and the CRONICAS Cohort Study Group Background: Physical inactivity and sedentary behaviors have been linked with impaired health outcomes. Establishing the physical inactiv- ity 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 seden- tarism (≥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. Keywords: physical activity, sedentary lifestyle, television, prevalence, Peru There is no doubt about the role of physical activity in accruing studies have reported that low levels of physical activity are common health gains.1 The benefits of physical activity have been widely in urban cities in Peru, for example 39% in Lima14 and 58% in documented in young2–4 and adult populations.5–7 Conversely, Arequipa.15 The opposite, higher levels of physical activity, has been sedentary lifestyle and physical inactivity have been established as documented in rural settings.14,16 These differences between rural risk factors for certain types of cancer such as breast or colorectal and urban areas could be due to urbanization, and understanding cancer.8,9 In addition, behavioral changes, such as slight reductions the role the built environment plays in this difference is important to in time spent sitting down, do have positive health effects.10 intervene. However, most of these were small studies conducted in A 10% reduction in the prevalence of insufficient physical specific sites, in isolation, which highlights the lack of nationwide activity by 2025 is 1 of the 9 global voluntary targets in the global data in Peru. action plan for the prevention and control of noncommunicable Peru is a diverse country with varied geographical scenarios diseases for the period 2013–2020.11 These country-level indicators spanning sea level, Andean mountains and Amazonian environ- should be monitored over time, yet Peru does not have a national ments, and with combinations of urban/rural and low/high-altitude survey to comply with such need.12 settings in each of them. The diversity of contexts, which will likely Physical activity is widely recognized as one of the key driv- impact on the profiles associated with physical activity, calls for ers of health changes related to urbanization.13 For example, some a better characterization of physical activity profiles in Peru and similar settings, especially since there is a lack of information regarding physical activity in high altitude settings with clear rural/ urban differences. These efforts could garner sufficient data and ini- Miranda ([email protected]), Carrillo-Larco, Gilman, Avilez, tiate momentum to step toward collection of nationwide prevalence Smeeth, Checkley, and Bernabe-Ortiz are with the CRONICAS Center of estimates to contribute to international monitoring mechanisms.11,12 Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, This study had 2 objectives: to report prevalence estimates Lima, Peru. Miranda is also with the Dept of Medicine, School of Medicine, of physical inactivity levels and TV watching, as a proxy of sed- Universidad Peruana Cayetano Heredia, Lima, Peru. Gilman and Checkley entarism, and to explore sociodemographical and neighborhood, are also with the Program in Global Disease Epidemiology and Control, as a proxy of built environment, factors associated with physical Department of International Health, Bloomberg School of Public Health, inactivity in 4 low-socioeconomic areas—Pampas de San Juan de Johns Hopkins University, Baltimore, MD; and the Asociación Benéfica Miraflores (highly urbanized), urban Puno, rural Puno and Tumbes PRISMA, Lima, Peru. Smeeth and Bernabe-Ortiz are with the Faculty (semiurban)—in Peru with a combination of rural/urban and low/ of Epidemiology and Population Health, London School of Hygiene and high altitude settings. We hypothesized that people in urban settings Tropical Medicine, London, United Kingdom. would be more physically inactive and sedentary than in rural areas. 654 Physical Inactivity in Peru 655 Methods Other Study Variables Study Design and Setting Demographic (sex, age); socioeconomic variables, based on number of years of education (6 years or less, 7–11 years, 12+ years) and The CRONICAS Cohort Study was designed to address the geo- socioeconomic status (measured using a wealth index based on graphical variation in the progression toward some noncommuni- assets and household facilities separately for each study site20 and cable diseases in Peru, and its methodology has been described in then combined into a single variable and presented in tertiles); study detail elsewhere.17 As a result, 4 Peruvian settings that differed by site (Lima, urban capital, sea level; urban Puno and rural Puno; and, level of urbanization and altitude, were included. Urban areas are Tumbes, semiurban, sea level) were recorded. defined as those sections which have at least 100 households grouped Behavioral risk factors included daily smoking (≥1 cigarette/ together. Rural areas have either dispersed households or less than day, self-report), heavy alcohol drinking (2 or more nights of 100 households grouped together. The sites were: Pampas de San alcohol intake in the past month and having ever drunk 6 or more Juan de Miraflores in Lima, a highly-urbanized low altitude setting drinks at a time, self-report), and fruits and vegetables intake (<5 (sea level), which has experienced significant but unplanned popula- portions per day, 5+ portions per day, self-report); body mass index tion growth; Puno, a high altitude area in the Peruvian Andes (3825 (normal weight, BMI = 18.5–24.9; overweight, BMI = 25–29.9; and m above sea level), divided into rural Puno and urban Puno, due obesity, BMI ≥30). Hypertension status (systolic blood pressure to the many small villages which surround the urban sections; and ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or self-report Tumbes, a semiurban low altitude (sea level), coastal setting where of physician diagnosis and current use of antihypertensive drugs)21 rural areas, comprising vast traditional landscapes, have become and type-2 diabetes (self-report of physician diagnosis and currently intermixed with rapidly-growing urban areas. For this analysis, receiving antihyperglycemic medication; or, fasting glucose ≥126 information of the baseline assessment, conducted between 2010 mg/dL)22 were also measured. and 2012, was used. Perceptions about neighborhood safety as a proxy of built environment characteristics (traffic, crosswalks, street illumina- Study Participants tion, walking safety during the day or night) were measured using questions of the Neighborhood Environmental Walkability Scale.23 Potential participants were randomly identified from the settings The aforementioned variables were treated as exposure vari- and stratified by age and sex. Subjects aged≥ 35 years with full-time ables and potential confounders in the regression models: when one residence in the area were invited to participate in the study. A sex- was the exposure of interest, the others were included as potential and age-stratified (35–44, 45–54, 55–64, and 65+ years) single-stage confounders in the regression analysis. random sampling procedure was performed using information of the most updated census in each site. Only 1 participant per household