Patterns and Determinants of Physical Inactivity in Rural and Urban Areas in Peru: A Population-Based Study

Abstract

Background: We report the prevalence of, and socio-demographical and built environment factors associated with physical inactivity in four resource-limited settings in Peru.Methods: Cross-sectional analysis of the CRONICAS cohort study. We used the IPAQ to define physical inactivity of leisure time and transport-related domains. Sedentarism (watching TV ≥2 hours per day), and perceptions about neighborhood's safety were also studied. Poisson regression models were conducted to determine prevalence ratios (PR) and 95% confidence intervals (95% CI) between exposure variables with outcomes of interest.Results: 3593 individuals were included: 48.5% males, mean age 55.1 (SD: 12.7) years. The 83.2% and 9.3% reported physical inactivity in the leisure time and transport-related domains, respectively. The 41.7 was sedentary. Rates varied according to sites (p<0.001). Being from rural sites was associated with 6%-13% higher prevalence of leisure time physical inactivity relative to Lima (urban). The pattern was different for transport-related physical inactivity: high altitude sites had 75%-50% lower prevalence. Too much traffic was associated with higher levels of transportrelated physical inactivity (PR=1.24; 95%CI 1.01-1.54).Conclusion: Our study showed high levels of physical inactivity and marked contrasting patterns by rural/urban low/high-altitude sites. It is necessary to generate synergies to expand nationwide physical activity surveillance systems. Physical activity is widely recognized as one of the key drivers of health changes related to urbanization. 11 For example, some studies have reported that low levels of physical activity are common in urban cities in Peru, for example 39% in Lima 12 and 58% in Arequipa. 13 The opposite, higher levels of physical activity, has been documented in rural settings. 12, 14 However, most of these studies were conducted in specific sites, in isolation.Peru is a diverse country with varied geographical scenarios spanning sea level, Andean mountains and Amazonian environments, and with combinations of urban/rural and low/high-altitude settings in each of them. The diversity of contexts, which will likely impact on the profiles associated with physical activity, calls for a better characterization of physical activity profiles in Peru and similar settings. These efforts could garner sufficient data and initiate momentum to step towards collection of nationwide prevalence estimates to contribute to international monitoring mechanisms. 9, 10This study had two objectives: to report prevalence estimates of physical inactivity levels, and to explore socio-demographical and neighborhood, as a proxy of built environment, factors associated 3Human Kinetics,

Geographic differences in overweight and obesity prevalence in Peruvian children, 2010–2015

“…In Peru, the rapid urbanisation has created an “obesogenic” environment, mainly in urban areas which report greater caloric intakes [ 10 ] and higher physical inactivity rates [ 11 , 12 ]. Loret De Mola et al, using the Peruvian National Demographic and Family Health Surveys (DHS) between 1996 and 2011 found that prevalence of overweight and obesity children in urban areas had decreased; however, this trend seems to continue until 2005, since the prevalence of overweight stabilised at around 12% [ 13 ].…”

Trajectories of body mass index and waist circumference in four Peruvian settings at different level of urbanisation: the CRONICAS Cohort Study

“… 28 29 Despite these arguments, a study with the same population showed that relative to Lima, urban and rural Puno have less prevalence of sedentarism and transport-related physical inactivity. 30 Consequently, physical activity may not yet be the main driver of the increased adiposity indexes.…”

Dietary practices, habits and physical activity levels of the Swahili community, Kenya in relation to obesity and chronic diseases of lifestyle

“…An increase in the use of passive modes of transport has been associated with declining physical activity levels (WHO, 2010). People do less labor-intensive work and watch television even in the poorer urban settings, compared to those living in rural settings (Miranda et al, 2016). Mombasa County is an urban area where majority of the people use public transport and are involved in jobs that are less labour-intensive hence sedentary behavior during occupational activities (MOT, 2015).…”