» Articles » PMID: 34574644

Socioeconomic Inequalities in Physical Activity and Sedentary Behaviour Among the Chilean Population: A Systematic Review of Observational Studies

Overview
Publisher MDPI
Date 2021 Sep 28
PMID 34574644
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Socioeconomic inequalities in physical (in)activity and sedentary behaviours are key mediators in obesity and health socioeconomic inequalities. Considering the high and uneven obesity rates in Chile, this review aims to systematically assess the socioeconomic inequalities in physical activity (PA) and sedentary behaviour (SB) among the Chilean population from different age groups. Peer-reviewed and grey literature were searched from inception until 31st December 2019 in PubMed, Scopus, PsycINFO, Web of Sciences and LILACS. Publications in English and Spanish, from observational studies that reported the comparison of at least one indicator of PA or SB between at least two groups of different socioeconomic positions (SEP), from the general Chilean population, were included. Data searches, screening, extraction, and quality assessment, using the Newcastle Ottawa Quality Assessment Scale for observational studies, were conducted by two independent researchers. Seventeen articles (from 16 studies) met the inclusion criteria (14 cross-sectional; two cohort). Across these, quality was considered low, medium and high for 19%, 69% and 13%, respectively. Results showed consistent evidence for a lower leisure-time PA and sitting time, and higher physical inactivity among adults from the lower, compared to the highest, SEP groups. Associations between SEP and total PA, moderate-to-vigorous PA, low PA, and transport and work-related PA were inconsistent. These findings provide insights to public health and physical activity researchers and policymakers aiming to reduce socioeconomic inequalities in PA and SB in Chile and other countries.

Citing Articles

Socioeconomic inequality in physical activity among adults in western Iran: a cross-sectional study.

Mohammadi N, Doosti-Irani A, Cheraghi Z Int J Equity Health. 2024; 23(1):273.

PMID: 39731155 PMC: 11673593. DOI: 10.1186/s12939-024-02362-6.


Sociodemographic Factors Related to Perceived Physical Activity on Chilean Adults after COVID-19 Pandemic.

Gallardo-Rodriguez R, Poblete-Valderrama F, Rodas-Kurten V, Vilas-Boas J Sports (Basel). 2024; 12(9).

PMID: 39330715 PMC: 11435602. DOI: 10.3390/sports12090238.


Exploring how people achieve recommended levels of physical activity, despite self-reported economic difficulties: a sense of coherence perspective.

Johansson L, Fransson E, Lingfors H, Golsater M BMC Prim Care. 2024; 25(1):105.

PMID: 38575903 PMC: 10993487. DOI: 10.1186/s12875-024-02354-z.


Impact of physical activity on disability-free and disabled life expectancies in middle-aged and older adults: Data from the healthy aging longitudinal study in Taiwan.

Chuang S, Chang Y, Wu I, Fang Y, Chan H, Wu R Geriatr Gerontol Int. 2024; 24 Suppl 1:229-239.

PMID: 38169087 PMC: 11503563. DOI: 10.1111/ggi.14796.


Traditional lifestyle factors partly mediate the association of socioeconomic position with intrahepatic lipid content: The Maastricht study.

Ren Z, Bosma H, Wesselius A, Eussen S, Kooi M, van der Kallen C JHEP Rep. 2023; 5(11):100855.

PMID: 37771365 PMC: 10522893. DOI: 10.1016/j.jhepr.2023.100855.


References
1.
Lee I, Shiroma E, Lobelo F, Puska P, Blair S, Katzmarzyk P . Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012; 380(9838):219-29. PMC: 3645500. DOI: 10.1016/S0140-6736(12)61031-9. View

2.
ONeill J, Tabish H, Welch V, Petticrew M, Pottie K, Clarke M . Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health. J Clin Epidemiol. 2013; 67(1):56-64. DOI: 10.1016/j.jclinepi.2013.08.005. View

3.
Dillman Carpentier F, Correa T, Reyes M, Smith Taillie L . Evaluating the impact of Chile's marketing regulation of unhealthy foods and beverages: pre-school and adolescent children's changes in exposure to food advertising on television. Public Health Nutr. 2019; 23(4):747-755. PMC: 7060093. DOI: 10.1017/S1368980019003355. View

4.
Ogilvie D, Fayter D, Petticrew M, Sowden A, Thomas S, Whitehead M . The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Med Res Methodol. 2008; 8:8. PMC: 2270283. DOI: 10.1186/1471-2288-8-8. View

5.
Jadue L, Vega J, Escobar M, Delgado I, Garrido C, Lastra P . [Risk factors for non communicable diseases: methods and global results of the CARMEN program basal survey]. Rev Med Chil. 2001; 127(8):1004-13. View