Skip to main content

Relationship between socio-demographic correlates and human development index with physical activity and sedentary time in a cross-sectional multicenter study



Socio-demographic correlates and human development index (HDI) are associated with self-reported physical activity, but only a few studies have focused on device-measured physical activity and sedentary time in Latin America. We examined the relationship between socio-demographic correlates and HDI with physical activity and sedentary time in a cross-sectional study.


We based our analyses on 2522 (53.1% women; 18–65 years [mean age 38.3 years]) adults drawn from the eight Latin America countries. Physical activity (light, moderate, vigorous, and moderate-to-vigorous intensity and steps) and sedentary time were assessed using Actigraph GT3X + accelerometers. Sex, age, and race/ethnicity were self-reported. The HDI country information was obtained from the United Nations Development Program.


For the age, ethnicity, vigorous physical activity and steps/day, there were significant differences between high and very high HDI countries. Women and younger age presented lower sedentary time than men and older. In moderate-to-vigorous physical activity, we found lower duration in women (-13.4 min/week), younger age (-0.1 min/week), and white/caucasian (-2.7 min/week) than men, older ages and mixed ethnicity. Women (-1266.5 steps/week) and very high HDI (-847.3 steps/week) presented lower steps than men and high HDI. Black (2853.9 steps/week), other (1785.4 steps/week), and white/caucasian ethnicity (660.6 steps/week) showed higher steps than mixed ethnicity.


Different socio-demographic correlates are associated with physical activity intensity; however, HDI is associated with vigorous physical activity and steps in the Latin American region, which can in turn guide policies to promote physical activity in the region.

Trial registration

ClinicalTrials.GovNCT02226627. Retrospectively registered on August 27, 2014.

Peer Review reports


High levels of physical activity and limited sedentary behavior have been associated with numerous health benefits, affecting not only high-income nations but also low- and middle-income countries [1,2,3]. One of the problems that affect both middle- and high-income countries is physical inactivity [4].

Physical activity is a complex behavior regulated by both individual and contextual factors [5]. Within the contextual factors, it has been described that the levels of physical activity are influenced by socio-demographic variables [6, 7]. An indicator that allows comparing countries considering these key aspects is the Human Development Index (HDI). The United Nations Development Programme describes the HDI as "a measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living" [8]. The HDI is composed of education, estimated as the expected years of schooling and average years of schooling; income or standard of living, estimated as gross national income per capita; and estimated health based on life expectancy at birth in years [9]. An advantage of the HDI is that it allows analytical comparisons between countries [10]. Countries with a higher HDI have been described as having higher levels of physical inactivity [11]. In Europe, Cameron et al. [12] found that higher socio-demographic correlates were associated with leisure-time physical activity but not with objectively measured physical activity intensity. These findings indicate that socio-demographic factors and HDI can be associated with physical activity intensity.

When the relationship between HDI and physical activity has been studied, physical activity was self-reported [13, 14], which is at risk of reporting bias [15]. Additional information on the association between HDI and objectively determined physical activity is, therefore, warranted. Unfortunately, there are few accelerometer data measured in Latin America since they are more expensive than subjective self-report methods [7]. Furthermore, there are large differences between self-reported and device-measured physical activity and sedentary time values [15]. Accordingly, correlation coefficients between minutes of physical activity and sedentary time from accelerometry and subjective self-report methods are low [15, 16].

At the same time, less evidence is available regarding socio-demographic and HDI in Latin American countries. Therefore, in a cross-sectional multicenter study, the present study aimed to determine the relationship between socio-demographic correlates and HDI with physical activity and sedentary time.


Study design and sample

The data for the current study was captured from the Latin American Study of Nutrition and Health (Estudio Latinoamericano de Nutrición y Salud, ELANS), which was conducted from 2014 to 2015 using a common design and comparable methods across countries. ELANS is a cross-sectional, epidemiological, multi-national survey that uses a large representative sample (15 to 65 years old) from eight countries (i.e., Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela) and focuses on urban populations. The ELANS protocol is registered in (#NCT02226627) and was approved by the Western Institutional Review Board (#20,140,605). Ethical approval to conduct this study was obtained from the ethical boards at each study site. This research was performed according to the ethical principles from the Declaration of Helsinki. Informed consent/assent was obtained from all participants before data collection. Full details of the ELANS (, are available elsewhere [5, 17].

To obtain representative samples, a complex and multistage clustered sampling design method was used, representing all regions for each country and randomly selecting the main cities. In each country, stratified recruitment of sample was done across sex, age, and socioeconomic status. In total, 92 cities participated in the ELANS (seven to 23 cities in each country). Details about participant sampling and recruitment strategies have been published elsewhere [5, 17].

A total of 9218 (4409 men) participants were included in the ELANS study. The sample with accelerometer data included 2732 participants aged 15–65 years, representing 29.6% of the total ELANS participants (N = 9218). Adolescents between 15 to 17 years were excluded from the current analyses as ELANS did not include adolescents who were younger than 15 years and because this study focused on the adult population. Therefore, the current manuscript only examines adults between 18 and 65 years of age, resulting in a final sample of 2522 participants. Details have been published elsewhere [5, 17].

Socio-demographics correlates

Sex (men and women), age (18 to 65 years), and race/ethnicity were collected for all participants using standard questionnaires during face-to-face interviews. Participants were asked about their race/ethnicity (mixed/caucasian, black, white, and other [i.e., Asian, Indigenous, Gypsy, and other]). Mixed/caucasian was defined as being born of a father and mother of different races/ethnicities. Further details can be found in a previous study [18].

Country human development index

The HDI is a composite index, ranging from 0 to 1, calculated using education, life expectancy, and per capita income [19]. This index was created by the United Nations Development Programme to rank countries on a scale of human development conceptualized in terms of the capabilities of humans within the countries to function [20].

The HDI information was obtained from the United Nations Development Programme, and the classification of the country was used categorically according to the original classification (low, medium, high or very high) [19, 21]. The participant’s countries were classified as high(0.70 to 0.79) or very high (≥ 0.80) HDI.

Physical activity and sedentary time

The Actigraph GT3X + accelerometers (Fort Walton Beach, FL, United States) were used to objectively monitor mean min/week of sedentary time, as well as the complete range of intensities of physical activity (including light, moderate, vigorous and moderate-to-vigorous physical activity), steps and sedentary time. The Actigraph GT3X + provides reliable and valid estimates of sedentary time, physical activity, and steps [22,23,24].

Data was collected via two home visits. In the first home visit, the designated participants received the accelerometer with instructions that contained a brief description of the device, details of how to wear it, and contact information. In addition, participants were given a diary to record wear-time per day for the following seven consecutive days. The accelerometer and diary were retrieved at the second home visit.

Participants were asked to wear the accelerometer while awake and remove them only for showering/bathing or other water activities and when sleeping. Participants were asked to wear the accelerometer on an elasticized belt at hip level on the right mid-axillary line. Participants were included in the analysis if they had at least five valid days of data with at least 10 h/day of wear time, including at least one weekend day. After excluding the nocturnal sleep period time, periods with more than 60 min of consecutive zero accelerometer counts were categorized as non-wear time [25]. The mean valid days of wear time and mean the number of hours of daily wear time within the analyzed sample with five or more valid days was 6.6 (95% confidence interval [CI]: 6.2; 7.0) and 15.3 h/day (95%CI: 15.1; 15.5), respectively [7].

The research team went to the participants’ homes to retrieve the devices following the 7-day measurement period. The team downloaded the data using the latest version of the ActiLife software (version 6.0; ActiGraph, Pensacola, FL). Data were collected at a sampling rate of 30 Hz and processed using 60-s epochs without the use of a filter [26]. Sedentary time was defined as time accumulated at < 100 activity counts/min, ≥ 101–1951 activity counts/min for light physical activity, ≥ 1952–5724 activity counts/min for moderate physical activity, ≥ 5725 activity counts/min for vigorous physical activity, and ≥ 1952 activity counts/min for moderate-to-vigorous physical activity [27, 28]. Additionally, we estimated the mean of steps count. Participants were categorized as meeting (≥ 150 min/week) or not meeting (< 150 min/week) MVPA guidelines as defined by the World Health Organization [29].

Statistical analysis

The Kolmogorov–Smirnov test and histograms were used to check data normality distribution. Descriptive statistics included absolute and relative frequencies, medians and interquartile range (IQR: 25th and 75th). High and very high HDI countries were compared using Mann–Whitney (continuous variables) and chi-square test (categorical variables).

Linear regression models (β unstandardized coefficient and 95% confidence intervals: 95%CI) were used to examine the relationship between socio-demographic correlates characteristics (sex, age and ethnicity) and HDI. Analysis were mutually adjusted for each other with sedentary time, physical activity intensity (min/week; sedentary time, light, moderate, vigorous, and moderate-to-vigorous) and steps/week. We also adjusted all models for countries, regions, and cities. All analyses were performed using SPSS V27 software (SPSS Inc., IBM Corp., Armonk, New York, NY, USA). A significance level of 5% was adopted.


There were no significant differences between the participants who were asked to wear an accelerometer and those who did not. The descriptive characteristics of the participants (n = 2522; 53.1% women; 18–65 years [mean age 38.3 years]) are shown in Table 1, stratified by country. Overall, 51.2% of participants were classified as mixed/caucasian ethnicity. The median sedentary time, light, moderate, vigorous, moderate-to-vigorous physical activity (min/day), and steps (counts/day) were 561.0, 306.1, 27.7, 0.0, 28.3, and 9697.8, respectively. The prevalence of not meeting moderate-to-vigorous physical activity guidelines was 35.8%.

Table 1 Demographic characteristics and device-measured physical activity and sedentary time, in overall and by coutnry

Brazil, Colombia, Ecuador, Peru, and Venezuela were classified as high, and Argentina, Chile, and Costa Rica were classified as very high HDI. The HDI scores ranged from 0.759 for Ecuador to 0.985 for Chile. Significant differences between high and very high HDI countries were observed for age, ethnicity, vigorous physical activity and steps/day. No significant differences between HDI countries were observed for sedentary time, light, moderate, and moderate-to-vigorous physical activity (Table 2).

Table 2  Characteristics (% or median [IQR]) of the sample by human development index country

Table 3 shows the results of the multivariate linear regression models for the effects of socio-demographic correlates and HDI on sedentary time and physical activity intensity, independent of country, region, and city. Women (-18.5 min/week) and participants of younger age (-0.7 min/week) presented lower sedentary time than men and those of older age. Participants of younger age (1.5 min/week), on the other hand, showed higher light physical activity than those of older age.

Table 3 Multivariate (β unstandardized coefficient) models for physical activity intensity

Overall, women (-12.6 min/week), participants of younger age (-0.1 min/week), and those of white/caucasian ethnicity (-2.8 min/week) presented lower moderate physical activity than men, older age and mixed ethnicity. Women (-0.7 min/week) and participants of younger age (-0.1 min/week) also showed lower vigorous physical activity than men and older age. On the other hand, another ethnicity (0.5 min/week) and very high HDI (0.3 min/week) was associated with higher vigorous physical activity than mixed ethnicity and high HDI. For moderate-to-vigorous physical activity, we found lower levels in women (-13.4 min/week), participants of younger age (-0.1 min/week), and white/caucasian (-2.7 min/week) compared to men, those of older ages and with mixed ethnicity. Overall, women (-1266.5 steps/week) and participants from countries with very high HDI (-847.3 steps/week) presented lower steps than men and those from high HDI. On the other hand, black (2853.9 steps/week), other (1785.4 steps/week) and white/caucasian ethnicity (660.6 steps/week) were associated with higher steps compared to mixed ethnicity, independently of country, region, and city (Table 3).


This study aimed to analyse the relationship between socio-demographic correlates and HDI with sedentary time and physical activity intensity. Our analysis, including data from 2522 adults (18–65 years) from Latin America, showed lower moderate and vigorous physical activity in women, participants of younger age and those of white/caucasian ethnicity. Further, participants from very high HDI countries showed higher vigorous physical activity and lower steps/week, respectively.

The sex disparity regarding physical activity level has been explored in different studies. Women are more inactive among adolescents [13] and adults [30]. Among adults, the result are controversial [31]. In a worldwide epidemiological study, including just Brazil from Latin America, the difference of physical inactivity between genders was more evident among HDI countries, with women being more inactive than men. In contrast, in the high HDI countries, the prevalence of physical inactivity was greater among men [11]. Gender inequality seems to be the key to understand the difference between sex regarding physical activity. In addition, gender inequality, concerns about stereotypes due to of insecurities around body image are important barriers [32]. Interventions to improve women's physical activity are needed, particularly in countries with lower HDI. In countries with higher HDI, women might have more opportunities to be active because of their purchasing power, and in many cases, there are lower crime rates, which allows women to engage in leisure-time physical activity outside the home.

The relationship between HDI and physical inactivity was previously explored. It showed a higher prevalence of physical inactivity in low HDI countries, although the study relied on self-reported physical activity [11]. On the other hand, based on World Bank Income, the highest income countries present a higher prevalence of not meeting physical activity recommendations based on self-reported physical activity [33]. Additionally, a worldwide epidemiological study with 168 countries showed that the prevalence of physical inactivity was more than twice as high in high-income countries than in low-income countries [30] and the highest levels of physical inactivity were observed in Latin American and Caribbean women. Among adolescents, the prevalence of engaging in physical activity 5 to 6 days/week was higher in countries with the highest HDI [13]. The discrepancy between studies can be explained, in part, based on compositional differences in the study sample and physical activity measurement method. These different results highlight an area for future studies to understand better the factors affecting the relationship between physical activity and HDI. Our study exposed that HDI presents an association with vigorous physical activity and steps/week. But we did not find an association between HDI and sedentary time, moderate and moderate-to-vigorous physical activity. Vigorous physical activity is prevalent among sports activities, practices in health clubs, gymnasiums and other private places. It can be assumed that the opportunity to be engaged with vigorous physical activity is better among very high HDI countries. Also, very high HDI countries have better built environment that encourages walking. Many studies have reported a positive association between the built environment and physical activity [34,35,36].

Along with previous studies, the results of the present study have several practical implications for public health policies. There is a need for a stronger investment in programs looking for gender equity, in general and specifically in the physical activity field. It could also help guide better access to physical activity in countries considering social inequity. Potential interventions aiming to increase physical activity should also consider variations in socio-demographic correlates and HDI and focus on groups with lower moderate-to-vigorous physical activity levels including those of white/caucasian ethnicity. In addition, local differences between countries need to be considered. For instance, Argentina, Brazil, Costa Rica and Venezuela presented higher proportion of white/caucasian ethnicity participants.

The strengths of this study included the large sample size with participants from eight countries from Latin America. There are relatively few studies that have objectively assessed sedentary time and physical activity intensities in Latin America since most international epidemiological studies have employed self-report methods [5, 18]. Objective assessments for sedentary time and physical activity are rare for population health surveys. The best available evidence must be used to support and guide action to decrease sedentary time and increase physical activity levels. A limitation of our study included the cross-sectional design, which prevents conclusions regarding causality from being established. Our evaluation of accelerometer-measured daily activity may also not represent the total population in the eight participating countries since participants were recruited from specific urban neighborhoods. Furthermore, accelerometers do not capture common activities such as cycling, resistance and static exercise, and carrying loads [37].


Different socio-demographic correlates are associated with physical activity intensity; There is also evidence for a country-specific influence of HDI on vigorous physical activity and steps per week in the Latin American region, which can guide policies to promote physical activity in the region. This process, initiated with national or regional physical activity surveillance, ultimately aims to improve physical activity levels and promote healthy lifestyles among Latin American adults.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due the terms of consent/assent to which the participants agreed but are available from the corresponding author on reasonable request. Please contact the corresponding author to discuss availability of data and materials.


  1. Wang Y, Nie J, Ferrari G, Rey-Lopez JP, Rezende LFM. Association of physical activity intensity with mortality: a national cohort study of 403681 US adults. JAMA Intern Med. 2021;181:203–11.

    Article  Google Scholar 

  2. Rezende LFM, Lee DH, Ferrari G, Giovannucci E. Confounding due to pre-existing diseases in epidemiologic studies on sedentary behavior and all-cause mortality: a meta-epidemiologic study. Ann Epidemiol. 2020;52:7–14.

    Article  Google Scholar 

  3. Nascimento W, Ferrari G, Martins CB, et al. Muscle-strengthening activities and cancer incidence and mortality: a systematic review and meta-analysis of observational studies. Int J Behav Nutr Phys Act. 2021;18:69.

    Article  Google Scholar 

  4. Hallal PC, Andersen LB, Bull FC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380:247–57.

    Article  Google Scholar 

  5. Ferrari G, Werneck AO, da Silva DR, et al. Is the perceived neighborhood built environment associated with domain-specific physical activity in Latin American adults? An eight-country observational study. Int J Behav Nutr Phys Act. 2020;17:125.

    Article  Google Scholar 

  6. Luis de Moraes Ferrari G, Kovalskys I, Fisberg M, et al. Original research Socio-demographic patterning of self-reported physical activity and sitting time in Latin American countries: findings from ELANS. BMC Public Health. 2019;19:1723.

    Article  Google Scholar 

  7. Ferrari GLM, Kovalskys I, Fisberg M, et al. Socio-demographic patterning of objectively measured physical activity and sedentary behaviours in eight Latin American countries: findings from the ELANS study. Eur J Sport Sci. 2020;20:670–81.

    Article  Google Scholar 

  8. UNDP. Human Development Index (HDI). In: Human Development Reports. 2019. Available:

  9. United Nations Development Programme. Overview Human Development Report 2016 Human Development for Everyone.

  10. Freire RCJ, Pieruccini-Faria F, Montero-Odasso M. Are human development Index dimensions associated with gait performance in older adults? A systematic review. Exp Gerontol. 2018;102:59–68.

    Article  Google Scholar 

  11. Dumith SC, Hallal PC, Reis RS, Kohl HW 3rd. Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. Prev Med. 2011;53:24–8.

    Article  Google Scholar 

  12. Cameron AJ, Van Stralen MM, Kunst AE, et al. Macroenvironmental factors including GDP per capita and physical activity in Europe. Med Sci Sports Exerc. 2013;45:278–85.

    Article  Google Scholar 

  13. Marques A, Henriques-Neto D, Peralta M, et al. Prevalence of physical activity among adolescents from 105 low, middle, and high-income countries. Int J Environ Res Public Health. 2020;17:3145.

    Article  Google Scholar 

  14. Werneck AO, Sadarangani KP, Ramirez-Velez R, et al. Macroeconomic, demographic and human developmental correlates of physical activity and sitting time among South American adults. Int J Behav Nutr Phys Act. 2020;17:163.

    Article  Google Scholar 

  15. Ferrari GLM, Kovalskys I, Fisberg M, et al. Comparison of self-report versus accelerometer - measured physical activity and sedentary behaviors and their association with body composition in Latin American countries. PLoS One. 2020;15:e0232420.

    Article  CAS  Google Scholar 

  16. Ferrari G, Werneck AO, Silva DR, et al. agreement between self-reported and device-based sedentary time among eight countries: findings from the ELANS. Prev Sci. 2021;22:1036–47.

    Article  Google Scholar 

  17. Fisberg M, Kovalskys I, Gomez G, et al. Latin American Study of Nutrition and Health (ELANS): rationale and study design. BMC Public Health. 2016;16:93.

    Article  CAS  Google Scholar 

  18. Ferrari G, Guzman-Habinger J, Chavez JL, et al. Sociodemographic inequities and active transportation in adults from Latin America: an eight-country observational study. Int J Equity Health. 2021;20:190.

    Article  Google Scholar 

  19. United Nations Development Programme. Índices e indicadores de desarrollo humano. Actualización estadística de. 2018. p. 113.

  20. Land KC. The human development index: objective approaches (2). In: Glatzer W, Camfield L, Møller V, Rojas M, editors. Global handbook of quality of life. Dordrecht, The Netherlands: Springer; 2015. p. 133–57.

    Chapter  Google Scholar 

  21. United Nations Development Programme. Human Development Reports. Accessed 11 Nov 2021. 

  22. Celis-Morales CA, Perez-Bravo F, Ibanez L, Salas C, Bailey ME, Gill JM. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One. 2012;7:e36345.

    Article  CAS  Google Scholar 

  23. Dowd KP, Szeklicki R, Minetto MA, et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2018;15:15.

    Article  Google Scholar 

  24. Chomistek AK, Yuan C, Matthews CE, et al. Physical activity assessment with the ActiGraph GT3X and doubly labeled water. Med Sci Sports Exerc. 2017;49:1935–44.

    Article  Google Scholar 

  25. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.

    Article  Google Scholar 

  26. Migueles JH, Cadenas-Sanchez C, Ekelund U, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47:1821–45.

    Article  Google Scholar 

  27. Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167:875–81.

    Article  Google Scholar 

  28. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications Inc accelerometer. Med Sci Sports Exerc. 1998;30:777–81.

    Article  CAS  Google Scholar 

  29. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62.

    Article  Google Scholar 

  30. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6:e1077–86.

    Article  Google Scholar 

  31. Zhou Y, Wu J, Zhang S, et al. Prevalence and risk factors of physical inactivity among middle-aged and older Chinese in Shenzhen: a cross-sectional study. BMJ Open. 2018;8:e019775.

    Article  Google Scholar 

  32. TheLancetPublic H. Time to tackle the physical activity gender gap. Lancet Public Health. 2019;4:e360.

    Article  Google Scholar 

  33. Rhodes RE, Janssen I, Bredin SSD, Warburton DER, Bauman A. Physical activity: health impact, prevalence, correlates and interventions. Psychol Health. 2017;32:942–75.

    Article  Google Scholar 

  34. Wasfi RA, Dasgupta K, Eluru N, Ross NA. Exposure to walkable neighbourhoods in urban areas increases utilitarian walking: longitudinal study of Canadians. J Transp Health. 2016;3:440–7.

    Article  Google Scholar 

  35. Frank LD, Iroz-Elardo N, MacLeod KE, Hong A. Pathways from built environment to health: a conceptual framework linking behavior and exposure-based impacts. J Transp Health. 2019;12:319–35.

    Article  Google Scholar 

  36. Feuillet T, Salze P, Charreire H, et al. Built environment in local relation with walking: why here and not there? J Transp Health. 2016;3:500–12.

    Article  Google Scholar 

  37. Ferrari G, Marques A, Barreira TV, et al. accelerometer-measured daily step counts and adiposity indicators among latin american adults: a multi-country study. Int J Environ Res Public Health. 2021;18:4641.

    Article  Google Scholar 

Download references


The authors would like to thank the staff and participants from each of the participating sites who made substantial contributions to Estudio Latinoamericano de Nutricion y Salud (ELANS).


Fieldwork and data analysis compromised in ELANS protocol was supported by a scientific grant from the Coca Cola Company, and by grant and/or support from Instituto Pensi/Hospital Infantil Sabara, International Life Science Institute of Argentina, Universidad de Costa Rica, Pontificia Universidad Católica de Chile, Pontificia Universidad Javeriana, Universidad Central de Venezuela (CENDES-UCV)/Fundación Bengoa, Universidad San Francisco de Quito, and Instituto de Investigación Nutricional de Peru. The funding sponsors had no role in study design; the collection, analyses, or interpretation of data; writing of the manuscript; or in the decision to publish the results.

Author information

Authors and Affiliations



G.F., conceived, designed, and helped to write and revise the manuscript; I.K., G.G., A.R., L.Y.C., M.Y.G., R.G.P., M.H-C., M.F., were responsible for coordinating the study, contributed to the intellectual content, and revise the manuscript, C.F-V., J.G-H, C.D., A.M., P.M., J.L.C., interpreted the data, helped to write and revise the manuscript. All authors contributed to the study design, critically reviewed the manuscript, and approved the final version.

Corresponding author

Correspondence to Gerson Ferrari.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was provided by the Western Institutional Review Board (#20140605), and by the ethical review boards of the participating institutions. ELANS is registered at Clinical Trials #NCT02226627. This research was performed according to the ethical principles from the Declaration of Helsinki. Written informed consent/assent was obtained from all individuals, before commencement of the study.

Consent for publication

Not applicable.

Competing interests

M.F. has received fees and consultancy payments from biotechnology, pharmaceutical and food and beverage companies. He has also received fees, payments for consulting and financing research studies without any restrictions, from government sources and non-profit entities. The rest of the authors also have no conflicts of interest to declare. None of the entities mentioned had or have any role in the design or preparation of this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ferrari, G., Farías-Valenzuela, C., Guzmán-Habinger, J. et al. Relationship between socio-demographic correlates and human development index with physical activity and sedentary time in a cross-sectional multicenter study. BMC Public Health 22, 669 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: