Ferrari et al. BMC Public Health (2022) 22:669 https://doi.org/10.1186/s12889-022-13117-9 RESEARCH Relationship between socio-demographic correlates and human development index with physical activity and sedentary time in a cross-sectional multicenter study Gerson Ferrari1,2*, Claudio Farías‑Valenzuela3, Juan Guzmán‑Habinger4,5, Clemens Drenowatz6, Adilson Marques7,8, Irina Kovalskys9, Georgina Gómez10, Attilio Rigotti11, Lilia Yadira Cortés12, Martha Cecilia Yépez García13, Rossina G. Pareja14, Marianella Herrera‑Cuenca15, Priscila Marconcin7, Javiera Lobos Chávez16 and Mauro Fisberg17,18 Abstract Background: 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. Methods: 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. Results: 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. Conclusions: 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: Clini calTr ials. Gov NCT02 226627. Retrospectively registered on August 27, 2014. Keywords: Human development index, Sedentary time, Physical activity, International study © The Author(s) 2022. 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 http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background 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 [1–3]. One of Open Access *Correspondence: gerson.demoraes@usach.cl 1 Universidad de Santiago de Chile (USACH), Escuela de Ciencias de la Actividad Física, el Deporte y la Salud, Chile, Las Sophoras 175, Estación Central, Santiago, Chile Full list of author information is available at the end of the article https://www.clinicaltrials.gov/ https://clinicaltrials.gov/ct2/show/NCT02226627 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s12889-022-13117-9&domain=pdf Page 2 of 8Ferrari et al. BMC Public Health (2022) 22:669 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 coun- tries considering these key aspects is the Human Devel- opment 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 school- ing 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 analyt- ical 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 activ- ity 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 objec- tively determined physical activity is, therefore, war- ranted. Unfortunately, there are few accelerometer data measured in Latin America since they are more expen- sive than subjective self-report methods [7]. Further- more, 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 coun- tries. 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. Methods 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 ClinicalTrials.gov (#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 princi- ples from the Declaration of Helsinki. Informed consent/ assent was obtained from all participants before data collection. Full details of the ELANS (https:// www. elans study. com/), are available elsewhere [5, 17]. To obtain representative samples, a complex and mul- tistage 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 soci- oeconomic 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, represent- ing 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 adoles- cents 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 partici- pants. 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 stand- ard questionnaires during face-to-face interviews. Par- ticipants were asked about their race/ethnicity (mixed/ caucasian, black, white, and other [i.e., Asian, Indig- enous, 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, cal- culated using education, life expectancy, and per cap- ita income [19]. This index was created by the United Nations Development Programme to rank countries on a scale of human development conceptualized in terms https://www.elansstudy.com/ https://www.elansstudy.com/ Page 3 of 8Ferrari et al. BMC Public Health (2022) 22:669 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 moni- tor mean min/week of sedentary time, as well as the complete range of intensities of physical activity (includ- ing light, moderate, vigorous and moderate-to-vigorous physical activity), steps and sedentary time. The Acti- graph GT3X + provides reliable and valid estimates of sedentary time, physical activity, and steps [22–24]. 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/bath- ing or other water activities and when sleeping. Par- ticipants 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 accu- mulated at < 100 activity counts/min, ≥ 101–1951 activ- ity 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 statis- tics 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 (cat- egorical variables). Linear regression models (β unstandardized coeffi- cient 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 per- formed using SPSS V27 software (SPSS Inc., IBM Corp., Armonk, New York, NY, USA). A significance level of 5% was adopted. Results There were no significant differences between the par- ticipants 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 classi- fied 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%. 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 dif- ferences between high and very high HDI countries were observed for age, ethnicity, vigorous physical activity and steps/day. No significant differences between HDI coun- tries were observed for sedentary time, light, moderate, and moderate-to-vigorous physical activity (Table 2). 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 Page 4 of 8Ferrari et al. BMC Public Health (2022) 22:669 Ta bl e 1 D em og ra ph ic c ha ra ct er is tic s an d de vi ce ‑m ea su re d ph ys ic al a ct iv ity a nd s ed en ta ry ti m e, in o ve ra ll an d by c ou tn ry SD S ta nd ar d de vi at io n, IQ R In te rq ua rt ile ra ng e, M VP A M od er at e- to -v ig or ou s ph ys ic al a ct iv ity Va ri ab le s O ve ra ll A rg en tin a Br az il Ch ile Co lo m bi a Co st a Ri ca Ec ua do r Pe ru Ve ne zu el a Sa m pl e si ze (n ) 25 22 27 1 52 2 27 4 31 9 24 7 24 9 30 2 33 8 A ge (y ea rs , m ea n (S D )) 38 .3 (1 3. 4) 40 .7 (1 3. 0) 39 .1 (1 3. 3) 38 .7 (1 3. 2) 39 .5 (1 3. 9) 38 .5 (1 2. 8) 36 .5 (1 3. 6) 37 .1 (1 3. 4) 36 .1 (1 3. 2) Se x (% ) M en 46 .9 42 .1 44 .1 46 .4 49 .8 47 .4 50 .2 47 .0 49 .7 W om en 53 .1 57 .9 55 .9 53 .6 50 .2 52 .6 49 .8 53 .0 50 .3 Et hn ic it y (% ) M ix ed /c au ca si an 51 .2 28 .3 19 .1 70 .0 58 .3 37 .2 91 .6 89 .5 44 .8 B la ck 6. 7 7. 3 20 .3 1. 7 6. 6 2. 8 1. 6 1. 0 5. 1 W hi te 34 .9 61 .6 42 .0 26 .1 29 .1 48 .4 3. 6 7. 5 44 .8 O th er 7. 2 2. 8 18 .6 2. 2 6. 0 11 .6 3. 2 2. 0 5. 3 D ev ic e‑ m ea su re d (m ed ia n [IQ R] ) S ed en ta ry ti m e (m in /d ay ) 56 1. 0 (4 90 .7 –6 35 .8 ) 57 1. 9 (5 01 .6 –6 48 .5 ) 54 9. 0 (4 77 .3 –6 18 .8 ) 54 3. 9 (4 69 –8 ‑6 24 .5 ) 55 4. 7 (4 94 .0 – 62 6. 4) 56 1. 7 (4 84 .6 –6 20 .3 ) 56 4. 1 (4 90 .7 –6 45 .8 ) 59 0. 3 (5 16 .4 –6 69 .6 ) 56 4. 0 (4 97 .0 –6 40 .7 ) L ig ht p hy si ca l a ct iv ‑ ity (m in /d ay ) 30 6. 1 (2 50 .7 –3 73 .4 ) 30 1. 8 (2 39 .9 –3 78 .1 ) 32 2. 3 (2 57 .4 –3 92 .1 ) 31 6. 4 (2 65 .1 –3 89 .0 ) 29 5. 4 (2 43 .5 – 36 2. 6) 28 6. 1 (2 34 .8 –3 52 .4 ) 30 9. 6 (2 52 .1 –3 76 .2 ) 30 7. 7 (2 51 .7 –3 74 .2 ) 30 0. 2 (2 50 .1 –3 54 .9 ) M or ad er at e ph ys ic al ac tiv ity (m in /d ay ) 27 .7 (1 6. 2– 45 .6 ) 26 .7 (1 5. 6– 44 .4 ) 25 .4 (1 5. 4– 43 .1 ) 33 .2 (2 2. 6– 50 .5 ) 30 .2 (1 6. 1– 44 .3 ) 24 .7 (1 2. 8– 41 .8 ) 30 .8 (1 7. 9– 52 .8 ) 29 .3 (1 6. 5– 51 .7 ) 24 .7 (1 4. 3– 42 .3 ) V ig or ou s ph ys ic al ac tiv ity (m in /d ay ) 0. 0 (0 .0 –0 .3 ) 0. 0 (0 .0 –0 .2 ) 0. 0 (0 .0 –0 .3 ) 0. 0 (0 .0 –0 .5 ) 0. 0 (0 .0 –0 .3 ) 0. 0 (0 .0 –0 .4 ) 0. 0 (0 .0 –0 .4 ) 0. 0 (0 .0 –0 .2 ) 0. 0 (0 .0 –0 .2 ) M VP A (m in /d ay ) 28 .3 (1 6. 4– 46 .4 ) 27 .6 (1 5. 7– 44 .8 ) 26 .3 (1 5. 7– 44 .2 ) 33 .5 (2 2. 7– 51 .0 ) 30 .6 (1 6. 1– 45 .0 ) 25 .7 (1 2. 8– 43 .8 ) 31 .3 (1 7. 9– 53 .1 ) 29 .7 (1 6. 5– 51 .8 ) 24 .7 (1 4. 3– 42 .3 ) S te ps (c ou nt s/ da y) 96 97 .8 (6 74 7. 3– 14 ,0 08 .2 ) 75 97 .7 (5 65 5. 8– 96 51 .8 ) 14 ,0 75 .6 (1 0, 14 6. 4– 17 ,2 43 ,6 ) 14 ,7 41 .1 (1 2, 11 5. 5– 17 ,7 26 .0 ) 75 07 .7 (5 74 1. 8– 99 54 .6 ) 70 86 .1 (5 26 9. 3– 90 91 .8 ) 76 59 .0 (5 82 4. 3– 10 2, 26 4) 79 54 .8 (5 97 8. 1– 10 ,5 10 .3 ) 12 ,0 81 .7 (8 55 4. 1– 15 ,6 20 .8 ) N ot m ee tin g M VP A gu id el in es (% ) 35 .8 39 .4 39 .3 21 .5 33 .2 42 .5 30 .9 33 .1 43 .2 Page 5 of 8Ferrari et al. BMC Public Health (2022) 22:669 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. Overall, women (-12.6  min/week), participants of younger age (-0.1  min/week), and those of white/cau- casian ethnicity (-2.8 min/week) presented lower mod- erate 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 vigor- ous 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 activ- ity, 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). Discussion This study aimed to analyse the relationship between socio-demographic correlates and HDI with sedentary time and physical activity intensity. Our analysis, includ- ing 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 Table 2  Characteristics (% or median [IQR]) of the sample by human development index country MVPA Moderate-to-vigorous physical activity a Mann–Whitney test (continuous variables) b chi square (categorical variables) p < 0.05 for comparisons between high and very high human development index Variables High human development index Very high human development index p‑value N 1.730 792 Country (n) 5 3 Sex (%) 0.274b Men 48 45 Women 52 55 Age (years) 37.9 39.3 0.010a Ethnicity (%) 0.002b Mixed/caucasian 54 43 Black 9 1 White 29 49 Other 8 7 Accelerometer data Sedentary time (min/day) 560.9 (493.2–638.9) 561.1 (485.3–629.7) 0.221a Light physical activity (min/day) 308.1 (251.8–374.4) 304.0 (248.2–374.3) 0.754a Moderate physical activity (min/day) 27.3 (15.8–45.8) 28.7 (17.0–45.3) 0.390a Vigorous physical activity (min/day) 0.0 (0.0–0.20) 0.0 (0.0–0.3) 0.040a MVPA (min/day) 27.8 (15.9–46.6) 29.7 (17.1–46.0) 0.296a Steps (counts/day) 9950.2 (6880.3–14,124.8) 9154.4 (6432.8–13,389.7) 0.019a Page 6 of 8Ferrari et al. BMC Public Health (2022) 22:669 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, par- ticularly 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 high- est income countries present a higher prevalence of not meeting physical activity recommendations based on self-reported physical activity [33]. Additionally, a world- wide 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 Car- ibbean 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 com- positional 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, gym- nasiums 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 [34–36]. Table 3 Multivariate (β unstandardized coefficient) models for physical activity intensity β regression coefficient with sedentary time and physical activity intensity (min/week) as dependent variable; adjustment: country, region, and city; CI Confidence interval, Ref: Reference, p < 0.05 Predictors Sedentary time (min/week) Light physical activity (min/week) Moderate physical activity (min/week) Estimates 95%CI p‑value Estimates 95%CI p‑value Estimates 95%CI p‑value Sex (ref. men) ‑18.53 ‑28.16; ‑8,89 < 0.001 6.37 ‑0.71; 13.45 0.078 ‑12.62 ‑14.52; 10.73 < 0.001 Age (years) ‑0.69 ‑1.05; ‑0.33 < 0.001 1.55 1.29; 1.82 < 0.001 ‑0.09 ‑0.17; ‑0.02 0.009 Ethnicity (ref. Mixed) Black 9.03 ‑11.35 – 29.42 0.385 7.29 ‑7.70 – 22.27 0.340 1.16 ‑2.86 – 5.18 0.571 Other ‑3.70 ‑22.28 – 14.88 0.696 ‑3.36 ‑17.03 – 10.30 0.630 ‑1.01 ‑4.67 – 2.65 0.589 White/cauca‑ sian 0.49 ‑10.19 – 11.18 0.928 ‑4.28 ‑12.14 – 3.57 0.285 ‑2.76 ‑4.86 – ‑0.65 0.010 Human devel‑ opment index (country; ref. high) ‑1.21 ‑12.03; 9.62 0.827 ‑4.25 ‑12.21; 3.71 0.295 1.31 ‑0.82; 3.45 0.227 Vigorous physical activity (min/week) Moderate‑to‑vigorous physical activity (min/week) Steps (count/week) Estimates 95%CI p‑value Estimates 95%CI p‑value Estimates 95%CI p‑value Sex (ref. men) ‑0.74 ‑0.94 – ‑0.54 < 0.001 ‑13.36 ‑15.32 – ‑11.41 < 0.001 ‑1266.48 ‑1667.83 – ‑865.14 < 0.001 Age (years) ‑0.02 ‑0.03 – ‑0.01 < 0.001 ‑0.11 ‑0.19 – ‑0.04 0.002 12.89 ‑2.14 – 27.92 0.093 Ethnicity (ref. Mixed) Black 0.09 ‑0.34 – 0.51 0.683 1.25 ‑2.89 – 5.39 0.554 2853.90 2004.68 – 3703.13 < 0.001 Other 0.51 0.13 – 0.90 0.009 ‑0.50 ‑4.27 – 3.28 0.796 1785.45 1011.20 – 2559.70 < 0.001 White/Cauca‑ sian 0.06 ‑0.17 – 0.28 0.613 ‑2.70 ‑4.87 – ‑0.53 0.015 660.60 215.46 – 1105.75 0.004 Human devel‑ opment index (country; ref. high) 0.26 0.03 – 0.49 0.024 1.57 ‑0.62 – 3.77 0.161 ‑847.28 ‑1298.31 – ‑396.26 < 0.001 Page 7 of 8Ferrari et al. BMC Public Health (2022) 22:669 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 pro- grams looking for gender equity, in general and specifi- cally 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 consid- ered. For instance, Argentina, Brazil, Costa Rica and Ven- ezuela presented higher proportion of white/caucasian ethnicity participants. The strengths of this study included the large sam- ple size with participants from eight countries from Latin America. There are relatively few studies that have objectively assessed sedentary time and physi- cal 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 popu- lation health surveys. The best available evidence must be used to support and guide action to decrease seden- tary time and increase physical activity levels. A limi- tation 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 neigh- borhoods. Furthermore, accelerometers do not capture common activities such as cycling, resistance and static exercise, and carrying loads [37]. Conclusion 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 activ- ity in the region. This process, initiated with national or regional physical activity surveillance, ultimately aims to improve physical activity levels and promote healthy life- styles among Latin American adults. Acknowledgements The authors would like to thank the staff and participants from each of the participating sites who made substantial contributions to Estudio Latinoamer‑ icano de Nutricion y Salud (ELANS). Authors’ contributions 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. Funding Fieldwork and data analysis compromised in ELANS protocol was supported by a scientific grant from the Coca Cola Company, and by grant and/or sup‑ port 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 Ven‑ ezuela (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; writ‑ ing of the manuscript; or in the decision to publish the results. 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. 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 institu‑ tions. 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, phar‑ maceutical and food and beverage companies. He has also received fees, pay‑ ments 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. Author details 1 Universidad de Santiago de Chile (USACH), Escuela de Ciencias de la Actividad Física, el Deporte y la Salud, Chile, Las Sophoras 175, Estación Central, Santiago, Chile. 2 Laboratorio de Rendimiento Humano, Grupo de Estudio en Educación, Actividad Física y Salud (GEEAFyS), Universidad Católica del Maule, Talca, Chile. 3 Instituto del Deporte, Universidad de las Américas, 9170022 Santiago, Chile. 4 Facultad de Ciencias, Universidad Mayor, Santiago, Chile. 5 Facultad de Ciencias, Especialidad medicina del deporte y la actividad física, Universidad Mayor, Santiago, Chile. 6 Division of Sport, Physical Activ‑ ity and Health, University of Education Upper Austria, Linz, Austria. 7 CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal. 8 ISAMB, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal. 9 Carrera de Nutrición, Facultad de Ciencias Médicas, Pontificia Universidad Católica Argentina, Buenos Aires, Argentina. 10 Departamento de Bioquímica, Escuela de Medicina, Universidad de Costa Rica, San José, Costa Rica. 11 Centro de Nutrición Molecular y Enfermedades Crónicas, Departamento de Nutrición, Diabetes y Metabolismo, Escuela de Medicina, Pontificia Universidad Católica, Santiago, Chile. 12 Departamento de Nutrición y Bioquímica, Pontificia Universidad Javeriana, Bogotá, Colombia. 13 Colégio de Ciencias de la Salud, Universidad San Francisco de Quito, Quito, Ecuador. 14 Instituto de Investi‑ gación Nutricional, La Molina, Lima, Peru. 15 Centro de Estudios del Desarrollo, Universidad Central de Venezuela (CENDES‑UCV)/Fundación Bengoa, Caracas, Venezuela. 16 Datrics, Santiago, Chile. 17 Centro de Excelencia em Nutrição e Dificuldades Alimentaes (CENDA), Instituto Pensi, Fundação José Luiz Egydio Setubal, Hospital Infantil Sabará, São Paulo, Brazil. 18 Departamento de Pediatria da Universidade Federal de São Paulo, São Paulo, Brazil. 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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub‑ lished maps and institutional affiliations. http://hdr.undp.org/en/content/human-development-index-hdi http://hdr.undp.org/en/content/human-development-index-hdi http://hdr.undp.org/sites/default/files/HDR2016_EN_Overview_Web.pdf http://hdr.undp.org/sites/default/files/HDR2016_EN_Overview_Web.pdf http://hdr.undp.org/en/content/human-development-index-hdi Relationship between socio-demographic correlates and human development index with physical activity and sedentary time in a cross-sectional multicenter study Abstract Background: Methods: Results: Conclusions: Trial registration: Background Methods Study design and sample Socio-demographics correlates Country human development index Physical activity and sedentary time Statistical analysis Results Discussion Conclusion Acknowledgements References