Men and women present different patterns of morbidity and mortality.1 Apart from biological differences, socially constructed gender norms, and historical gender inequalities can have harmful effects on health behaviors, including physical activity (PA).2 Gender norms shape behaviors from early childhood,3 influencing not only children’s behaviors but the encouragement received for active play and PA,4,5 contributing to the lower levels of PA observed in girls compared with boys.6 These gender differences in PA increase with age,7 since girls often experience less socioecological beneficial factors at the individual, family, school, and environmental levels, exacerbating gender inequalities in adolescence.7,8 In adulthood, for most women, the double burden of having a job and taking on most of the housework, motherhood, and lack of family support also contribute to lower levels of PA.9
Gender or sex inequalities in PA levels have been reported in different age groups and countries with different economic contexts.10–14 Globally, a gender difference in the prevalence of meeting PA recommendations of 7.1 percentage points was reported in adolescence,10 and around 8.3 percentage points in adulthood.15 This inequality ranges from the first year of life16 to older age.17 However, despite evidence that sex is a consistent correlate of PA at different ages,18 most studies investigating gender inequalities in PA levels focused on adolescents1,12,13 or adults.11,19 The literature still lacks studies exploring these inequalities in children and older adults.
Moreover, most studies assess PA through self-reported measures,11–14,19 which are susceptible to social-desirability and information bias. This study aims to evaluate gender inequalities in PA across different age groups using accelerometer data from the Pelotas (Brazil) cohorts, including birth cohorts (BCs) and the “Como vai?” cohort study addressing older adults.
Materials and Methods
Participants
We are using data from 5 cohort studies conducted in Pelotas (Brazil), a city a population of 343.826 inhabitants in 2021 and a GDP per capita of R$ 27,586.96 (U$ 5290.15).20 The first BC was established in 1982 and additional cohorts were set up in 1993, 2004, and 2015, following all live births in the urban area of Pelotas for each respective year. These studies aim to monitor and examine associations between lifestyle, environmental and social exposures, and health outcomes across the life course, and to examine differences across generations. Furthermore, in 2014, The “Como vai?” study was initially designed as a cross-sectional survey of older adult population living in the urban area of Pelotas, which afterward became a cohort study. Detailed information on each cohort can be found elsewhere.21–27
Our study included only follow-ups with information on accelerometry, which resulted in samples with ages: 1, 2, 4 (2015 BC); 6, 11, 15 (2004 BC); 18, 22 (1993 BC); 30 (1982 BC); and 60 years or more (“Como vai?”).
Ethics
The 1982, 1993, and 2004 Pelotas BCs study protocols were approved by the Medical School Ethics Committee of the Federal University of Pelotas (respective registration numbers: 16/12; 1.250.366; 40601116) and the 2015 BC, by the School of Physical Education Ethics Committee of the Federal University of Pelotas (registration 26746414.5.0000.5313). The “Como vai?” study protocol was approved by the Research Ethics Committee of the Faculty of Sciences of the Federal University of Pelotas (registration 24538513.1.0000.5317). The participation of the individuals in each study was voluntary, and informed consent was obtained from all participants.
Accelerometry
The protocol for the Pelotas cohorts comprises 24 hours of accelerometer data collection for all ages, including sleep and water activities. For 1- and 2-year-olds, the accelerometer was placed on the left wrist, and data were collected for 2 days.28 Four-year-old children wore the device on their left wrist for 7 days, while older participants wore it on the nondominant wrist for 7 days. In the 6-, 18-, and 30-year follow-ups of 2004, 1993, and 1982 BCs, participants wore the accelerometer on the nondominant wrist from 4 to 7 days.29
In the earlier accelerometry data collections (6, 18, 30, and 60+ y, conducted between 2010 and 2014) GENEActive (Activinsights; sampling rate: 85.7 Hz; ±8 g dynamic range) was used, while in more recent follow-ups (1, 2, 4, 11, 15, and 22 years, conducted from 2015 onwards), the device used was the ActiGraph GT3X/GT3X+ (ActiGraph; sampling rate: 60 Hz; ±8 g dynamic range). High comparability between these brands has previously been reported when raw data is analyzed.30,31 For this reason, we analyzed the raw signal, which are less affected by company-specific filters.
Data were processed with the GGIR package (version 2.2) in R software.32 This process included verification of sensor calibration error, detection of sustained abnormally high values, and nonwear detection.33,34 Vector magnitude was calculated using Euclidian Norm Minus One metric to summarize acceleration from axes x, y, and z into a single-dimensional signal (
Valid accelerometer data was based on the minimum number of measurement days included, calculated using the Spearman–Brown formula, which uses the intraclass correlation coefficient combined with the Spearman–Brown prophecy.35 Balancing the reliability values and the number of losses, we considered a minimum of 1 day for 1-year-old infants, 2 days for 4-, 6-, 18-, and 30-year-old participants, and 3 days for other ages. This method was described in detail in previous publications.28,36
The 2 main outcome variables used in our study were overall PA and moderate to vigorous PA (MVPA). Overall PA was defined as the average Euclidean Norm Minus One per day, expressed in milli g (mg). MVPA was defined as the average of minutes spent above the threshold of 100 mg per day,37 using 5-minute bouts. We presented both outcome measures to better represent PA behavior in our sample, including the total volume of PA (overall PA) and structured PA (MVPA). The MVPA variable was calculated for individuals aged 6 years or older.
Gender Inequalities
The present study used the term inequality to express differences between individuals without the judgment of how just or avoidable those differences are.38 Gender was based on sex assigned at birth (male or female) for each follow-up of the cohort studies included.
Statistical Analysis
To illustrate gender inequalities in PA, we presented the means of overall PA and MVPA in men and women for each follow-up in an equiplot (equidade.org/equiplot). The means, 95% CIs, and T test results are shown in the Supplementary Materials (available online).
Absolute inequalities (gender differences) were calculated by subtracting women’s mean PA (overall PA and MVPA) from men’s mean PA, with positive values indicate a higher mean among men. Relative inequalities were calculated as the ratio between women’s and men’s mean PA, where values above 1 indicate higher PA mean among men. For both absolute and relative inequalities, 95% CIs were calculated using the bootstrap method, which provides the standard errors based on drawing different samples from the same data (resampling).39
We also analyzed our data stratified for socioeconomic status, aiming to illustrate the PA intersectionality between gender and wealth. The concept of intersectionality, first introduced by Collins,40 refers to the simultaneous overlapping of multiple forms of inequalities.41 We used a wealth index using items, such as house characteristics and household assets, extracting the first component of a principal component analysis.42 This component was divided into quintiles, with the first (Q1) representing the poorest group and the last (Q5) representing the wealthiest group. We present means of overall PA and MVPA variables in Q1 and Q5 for men and women in an equiplot. The means, 95% CI, and P values obtained through t tests for the intersectionality analysis are available in the Supplementary Materials (available online).
We used a meta-analysis approach with random effect to graphically represent gender inequalities in overall PA and MVPA for each age group. Also, a sensitivity analysis was carried out to verify whether the gender gap is influenced by the amount of PA. To answer this research question, we divided the overall PA (mg) and minutes of MVPA in deciles and presented the gender gap in the 10% least active (first decile) and 10% most active (10th decile). The results of the sensitivity analysis are presented in the Supplementary Materials (available online).
All statistical analyses were performed using Stata 17 (StataCorp. 2021)
Results
The present study includes data from 5 Pelotas cohort studies, comprising, on average, 2590 participants per follow-up with valid accelerometry data (Table 1). Sample size across cohorts ranged from 965 in the “Como vai?” study to 3462 in the 18-year follow-up (1993 BC). Table 1 shows the comparison between the original cohorts and the analytical samples, stratified by gender and wealth quintiles (first and fifth). The 30-year follow-up (1982 BC) had a higher proportion of women (P = .012) in the analytical sample. None of the remaining comparisons were statistically significant, indicating that the analytical sample is representative of the original cohort when considering gender and wealth distribution.
Description of Original Cohorts and Analytical Samples for All 5 Cohort Studies, Stratified by Gender and Wealth Quintiles
| Cohorts’ follow-ups | Gender | Wealth quintilesa and gender | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Poorest (Q1) | Wealthiest (Q5) | |||||||||||
| All | Men | Women | All | Men | Women | P | All | Men | Women | P | ||
| N | n (%) | n (%) | P | N | n (%) | n (%) | N | n (%) | n (%) | |||
| 2015 Cohort | ||||||||||||
| Original cohort (reference) | 4275 | 2164 (50.6) | 2111 (49.4) | — | 826 | 429 (51.9) | 397 (48.1) | — | 825 | 439 (53.2) | 386 (46.8) | — |
| 1 y | 2974 | 1550 (52.1) | 1424 (47.9) | .214 | 590 | 313 (53.1) | 277 (46.9) | .706 | 507 | 284 (56.0) | 223 (44.0) | .336 |
| 2 y | 2645 | 1363 (51.5) | 1282 (48.5) | .473 | 507 | 264 (52.1) | 243 (47.9) | 1.000 | 461 | 253 (54.9) | 208 (45.1) | .600 |
| 4 y | 2955 | 1493 (50.5) | 1462 (49.5) | .943 | 578 | 305 (52.8) | 273 (47.2) | .786 | 523 | 288 (55.1) | 235 (44.9) | .537 |
| 2004 Cohort | ||||||||||||
| Original cohort (reference) | 4228 | 2193 (51.9) | 2035 (48.1) | — | 707 | 359 (50.8) | 348 (49.2) | — | 653 | 349 (53.4) | 304 (46.6) | — |
| 6 y | 2604 | 1341 (51.5) | 1263 (48.5) | .784 | 445 | 227 (51.0) | 218 (49.0) | .952 | 402 | 211 (52.5) | 191 (47.5) | .799 |
| 11 y | 3348 | 1722 (51.4) | 1626 (48.6) | .711 | 553 | 277 (50.1) | 276 (49.9) | .821 | 518 | 274 (52.9) | 244 (47.1) | .860 |
| 15 y | 1484 | 738 (49.7) | 746 (50.3) | .165 | 230 | 115 (50.0) | 115 (50.0) | .879 | 246 | 117 (47.6) | 129 (52.4) | .117 |
| 1993 Cohort | ||||||||||||
| Original cohort (reference) | 5248 | 2603 (49.6) | 2645 (50.4) | — | 863 | 445 (51.6) | 418 (48.4) | — | 856 | 436 (50.9) | 420 (49.1) | — |
| 18 y | 3462 | 1710 (49.4) | 1752 (50.6) | .861 | 650 | 339 (52.2) | 311 (47.8) | .835 | 640 | 310 (48.4) | 330 (51.6) | .347 |
| 23 y | 2783 | 1350 (48.5) | 1433 (51.5) | .360 | 517 | 259 (50.1) | 258 (49.9) | .617 | 530 | 255 (48.1) | 275 (51.9) | .320 |
| 1982 Cohort | ||||||||||||
| Original cohort (reference) | 5913 | 3037 (51.4) | 2876 (48.6) | — | 999 | 528 (52.9) | 471 (47.1) | — | 794 | 378 (47.6) | 416 (52.4) | — |
| 30 y | 2680 | 1298 (48.4) | 1382 (51.6) | .012 | 489 | 235 (48.1) | 254 (51.9) | .087 | 332 | 147 (44.3) | 185 (55.7) | .326 |
| COMO VAI? Study | ||||||||||||
| Original cohort (reference) | 1451 | 537 (37.0) | 914 (63.0) | — | 282 | 94 (33.3) | 188 (66.7) | — | 273 | 109 (39.9) | 164 (60.1) | — |
| ≥60 y | 965 | 364 (37.7) | 601 (62.3) | .731 | 180 | 56 (31.1) | 124 (68.9) | .684 | 181 | 73 (40.3) | 108 (59.7) | 1.000 |
aWealth quintiles at birth for the 2015 and 2004 cohorts, at 2 years for the 1982 cohort, at 11 years for the 1993 cohort, and at baseline for the COMO VAI? Study.
The gender gap for overall PA and MVPA is illustrated in Figure 1. For overall PA, the gap starts among 1-year-old infants and increasingly widens as PA levels rise, up to 11 years, when the gender gap peaked. There is a drastic decrease in overall PA between 11 and 15 years for both genders, although men still presented higher levels of PA, followed by some stability throughout adulthood. Among older adults, the PA decreases to a lower level than in infants and there is no longer a gender gap (Figure 1A).
Gender gap for overall PA expressed in mg (A) and minutes spent in MVPA (B) among participants of 5 Pelotas cohort studies. ENMO indicates Euclidean Norm Minus One; MVPA, moderate to vigorous PA; PA, physical activity.
Citation: Journal of Physical Activity and Health 21, 11; 10.1123/jpah.2024-0018
Regarding MVPA (Figure 1B), from 6 to 15 years, there is a declining pattern for PA with stability in the gender gap. At the age of 18, adolescent men showed an increase in MVPA, which is not followed by women, resulting in the widest gender gap. Between the ages of 22 and 30 years, both MVPA and gender gap show little variation, and a steep decrease is observed for older adults, although the gender gap remains, with older men spending more time in MVPA than older women.
The absolute gender inequalities for overall PA and MVPA are described in Figure 2. For overall PA (Figure 1A), the widest gender gap was found at 11 years (difference 9.8 mg; 95% CI, 8.7–11.0), while for MVPA (Figure 1B), the widest difference was found at 18 years (difference 32.9 min; 95% CI, 30.1–35.7). For both overall PA and MVPA, men had higher PA than women in virtually all ages, although there was no gap between men and women in overall PA among older adults (Figure 2A).
Absolute gender inequalities for overall PA expressed in mg (A) and minutes spent in MVPA (B) among participants of 5 Pelotas cohort studies. ENMO indicates Euclidean Norm Minus One; MVPA, moderate to vigorous PA; PA, physical activity.
Citation: Journal of Physical Activity and Health 21, 11; 10.1123/jpah.2024-0018
Regarding relative gender inequalities (Figure 3A and 3B), 18-year-olds presented the highest gender inequalities for overall PA (ratio 1.24; 95% CI, 1.22–1.26), while older adults presented the lowest inequalities (ratio 1.02; 95% CI, 0.98–1.07) although not statistically significant. On the other hand, for MVPA older adults presented the highest relative inequalities (ratio 2.0; 95% CI, 1.92–2.08), while 6-year-olds had the lowest inequalities (ratio 1.47; 95% CI, 1.40–1.54).
Relative gender inequalities for overall PA expressed in mg (A) and minutes spent in MVPA (B) among participants of 5 Pelotas cohort studies. ENMO indicates Euclidean Norm Minus One; MVPA, moderate to vigorous PA; PA, physical activity.
Citation: Journal of Physical Activity and Health 21, 11; 10.1123/jpah.2024-0018
Figure 4 shows the gender gap for PA across the first (Q1, 20% poorest) and last (Q5, 20% richest) wealth quintiles. The double stratification for overall PA showed the higher acceleration among the poorest groups in all ages, although the wealthiest men have similar overall PA levels to the poorest women from the age of 11 years onwards. The highest gaps are shown at ages 11 and 18 years, with the poorest men showing around 20 mg of difference from the wealthiest women (Figure 4A). For MVPA, during childhood and early adolescence (1 and 11 y), boys showed more MVPA than girls, regardless of wealth. From 15 years to adulthood, the poorest women show results similar to those of the wealthiest men. The highest gap is at age 18 years when the poorest men show around 60 minutes of MVPA more than the wealthiest women.
Gender gap for overall PA expressed in mg (A) and minutes spent in MVPA (B) between the first and last wealth quintiles among participants of 5 Pelotas cohort studies. ENMO indicates Euclidean Norm Minus One; MVPA, moderate to vigorous PA; PA, physical activity.
Citation: Journal of Physical Activity and Health 21, 11; 10.1123/jpah.2024-0018
Gender inequalities in overall PA and MVPA for the highest and lowest PA deciles are shown in Supplementary Figure S1 in (Supplementary Materials [available online]). The highest decile of overall PA (D10) showed increasing gender inequalities from 1 to 11 years, followed by stability during adulthood and a drastic decline among older adults. The widest gap for overall PA in D10 was observed at age 11, with approximately 20 mg of difference between boys and girls. The MVPA gap in D10 also increased with age until the end of adolescence (18 y), when the highest gap was found (∼70 min) and narrowed thereafter.
Discussion
This work describes the gender gap for PA in 5 Pelotas cohort studies, including a broad age range of participants. We attempted to describe the patterns of absolute and relative PA gender inequalities in multiple age groups by exploring overall PA and MVPA, aiming at representing both everyday activities and structured PA, such as sports and exercises. Our findings showed that both overall PA and MVPA were consistently higher among men.
We presented absolute and relative measures of inequalities. While absolute measures have easier interpretation, relative measures can better express proportional differences, allowing comparisons at different scales. They are complementary because their interpretability differs according to the magnitude of outcomes occurrence.43 For example, when there is a lower occurrence there could be marked relative inequalities, but modest absolute inequalities. In our case, older adults presented a low level of MVPA so the absolute inequality measure identified a difference of only 7 minutes between men and women. On the other hand, the relative inequality measure identified a mean of MVPA in men 2 times higher than in women for this group—the highest relative inequality among all age groups. This does not mean that one measure is better than the other, but prefereably, both measures should be shown to improve interpretability and better inform policymakers’ decisions.44
It is important to underline that overall PA measured by accelerometry reflects any activity performed throughout the day, including daily routine activities and intermittent and sporadic movements, and not just structured PA. Also, PA described in mg presents even less interpretability of the data, hampering its translation into daily activities. However, recent evidence has shown that, among inactive adults, the minimum clinically important difference of approximately 1 mg corresponds to about 5 to 6 minutes of brisk walking per day, and an increase in average daily acceleration of this magnitude is associated with reduced risk of all-cause mortality (hazard ratio: 0.95; 95% CI, 0.94–0.96).45 When applying this example to our results, the absolute gender gap of 20 mg found for 11 years olds in the present study, would correspond to approximately 100 to 120 minutes of daily brisk walking.
The PA gender gap starts early in life, with inequalities already found in 1-year-old infants. The intrinsic societal restrictive gender norms are the main drivers of this discrepancy in PA, which indirectly shapes how parents raise their children, including how to dress a young girl for a play-day, which toys are gifted to boys and girls, and above all, the level of incentive to active play they receive.46 As a result, young girls often receive less favorable influences at the individual, family, school, and environmental levels.47 When discussing PA promotion at early ages, it is imperative to highlight the tracking effect of PA, where behaviors acquired during childhood are more likely to be carried out to adolescence and adulthood.48
Another relevant finding is that transition phases, from childhood to adolescence (11 y old) and from adolescence to early adulthood (18 y old), showed the widest gender inequalities for overall PA and MVPA, respectively. Some of the barriers surrounding women’s PA might be more evident in adolescence, especially body dissatisfaction and feeling uncomfortable during the practice, in addition to less social support from peers, family, and teachers than their male counterparts, and less access to safe and welcoming facilities, equipment, transport, and training.43,49 These factors shape female attitudes toward PA throughout adolescence and could affect this behavior throughout life,49 which might be intensified by transition life events, such as entering university and/or the job market, and above all, motherhood which often lead to creating even more barriers for women regarding PA practice.50,51
As mentioned before, in our study, the highest relative gender inequality was found among older adults, with men having twice the MVPA level of women at age 60 or more. A previous study also using the “Como vai?” study data has shown that older women spent more time in light-intensity PA than older men, with oldest participants (80 y +), those currently not working (retired or unemployed), and those reporting poor self-perceived health having lower levels of both light PA and MVPA.52 A decline in PA levels is expected with advanced age, due to loss in mobility, general health status, and self-efficacy that might happen in this age group.53,54 Although doing light-intensity PA is better than the absence of PA practice, it is important to acknowledge the positive effects of strength, mobility, and higher intensity exercises for this age group which could help prevent negative health outcomes while improving health and quality of life.55
Our results on the PA’s intersectionality between gender and wealth showed higher acceleration among the poorest groups, with the 20% richest women being the least active group, and the 20% poorest men being the most active group. To better interpret this result, it is imperative to understand that accelerometry-based overall PA includes all PA domains: leisure time, occupational, commuting, and domestic. This is a limitation of accelerometry, since this measure lacks contextual information on the domains and types of PA, and then we observe controversial wealth patterns for this behavior.56 In this sense, the literature consistently reports a higher level of domestic, occupational, and commuting PA among socioeconomically vulnerable populations when considering self-reported measures.57 On the other hand, the richest groups usually have a privileged position in PA practice, being able to pay for private and expensive activities, and taking place during their leisure time.58 Further questionnaire-based research is needed to better understand the nuances of different types and domains of PA according to income and sex. Also, future studies should aim to explore intersectionalities among further dimensions of inequalities, such as ethnicity, area of residence, occupation, religion, disability, and other context-specific relevant dimensions.
The sensitivity analysis revealed interesting results when stratifying PA levels by gender and the amount of PA. For both outcomes (overall PA and MVPA), we identified a clear pattern of a higher gender gap when looking at the most active group. These results highlight that women, in general, present a much lower level of PA than men, even among the most active group. In other words, the highest decile of PA is a very selected part of the sample (with an average of close to 100 min of MVPA). However, we identified a huge difference between most active men and women already at younger ages. Even the most active women are not close to most active men. These findings suggest that the gender gap is also evident in women with greater opportunities, support, and facilitators for PA practice.
Limitations
Because we described data from 5 different cohort studies, with data collections spanning from 2010 to 2019, this analysis is not completely longitudinal and the sample represents different generations, which might impact the PA estimates shown. Consequently, conclusions should be based on descriptive assumptions of a wide age range instead of a lifecycle analysis. Also, although raw data minimizes the differences between the 2 device brands used, some residual distinctions could remain. Similarly, the differences in wear time and placement of the device could have an impact in the PA measures. Finally, at 15 years, the follow-up was interrupted by the COVID-19 pandemic, which could have impacted PA results. However, since men and women wore the same device, same placement, and same settings at each follow-up, gender inequalities were unlikely to be affected by those factors.
Conclusions
Our findings demonstrated that PA gender inequalities start at an early age and intensify in transition periods of life but, also, relative gender inequalities are marked among older adults. Actions to promote PA in women must consider a life cycle strategy, starting at an early age, intensifying actions at transition periods, yet still being mindful of older adults’ needs. Future studies should aim to understand the determinants of gender inequalities throughout the lifecourse, so that new policies and interventions can act on tacking on the causes of gender inequalities.
Acknowledgments
The authors would like to thank all members from the Grupo de Estudos e Pesquisa em Acelerometria (GEPEA) for the valuable scientific discussions during the conception of this manuscript. Authors Contributions: Drafting of the manuscript: Ricardo, Wendt, Tornquist. Data analysis and interpretation: Ricardo. Scientific advisors: Gonçalves, Wehrmeister, da Silva, Tovo-Rodrigues, Santos, Barros, Matijasevich, Hallal, Domingues, Ekelund, Bielemann, Crohechemore-Silva. Review and approval of the final version of this manuscript: All authors. Funding Source: Ricardo is supported by the Medical Research Council MC_UU_00006/5. This article is based on data from 4 “Pelotas Birth Cohort” studies conducted by Postgraduate Program in Epidemiology at Universidade Federal de Pelotas with the collaboration of the Brazilian Public Health Association (ABRASCO). The cohorts were funded by Wellcome Trust, The International Development Research Center, World Health Organization, Overseas Development Administration, European Union, National Support Program for Centers of Excellence (PRONEX), Children’s Pastorate, Fundação de Amparo a Pesquisa do Estado do Rio Grande do Sul, the Brazilian National Research Council (CNPq), and the Brazilian Ministry of Health.
References
- 1.↑
de Looze M, Elgar FJ, Currie C, Kolip P, Stevens GWJM. Gender inequality and sex differences in physical fighting, physical activity, and injury among adolescents across 36 countries. J Adolesc Health. 2019;64(5):657–663. doi:
- 2.↑
Hawkes S, Buse K. Gender and global health: evidence, policy, and inconvenient truths. The Lancet. 2013;381(9879):1783–1787. doi:
- 3.↑
Halim MLD, Ruble DN, Tamis-LeMonda CS, Shrout PE, Amodio DM. Gender attitudes in early childhood: behavioral consequences and cognitive antecedents. Child Dev. 2017;88(3):882–899. doi:
- 4.↑
Nilsen AKO, Anderssen SA, Ylvisaaker E, Johannessen K, Aadland E. Physical activity among Norwegian preschoolers varies by sex, age, and season. Scand J Med Sci Sports. 2019;29(6):862–873. doi:
- 5.↑
Mattocks C, Hines M, Ness A, et al. Associations between sex-typed behaviour at age 3½ and levels and patterns of physical activity at age 12: the Avon longitudinal study of parents and children. Arch Dis Child. 2010;95(7):509–512. doi:
- 6.↑
Arts J, Drotos E, Singh AS, Chinapaw MJM, Altenburg TM, Gubbels JS. Correlates of physical activity in 0- to 5-year-olds: a systematic umbrella review and consultation of international researchers. Sports Med. 2023;53(1):215–240. doi:
- 7.↑
Schlund A, Reimers AK, Bucksch J, Linder S, Demetriou Y. Sex/gender considerations in school-based interventions to promote children’s and adolescents’ physical activity: a systematic review. German J Exerc Sport Res. 2021;51(3):257–268. doi:
- 8.↑
Telford RM, Telford RD, Olive LS, Cochrane T, Davey R. Why are girls less physically active than boys? Findings from the LOOK longitudinal study. PLoS One. 2016;11(3):e0150041. doi:
- 9.↑
Evenson KR, Aytur SA, Borodulin K. Physical activity beliefs, barriers, and enablers among postpartum women. J Womens Health. 2009;18(12):1925. doi:
- 10.↑
Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020;4(1):23–35. doi:
- 11.↑
Mielke GI, da Silva ICM, Kolbe-Alexander TL, Brown WJ. Shifting the physical inactivity curve worldwide by closing the gender gap. Sports Med. 2017;48(2):481–489. doi:
- 12.↑
Brazo-Sayavera J, Aubert S, Barnes JD, González SA, Tremblay MS. Gender differences in physical activity and sedentary behavior: results from over 200,000 Latin-American children and adolescents. PLoS One. 2021;16(8):e0255353. doi:
- 13.↑
Ricardo LIC, Wendt A, Costa CDS, et al. Gender inequalities in physical activity among adolescents from 64 Global South countries. J Sport Health Sci. 2022;11(4):509–520. doi:
- 14.↑
Werneck AO, Baldew SS, Miranda JJ, Díaz Arnesto O, Stubbs B, Silva DR. Physical activity and sedentary behavior patterns and sociodemographic correlates in 116,982 adults from six South American countries: The South American physical activity and sedentary behavior network (SAPASEN). Int J Behav Nutr Phys Act. 2019;16(1):839. doi:
- 15.↑
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(10):e1077–e1086. doi:
- 16.↑
Ricardo LIC, Da Silva ICM, De Andrade Leão OA, Domingues MR, Wehrmeister FC. Objectively measured physical activity in one-year-old children from a Brazilian cohort: levels, patterns and determinants. Int J Behav Nutr Phys Act. 2019;16(1):895. doi:
- 17.↑
Lin YC, Yeh MC, Chen YM, Huang LH. Physical activity status and gender differences in community-dwelling older adults with chronic diseases. J Nurs Res. 2010;18(2):88–97. doi:
- 18.↑
Aleksovska K, Puggina A, Giraldi L, et al. Biological determinants of physical activity across the life course: a “Determinants of Diet and Physical Activity” (DEDIPAC) umbrella systematic literature review. Sports Medicine—Open. 2019;5(1):173. doi:
- 19.↑
Wendt A, Ricardo LIC, Costa CS, Knuth AG, Tenorio MCM, Crochemore-Silva I. Socioeconomic and gender inequalities in leisure-time physical activity and access to public policies in Brazil From 2013 to 2019. J Phys Act Health. 2021;18(12):1503–1510. doi:
- 20.↑
IBGE. | Cidades@ | Rio Grande do Sul | Pelotas | Panorama. 2022. https://cidades.ibge.gov.br/brasil/rs/pelotas/panorama. Accessed September 20, 2022.
- 21.↑
Santos IS, Barros AJD, Matijasevich A, Domingues MR, Barros FC, Victora CG. Cohort profile: the 2004 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2011;40(6):1461–1468. doi:
- 22.
Horta BL, Gigante DP, Gonçalves H, et al. Cohort profile update: the 1982 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2015;44(2):441–441e. doi:
- 23.
Victora CG, Barros FC. Cohort profile: the 1982 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2006;35(2):237–242. doi:
- 24.
Victora CG, Hallal PC, Araújo CLP, Menezes AMB, Wells JCK, Barros FC. Cohort profile: the 1993 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2008;37(4):704–709. doi:
- 25.
Gonçalves H, Wehrmeister FC, Assunção MCF, et al. Cohort profile update: the 1993 Pelotas (Brazil) Birth Cohort follow-up at 22 years. Int J Epidemiol. 2018;47(5):1389–1390E. doi:
- 26.
Bielemann RM, LaCroix AZ, Bertoldi AD, et al. Objectively measured physical activity reduces the risk of mortality among Brazilian older adults. J Am Geriatr Soc. 2020;68(1):137–146. doi:
- 27.↑
Hallal PC, Bertoldi AD, Domingues MR, et al. Cohort profile: the 2015 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2018;47(4):1048–1048H. doi:
- 28.↑
Ricardo LIC, Da Silva ICM, Martins RC, et al. Protocol for objective measurement of infants’ physical activity using accelerometry. Med Sci Sports Exerc. 2018;50(5):1084–1092. doi:
- 29.↑
da silva ICM, van Hees VT, Ramires VV, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. 2014;43(6):1959–1968. doi:
- 30.↑
Rowlands AV. Moving forward with accelerometer-assessed physical activity: two strategies to ensure meaningful, interpretable, and comparable measures. Pediatr Exerc Sci. 2018;30(4):450–456. doi:
- 31.↑
Rowlands AV, Yates T, Davies M, Khunti K, Edwardson CL. Raw Accelerometer data analysis with GGIR R-package: does accelerometer brand matter? Med Sci Sports Exerc. 2016;48(10):1935–1941. doi:
- 32.↑
Migueles JH, Rowlands AV, Huber F, Sabia S, Hees VTV. GGIR: a research community–driven open source r package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J Meas Phys Behav. 2019;2(3):188–196. doi:
- 33.↑
van Hees VT, Fang Z, Langford J, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol. 2014;117(7):738–744. doi:
- 34.↑
van Hees VT, Gorzelniak L, Dean León EC, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013;8(4):e61691. doi:
- 35.↑
Eisinga R, Grotenhuis MT, Pelzer B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int J Public Health. 2013;58(4):637–642. doi:
- 36.↑
Ricardo LIC, Wendt A, Galliano LM, et al. Number of days required to estimate physical activity constructs objectively measured in different age groups: findings from three Brazilian (Pelotas) population-based birth cohorts. PLoS One. 2020;15(1):6017. doi:
- 37.↑
Hildebrand M, van Hees VT, Hansen BH, Ekelund U. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46(9):1816–1824. doi:
- 38.↑
Pan American Health Association. Methodological summaries: measuring inequity in health. Epidemiol Bull. 1999;20(1):11.
- 40.↑
Hill Collins P. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. Routledge; 2000.
- 41.↑
Bose CE. Patricia Hill Collins Symposium Intersectionality and Global Gender Inequality. 2012. doi:
- 42.↑
Filmer D, Pritchett LH. Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India. Demography. 2001;38(1):115–132. doi:
- 43.↑
Duffey K, Barbosa A, Whiting S, et al. Barriers and facilitators of physical activity participation in adolescent girls: a systematic review of systematic reviews. Front Public Health. 2021;9:1529. doi:
- 44.↑
Hosseinpoor AR, Bergen N, Barros AJD, Wong KLM, Boerma T, Victora CG. Monitoring subnational regional inequalities in health: measurement approaches and challenges. Int J Equity Health. 2016;15(1):307. doi:
- 45.↑
Rowlands A, Davies M, Dempsey P, Edwardson C, Razieh C, Yates T. Wrist-worn accelerometers: recommending ∼1.0 mg as the minimum clinically important difference (MCID) in daily average acceleration for inactive adults. Br J Sports Med. 2021;55(14):814–815. doi:
- 46.↑
Ricardo LIC, da Silva ICM, de Andrade Leão OA, Domingues MR, Wehrmeister FC. Objectively measured physical activity in one-year-old children from a Brazilian cohort: levels, patterns and determinants. Int J Behav Nutr Phys Act. 2019;16(1):895. doi:
- 47.↑
Telford RM, Telford RD, Olive LS, Cochrane T, Davey R. Why are girls less physically active than boys? Findings from the LOOK longitudinal study. PLoS One. 2016;11(3):e0150041. doi:
- 48.↑
Telama R, Yang X, Leskinen E, et al. Tracking of physical activity from early childhood through youth into adulthood. Med Sci Sports Exerc. 2014;46(5):955–962. doi:
- 49.↑
The Lancet Public Health. Time to tackle the physical activity gender gap. Lancet Public Health. 2019;4(8):e360. doi:
- 50.↑
Chen X, Kemperman A, Timmermans H. Socio-demographics, neighborhood characteristics, time use, and leisure-time physical activity engagement patterns over the life course. SSM Popul Health. 2022;19:101244. doi:
- 51.↑
Evenson KR, Aytur SA, Borodulin K. Physical activity beliefs, barriers, and enablers among postpartum women. J Womens Health. 2009;18(12):1925–1934. doi:
- 52.↑
Ramires VV, Wehrmeister FC, Böhm AW, et al. Physical activity levels objectively measured among older adults: a population-based study in a Southern city of Brazil. Int J Behav Nutr Phys Act. 2017;14(1):465. doi:
- 53.↑
Milanović Z, Pantelić S, Trajković N, Sporiš G, Kostić R, James N. Clinical interventions in aging age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging. 2013;8:549–556. doi:
- 54.↑
Mcauley E, Szabo A, Gothe N, Olson EA. Self-efficacy: implications for physical activity, function, and functional limitations in older adults. Am J Lifestyle Med. 2011;5(4):361–369. doi:
- 55.↑
Langhammer B, Bergland A, Rydwik E. The importance of physical activity exercise among older people. Biomed Res Int. 2018;2018:823. doi:
- 56.↑
Kari JT, Pehkonen J, Hirvensalo M, et al. Income and physical activity among adults: evidence from self-reported and pedometer-based physical activity measurements. PLoS One. 2015;10(8):651. doi:
- 57.↑
Beenackers MA, Kamphuis CBM, Giskes K, et al. Socioeconomic inequalities in occupational, leisure-time, and transport related physical activity among European adults: a systematic review. Int J Behav Nutr Phys Act. 2012;9(1):116. doi:
- 58.↑
da Silva ICM, Mielke GI, Bertoldi AD, et al. Overall and leisure-time physical activity among Brazilian adults: national survey based on the global physical activity questionnaire. J Phys Act Health. 2018;15(3):212–218. doi:
