Abstract(resumo):

The objective of this study was to investigate the determinants of changes in leisure time physical activity among participants in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). This cohort study included 13,707 participants from ELSA-Brasil. Leisure time physical activity was measured at two time points (2008-2010 and 2012-2013), with mean follow-up duration of 3.8 ± 0.4 years, and the International Physical Activity Questionnaire was used. Sociodemographic, environmental, clinical and laboratory variables were collected through questionnaires and specific exams and measured at baseline. Crude and adjusted relative risk and 95% confidence interval were estimated using the Poisson regression with robust variance. Most participants were females (54.6%) and aged 35-50 years (47.5%). Among men, obesity increased the risk of being physically inactive, while those with higher education living in environments suitable for the practice of physical activities in the neighborhood reduced this risk. For women, higher education and living in environments suitable for the practice of physical activities in the neighborhood were associated with a shift from physically inactive to active; however, obesity and smoking increased the risk of becoming inactive.

Keywords(palavra-chave):

motor activity, exercise, environment, follow-up studies.

Content(conteúdo):

Introduction

The regular practice of leisure time physical activity (LTPA) has been associated to reduction of general mortality in adults and older adults1,2. Changes in LTPA are influenced by different risk factors, from physical to environmental3,4. On the other hand, it is known that physical inactivity is associated to innumerable diseases, which generate high costs to health systems worldwide5–7.
In literature, the LTPA measurement is more common in a single moment in time8, but when its changes are evaluated, its variation can affect health outcomes9. In longitudinal studies, it was observed that individuals who practiced regular physical activity had lower risk of death during follow-up, compared to those who were physically inactive, even after adjustment for variables of interest. It was also observed that being physically active in only one evaluation already increased the chances of being healthy, which varied among individuals as they age10,11.
The literature indicates that several variables can influence changes in levels of physical activity in the adult population, including body mass index, triglycerides, cholesterol, age, social class, hypertension and diabetes12,13. However, most studies assess these changes in children, adolescents, or early adulthood 14–16.
Although the international literature have presented studies with changes in the physical activity standards in adults, in Latin America, studies of this type are still scarce, as there are few large-scale longitudinal studies with the adult population investigating multiple outcomes8,17. It is highlighted that these studies are of great importance to understand possible factors that may interfere in the practice of physical activity, and consequently, in the prevention of chronic diseases and reduction of costs in the health sector. The aim of this study was to investigate the determinants of changes of leisure time physical activity in participants in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods

Population and Sample

The study population consists of participants in the ELSA-Brasil, which is a cohort study with 15,105 public servants, active employees or retirees, from both sexes, aged 35-74 years at baseline, from six educational institutions in the following Brazilian cities: Salvador, Belo Horizonte, Vitória, Porto Alegre, Rio de Janeiro and São Paulo. The study outline and the cohort profile had already been published18,19. In this study, data were analyzed from baseline (2008-2010) and from the first follow-up (2012-2014) of individuals who answered the questionnaire on physical activity, and who had complete sociodemographic, environmental, clinical and laboratory profile data, with a total of 13,107 participants. The average follow-up time was 3.8±0.4 years, varying from 2.6 to 6.0 years. The study was approved by all committees of ethics and research of the institutions in this study. All participants signed the free and informed consent form.

Data collection

Data were collected by a team of trained interviewers and evaluators to perform the study protocol, who were supervised, certified and re-certified by qualified professionals. Face-to-face interviews, anthropometric measurements, clinical exams and blood collections were performed20.

Physical activity Measurement

Physical activity was measured through the long-version International Physical Activity Questionnaire (IPAQ), with questions on physical activity frequency, duration and intensity 21. The amount of physical activity was reported in minutes/week, consisting of the multiplication of weekly frequency by the duration of each physical activity. For this study, the LTPA domain was used which was categorized according to the guidelines at the time of data collection as insufficiently active (< 150 minutes per week of moderate physical activity or walks, and/or < 60 minutes per week of vigorous physical activity, or < 150 minutes per week of any combination of vigorous physical activity and walks) and physically active (? 150 minutes per week of moderate physical activity or walks, and/or ? 60 minutes per week of vigorous physical activity of any combination of any vigorous, moderate physical activity or walks).

Sociodemographic, Environmental, Clinical and Laboratory Profile

Sociodemographic characteristics, including age, schooling, family income and functional status, were self-declared. Age was categorized in three stratums (34-50, 51-59 and ? 60 years), schooling into incomplete elementary school, complete elementary school, high school and higher education. Functional status was categorized into active and retired.
The physical environment variables were obtained by using two scales: adequate environment for the practice of physical activity in the neighborhood (9 items) and neighborhood safety (3 items). Answers varied from 1 to 5 (totally agree to totally disagree). In Brazil and in other countries, these scales showed appropriate psychometric properties22–24. In the scale of evaluation of appropriate environment for physical activities – in ELSA-Brasil – grades ranged from 9 to 45, showing perception of higher quality and perception of lower quality, respectively. A cohort score was created to characterize perception groups: perception of higher quality (score ? 18) and perception of lower quality (score > 18). This criterion was chosen because scores lower than 18 mean that most of the answers varied from “totally agree” to “partially agree”, showing “higher walkability”, while punctuations higher than 18 were concentrated in “totally disagree” and “partially disagree”, showing “lower walkability”25. Regarding the safety perception in the neighborhood, scores varied from 3 to 12, showing perception of higher and lower neighborhood safety, respectively. Cutoff points were ? 6 for higher safety and > 6 for lower safety26.
Current smoking habit was stratified into smokers and non-smokers. Weight and height were measured by wearing the study clothes and on bare feet. For the height measurement, Seca stadiometer was used, with participant standing with his/her back to the stadiometer and head in the Frankfurt plan. To verify weight, Toledo scale with capacity of up to 200kg was used. Obese patients were identified by body mass index (BMI), using the following equation: BMI= weight / height (m)², and the adopted cutoff point was: up to 24.9 – normal weight, from 25 to 29.9 – overweight and over 30.0 – obesity. To identify abdominal adiposity, the waist circumference (WC) was used, measured in the mean point between the lower edge of the costal arc, and for the iliac crest in the median axillary line, adopting cutoff point for men WC ? 88 cm, and for women WC ? 84 cm27.
Blood pressure was measured with the individual in the sitting position, after a 5-minute rest, with a validated oscillometer (Omron HEM 705CPINT, Tokyo, Japan). Three measurements were performed with intervals of 1 minute and the average of the two last readings was considered. Hypertension was considered if the participant belonged to at least one of the following criteria: systolic blood pressure ? 140 mmHg or diastolic blood pressure ? 90 mmHg, use of anti-hypertensive medicine in the two weeks prior to the interview28.
Blood samples were obtained by venous puncture after a 12-hour nocturnal fasting. Subsequently, samples were stored and transported to the central laboratory of ELSA-Brasil. Total Cholesterol, HDL Cholesterol (HDL-C) and triglycerides were determined by the enzymatic colorimetric method, and LDL Cholesterol (LDL-C) was calculated using the Friedewald equation.
Participants categorized as diabetics were those that who self- declared that their doctors had diagnosed the disease, had used hypoglycemic medicine or insulin in the last two weeks, presented fasting glycoses ? 126 mg/dL, post-load glycose of 2 hours ? 200 mg/dL or glycated hemoglobin (HbA1c) ? 6.5%29. The presence of dyslipidemia was classified when participants showed hypercholesterolemia (LDL Cholesterol ? 160 mg/dL), hypertriglyceridemia (triglycerides ? 150 mg/dL) or HDL reduced cholesterol (men < 40 mg/dL and women< 50 mg/dL)30. All variables in the sociodemographic, environmental, clinical and laboratory profiles refer to baseline (2008-2010).

Statistical Analysis

The chi-square test was used to verify the differences between the prevalence of physical activities between waves. Leisure time physical activity was the dependent variable. Independent variables were grouped into social factors (age, schooling and functional status); environmental factors (perception of neighborhood safety and adequate environment for the practice of physical activities); and clinical and behavioral factors (smoking, BMI, abdominal obesity, hypertension, diabetes and dyslipidemia).
The prevalence of LTPA in the two periods and the stratum of each independent variable were presented with their respective confidence interval (95%CI). All analyses were stratified according to gender. The changes of physical activity were analyzed in four groups based on the categories of physical activities at baseline, which were named: inactive-inactive, inactive-active, active-inactive and active-active. For association analysis, Poisson regression with robust variance was used and the change in physical activity was used as the outcome. Two cohorts were created according to the categories at baseline: active and inactive. The groups of interest were those that became active in the inactive cohort (reference group= those who remained inactive) and those who became inactive for the active cohort (reference group = those who remained active). The relative risk (RR) was calculated for the crude and adjusted analyses respectively and with respective 95% confidence intervals.
The adjusted analysis was grouped into three levels to discriminate potential associated factors. The strategy adopted for the input of variables in the levels was using the forward method in the following order: level 1 (age, schooling and functional category), level 2 (perception of neighborhood safety and adequate environment for the practice of physical activity) and level 3 (smoking, BMI, abdominal obesity, hypertension, diabetes and dyslipidemia). During the phases of the multivariate analysis, variables p?0.10 remained in the level. Data were analyzed using the Stata 12.0 statistical software (Stata Corporation, College Station, United States).

Results

Among the 13,707 participants, 54.6% were women. Among them, 47.2% aged 34 - 50 years, 55.1% reported to have completed higher education, 22.2% were retired, 77.1% did not feel safe in the neighborhood, 52.3% considered the neighborhood adequate for the practice of physical activity, 12.0% were smokers, 24.5% were obese, 31.3% had hypertension and 16.1% had diabetes. Among men, 47.9% aged 34 - 50 years, 51.5% reported to have completed higher education, 15.1% were retired, 75.3% did not feel safe in the neighborhood, 13.4% were smokers, 20.3% were obese, 39.0% had hypertension, 22.3% had diabetes and 50.1% had dyslipidemias (Table 1).
At baseline, 39.0% of participants were physically active, being that men (44.7%) were more active than women (34.2%). In the second period, the proportion of physically active individuals increased (42.2%) and statistically significant difference was observed both in the general population and the population stratified according to gender (p<0.05) (Figure 1). The proportion of individuals who were active at baseline and who became inactive was similar among genders (about 13%). The same was observed among individuals who were physically inactive (about 16%). However, for those who had not changed status in the two stratums, 49.7% of women remained inactive, and among those who remained active, higher proportion of men was observed (31.8%) (Figure 2).
Among men, in the adjusted analyses, there no association with any variable in the non-active cohort at baseline was found. For women, having completed higher education (RR= 1.59; 95% CI: 1.13 - 2.22) and adequate environment for the physical activity in the neighborhood (RR= 1.19; 95% CI: 1.08 - 1.33) were associated with change from physically inactive to active (Table 2).
On the other hand, the variable associated to higher risk of being physically inactive in the adjusted analysis, among men, was obesity (RR= 1.44; 95% CI: 1.15 - 1.81), showing that this body mass index standard reduces the probability of becoming physically active; nevertheless, participants who reported to have completed higher education (RR= 0.60; 95% CI: 0.47 - 0.77) and who have adequate environment for the practice of physical activity in the neighborhood (RR= 0.86; 95% IC: 0.76 - 0.98) reduced the risk of being physically inactive. Among women, being obese (RR= 1.39; 95% CI: 1.14 - 1.68) and smoker (RR= 1.21; 95% CI: 1.04 - 1.42) have higher risk of becoming inactive, however, older individuals (51 to 59 years of age RR= 0.77; 95% CI: 0.68 - 0.87 / ? 60 years of age RR= 0.66; 95% CI: 0.56 - 0.77), those who reported to have completed higher education (RR= 0.65; 95% CI: 0.48 - 0.87) and those who have adequate environment for the practice of physical activity in the neighborhood (RR= 0.84; 95% CI: 0.75 - 0.94) show lower risk of becoming inactive (Table 3).

Discussion

ELSA-Brasil is the first study to analyze changes of physical activity in adults and their determinants in different regions in the country, with repeated measures, which found that obese men had higher risk of remaining inactive, while those with higher education and suitable environment for the practice of physical activity in their neighborhood had lower risk of becoming inactive. Among women, having higher education and suitable environment for the practice of physical activity in the neighborhood favored the transition from inactive to active. On the other hand, obesity and smoking increased the risk of becoming inactive, while older age, higher education, and suitable environment for the practice of physical activity in the neighborhood reduced the risk of becoming inactive. In Latin America, studies of this type are very scarce8, and only show trend studies with adults from different samples31,32.
In a study carried out in Canada, it was observed that individuals who were less inclined to decrease the levels of physical activity for both genders had as determinants higher education, being retired, became or remained regular alcohol consumers and developed or continued to have some type of chronic disease33. In this study, having complete higher education was a determiner for becoming or remaining active in both genders, and being older than 50 years for women; however, having any chronic disease (hypertension or diabetes), and being retired was not a determiner for the change in physical activity standards. Specific attention must be given to the population with low schooling, because they are susceptible to reductions in the levels of physical activity, as this population does not always have the financial resources to have access to practice physical activity in gyms, clubs and other private spaces34. On the other hand, regarding age, there is evidence that in some regions, while the population ages, their general levels of physical activity increase with time35. It can be hypothesized that with advancing age, there may be a reduction in demands from family and work and greater opportunity to adopt healthier behaviors, including an active lifestyle.
In a study carried out in Finland, it was found that compared to those who remained with low levels of physical activity, participants who increased their levels were less inclined to show cardiometabolic risk factors36. In our study, obesity was the only cardiometabolic predictor that had an impact in the change of physical activity standards, as an inverse association to the practice of leisure time physical activity. In a cross-sectional analysis in the cohort ELSA Brasil, beneficial effects of leisure time physical activity on cardiometabolic health was observed37. However, it was also shown that obesity is associated to low levels of physical activity in commuting38. The literature has shown that interventions with physical activity are able to reduce obesity and cardiometabolic risk39. In addition, weight gain in adulthood is associated to physical inactivity standards, which highlights the importance of the adoption of a healthy lifestyle40.
It is important to highlight that adequate environment for the practice of physical activity in the neighborhood was the most associated characteristic to the change of physical activity standards in our study. In cross-sectional analyses (ELSA Brasil), this association was also shown25,41. However, in the CARDIA study carried out with an adult population, no neighborhood characteristic was associated to changes of physical activity standards in 10 years. In spite of the difference in the results from CARDIA, the results of this study demonstrate the positive influence of the appropriate environment for the practice of physical activity, highlighting the importance of governmental policies that enable public spaces with a safe environment and equipment that stimulate the practice of physical activities. In addition to educational information showing the importance of the regular practice of physical activity, for health and well-being42. It was also observed that a safe, accessible and aesthetically pleasant environment has positive influences among adults43.
Our results show that factors such as education and urban infrastructure play a protective role on physical inactivity, while obesity and smoking are associated with higher risk, with striking differences between men and women. These diferences occur due to some sociocultural barriers that are mainly faced by women, among them, insecurity, lack of proper environment for physical activity, stereotypes, domestic tasks, double workday and lack of investment in physical activity promotion programs44. This inequality is a challenge that must be recognized in research on changes in physical activity and public policies, and therefore, emphasis must be placed on addressing gender inequality.
Significant increase of the prevalence of physically active individuals was observed in the second segment. Dai et al analyzed prospective population data in Canada, and with the same sample size of ELSA-Brasil, and found reduction in the practice of physical activity (32%) in the follow-up, if compared to baseline for both genders33. In our study, it was identified that individuals who became inactive, accounted for around 13% for both gender and 16% of the sample for those who became active, for both genders. A cohort in the public health sector with adults carried out in Finland showed that 22% increased their physical activity standards, and 27% showed a decrease36. This increase of the practice of physical activity found in our study, although modest, can be due to public policies encouraging the practice of physical activities, which have been implemented in Brazil45.
Data from this study come from a cohort of workers from Brazilian public institutions, and despite it shows participants from different regions in the country, extrapolating these results should be performed with care, because they may not represent the general population. Another possible limitation is that physical activity and the physical environment scales were measured through questionnaires, but these instruments are used and accepted in a great number of studies46. Only two time points were assessed, which may be a period relatively short to assess changes, as this assessment captures a single time point and may not reflect long-term behavior.

Conclusion

In conclusion, this study demonstrated that obesity, higher education and adequate environment for the practice of physical activity in the neighborhood can be determining factors for changes in the level of physical activity among men. For women, higher education, obesity, smoking, adequate environment for the practice of physical activity in the neighborhood and age can interfere with changes in the levels of physical activity. Our findings aggregate new knowledge on possible contemporary factors that change the practice of physical activity and that are relevant for Brazil, once they can influence public policies, nor only in the health sector, but also in the social and urbanization areas, because it was observed that the characteristics of the neighborhood were associated to changes in the practice of physical activities, in most of the analyses. The replication of this model of analysis in other cohorts and generations in Latin America is recommended to strengthen the scientific evidence on causal relations for the change in physical activity standards, once the identification of determinants of an active behavior, presupposes the identification of multicausality of this phenomenon and the need for more effective strategies to generate more favorable changes in the levels of public health.




Acknowledgments

The authors would like to to express their gratitude to the volunteers who participated in this study.

Declaration of conflicting interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding statement

The ELSA-Brasil baseline study was supported by the Brazilian Ministry of Health (Science and Technology Department) and the Brazilian Ministry of Science and Technology (Financiadora de Estudos e Projetos and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grants 01 06 0010.00 RS, 01 06 0212.00 BA, 01 06 0300.00 ES, 01 06 0278.00 MG, 01 06 0115.00 SP, 01 06 0071.00 RJ).



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