Introduction
Physical activity provides a wide range of benefits, including psychological well-being and quality of life.Reference Hakimm, Petrovitch and Burchfiel1–Reference Penedo and Dahn3 In addition, some of the main health problems worldwide, such as obesity and atherosclerosis, have sedentary behavior as a risk factor.Reference Green, Maiorana, O’Driscoll and Taylor4–Reference Horber, Kohler, Lippuner and Taeger6 Low physical activity levels are associated with elevated risk of mortality in adults compared with individuals who report moderate or high levels of activity.Reference Hakimm, Petrovitch and Burchfiel1–Reference Bijnen, Caspersen and Feskens7
Different factors have been proposed as determinants of an active lifestyle in adults, including demographic, biological and emotional variables in addition to environmental conditions. Few studies have investigated the perinatal factors that influence the levels of physical activity in adulthood, although these associations have been demonstrated in animal models.Reference Zimmerberg and Shartrand8, Reference Matthews, Hall, Wilkinson and Robbins9 Female gender, high maternal social status and number of pregnancies are correlated with sedentary behavior.Reference Hallal, Wells, Reichert, Anselmi and Victora10 Some studies propose that low birth weight is inversely related to sedentary behavior, mainly in females.Reference Azevedo, Horta, Gigante, Vitora and Barros11
Socioeconomic status is also related to physical activity, mainly in young adults.Reference Hanson and Chen12 Social insecurity is associated with low physical activity as social violence decreases the practice of leisure in public spaces.Reference Weir, Etelson and Brand13, Reference Molnar, Gortmaker, Bull and Buka14 Some studies have shown that underprivileged social groups demonstrate higher physical activity considering the high physical demands of their jobs.Reference Rahkonen, Laaksonen, Martikainen, Roos and Lahelma15 On the other hand, higher social strata can often invest time and money to engage in social leisure activities.Reference Lee16
In view of the above considerations, our objective was to investigate early and late variables that could possibly impact an individual’s propensity to sedentary behavior, based on a theoretical model named ‘Similarities in the inequalities’. This model points out the similarities of some health outcomes considering privileged and underprivileged social groups. In this case, access to technologies by the former and scarcity of health resources for the latter play a fundamental role in the process, as illustrated by the similarities in low-birth-weight and obesity rates among extreme social groups in Brazil.Reference Silveira, Portella and Goldani17, Reference Silva, Barbieri, Gomes and Bettiol18
Method
This was a longitudinal, prospective cohort study involving participants born in the municipality of Ribeirão Preto (state of São Paulo, southeast of Brazil) from 1 June 1978 to 31 May 1979. During this period, 9067 liveborn infants delivered at Ribeirão Preto hospitals (98% of the total number of liveborns for the same period) participated in the study. For the follow-up of the cohort, babies whose mothers were not from Ribeirão Preto and did not reside in this city at the time of delivery were excluded from the study. Thus, 6973 liveborns remained in the study (6827 singletons and 146 twin deliveries). Of the 6827 singletons, 246 died during the first year of life and 97 died before the age of 20, for a total of 343 deaths.Reference Oliveira, Bettiol, Barbieri, Gutierrez and Azenha19 Data about mothers and children, including anamnesis and anthropometry were collected by trained personnel at the time of birth. The study was approved by the ethics committee of the University Hospital, Medical School of Ribeirão Preto.
The number of individuals invited for further evaluation was calculated, based in an attempt to permit the estimate of prevalences that are within the 50% range with a relative precision of 1.8%, and prevalences that are about 10% with a relative precision of 1.1%, with a confidence level of 95%. Considering a 50% prevalence of the effect, the sample size should be able to detect differences between proportions of 12%, with a prevalence of exposure of 10% and difference of 10% with a prevalence of exposure of about 20%, working with a probability of type I error of 5% and with the power of the study fixed at 80%. The sample size should also permit the detection of small differences between means for variables with a high standard deviation, with a 5% probability of type I error and with a power of >90%. To satisfy these assumptions, the final sample size corresponded to nearly 30% of the eligible population (6484 alive at 20 years), corresponding to 1946 individuals.
For the current study, a total of 5665 individuals were located. On the basis of the geo-economic characterization of the city, one of each three individuals belonging to the same geographic area was contacted. In case of a refusal, impossibility to participate or inability to locate an individual, contact was made with the next name on the list. In this process, 705 individuals had to be replaced because of refusal (209 cases), imprisonment (31 cases), death after 20 years of age (34 cases) and failure to appear for the interview (431 cases). Thus, 2103 young adults effectively participated in the study, corresponding to 32.4% of the eligible population.
Between April 2002 and May 2004, the team applied a detailed lifestyle history questionnaire (including information on physical activity) and a socioeconomic questionnaire, in addition to performing physical examination and anthropometric assessment in these individuals. A detailed description of the cohort and a comparison of this random sample with the original population have been published.Reference Barbieri, Bettiol and Silva20, Reference Goldani, Barbieri, Silva and Bettiol21 Briefly, the 2004 sample was comparable to the original population with regard to birth weight, birth length and maternal age, although the sample was slightly wealthier considering maternal income. For the purpose of this study, only singletons were included in the analyses. Therefore, in the current study, we analyzed data from 2057 individuals.
Social data were obtained on two occasions; maternal data were obtained by a standardized questionnaire applied to the mothers soon after delivery and demographic information was collected from official records.Reference Barbieri, Bettiol and Silva20 The participants’ data were obtained using a standardized questionnaire on the occasion of their return for evaluation at 23–25 years of age. In Brazil, 5–8 years represent ‘middle school,’ 9–11 years represent ‘high school’ completion and ⩾ 12 represent University education.
Physical activity information was obtained using the short version of the International Physical Activity Questionnaire (IPAQ)22 validated for use in Brazil, which contains questions about activity in different situations including work and domestic tasks. The recall time used in this study was 1 week.Reference Kriska and Caspersen23
To classify the participants’ physical activity levels, we used the definition of Metabolic Equivalent of Task (METs)-min/week. By convention, 1 MET is considered as the resting metabolic rate obtained during quiet sitting. IPAQ responses were then quantified in METs using constants of conversion.Reference Ainsworth, Haskell and Whitt24 Thus, participants were categorized as follows: (1) active: ⩾7 days of combined activities reaching at least 3000 METs or intense activity for at least 3 days reaching a minimum of 1500 METs; (2) sufficiently active: five or more days of combined activities reaching at least 600 METs or ⩾5 days of walking for a minimum of 30 min/day or ⩾5 days of moderate activity for at least 30 min/day or ⩾3 days of intense activity for at least 20 min/day; and (3) sedentary: when the level of physical activity was below the above descriptions. As our objective was to study factors that determine sedentary behavior in this sample, we grouped categories 1 and 2 (active and sufficiently active) into a single category to compare it with the category 3 (sedentary).
The independent variables considered were maternal schooling stratified into four groups (0–4, 5–8, 9–11 and >12 years of education); participant’s schooling level stratified into two groups (0–8 and ⩾9 years of education); birth weight categorized into <2500 g and ⩾2500 g; gender; regarding smoking during pregnancy, mothers were classified as smokers and non-smokers and adult participants were classified as smokers, non-smokers and ex-smokers.
Poisson regression was used to estimate the relative risk (RR) for sedentary behavior according to the various categories. We performed three Poisson regression models: (1) early model considering the variables birth weight, maternal income, schooling and smoking during gestation, collected immediately after birth, adjusted for gestational age (last menstrual period in weeks); (2) late model considering gender, participants’ schooling and smoking and body mass index; and (3) combined model (early + late), considering only the variables that were statistically significant in the previous models and adjusting for gestational age. Finally, we performed an analysis of the interaction considering the variables included in the last model. For all analyses, the level of significance was set at 5%. The software used for statistical analysis was STATA 9.0.
Results
The general rate of sedentary behavior in this sample was 49.6%. The variables that showed a statistically significant association with sedentary behavior were birth weight, maternal schooling, participants’ schooling and gender. There was an increased prevalence of sedentary behavior in individuals born lighter than 2500 g (P = 0.018), in women (P < 0.001) and in families with lower educational levels (both mother, P = 0.027 and participant, P = 0.033). On the other hand, maternal and individual’s smoking was not associated with this outcome (P = 0.947and P = 0.207, respectively). The wealth distribution measured in minimal wages (MW) earned per month was quite consistent across the cohort (<MW – 11.5%; between 3 and 4.9 MW – 24.1%; between 5 and 9.9 MW – 33%; between 10 and 19.9 MW – 21.1%; and >20 MW – 10.3%).
Table 1 depicts the early Poisson regression model, showing that being born smaller than 2500 g (P = 0.043) was the sole as risk factor for the young adults to be sedentary.
Table 1 Early model for sedentary behavior considering birth weight, maternal schooling, smoking and income
SB, sedentary behavior; RR, relative risk; CI, confidence interval; MW, minimal wage.
aAdjusted for gestational age.
In the late model (Table 2), we observed an association between sedentary behavior and female gender (P < 0.001) as well as participants’ lower educational levels (P = 0.037).
Table 2 Late model for sedentary behavior considering gender, participants’ schooling, smoking and BMI
SB, sedentary behavior; RR, relative risk; CI, confidence interval; BMI, body mass index.
Table 3 shows the results for the combined model, in which we analyzed variables that were significant in the early and late models (participants’ schooling, gender and birth weight). In this model, only gender and schooling of participants remained significant (P < 0.001 and P = 0.027, respectively), with women and less educated individuals showing a higher risk of being sedentary. Low birth weight had a marginal effect on sedentary behavior (P = 0.079).
Table 3 Combined model for sedentary behavior considering birth weight, gender and participants’ schooling
RR, relative risk; CI, confidence interval.
aAdjusted for gestational age.
In Table 4, we show the results of the interaction analysis between an early variable (birth weight) and a variable from the late environment (participants’ schooling). Individuals with low birth weight and low educational level showed a similar probability to have sedentary behavior in adulthood when compared with individuals having higher educational levels and adequate birth weight (RR = 0.919, 95%CI = 0.645–1.309). On the other hand, poor schooling with adequate birth weight or higher level of education with low birth weight showed an increased risk for sedentary behavior (RR = 1.149, 95%CI = 1.022–1.292 and 1.306, 95%CI = 1.098–1.554, respectively).
Table 4 Interaction between participants’ schooling and birth weight on sedentary behavior using the multiple Poisson regression model
RR, relative risk; CI, confidence interval; LBW, low birth weight.
aAdjusted for gestational age, maternal schooling and gender.
Discussion
In this study, we aimed to identify early and late factors associated with sedentary behavior in adulthood. The different models demonstrated that both biological characteristics and social factors are correlated with the prevalence of sedentary behavior in adulthood. We also showed an interaction between an early biological factor (birth weight) and a late social one (participants’ schooling).
When analyzing variables in the early model, we demonstrated that both birth weight and low-family level of education influence the prevalence of sedentary behavior in adulthood. Brazil has been characterized by its high level of social inequalities strongly affecting the pattern of health and disease.Reference Silveira, Portella and Goldani17 Regarding social background, we found that, as expected, poor schooling has a major impact on the risk to be sedentary in adulthood. However, a study carried out in Finland showed that most workers from lower socioeconomic classes perform activities that require physical strength,Reference Rahkonen, Laaksonen, Martikainen, Roos and Lahelma15 and this obviously increases the physical activity levels in the lower socioeconomic strata in this country. The results of our study agree with the cited study in the fact that socioeconomic status influences physical activity, although the direction by which social variables affect sedentary behavior may depend on the specific economic and geographic context of the country.
Extreme unequal backgrounds co-exist in a complex scenario promoting similar health outcomes. We named this particular pattern of health and disease ‘Similarities in the inequalities’Reference Silveira, Portella and Goldani17 model. In this study (Table 4), we showed that individuals with normal birth weight with higher levels of education have a similar probability to be sedentary to low-birth-weight individuals with poor schooling. Therefore, opposed perinatal backgrounds, when facing unequal social achievement during the life-course, have the same impact over this behavioral health outcome. In the less privileged social group, physical strength at the workplace imposes a higher level of activity. On the other hand, wealthy individuals are more prone to be engaged in a healthy lifestyle and leisure activities. In our opinion, this is a peculiar model found especially in countries with a high level of social inequalities like Brazil, but more studies are needed to explore this possibility.
Considering biological variables, low birth weight has been proposed as a proxy for a poor intrauterine environment in different studies,Reference Barker25 and has been associated with several pathological outcomes in adulthood such as cardiovascular disease,Reference Forsen, Eriksson, Osmond and Barker26 insulin resistance,Reference Forsen, Eriksson and Tuomilehto27, Reference Soto and Mericq28 atherosclerosisReference Barker, Martyn, Osmond, Hales and Fall29, Reference Davies, Smith, Ben-Shlomo and Litchfield30 and overweight.Reference Laitinen, Pietilainen, Wadsworth, Sovio and Jarvelin31 Our group has previously shown that women born with intrauterine growth restriction prefer to eat more palatable food in adulthood.Reference Barbieri, Portella and Silveira32 In this case, lack of nutrition during gestation would create a tendency in the developing individual to establish a pattern of biological functioning in which energy storage is facilitated and the expenditure is minimal. Hence, these individuals naturally prefer caloric and highly palatable foods and spontaneously decrease energy expenditure, being less physically active in adulthood.Reference Vickers, Breier, McCarthy and Gluckman33
The gender differences reported here reflect the findings from several other studies in which women were found to be more sedentary than men.Reference Martinez-Gonzalez, Varo and Santos34–Reference Costa, Werneck, Lopes and Faerstein36 In this study, gender was considered as a late variable because the effect of the gender upon sedentary behavior appears to be more relevant in later life as compared with its effects at birth. It has been shown that activity levels are not affected by the gender early in life.Reference O’Brien and Huston37 However, activity is clearly different between the genders in adulthoodReference Martinez-Gonzalez, Varo and Santos34–Reference Costa, Werneck, Lopes and Faerstein36 and later life,Reference Chipperfield, Newall, Chuchmach, Swift and Haynes38 probably because the effect of sex on sedentary behavior is more related to the gender’s social role and arrangements than to biological components. Social and cultural factors determine differences regarding sedentary behavior when comparing the genders.Reference Monteiro, Conde and Matsudo39 Women engage in physical activities mainly for health or esthetical reasons, whereas men relate physical activity to pleasure.Reference Azevedo, Araújo and Reichert40 The gender effect reported in our study reproduces the findings of several other studies addressing physical activity using the same questionnaire.Reference Santos, Santos, Ribeiro and Mota41–Reference Bergman, Grjibovski, Hagströmer, Bauman and Sjöström46 IPAQ has questions about physical activity performed at the workplace and transportations, as well as during the daily-life activities, exercise and leisure, providing examples to the responders that are mostly applicable to both genders.
From a strictly biological point of view, the findings of our study are compatible with ‘Match and Mismatch’ model proposed by Gluckman and Hanson in 2004.Reference Gluckman and Hanson47 This model suggests that the developing individual has the capacity of foreseeing the environment in which it will grow, using maternal hormonal signs through the placenta and/or through lactation. These signs allow the individual to adjust its physiology according to such prediction. If the prediction is correct, the risk for illnesses is minimal. However, if the prediction is erroneous, there is an increase in the risk for illnesses. The risk for diseases is, therefore, the result of the degree of ‘Match or Mismatch’ between the environment predicted by the individual during the period of high plasticity and development and the actual environment in which this individual will live in mature ages. In our study, low birth weight was a marker of a ‘poor’ intrauterine environment and therefore the developing fetus predicted that the external environment would also be frugal in resources. However, if the prediction is wrong and the individual grows in a ‘rich’ environment (by means of a better socioeconomic status), ‘mismatch’ occurs and in fact this group will show a higher prevalence of sedentary behavior, probably with a higher risk for the development of chronic illnesses in the long term. On the other hand, in the group in which there is a ‘match’, this effect does not appear.
The study has some limitations. A potential bias would be the generalization of findings from individuals born in a specific city during a specific year to the general population. In addition, in the final analysis only 35 cases that had low birth weight and ⩾ 8 of education. However, we find that these results are interesting and should be noticed, warranting further reproduction of the findings, if possible with a measure of income as well as schooling.
In conclusion, we showed that early and late factors have a complex interaction, affecting the prevalence of sedentary behavior in young adults, and possibly this is correlated with the individual variability in the risk for chronic illnesses in the population. We propose two complementary theoretical models that may explain such interaction. First of all, the ‘Similarities in the inequalities’ basis points out that unequal perinatal characteristics and differences in the access to adequate social standards promote similarities in a health outcome, in this case sedentary behavior. The phenomenon can also be understood by means of a discrepancy between the early phenotypic programming and late environmental conditions. Both proposals are valuable at the individual level for awareness about risk factors and health promotion. Furthermore, if applied in a broader context, this knowledge could contribute to primary care policies and as a background for future studies assessing these associations on a mechanistic level.
Acknowledgements
This study was funded by Brazilian national agencies: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
Statement of Interest
The authors declare that there are no competing financial interests in relation to this study.
