Abstract
The present study aimed to verify the prevalence and association of sedentary behavior and its breaks with obesity and cardiovascular risk factors in teaching professionals. The sample was composed by 245 public school teachers (186 women and 59 men), with a mean age of 45 yr. Sedentary behavior was evaluated by self-reported screen time in different devices (television, computer, cellphone/tablet), and sedentary breaks at work and leisure were assessed by a Likert scale (never, rarely, sometimes, often, always). Cardiovascular risk factors (overweight/obesity, abdominal obesity, blood pressure, and heart rate) were objectively collected by trained individuals in the work environment of the teachers. Logistic Binary Regression models were adjusted for confounding factors (age, sex, and socioeconomic status). The prevalence of sedentary behavior was 55.3% in the sample. High sedentary behavior was associated to abdominal obesity (OR=2.21 [CI=1.23–3.97]). No association was observed between sedentary breaks at work and independent variables, however teachers with high sedentary breaks at leisure time were less likely to present high blood pressure (OR=0.58 [CI=0.32–0.98]). In conclusion, high sedentary behavior was associated with abdominal obesity, and high sedentary breaks in leisure time were associated to lower chances of high blood pressure among public school teachers.
Keywords: Screen time, Sedentary breaks, Abdominal Obesity, Blood pressure, Teachers
Introduction
Sedentary behavior corresponds to activities of energy expenditure less than or equal to 1.5 metabolic equivalent, performed in a sitting or reclining position1). The sedentary behavior has become a significant concern for public health, once its high level has been associated with an increased risk of cardiovascular diseases, obesity, adverse metabolic profiles, some cancers, and mortality2,3,4).
The increase in high sedentary behavior over the years has been attributed to changes in transportation, entertainment, and work environments5), which are related to a drastic reduction in the daily demands of physical activity6). Adult population spends almost 55–57% of their time in sedentary behaviors, which corresponds of almost 8 h per day7). An epidemiological research indicates that prevalence of high sedentary behavior ranges from 60 to 71% worldwide8).
Nevertheless, sedentary behavior may occur in different domains of daily life, as work environment, leisure activities, and passive transport. Therefore, different strategies can be used to estimate the sedentary time: in its totality, by domain or through specific behavior9). Recent studies analyzed sedentary time spent at occupational activities, which represents a large part of the waking hours of workers, once many professionals have high amount of time in sitting position at their jobs10, 11). Otherwise, frequent breaks in sedentary time have been investigated as a way to mitigate the health impairments of sedentary behavior4, 12).
However, is not consensual in literature the association of sedentary behavior in other domains with cardiometabolic risk factors among predominantly non-sedentary workers, as teachers. Higher physically demanding jobs has been associated with longer sitting time at leisure, however, the relationship of work activities with sedentary behavior outside the work environment is still unclear13). Teachers present a high workload at school environment, by remaining in orthostatic position for up to 95% of their activities14,15,16,17), which requires prolonged isometric contraction to oppose gravity18). Thus, teachers are considered as having a non-sedentary profile19) and previous studies did not investigate whether the sedentary behavior patterns outside the work environment were related to cardiovascular risk factors, as obesity and high blood pressure among teachers.
Therefore, this study aimed to verify the sedentary behavior patterns (overall sedentary behavior and breaks in sedentary behavior at work and leisure) and analyze its associations with cardiovascular risk factors in public school teachers.
Methods
This observational study has a cross-sectional design and was developed by the Group of Studies in Physical Activity and Health of the Faculty of Sciences and Technology from Sao Paulo State University, Campus of Presidente Prudente. All procedures performed in the study were approved by the Institution’s Ethics and Research Committee (process number 72191717.9.0000.5402). All the participants were informed about the procedures and objectives of the research and those who agreed to participate signed the Informed Consent Term.
Sample selection and inclusion criteria
The study was conducted in the city of Presidente Prudente, located in the Southeast region of Brazil, which had an estimated population of 207,625 inhabitants and a Human Development Index (HDI) of 0.84620).
According to the Education Department of the city, there are approximately 650 teachers distributed in 23 public schools. All these schools were visited and invited to participate in the research. The data collection was performed during the collective pedagogical work class, at time when all teachers were present, in a previous scheduled date, at the work environment of the teachers, so as not to interfere in the pedagogical activities of the schools.
In addition, the details of the study were communicated by the coordinator to the teachers at least one week in advance and the following inclusion criteria were defined: i) be an effective teacher of the school; ii) not have performed exhaustive exercises for at least 24 h prior to evaluation of hemodynamic variables; iii) participate in all procedures of research (questionnaires, anthropometry, and measurement of cardiovascular parameters); iv) signed the Informed Consent Term.
Sample calculation
The calculation of minimum sample size considered a prevalence of outcome (high sedentary behavior) of 50%, used in epidemiological studies (Agranonik and Hirakata)21), the population of 650 public school teachers, confidence interval of 95%, a power of test of 80%, and a maximum tolerable error of 5%, which provided a minimum simple random sample of 242 teachers. For the sample selection, all the 23 schools in the city were contacted, but only 13 allowed the collection of data and all these schools were assessed.
Organization of data collection
The collection of data was performed at the school environment by previously trained researchers, so that any doubts were promptly resolved. Measurements of anthropometry (weight, height, and waist circumference), resting heart rate, and blood pressure were performed in specific rooms provided by the management of the schools participating in the study. In order to avoid possible constraints in the anthropometric evaluation, the male teachers were evaluated by male researchers and female teachers by female researchers.
Sedentary behavior
The subjective model used to assess sedentary behavior was based on the questionnaires provided by The Sedentary Behavior Research Network (SBRN)22), by the number of daily hours in a typical weekday in which the teachers spent watching television, using computer or cellphone/tablet, and spent in sitting position. The total sedentary behavior was obtained by the sum of the responses for each sedentary behavior, which were classified as follows: i) less than 1 h (0 h computed); ii) more than 1 h but less than 2 h (1 h computed); iii) more than 2 h but less than 3 h (2 h computed); iv) more than 3 h but less than 4 h (3 h computed); v) more than 4 h but less than 5 h (4 h computed); and vi) more than 5 h (5 h computed). Individuals who reported the sum of television, cell/tablet, computer, and sitting time equal to or greater than 8 h per day were classified as “high sedentary behavior”. This cut-off point was adopted as it is in accordance with the criteria recommended by Van der Ploeg et al23).
The breaks in sedentary behavior at work and leisure were obtained through the following questions:
-In your work environment, how often do you get up to go to the bathroom, drink water, or perform another activity that requires standing or walking for at least a short time?
-In your leisure time, how often do you get up to go to the bathroom, drink water, or perform another activity that requires standing or walking for at least a short time?
The response options were presented using a Likert scale, considering the options: never; rarely, sometimes, often, and always. According to the response, the sample was further classified as “high breaks in sedentary behavior” (‘often’ and ‘always’), and as “low breaks in sedentary behavior” (‘never’, ‘rarely’, and ‘sometimes’) for both domains of work and leisure time.
Anthropometry
Anthropometric variables were measured with participant barefoot and wearing light clothing on the day of the assessments. Body mass, height, and waist circumference were evaluated. Body mass was measured using a digital scale (Plenna®, Sao Paulo, Brazil) with an accuracy of 0.1 kg and height was measured by means of a portable stadiometer (Sanny®, American Medical of Brazil, Sao Paulo, Brazil) with a maximum extension of 2.2 meters and a precision of 0.1 cm. After taking these two measures, the body mass index (BMI) was calculated by division of body mass by the height squared. Subsequently, the teachers were classified as: I) eutrophic; II) with excess weight, subjects with a BMI equal to or greater than 25 kg/m2.
Waist circumference was collect in the middle-point between the iliac crest and the last rib, by an inextensible tape with precision in millimeters (mm). The participants were classified as with or without abdominal obesity, according to gender, using the National Cholesterol Education Program (NCEP)24) cut-off points of 102 cm for men and 88 cm for women.
Blood pressure
A digital oscillometric device (OMRON brand, model HEM-742) was used to collect the measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP). All the measurements were taken in the left arm, with the individuals seated at rest for a minimum of five minutes. The cut-off points recommended by the VI Guidelines for Hypertension25) were adopted, in which individuals with blood pressure equal to or greater than 140/90 were considered as presenting high blood pressure. Teachers were questioned about diagnostic of hypertension and use of blood pressure lowering drugs. Those teachers who report to have medical diagnostic of hypertension and/or to take lowering blood pressure drugs were considered as having high blood pressure, independently of their blood pressure values at data collection.
Heart rate
The digital oscillometric device (OMRON brand, model HEM-742) was also used to assess the resting heart rate, with the participant seated at rest for at least five minutes prior the collect. The resting heart rate was divided into quartiles and teachers in the highest quartile (Q4) were considered as presenting high resting heart rate.
Socioeconomic condition
The Brazilian Economic Classification Criteria26) was used to assess the socioeconomic condition of the sample. This instrument considers the level of education, and the presence and quantity of certain rooms and consumer goods at home (i.e. television, DVD player, bathrooms, car, washing machine, freezer) and classifies the sample into economic classes according its specific scoring, from higher to lower: A1, A2, B1, B2, C1, C2, D, and E. For the characterization of sample, the socioeconomic condition was classified according to the power of consumption criteria of instrument in socioeconomic classes high (A1, A2), medium (B1, B2, C1), and low (C2, D, E).
Statistical analysis
Characterization variables of the sample are expressed as mean and standard deviation for continue variables and as frequency for categorical variables. The mean differences were analyzed by t-test for independent samples and the association between high sedentary behavior and sedentary breaks with independent variables (obesity, high blood pressure, high resting heart rate) was assessed by the χ2 test. All variables were considered as independent variables in the multiple model, evaluated by binary logistic regression, in its unadjusted and adjusted form (sex, age, and socioeconomic condition). The statistical significance adopted was 5% and a confidence interval of 95%. The statistical package SPSS version 15.0 was used for all analyses.
Patient and public involvement
Patients and or public were not involved in the research.
Results
The sample consisted of 245 individuals (approximately 38% of the city’s teachers), of which 186 were female (76%) and 59 male (24%), with a mean age of 45.20 ± 10.42 yr. The prevalence of socioeconomic status in the sample was 5.7% of high, 91.0% of medium, and 3.3% of low socioeconomic class. The prevalence of sedentary behavior in the teachers participating in this study was 55.3%, and this prevalence was higher in male teachers, 69.5%; in women the prevalence was 50.8% (p=0.018). The characterization variables of sample were stratified according to the level of sedentary behavior (low or high) and are presented in the Table 1. Teachers with high sedentary behavior presented lower age (43.5 yr vs. 47.3 yr, p=0.006) and higher waist circumference (89.6 cm vs. 85.8 cm, p=0.038) than those teachers with low sedentary behavior.
Table 1. Characterization of the sample.
| Variables | Low SB Mean (SD) |
High SB Mean (SD) |
p-value* |
|---|---|---|---|
| Age (yr) | 47.27 (9.93) | 43.53 (10.53) | 0.006 |
| Weight (kg) | 72.09 (15.97) | 75.91 (17.41) | 0.078 |
| Height (cm) | 163.25 (7.95) | 165.23 (8.89) | 0.070 |
| Body mass index (kg/m2) | 27.03 (5.50) | 27.66 (5.32) | 0.360 |
| Waist circumference (cm) | 85.80 (13.73) | 89.58 (14.63) | 0.038 |
| SBP (mmHg) | 126.05 (17.58) | 125.39 (17.72) | 0.771 |
| DBP (mmHg) | 78.09 (11.22) | 79.56 (11.21) | 0.310 |
| HR (mmHg) | 77.68 (12.23) | 78.83 (12.05) | 0.462 |
*p-value of t-test for independent samples. SB: sedentary behavior; SD: standard deviation; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate.
Table 2 presents information of association between high sedentary behavior and independent variables. Teachers with abdominal obesity presented a prevalence of high sedentary behavior higher than teachers without abdominal obesity (65.2% vs. 46.6%, p=0.005). Abdominal obesity was observed in 112 teachers, which corresponds to 45.7% of sample.
Table 2. Prevalence of cardiovascular risk factors according to high sedentary behavior in public school teachers.
| Variables | Total (n=245) N |
High SB (n=135) n (%) |
p-value* | |
|---|---|---|---|---|
| Body mass index | ||||
| Normal | 98 | 51 (52.0) | 0.512 | |
| Overweight | 147 | 84 (57.1) | ||
| Waist circumference | ||||
| Normal | 133 | 62 (46.6) | 0.005 | |
| Abdominal obesity | 112 | 73 (65.2) | ||
| Blood pressure | ||||
| Normal | 140 | 78 (55.7) | 0.926 | |
| High | 105 | 57 (54.3) | ||
| Heart rate | ||||
| Normal | 180 | 101 (56.1) | 0.702 | |
| High | 65 | 34 (52.3) | ||
*p-value for χ2 test. SB: Sedentary behavior.
Table 3 presents information on the magnitude of associations between sedentary behavior and independent variables. Teachers with high sedentary behavior were more than twice as likely to have abdominal obesity when compared to teachers with low sedentary behavior, regardless sex, age, and socioeconomic status (Odds ratio= 2.21, 95% CI: 1.23; 3.97, p=0.008). No association was observed of high sedentary behavior with overweight, high blood pressure, and high heat rate among teachers.
Table 3. Association between high sedentary behavior and cardiovascular risk factors in public school teachers.
| Variables | Not adjusted | Adjusted | |||||
|---|---|---|---|---|---|---|---|
| OR | 95%CI | p-value | OR | 95%CI | p-value | ||
| Body mass index | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Overweight | 1.22 | 0.73–2.05 | 0.432 | 1.33 | 0.78–2.30 | 0.296 | |
| Waist circumference | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Abdominal obesity | 2.14 | 1.27–3.59 | 0.004 | 2.21 | 1.23–3.97 | 0.008 | |
| Blood pressure | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.94 | 0.56–1.57 | 0.824 | 1.08 | 0.63–1.85 | 0.780 | |
| Heart rate | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.85 | 0.49–1.51 | 0.597 | 0.90 | 0.49–1.64 | 0.896 | |
Analysis adjusted by sex, age, and socioeconomic status. OR: odds ratio; CI: confidence interval.
Table 4 presents information about high breaks in sedentary behavior at work and independent variables. No association was observed between high breaks in sedentary behavior at work and overweigh, abdominal obesity, high blood pressure, and high heart rate for both unadjusted and adjusted analysis.
Table 4. Association between high breaks in sedentary behavior at work and cardiovascular risk factors in public school teachers.
| Not adjusted | Adjusted | ||||||
|---|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | ||
| Body mass index | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Overweight | 0.74 | 0.42–1.29 | 0.299 | 0.78 | 0.49–1.39 | 0.409 | |
| Waist circumference | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Abdominal obesity | 0.99 | 0.58–1.72 | 0.987 | 0.92 | 0.51–1.68 | 0.810 | |
| Blood pressure | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.90 | 0.52–1.55 | 0.713 | 1.00 | 0.57–1.76 | 0.983 | |
| Heart rate | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.93 | 0.53–1.78 | 0.931 | 0.89 | 0.47–1.69 | 0.732 | |
Analysis adjusted by sex, age, and socioeconomic status. OR: odds ratio; CI: confidence interval.
Table 5 presents the association of high breaks in sedentary behavior at leisure time with independent variables. Teachers who report high sedentary breaks at leisure were 44% less likely to have high blood pressure than those teachers who report low sedentary breaks in unadjusted analysis. This association remained significant even after adjustment for sex, age, and socioeconomic status (Odds ratio=0.58, 95% CI: 0.32; 0.98, p=0.042). There was no association between high breaks in sedentary behavior and overweight, abdominal obesity, and elevated heart rate among teachers.
Table 5. Association between high breaks in sedentary behavior at leisure and cardiovascular risk factors in public school teachers.
| Not adjusted | Adjusted | ||||||
|---|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | ||
| Body mass index | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Overweight | 0.65 | 0.36–1.15 | 0.139 | 0.67 | 0.37–1.20 | 0.176 | |
| Waist circumference | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| Abdominal obesity | 0.69 | 0.40–1.20 | 0.195 | 0.70 | 0.38–1.28 | 0.252 | |
| Blood pressure | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.56 | 0.32–0.97 | 0.042 | 0.58 | 0.32–0.98 | 0.042 | |
| Heart rate | |||||||
| Normal | 1.00 | Reference | 1.00 | Reference | |||
| High | 0.65 | 0.36–1.19 | 0.170 | 0.55 | 0.29–1.03 | 0.063 | |
Analysis adjusted by sex, age, and socioeconomic status. OR: odds ratio; CI: confidence interval.
Discussion
The results of this study showed a predominance of female teachers, and a prevalence of sedentary behavior of 55.3%. High sedentary behavior was related to abdominal obesity, with teachers who reported this behavior being 2 times more likely to present abdominal obesity. Regarding breaks in sedentary behavior: at work, there were no significant associations with any of the studied variables; however, in leisure, it was observed that teachers who interrupted sitting time more often were 42% less likely to present high blood pressure, even after adjustment for confounders.
The prevalence of high sedentary behavior in the present study was lower than findings reported by a systematic review of Rezende et al.27), who observed a general prevalence of 62% of high sedentary behavior in a wide sample of adults from 54 countries. This difference may be related to the high prevalence of female teachers in the present study, which was also observed in a previous study among teachers15). Women tend to be less sedentary than men due to a double journey between work and domestic tasks15).
It was observed at the present study that teachers with high sedentary behavior presented significantly lower age than teachers with low sedentary behavior. A possible hypothesis may be related to the type of sedentary behavior assessed in the present study, which was in regard screen time. It was observed that younger adults use more breadth of technology than older adults28, 29), which may result in a wide range of daily tasks through screen devices, for both work and entertainment activities, increasing their screen time in different devices and, consequently, overall sedentary behavior.
Sedentary behavior can occur in different domains of daily life, in leisure, work, or travel. In our study, it was observed an association between high sedentary behavior and abdominal obesity. This finding was in accordance to previous studies in literature among teachers30) and overall adult population6, 31,32,33). Thorp et al.34) observed that just a 1-h increase in daily TV viewing has already been associated with increased waist circumference. A possible hypothesis for this association is that sedentary activities promotes a lower energy expenditure and take place of other daily activities even of light intensity, as well as encourages the consumption of high caloric foods35), which may result in higher adiposity.
Although previous studies showed that sedentary behavior was associated with an increased risk of hypertension36) and higher mortality rates for cardiovascular diseases37), the present study observed no association between high sedentary behavior and high blood pressure among teachers. Otherwise, teachers who report high breaks in sedentary behavior al leisure time were less likely to have high blood pressure than teachers who reported low sedentary breaks. Convergently with our findings, other studies previously observed benefits of sedentary breaks in cardiometabolic health4, 19, 35, 38). A possible hypothesis is that individuals who perform more breaks in sedentary activities have higher total energy expenditure than those who break less, which may prevent body fat gain and increase muscular contractions, lowering risk of developing harmful alterations in metabolic markers19).
The breaks in sedentary behavior at work environment was not associated with cardiovascular risk factors in the present study. A possible reason may be due to teachers has the majority of their workload standing14,15,16,17), which represents a predominantly non-sedentary work activities and could not be significantly affected by breaks in this domain (work). Otherwise, leisure time activities of teachers may be more susceptible to sedentary choices, as television viewing, computer and cellphone use, and consequently be significantly affected by sedentary breaks at this domain. Another factor is that sedentary behavior has been associated with unhealthy metabolic health, regardless of physical activity levels39) and high sedentary breaks at leisure may be able to mitigate the health impairments of sedentary behavior by reducing its accumulation in longer periods and by decreasing the sedentary time overall. Healy et al.40) suggest that reductions of 1–2 h in sedentary time can already result in substantial reductions in the risk of cardiovascular disease.
As limitations, the cross-sectional design of the study does not allow to infer about cause and effect relationships. Another limiting factor is that self-report information of sedentary behavior may be subject to biases, although has been able to assess the specific sedentary behavior of a domain (e.g., work-related, entertainment)41). Besides that, the use of lowering heart rate drugs was not assessed in the sample and may compromise the findings.
Otherwise, among the strength of the study, is important to highlight the randomly selected sample and analysis adjusted for confounding factors, as sex, age, and socioeconomic status. Besides that, the present study focused on different patterns of sedentary behavior (sedentary behavior and sedentary breaks), as well as different domains of occurrence (at work and at leisure) and analyzed its association with cardiometabolic risk factors among teachers, while majority of studies at school environment were focused only in students. It should also be noted that the data collection was performed at the work environment (school) and cardiovascular risk factors were objectively measured.
In summary, a prevalence of high sedentary behavior of 55.3% was observed. High sedentary behavior was related to the high prevalence of abdominal obesity, and the teachers who reported this behavior were 2 times more likely to present abdominal obesity. Regarding breaks in sedentary behavior at work, there were no significant associations with any of the studied variables. However, teachers who report high sedentary behavior at leisure time were 42% less likely to present high blood pressure, even after adjustment for confounders. As practical applications, encouraging frequent breaks in sedentary activities, even brief periods of light intensity physical activities, may be a viable and effective approach to reducing cardiovascular risk factors among teachers.
Acknowledgments
The authors would like to thank the Educational Department of Presidente Prudente, the manager and teachers from the assessed schools which allowed and participate of the research, and the CAPES−Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil for the funding in part of the study (Finance code 001).
References
- 1.Sedentary Behaviour Research Network (2012) Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab 37, 540–2. [DOI] [PubMed] [Google Scholar]
- 2.Mailey EL, Rosenkranz SK, Casey K, Swank A. (2016) Comparing the effects of two different break strategies on occupational sedentary behavior in a real world setting: a randomized trial. Prev Med Rep 4, 423–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Pate RR, O’Neill JR, Lobelo F. (2008) The evolving definition of “sedentary”. Exerc Sport Sci Rev 36, 173–8. [DOI] [PubMed] [Google Scholar]
- 4.Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, Owen N. (2008) Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care 31, 661–6. [DOI] [PubMed] [Google Scholar]
- 5.Compernolle S, De Cocker K, Teixeira PJ, Oppert JM, Roda C, Mackenbach JD, Lakerveld J, McKee M, Glonti K, Rutter H, Bardos H, Cardon G,, De Bourdeaudhuij I, WP3 SPOTLIGHT group (2016) The associations between domain-specific sedentary behaviours and dietary habits in European adults: a cross-sectional analysis of the SPOTLIGHT survey. BMC Public Health 16, 1057–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Owen N, Healy GN, Matthews CE, Dunstan DW. (2010) Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 38, 105–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Warren TY, Barry V, Hooker SP, Sui X, Church TS, Blair SN. (2010) Sedentary behaviors increase risk of cardiovascular disease mortality in men. Med Sci Sports Exerc 42, 879–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Brito WF, Santos CL, Marcolongo AA, Campos MD, Bocalini DS, Antonio EL, Silva Junior JA, Tucci PJ, Serra AJ. (2012) Physical activity levels in public school teachers. Rev Saude Publica 46, 104–9. [DOI] [PubMed] [Google Scholar]
- 9.van Uffelen JGZ, Heesch KC, Hill RL, Brown WJ. (2011) A qualitative study of older adults’ responses to sitting-time questions: do we get the information we want? BMC Public Health 11, 458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kikuchi H, Inoue S, Odagiri Y, Inoue M, Sawada N,, Tsugane S, Japan Public Health Centre (JPHC) study group (2015) Occupational sitting time and risk of all-cause mortality among Japanese workers. Scand J Work Environ Health 41, 519–28. [DOI] [PubMed] [Google Scholar]
- 11.van Uffelen JGZ, Wong J, Chau JY, van der Ploeg HP, Riphagen I, Gilson ND, Burton NW, Healy GN, Thorp AA, Clark BK, Gardiner PA, Dunstan DW, Bauman A, Owen N, Brown WJ. (2010) Occupational sitting and health risks: a systematic review. Am J Prev Med 39, 379–88. [DOI] [PubMed] [Google Scholar]
- 12.Owen N. (2012) Sedentary behavior: understanding and influencing adults’ prolonged sitting time. Prev Med 55, 535–9. [DOI] [PubMed] [Google Scholar]
- 13.Saidj M, Menai M, Charreire H, Weber C, Enaux C, Aadahl M, Kesse-Guyot E, Hercberg S, Simon C, Oppert JM. (2015) Descriptive study of sedentary behaviours in 35,444 French working adults: cross-sectional findings from the ACTI-Cités study. BMC Public Health 15, 379–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Vedovato TG, Monteiro I. (2014) Health conditions and factors related to the work ability of teachers. Ind Health 52, 121–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Cardoso JP, Ribeiro IQB, Araújo TM, Carvalho FM, Reis EJFB. (2009) Prevalência de dor musculoesquelética em professores. Rev Bras Epidemiol 12, 604–14. [Google Scholar]
- 16.Dias DF, Loch MR, González AD, Andrade SM, Mesas AE. (2017) Insufficient free-time physical activity and occupational factors in Brazilian public school teachers. Rev Saude Publica 51, 68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Delcor NS, Araújo TM, Reis EJFB, Porto LA, Carvalho FM, Oliveira e Silva M, Barbalho L, de Andrade JM. (2004) Labor and health conditions of private school teachers in Vitória da Conquista, Bahia, Brazil. Cad Saude Publica 20, 187–96. [DOI] [PubMed] [Google Scholar]
- 18.Meneguci J, Santos DAT, Silva RB, Santos RG, Sasaki JE, Tribess S, Damião R, Virtuoso Júnior JS. (2015) Comportamento Sedentário: conceito, implicações fisiológicas e os procedimentos de avaliação. Rev Motricidade 11, 160–74. [Google Scholar]
- 19.Vaz M, Bharathi AV. (2004) How sedentary are people in “sedentary” occupations? The physical activity of teachers in urban South India. Occup Med (Lond) 54, 369–72. [DOI] [PubMed] [Google Scholar]
- 20.A cidade—site oficial da prefeitura municipal de Presidente Prudente (2017) http://www.presidenteprudente.sp.gov.br/site/acidade.xhtml. Accessed June 14, 2017.
- 21.Agranonik M, Hirakata VN. (2011) Cálculo de tamanho de amostra: proporções. Rev HCPA 31, 382–8. [Google Scholar]
- 22.Sedentary Behaviour Research Network (2016) http://www.sedentarybehaviour.org/sedentary-behaviour-questionnaires/. Accessed January 12, 2016.
- 23.van der Ploeg HP, Chey T, Korda RJ, Banks E, Bauman A. (2012) Sitting time and all-cause mortality risk in 222 497 Australian adults. Arch Intern Med 172, 494–500. [DOI] [PubMed] [Google Scholar]
- 24.Grundy SM, Cleeman JI, Merz CNB, Brewer HB, Jr, Clark LT, Jr, Hunninghake DB, Pasternak RC, Smith SC, Jr, Stone NJ, Coordinating Committee of the National Cholesterol Education Program (2004) Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines. J Am Coll Cardiol 44, 720–32. [DOI] [PubMed] [Google Scholar]
- 25.Brasileira de Cardiologia S, Brasileira de Hipertensão S, Brasileira de Nefrologia S. (2010) VI Diretrizes Brasileiras de Hipertensão. Arq Bras Cardiol 95, 1–51. [PubMed] [Google Scholar]
- 26.Instituto Brasileiro de Opinião Pública e Estatística (2009) Levantamento sócio econômico. http://www.abep.org/novo/CMS/Utils/FileGenerate.ashx?id=46. Accessed March 8, 2016.
- 27.Rezende LFM, Sá TH, Mielke GI, Viscondi JYK, Rey-López JP, Garcia LMT. (2016) All-cause mortality attributable to sitting time: analysis of 54 countries worldwide. Am J Prev Med 51, 253–63. [DOI] [PubMed] [Google Scholar]
- 28.Czaja SJ, Charness N, Fisk AD, Hertzog C, Nair SN, Rogers WA, Sharit J. (2006) Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychol Aging 21, 333–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Olson KE, O’Brien MA, Rogers WA, Charness N. (2011) Diffusion of technology: frequency of use for younger and older adults. Ageing Int 36, 123–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mota Júnior RJ. (2016) Avaliação dos fatores de risco cardiovascular e síndrome metabólica em professores da Educação Básica da rede privada de Viçosa, Minas Gerais [dissertação]. Viçosa (Minas Gerais): Universidade Federal de Viçosa.
- 31.Cooper AR, Sebire S, Montgomery AA, Peters TJ, Sharp DJ, Jackson N, Fitzsimons K, Dayan CM, Andrews RC. (2012) Sedentary time, breaks in sedentary time and metabolic variables in people with newly diagnosed type 2 diabetes. Diabetologia 55, 589–99. [DOI] [PubMed] [Google Scholar]
- 32.Owen N, Sparling PB, Healy GN, Dunstan DW, Matthews CE. (2010) Sedentary behavior: emerging evidence for a new health risk. Mayo Clin Proc 85, 1138–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Katzmarzyk PT, Church TS, Craig CL, Bouchard C. (2009) Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc 41, 998–1005. [DOI] [PubMed] [Google Scholar]
- 34.Thorp AA, Owen N, Neuhaus M, Dunstan DW. (2011) Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am J Prev Med 41, 207–15. [DOI] [PubMed] [Google Scholar]
- 35.Calegari K. (2012) Associação entre compulsão alimentar e sedentarismo: fatores que levam a obesidade. Rev Obesidade Nutr Emagrecimento 6, 242–53. [Google Scholar]
- 36.Guerra PH, Mielke GI, Garcia LMT. (2014) Comportamento sedentário. Rev Corpoconsciencia 18, 23–6. [Google Scholar]
- 37.Sardinha LB, Magalhães J. (2012) Comportamento Sedentário—epidemiologia e Relevância. Rev Factores Risco 27, 54–64. [Google Scholar]
- 38.Chastin SFM, Egerton T, Leask C, Stamatakis E. (2015) Meta-analysis of the relationship between breaks in sedentary behavior and cardiometabolic health. Obesity (Silver Spring) 23, 1800–10. [DOI] [PubMed] [Google Scholar]
- 39.Bowden Davies KA, Sprung VS, Norman JA, Thompson A, Mitchell KL, Harrold JOA, Finlayson G, Gibbons C, Wilding JPH, Kemp GJ, Hamer M, Cuthbertson DJ. (2019) Physical activity and sedentary time: association with metabolic health and liver fat. Med Sci Sports Exerc 51, 1169–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Healy GN, Matthews CE, Dunstan DW, Winkler EA, Owen N. (2011) Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003-06. Eur Heart J 32, 590–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE. (2011) Measurement of adults’ sedentary time in population-based studies. Am J Prev Med 41, 216–27. [DOI] [PMC free article] [PubMed] [Google Scholar]