In our cohort we observed a number of new findings. First, a high level of SB was reported (9.1 h/day at weekdays; 7.4 h/day at weekend days), despite 86% of our population meeting the recommended physical activity dose. Second, we found that the majority of sedentary time was spent during leisure time activities and not at work. Third, younger age, male sex, being unmarried, higher education level, being employed, a higher BMI and higher physical activity levels were independently associated with higher levels of total SB, although this link was not consistently present across all SB domains. Other factors such as smoking status, cancer and health status were associated with specific domains of SB. Our results suggest that, SB is highly prevalent in physically active subjects and that correlates of SB differ across the various domains. Interventions to reduce SB might benefit from domain-specific targets, whilst the relative importance of these SB domains may differ between groups and/or individuals.
Prevalence of sedentary behavior
In our study, we found a median total sedentary time of 9.1 h/day on weekdays and 7.4 h/day on weekend days. These results highlight that SB is not only present in the general population, but also highly prevalent in physically active individuals. This observation suggests that public health interventions to reduce sedentary behavior should be developed on a population-wide scale. Interestingly, the amount of sedentary time in our physically active cohort was ~ 1–4 h/day higher compared to previous studies [
10,
18‐
21]. This discrepant finding may relate to the observation that Dutch individuals sit more compared to other European countries [
18].
Interestingly, most sedentary time was spent during leisure time activities rather than during work. This observation differs from previous findings [
21‐
23]. The difference might be partially explained by the relatively high percentage of older individuals who stopped working (25%) in our population compared to previous articles [
21‐
23] (all working adults). Another explanation relates to differences in the physical activity levels of the profession, but most individuals in our population were highly educated, who typically perform desk-based office work. Alternatively, occupational sitting time was derived from a single item in the questionnaire, whereas leisure time sitting was calculated from seven items. Hence, study participants may have been reluctant to score a high sedentary time on a single item. Another study using the Sedentary Behavior Questionnaire also found higher levels of leisure time sitting compared to sedentary time at work [
14]. Nonetheless, our observations may have important implications for SB interventions in physically active individuals. Since the time spent sedentary during leisure time is significantly higher compared to occupational sitting, workplace interventions for reducing total sedentary time might have limited effects in our population. Possibly, interventions focused on reducing SB during leisure time (e.g. watching TV, eating and drinking, and computer use) may be more relevant, especially since this type of SB counts for 51% of the total SB time in our population.
Correlates of total and domain-specific sedentary behavior
Younger age, male sex, being unmarried, higher education level, being employed and a higher BMI were independently associated with higher levels of total SB. SB correlates identified in the present study align with previous findings [
8,
21‐
26], but the direction of the association is different for age, smoking status and physical activity. In our study, age was negatively associated with high levels of sedentary time, which is in contrast to other studies, which found a positive association [
20,
27]. A potential explanation for this distinction is that adults in our study, aged 25–64, reported much higher levels of sedentary time (median 9.3 h/day), compared to other studies (4–8 h/day) [
18,
20,
27]. In contrast, our older adult population reported relatively lower levels of sedentary time [
28]. A previous study found that retired individuals had higher levels of leisure time SB and physical activity [
29], which is comparable with our study results in individuals of ≥65 years old. Former smoking was associated with lower levels of occupational sedentary time and with higher levels of leisure time sitting, compared to individuals who never smoked. In addition, we did not find any association in smokers, which is surprising and may relate to the low number (
n = 477, 6%) of smokers in our population. Previous literature has shown that current smoking was associated with TV viewing, but not with total sedentary time [
30]. Finally, physical activity was only associated with total sedentary time and not in different domains of SB. A cross-sectional study in 34,555 working adults found that physically active individuals were less sedentary in all domains [
23]. The difference between our study and previous findings might be explained by the fact that our participants are mostly active, so maybe the amount of physical activity does not influence domains of SB in an already active population. Another explanation for the discrepant findings of the present study may relate to differences in cohort characteristics. Our cohort includes individuals who received mostly higher education, reporting high levels of physical activity, a low smoking status (6%), and a high self-reported health status (90% good to very good), which is different from most general population cohorts [
22‐
24,
31].
Poor health status related to higher volumes of leisure time sitting, which is similar with findings from other studies [
31]. However, heath-related issues such as cardiovascular diseases, hypertension, hypercholesterolemia and diabetes mellitus were not associated with sedentary time, which is in contrast to other studies suggesting that patients with cardiovascular diseases and diabetes mellitus are more sedentary compared to healthy controls [
32,
33]. Opposite associations were also found for the relationship between cancer and sedentary time [
34]. An important difference between our study and previous work is that the majority of the patient population in our study is physically active, might have a healthier lifestyle (only 6% smokers) and had a good self-reported health status (90% reported good to very good), which is not the case in most general patient populations. This may partially explain the different findings in our population. Still, future studies are needed to confirm our results. Especially studies in large populations investigating combinations of different correlates using multivariable regression models are necessary, because most current studies investigated only small groups of correlates. At least, our results suggest that presence of disease by definition is not automatically related to larger volumes of SB.
In this study, we hypothesized that subject-, lifestyle- and health- characteristics relate to SB, but that the magnitude of these associations may differ across SB domains. Indeed, education levels were associated with occupational SB but not with leisure and transportation SB. In addition, the association with age and cancer had a different direction for each domain. These findings indicate that some, but not all, SB correlates are domain specific, suggesting that tailored interventions may be needed to reduce SB across different domains and in specific target groups. Stratified analyses between active and inactive individuals confirmed our main analyses, but some differences were found. For example, discrepant results were found for marital status, BMI, some factors of disease history and lifestyle. Further, in this study we found opposite directions for association between age, current smoking status and physical activity, and high levels of SB compared to other previous mentioned literature. Although the directions were similar for active and inactive individuals in this study, the magnitude was larger in the active individuals. Further studies should determine, whether these differences in associations between active and inactive individuals do exist or whether this due to a lower sample size of inactive individuals.
Strengths and limitations
The strengths of this study include a large cohort of mostly physically active individuals with a broad range of potential correlates. In addition, we used an extended questionnaire to inquire SB in three domains. We also asked the participants about their sleeping time and their physical activities to get a 24-h overview. However, limitations of our study include self-reported data on SB, physical activity and disease history, which all may cause measurement errors. In addition, occupation SB was measures with only one items, whereas leisure time SB was measures with seven items. Self-reported SB may be underestimated [
35] and self-reported physical activity [
36] overestimated compared to objectively measured SB and physical activity. However, due to the sample size of the study it was not feasible to use objective measures for SB and physical activity. In addition, objective measures of SB could not distinguish between different domains in which SB could take place. Future studies are needed to examine correlates of objectively measured sedentary time and patterns of SB, but in order to distinguish between domains, objective measures should be combined with subjective measures. Finally, we performed cross-sectional analyses of the associations between subject characteristics and sedentary time. Although the aim of the study was not to examine causation, it could be informative to investigate which correlates are associated with reductions in sedentary time using repeated measurements of SB. This may further improve interventions which aim to reduce sedentary time.