Skip to main content
Download PDF

Associations of accelerometer-based sedentary bouts with adiposity markers among German adults – results from a cross-sectional study

BMC Public Health volume 23, Article number: 469 (2023) Cite this article

Abstract

Background

Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.

Methods

In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m− 2) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.

Results

Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m− 2 (SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; p = 0.047) and the daily number of >30-minute bouts was positively associated with WC (b = 0.330; p = 0.001). All other associations were not statistically significant.

Conclusion

The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.

Trial registration

Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).

Peer Review reports

Introduction

Higher amounts of sedentary behaviour have been found to be associated with a range of health risks including incidence of cardiovascular disease [1, 2], type-2 diabetes [1, 3, 4], and cardio-metabolic risk factors such as higher waist circumference (WC) and body mass index (BMI) [4]. Also, evidence on detrimental relationships of total sedentary time with cardiovascular [3, 5] and all-cause mortality [3, 6] has accumulated. Amongst other eminent scientific authorities [7, 8], the World Health Organization recommends in their recently updated guidelines on physical activity and sedentary behaviour that ‘adults should limit the amount of time spent being sedentary’ [9].

In addition to limiting total sedentary time, several countries e.g., Australia [10], Canada [11], Germany [12], or the United Kingdom [13] have included the recommendation to break up prolonged periods of sitting in their national public health guidelines. Since the 2000s, evidence on deleterious effects of sedentary behaviour patterns has grown suggesting that prolonged sitting periods without interruptions may increase cardio-metabolic health risks in addition to those raised from total amounts of sedentary time. According to the Sedentary Behavior Research Network, sedentary behaviour patterns can be defined as ‘the manner in which sedentary behaviour is accumulated throughout the day or week while awake’, with a sedentary bout being defined as ‘a period of uninterrupted sedentary time’ and a sedentary break as ‘a non-sedentary bout in between two sedentary bouts’ [14]. There is some evidence from observational studies on associations between various indicators of sedentary behaviour patterns and cardio-metabolic biomarkers, such as WC [15,16,17,18,19,20], BMI [17, 18, 20, 21], triglycerides [15, 17, 20], and 2-hour plasma glucose [15]. Few studies have investigated prospective outcomes such as incidence of cardiovascular disease [2, 22] or all-cause mortality [22,23,24,25,26]. As findings on deleterious associations are not consistent across and within studies, experts come to conclude that the existing evidence to date remains insufficient and inconclusive [7, 9, 27].

Sedentary bouts and breaks have been operationalized using various minimum durations with the study that first introduced the concept classifying each ≥ 1 min interruption after ≥ 1 min of sedentary time as a break [15, 16]. The choice of minimum durations affect the number of both breaks and bouts being observed [28]. In the aforementioned study [15], quartiles of breaks in sedentary time with metabolic risk variables were reported showing that participants with more than 673 sedentary breaks across the entire data-collection period had a significantly lower WC than those with less than 506 breaks. However, these results may be hard to translate into a feasible and acceptable public health message. Thus, several studies used cut offs in order to differentiate between sedentary bouts with respect to their length [17,18,19, 24, 26]. Among the variety of thresholds applied, the most frequently used was > or ≥ 30 min, respectively, in order to examine health risks of prolonged sitting periods. To additionally differentiate between bouts of short and moderate length and yet with respect to practicability of sedentary interruptions in everyday life, setting another threshold at a duration of not shorter than 10 min seems reasonable.

One issue that has been discussed in the literature is, whether benefits of sedentary breaks simply reflect favourable effects of higher amounts of physical activity that is performed during those breaks [27]. Most of the aforementioned studies took account of total sedentary time and moderate-to-vigorous physical activity (MVPA) as potential confounders. However, some studies did not adjust for light physical activity (LPA) [2, 15, 17, 19, 23], possibly due to problems arising from collinearity between the device-based measures. In recent years, compositional data analysis (CoDA) [29, 30] has gained more and more attention, as this approach enables to simultaneously account for relative amounts of total sedentary time, LPA, and MVPA. Thus, applying CoDA strengthens the rationale behind the attempt to answer the research question whether to promote breaking up sedentary time in addition to already established recommendations on increasing physical activity and reducing total amounts of sedentary time.

Among the variety of cardio-metabolic biomarkers, WC and BMI are easy to assess and with results straightforward to communicate to the public. To our best knowledge, this is the first study among German adults that investigated associations between sedentary behaviour patterns and cardio-metabolic biomarkers. Thus, the aim of our study was to investigate associations of short sedentary bouts with a length of 1 to 10 min, moderate sedentary bouts with a length of >10 to 30 min, and long sedentary bouts with a length of >30 min with two indicators of adiposity, i.e., WC and BMI, accounting for accelerometer-based time-use compositions (i.e., total sedentary time and physical activity).

Methods

Participants and procedure

We combined socio-demographic, anthropometric, and accelerometer data from apparently healthy adults, collected in three previous studies. All studies took place in the area of Greifswald in Northern Germany between 2012 and 2018. Detailed description of the design and sampling procedures for each study are reported elsewhere [31,32,33]. In short, study 1 (number of the ethical approval: BB 64/07) [31] was a cross-sectional study comprising a two-stage cardio-preventive screening and examination program. Participants were recruited in general practices, job centres, and via statutory health insurance between June 2012 and December 2013. A subsample of 231 participants wore an accelerometer for seven consecutive days. Study 2 (BB 002/15a) [32] was a longitudinal study to investigate the feasibility of a computerized, tailored letter intervention to increase physical activity and to reduce sedentary time. The sample was randomly drawn from those of study 1 who agreed to be contacted again. The study was conducted between February 2015 and August 2016. For the present analysis, only data from baseline measurements were used derived from a sample of 175 participants. Further, participants from study 2 were excluded if they already participated in accelerometry in study 1. Study 3 (BB 076/18) [33] was a cross-sectional study to investigate the agreement of self-reported and accelerometer-based physical activity measures. Participants were recruited at a shopping mall between May and December 2018 and the final sample comprised 365 individuals.

Data from participants were included in the current analyses if (i) socio-demographic, anthropometric, and accelerometer data were complete, (ii) participants had no history of cardiovascular events (myocardial infarction, stroke) or vascular intervention, no diabetes mellitus, and a BMI ≤ 35 kg m− 2, (iii) the same accelerometer wearing protocol was applied for all participants, and (iv) the accelerometer was worn for ≥ 10 h on ≥ 4 days, regardless of whether these days were weekend days or not [34]. The total sample of the present analysis comprised 460 participants.

Measures

Waist circumference and body mass index

WC (cm) and BMI (kg m− 2) were assessed at the cardiovascular examination center of the University Medicine Greifswald by trained and certified medical staff. WC was measured midway between lowest rib and iliac crest using an inelastic tape. Body weight and height were measured with digital scales (Soehnle Industrial solutions GmbH, catalog number SOEHNLE 7720 and ADE GmbH & Co., catalog number MZ 10020, respectively). BMI was calculated by dividing body weight in kg by height in m squared.

Accelerometer-based measures

Physical activity and sedentary time were obtained using tri-axial ActiGraph Model GT3X + accelerometers (Pensacola, FL) worn on the right hip attached to an elastic belt for seven consecutive days. Participants were instructed to wear the accelerometer during waking hours and to put it off for water-based activities such as morning hygiene or swimming. Using ActiLife version 6.13.3 (ActiGraph, Pensacola, FL), the accelerometers were initialized at a sampling rate of 100 Hz (study 1 and 2) or 30 Hz (study 3) and raw data were integrated into 10 s epochs. Data from the vertical axis were used. ActiGraph accelerometers provide counts as the output metric. To identify accelerometer wear time as well as time spent in different intensities of physical activity, intensity cut points were applied according to Troiano and colleagues [35]: Wear time was determined by removing non-wear time defined as at least 60 min of consecutive zero counts, allowing for 2 min of counts between 0 and 100. Time spent in MVPA was determined by summing minutes per day where the accelerometer count met the intensity-threshold criterion of 2020 counts/minute (i.e., activities of three metabolic equivalents of task or more such as brisk walking). LPA was defined as 100–2019 counts/minute. Time with less than 100 counts/minute was defined as sedentary time [35]. Because time spent in physical activity and time spent sedentary are compositional components of total time (i.e., accelerometer wear time), these variables were expressed as proportions of total time (sedentary time, LPA, and MVPA) and then isometric log-ratio transformed [22, 30] to the following z parameters that were subsequently used as covariates in analyses.

$${z_1}\, = \,\surd \frac{2}{3}{\rm{ln}}\frac{{sedentary\,time}}{{\sqrt {LPA\,{\rm{x}}\,MVPA} }}$$
(1)
$${z_2}\, = \,\surd \frac{1}{2}{\rm{ln}}\frac{{LPA}}{{MVPA}}$$
(2)

A sedentary bout ended when sedentary time was interrupted for ≥ 1 min in which the accelerometer count rose up to or above 100 counts/minute. The mean daily number of bouts with a length of 1 to 10 min, >10 to 30 min, and >30 min was analysed.

Covariates

Sex, age, school education (< 10 years, 10 years, >10 years), employment (employed, unemployed or retired), and current smoking (yes, no) were obtained by a self-administered questionnaire. Variables related to data collection included study (study 1, study 2, study 3) and season of data collection (spring or summer, autumn or winter).

Statistical analysis

Multilevel mixed-effects linear regressions were used to examine the associations of sedentary bouts with WC and BMI including study as a higher-level group variable. Models were estimated using the xtmixed command in Stata version 15.1 (Stata Corp, 2017). A maximum likelihood estimator with robust standard errors was chosen. P values below 0.05 were considered significant. Several models were calculated for each outcome in the following way. First, WC was regressed on the number of sedentary 1-to-10-minute bouts per day with adjustment for basic covariates including sex and age (basic model). Second, the following covariates were added to the model: school education, employment, current smoking, season of data collection [36], and composition of accelerometer-based time use in terms of the isometric log-ratio transformed z parameters (adjusted model). Adding the latter enabled to account for potential confounding of associations between sedentary bouts and adiposity markers with relative amounts of total sedentary time and physical activity. Likelihood ratio tests were used to compare models including a quadratic term of the continuous covariate age to linear models in order to test for nonlinearity. Third, the associations of the number of sedentary >10-to-30-minute bouts and >30-minute bouts with WC were analysed in one basic model and one adjusted model each. The same procedure was applied to analyse the associations between sedentary bouts and BMI. To provide a visual representation of the associations in the adjusted models, marginal means for the associations of quartiles of bouts with WC and BMI were estimated and presented in column diagrams. Secondary analyses were conducted separately for women and men.

Results

Sample characteristics

Characteristics of the total sample (N = 460) and separately for women (66%) and men are described in Table 1. Participants were, on average, 57.1 (standard deviation, SD 8.5) years old, 36% were highly educated. The mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m− 2 (SD 3.8). On average, participants wore the accelerometer for 14.7 h day− 1 (SD 1.5) and spent 10.1 h day− 1 (SD 1.7) sedentary, 3.8 h day− 1 (SD 1.0) in LPA and 0.8 h day− 1 (SD 0.5) in MVPA. Further details on accelerometer-based measures are shown in Table 1.

Table 1 Sample characteristics (N = 460)
Full size table

Associations between number of sedentary bouts and adiposity markers

The number of sedentary 1-to-10-minute bouts per day were inversely related with WC and BMI in the basic models (b = -0.048; p = 0.014 and b = -0.018; p < 0.001, respectively; Table 2). In the adjusted models, the association with WC became non-significant (b = -0.063; p = 0.131), whereas the association with BMI remained significant (b = -0.027; p = 0.047). Associations between the number of sedentary >10-to-30-minute bouts per day with WC and BMI were not significant both in the basic models (b = 0.053; p = 0.551 and b = 0.018; p = 0.516, respectively) and in the adjusted models (b = -0.139; p = 0.401 and b = -0.036; p = 0.262, respectively). The number of >30-minute bouts per day was positively associated with WC (b = 0.590; p = 0.004) and BMI (b = 0.184; p = 0.009) in the basic models. In the adjusted models, the association with WC was attenuated but remained significant (b = 0.330; p = 0.001) whereas the association with BMI was attenuated and no longer significant (b = 0.113; p = 0.184).

Table 2 Multilevel mixed-effects linear regression models of the association of sedentary bouts with adiposity markers (N = 460)
Full size table

To provide a visual representation of the effect size of the associations in the adjusted models, Fig. 1 shows the estimated marginal means for the associations of quartiles of the number of sedentary bouts per day with WC and BMI. Compared to those in the lowest quartile of 1-to-10-minute bouts, those in the third quartile had, on average, a 3.73 cm lower WC (p = < 0.001) and a 0.66 kg m− 2 lower BMI (p = < 0.001) whereas those in the highest quartile had a 3.51 cm lower WC (p = 0.001) and a 1.19 kg m− 2 lower BMI (p = 0.017). Compared to those in the lowest quartile of >30-minute bouts, those in the highest quartile had a 1.44 cm higher WC (p = 0.040). The results for analyses separated by sex are presented in additional files [see Supplementary Material 1 and 2].

Fig. 1
figure 1

Quartiles of the number of sedentary 1-to-10-minute bouts (a and b), >10-to-30-minute bouts (c and d) and >30-minute bouts per day (e and f) with waist circumference (left column) and body mass index (right column). Multilevel mixed-effects linear regression models included study as higher-level group variable. Estimated marginal means (95% CI) adjusted for sex, age, age squared, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use (i.e., total time spent sedentary as well as in light and moderate-to-vigorous physical activity. Cut points for quartiles were 76.84, 93.71, and 111.71 bouts per day for 1-to-10-minute bouts; 10.85, 13.35, and 15.71 bouts per day for >10-to-30-minute bouts; and 2.16, 3.15, and 4.50 bouts per day for >30-minute bouts; *P < 0.05, **P < 0.01, and ***P < 0.001 compared to quartile 1

Full size image

Discussion

In this observational study using combined data from three different studies, we examined cross-sectional associations of short (1 to 10 min), moderate (>10 to 30 min), and long (>30 min) sedentary bouts with WC and BMI in subjects without prevalent cardiovascular disease. Our data revealed three main findings: first, there was a statistically significant inverse relationship of the daily number of short sedentary bouts with BMI but not with WC. Second, the daily number of moderate sedentary bouts was not related to WC or BMI. Third, the daily number of long sedentary bouts was significantly associated with a higher WC but not with BMI.

A number of studies investigated associations between sedentary behaviour patterns and obesity metrics [15,16,17,18,19,20,21]. However, it is difficult to compare reported results due to methodological differences including, but not limited to, sensing method of the device (accelerometer-based or inclinometer-based), the cut-points chosen to classify sedentary behaviour, and applied definitions of sedentary bouts and breaks [28]. Our results are in line with a number of previous studies that found modest associations of sedentary behaviour patterns with WC [15,16,17,18, 21] and BMI [15, 17, 18, 21]. For example, an Australian observational study among 678 middle-aged to older adults investigating various inclinometer-based measures showed that frequently interrupted sitting (compared to patterns with relatively more prolonged sitting) was beneficially associated with WC and BMI [17]. In a Danish study among 692 blue-collar workers applying isotemporal substitution modelling, it was found that replacing long sedentary bouts (>30 min) with brief sedentary bouts (≤ 5 min) was associated with lower levels of adiposity markers including WC and BMI [18]. A meta-analysis published in 2015 investigated the relationship between the frequency of sedentary interruptions and cardio-metabolic health in adults [21]. Results from the included observational studies revealed inverse associations of interruptions with WC and BMI.

In our study, associations of short sedentary bouts were only statistically significant for BMI but not for WC. However, associations of quartiles of short bouts showed that compared to those with the lowest number of bouts (i.e., first quartile) individuals with the highest number of bouts (i.e., third and fourth quartile) had not only a significantly lower BMI, but also a significantly lower WC. Thus, the relationship with WC may be curvilinear (Fig. 1a) which may be the reason why the linear association was not statistically significant. In contrast to the aforementioned studies [17, 18, 21], we did not find a statistically significant association of long sedentary bouts with BMI. However, some of these studies did not account for LPA [15, 17] or total sedentary time [17] in some of their investigated associations which may have led to over-estimated relationships of sedentary behaviour patterns with BMI. For example, in a Finish cohort study comparing groups with different profiles of sedentary accumulation patterns, statistically significant associations of more fragmented patterns with lower BMI disappeared after adjustment for total sedentary time [20]. In our analysis, we simultaneously accounted for amounts of total sedentary time, LPA, and MVPA by applying CoDA, which may be a contributing factor to the difference in findings on BMI compared to previous studies. Further, it should be noted that the high complexity of obesity-related health risks may not be captured by weight-based measures such as BMI [37, 38]; and that BMI has been shown to be less accurate in health risk prediction than measures of body fat distribution including WC [39, 40].

Our results provide some evidence for beneficial associations of short sedentary bouts and for unfavourable associations of long sedentary bouts with adiposity markers. These findings suggest that obesity-related risk factors might be improved if sitting time is frequently interrupted and if sitting periods that last longer than 30 min are avoided. It has been discussed in the literature, whether benefits of sedentary breaks solely stem from favourable effects of higher amounts of physical activity [27]. As we found statistically significant associations of sedentary bouts with adiposity markers after simultaneous adjustment for MVPA and LPA as well as total sedentary time, the accumulation pattern of sedentary time seems to be a relevant factor. Indeed, suggestions have been made on the physiological mechanisms underlying the beneficial effects of regularly interrupting prolonged sitting on the cardiovascular system, such as the maintenance of the muscle pump and blood flow [41]. From a public health perspective, the results of this study provide some indication that obesity-related health risks may be improved if total sedentary time is accumulated in more short and fewer long sedentary bouts. Whilst interrupting sitting every thirty minutes might be feasible and acceptable, breaking up sitting every ten minutes seems highly impractical to be implemented in everyday life. Thus, defining quantitative recommendations on specific thresholds of bout durations remains a challenge.

Strengths and limitations

This study investigated sedentary behaviour patterns measured by device in a moderate-sized sample of middle-aged to older adults in Germany. Results add data to the literature on associations between uninterrupted sitting time and adiposity markers. We used short, moderate, and long sedentary bouts as measures of sitting patterns and we addressed WC and BMI as generally acknowledged health risk factors to enable inferring a straightforward public health message. Furthermore, we adjusted our analyses for the composition of accelerometer-based time use to draw conclusions on the benefit of interrupting sedentary time in addition to total sedentary time and physical activity levels.

Some limitations of this study should be considered. First, our findings may not be generalizable to the whole general population. Similar to other accelerometer studies, the proportion of non-participants was high and selection bias of highly motivated and physically active individuals is likely. Second, hip-worn accelerometers used in this study assess sedentary time from data indicating a lack of movement (< 100 counts/minute) compared to more movement (≥ 100 counts/minute). As other stationary behaviour such as standing may be captured below this threshold, sitting data might be biased [42,43,44]. As discussed above, however, our data revealed results similar to those of a previous study using inclinometers [17], which assess body posture to classify sedentary time. Third, some participants in our study may have worn the accelerometer during night’s sleep, indicated by high amounts of wear time (e.g., a number of seven participants (1.5%) showed an average daily wear time of >20 h per day. Thus, there is the possibility of sedentary time being conflated with sleep time if the accelerometer has not been removed [45]. A sensitivity analysis under exclusion of the seven participants revealed results similar to the results of our main analyses. Only the association between >30-minute bouts and WC was no longer significant (b = 0.395; p = 0.065). However, as diaries were not used in this study, participants’ sleep time could not be verified and conclusions on this divergence should be drawn with caution. Fourth, we combined accelerometer data from three different studies that applied different sampling rates, i.e. 100 Hz [31, 32] and 30 Hz [33]. This may have caused bias as sampling rate affects the processing of raw acceleration data to activity counts [46]. However, this applies mainly to higher-intensity activities [46] which were less prevalent among our participants. Fifth, we used WC and BMI as indicators of obesity. However, there are other measures that assess body fat distribution as more proximate measures of obesity, such as percent body fat. In our study, obesity-measures other than WC and BMI were not available. Finally, conclusions on the direction of causality cannot be drawn from this cross-sectional study. Longitudinal studies suggest that obesity predicts future amounts of sedentary time whereas associations of the reverse direction remain less evident [27, 47, 48]. The same may apply for associations between obesity and sedentary behaviour patterns. However, evidence on prospective outcomes such as risk for cardiovascular disease [2] and all-cause mortality [22, 23, 26] has accumulated in recent years. For example, in a study among 4,510 U.S. National Health and Nutrition Examination Survey participants investigating accelerometer-based sedentary ≥ 30-minute bouts using latent class analysis it was found that the class with the highest percentage of the day in sedentary bouts had a higher risk of all-cause mortality than the class with the fewest sedentary bouts [26].

Despite these limitations, our finding that sedentary bouts of short, moderate, and long length were differently associated with obesity indicators among 460 individuals deserves further research. If the relationships revealed in this study are found in larger samples, other populations, and within prospective longitudinal studies that allow for inferences on causality, recommendations on sedentary behaviour should explicitly address interruptions of prolonged sitting.

Conclusion

In a sample of 460 apparently healthy middle-aged to older adults, the daily number of sedentary bouts lasting 1 to 10 min was significantly associated with lower BMI but not with WC and the number of bouts lasting >30 min was significantly associated with higher WC but not with BMI. These relationships persisted independent of time spent in sedentary behaviour, LPA and MVPA. Besides limiting total sedentary time or increasing physical activity, frequent interruptions of sedentary time might improve obesity-related risk factors. Thus, the results of this study to some extent support sedentary behaviour guidelines that promote regular interruptions of sitting.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions associated with anonymity of participants but are available from the corresponding author on reasonable request. The data is shared with researchers who submit a methodologically sound proposal to achieve the aims of the approved proposal. Requests in this regard should be directed to the corresponding author to gain access. Requestors must sign a data access agreement ensuring data usage in compliance with the statement given in the informed consent procedure and with the German data protection law, that the data will not be transferred to others, and that the data will be deleted after the intended analysis has been completed.

Abbreviations

BMI:

Body mass index

CoDA:

Compositional Data Analysis

LPA:

Light physical activity

MVPA:

Moderate-to-vigorous physical activity

SD:

Standard deviation

WC:

Waist circumference

References

  1. Bailey DP, Hewson DJ, Champion RB, Sayegh SM. Sitting time and risk of cardiovascular disease and diabetes: a systematic review and meta-analysis. Am J Prev Med. 2019;57(3):408–16.

    Article  PubMed  Google Scholar 

  2. Bellettiere J, LaMonte MJ, Evenson KR, Rillamas-Sun E, Kerr J, Lee IM, et al. Sedentary behavior and cardiovascular disease in older women: the Objective Physical Activity and Cardiovascular Health (OPACH) Study. Circulation. 2019;139(8):1036–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Patterson R, McNamara E, Tainio M, de Sá TH, Smith AD, Sharp SJ, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol. 2018;33(9):811–29.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ahmad S, Shanmugasegaram S, Walker KL, Prince SA. Examining sedentary time as a risk factor for cardiometabolic diseases and their markers in South Asian adults: a systematic review. Int J Public Health. 2017;62(4):503–15.

    Article  CAS  PubMed  Google Scholar 

  5. Ekelund U, Brown WJ, Steene-Johannessen J, Fagerland MW, Owen N, Powell KE, et al. Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850 060 participants. Br J Sports Med. 2019;53(14):886–94.

    Article  PubMed  Google Scholar 

  6. Ekelund U, Tarp J, Steene-Johannessen J, Hansen BH, Jefferis B, Fagerland MW, et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ. 2019;366:l4570.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Katzmarzyk PT, Powell KE, Jakicic JM, Troiano RP, Piercy K, Tennant B, et al. Sedentary behavior and health: update from the 2018 Physical Activity Guidelines Advisory Committee. Med Sci Sports Exerc. 2019;51(6):1227–41.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Young DR, Hivert MF, Alhassan S, et al. Sedentary behavior and cardiovascular morbidity and mortality: a science advisory from the American Heart Association. Circulation. 2016;134:e262–79.

    Article  PubMed  Google Scholar 

  9. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020.

    Google Scholar 

  10. Department of Health. Australia’s physical activity and sedentary behaviour guidelines for adults (18–64 years). Canberra, Australia, 2014.

  11. Ross R, Chaput JP, Giangregorio LM, Janssen I, Saunders TJ, Kho ME, et al. Canadian 24-Hour Movement Guidelines for adults aged 18–64 years and adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10):57–s102. (Suppl. 2)).

    Article  Google Scholar 

  12. Füzéki E, Vogt L, Banzer W. [German National Physical Activity Recommendations for adults and older adults: methods, database and Rationale]. Gesundheitswesen. 2017;79(S 01):20–s8.

    Google Scholar 

  13. Department of Health and Social Care.UK Chief Medical Officers’ Physical Activity Guidelines. England, 2019.

  14. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Healy GN, Dunstan DW, Salmon J, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31:661–6.

    Article  PubMed  Google Scholar 

  16. Healy GN, Matthews CE, Dunstan DW, et al. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J. 2011;32:590–7.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bellettiere J, Winkler EAH, Chastin SFM, Kerr J, Owen N, Dunstan DW, et al. Associations of sitting accumulation patterns with cardio-metabolic risk biomarkers in australian adults. PLoS ONE. 2017;12(6):e0180119.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Gupta N, Heiden M, Aadahl M, Korshøj M, Jørgensen MB, Holtermann A. What is the effect on obesity indicators from replacing prolonged sedentary time with brief sedentary bouts, standing and different types of physical activity during working days? A cross-sectional accelerometer-based study among blue-collar workers. PLoS ONE. 2016;11(5):e0154935.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Husu P, Suni J, Tokola K, Vähä-Ypyä H, Valkeinen H, Mäki-Opas T, et al. Frequent sit-to-stand transitions and several short standing periods measured by hip-worn accelerometer are associated with smaller waist circumference among adults. J Sports Sci. 2019;37(16):1840–8.

    Article  PubMed  Google Scholar 

  20. Farrahi V, Kangas M, Kiviniemi A, Puukka K, Korpelainen R, Jämsä T. Accumulation patterns of sedentary time and breaks and their association with cardiometabolic health markers in adults. Scand J Med Sci Sports. 2021;31(7):1489–507.

    Article  PubMed  Google Scholar 

  21. Chastin SFM, Egerton T, Leask C, et al. Meta-analysis of the relationship between breaks in sedentary behavior and cardiometabolic health. Obesity. 2015;23:1800–10.

    Article  PubMed  Google Scholar 

  22. Dempsey PC, Strain T, Winkler EAH, Westgate K, Rennie KL, Wareham NJ, et al. Association of accelerometer-measured sedentary accumulation patterns with incident cardiovascular disease, cancer, and all-cause mortality. J Am Heart Assoc. 2022;11(9):e023845.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Diaz KM, Howard VJ, Hutto B, Colabianchi N, Vena JE, Safford MM, et al. Patterns of sedentary behavior and mortality in U.S. middle-aged and older adults: a national cohort study. Ann Intern Med. 2017;167(7):465–75.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Diaz KM, Duran AT, Colabianchi N, Judd SE, Howard VJ, Hooker SP. Potential effects on mortality of replacing sedentary time with short sedentary bouts or physical activity: a national cohort study. Am J Epidemiol. 2019;188(3):537–44.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jefferis BJ, Parsons TJ, Sartini C et al. Objectively measured physical activity, sedentary behaviour and all-cause mortality in older men: does volume of activity matter more than pattern of accumulation? Br J Sports Med. 2019;53:1013–20.

  26. Evenson KR, Herring AH, Wen F. Accelerometry-assessed latent class patterns of physical activity and sedentary behavior with mortality. Am J Prev Med. 2017;52(2):135–43.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Stamatakis E, Ekelund U, Ding D, Hamer M, Bauman AE, Lee IM. Is the time right for quantitative public health guidelines on sitting? A narrative review of sedentary behaviour research paradigms and findings. Br J Sports Med. 2019;53(6):377–82.

    Article  PubMed  Google Scholar 

  28. Boerema ST, van Velsen L, Vollenbroek MM, Hermens HJ. Pattern measures of sedentary behaviour in adults: a literature review. Digit Health. 2020;6:2055207620905418.

    PubMed  PubMed Central  Google Scholar 

  29. Gupta N, Rasmussen CL, Holtermann A, Mathiassen SE. Time-based data in occupational studies: the whys, the hows, and some remaining challenges in Compositional Data Analysis (CoDA). Ann Work Expo Health. 2020;64(8):778–85.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel Compositional Data Analysis approach. PLoS ONE. 2015;10(10):e0139984.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Guertler D, Meyer C, Dörr M, Braatz J, Weymar F, John U, et al. Reach of individuals at risk for cardiovascular disease by proactive recruitment strategies in general practices, job centers, and health insurance. Int J Behav Med. 2017;24(1):153–60.

    Article  PubMed  Google Scholar 

  32. Voigt L, Baumann S, Ullrich A, Weymar F, John U, Ulbricht S. The effect of mere measurement from a cardiovascular examination program on physical activity and sedentary time in an adult population. BMC Sports Sci Med Rehabil. 2018;10:1.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Voigt L, Ullrich A, Siewert-Markus U, Dörr M, John U, Ulbricht S. Visualization of intensity levels to reduce the gap between self-reported and directly measured physical activity. J Vis Exp. 2019;145:e58997.

  34. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47(9):1821–45.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8.

    Article  PubMed  Google Scholar 

  36. Turrisi TB, Bittel KM, West AB, Hojjatinia S, Hojjatinia S, Mama SK, et al. Seasons, weather, and device-measured movement behaviors: a scoping review from 2006 to 2020. Int J Behav Nutr Phys Act. 2021;18(1):24.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Andreacchi AT, Oz UE, Bassim C, Griffith LE, Mayhew A, Pigeyre M, et al. Clustering of obesity-related characteristics: a latent class analysis from the canadian longitudinal study on aging. Prev Med. 2021;153:106739.

    Article  PubMed  Google Scholar 

  38. Samouda H, Ruiz-Castell M, Karimi M, Bocquet V, Kuemmerle A, Chioti A, et al. Metabolically healthy and unhealthy weight statuses, health issues and related costs: findings from the 2013–2015 European Health Examination Survey in Luxembourg. Diabetes Metab. 2019;45(2):140–51.

    Article  CAS  PubMed  Google Scholar 

  39. Nimptsch K, Konigorski S, Pischon T. Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine. Metabolism. 2019;92:61–70.

    Article  CAS  PubMed  Google Scholar 

  40. Gažarová M, Bihari M, Lorková M, Lenártová P, Habánová M. The Use of different anthropometric indices to assess the body composition of young women in relation to the incidence of obesity, sarcopenia and the premature mortality risk. Int J Environ Res Public Health. 2022;19(19):12449.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Higgins S, Pomeroy A, Bates LC, Paterson C, Barone Gibbs B, Pontzer H, et al. Sedentary behavior and cardiovascular disease risk: an evolutionary perspective. Front Physiol. 2022;13:962791.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Barreira TV, Zderic TW, Schuna JM, Hamilton MT, Tudor-Locke C. Free-living activity counts-derived breaks in sedentary time: are they real transitions from sitting to standing? Gait Posture. 2015;42(1):70–2.

    Article  PubMed  Google Scholar 

  43. Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561–7.

    Article  PubMed  Google Scholar 

  44. Aguilar-Farías N, Brown WJ, Peeters GMEEG. ActiGraph GT3X + cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport. 2014;17(3):293–9.

    Article  PubMed  Google Scholar 

  45. Wilson JJ, Skjødt M, McMullan I, Blackburn NE, Giné-Garriga M, Sansano-Nadal O, et al. Consequences of choosing different settings when processing hip-based accelerometry data from older adults: A practical approach using baseline data from the SITLESS study. J Meas Phys Behav. 2020;3(2):89–99.

  46. Brønd JC, Arvidsson D. Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts. J Appl Physiol. 2016;120(3):362–9.

    Article  PubMed  Google Scholar 

  47. Golubic R, Wijndaele K, Sharp SJ, Simmons RK, Griffin SJ, Wareham NJ, et al. Physical activity, sedentary time and gain in overall and central body fat: 7-year follow-up of the ProActive trial cohort. Int J Obes (Lond). 2015;39(1):142–8.

    Article  CAS  PubMed  Google Scholar 

  48. Ekelund U, Brage S, Besson H, et al. Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality? Am J Clin Nutr. 2008;88:612–7.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported and funded by grants from the Federal Ministry of Education and Research as part of the German Centre for Cardiovascular Research, DZHK, grant number 81/Z540100152 (original studies 1 and 2) and grant number D347000002 (original study 3). The DZHK had no direct role in the development of methodology, the acquisition, analysis, and interpretation of data or in writing the manuscript.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations

  1. Department of Prevention Research and Social Medicine, Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, Greifswald, D-17475, Germany

    Lisa Voigt, Antje Ullrich, Diana Guertler, Ulrich John & Sabina Ulbricht

  2. German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany

    Lisa Voigt, Antje Ullrich, Stefan Groß, Diana Guertler, Marcus Dörr, Neeltje van den Berg, Ulrich John & Sabina Ulbricht

  3. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany

    Lisa Voigt, Stefan Groß & Marcus Dörr

  4. Molecular Epidemiology Research Group, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Lina Jaeschke

  5. Section Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

    Neeltje van den Berg

Authors
  1. Lisa Voigt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antje Ullrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Groß
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diana Guertler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lina Jaeschke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcus Dörr
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Neeltje van den Berg
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ulrich John
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sabina Ulbricht
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LV, AU, and SU planned und designed the study. LV, MD, and SU managed participants’ recruitment and data collection. LV, AU, DG, and SU analysed and interpreted the data. LV drafted the manuscript. SU supervised the writing and AU, SG, DG, LJ, MD, NvdB, and UJ provided additional input. All authors read, critically revised, and approved the final version of the manuscript.

Corresponding author

Correspondence to Lisa Voigt.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Further, this study was conducted in accordance with the current guidelines of good clinical practice, and in line with CONSORT guidelines for non-pharmacological studies. The ethics committee of the University Medicine Greifswald approved the study protocols of all included studies (study 1: number BB 64/07; study 2: number BB 002/15a; study 3: number BB 076/18). Written informed consent was obtained before inclusion of study participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Voigt, L., Ullrich, A., Groß, S. et al. Associations of accelerometer-based sedentary bouts with adiposity markers among German adults – results from a cross-sectional study. BMC Public Health 23, 469 (2023). https://doi.org/10.1186/s12889-023-15304-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-15304-8

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us