Childhood obesity is a strong predictor of youth and adult morbidity (1), with abdominal obesity playing a key role in the cluster of risk factors associated with metabolic syndrome and cardiovascular diseases (2). The prevalence values for obesity in Portuguese children are around 8% to 9%, with overweight reaching 18% in children and 24% for adolescents (3). An unfavorable association between sedentary behavior and body composition profile of children (4,5), and with general health as well, has been documented (6). Prospective observations suggest that increasing sedentary time (ST) by 1%, from 7 to 15 yr was associated with an increase in body mass index (BMI) of 0.01 kg·m−2 and in fat mass index (FMI) of 0.15 kg·m−2 (7). Consequently, it has been advocated that ST should be considered an independent risk factor for health in youth, distinct from physical activity (PA) (8). Further, observational data indicates that higher moderate-to-vigorous PA (MVPA) is associated with better cardiometabolic risk factors and fatness phenotype in children, regardless of time spent in ST (9–13).
Emerging evidence suggest that the way we accumulate ST may be more relevant than total ST for children’s health (14,15), with recent findings suggesting that interrupting ST and limiting prolonged bouts of ST may be beneficial to weight status (16,17), and cardiometabolic risk (17). One previous investigation has observed an inverse association for the number of shorter sedentary bouts (1–4 min) with cardiovascular risk factors and BMI, whereas the number of sedentary bouts with longer length (10–14 min) was related to a deleterious waist circumference, fasting glucose, and BMI (18). However, none of the previous studies examined the associations between the number of sedentary bouts with varying length and total/abdominal adiposity (assessed using a reference method).
Patterns of ST and PA have also been related to youth’s cardiorespiratory fitness (CRF) (14,19), whereas on the other hand, low CRF is associated with an unfavorable body composition (20,21). Thus, some of the previous associations reported in the literature (16–18) relating patterns of ST with health and body composition may have been mediated by CRF. A recent study observed that, in adults, CRF mediated the prospective associations of 10-yr changes in sedentary behavior and MVPA with changes in a cardiometabolic risk score, including waist circumference, fasting glucose, high-density lipoprotein cholesterol, triglycerides and blood pressure, and with waist circumference itself (22).
Recently, inverse cross-sectional associations between MVPA and adiposity indicators were found, whereas no associations were observed for total ST with the same outcomes (13). Here, we aim to extend these findings with a more thorough understanding of the relationships of patterns of ST and MVPA, with total/regional adiposity. Furthermore, we provide a new insight on the potential mediating role of CRF on the associations between patterns of ST and adiposity indexes. CRF was adjusted for fat-free mass (FFM) (23) as it is considered the best expression of CRF in children and youth.
METHODS
Participants
Participants were recruited from public schools in Oeiras, Portugal. These schools participated in a school-based cluster randomized controlled trial (clinical trial registry: ISRCTN76013675) to evaluate the impact of an intervention in childhood obesity between 2010 and 2011, as described previously (13). The present investigation included a subsample of 333 children (172 girls) that had valid data on CRF. All participants’ guardians were informed about the aims of the investigation and gave written informed consent for their children to participate. The investigation was approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon, and conducted in accordance with the World Medical Association’s Declaration of Helsinki on human studies (24).
Body composition measures
Participants were weighed to the nearest 0.1 kg wearing minimal clothing and without shoes on an electronic scale (model 799, Seca, Hamburg, Germany). Height was measured without shoes to the nearest 0.1 cm (model 220, Seca, Hamburg, Germany), according to the standardized procedures described elsewhere (25).
Dual-energy X-ray absorptiometry (DXA) (Hologic Explorer-W, fan-beam densitometer, software QDR for windows version 12.4; Hologic, Waltham, MA) was used to estimate total fat mass (FM), abdominal FM and FFM. Total FMI and abdominal FMI (FMIabd) were calculated by dividing the total and abdominal fat mass by the square of the height (kg·m−2). A whole-body scan was performed and the attenuation of X-rays pulsed between 70 and 140 kV synchronously with the line frequency for each pixel of the scanned image was measured. Abdominal body fat mass was measured through partial analyses of the DXA scan, based on regions of interest. The region of interest was marked by one rectangle, set between the upper margin of the second lumbar vertebra and the bottom lower margin of the fourth lumbar vertebra, the sides being defined by the continuation of the lateral limits of the rib cage. Following the protocol for DXA described by the manufacturer, a phantom with six fields of acrylic and aluminum of varying thickness and known absorptive properties was scanned alongside each participant to serve as an external standard for the analyses of different tissue components. The same laboratory technician positioned the participants, performed the scans and executed the analyses according to the operator’s manual using the standard analysis protocol. Based on 10 participants, the coefficient of variation in our laboratory for FM and abdominal FM were 1.7% and 0.01%, respectively.
Sedentary patterns and physical activity
Participants were asked to wear an accelerometer (model GT1M model; Actigraph, Pensacola, FL) on the right hip, near the iliac crest during four consecutive days, including two weekdays and two weekend days (26). The delivery of the accelerometers to the participants, as well as the instructions on how to wear the device, was made in person. After the assessment period, a researcher received the accelerometers directly from the participants. The devices were activated on the first day and the data recorded in 15-s epochs. We used 15-s epochs to capture all spontaneous physical activity, typical of children, while also avoiding biased results from high-frequency sampling intervals (27). The device activation and data download and processing were performed using the software Actilife Lifestyle (v.6.10.0; Actigraph). For the analyses, a valid day was defined as having ≥600 min of wear time. Apart from when the accelerometer was removed (e.g., sleeping or water activities), periods of at least 60 min of consecutive zero activity intensity counts were also considered as non–wear time. The investigation included the results from participants with at least three valid days (including two weekdays and one weekend day).
To determine the mean time in each activity intensity (sedentary, light, and MVPA), we used cutoff values as follows: ST < 100 counts per minute; light 100–2295 counts per minute; MVPA ≥ 2296 counts per minute (28). A sedentary bout was defined as a continuum period where the counts per minute were below 100. For each participant, the number of sedentary bouts lasting 1 to 4 min, 5 to 9 min, 10 to 14 min, and ≥15 min were calculated. The rationale for the use of these specific intervals was based on a previous study with similar methodology and sample (18). However, due to the low frequency of bouts beyond the 15-min interval (3.9 ± 1.7), we used the same approach as the one in Saunders paper, but we decided to collapse all the bouts above 15 min into one category (>15 min). Because ST is highly correlated with sedentary bouts of varied lengths, and to avoid multicollinearity in the models, we adjusted each sedentary bout individually. Sedentary bouts were regressed on ST and wear time, and the unstandardized residuals summed to the mean (sedentary bouts) were used in the analyses. A similar procedure was performed to adjust total ST to wear time. A break in ST (BST) was considered as an interruption of ST with at least 1-min duration, in which the counts registered by the accelerometer rose above the threshold of 100 counts per minute. The number of BST was divided by the number of hours in ST (BST/ST) and this variable was used in the analysis. Using this ratio instead of total breaks allows understanding the number of interruptions for each hour spend sedentary.
Cardiorespiratory fitness
Cardiorespiratory fitness was indirectly determined by a maximal ergometer cycle test with progressively increasing workload using an electronically braked ergometer (Monark 828 E Ergomedic; Monark, Sweden). Initial and incremental workloads were 20 W for children weighing <30 kg and 25 W for children ≥30 kg (29). The workload was increased every 3 min until the peak effort of the participants was reached. Heart rate was recorded continuously (Polar Electro Oy, Finland) throughout the test. Criteria defined for a peak effort were heart rate > 185 bpm or the subjective judgment by the observer that the participant could no longer continue, even after encouragement. Peak power output and peak oxygen consumption (mL·min−1) were calculated according to the formulas by Hansen et al. (30). The test has been previously validated against direct measurement of peak oxygen consumption (29). Peak oxygen consumption was normalized by FFM (mL·kg−1·min−1) and termed CRF from here on (23).
Statistical analysis
Data analyses were performed using IBM SPSS Statistics version 22.0, 2013 (SPSS Inc., an IBM Company, Chicago, IL). Descriptive statistics including means ± SD were calculated for all outcome measurements. Comparisons between sexes were performed using independent sample t-test or the nonparametric Mann–Whitney–Wilcoxon approach. Pearson correlations were conducted to verify the relation between accelerometer variables.
Mediation analysis was conducted separately with the PROCESS SPSS macro (v2. 16.3, model 4) (31). This macro analyses the significance of total and specific indirect effects using bootstrap procedures (resampling of 5000 bootstrap samples was used), which do not require assumptions of normality of the sampling. For CRF mediation analysis we first tested the total (path c) and direct (path c′, with CRF adjustment) effect between X (ST patterns and PA variables) and Y (FMI and FMIabd) to understand if there was an association of ST patterns and PA with adiposity (Fig. 1A). After, we analyzed path a (the effect of X on CRF) and path b (the effect of CRF on Y, excluding the effect of the X). Cardiorespiratory fitness mediation effects were described by calculating the effect ratios which express the amount of the total effect that is explained by the indirect effect via the mediator (CRF). Interactions between sex and these associations were assessed with no significant findings, thus the analyses were not stratified by sex, but rather it was included in the models as a confounder. All reported coefficients are unstandardized and adjusted for MVPA (except when exposure), sex, and age (months). Statistical significance was set at 5%.
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FIGURE 1: Mediation analysis (paths described in A) of bouts (B: 1–4 min; C: 5–9 min; D: 10–14 min; E: ≥15 min), breaks (F) and total ST (G) and physical activity (H), with total fat and abdominal fat indexes. Sed bouts, sedentary bouts.
RESULTS
Descriptive characteristics of the participants are presented in Table 1, for both sexes and for the overall sample. Children were categorized as overweight and obese according to the World Health Organization criteria (32). Prevalence of children who met recommended levels of 60 min·d−1 of MVPA are also presented (33).
Table 2 shows the bivariate correlations between unadjusted accelerometer variables. Sedentary time was inversely associated with MVPA (r = −0.40, P < 0.001) and BST/ST (r = −0.77; P < 0.001) and positively related to the number of sedentary bouts of all lengths (r ≥ 0.64; P < 0.001).
Figure 1 shows the results for the mediation analysis. The number of shorter sedentary bouts (1–4 min) (Fig. 1B) and MVPA (Fig. 1H) were independently and negatively associated with both total and abdominal FMI (path c, total effects), while positive associations were observed for longer sedentary bouts (5–9, 10–14, and ≥15min) (Figs. 1C, D, and E). The BST/ST and total ST were not associated with FMI and FMIabd (Figs. 1F and G).
When analyzing path a, we observed that CRF was not associated with sedentary bouts of all lengths, BST/ST, and total ST, independently of MVPA, sex and age. Conversely, while using the same adjustments, a positive association was observed between CRF and MVPA. A mediation effect by CRF was observed for sedentary bouts of 1 to 4 min with FMI, sedentary bouts 5 to 9 min with FMIabd, and sedentary bouts ≥15 min and MVPA with both adiposity indexes (P < 0.05). The ratio of an indirect to a total effect of X (MVPA and sedentary bouts) on Y (FMI) varied between 7.2% (for sedentary bouts of 1 to 4 min with FMI) to 12.2% (for MVPA with FMI). For the associations with FMIabd the effect varied between 7.8% for sedentary bouts of 5 to 9 min and 13.9% for sedentary bouts ≥15 min.
Figure 2 summarizes the results for the direction of the associations of sedentary patterns and MVPA, with adiposity phenotypes, and the results of the mediation analysis. Overall, we observed that CRF mediated the associations of sedentary bouts (1–4 and ≥15 min) and MVPA with FMI. For FMIabd, CRF mediated the associations of sedentary bouts (5–9 and ≥15 min) and MVPA.
FIGURE 2: Associations between bouts, breaks and total ST and MVPA with total fat and abdominal fat indexes: the mediating role of cardiorespiratory fitness.
TABLE 1: Mean and standard deviation values for participants’ demographic data, anthropometric measurements, body composition, physical activity, and ST characteristics.
TABLE 2: Bivariate associations between unadjusted accelerometer variables.
DISCUSSION
Our results suggest that MVPA and the number of shorter sedentary bouts (1–4 min) are favorably related to lower levels of total and abdominal adiposity. Conversely, accumulating sedentary bouts of higher length (5–9, 10–14, and ≥15 min) are associated with higher adiposity, regardless of MVPA. Total ST and BST/ST were not associated with total and abdominal fat mass. Mediation analyses suggest that CRF mediates the associations of MVPA and the number of sedentary bouts with adiposity indexes. However, this mediation effect was only observed for sedentary bouts (1–4 and ≥15min) and MVPA with FMI, and sedentary bouts (5–9 and ≥15min) and MVPA with FMIabd. These results raise awareness to the importance of engaging in regular physical activity to improve CRF, with a consequent impact on adiposity. Notwithstanding, avoiding longer periods in ST may also be relevant for a favorable adiposity profile.
Concerns about childhood obesity have been growing in the past decades regarding the possibility of affecting the stability of the health care system, particularly when taking into account that adiposity increases with age (1). Within the different adiposity phenotypes, abdominal adiposity has been strongly related to different cardiovascular and metabolic risk factors (34). Our cross-sectional data suggest a favorable association between short sedentary bouts (1–4 min) and total/abdominal adiposity, and that longer sedentary bouts may be deleterious for adiposity profile. These observations corroborate previous findings (18), in which sedentary bouts lasting 1 to 4 min were associated with a lower cardiometabolic risk and BMI. Similar to our findings, these authors also observed that longer sedentary bouts were associated with a higher BMI, although only in boys (18). Together, our investigation substantiates the hypothesis that children who accumulate ST in shorter periods may present beneficial adiposity phenotypes.
Possible mechanisms that may explain the role of shorter sedentary bouts for a better adiposity profile may be related to a contribution of shorter sedentary bouts to higher levels of overall physical activity and thus a negative energy balance. In adults, it has been previously observed that the transition from sitting to standing and return to sitting had a metabolic cost of 0.32 kcal·min−1 above sitting, which is higher when compared to just standing (0.07 kcal·min−1) (35). An acute experimental study (36) indicated that interrupting ST was associated with decreased levels of insulin (32%), C-peptide (17%), and blood glucose (7%), when compared with a continuous sitting condition. Even acknowledging some controversial findings regarding the benefit of breaking up ST in youth (37), it may be hypothesized that children who spend longer bouts in sedentary pursuits may present higher levels of circulating insulin, which promotes lipogenesis and fatty acids uptake by the adipose tissue, possibly aggravating insulin resistance and increasing total adiposity (38).
Consistent with previous studies (10,11,14,39), a negative relationship between MVPA and adiposity was observed. Additionally, we did not observe an association between total ST and adiposity, when adjusting for MVPA. A similar trend was observed in a systematic review (40), however, specific sedentary behaviors such as TV viewing might be associated with adiposity, regardless of total physical activity, which can be explained by residual confounding (40).
Patterns of ST and MVPA may influence CRF in youth (14,19). On the other hand, CRF has also been related to adiposity (20,21). In fact, it has recently been suggested that CRF mediates the associations of ST and MVPA with body fatness in adults (41), as well as the associations between MVPA and metabolic syndrome risk factors in children (42). Our findings extend the previous results for adiposity, by suggesting for the first time that CRF mediates the association between sedentary bouts of different lengths and total/regional adiposity in children. This means that ST patterns and MVPA may be associated with adiposity, in part due to its associations with CRF. For instance, CRF explained 11% and 14% of the association of longer sedentary bouts (>15 min) with both FMI and FMIabd, respectively (Fig. 2).
There is a potential reason that explains our results regarding the mediation effect for the associations between the shorter and prolonged sedentary bouts with FMI. The number or frequency of bouts of 1 to 4 min is much higher, when compared to the other bout categories, meaning that there is more opportunity for the CRF to mediate part of the associations with FMI. On the other end, prolonged bouts (>15 min) entail periods of continuous ST that can go beyond 15 min (e.g., 2 or 3 h). Thus, both observational (43) and experimental (36) evidence suggest that spending prolonged time in sedentary pursuits has a negative impact on health-related outcomes, which is attenuated when interrupted more frequently. The fact that CRF mediated the associations between these prolonged bouts and FMI was somehow expected, due to the higher health risk associated with these bouts. Finally, there was no mediation effect regarding the bout categories of 5 to 9 min and 10 to 14 min, probably because they had a much lower frequency, and they do not compromise children’s health in the same way as longer bouts do.
Our cross-sectional data generate the hypothesis that children who accumulate sedentary bouts of longer length (i.e., ≥15 min) may have a more favorable adiposity profile, if CRF levels are high. On the other end, children who accumulate shorter sedentary bouts (1–4 min), but with low CRF values, may have an unfavorable adiposity profile, but only for total adiposity. In a 10-yr follow-up study in adults, self-reported increases in MVPA and decreases in ST resulted in improved CRF, which was related to beneficial effects on cardiometabolic health (31). Our findings corroborate this, as we observed that the associations between MVPA and ST patterns and adiposity are partly mediated by CRF.
As far as BST and ST are concerned, we observed no association with adiposity, which contradicts previous findings showing an unfavorable association for ST with adiposity in children (4,5,7). However, the present results are in accordance with those from a recent meta-analysis that suggests limited evidence for an association between ST and adiposity in youth (37). From the same meta-analysis, including data from six studies analyzing the association of BST with adiposity, only one study (18) suggested an association between BST and adiposity, although participants in this study all had a family history of obesity.
The strengths of our investigation include a relatively large sample size of children, including objectively assessed physical activity, which substantially reduces the likelihood of misclassification bias. Furthermore, DXA scans provide a more valid measurement of adiposity, when compared to traditional anthropometric measurements, and provides us with FFM measurement. This allowed us to normalize CRF values using FFM, as a proxy of oxygen consumption on the metabolically active mass. The present investigation also has some limitations that warrant discussion. For instance, the cross-sectional nature of this investigation limits possible inferences of causality. Thus, the direction of the observed associations cannot be established and may be bidirectional. Moreover, a careful interpretation is warranted regarding the mediation analysis, since a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis (44). The participants were restricted to children age 9 to 11 yr old, and therefore the results cannot be generalized to children of younger or older ages. No dietary records were assessed during the investigation, and because both energy expenditure and caloric intake affect body composition, one cannot neglect the importance of dietary intake on children’s body composition. In addition, it has been recognized that different types of sedentary pursuits (e.g., screen time) entail distinct associations with health parameters, thus using accelerometer-related ST, it is not possible to infer about the impact of these different sedentary behaviors on FMI and FMIabd. Due to limited available confounders, there may be residual confounding as a result of unmeasured variables, such as parental BMI, birth weight, or socioeconomic status.
Our findings suggest that total ST and BST/ST were not associated with adiposity phenotypes when accounting for MVPA. However, it may be important to consider the length of sedentary bouts in which children partake their ST, as well as the time spent in MVPA. These associations on different adiposity phenotypes are partially explained by a mediating effect of CRF. From a prevention point of view, early childhood is an important timeframe for preventing obesity and should be targeted by programs using a multifaceted approach with special emphasis on increasing children’s physical activity of higher intensity to improve their CRF levels.
The authors declare that they have no conflict of interest. The results of the present study do not constitute endorsement by ACSM and are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
This work was supported by the Portuguese Foundation for Science and Technology (grant PTDC/DES/108372/2008). D. A. S., J. P. M., and P. B. J. are supported by a scholarship from the Portuguese Foundation for Science and Technology (grants D. A. S., SFRH/BPD/92462/2013; J. P. M., SFRH/BD/85742/2012; P. B. J., SFRH/BPD/115977/2016).
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