Introduction

An increasing bulk of evidence suggests that physical activity (PA) is associated with numerous health benefits, and youth are encouraged to engage in a minimum of 60 min per day of moderate-to-vigorous PA (MVPA) (≥4 metabolic equivalent of tasks (METs)) [42, 71]. However, in the past decade, there has been a rapidly growing interest in sedentary behaviour (Latin sedere, ‘to sit’) defined as any waking behaviour characterized by low energy expenditure (≤1, 5 METs) while in a sitting or reclining posture [4]. Emerging data [35, 36, 38, 68] reveal an association between the amount of sedentary behaviour, the pattern in which it is accumulated, and increased cardio-metabolic risk in the adult population. Possible underlying mechanisms for adverse health effects are complex, but they are at least partially connected to changes in lipoprotein lipase activity [31, 34, 61] caused by reduced muscle contractions. Data suggest that youth accumulate up to 9 h of objectively measured daily sedentary behaviour [20, 65, 78], and the average daily minutes in sedentary behaviour tend to increase by age [20, 78]. A few years ago, Tremblay and associates [73] reported an association between sedentary behaviour and unfavourable body composition, increased risk for metabolic syndrome and cardio-vascular disease in youth. In response to this finding, Canadian recommendations [72] suggest that youth should limit their time spent sedentary during waking hours. These conclusions appear to be primarily based on self- or parent-reported screen-based sedentary behaviour, and evidence suggests that such proxy measure fail to represent total accumulated sedentary behaviour [77]. In connection with this finding, correlation coefficients of 0.08 (television (TV) time) [10] and 0.06 (screen time) [70] have been reported between youths’ self- or parent-reported screen-related time and their volumes of objectively measured sedentary behaviour. Moreover, a very weak-to-negligible correlation (i.e. r=0.05) was found between sedentary bouts and TV time, suggesting that prolonged sedentary behaviour is not necessarily accumulated in front of the TV set [10]. This indicates that youth accumulate high volumes of sedentary behaviour outside the context of TV, and therefore, sitting during the school day, at home while doing homework and other similar activities should also be recognized as sedentary. Objective measurement devices such as accelerometers have the advantages that they provide an accurate estimation of the total accumulated volume of sedentary behaviour throughout the day and, thus, are not limited to a certain context [9, 48]. Accelerometers are valid motion sensors and are considered a reference method to accurately classify sedentary behaviour, as described in details elsewhere [48]. Although self- or parent-reported screen time suggests an association between sedentary behaviour and increased cardio-metabolic risk, when looking at objective measures, the association is less certain. Therefore, the objective of this paper was to review studies that examine the association between volume and pattern of accelerometer-measured sedentary behaviour and cardio-metabolic risk in youth.

Method

Inclusion criteria

The following inclusion criteria were applied: (i) youth participants (age range 6–19); (ii) accelerometer-measured volume and/or pattern of sedentary behaviour and its association with ≥1 cardio-metabolic outcome; and (iii) published, in press or accepted in an English language, peer-reviewed journal between January 2000 and October 2013. Articles were not excluded on the basis of study design (e.g. cross-sectional and longitudinal studies), selection of accelerometer brand and/or model (e.g. ActiGraph or Actical) or process for collecting and/or analysing accelerometer raw data (e.g. choice of epoch or cut-point for sedentary behaviour). However, articles examining clusters of activity behaviour and their associations with cardio-metabolic risk were excluded due to difficulties in determining the isolated effect of sedentary behaviour.

For the purpose of this review, the term ‘volume’ refers to total accumulated time spent in sedentary behaviour. Furthermore, the term ‘pattern’ refers to both ‘bouts’ and ‘breaks’ in sedentary behaviour. Although the duration varies across studies, the term ‘bout’ is defined as prolonged sedentary behaviour (i.e. consecutive minutes of sedentary behaviour), while the term ‘break’ is defined as any interruption of sedentary behaviour. These definitions have been employed in previous literature [21, 34, 35].

Literature search

In October 2013, searches for potential articles were undertaken in the electronic bibliographic databases PubMed and SportDiscus. The key words ‘sedentar*’ (e.g. ‘sedentary behaviour’, ‘sedentary time’, ‘sedentary lifestyle’, ‘sedentariness’ and ‘sedentarism’) and ‘objective*’ (e.g. ‘objective measurements’,‘objectively measured’, ‘objectively assessed’ and ‘objectively determined’) were selected in addition to ‘accelerometer’, as well as ‘children’, ‘adolescents’ and ‘youth’, to restrict the search outcome to a priori inclusion criteria. These key words were used in conjunction with the following individual markers of cardio-metabolic outcomes: ‘body mass index’, ‘weight’, ‘overweight’, ‘obesity’, ‘blood pressure’, ‘cholesterol’, ‘triglycerides’, ‘glucose’, ‘insulin’ and ‘inflammatory markers’, employed in different combinations. All key words were limited to words appearing in the title and/or abstract. Furthermore, in October 2013, electronic platforms (e.g. Sedentary Behaviour Research Network (SBRN)) were searched for articles recently accepted for publication.

Screening procedure

Articles were imported into an electronic-based reference management library created especially for this review. Duplicates were initially removed using the reference management system, and remaining duplicates were removed manually by the authors. Subsequently, titles and abstracts were independently screened in an unblinded manner by the authors, and any differences during this process were reconciled by discussion and mutual agreement, and thus, consensus was obtained for all included articles. Furthermore, full text copies were obtained for all articles meeting the initial screening and the two reviewers examined the articles, and similarly to the initial screening, any uncertainty of inclusion during this process was resolved by consensus between the authors. Furthermore, the first author searched reference lists of selected reviews and included primary source articles, as well as articles published on electronic platforms, to find additional articles of interest.

Extraction process

Data were extracted by the first author using a standardized data extraction sheet and tabulated. Subsequently, the data were checked for accuracy by the second author. Furthermore, tables were created, and information regarding study characteristics and results was extracted. Results of the reviewed studies were interpreted as statistically significant at an alpha level ≤0.05. The table (Table 2) summarizing the study results was divided in two sections: section one for studies adjusting for covariates including time spent in MVPA and section two for studies controlling for covariates but not including MVPA. MVPA was separated as a factor in this study because evidence suggests that MVPA is an important contributor to metabolic health [26]. The reviewers were blinded to neither the authors nor the journals of these articles when extracting and checking data.

Methodological quality index assessment

The two reviewers assessed the methodological quality (i.e. risk of bias) using a previously proposed checklist [24], and differences during the scoring process were discussed until consensus was reached. The original checklist consists of 27 items (maximal quality index 32, with a higher index reflecting greater methodological quality) across five domains, including reporting, external validity, internal validity (i.e. bias and confounding) and power. The reviewers modified the checklist to fit methodological quality assessment of the reviewed observational studies (i.e. cross-sectional and longitudinal studies) with a maximal score of 22. A quality index was calculated for each study and expressed as a number of compliant items by the percentage of the total applicable items. Item 27 was simplified, awarding the score 1 for studies with a power/sample size calculation and 0 when no calculation was detected (or it was unable to be determined). Furthermore, studies adjusting for covariates including MVPA were considered as the highest level of evidence (section one in Table 2). Articles were not excluded due to methodological quality.

Results

When duplicates were removed, a total of 1,152 potential articles were retrieved from the initial electronic bibliographic database search. Subsequently, titles and abstracts were screened and full-text copies were obtained for 40 articles. Of these, ten did not specifically examine the association between volume and/or pattern of accelerometer-measured sedentary behaviour and markers of cardio-metabolic risk, leaving 30 articles meeting a priori inclusion criteria. Furthermore, 12 and 3 articles, respectively, were found after screening reference lists of selected published reviews and included primary source articles, as well as searching electronic platforms. Thus, a total of 45 articles were considered eligible for inclusion.

Table 1 provides a summary of methods used in each individual study, and Table 2 displays characteristics of individual studies and study results.

Table 1 Methods used when examine the association between volume and pattern of objectively measured sedentary behaviour and cardio-metabolic risk in youth
Full size table
Table 2 Association between volume and pattern of objectively measured sedentary behaviour and cardio-metabolic risk in youth
Full size table

Assessment of risk for bias

An assessment of methodological quality was completed for all included articles. The average quality index percentage was 77.2 (range 64–91 %). Approximately 51 % of the reviewed studies adjusted for covariates including MVPA.

Volume and pattern of sedentary behaviour

Markers of obesity

Numerous observational studies [1, 5, 7, 11, 21, 22, 29, 33, 41, 49, 69, 70, 74 76] have reported a null association between volume of objectively measured sedentary behaviour and body mass index (BMI), with two studies [21, 22] employing representative data from the Canadian Health Measures Survey (CHMS). Two cross-sectional studies [1, 49] detected no association between volume of sedentary behaviour and being overweight or obese (defined according to the sex- and age-specific BMI cut-off [19]) in youth. Fewer evidence [11, 17, 21, 56, 69, 75] indicates an association between volume and pattern of sedentary behaviour and BMI. One study [17] reported greater BMI in youth who accumulated the highest volumes of sedentary behaviour when compared with their least sedentary peers. Another study [75] observed a correlation between volume of sedentary behaviour and BMI in girls, but not boys. Three studies [11, 21, 69] reported that the pattern in which sedentary behaviour is accumulated may be important for BMI, although this finding was limited to certain durations and periods of discretionary free time. In addition, one study [56] found that volume of sedentary behaviour was associated with increased BMI at the 50th, 75th and 90th BMI percentile between ages 9 and 15, although this association attenuated towards the 50th percentile.

Both large- [10, 15, 21, 22, 25, 69, 70] and small-scale [3, 12, 17, 29, 33, 40, 41, 58, 62] observational studies have reported a null association between volume of sedentary behaviour and waist circumference (WC). One of these studies [10] observed no association between patterns of sedentary behaviour and WC. Furthermore, one study [50] suggest no differences between highly and less sedentary youth in terms of WC. Accumulating evidence also suggests no association between volume of sedentary behaviour and other markers of body fat [2, 5, 14, 27, 29, 46, 50, 53–55, 57, 70, 74]. However, one study [17] reported greater WC in highly sedentary youth in comparison with their less sedentary peers. Two studies [8, 41] found a weak-to-moderate positive correlation between sedentary behaviour and WC. One study [21] reported that prolonged sedentary bouts after 3 pm were associated with increased WC, whereas interruption of sedentary behaviour was negatively associated with WC in boys ages 11–14. Similarly, one study [69] found that shorter bouts of sedentary were negatively associated with WC in girls with a family history of obesity. Some studies have also reported a weak-to-moderate positive association [8, 25, 46, 62, 74, 75] between volume of sedentary behaviour and markers of body fat, with one study [46] detecting a weak negative correlation between number of sedentary breaks and body fat.

Blood pressure

A vast majority of cross-sectional studies support a null association between objectively measured sedentary behaviour and systolic and diastolic blood pressure (SBP and DBP) and median BP [3, 10, 12, 15, 21, 23, 33, 40, 50, 52], with one study [10] reporting no association between sedentary breaks and bouts and BP. Conversely, two studies [25, 30] reported a positive association between volume of sedentary behaviour, SBP and DBP. In addition, one study [50] reported differences between quartiles of sedentary behaviour and SBP, with less sedentary youth reporting healthier SBP.

Insulin

A null association has been found between volume [17, 37, 64, 69] and pattern [67, 69] of sedentary behaviour and markers of insulin, including samples with at least one obese parent [69]. When stratified by sedentary quartiles, two studies [17, 66] reported unfavourable levels of insulin in the fourth, in comparison to the first, quartile. In addition, four studies [8, 25, 37, 66] have reported weak associations between volume of sedentary behaviour and insulin, though only one [37] adjusted for time spent in MVPA.

Glucose

Most cross-sectional studies [3, 15, 17, 40, 64, 69] have reported a null association between volume of sedentary behaviour and glucose. One study [67] detected no difference between a prolonged bout of sitting, interrupted sitting and glucose levels. Conversely, a few studies [12, 25] have found a weak positive association between volume of sedentary behaviour and glucose. One study [69] reported an association between sedentary bouts lasting 10–14 min and increased levels of glucose in girls with at least one obese parent. Another study [50] compared tertiles of sedentary behaviour and concluded that youth who accumulated higher proportions of sedentary behaviour had less favourable levels of glucose compared to their less sedentary peers.

Blood lipids

Studies [3, 10, 12, 15, 17, 18, 21, 25, 40, 69] enrolling youth from Australia, Europe and North America support a null association between volume of sedentary behaviour and blood lipids. Two studies found no association between the pattern in which volume of sedentary behaviour was accumulated and non-high-density lipoprotein cholesterol (HDL-C) [10] and HDL-C [69], while another study [67] detected no difference between prolonged sitting, interrupted sitting and low-density lipoprotein cholesterol (LDL-C), HDL-C and triglycerides. One study [69] found a negative association between bouts lasting 15–29 min and triglycerides in boys with at least one obese parent. One small study reported no difference between tertiles of sedentary behaviour and LDL-C, HDL-C and total cholesterol [50]. However, one study [25] reported a weak positive association when analysing volume of sedentary behaviour together with triacylglycerol. Another study [50] detected less favourable levels of triglycerides in youth who accumulated high volumes of sedentary behaviour compared to their less sedentary peers.

Clustered cardio-metabolic risk and inflammatory markers

Some studies have reported a null association between volume of sedentary behaviour and individual and clustered cardio-metabolic risk [3, 17, 39, 40, 69], and studies have also found a null association between volume of sedentary behaviour and individual inflammatory markers [10, 51, 69]. Some studies suggest a null association between numbers of sedentary bouts [10, 17] and breaks [10] and clustered cardio-metabolic risk. Three studies [3, 10, 17] detected no difference between highly versus less sedentary youth when related to clustered cardio-metabolic risk. Conversely, two large studies [2, 25] support an association between volume of sedentary behaviour and cardio-metabolic risk. One study [69] found a positive association between number of sedentary breaks, shorter sedentary bouts and reduced cardio-metabolic risk in a sample of 522 youth with a family history of obesity. This study also suggests a negative association between prolonged bouts of sedentary behaviour and inflammatory marker [69]. One study [50] reported that, in comparison to highly sedentary, less sedentary youth experienced reduced cardio-vascular risk.

Discussion

Research on sedentary behaviour is in its infancy, yet it is expanding rapidly, and the current review provides important insights regarding the association between objectively measured sedentary behaviour and cardio-metabolic risk in youth. Most reviewed studies appear to have examined volume of sedentary behaviour and its association with markers of obesity, yet limited evidence supports such association when adjusting for MVPA. The importance of sedentary bouts and breaks appears to be inconsistent; some evidences, however, indicate that prolonged sedentary bouts are positively associated with obesity, whereas sedentary breaks may be beneficial and, thus, negatively associated with obesity. The longitudinal association between volume of sedentary behaviour and obesity is rather unexplored though previous work suggests a weak association when taking study quality into account.

A vast majority of evidence does not support an association between volume of sedentary behaviour and BP, even though it should be acknowledged that three studies reported such association. Two of these, however, utilized a relatively high cut-point (i.e. <500 CPM) to estimate sedentary behaviour, suggesting greater volume of sedentary behaviour can increase the likelihood of detecting an unfavourable association. None of the reviewed studies reported an association between volume of sedentary behaviour and BP when adjusting for covariates including MVPA.

We found limited evidence to support an association between objectively measured sedentary behaviour and glucose. No study reported such association when adjusting for time spent in MVPA. Likewise, reviewed studies indicate a weak association between objectively measured sedentary behaviour and markers of insulin. Because this review’s authors found few studies that explore this connection, future work ought to continue examine the effect of objectively measured sedentary behaviour and its association with glucose and insulin.

Taken together, reviewed evidence suggests a lack of association between objectively measured sedentary behaviour and blood lipids, though it should be recognized that the opposite has been reported. However, the only two studies reporting an association between objectively measured sedentary behaviour and blood lipids did not adjust for time spent in MVPA.

Most of the reviewed studies indicate a null association between volume of sedentary behaviour and clustered cardio-metabolic risk. This conclusion is unchanged when taking study quality into consideration. However, it is noteworthy that one study suggests that the manner in which sedentary behaviour is accumulated has an association with clustered cardio-metabolic risk; frequent breaks in sedentary behaviour are beneficial for youth with a family history of obesity.

We found only three studies that examine the association between sedentary behaviour and inflammatory markers; thus, additional research is urgent. However, based on these studies, volume of sedentary behaviour is not associated with inflammatory markers.

Although youth accumulate roughly 6 to 8 h of sedentary behaviour throughout the day, limited evidence indicates an association between objectively measured sedentary behaviour and individual and clustered cardio-metabolic risk. To support this further, Ekelund and colleagues [26] used data from the International Children’s Accelerometry Database, comprising 20,871 youth, and employed a meta-analytic approach to examine the association between volume of objectively measured sedentary behaviour, MVPA and indicators of increased cardio-metabolic risk. Evidence was only found to suggest an association between volume of sedentary behaviour and insulin, yet this association attenuated towards the null when additionally adjusting for time spent in MVPA. A similar pattern was observed in some of the reviewed studies [5, 18, 70, 55] indicating that MVPA may be more important than volume of sedentary behaviour in relation to cardio-metabolic risk in the youth population. Collectively, we found few studies reporting an association between volume of sedentary behaviour and cardio-metabolic risk when controlling for MVPA. As partially suggested above, time spent in MVPA [11, 14, 15, 22, 29, 40, 46, 54, 76], or simply VPA [14, 33], was associated with reduced cardio-metabolic risk independent of volume of objectively measured sedentary behaviour even though most youth would be classified as physically inactive (i.e. not meeting current PA recommendations [4]). Therefore, it appears critical to stress additional health benefits associated with MVPA, not necessarily simply to advocate for reducing sedentary behaviour. For example, in addition to improvements in components of cardio-metabolic health, body composition and cardio-respiratory fitness [42], MVPA appears to be important for stimulating and improving bone mineral content [43] which is unlikely to occur when engaging in light intense PA.

Previous reviews [63, 73] have predominantly incorporated subcomponents of sedentary behaviour, and therefore, it is not surprising that this review provides results that are somewhat contrary to prior observations. Since other studies have focused on certain subcomponents of sedentary behaviour, such as time spent in front of the TV, which are clearly associated with unhealthy eating and increased beverage consumption [47, 59, 60], this association may not be detected when considering total accumulated time spent in sedentary behaviour. With this in mind, limiting screen-based sedentary behaviour is an important challenge for public health authorities and organizations even though this may be difficult due to strong habitual component and environmental cues [6].

Some limitations should be taken into consideration when interpreting the present results. Most reviewed studies are cross-sectional in nature, and as a consequence, the long-term effects of objectively measured sedentary behaviour appear to be rather unexplored. In addition, few studies have examined the effect of prolonged sedentary behaviour and its association with cardio-metabolic health in youth. Moreover, there is a gap in the literature regarding ideal accelerometer-data proceeding methods [45], and consensus is urgently required since different methodological approaches complicate the comparability between studies. For example, the reviewed studies have collected sedentary behaviour in 5- to 60-s epochs and employed cut-points between <50 and <1,100 CPM though <100 CPM is the most common (65 % of cases) and is an appropriate cut-point [28]. This is a limitation since evidence suggests that the choice of cut-point will influence the association between sedentary behaviour and cardio-metabolic risk, with higher cut-points producing stronger associations [2]. Even though accelerometers provide accurate estimate of sedentary behaviour, the devices may categorize standing as sedentary behaviour as well [13, 16] even though it does not meet the definition of this behaviour: both <1.6 MET and a sitting or reclining posture [4]. To overcome this bias, future studies could use inclinometer output to distinguish between postures [44], yet some evidence suggests that these outputs are not an appropriate indicator for youth’s posture since misclassifications are common [32]. Furthermore, accelerometers do not provide distinctions between sedentary behaviour so it is not possible to determine the contexts volumes and patterns that sedentary behaviour are accumulated. This is important in studies investigating the association between subcomponents of sedentary behaviour in relation to cardio-metabolic health.

Conclusion

Available evidence suggests a weak association between volume and pattern of objectively measured sedentary behaviour and individual and clustered cardio-metabolic risk in youth when adjusting for time spent in MVPA. Therefore, future studies should examine the association between objectively measured sedentary behaviour and cardio-metabolic risk independent of MVPA. Finally, we suggest that youth should be encouraged to engage in recommended levels of MVPA and reduce excessive time spent in screen-based sedentary behaviour.