Sedentary behavior, typically defined as activities requiring very low energy expenditure that occur while sitting or lying down, has been the subject of increasing epidemiological research in recent years (14,17,31). During childhood, sedentary behavior increases with age, and there is evidence of deleterious associations between various markers of sedentary behavior, such as TV viewing and accelerometer-assessed sedentary time, and health outcomes (6,20,29,38). Accordingly, public health guidelines in the United Kingdom and other countries recommend that, alongside regular participation in moderate- to vigorous-intensity physical activity (MVPA), people of all ages should minimize the amount of time spent being sedentary for prolonged periods (34,37).
To elucidate clearly the associations between sedentary behavior and health outcomes, accurate information on sedentary behavior exposure is essential (41). In this regard, objective methods of measurement are preferable to self-report approaches, which are susceptible to various forms of bias, particularly when used with children (4,8). Accordingly, accelerometry is increasingly being used in epidemiological research to obtain estimates of total sedentary time, in addition to information on levels of physical activity (2,7,9,28,32). However, a widely acknowledged limitation in the use of accelerometry is the lack of consensus regarding data processing and the various decisions that are made in the derivation of exposure (or outcome) measures (8,19,27). A key area of debate is in the selection of a count threshold, below which it is deemed likely that the participant is engaged in sedentary behavior. A number of count thresholds, or cut points, for sedentary behavior have been proposed for use in young people, ranging from 10 to 1592 counts per minute (CPM) (15,22,23,25,33,39,40). A further consideration in accelerometer data processing is how to distinguish time where the participant is wearing the monitor but not moving (i.e., participant is sedentary) from the time when the device has been removed. Typically, this is achieved by imposing a limit on the duration of consecutive zero counts (zero strings), above which it is adjudged that the monitor has been removed. These segments of zero counts are typically removed from further analysis, thus affecting the accumulated sedentary time. Again, the choice of zero string criteria varies considerably in the literature, ranging from 10 to 60 min of consecutive zero counts (19,20,30).
Disparities in data processing protocols create uncertainty as to whether obtained exposure variables reflect behaviorally similar or distinct constructs, potentially limiting comparability between studies and hindering evidence synthesis. The present study was devised to examine whether the use of different count thresholds and zero string criteria influenced observed associations of sedentary time with adiposity and clustered metabolic risk in a large, heterogeneous sample of children and adolescents.
Data are from the European Youth Heart Study (EYHS), a mixed longitudinal study designed to examine personal, environmental, and lifestyle influences on CVD risk factors in European children. The rationale, aims, study design, selection criteria, and sample size have been described in detail previously (26).
Briefly, at each study location, a defined population of children was identified, and from this population, a two-stage cluster sample was randomly selected. The primary sampling unit was schools, and the secondary unit was classes within schools. A minimum of 20 schools were randomly selected from local authority lists within appropriate age, sex, and socioeconomic strata, using probability proportional to school size. The overall response rate was 73% of those who were eligible and was similar across age and sex groups. Written informed consent was obtained from a parent or guardian, and the study procedures were explained verbally to all children. Ethical approval for the study was obtained from the local research ethics committee in each study region.
A total of 2162 children age 9–10 yr (1032 boys and 1130 girls) and 2057 adolescents age 15–16 yr (952 boys and 1105 girls) from three geographically defined areas in Europe (Odense, Denmark; Tartu, Estonia; and Madeira, Portugal) participated in the study. From the Danish center, data are used from two waves of assessment (1997 and 2003), including 358 children who participated at both time points.
Weight and height were measured, without shoes, using standardized techniques. Four skinfold thickness measurements (triceps, biceps, subscapula, and suprailiac) were taken on the left side of the body in duplicate or triplicate (16). The two closest measurements at each site were averaged, and the sum of the four skinfolds was used as an indicator of adiposity. Sexual maturity was assessed by the data collectors using the five-stage scale for breast development in girls and pubic hair in boys (36). Resting systolic and diastolic blood pressure levels were measured in the sitting position, after 5 min of sitting rest, with a Dinamap vital signs monitor (Critikron, Tampa, FL).
Overnight fasting blood samples were taken in the morning from the antecubital vein. Samples were handled and analyzed for insulin, glucose, triglycerides, and HDL-cholesterol as previously described (10).
Clustered metabolic risk score
Broadly based on the definition proposed by the World Health Organization (1), a standardized continuously distributed variable for clustered metabolic risk was calculated (3). This variable was derived by standardizing (z-scores) and then averaging the following continuously distributed metabolic syndrome components: blood pressure ([systolic blood pressure + diastolic blood pressure] / 2), fasting glucose, insulin, inverted HDL-cholesterol, and triglycerides. Before standardization, triglyceride and insulin values were logarithmically (ln) transformed because of skewness of distribution. No adiposity component was included in the clustered risk index because this was examined as a separate outcome. All z-scores were derived by sex and age groups. The purpose of using continuously distributed variables, rather than dichotomized outcomes, was to maximize statistical power (24).
Physical activity and sedentary time
Free-living physical activity and sedentary time were assessed with an MTI ActiGraph accelerometer (Model 7164; Fort Walton Beach, FL). Children were asked to wear the accelerometer for at least two weekdays and two weekend days during the daytime, except while bathing and other water-based activities. Activity data were stored on a minute-by-minute basis and were downloaded to a PC before analysis. A special written program (MAHUffe; www.mrc-epid.cam.ac.uk) was used for data cleaning, classification of wear/nonwear, and summation of sedentary time and total physical activity.
A minimum of 500 min of registered wear time was required for inclusion of a day in analysis. No criterion for a minimum number of days of data required for inclusion in analysis was applied; this allows variation of the number of person-days of data available for analysis between zero string protocols, which would reflect common analytical scenarios. A total of 2327 participants (age 9–10 yr: 484 boys and 547 girls; age 15–16 yr: 575 boys and 721 girls) provided at least one valid day of accelerometer data under the most inclusive zero string criterion (>100 min of consecutive zeros); characteristics for this sample are provided in Table 1.
The following cut points for determination of sedentary behavior were examined: 100 (20), 500 (30), 800 (22), and 1100 (25) CPM, reflecting a range of frequently used criteria in the literature. The following criteria for determination of nonwear time were examined: 10 (30), 20 (5), 60 (20), and 100 min of consecutive zero counts. Raw data were processed according to all possible combinations of cut point and nonwear criteria while keeping all other processing options constant. Thus, 16 accelerometry-derived sedentary time variables were obtained. The main summary variable was sedentary time in hours per day.
Data were analyzed using STATA version 11 (Stata Corporation, College Station, TX). Descriptive statistics are presented as mean and SD or geometric mean and 95% confidence interval (CI; 1.96 × SD on a log scale). Associations of sedentary time with adiposity and clustered metabolic risk were assessed using multilevel cross-sectional time series regression, with random effects at the individual level. Separate models were constructed for each of the 16 sedentary time variables. All associations were adjusted for age group, age, sex, study location, sexual maturity, day of the week, season, and registered wear time (Model 1). Models for clustered metabolic risk were additionally adjusted for adiposity. To examine whether associations were independent of the overall physical activity, models were additionally adjusted for average activity intensity (total accelerometer counts per registered minute; Model 2). Meta-analysis was used to obtain pooled estimates of the exposure–outcome association over all processing protocols. Metaregression was used to determine the influence of zero string and cut point settings on these associations. In calculating the overall effect estimates and the associated CI, meta-analytic methods were adapted to account for nonindependence of estimates obtained under each processing protocol. Forest plots are used as a visual aid to the interpretation of the 16 effect estimates obtained, separately for each outcome. Statistical significance was accepted at a level of P < 0.05.
Descriptive characteristics of participants are shown in Table 1. Data are presented for 2327 participants providing at least 1 d of valid accelerometer data under a 100-min zero string protocol. The choice of zero string threshold affects the duration of registered wear time each day. As a result, the number of person-days of observation that met the minimum 500 min of registered wear time differed under each zero string protocol. The 10-, 20-, 60-, and 100-min protocols yielded data on 9581, 9701, 9970, and 10,236 person-days, respectively. Children who were excluded under a 60-min zero string protocol were significantly younger than those included in the 100-min zero string analysis (P = 0.005); however, there were no other age or sex differences for those excluded under the 10-, 20-, or 60-min consecutive zero criteria in either age group. Children excluded from analyses under 10- and 20-min protocols had higher body mass index (BMI; P = 0.02 and P = 0.008) and sum of skinfolds (P = 0.006, and P = 0.005) compared with those analyzed under a 100-min consecutive zero protocol. Adolescents excluded under a 60-min zero string protocol had a higher BMI than those included in the 100-min zero string analysis (P = 0.04).
Estimates of sedentary time ranged from 4.8 to 11.9 h·d−1 and differed significantly by both zero string criteria and cut point (Table 2). In the investigated range (zero string = 10–100 min; cut point = 100–1100 CPM), sedentary time followed a power-law relationship with a zero string exponent of 0.03 and cut point exponent of 0.27:
Thus, under a constant zero string criterion, a twofold increase in the accelerometer cut point (e.g., from 100 to 200 CPM) increased estimated sedentary time by a factor of 1.2 (20.27 = 1.2). Shared variance between sedentary time and overall physical activity was less than 17% (Table 3).
At the basic level of adjustment (Model 1), a positive association between sedentary time and clustered metabolic risk was observed for all 16 of the sedentary time variables. After further adjustment for overall physical activity (Model 2), associations were attenuated but remained statistically significant for all but two of the sedentary time variables (zero string = 60 min/cut point = 100 CPM; zero string = 100 min/cut point 100 CPM). β coefficients and 95% CI for the association of each sedentary time variable with clustered metabolic risk are presented in the supplementary material (Table, Supplemental Digital Content 1, http://links.lww.com/MSS/A249, Associations of accelerometer-determined sedentary time with clustered metabolic risk). Regression coefficients (Model 2) and an overall pooled effect estimate for the association between sedentary time and clustered metabolic risk are presented in the forest plot (Fig. 1); the meta-analytical average across protocols was significant (β = 0.0051, 95% CI = 0.0018–0.0085). The plot suggested a nonlinear influence of cut point on the sedentary time–metabolic risk association, which was confirmed by meta-regression analysis; in log–log space, we found a positive effect of cut point on the association between sedentary time and metabolic risk (β = 0.42; 95% CI = 0.22–0.62) but no significant effect modification by zero string (β = −0.19; 95% CI = −0.40 to 0.012). The relationship was as follows:
For the association between sedentary time and adiposity, significant negative relationships were observed for exposure variables derived using a 10- or 20-min zero string criteria, except those for which a 1100-CPM cut point was used (Model 1). In models that included adjustment for overall physical activity (Model 2), there was a general attenuation of associations toward the null; significant negative associations with adiposity remained for sedentary time variables obtained using zero string criteria of 10 and 20 min with a cut point of 100 CPM only. β coefficients and 95% CI for the association of each sedentary time variable with adiposity are presented in the supplementary material (Table, Supplemental Digital Content 2, http://links.lww.com/MSS/A250, Associations of accelerometer-determined sedentary time with adiposity). Regression coefficients (Model 2) and an overall pooled effect estimate for the association between sedentary time and adiposity are presented in the forest plot (Fig. 2). In pooled analyses, no significant association between sedentary time and adiposity was observed (β = −0.0035; 95% CI = −0.0079 to 0.00096). In the metaregression analysis, there was no evidence that the association between sedentary time and adiposity was moderated by zero string (β = −0.20; 95% CI = −0.66 to 0.27) or cut point (β = 0.22; 95% CI = −0.24 to 0.68). The relationship was as follows:
In sensitivity analyses, we restricted our statistical models to participants providing >2 d of accelerometer data; results were not substantively affected by this change in inclusion criteria (data not shown).
The aim of this study was to investigate whether the choice of cut point and zero string criteria influenced associations of accelerometer-assessed sedentary time with adiposity and clustered metabolic risk in children and adolescents. Analyses revealed a positive association between sedentary time and clustered metabolic risk that was independent of overall physical activity (accelerometer CPM). In general, the magnitude of the sedentary behavior–metabolic risk association was greater when a higher cut point was used in deriving the exposure variable, but no significant effect of the zero string criteria was identified. No significant overall association between sedentary time and adiposity was observed, and no evidence was found that this association was influenced by the choice of zero string or cut point protocol.
In descriptive analyses, we noted a significant effect of both zero string and cut point on the estimates of daily sedentary time. Estimated sedentary time followed a power law relationship with the choice of zero string and cut point. The cut point exponent, however, was notably larger than that obtained for the zero string criteria, reflecting a much greater influence on the estimation of sedentary time. It should be noted that the exponents reported here are specific to the analytical sample and to the range of processing criteria investigated. Nonetheless, expressing the influence of data processing criteria on sedentary time estimates as a power function may facilitate greater comparability between studies by enabling recalculation of sedentary times obtained from varying criteria under a set of common rules. This may be of particular value for surveillance purposes, allowing prevalence estimates to be compared more appropriately between studies. In the dropout analysis, we found evidence of possible selection by adiposity under the different zero string protocols, possibly reflecting different patterns in the accumulation of sedentary time. Further research exploring patterns of sedentary time accumulation across diverse populations will help to establish whether processing protocols should be stratified by demographic or anthropometric characteristics.
In this study, objectively assessed sedentary time was positively associated with continuous metabolic risk, independent of overall physical activity. A previous study of European adolescents also reported that sedentary time was positively associated with a cardiovascular risk index, but this analysis did not include adjustment for physical activity (18). In participants age 6–19 yr from the National Health and Nutrition Examination Survey, sedentary time was not associated with a categorical cardiometabolic risk score (5). This study derived sedentary time using a cut point of 100 CPM and a zero string threshold of 20 min; in the current study, sedentary time obtained using this protocol was positively associated with metabolic risk (Table, Supplemental Digital Content 1, http://links.lww.com/MSS/A249). The inclusion of different metabolic health indicators and contrasting approaches to statistical analysis may account for these conflicting results. Findings from studies examining the association between sedentary time and individual cardiometabolic risk factors have also been mixed (11,30). The association between sedentary time and metabolic indicators is typically attenuated after adjustment for physical activity, and in combined analyses associations are usually stronger for MVPA compared to sedentary time. We found no overall association between sedentary time and sum of skinfold-assessed adiposity in the current study. Unexpectedly, β coefficients for the association between sedentary time and adiposity were negative, although associations were not significant for the majority of the derived exposure variables or in the pooled estimate. Findings are broadly consistent with previous research on this topic (11,18,21,35). It is important to establish whether sedentary time exerts an influence on health independently of physical activity. Statistical adjustment for MVPA becomes potentially problematic when using cut points from the higher end of the published range because of collinearity. In the current study, we adjust for overall physical activity (total accelerometer counts per registered minute) to avoid this problem and optimize the comparability between processing protocols.
From the metaregression analysis, we found that the association between sedentary time and clustered metabolic risk was moderated by the choice of cut point. In general, stronger associations between sedentary time and metabolic risk were observed as the cut point increased. However, as may be inferred from the forest plot (Fig. 1), the moderating influence of cut point on the sedentary time–metabolic risk association was curvilinear, exerting greatest influence at the lower end of the count threshold continuum. Findings suggest the need for caution in comparing results from studies exploring the association between accelerometer-assessed sedentary time and metabolic health in which different cut points have been used to derive the exposure variable. To better characterize the shape of the effect modification by cut point, further research could examine associations between sedentary time and metabolic health across a greater number and wider range of accelerometer cut points. Studies exploring the influence of other data processing decisions, such as epoch length, on the associations between sedentary time and metabolic health would also be valuable.
The mechanisms linking sedentary behavior with metabolic health are not well understood. Drawing predominantly on work in animal models, Hamilton et al. (12,13) have proposed that the metabolic pathways linking sedentary behavior with adverse health outcomes may be biologically distinct from those associated with physical activity. However, sedentary time variables constructed using cut points of 500, 800, and 1100 CPM will likely include activities that are behaviorally and physiologically more demanding than sitting, yet our data suggest that variables derived under these protocols may be more strongly associated with clustered metabolic risk. It may be that accelerometer-derived estimates of sedentary time using higher cut points better reflect exposure to particular sedentary behaviors, such as television viewing and computer gaming, which may adversely influence metabolic health through other pathways (5,10). Alternatively, it may be that, in children and adolescents, not only sedentary time but also a combination of both high sedentary time and high levels of light-intensity activity are adversely associated with metabolic health. Further work exploring the mechanisms linking individual sedentary behaviors and overall sedentary time with metabolic health is required to establish whether observed associations are truly causal and identify targets for intervention.
Strengths of the current study include broadly representative accelerometer processing criteria for defining sedentary time in a large heterogeneous sample of children on whom a wide range of anthropometric and physiological outcomes were available. The following limitations are acknowledged. First, the study is cross-sectional; thus, it is not possible to establish the temporal sequence of the observed associations. Second, exposure–outcome associations described in this study may be subject to residual confounding because of measurement error or failure to adjust for other confounding factors. We prioritized statistical power in our analyses and thus decided not to adjust for certain covariates (e.g., socioeconomic status) for which data were missing for many participants. However, the principal aim of this analysis was not to explore associations between sedentary time and metabolic health per se but rather to assess whether these associations were moderated by accelerometer data processing criteria. We acknowledge that effect estimates may be subject to a degree of residual confounding but maintain that the primary results of the analysis remain valid.
This study identified an independent positive association between sedentary time and clustered metabolic risk. The magnitude of this association was moderated by the cut point used in deriving the exposure variable, such that higher cut points typically produced stronger associations. This study adds to a growing body of literature exploring health outcomes of sedentary behavior in youth and suggests that, dependent on the outcome under study, it may not be appropriate to compare directly the results of studies using different accelerometer cut points for sedentary time.
This study was supported by grants from the following organizations: the Danish Heart Foundation, the Danish Medical Research Council Health Foundation, the Danish Council for Sports Research, the Foundation in Memory of Asta Florida Bolding ReneôÇe Andersen, the Faculty of Health Sciences at the University of Southern Denmark, the Estonian Science Foundation (grant numbers 3277 and 5209), and the Medical Research Council, United Kingdom. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors are very grateful to the participants and their families who gave their time to the study. The authors would also like to acknowledge all the members of the EYHS Group.
The work of Andrew Atkin was supported, in part, by the Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration Public Health Research Centre of Excellence (RES-590-28-0002). Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The work of Soren Brage was supported by the Medical Research Council (MC_U106179473).
The authors (Atkin, Ekelund, Møller, Froberg, Sardinha, Andersen, and Brage) have no conflicts of interest relevant to this article to disclose.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetes Med
. 1998; 15 (7): 539–53.
2. Basterfield L, Adamson AJ, Frary JK, Parkinson KN, Pearce MS, Reilly JJ. Longitudinal study of physical activity and sedentary behavior
in children. Pediatrics
. 2011; 127 (1): e24–30.
3. Brage S, Wedderkopp N, Ekelund U, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care
. 2004; 27 (9): 2141–8.
4. Bryant MJ, Lucove JC, Evenson KR, Marshall S. Measurement of television viewing in children and adolescents: a systematic review. Obes Rev
. 2007; 8 (3): 197–209.
5. Carson V, Janssen I. Volume, patterns, and types of sedentary behavior
and cardio-metabolic health
in children and adolescents: a cross-sectional study. BMC Public Health
. 2011; 11: 274.
6. Chinapaw MJ, Proper KI, Brug J, van Mechelen W, Singh AS. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev
. 2011; 12 (7): e621–32.
7. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep
. 2011; 22 (1): 15–23.
8. Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. J Appl Physiol
. 2008; 105 (3): 977–87.
9. Corder K, van Sluijs EM, Ekelund U, Jones AP, Griffin SJ. Changes in children’s physical activity over 12 months: longitudinal results from the SPEEDY study. Pediatrics
. 2010; 126 (4): e926–35.
10. Ekelund U, Brage S, Froberg K, et al. TV viewing and physical activity are independently associated with metabolic risk in children: the European Youth Heart Study. PLoS Med
. 2006; 3 (12): e488.
11. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA
. 2012; 307 (7): 704–12.
12. Hamilton MT, Hamilton DG, Zderic TW. Exercise physiology versus inactivity physiology: an essential concept for understanding lipoprotein lipase regulation. Exerc Sport Sci Rev
. 2004; 32 (4): 161–6.
13. Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes
. 2007; 56 (11): 2655–67.
14. Katzmarzyk PT. Physical activity, sedentary behavior
, and health: paradigm paralysis or paradigm shift? Diabetes
. 2010; 59 (11): 2717–25.
15. Krishnaveni GV, Mills IC, Veena SR, et al. Accelerometers for measuring physical activity behavior in Indian children. Indian Pediatr
. 2009; 46 (12): 1055–62.
16. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual
. Champaign (IL): Human Kinetics; 1991.
17. Marshall SJ, Ramirez E. Reducing sedentary behavior
: a new paradigm in physical activity promotion. Am J Lifestyle Med
. 2011; 5 (6): 518–30.
18. Martinez-Gomez D, Eisenmann JC, Gomez-Martinez S, Veses A, Marcos A, Veiga OL. Sedentary behavior
, adiposity and cardiovascular risk factors in adolescents. The AFINOS study. Rev Esp Cardiol
. 2010; 63 (3): 277–85.
19. Masse LC, Fuemmeler BF, Anderson CB, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S544–54.
20. Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol
. 2008; 167 (7): 875–81.
21. Mitchell JA, Mattocks C, Ness AR, et al. Sedentary behavior
and obesity in a large cohort of children. Obesity
. 2009; 17 (8): 1596–602.
22. Puyau MR, Adolph AL, Vohra FA, Butte NF. Validation and calibration of physical activity monitors in children. Obes Res
. 2002; 10 (3): 150–7.
23. Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF. Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc
. 2004; 36 (9): 1625–31.
24. Ragland DR. Dichotomizing continuous outcome variables: dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology
. 1992; 3 (5): 434–40.
25. Reilly JJ, Coyle J, Kelly L, Burke G, Grant S, Paton JY. An objective method for measurement of sedentary behavior
in 3- to 4-year olds. Obes Res
. 2003; 11 (10): 1155–8.
26. Riddoch C, Edwards D, Page A, et al. The European Youth Heart Study—cardiovascular disease risk factors in children: rationale, aims, study design, and validation of methods. J Phys Act Health
. 2005; 2 (1): 115–29.
27. Rowlands AV. Accelerometer assessment of physical activity in children: an update. Pediatr Exerc Sci
. 2007; 19 (3): 252–66.
28. Ruiz JR, Ortega FB, Martinez-Gomez D, et al. Objectively measured physical activity and sedentary time in European adolescents: the HELENA Study. Am J Epidemiol
. 2011; 174 (2): 173–84.
29. Salmon J, Tremblay MS, Marshall SJ, Hume C. Health risks, correlates, and interventions to reduce sedentary behavior
in young people. Am J Prev Med
. 2011; 41 (2): 197–206.
30. Sardinha LB, Andersen LB, Anderssen SA, et al. Objectively measured time spent sedentary is associated with insulin resistance independent of overall and central body fat in 9- to 10-year-old Portuguese children. Diabetes Care
. 2008; 31 (3): 569–75.
31. Sedentary Behaviour Research Network. Letter to the Editor: Standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab
. 2012; 37 (3): 540–2.
32. Sherar LB, Griew P, Esliger DW, et al. International children’s accelerometry
database (ICAD): design and methods. BMC Public Health
. 2011; 11 (1): 485.
33. Sirard JR, Trost SG, Pfeiffer KA, Dowda M, Pate R. Calibration and evaluation of an objective measure of physical activity in preschool children. J Phys Act Health
. 2005; 2 (3): 345–57.
34. Chief Medical Officers of England, Scotland, Wales and Northern Ireland. Start Active Stay Active. A report on physical activity for health from the four home countries’ Chief Medical Officers. UK Department of Health; 2011. Available from: http://www.dh.gov.uk/prod_consum_dh/groups/dh_digitalassets/documents/digitalasset/dh_128210.pdf
35. Steele RM, van Sluijs EM, Cassidy A, Griffin SJ, Ekelund U. Targeting sedentary time or moderate- and vigorous-intensity activity: independent relations with adiposity in a population-based sample of 10-y-old British children. Am J Clin Nutr
. 2009; 90 (5): 1185–92.
36. Tanner JM. Growth at Adolescence
. Oxford (UK): Blackwell Scientific Publications; 1962.
37. Tremblay MS, LeBlanc AG, Janssen I, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab
. 2011; 36 (1): 59–64.
38. Tremblay MS, Leblanc AG, Kho ME, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act
. 2011; 8 (1): 98.
39. Treuth MS, Schmitz K, Catellier DJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc
. 2004; 36 (7): 1259–66.
40. Van Cauwenberghe E, Labarque V, Trost SG, DB I., Cardon G. Calibration and comparison of accelerometer cut points in preschool children. Int J Pediatr Obes
. 2011; 6 (2–2): e582–9.
41. Wareham NJ, Rennie KL. The assessment of physical activity in individuals and populations: why try to be more precise about how physical activity is assessed? Int J Obes Relat Metab Disord
. 1998; (22 Suppl 2): S30–8.