Skip to main content
Download PDF
Download ePub

Substituting prolonged sedentary time and cardiovascular risk in children and youth: a meta-analysis within the International Children’s Accelerometry database (ICAD)

International Journal of Behavioral Nutrition and Physical Activity volume 16, Article number: 96 (2019) Cite this article

Abstract

Background

Evidence on the association between sitting for extended periods (i.e. prolonged sedentary time (PST)) and cardio-metabolic health is inconsistent in children. We aimed to estimate the differences in cardio-metabolic health associated with substituting PST with non-prolonged sedentary time (non-PST), light (LIPA) or moderate-to-vigorous physical activity (MVPA) in children.

Methods

Cross-sectional data from 14 studies (7 countries) in the International Children’s Accelerometry Database (ICAD, 1998–2009) was included. Accelerometry in 19,502 participants aged 3–18 years, together with covariate and outcome data, was pooled and harmonized. Iso-temporal substitution in linear regression models provided beta coefficients (95%CI) for substitution of 1 h/day PST (sedentary time accumulated in bouts > 15 min) with non-PST, LIPA or MVPA, for each study, which were meta-analysed.

Results

Modelling substitution of 1 h/day of PST with non-PST suggested reductions in standardized BMI, but estimates were > 7-fold greater for substitution with MVPA (− 0.44 (− 0.62; − 0.26) SD units). Only reallocation by MVPA was beneficial for waist circumference (− 3.07 (− 4.47; − 1.68) cm), systolic blood pressure (− 1.53 (− 2.42; − 0.65) mmHg) and clustered cardio-metabolic risk (− 0.18 (− 0.3; − 0.1) SD units). For HDL-cholesterol and diastolic blood pressure, substitution with LIPA was beneficial; however, substitution with MVPA showed 5-fold stronger effect estimates (HDL-cholesterol: 0.05 (0.01; 0.10) mmol/l); diastolic blood pressure: − 0.81 (− 1.38; − 0.24) mmHg).

Conclusions

Replacement of PST with MVPA may be the preferred scenario for behaviour change, given beneficial associations with a wide range of cardio-metabolic risk factors (including adiposity, HDL-cholesterol, blood pressure and clustered cardio-metabolic risk). Effect estimates are clinically relevant (e.g. an estimated reduction in waist circumference of ≈1.5 cm for 30 min/day replacement). Replacement with LIPA could be beneficial for some of these risk factors, however with substantially lower effect estimates.

Background

Maintenance of sufficient levels of moderate-to-vigorous physical activity (MVPA) is a well-accepted lifestyle target for optimal health and development in young people [1]. Sedentary time, i.e. any waking behaviour with low energy expenditure, while in sitting/reclining/lying posture [2], has recently gained attention as a potential additional health risk factor, and is highly prevalent, even in children [3, 4]. Lower fitness levels and unfavourable cardiovascular risk profiles have been associated with excessive self-reported TV viewing, independent of MVPA [5]. For accelerometry-assessed total sedentary time, such associations are less consistent in children and adolescents [1, 3,4,5].

Total sedentary time is, however, accumulated in varying patterns. Some tend to accumulate predominantly long sedentary bouts whereas others display more regular interruptions, while still accruing similar volumes of total sedentary time [6]. These diverse accumulation patterns may have different associations with health indicators, which cannot be detected by only examining total volume of sedentary time. In adults, evidence from experimental and observational research indicates a protective effect of regular interruptions in sitting time on specific cardio-metabolic biomarkers, including adiposity, glucose metabolism and inflammation, even when these interruptions are only of light intensity [6]. In paediatric populations, far fewer studies have examined this. Recent reviews in this age group concluded that the current evidence on associations between objectively measured accumulation patterns of sedentary time and cardio-metabolic health is inconsistent [3,4,5, 7]. These reviews could not include meta-analyses due to methodological differences among contributing studies [3,4,5, 7]. Despite this low level of evidence, some national public health authorities have implemented recommendations for paediatric populations to limit sitting time for “extended” periods (i.e. prolonged sedentary time (PST)) [8, 9].

If PST were cardio-metabolically harmful, guidelines designed to minimise PST should be fully informed by the relative merits of replacing it with other activity behaviours. Total awake time in the day is finite and the impact of reductions in PST on health depends on what behaviour it is displaced with, whether this is non-prolonged sedentary time (non-PST), light-intensity physical activity (LIPA) or MVPA [6]. Some of these activity subcomponents may be more amenable to behaviour change than others [10], further reinforcing the need to examine the potential impact of these different substitution scenarios. This is however largely unexplored in children and adolescents using device-based measures [5], certainly on a sufficiently large scale to allow for full examination of differences in such substitution effects by sex and age.

Using the International Children’s Accelerometry Database (ICAD), containing pooled accelerometry and cardio-metabolic health data from an extensive number of studies in children and adolescents worldwide, we aimed to examine 1) the independent associations between PST and cardio-metabolic health, as well as 2) the associations following iso-temporal substitution modelling of replacing PST with other activity, using a meta-analytical approach on harmonized data [11]. Such insights are important to guide intervention design and strengthen the evidence-base underpinning public health guidelines in this age group.

Methods

Participants

Data from 21 studies in children aged 3–18, with, as a minimum, objectively measured activity and sedentary time by a waist-worn accelerometer (Actigraph Corp., Pensacola, FL, USA), as well as sex, age and measured height and weight, were pooled in the ICAD. A detailed description of the ICAD aims, design, study characteristics and covariate measurement has previously been published [11]. Participants of 14 of the 21 studies which provided information on at least one cardio-metabolic variable, more specifically waist circumference (the most commonly measured), constituted the sampling frame for the current cross-sectional analysis (n = 20,296). After excluding those individuals with insufficient valid accelerometry data (n = 788) and invalid data for some of the outcome measures (n = 6), a total sample of 19,502 children were included in analyses. The study complies to the Declaration of Helsinki, all study protocols were approved by local ethics committees and informed consent was obtained from all subjects or their legal authorized representative.

Anthropometric and cardio-metabolic risk variables

Height, weight and waist circumference were measured in all 14 included studies. Height and weight were measured objectively using standardized methods, and used to derive body mass index (BMI; weight(kg)/(height(m)2)). Age- and sex-specific BMI standard deviation scores (BMI z-scores) were calculated based on the LMS method [12]. Waist circumference was measured by metal anthropometric tape just above the iliac crest in one study [13], and halfway between the lower rib margin and the iliac crest in the other studies, following gentle expiration [14,15,16,17,18,19,20,21,22] (on the skin in all studies, except two that allowed light clothing [17, 20]). Due to the difference in measurement method between studies and the lack of reference data covering the entire age range of the ICAD sample, waist circumference was not standardized, similar to previous ICAD analyses [1].

Ten studies provided measurements of systolic and diastolic blood pressure, as described in detail elsewhere [1, 15, 18, 19, 22,23,24,25]. Fasting triglycerides and HDL-cholesterol levels were assessed in 8 studies [15, 16, 22,23,24, 26], and fasting glucose and insulin in 7 studies [15, 16, 22,23,24] using standardized clinical procedures [1]. Six participants with unrealistic values for fasting glucose and insulin were excluded from analyses.

A clustered cardio-metabolic risk score (CCMR) was calculated based on indicators of central adiposity (waist circumference), lipid (triglycerides and HDL-cholesterol) and glucose (fasting glucose and insulin) metabolism and blood pressure (systolic and diastolic blood pressure) [27]. Sex-specific standardization of each individual variable was based on the mean and SD of all boys and girls without missing data for the respective variable. Triglycerides and insulin values were first normalized (log 10), due to their skewed distribution. After averaging systolic and diastolic blood pressure and inverting HDL-cholesterol z-scores, CCMR was calculated by summing individual z-scores and dividing by 6 (i.e. the number of contributing components). The same score was also calculated without the central adiposity component (CCMRno adip) for models with additional adjustment for waist circumference [27]. These risk scores were calculated for participants with data on all contributing cardio-metabolic variables.

Objectively measured sedentary time and physical activity

All accelerometry data were processed centrally using open-source software [28]. Higher resolution files (epoch length < 60 s) were reintegrated to 60 s epoch resolution and all time between midnight and 7 am was discarded. All epochs with an intensity > 30,000 counts per minute (cpm) were classified as non-valid. Non-wear time was defined as bouts lasting ≥60 min of consecutive zeros. Children with ≥1 valid day, each containing ≥500 min of monitor wear time (from 7 am to midnight), passed the inclusion criteria (n = 19,695). Out of these, all files previously deemed invalid (n = 201) [11], as well a small number of additional files generated by mechanically faulty monitors (n = 14) were excluded. The latter 14 cases were verified as corrupt data via visual screening of files, identified due to implausible plateaus at low or high acceleration levels.

All valid wear time registering acceleration < 100 cpm was defined as total sedentary time. Cut-offs were ≥ 100 cpm and < 3000 cpm for LIPA, and ≥ 3000 cpm for time spent in MVPA [1]. PST was characterized as the duration of sedentary time accumulated in bouts > 15 min (PST > 15 min) and > 30 min (PST > 30 min) [29]. PST defined by minimal bout durations > 60 min showed minimal prevalence in this age group (< 1% of total sedentary time) and was therefore not considered. Non-PST of ≤15 min and ≤ 30 min was calculated as the difference between total sedentary and the respective PST volumes. All variables were expressed in h/day.

Statistical analysis

Descriptive characteristics were calculated for the total sample and separately by sex (mean (standard deviation (SD)) for normally distributed, medians (interquartile range (IQR)) for non-normally distributed and n (%) for categorical variables). Spearman correlations were calculated between sedentary and activity time components.

Linear regression models were fit within each study to estimate associations between PST and the cardio-metabolic risk factors. For each of the exposure-outcome combinations, multi-collinearity (variance inflation factor), homoscedasticity (plot of residuals versus fitted values) and normality of residuals (histogram) were checked in the largest study with greatest variability in age, ethnicity, and cardio-metabolic variables measured [23]. Models were initially adjusted for sex, age (years) and monitor wear time (hours/day; Model A). Subsequently, we added MVPA time (Model B) and waist circumference (Model C) to examine independence of these covariates. Random effects meta-analysis was used to derive a pooled regression coefficient (95% CI) across studies. Sex interactions were examined within each study in Model A and meta-analysed. Age (< 12 versus ≥12) interactions were examined, within each study with an age range incorporating 12 years (≥5 studies, depending on outcome), and meta-analysed. Age interactions were examined in the total group when no sex interaction was found and separately within boys and girls when a significant sex interaction was found.

We then examined iso-temporal substitution [30] of PST with non-PST, LIPA and MVPA for those outcome variables which showed a significant association with PST in Model A described above, providing results for the total group where no sex interaction was found and additionally for both sexes separately where a sex interaction was found. The iso-temporal substitution models within each study included sex (where applicable), age, wear time, non-PST, LIPA and MVPA. Regression coefficients (95% CI) of the included activity components in these models provide an estimate of the change in the outcome variable when increasing that type of activity by 1 h/day while decreasing PST by the same duration and holding other activity components constant [30], and were also meta-analysed.

A first set of sensitivity analyses was conducted in a smaller set of children who provided ≥3 valid days of accelerometry data, including ≥1 weekend day. A second set of sensitivity analyses was conducted, adjusting for a more comprehensive set of confounding variables, more specifically ethnicity (white/non-white), parental socio-economic status (SES; household income (categorical)), birth weight (kg) and sexual maturity (breast development in girls and pubic hair in boys, Tanner), where available. Due to missing data for these covariates, these analyses were performed in smaller samples. As this was an exploratory analysis of observational data and not a confirmatory analysis of a clinical trial, we did not correct for multiple testing. All analyses were conducted using Stata version 14 (Stata Statistical Software. College Station, TX: StataCorp LP. 2015) and statistical significance was set at p < 0.05.

Results

Descriptive characteristics

Characteristics of all included participants are provided in Table 1. The mean (SD) contribution to total sedentary time was 30.8% (12.9%) for PST > 15 min (boys: 30.3% (13.1%); girls: 31.3% (12.6%))) and 14.1% (10.7%) for PST > 30 min (boys: 13.9% (11.0%); girls: 14.2% (10.4%)). PST > 15 min and PST > 30 min were highly correlated (Table 2). Additional file 1: Table S1 provides individual cohort characteristics as well as descriptive statistics for the outcome variables within each cohort.

Table 1 Baseline descriptive characteristics of all participants and stratified by sex, ICAD
Full size table
Table 2 Spearman correlations between sedentary and activity time covariates
Full size table

Prolonged sedentary time and cardio-metabolic risk

More PST > 15 min was independently associated with higher standardized BMI, waist circumference, systolic and diastolic blood pressure and CCMR and lower HDL-cholesterol after adjustment for confounders in Model A (Table 3). Significant sex interactions were found for most outcomes, except standardized BMI. The associations of PST > 15 min with waist circumference, HDL-cholesterol, CCMR and CCMRno adip were stronger in boys; there was a stronger positive association with diastolic blood pressure in girls. After adjustment for MVPA, most associations were attenuated and no longer significant, except for the inverse association with HDL-cholesterol in boys and the positive association with diastolic blood pressure in girls (Model B). The latter two associations were non-significant following further adjustment for waist circumference (Model C). There was no evidence that any of the PST > 15 min/outcome associations differed between age groups. Results for PST > 30 min were similar compared to those for PST > 15 min (Additional file 1: Table S2), which is commensurate with the fairly high correlation between both exposure variables (Table 2). Therefore only PST > 15 min was taken forward for further analyses. Sensitivity analyses in the subsample with ≥3 valid wear days including ≥1 valid weekend day showed comparable patterns of findings (Additional file 1: Table S3). Sensitivity analysis, allowing for more comprehensive confounding adjustment where available, showed somewhat smaller effect estimates compared to those in the main analyses (Additional file 1: Table S4).

Table 3 Cross-sectional associations between prolonged sedentary time and cardio-metabolic risk, ICAD
Full size table

Isotemporal substitution of prolonged sedentary time and cardio-metabolic risk

Substitution of 1 h/day of PST > 15 min with the same duration of non-PST was estimated to reduce standardized BMI by 0.06 SD units (Table 4 and Additional file 1: Figure S1-S7). However, substitution with the same amount of MVPA was associated with a > 7-fold greater estimated reduction in standardized BMI (0.44 SD units). For waist circumference, systolic blood pressure, CCMR and CCMRno adip, only reallocation to MVPA was significantly associated with these outcomes. A 1 h/day substitution was associated with a 3.1 cm lower waist circumference (boys: 3.5 cm; girls: 2.7 cm), 1.5 mmHg lower systolic blood pressure (boys: 1.5 mmHg; girls: 1.9 mmHg) and a 0.18 SD unit lower CCMR (boys: 0.15 SD units; girls: 0.22 SD units). For HDL-cholesterol in boys (0.01 mmol/l) and diastolic blood pressure in the total group (− 0.2 mmHg) and girls (− 0.3 mmHg), substitution with LIPA suggested potential beneficial effects. However, substitution by MVPA resulted in stronger effect estimates, both for HDL-cholesterol (total group: 0.05 mmol/l; girls: 0.07 mmol/l; boys: 0.05 mmol/l) and diastolic blood pressure (total group: − 0.8 mmHg; girls: − 1.3 mmHg). Interestingly, substitution by non-PST was associated with higher levels of diastolic blood pressure (total group and boys: 0.3 mmHg; girls: 0.4 mmHg).

Table 4 Cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD
Full size table

Discussion

Constituting almost a third of all sedentary time and a substantial proportion (16%) of daily waking wear time in this age group, PST > 15 min is a potentially important target for behaviour change. Modelling 1 h/day substitutions of PST with higher intensity activity suggested beneficial effects on most cardio-metabolic health outcomes examined; however, a moderate-to-vigorous intensity of replacement activity seemed preferable. Replacement with LIPA was only associated beneficially with HDL cholesterol (boys) and diastolic blood pressure. Similarly, findings suggested that replacement with more interrupted sedentary time may result in limited benefit, based on weak associations with only BMI and diastolic blood pressure (the latter in the unexpected direction).

Effect estimates for replacement with MVPA are clinically relevant. For example, the estimated reduction found for waist circumference of ≈3 cm would be associated with an approximately 6% lower risk of cardiovascular events [31]. A 1 h/day replacement with MVPA may however be difficult to achieve, as it is equivalent to an increase of 200% of mean observed MVPA in this sample, compared to a potentially more feasible increase by 16 and 24% of mean observed LIPA and non-PST, respectively. However, when modelling 30 min/day replacements with MVPA, the estimated reductions in waist circumference and subsequent incident CVD risk would be ≈1.5 cm and 3%, respectively, which are still clinically important [31]. Compared to a recent cross-sectional study examining substitution of total, rather than prolonged sedentary time, by MVPA, we found stronger effect estimates for systolic blood pressure and HDL-cholesterol, albeit comparable effect estimates for waist circumference [32]. This supports the notion that focusing on replacements of prolonged sedentary time may be a more efficient way to preserve cardio-metabolic health in children, rather than focusing on all sedentary time.

Observational studies in children on accumulation patterns in sedentary time and cardio-metabolic health have focused on independent rather than iso-temporal associations. Differences in exposure definition, accelerometry methodology, analysis strategy and to some extent study population between our meta-analysis and other singular studies complicate comparisons of results [3,4,5]. However, attenuation of most associations following adjustment for MVPA is in line with the literature [3, 4]. Sex-specific associations were similarly found in some studies that suggested somewhat stronger associations in boys [33, 34], although this was not corroborated by others [35, 36]. Although true biological differences could explain our findings, a more plausible explanation might be found in the type of prolonged sitting. For example, boys engage in more screen time, which is more strongly associated with impaired cardiovascular health [37, 38]. This could be mediated through dietary alterations, or the interplay with the timing of energy-dense meal consumption [38]. Prolonged sitting following energy-dense meals has been shown to exacerbate post-prandial glucose and lipid excursions [39, 40]. Future work should examine whether PST at specific times of the day is more strongly associated with cardio-metabolic health, and hence whether it would be more effective, but also feasible, to replace PST by other activities during these time periods. Insights into mechanistic pathways between prolonged sitting and cardio-metabolic health are still limited, need further scrutiny and are predominantly based on animal and adult human populations. However, hypothesized mechanisms include reductions in muscular demand, blood flow, lipid oxidation, muscle/liver insulin sensitivity and vascular function, and increased body insulin resistance, ectopic fat storage, and oxidative stress [41]. Finally, even though our findings suggest most beneficial effects for replacement with MVPA, the optimal accumulation patterns need further investigation, including MVPA bout durations and activity type (aerobic versus resistance). This may well differ depending on the outcome (e.g. glucose or lipid metabolism, vascular function) [39, 42].

Important strengths of this study include the large sample size and wide age range, allowing for investigation of moderation effects by sex and age, which is more challenging in smaller cohort and intervention studies. The great heterogeneity of included study samples in terms of ethnicity, obesity status and general health profile also increases external validity of the findings. Second, meta-analysis rather than pooled analysis allowed for better control of residual confounding through sensitivity analyses allowing differential level of adjustment between studies. Third, harmonization of all exposure variables further increased robustness of the results and allowed for estimation of non-iso-temporal and iso-temporal effect estimates, which is novel in children and youth, especially on this scale. All exposures were also measured by accelerometry, rather than by self-report, avoiding biases associated with the latter. Finally, our characterization of prolonged sedentary time based on time spent in sedentary bouts of minimal duration is more optimal compared to other methods, such as breaks in sedentary time [6, 29].

The following limitations however also need to be considered. The observational and cross-sectional study design prevents conclusions in terms of causality and effect estimates are based on statistical modeling rather than behavioural change. It is, however, unlikely that children with less healthy profiles for non-adiposity risk factors (mostly subclinical in this age group) would have consequentially changed their sedentary time accumulation habits. The alternative direction of causality, i.e. prolonged sitting increasing cardio-metabolic risk, is in line with findings from intervention studies in children, showing acute beneficial effects of interrupting sitting time by activity breaks on glucose, lipid and vascular function [39, 42]. The accelerometry used in the included studies does not distinguish between postures, which may have resulted in a certain degree of misclassification between standing and non-standing posture. Participants on average wore the accelerometers for 5 days, which may not fully reflect their habitual activity levels due to high within-individual variability. As previously estimated, this may have led to an underestimation of the true magnitude of associations by 50% [1]. Sensitivity analyses excluding those without valid measurement on a weekend day and at least three valid days did not alter results. The latter, in combination with high wear time adherence during awake time (i.e. mean wear time of 13 h/day across an average of 5 days) also suggests that our findings are robust to alternative valid day definitions. Although our intensity threshold for MVPA could be considered fairly high, implementing a threshold of 2000 cpm did not materially change results (data not shown). Finally, due to data unavailability we could not include dietary intake as a covariate in our analyses. Hence residual confounding may be at play, also for variables which were not available in all studies and were hence only included for some studies in our sensitivity analyses. Future studies should aim to examine the extent of this issue more comprehensively.

Conclusions

Our findings suggest that replacements of PST with higher-intensity activity, preferably MVPA, are beneficially associated with adiposity, HDL-cholesterol, blood pressure and clustered cardio-metabolic risk in children and adolescents, with clinically relevant effect estimates. Future observational studies, with accelerometry and cardio-metabolic profiling at multiple time-points, should examine whether these iso-temporal associations persist in longitudinal analysis, to gain further insights in these associations in the long term according to important population strata such as age and sex. These should be accompanied by further lab-based and free-living intervention studies, respectively examining the acute and more chronic effects of such substitutions on cardio-metabolic health in children.

Availability of data and materials

The datasets supporting the conclusions of this article are available via http://www.mrc-epid.cam.ac.uk/Research/Studies/ICAD/.

Abbreviations

ALSPAC:

Avon Longitudinal Study of Parents and Children

BMI:

Body mass index

BP:

Blood pressure

CCMR:

Clustered cardio-metabolic risk

CCMRno adip :

Clustered cardio-metabolic risk without central adiposity component

cpm:

Counts per minute

CSCIS:

Copenhagen School Child Intervention Study

EYHS:

European Youth Heart Study

ICAD:

International Children’s Accelerometry Database

IQR:

Interquartile range

KISS:

Kinder Sportstudie

LIPA:

Light-intensity physical activity

MAGIC:

Movement and Activity Glasgow Intervention in Children

MVPA:

Moderate-to-vigorous physical activity

NHANES:

National Health and Nutrition Examination Survey

non-PST:

Non-prolonged sedentary time

PEACH:

Personal and Environmental Associations with Children’s Health

PST:

Prolonged sedentary time

SD:

Standard deviation

SES:

Socio-economic status

SPEEDY:

Sport, Physical Activity and Eating Behavior: Environmental Determinants in Young People

Triglyc:

Triglycerides

Waist:

Waist circumference

References

  1. Ekelund U, Luan J, Sherar SB, Esliger DW, Griew P, Cooper A. International Children's Accelerometry database (ICAD) collaborators. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents JAMA. 2012;15:704–12.

    Google Scholar 

  2. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, Chastin SFM, Altenburg TM, Chinapaw MJM. Sedentary behavior research network (SBRN) - terminology consensus project process and outcome. Int J Behav Nutr Phys Act. 2017;14:75.

    Article  Google Scholar 

  3. Froberg A, Raustorp A. Objectively measured sedentary behaviour and cardio-metabolic risk in youth: a review of evidence. Eur J Pediatr. 2014;173:845–60.

    Article  Google Scholar 

  4. Cliff D, Hesketh K, Vella S, Hinkley T, Tsiros M, Ridgers N, Carver A, Veitch J, Parrish A, Hardy L, Plotnikoff R, Okely A, Salmon J, Lubans D. Objectively measured sedentary behaviour and health and development in children and adolescents: systematic review and meta-analysis. Obes Rev. 2016;17:330–44.

    Article  CAS  Google Scholar 

  5. Carson V, Hunter S, Kuzik N, Gray C, Poitras V, Chaput J, Saunders T, Katzmarzyk P, Okely A, Connor Gorber S, Kho M, Sampson M, Lee H, Tremblay M. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab. 2016;41:S240–S65.

    Article  Google Scholar 

  6. Chastin SF, Egerton T, Leask C, Stamatakis E. Meta-analysis of the relationship between breaks in sedentary behavior and cardiometabolic health. Obesity. 2015;23:1800–10.

    Article  Google Scholar 

  7. Verswijveren SJJM, Lamb KE, Bell LA, Timperio A, Salmon J, Ridgers ND. Associations between activity patterns and cardio-metabolic risk factors in children and adolescents: A systematic review. PLoS One. 2018;13:e0201947.

    Article  Google Scholar 

  8. Department of Health: Start active, stay active - A report on physical activity for health from the four home countries` Chief Medical Officers. 2011.

    Google Scholar 

  9. Tremblay M, Carson V, Chaput J, Connor Gorber S, Dinh T, Duggan M, Faulkner G, Gray C, Gruber R, Janson K, Janssen I, Katzmarzyk P, Kho M, Latimer-Cheung A, LeBlanc C, Okely A, Olds T, Pate R, Philips A, Poitras V, Rodenburg S, Sampson M, Saunders T, Stone J, Stratton G, Weiss S, Zehr L. Canadian 24-hour movement guidelines for children and youth: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2016;41:S311–S27.

    Article  Google Scholar 

  10. Fairclough SJ, Hackett AF, Davies IG, Gobbi R, Mackintosh KA, Warburton GL, Stratton G, van Sluijs EM, Boddy LM. Promoting healthy weight in primary school children through physical activity and nutrition education: a pragmatic evaluation of the CHANGE! Randomised intervention study. BMC Public Health. 2013;13:626.

    Article  Google Scholar 

  11. Sherar L, Griew P, Esliger D, Cooper A, Ekelund U, Judge K, Riddoch C. International children's accelerometry database (ICAD): design and methods. BMC Public Health. 2011;11:485.

    Article  Google Scholar 

  12. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995;73:25–9.

    Article  CAS  Google Scholar 

  13. Centers for Disease Control and Prevention: National Health and Nutrition Examination Survey. Anthropometry and physical activity monitor procedures manual. 2005.

    Google Scholar 

  14. Niederer I, Kriemler S, Zahner L, Burgi F, Ebenegger V, Hartmann T, Meyer U, Schindler C, Nydegger A, Marques-Vidal P, Puder JJ. Influence of a lifestyle intervention in preschool children on physiological and psychological parameters (Ballabeina): study design of a cluster randomized controlled trial. BMC Public Health. 2009;9:94.

    Article  Google Scholar 

  15. Riddoch C, Edwards D, Page A, Froberg K, Anderssen S, Wedderkopp N, Brage S, Cooper A, Sardinha L, Harro M, Klasson Heggebo L, van Mechelen W, Boreham C, Ekelund U, Andersen L. The European youth heart study - cardiovascular risk factors in children: rationale, aims, study design and validation of methods. J Phys Act Health. 2005;2:115–29.

    Article  Google Scholar 

  16. Zahner L, Puder JJ, Roth R, Schmid M, Guldimann R, Puhse U, Knopfli M, Braun-Fahrlander C, Marti B, Kriemler S. A school-based physical activity program to improve health and fitness in children aged 6-13 years ("kinder-Sportstudie KISS"): study design of a randomized controlled trial [ISRCTN15360785]. BMC Public Health. 2006;6:147.

    Article  Google Scholar 

  17. van Sluijs EMF, Skidmore PML, Mwanza K, Jones AP, Callaghan AM, Ekelund U, Harrison F, Harvey I, Panter J, Wareham NJ, Cassidy A, Griffin SJ. Physical activity and dietary behaviour in a population-based sample of British 10-year old children: the SPEEDY study (sport, physical activity and eating behaviour: environmental determinants in young people). BMC Public Health. 2008;8:388.

  18. Reilly JJ, Kelly L, Montgomery C, Williamson A, Fisher A, McColl JH, Lo CR, Paton JY, Grant S. Physical activity to prevent obesity in young children: cluster randomised controlled trial. BMJ. 2006;333:1041.

    Article  Google Scholar 

  19. Victora CG, Hallal PC, Araujo CL, Menezes AM, Wells JC, Barros FC. Cohort profile: the 1993 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2008;37:704–9.

    Article  Google Scholar 

  20. Page AS, Cooper AR, Griew P, Jago R. Independent mobility, perceptions of the built environment and children's participation in play, active travel and structured exercise and sport: the PEACH project. Int J Behav Nutr Phys Act. 2010;7:17.

    Article  Google Scholar 

  21. Golding J, Pembrey M, Jones R. ALSPAC--the Avon longitudinal study of parents and children. I Study methodology Paediatr Perinat Epidemiol. 2001;15:74–87.

    Article  CAS  Google Scholar 

  22. Bugge A, El-Naaman B, Dencker M, Froberg K, Holme IM, McMurray RG, Andersen LB. Effects of a three-year intervention: the Copenhagen school child intervention study. Med Sci Sports Exerc. 2012;44:1310–7.

    Article  Google Scholar 

  23. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. Atlanta: Laboratory Procedures Manual; 2005.

  24. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. Atlanta: Laboratory Procedures Manual; 2010.

  25. 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;40:181–8.

    Article  Google Scholar 

  26. Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, Anderssen SA. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (the European youth heart study). Lancet. 2006;368:299–304.

    Article  Google Scholar 

  27. Wijndaele K, Orrow G, Ekelund U, Sharp SJ, Brage S, Griffin SJ, Simmons RK. Increasing objectively measured sedentary time increases clustered cardiometabolic risk: a 6 year analysis of the ProActive study. Diabetol. 2014;57:305–12.

    Article  CAS  Google Scholar 

  28. White T: Physical Activity Monitor Processing. https://doi.org/10.5281/zenodo.1187043 2018.

    Google Scholar 

  29. Wijndaele K, Healy GN. Sitting and chronic disease: where do we go from here? Diabetol. 2016;59:688–91.

    Article  Google Scholar 

  30. Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170:519–27.

    Article  Google Scholar 

  31. de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28:850–6.

    Article  Google Scholar 

  32. Hansen BH, Anderssen SA, Andersen LB, Hildebrand M, Kolle E, Steene-Johannessen J, Kriemler S, Page AS, Puder JJ, Reilly JJ, Sardinha LB, van Sluijs EMF, Wedderkopp N, Ekelund U. International Children's Accelerometry Database (ICAD) Collaborators. Cross-sectional associations of reallocating time between sedentary and active behaviours on cardiometabolic risk factors in young people: an International Children's Accelerometry Database (ICAD) analysis. Sports Med. 2018;48:2401–12.

    Article  Google Scholar 

  33. Colley RC, Garriguet D, Janssen I, Wong SL, Saunders TJ, Carson V, Tremblay MS. The association between accelerometer-measured patterns of sedentary time and health risk in children and youth: results from the Canadian health measures survey. BMC Public Health. 2013;13:200.

    Article  Google Scholar 

  34. Saunders TJ, Tremblay MS, Mathieu ME, Henderson M, O'Loughlin J, Tremblay A, Chaput JP. Associations of sedentary behavior, sedentary bouts and breaks in sedentary time with cardiometabolic risk in children with a family history of obesity. PLoS One. 2013;8:e79143.

    Article  Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. Cliff DP, Jones RA, Burrows TL, Morgan PJ, Collins CE, Baur LA, Okely AD. Volumes and bouts of sedentary behavior and physical activity: associations with cardiometabolic health in obese children. Obesity. 2014;22:E112–E8.

    Article  CAS  Google Scholar 

  37. Marshall SJ, Gorely T, Biddle SJ. A descriptive epidemiology of screen-based media use in youth: a review and critique. J Adolesc. 2006;29:333–49.

    Article  Google Scholar 

  38. Patterson R, McNamara E, Tainio M, de Sa TH, Smith AD, Sharp SJ, Edwards P, Woodcock J, Brage S, Wijndaele K. 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;33:811–29.

    Article  Google Scholar 

  39. Belcher BR, Berrigan D, Papachristopoulou A, Brady SM, Bernstein SB, Brychta RJ, Hattenbach JD, Tigner IL Jr, Courville AB, Drinkard BE, Smith KP, Rosing DR, Wolters PL, Chen KY, Yanovski JA. Effects of interrupting Children's sedentary behaviors with activity on metabolic function: a randomized trial. J Clin Endocrinol Metab. 2015;100:3735–43.

    Article  CAS  Google Scholar 

  40. Fletcher EA, Salmon J, McNaughton SA, Orellana L, Wadley GD, Bruce C, Dempsey PC, Lacy KE, Dunstan DW. Effects of breaking up sitting on adolescents' postprandial glucose after consuming meals varying in energy: a cross-over randomised trial. J Sci Med Sport. 2018;21:280–5.

    Article  Google Scholar 

  41. Dempsey PC, Matthews CE, Dashti G, Doherty AR, Berguignon A, van Roekel EH, Dunstan DW, Wareham NJ, Yates TE, Wijndaele K, Lynch BM. Sedentary behaviour and chronic disease: mechanisms and future directions. J Phys Act Health. in revision.

  42. McManus AM, Ainslie PN, Green DJ, Simair RG, Smith K, Lewis N. Impact of prolonged sitting on vascular function in young girls. Exp Physiol. 2015;100:1379–87.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all participants and funders of the original studies that contributed data to ICAD. The ICAD collaborators include: prof LB Andersen, Department of Teacher Education and Sport, Western Norwegian University of applied sciences, Sogndal, Norway (Copenhagen school child intervention study (CoSCIS)); prof S Anderssen, Norwegian School for Sport Science, Oslo, Norway (European youth heart study (EYHS), Norway); Dr. AJ Atkin, MRC Epidemiology Unit & Centre for diet and activity research, University of Cambridge, UK; prof G Cardon, Department of Movement and Sports Sciences, Ghent University, Belgium (Belgium pre-school study); Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Hyattsville, MD USA (National Health and nutrition examination survey (NHANES)); Dr. R Davey, Centre for Research and Action in public health, University of Canberra, Australia (Children’s health and activity monitoring for schools (CHAMPS)); prof U Ekelund, Norwegian School of Sport Sciences, Oslo, Norway & MRC epidemiology unit, University of Cambridge, UK; Dr. DW Esliger, School of Sports, exercise and health sciences, Loughborough University, UK; Dr. P Hallal, postgraduate program in epidemiology, Federal University of Pelotas, Brazil (1993 Pelotas birth cohort); Dr. BH Hansen, Norwegian School of Sport Sciences, Oslo, Norway; prof KF Janz, Department of Health and Human Physiology, Department of Epidemiology, University of Iowa, Iowa City, US (Iowa bone development study); prof S Kriemler, epidemiology, biostatistics and prevention institute, University of Zürich, Switzerland (kinder-Sportstudie (KISS)); Dr. N Møller, University of Southern Denmark, Odense, Denmark (European youth heart study (EYHS), Denmark); Dr. K Northstone, Bristol medical school, University of Bristol, UK (Avon longitudinal study of parents and children (ALSPAC)); Dr. a Page, Centre for Exercise, nutrition and health sciences, University of Bristol, UK (personal and environmental associations with Children’s health (PEACH)); prof R Pate, Department of Exercise Science, University of South Carolina, Columbia, US (physical activity in pre-school children (CHAMPS-US) and project trial of activity for adolescent girls (project TAAG)); Dr. JJ Puder, Service of Endocrinology, diabetes and metabolism, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Switzerland (Ballabeina study); prof JJ Reilly, physical activity for health group, School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK (movement and activity Glasgow intervention in children (MAGIC)); prof J Salmon, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia (children living in active Neigbourhoods (CLAN)); prof LB Sardinha, exercise and health laboratory, Faculty of Human Movement, Universidade de Lisboa, Lisbon, Portugal (European youth heart study (EYHS), Portugal); Dr. LB Sherar, School of Sports, exercise and health sciences, Loughborough University, UK; Dr. a Timperio, Centre for Physical Activity and Nutrition Research, Deakin University Melbourne, Australia (healthy eating and play study (HEAPS)); Dr. EMF van Sluijs, MRC Epidemiology Unit & Centre for diet and activity research, University of Cambridge, UK (sport, physical activity and eating behaviour: environmental determinants in young people (SPEEDY))

Funding

This work was supported by the National Prevention Research Initiative [grant number: G0701877] (http://www.mrc.ac.uk/research/initiatives/national-prevention-research-initiative-npri/). The funding partners relevant to this award are: British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Medical Research Council; Research and Development Office for the Northern Ireland Health and Social Services; Chief Scientist Office; Scottish Executive Health Department; The Stroke Association; Welsh Assembly Government and World Cancer Research Fund. This work was additionally supported by the Medical Research Council [grant numbers MC_UU_12015/3, MC_UU_12015/7], The Research Council of Norway [grant number 249932/F20], Bristol University, Loughborough University and Norwegian School of Sport Sciences. We also gratefully acknowledge the contribution of Prof Chris Riddoch, Prof Ken Judge, Prof Ashley Cooper and Dr. Pippa Griew to the development of ICAD. This work was further supported by a British Heart Foundation Intermediate Basic Science Research Fellowship [grant number FS/12/58/29709].

Author information

Author notes

  1. Søren Brage and Ulf Ekelund contributed equally to this work.

Authors and Affiliations

  1. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Box 285, Addenbrooke’s Hospital Hills Road Cambridge, Cambridge, CB2 0QQ, UK

    Katrien Wijndaele, Thomas White, Esther M. F. van Sluijs, Stephen J. Sharp & Søren Brage

  2. Faculty of Teacher Education and Sport, Campus Sogndal, Western Norway University of Applied Sciences, Bergen, Norway

    Lars Bo Andersen, Niels Wedderkopp & Mathias Ried-Larsen

  3. Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway

    Lars Bo Andersen, Elin Kolle & Ulf Ekelund

  4. Centre for Research in Childhood Health, Exercise Epidemiology Unit, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

    Anna Bugge, Niels Wedderkopp & Mathias Ried-Larsen

  5. Physiotherapy, University College Copenhagen, Copenhagen, Denmark

    Anna Bugge

  6. Bristol Medical School, University of Bristol, Bristol, UK

    Kate Northstone

  7. Institute of Social and Preventive Medicine, University of Zurich, Zürich, Switzerland

    Susi Kriemler

  8. Centre for Exercise, Nutrition and health Sciences, School for Policy Studies, University of Bristol, Bristol, UK

    Angie S. Page

  9. Lausanne University Hospital, Lausanne, Switzerland

    Jardena J. Puder

  10. School of Psychological Sciences and Health, University of Strathclyde, Glasgow, Scotland

    John J. Reilly

  11. Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal

    Luis B. Sardinha

  12. UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge, Cambridge, UK

    Esther M. F. van Sluijs

Authors
  1. Katrien Wijndaele
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas White
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lars Bo Andersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Bugge
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elin Kolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kate Northstone
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Niels Wedderkopp
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mathias Ried-Larsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Susi Kriemler
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Angie S. Page
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jardena J. Puder
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. John J. Reilly
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Luis B. Sardinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Esther M. F. van Sluijs
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Stephen J. Sharp
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Søren Brage
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ulf Ekelund
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

On behalf of the International Children’s Accelerometry Database (ICAD) Collaborators

  • L. B. Andersen
  • , S. Anderssen
  • , A. J. Atkin
  • , G. Cardon
  • , R. Davey
  • , U. Ekelund
  • , D. W. Esliger
  • , P. Hallal
  • , B. H. Hansen
  • , K. F. Janz
  • , S. Kriemler
  • , N. Møller
  • , K. Northstone
  • , A. Page
  • , R. Pate
  • , J. J. Puder
  • , J. J. Reilly
  • , J. Salmon
  • , L. B. Sardinha
  • , L. B. Sherar
  • , A. Timperio
  •  & E. M. F. van Sluijs

Contributions

KW conceptualized the study, analysed and interpreted the data, drafted the initial manuscript, and revised the manuscript. SB and UE interpreted the data, and reviewed and revised the manuscript. TW processed the accelerometry data and reviewed and revised the manuscript. SJS provided statistical advice and reviewed and revised the manuscript. LBA, AB, EK, KN, NW, MRL, SK, ASP, JJP, JJR, LBS, EMFvS contributed data from original studies and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. KW is the guarantor of this work.

Corresponding author

Correspondence to Katrien Wijndaele.

Ethics declarations

Ethics approval and consent to participate

All study protocols were approved by local ethics committees and informed consent was obtained from all subjects or their legal authorized representative [13,14,15,16,17,18,19,20,21,22].

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Supplementary information

Additional file 1: Table S1.

Individual cohort characteristics and descriptive statistics of cardio-metabolic outcome variables, ICAD. Table S2. Cross-sectional associations between prolonged sedentary time, defined as > 30 min bouts, and cardio-metabolic risk, ICAD. Table S3. Cross-sectional associations between prolonged sedentary time and cardio-metabolic risk, ICAD (> = 3 valid day with > = 1 valid weekend day). Table S4. Cross-sectional associations between prolonged sedentary time and cardio-metabolic risk with more elaborate adjustment for confounding, ICAD (> = 1 valid day). Figure S1. Meta-analysis for standardised BMI, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S2. Meta-analysis for waist circumference, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S3. Meta-analysis for HDL cholesterol, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S4. Meta-analysis for systolic blood pressure, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S5. Meta-analysis for diastolic blood pressure, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S6. Meta-analysis for clustered cardio-metabolic risk, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD. Figure S7. Meta-analysis for clustered cardio-metabolic risk excluding waist circumference, showing regression coefficients (95% CI) from cross-sectional iso-temporal models examining substitution of prolonged sedentary time with non-prolonged sedentary time, light and moderate-to-vigorous physical activity, ICAD (DOCX 173 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wijndaele, K., White, T., Andersen, L.B. et al. Substituting prolonged sedentary time and cardiovascular risk in children and youth: a meta-analysis within the International Children’s Accelerometry database (ICAD). Int J Behav Nutr Phys Act 16, 96 (2019). https://doi.org/10.1186/s12966-019-0858-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12966-019-0858-6

Keywords

  • ICAD
  • ALSPAC
  • Prolonged sitting
  • Cardio-metabolic
  • Iso-temporal
  • Physical activity

International Journal of Behavioral Nutrition and Physical Activity

ISSN: 1479-5868

Contact us