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Epidemiology and Population Health

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE)

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Abstract

Background/Objectives

Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined.

Methods

Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score).

Results

After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251–0.449; all p < 0.001) and higher SB breaks (b = −0.005–−0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = −0.077–−0.649; p from <0.001–0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well.

Conclusions

Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

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Fig. 1: Density plots comparing the distributions of sedentary behavior (SB) variables when assessed by the SBmovement, SBposture, and SBcombined methods.
Fig. 2: Two-dimensional density plots depicting joint distributions of sedentary behavior (SB) variables with moderate-to-vigorous physical activity (MVPA).

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Code availability

Code from the analysis is available on request.

References

  1. 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;56:2655–67.

    Article  CAS  PubMed  Google Scholar 

  2. Young DR, Hivert MF, Alhassan S, Camhi SM, Ferguson JF, Katzmarzyk PT, et al. Sedentary behavior and cardiovascular morbidity and mortality: a science advisory from the American Heart Association. Circulation. 2016;134. https://www.ahajournals.org/doi/10.1161/CIR.0000000000000440.

  3. Pate RR, O’neill JR, Lobelo F. The evolving definition of” sedentary”. Exerc Sport Sci Rev. 2008;36:173–8.

    Article  PubMed  Google Scholar 

  4. Gibbs BB, Hergenroeder AL, Katzmarzyk PT, Lee IM, Jakicic JM. Definition, measurement, and health risks associated with sedentary behavior. Med Sci Sports Exerc. 2015;47:1295–300.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Reilly JJ, Janssen X, Cliff DP, Okely AD. Appropriateness of the definition of ‘sedentary’ in young children: whole-room calorimetry study. J Sci Med Sport. 2015;18:565–8.

    Article  PubMed  Google Scholar 

  6. Saint-Maurice PF, Kim Y, Welk GJ, Gaesser GA. Kids are not little adults: what MET threshold captures sedentary behavior in children? Eur J Appl Physiol. 2016;116:29–38.

    Article  PubMed  Google Scholar 

  7. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(Jun):75 https://doi.org/10.1186/s12966-017-0525-8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sedentary Behaviour Research Network. Letter to the Editor: Standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 2012;37:540–2.

    Article  Google Scholar 

  9. Crouter SE, Hibbing PR, LaMunion SR. Use of objective measures to estimate sedentary time in youth. J Meas Phys Behav. 2018;1:136–42.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Janssen X, Cliff DP. Issues related to measuring and interpreting objectively measured sedentary behavior data. Meas Phys Educ Exerc Sci. 2015;19:116–24.

    Article  Google Scholar 

  11. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167:875–81.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  13. Carlson JA, Ridgers ND, Nakandala S, Zablocki R, Tuz-Zahra F, Bellettiere J, et al. CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children. Int J Behav Nutr Phys Act. 2022;19(Aug):109 https://doi.org/10.1186/s12966-022-01349-2.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SFM, et al. Utilization and harmonization of adult accelerometry data: review and expert consensus. Med Sci Sports Exerc. 2015;47:2129–39.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cliff DP, Hesketh KD, Vella SA, Hinkley T, Tsiros MD, Ridgers ND, et al. 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  PubMed  Google Scholar 

  16. Katzmarzyk PT, Barreira TV, Broyles ST, Champagne CM, Chaput JP, Fogelholm M, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health. 2013;13(Sep):900 https://doi.org/10.1186/1471-2458-13-900.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Treuth MS, Schmitz K, Catellier DJ, McMurray RG, Murray DM, Almeida MJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc. 2004;36:1259–66.

    PubMed  PubMed Central  Google Scholar 

  18. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85:660–7.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen KY, Bassett DR. The technology of accelerometry-based activity monitors: Current and future. Med Sci Sports Exerc. 2005;37:S490–500.

    Article  PubMed  Google Scholar 

  20. Cain KL, Conway TL, Adams MA, Husak LE, Sallis JF. Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension. Int J Behav Nutr Phys Act. 2013;10:51 https://doi.org/10.1186/1479-5868-10-51.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hibbing PR, Bassett DR, Crouter SE. Modifying accelerometer cut-points affects criterion validity in simulated free-living for adolescents and adults. Res Q Exerc Sport. 2020;91:514–24.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Troiano RP, Berrigan D, Dodd KW, MâSse LC, Tilert T, Mcdowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.

    Article  PubMed  Google Scholar 

  23. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37:S523–30.

    Article  PubMed  Google Scholar 

  24. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Stat Methodol. 1995;57:289–300.

    Google Scholar 

  25. Katzmarzyk PT, Barreira TV, Broyles ST, Champagne CM, Chaput JP, Fogelholm M, et al. Physical activity, sedentary time, and obesity in an international sample of children. Med Sci Sports Exerc. 2015;47:2062–9.

    Article  PubMed  Google Scholar 

  26. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, 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 https://doi.org/10.1186/1479-5868-8-98.

    Article  Google Scholar 

  27. Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab. 2016;41:S240–65.

    Article  PubMed  Google Scholar 

  28. Biddle SJH, García Bengoechea E, Wiesner G. Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality. Int J Behav Nutr Phys Act. 2017;14(Mar):43 https://doi.org/10.1186/s12966-017-0497-8.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dumuid D, Stanford TE, Pedišić Ž, Maher C, Lewis LK, Martín-Fernández JA, et al. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach. BMC Public Health. 2018;18(Dec):311 https://doi.org/10.1186/s12889-018-5207-1.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28:846–57.

    Article  PubMed  Google Scholar 

  31. Saunders TJ, Larouche R, Colley RC, Tremblay MS. Acute sedentary behaviour and markers of cardiometabolic risk: a systematic review of intervention studies. J Nutr Metab. 2012;2012:1–12. https://doi.org/10.1155/2012/712435.

    Article  CAS  Google Scholar 

  32. Saunders TJ, Atkinson HF, Burr J, MacEwen B, Skeaff CM, Peddie MC. The acute metabolic and vascular impact of interrupting prolonged sitting: A systematic review and meta-analysis. Sports Med. 2018;48:2347–66.

    Article  PubMed  Google Scholar 

  33. Saunders TJ, McIsaac T, Douillette K, Gaulton N, Hunter S, Rhodes RE, et al. Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45:S197–217.

    Article  PubMed  Google Scholar 

  34. Owen N, Healy GN, Dempsey PC, Salmon J, Timperio A, Clark BK, et al. Sedentary behavior and public health: integrating the evidence and identifying potential solutions. Annu Rev Public Health. 2020;41:265–87.

    Article  PubMed  Google Scholar 

  35. Wheeler MJ, Green DJ, Cerin E, Ellis KA, Heinonen I, Lewis J, et al. Combined effects of continuous exercise and intermittent active interruptions to prolonged sitting on postprandial glucose, insulin, and triglycerides in adults with obesity: a randomized crossover trial. Int J Behav Nutr Phys Act. 2020;17(Dec):152 https://doi.org/10.1186/s12966-020-01057-9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Homer AR, Taylor FC, Dempsey PC, Wheeler MJ, Sethi P, Townsend MK, et al. Frequency of interruptions to sitting time: Benefits for postprandial metabolism in type 2 diabetes. Diabetes Care. 2021;44:1254–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Higgins S, Pomeroy A, Bates LC, Paterson C, Barone Gibbs B, Pontzer H, et al. Sedentary behavior and cardiovascular disease risk: an evolutionary perspective. Front Physiol. 2022;13(Jul):962791 https://doi.org/10.3389/fphys.2022.962791.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bergouignan A, Latouche C, Heywood S, Grace MS, Reddy-Luthmoodoo M, Natoli AK, et al. Frequent interruptions of sedentary time modulates contraction- and insulin-stimulated glucose uptake pathways in muscle: ancillary analysis from randomized clinical trials. Sci Rep. 2016;6(Aug):32044 https://doi.org/10.1038/srep32044.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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(Aug):e0201947 https://doi.org/10.1371/journal.pone.0201947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bejarano CM, Gallo LC, Castañeda SF, Garcia ML, Sotres-Alvarez D, Perreira KM, et al. Patterns of sedentary time in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) youth. J Phys Act Health. 2021;18:61–9.

    Article  PubMed  Google Scholar 

  41. Pfeiffer KA, Clevenger KA, Kaplan A, Van Camp CA, Strath SJ, Montoye AHK. Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review. Physiol Meas. 2022. https://doi.org/10.1088/1361-6579/ac89ca.

  42. Strath SJ, Pfeiffer KA, Whitt-Glover MC. Accelerometer use with children, older adults, and adults with functional limitations. Med Sci Sports Exerc. 2012;44:S77–85.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Greenwood-Hickman MA, Nakandala S, Jankowska MM, Rosenberg D, Tuz-Zahra F, Bellettiere J, et al. The CNN Hip Accelerometer Posture (CHAP) method for classifying sitting patterns from hip accelerometers: a validation study. Med Sci Sports Exerc. 2021;53:2445–54.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bellettiere J, Nakandala S, Tuz-Zahra F, Winkler EAH, Hibbing PR, Healy GN, et al. CHAP-adult: a reliable and valid algorithm to classify sitting and measure sitting patterns using data from hip-worn accelerometers in adults aged 35+. J Meas Phys Behav. 2022;5:215–23.

    Article  Google Scholar 

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Acknowledgements

This study was funded by NIH R01DK114945. The parent study (ISCOLE) was funded by The Coca-Cola Company. With the exception of requiring that the study be global in nature, the parent study funder had no role in the design and conduct of the study.

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Authors and Affiliations

Authors

Contributions

Designed and implemented parent study: PK. Acquired funding for present study: LN, JC, AL, MJ, AK. Developed the methods: JC, SN, JB, JZ, AK, LN, MAGH. Processed data: CS, PH, PK. Devised analysis: PH, JC, LN, MJ. Drafted manuscript: PH, JC, LN. Reviewed, revised, and approved manuscript: All authors.

Corresponding author

Correspondence to Paul R. Hibbing.

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Competing interests

This study was funded by NIH R01DK114945. The parent study (ISCOLE) was funded by The Coca-Cola Company. With the exception of requiring that the study be global in nature, the parent study funder had no role in the design and conduct of the study. The authors declare no conflicts of interest.

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Hibbing, P.R., Carlson, J.A., Steel, C. et al. Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Obes 47, 1100–1107 (2023). https://doi.org/10.1038/s41366-023-01364-8

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