Skip to main content
SpringerLink
Log in

Dynamics of Sedentary Behaviours and System-Based Approach: Future Challenges and Opportunities in the Life-Course Epidemiology of Sedentary Behaviours

  • Chapter
  • First Online:
Sedentary Behaviour Epidemiology

Part of the book series: Springer Series on Epidemiology and Public Health ((SSEH))

  • 224 Accesses

Abstract

This chapter challenges our current thinking about sedentary behaviour and offers new paradigms to move forward to understand the complex nature of sedentary behaviours and their determinants. Sedentary behaviours are ubiquitous and changing in nature over time: with advances in media and IT technology, television (TV) time is decreasing, but overall screen time is growing. Understanding the non-linear temporal dynamics of sedentary behaviours and how people accumulate, or break, sitting time appears a crucial step to design innovative strategies. Since multiple factors at different levels (proximal, distal) are interacting to drive sedentary time, new perspectives combining a life-course perspective and complexity science are needed. System-based approach and adaptive dynamical systems modelling will help model the interaction between factors and feedback loops. A system-based framework for the study of sedentary behaviours called SOS (Systems of Sedentary behaviours) has been established by a transdisciplinary research group within the framework of the European DEDIPAC Knowledge Hub. Novel methods of enquiry are required to progress the field, including methodologies for analysis such as probabilistic modelling techniques (Bayesian Networks), simulation studies investigating different scenarios of possible societal changes and their effect on sedentary behaviours, and innovations in measuring accurately other dimensions such as context and type of sedentary behaviours. Finally, future opportunities for innovative data collection (e.g., ecological momentary assessment) and analysis (big data) and innovative interventions (natural experiments, just-in-time adaptive interventions, solutionist, and participatory approach) are highlighted for their potential to benefit sedentary behaviours research and work more efficiently towards public health solutions to tackle this new threat of modern life.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dietz WH. The role of lifestyle in health: the epidemiology and consequences of inactivity. Proc Nutr Soc. 1996;55:829–40.

    Article  Google Scholar 

  2. Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population-health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38:105.

    Article  Google Scholar 

  3. Owen N, Sugiyama T, Eakin EE, Gardiner PA, Tremblay MS, Sallis JF. Adults’ sedentary behavior: determinants and interventions. Am J Prev Med. 2011;41:189–96.

    Article  Google Scholar 

  4. Sallis JF, Owen N, Fisher E. Ecological models of health behavior. In: Health behavior: Theory, research, and practice. San Francisco: Jossey-Bass/Wiley; 2015. p. 5.

    Google Scholar 

  5. Chastin S, Granat MH. Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity. Gait Posture. 2010;31:82–6.

    Article  Google Scholar 

  6. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Ment Health Phys Act. 2013;6:55–6.

    Article  Google Scholar 

  7. Stamatakis E, Coombs N, Jago R, Gama A, Mourão I, Nogueira H, Rosado V, Padez C. Type-specific screen time associations with cardiovascular risk markers in children. Am J Prev Med. 2013;44:481–8.

    Article  Google Scholar 

  8. Stamatakis E, Davis M, Stathi A, Hamer M. Associations between multiple indicators of objectively-measured and self-reported sedentary behaviour and cardiometabolic risk in older adults. Prev Med. 2012;54:82–7.

    Article  Google Scholar 

  9. Leask CF, Harvey JA, Skelton DA, Chastin SF. Exploring the context of sedentary behaviour in older adults (what, where, why, when and with whom). Eur Rev Aging Phys Act. 2015;12:1–8.

    Article  Google Scholar 

  10. Kesse-Guyot E, Charreire H, Andreeva VA, Touvier M, Hercberg S, Galan P, Oppert J-M. Cross-sectional and longitudinal associations of different sedentary behaviors with cognitive performance in older adults. PLoS One. 2012;7(10):e47831.

    Article  Google Scholar 

  11. Cardon GM, De Clercq DL, De Bourdeaudhuij IM. Back education efficacy in elementary schoolchildren: a 1-year follow-up study. Spine. 2002;27:299–305.

    Article  Google Scholar 

  12. Hinckson E, Salmon J, Benden M, Clemes SA, Sudholz B, Barber SE, Aminian S, Ridgers ND. Standing classrooms: research and lessons learned from around the world. Sports Med. 2016;46:977–87.

    Article  Google Scholar 

  13. Liao Y, Liao J, Durand CP, Dunton GF. Which type of sedentary behaviour intervention is more effective at reducing body mass index in children? A meta-analytic review. Obes Rev. 2014;15:159–68.

    Article  Google Scholar 

  14. Biddle SJ, O’Connell S, Braithwaite RE. Sedentary behaviour interventions in young people: a meta-analysis. Br J Sports Med. 2011;45:937–42.

    Article  Google Scholar 

  15. Gardner B, Smith L, Lorencatto F, Hamer M, Biddle SJ. How to reduce sitting time? A review of behaviour change strategies used in sedentary behaviour reduction interventions among adults. Health Psychol Rev. 2016;10:89–112.

    Article  Google Scholar 

  16. Chastin SF, Fitzpatrick N, Andrews M, DiCroce N. Determinants of sedentary behavior, motivation, barriers and strategies to reduce sitting time in older women: a qualitative investigation. Int J Environ Res Public Health. 2014;11:773–91.

    Article  Google Scholar 

  17. Greenwood-Hickman MA, Renz A, Rosenberg DE. Motivators and barriers to reducing sedentary behavior among overweight and obese older adults. The Gerontologist. 2016;56:660–8.

    Article  Google Scholar 

  18. Stierlin AS, De Lepeleere S, Cardon G, Dargent-Molina P, Hoffmann B, Murphy MH, Kennedy A, O’donoghue G, Chastin SF, De Craemer M. A systematic review of determinants of sedentary behaviour in youth: a DEDIPAC-study. Int J Behav Nutr Phys Act. 2015;12:1–19.

    Article  Google Scholar 

  19. Leatherdale ST, Wong SL. Modifiable characteristics associated with sedentary behaviours among youth. Int J Pediatr Obes. 2008;3:93–101.

    Article  Google Scholar 

  20. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA. 2003;289:1785–91.

    Article  Google Scholar 

  21. Bucksch J, Sigmundova D, Hamrik Z, Troped PJ, Melkevik O, Ahluwalia N, Borraccino A, Tynjälä J, Kalman M, Inchley J. International trends in adolescent screen-time behaviors from 2002 to 2010. J Adolesc Health. 2016;58:417–25.

    Article  Google Scholar 

  22. Matrix S. The Netflix effect: teens, binge watching, and on-demand digital media trends. Jeunesse: young people, texts, cultures. 2014;6:119–38.

    Article  Google Scholar 

  23. Brasel SA, Gips J. Media multitasking behavior: concurrent television and computer usage. Cyberpsychol Behav Soc Netw. 2011;14:527–34.

    Article  Google Scholar 

  24. Christensen CG, Bickham D, Ross CS, Rich M. Multitasking with television among adolescents. J Broadcast Electron Media. 2015;59:130–48.

    Article  Google Scholar 

  25. Choudrie J, Grey S, Tsitsianis N. Evaluating the digital divide: the silver Surfer’s perspective. Electron Gov Int J. 2010;7(2):148–67.

    Google Scholar 

  26. Stephens SK, Winkler EA, Trost SG, Dunstan DW, Eakin EG, Chastin SF, Healy GN. Intervening to reduce workplace sitting time: how and when do changes to sitting time occur? Br J Sports Med. 2014;48:1037–42.

    Article  Google Scholar 

  27. Dowd KP, Harrington DM, Bourke AK, Nelson J, Donnelly AE. The measurement of sedentary patterns and behaviors using the activPAL™ professional physical activity monitor. Physiol Meas. 2012;33:1887.

    Article  Google Scholar 

  28. Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PC, Peng C-K, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci. 2002;99:2466–72.

    Article  Google Scholar 

  29. Vázquez A, Oliveira JG, Dezsö Z, Goh K-I, Kondor I, Barabási A-L. Modeling bursts and heavy tails in human dynamics. Phys Rev E. 2006;73:036127.

    Article  Google Scholar 

  30. Nicolai S, Benzinger P, Skelton DA, Aminian K, Becker C, Lindemann U. Day-to-day variability of physical activity of older adults living in the community. J Aging Phys Act. 2010;18(1):75–86.

    Article  Google Scholar 

  31. Chastin SF, Winkler EA, Eakin EG, Gardiner PA, Dunstan DW, Owen N, Healy GN. Sensitivity to change of objectively-derived measures of sedentary behavior. Meas Phys Educ Exerc Sci. 2015;19:138–47.

    Article  Google Scholar 

  32. 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 

  33. Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, Owen N. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31:661–6.

    Article  Google Scholar 

  34. Pedišić Ž. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research—the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;46:135–46.

    Google Scholar 

  35. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. 2015;10:e0139984.

    Article  Google Scholar 

  36. Chaput J-P, Carson V, Gray CE, Tremblay MS. Importance of all movement behaviors in a 24 hour period for overall health. Int J Environ Res Public Health. 2014;11:12575–81.

    Article  Google Scholar 

  37. Charreire H, Casey R, Salze P, Kesse-Guyot E, Simon C, Chaix B, Banos A, Badariotti D, Touvier M, Weber C. Leisure-time physical activity and sedentary behavior clusters and their associations with overweight in middle-aged French adults. Int J Obes. 2010;34:1293–301.

    Article  Google Scholar 

  38. Buman MP, Winkler EA, Kurka JM, Hekler EB, Baldwin CM, Owen N, Ainsworth BE, Healy GN, Gardiner PA. Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol. 2014;179:323–34.

    Article  Google Scholar 

  39. Pearson N, Biddle SJ. Sedentary behavior and dietary intake in children, adolescents, and adults: a systematic review. Am J Prev Med. 2011;41:178–88.

    Article  Google Scholar 

  40. Santaliestra-Pasías A, Mouratidou T, Huybrechts I, Beghin L, Cuenca-García M, Castillo M, Galfo M, Hallstrom L, Kafatos A, Manios Y. Increased sedentary behaviour is associated with unhealthy dietary patterns in European adolescents participating in the HELENA study. Eur J Clin Nutr. 2014;68:300–8.

    Article  Google Scholar 

  41. Gubbels JS, van Assema P, Kremers SP. Physical activity, sedentary behavior, and dietary patterns among children. Curr Nutr Rep. 2013;2:105–12.

    Article  Google Scholar 

  42. Lipsky LM, Iannotti RJ. Associations of television viewing with eating behaviors in the 2009 health behaviour in school-aged children study. Arch Pediatr Adolesc Med. 2012;166:465–72.

    Article  Google Scholar 

  43. Charreire H, Kesse-Guyot E, Bertrais S, Simon C, Chaix B, Weber C, Touvier M, Galan P, Hercberg S, Oppert J-M. Associations between dietary patterns, physical activity (leisure-time and occupational) and television viewing in middle-aged French adults. Br J Nutr. 2011;105:902–10.

    Article  Google Scholar 

  44. Chastin SF, Buck C, Freiberger E, Murphy M, Brug J, Cardon G, O’Donoghue G, Pigeot I, Oppert J-M. Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2015;12:1–12.

    Article  Google Scholar 

  45. O’donoghue G, Perchoux C, Mensah K, Lakerveld J, Van Der Ploeg H, Bernaards C, Chastin SF, Simon C, O’gorman D, Nazare J-A. A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach. BMC Public Health. 2016;16:1–25.

    Article  Google Scholar 

  46. Ng SW, Popkin BM. Time use and physical activity: a shift away from movement across the globe. Obes Rev. 2012;13:659–80.

    Article  Google Scholar 

  47. Milton K, Gale J, Stamatakis E, Bauman A. Trends in prolonged sitting time among European adults: 27 country analysis. Prev Med. 2015;77:11–6.

    Article  Google Scholar 

  48. Loyen A, van der Ploeg HP, Bauman A, Brug J, Lakerveld J. European sitting championship: prevalence and correlates of self-reported sitting time in the 28 European Union member states. PLoS One. 2016;11:e0149320.

    Article  Google Scholar 

  49. Glass TA, Goodman SN, Hernán MA, Samet JM. Causal inference in public health. Annu Rev Public Health. 2013;34:61.

    Article  Google Scholar 

  50. Susser M. Does risk factor epidemiology put epidemiology at risk? Peering into the future. J Epidemiol Community Health. 1998;52:608–11.

    Article  Google Scholar 

  51. Co-operation OE. Development: applications of complexity science for public policy. Paris: OECD; 2008.

    Google Scholar 

  52. Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. Am J Public Health. 2006;96:452–8.

    Article  Google Scholar 

  53. Leischow SJ, Milstein B. Systems thinking and modeling for public health practice. Am Public Health Assoc. 2006;96:403–5.

    Article  Google Scholar 

  54. Luke DA, Stamatakis KA. Systems science methods in public health: dynamics, networks, and agents. Annu Rev Public Health. 2012;33:357.

    Article  Google Scholar 

  55. Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, Mardell J, Parry V. Foresight. Tackling obesities: future choices. Project report. Foresight Tackling obesities: future choices Project report 2007.

    Google Scholar 

  56. Richard L, Gauvin L, Raine K. Ecological models revisited: their uses and evolution in health promotion over two decades. Annu Rev Public Health. 2011;32:307–26.

    Article  Google Scholar 

  57. Lakerveld J, van der Ploeg HP, Kroeze W, Ahrens W, Allais O, Andersen LF, Cardon G, Capranica L, Chastin S, Donnelly A. Towards the integration and development of a cross-European research network and infrastructure: the DEterminants of DIet and physical ACtivity (DEDIPAC) knowledge hub. Int J Behav Nutr Phys Act. 2014;11:1–10.

    Article  Google Scholar 

  58. Chastin SF, De Craemer M, Lien N, Bernaards C, Buck C, Oppert J-M, Nazare J-A, Lakerveld J, O’Donoghue G, Holdsworth M. The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study. Int J Behav Nutr Phys Act. 2016;13:1–13.

    Article  Google Scholar 

  59. Migueles JH, Aadland E, Andersen LB, Brønd JC, Chastin SF, Hansen BH, Konstabel K, Kvalheim OM, McGregor DE, Rowlands AV. GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies. Br J Sports Med. 2022;56:376–84.

    Article  Google Scholar 

  60. Aitchison J. The statistical analysis of compositional data. J R Stat Soc: Series B (Methodological). 1982;44:139–60.

    MathSciNet  Google Scholar 

  61. Ding D, Do A, Schmidt H-M, Bauman AE. A widening gap? Changes in multiple lifestyle risk behaviours by socioeconomic status in New South Wales, Australia, 2002–2012. PLoS One. 2015;10:e0135338.

    Article  Google Scholar 

  62. Harding NJ. Application of Bayesian networks to problems within obesity epidemiology. The University of Manchester (United Kingdom); 2011.

    Google Scholar 

  63. Finegood DT, Merth TD, Rutter H. Implications of the foresight obesity system map for solutions to childhood obesity. Obesity. 2010;18:S13.

    Article  Google Scholar 

  64. Jagosh J. Realist synthesis for public health: building an ontologically deep understanding of how programs work, for whom, and in which contexts. Annu Rev Public Health. 2019;40:361–72.

    Article  Google Scholar 

  65. Owen N, Bauman A, Brown W. Too much sitting: a novel and important predictor of chronic disease risk? Br J Sports Med. 2009;43:81–3.

    Article  Google Scholar 

  66. Mensah K, Maire A, Oppert J-M, Dugas J, Charreire H, Weber C, Simon C, Nazare J-A. Assessment of sedentary behaviors and transport-related activities by questionnaire: a validation study. BMC Public Health. 2016;16:1–9.

    Article  Google Scholar 

  67. Busschaert C, De Bourdeaudhuij I, Van Holle V, Chastin SF, Cardon G, De Cocker K. Reliability and validity of three questionnaires measuring context-specific sedentary behaviour and associated correlates in adolescents, adults and older adults. Int J Behav Nutr Phys Act. 2015;12:1–13.

    Article  Google Scholar 

  68. Mann S, Nolan J, Wellman B. Sousveillance: inventing and using wearable computing devices for data collection in surveillance environments. Surveill Soc. 2003;1:331–55.

    Article  Google Scholar 

  69. Kerr J, Marshall SJ, Godbole S, Chen J, Legge A, Doherty AR, Kelly P, Oliver M, Badland HM, Foster C. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med. 2013;44:290–6.

    Article  Google Scholar 

  70. Doherty AR, Smeaton AF. Automatically segmenting lifelog data into events. In: 2008 Ninth international workshop on image analysis for multimedia interactive services. New York: IEEE; 2008. p. 20–3.

    Chapter  Google Scholar 

  71. Kelly P, Marshall SJ, Badland H, Kerr J, Oliver M, Doherty AR, Foster C. An ethical framework for automated, wearable cameras in health behavior research. Am J Prev Med. 2013;44:314–9.

    Article  Google Scholar 

  72. Harvey J, Chastin S, Skelton D. The art and science of defining the context of sedentary behaviour by older adults, using interview, activity monitoring and body worn cameras. Clin Rehabil. 2015;29:314.

    Google Scholar 

  73. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc. 2013;45:2193.

    Article  Google Scholar 

  74. Loveday A, Sherar LB, Sanders JP, Sanderson PW, Esliger DW. Technologies that assess the location of physical activity and sedentary behavior: a systematic review. J Med Internet Res. 2015;17:e4761.

    Article  Google Scholar 

  75. Maher JP, Rebar AL, Dunton GF. Ecological momentary assessment is a feasible and valid methodological tool to measure older adults’ physical activity and sedentary behavior. Front Psychol. 2018;9:1485.

    Article  Google Scholar 

  76. Degroote L, DeSmet A, De Bourdeaudhuij I, Van Dyck D, Crombez G. Content validity and methodological considerations in ecological momentary assessment studies on physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2020;17:1–13.

    Article  Google Scholar 

  77. Giurgiu M, Niermann C, Ebner-Priemer U, Kanning M. Accuracy of sedentary behavior–triggered ecological momentary assessment for collecting contextual information: development and feasibility study. JMIR Mhealth Uhealth. 2020;8:e17852.

    Article  Google Scholar 

  78. Chastin SFM, Schwarz U, Skelton DA. Development of a consensus taxonomy of sedentary behaviors (SIT): report of Delphi round 1. PLoS One. 2013;8:e82313.

    Article  Google Scholar 

  79. Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives, vol. 31. Oxford: Oxford University Press; 2002. p. 285–93.

    Google Scholar 

  80. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology. J Epidemiol Community Health. 2003;57:778.

    Article  Google Scholar 

  81. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. Am J Prev Med. 2011;41:207–15.

    Article  Google Scholar 

  82. Rezende LFM, Rey-López JP, Matsudo VKR, Luiz OC. Sedentary behavior and health outcomes among older adults: a systematic review. BMC Public Health. 2014;14:1–9.

    Article  Google Scholar 

  83. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, Goldfield G, Gorber SC. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:1–22.

    Article  Google Scholar 

  84. Brug J, Chinapaw M. Determinants of engaging in sedentary behavior across the lifespan; lessons learned from two systematic reviews conducted within DEDIPAC. Int J Behav Nutr Phys Act. 2015;12:1–4.

    Article  Google Scholar 

  85. Council MR. Maximising the value of UK population cohorts. 2014.

    Google Scholar 

  86. Celis-Morales CA, Lyall DM, Petermann F, Anderson J, Ward J, Iliodromiti S, Mackay DF, Welsh P, Bailey ME, Pell J. Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK biobank study. Int J Obes. 2019;43:1526–38.

    Article  Google Scholar 

  87. Pearson H. Massive UK baby study cancelled. Nature. 2015;526:620–1.

    Article  Google Scholar 

  88. Gandomi A, Haider M. Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag. 2015;35:137–44.

    Article  Google Scholar 

  89. Swan M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health. 2009;6:492–525.

    Article  Google Scholar 

  90. Petticrew M, Cummins S, Ferrell C, Findlay A, Higgins C, Hoy C, Kearns A, Sparks L. Natural experiments: an underused tool for public health? Public Health. 2005;119:751–7.

    Article  Google Scholar 

  91. Craig P, Katikireddi SV, Leyland A, Popham F. Natural experiments: an overview of methods, approaches, and contributions to public health intervention research. Annu Rev Public Health. 2017;38:39.

    Article  Google Scholar 

  92. Ogilvie D, Adams J, Bauman A, Gregg EW, Panter J, Siegel KR, Wareham NJ, White M. Using natural experimental studies to guide public health action: turning the evidence-based medicine paradigm on its head. J Epidemiol Community Health. 2020;74:203–8.

    Article  Google Scholar 

  93. Crane M, Bohn-Goldbaum E, Grunseit A, Bauman A. Using natural experiments to improve public health evidence: a review of context and utility for obesity prevention. Health Res Policy Syst. 2020;18:1–13.

    Article  Google Scholar 

  94. Sundmaeker H, Guillemin P, Friess P, Woelfflé S. Vision and challenges for realising the internet of things. Cluster of European research projects on the internet of things. European Commision. 2010;3:34–6.

    Google Scholar 

  95. Bhattacharya S, Pandey M. Internet of things for green technology. In: Green engineering and technology. Boca Raton: CRC Press; 2021. p. 243–58.

    Chapter  Google Scholar 

  96. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52:446–62.

    Article  Google Scholar 

  97. Khoury MJ, Ioannidis JP. Big data meets public health. Science. 2014;346:1054–5.

    Article  Google Scholar 

  98. Morozov E. To save everything, click here: the folly of technological solutionism. J Inf Policy. 2014;4:173–5.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Health and Life Science, Glasgow Caledonian University, Glasgow, UK and Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium

    Sebastien F. M. Chastin

  2. Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium and Flemish Research Foundation, Brussels, Belgium

    Sofie Compernolle

  3. Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium and Flemish Research Foundation, Brussels, Belgium

    Marieke De Craemer

  4. Department of Nutrition, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Sorbonne Université Institute of Cardiometabolism and Nutrition (ICAN), Paris, France

    Jean-Michel Oppert

  5. Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium

    Greet Cardon

Authors
  1. Sebastien F. M. Chastin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sofie Compernolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marieke De Craemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jean-Michel Oppert
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Greet Cardon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastien F. M. Chastin .

Editor information

Editors and Affiliations

  1. Epidemiology and Preventive Medicine, Universität Regensburg, Regensburg, Germany

    Michael F. Leitzmann

  2. Epidemiology and Preventive Medicine, Universität Regensburg, Regensburg, Germany

    Carmen Jochem

  3. Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Tirol, Austria

    Daniela Schmid

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chastin, S.F.M., Compernolle, S., De Craemer, M., Oppert, JM., Cardon, G. (2023). Dynamics of Sedentary Behaviours and System-Based Approach: Future Challenges and Opportunities in the Life-Course Epidemiology of Sedentary Behaviours. In: Leitzmann, M.F., Jochem, C., Schmid, D. (eds) Sedentary Behaviour Epidemiology. Springer Series on Epidemiology and Public Health. Springer, Cham. https://doi.org/10.1007/978-3-031-41881-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41881-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41880-8

  • Online ISBN: 978-3-031-41881-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation