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Heine, M. Physical Activity in Cardiometabolic Disease. Encyclopedia. Available online: (accessed on 17 April 2024).
Heine M. Physical Activity in Cardiometabolic Disease. Encyclopedia. Available at: Accessed April 17, 2024.
Heine, Martin. "Physical Activity in Cardiometabolic Disease" Encyclopedia, (accessed April 17, 2024).
Heine, M. (2021, December 06). Physical Activity in Cardiometabolic Disease. In Encyclopedia.
Heine, Martin. "Physical Activity in Cardiometabolic Disease." Encyclopedia. Web. 06 December, 2021.
Physical Activity in Cardiometabolic Disease

Cardiometabolic disease begins with insulin resistance and then progresses to the clinically identifiable high-risk states of metabolic syndrome and prediabetes, before it leads to type 2 diabetes (T2DM) and cardiovascular disease (CVD). In low-to-middle-income countries (LMICs), the burden attributable to non-communicable disease (including CVD and T2DM) increased from 37.8% of total disability-adjusted life years (DALYs) in 1990 to 66.0% in 2019, with a similar pattern in upper-middle-income countries as well. Cardiometabolic disease imposes a large financial burden on patients and households, while increasing vulnerability to poverty.

physical activity diabetes cardiovascular disease metabolic syndrome qualitative review systems thinking

1. Introduction

Prevention of cardiometabolic diseases, including T2DM and CVD, includes maintaining a healthy weight, eating healthily, avoiding tobacco use, and being physically active [1]. Countries where the burden of disease is shifting rapidly are struggling to deliver primary and secondary preventative interventions [2]. Public health approaches are failing to address the crucial risk factors (such as physical inactivity) globally [2], while interventions focused on individual lifestyle modifications are largely absent due to intricate and complex resource constraints [3][4][5][6][7]. While high-income countries bear a larger proportion of the economic burden (80% of economic cost), LMICs have a larger proportion of the disease burden (75% of DALYs) [8]. To effectively address the burden of physical inactivity in LMICs, in relation to the increasing burden of cardiometabolic disease, it is imperative that we understand the drivers of physical inactivity (along with the other risk factors), from a primary and secondary preventative point of view. The World Health Organisation (WHO) physical activity and sedentary behaviour guidelines development group argues that there is a specific need for more studies in LMICs that aim to identify how various sociodemographic factors (e.g., age, sex, and socioeconomic status) inform physical activity or modify the health effects of physical activity in an attempt to address global health disparities [9].

Depending on the design, studies may be informed by preconceived conceptual frameworks for behaviour change (e.g., Theory of Planned Behaviour). While such conceptual frameworks have helped to clarify (physical activity) behaviour, they have been criticised for their often linear and phased perceptions of behaviour, which are insensitive to environmental influences [10][11]. Emerging health behaviour models using the Socio-Ecological Framework (which includes social factors, policy, and environmental factors) or Complexity Theory may be more conducive to the complex nature of behaviour [11], particularly in resource-constrained settings. Quantitative methods have been used widely to identify determinants of and factors associated with physical activity. Such studies provide clear quantitative evidence for the relationship between physical activity and a select number of potential determinants (e.g., the relationship between physical activity and built environment). Albeit valuable, these studies may be limited in their scope and comprehensiveness when accounting for the complexity of aspects associated with physical activity within a single study design.

Alternatively, qualitative studies may provide better insight into the real-world challenges and experiences related to physical activity, unrestricted by prior variable selection. Neither existing qualitative nor quantitative research has been able to fully capture the complex system of physical activity behaviour. However, qualitative research may help to develop an understanding of the people, the practices, and the policies behind the mechanisms and interventions [12].

2. Discussion

In the literature, behavioural change theories such as the Social Cognitive Theory (SCT), the Theory of Planned Behaviour (TPB), Self-Determination Theory (SDT), and the Transtheoretical Model (TTM) have been dominant approaches in understanding the determinants and correlates of physical activity [10][11]. These theories have generally viewed change as a linear, deterministic process based on the interaction of cognitive factors such as knowledge, intention, attitudes, beliefs, and efficacy and intention [13]. Although the utilisation of these theories has informed our understanding of the psychological factors and mechanisms that influence physical activity behaviour [10], physical inactivity remains one of the most important health problems of our time [14]. It has become clear that behaviour, and behaviour change, are a complex phenomenon, influenced by multiple factors [10]. In this sense, socio-ecological models of health behaviour that focus on individual, social, policy, and environmental-related factors may be particularly useful in aiding our understanding of physical activity. As a complex system, a socio-ecological framework sees behaviour as the result of direct, indirect, and interactive influences from factors of multiple levels of the system [11]. Similarly, the findings of this study point toward the multiple interactions, across multiple levels of the person’s ecological system, contributing to an environment (both internal and external to the individual) that either enables or restricts physical activity participation. In line with a systems thinking approach, physical activity behaviour may be influenced by an almost infinite combination of barriers and facilitators [13]. However, the identification of recurrent patterns may be used to develop targeted interventions.
Throughout this review, there were several factors that, in quantitative research, could be classified as effect modifiers and/or confounders yet which were challenging to account for in this qualitative meta-synthesis. Some transpired more explicitly, such as gender, whereas others were less tangible, such as temporal aspects or “geographical context”. With respect to the temporal nature of physical activity behaviour, people would describe a social and physical upbringing in which physical inactivity was implicit—cumulative exposure to various risk factors in conjunction with a potential epigenetic predisposition [15][16]. Geographically, barriers such as safety/violence, air pollution, neighbourhood walkability, and access to physical activity programs appear more prevalent factors in urban settings [17][18]. Conversely, the role of manual labour and subsistence farming in rural settings may affect the relative (perceived) value of physical activity in risk reduction or secondary prevention. Hence, in particularly in rural areas, the role of physical activity in the primary and secondary prevention of cardiometabolic disease may not be so explicit, and other risk factors may be more prevalent [19][20][21]. The impact of changing context (e.g., urbanisation) on physical activity did not reflect explicitly in the factors identified, despite compelling evidence that, for instance, urbanisation or migration impact physical activity participation [22][23][24]. The impact of time has not been fully captured in any of the prevailing models of behaviour [10]. Finally, women appeared more at risk for physical inactivity (particularly in relation to prevailing family roles impacting employment and power dynamics) and appeared to report more barriers to physical activity in relation to safety, cultural or religious norms, and stigmatisation. In this light, there may be a case for a gender-specific approach in addressing physical activity in contexts where this is applicable [25].


  1. Chatterjee, S.; Khunti, K.; Davies, M.J. Type 2 Diabetes. Lancet 2017, 389, 2239–2251.
  2. Murray, C.J.L.; Abbafati, C.; Abbas, K.M.; Abbasi, M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abdollahi, M.; Abedi, P.; Abedi, A.; Abolhassani, H.; et al. Five Insights from the Global Burden of Disease Study 2019. Lancet 2020, 396, 1135–1159.
  3. Pesah, E.; Turk-Adawi, K.; Supervia, M.; Lopez-Jimenez, F.; Britto, R.; Ding, R.; Babu, A.; Sadeghi, M.; Sarrafzadegan, N.; Cuenza, L.; et al. Cardiac Rehabilitation Delivery in Low/Middle-Income Countries. Heart 2019, 105, 1806–1812.
  4. Heine, M.; Fell, B.L.; Robinson, A.; Abbas, M.; Derman, W.; Hanekom, S. Patient-Centred Rehabilitation for Non-Communicable Disease in a Low-Resource Setting: Study Protocol for a Feasibility and Proof-of-Concept Randomised Clinical Trial. BMJ Open 2019, 9, e025732.
  5. Jesus, T.S.; Landry, M.D.; Hoenig, H. Global Need for Physical Rehabilitation: Systematic Analysis from the Global Burden of Disease Study 2017. Int. J. Environ. Res. Public Health 2019, 16, 980.
  6. van Zyl, C.; Badenhorst, M.; Hanekom, S.; Heine, M. Unravelling “Low-Resource Settings”: A Systematic Scoping Review with Qualitative Content Analysis. BMJ Glob. Health 2021, 6, e005190.
  7. Heine, M.; Lupton-Smith, A.; Pakosh, M.; Grace, S.L.; Derman, W.; Hanekom, S. Exercise-Based Rehabilitation for Non-Communicable Disease in Low-Resource Settings–A Systematic Scoping Review. BMJ Glob. Health 2019, 4, e001833.
  8. Ding, D.; Lawson, K.D.; Kolbe-Alexander, T.L.; Finkelstein, E.A.; Katzmarzyk, P.T.; van Mechelen, W.; Pratt, M. The Economic Burden of Physical Inactivity: A Global Analysis of Major Non-Communicable Diseases. Lancet 2016, 388, 1311–1324.
  9. DiPietro, L.; Al-Ansari, S.S.; Biddle, S.J.H.; Borodulin, K.; Bull, F.C.; Buman, M.P.; Cardon, G.; Carty, C.; Chaput, J.-P.; Chastin, S.; et al. Advancing the Global Physical Activity Agenda: Recommendations for Future Research by the 2020 WHO Physical Activity and Sedentary Behavior Guidelines Development Group. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 143.
  10. Buchan, D.S.; Ollis, S.; Thomas, N.E.; Baker, J.S. Physical Activity Behaviour: An Overview of Current and Emergent Theoretical Practices. J. Obes. 2012, 2012, 546459.
  11. Rhodes, R.E.; McEwan, D.; Rebar, A.L. Theories of Physical Activity Behaviour Change: A History and Synthesis of Approaches. Psychol. Sport Exerc. 2019, 42, 100–109.
  12. Centre for Reviews and Dissemination. CRD’s Guidance for Undertaking Reviews in Healthcare; York Publishing Services: York, UK, 2009.
  13. Resnicow, K.; Vaughan, R. A Chaotic View of Behavior Change: A Quantum Leap for Health Promotion. Int. J. Behav. Nutr. Phys. Act. 2006, 3, 25.
  14. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Worldwide Trends in Insufficient Physical Activity from 2001 to 2016: A Pooled Analysis of 358 Population-Based Surveys with 1·9 Million Participants. Lancet Glob. Health 2018, 6, e1077–e1086.
  15. Ivey, K.L.; Nguyen, X.-M.T.; Posner, D.; Rogers, G.B.; Tobias, D.K.; Song, R.; Ho, Y.-L.; Li, R.; Wilson, P.W.; Cho, K. The Structure of Relationships between the Human Exposome and Cardiometabolic Health: The Million Veteran Program. Nutrients 2021, 13, 1364.
  16. Samodien, E.; Abrahams, Y.; Muller, C.; Louw, J.; Chellan, N. Non-Communicable Diseases—A Catastrophe for South Africa. S. Afr. J. Sci. 2021, 117.
  17. Cleland, C.; Reis, R.; Hino, A.; Hunter, R.; Fermino, R.; Paiva, H.; Czestschuk, B.; Ellis, G. Built Environment Correlates of Physical Activity and Sedentary Behaviour in Older Adults: A Comparative Review between High and Low-Middle Income Countries. Health Place 2019, 57, 277–304.
  18. Elshahat, S.; O’Rorke, M.; Adlakha, D. Built Environment Correlates of Physical Activity in Low- and Middle-Income Countries: A Systematic Review. PLoS ONE 2020, 15, e0230454.
  19. Allen, L.; Williams, J.; Townsend, N.; Mikkelsen, B.; Roberts, N.; Foster, C.; Wickramasinghe, K. Socioeconomic Status and Non-Communicable Disease Behavioural Risk Factors in Low-Income and Lower-Middle-Income Countries: A Systematic Review. Lancet Glob. Health 2017, 5, e277–e289.
  20. Mumu, S.J.; Fahey, P.P.; Ali, L.; Rahman, A.K.M.F.; Merom, D. Seasonal Variations in Physical Activity Domains among Rural and Urban Bangladeshis Using a Culturally Relevant Past Year Physical Activity Questionnaire (PYPAQ). J. Environ. Public Health 2019, 2019, 2375474.
  21. Padrão, P.; Damasceno, A.; Silva-Matos, C.; Prista, A.; Lunet, N. Physical Activity Patterns in Mozambique: Urban/Rural Differences during Epidemiological Transition. Prev. Med. 2012, 55, 444–449.
  22. Addo, I.Y.; Brener, L.; Asante, A.D.; de Wit, J. Determinants of Post-Migration Changes in Dietary and Physical Activity Behaviours and Implications for Health Promotion: Evidence from Australian Residents of Sub-Saharan African Ancestry. Health Promot. J. Aust. 2019, 30 (Suppl. S1), 62–71.
  23. Huang, N.-C.; Kung, S.-F.; Hu, S.C. The Relationship between Urbanization, the Built Environment, and Physical Activity among Older Adults in Taiwan. Int. J. Environ. Res. Public Health 2018, 15, 836.
  24. Assah, F.K.; Ekelund, U.; Brage, S.; Mbanya, J.C.; Wareham, N.J. Urbanization, Physical Activity, and Metabolic Health in Sub-Saharan Africa. Diabetes Care 2011, 34, 491–496.
  25. Sharkey, T.; Whatnall, M.C.; Hutchesson, M.J.; Haslam, R.L.; Bezzina, A.; Collins, C.E.; Ashton, L.M. Effectiveness of Gender-Targeted versus Gender-Neutral Interventions Aimed at Improving Dietary Intake, Physical Activity and/or Overweight/Obesity in Young Adults (Aged 17–35 Years): A Systematic Review and Meta-Analysis. Nutr. J. 2020, 19, 78.
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