The Science of Physical Activity & Health

Being physically active on a weekly basis:

• Lowers your risk of:
  • Dying prematurely (USDHHS, 2008; Byberg et al., 2009; Samitz, Egger and Zwahlen, 2011; Wen et al., 2011)
  • Coronary heart disease (USDHHS, 2008; Fletcher et al., 1996)
  • Stroke (USDHHS, 2008; Hu et al., 2000)
  • Type-2 diabetes and metabolic syndrome (WHO, 2009; USDDHS, 2008; Diabetes Prevention Program [DPP] Research Group, 2003; DPP Research Group, 2002; Sigal et al., 2006; Wei et al., 1999)
  • Hypertension (USDHHS, 2008; Barlow et al., 2006; Morris, 1994)
  • Adverse lipid profile (USDHHS, 2008)
  • Colon, breast and endometrial cancers (WHO, 2009; Knols et al., 2005; Holmes et al., 2005; Fong et al., 2012; Slattery and Potter, 2002)
  • Respiratory illnesses (Garcia-Aymerich et al., 2006)
  • Hip fractures (USDHHS, 2008)
• Decreases feelings of depression and anxiety (Dunn et al., 2005)
• Reduces abdominal obesity and helps in weight loss, weight maintenance and prevention of weight gain (USDHHS, 2008)
• Prevents falls and improves functional health for older adults (Blair, 2009; USDHHS, 2008; Department of Health, 2004)
• Improves cognitive function (USDHHS, 2008: Larson et al., 2006)
• Increases bone density (USDHHS, 2008)
• Improves quality of sleep (USDHHS, 2008)

The patent pending system incorporates robust proprietary algorithms, which are driven by on-body data, a multidimensional understanding of physical activity and disease specific guidelines. This unique data analysis ensures that our outputs are accurate and actionable, facilitating effective evaluation, contextualised feedback and supports autonomous behaviour change. The engine enables the safe, controlled and consistent use of Physical Activity for healthcare.

The current use and understanding of Physical activity is based on generic guidelines, which are slightly ambiguous. This lack of personalisation has made them difficult to interpret for the necessary behaviour change, in the context of individual disease risk or management.

The emergence of wearable technology has introduced an element of personalisation and measurement for Physical Activity, however this is still insufficient to be appropriate for healthcare. There is, in many cases, a question over accuracy, which is often unpublished, but more importantly the data provided is neither meaningful or valuable in the context of health. The display of abstract numbers quantifying activity, although well designed, provide no insight into to your health or actions necessary to change. This also creates a danger that individuals will form an erroneous opinion of their physical activity, preventing them from harnessing its benefits, due to the focus put on single activity dimensions (Thompson et al., 2015).

Understanding that Physical Activity is a much more heterogeneous behaviour, with various dimensions known to have clear biological and health benefits (Thompson and Batterham, 2013; Metcalfe et al., 2012; Department of Health, 2011a; Department of Health, 2011b; Healy et al., 2011; Tremblay et al., 2010; Lee et al. 2010; Helmerhurst et al., 2009; Burgomaster et al., 2008; Centers for Disease Control and Prevention, 2008; Healy et al., 2008; Rakobowchuk et al., 2008; Institute of Medicine, 2005; Brooks et al., 2004; Gibala, 2007; World Health Organisation [WHO], 2000), is key to its effective use as a digital medicine. By using a multidimensional view it provides an accurate evaluation of physical activity, is more educational, and enables the personalisation of activity prescription (Thompson et al., 2015).

There are different ways to harness the protective properties of physical activity because it has multiple independent biologically-important dimensions. Focusing on one dimension alone, like minutes of activity or the number of steps taken, creates a danger of developing a false picture of activity which may be detrimental to personal health outcomes.

The research, published in the Exercise and Sport Sciences Reviews (click here for article), a journal of the American College of Sports Medicine, from our partners at the University of Bath shows robust evidence that personalised multidimensional physical activity profiling is crucial to providing an accurate assessment of an individual’s physical activity and for providing precise tailored advice. It is also integral to influencing successful behaviour change through greater understanding and offering activity choices tailored to an individual’s needs.

The multi-dimensional physical activity profiles visualised in the KiActiv^® system, created with patients and healthcare professionals, are proven to be easily understood, enhance knowledge of physical activity and inspire confidence to change and self-manage (Western et al., 2015).

To support this behaviour change, our system is grounded in Self-Determination Theory, a motivational theory which has strong empirical support in the context of health (Williams, 2002; Fortier et al., 2009), including in the context of physical activity (Biddle and Nigg, 2000; Fortier and Kowal, 2007; Wilson, Mack and Grattan, 2008).