Physical Activity and Health Across the Lifespan (PAHAL)

Current physical activity guidelines for children and adults recommend overall amounts of activity to be achieved over a day/week. However, little is known about how this activity should be accumulated, despite evidence that different patterns of physical activity are required to reduce the risk of different diseases. For example, physical activity frequency is important for insulin sensitivity and glycaemic control; intensity (walking pace) is for heart disease risk, and type (sharp bursts of weight-bearing activity) is for bone health.

Although total amounts of physical activity are known to affect health they may not be sufficient to explain differences in health outcomes; therefore, particular temporal patterns of physical activity behaviour may be critical.

Our research is focused on trying to characterise detailed patterns of physical activity to determine which patterns are most strongly associated with which disease or condition. This is important for public health messages about how much of what type of activity is important and would also help to understand adherence to specific physical activity messages following intervention.

Our interdisciplinary research specialisms link pre-clinical laboratory and fundamental research with epidemiological and intervention studies to enhance our understanding of the relationship between physical activity and health across the lifespan.

Our expertise

Our group comprises diverse expertise including:

  • Adult and paediatric health and exercise physiology
  • Physical activity measurement methodology
  • Physical activity epidemiology and public health
  • Evaluation and design of physical activity interventions
  • Associations of physical activity, bone health and injury
  • Psychology of exercise and physical activity
  • Physical activity data modelling and analysis

Research specialisms in physical activity

Preclinical research

In our designated laboratory facility we are currently exploring the physiological mechanisms which underpin associations between health markers and the low-level habitual physical activity and inactivity which comprises so much of daily life. This work will ultimately translate into identifying the minimum and optimum effective doses of physical activity to benefit health outcomes in various populations and to maximise adherence to physical activity interventions.

As a result of combining biomechanics with physical activity measurement, we are also continuing to develop methods in the laboratory using accelerometry to quantify loading beneficial to bone health and injury prevention. We are currently testing these methods in free-living populations to explore associations between habitual physical activity relevant to bone and estimates of bone health and training characteristics associated with running related injuries.

Physical activity measurement

Small, wearable devices called accelerometers enable us to collect very detailed information on the patterns of physical activity (intensity, frequency, duration), as well as the total amount of activity, that people do in their everyday lives. They do this by measuring activity up to 100 times per second, for every second of the day, thus giving us around 4 million data points per person when worn typically for a week. We have collected an analysed accelerometer data for a number of large studies involving various clinical groups, located in the University of Exeter College of Medicine and Health and external collaborators.

Analysis methods

In collaboration with accelerometer manufacturers and colleagues in Computer Science and Maths we are developing new methods of data processing and analysis that will lead to rapid developments in our knowledge of the associations between patterns of physical activity and health. In addition, we have a programme of research designed to develop methods for translating raw acceleration signals into physical activity and sedentary behaviours such as walking, running, sitting etc.

The overall aim of our work is to develop optimal age-specific activity recommendations and identify disease-specific physical activity ‘prescriptions’.

Epidemiology of physical activity and sedentary behaviour

In collaboration with colleagues from University College London we have published articles examining how patterns of sedentary behaviour are associated with a range of health outcomes. We intend to use new classifications of temporal patterns of physical activity, derived from accelerometer data, to examine how they alter our understanding of the relationship between physical and health.

Determinants of physical activity and evaluation of physical activity interventions

Our research seeks to identify the personal, social and environmental correlates of specific patterns of physical activity in order to inform the design of interventions and guide policy. We have combined accelerometer and GPS technologies to objectively record the location of physical activity and how this varies according to population group and geographical location. We also seek to evaluate the effectiveness of physical activity interventions (and associated mechanisms) for enhancing physical and psychological health and well-being.

Group members

Partnerships

Academics in the group collaborate widely with colleagues from the University of Exeter College of Medicine and Health, industry and other academic institutions.

We are currently engaged in a number of intervention studies, laboratory studies and epidemiological studies that involve both clinical and non-clinical studies of the effectiveness of interventions to:

  • reduce childhood obesity;
  • increase children’s physical activity and increase physical activity in heart failure patients.

Our researchers receive funds from the National Institute of Health Research (HTA), Medical Research Council and partners in the National Prevention Research Initiative (NPRI), Wellcome Trust and Activinsights Ltd.

Industry partners

Activinsights currently sponsor a PhD studentship, the overall aim of which is to use a combination of mathematics, statistical analysis and software development to automatically identify the activity (sitting, walking, running, etc) that the wearer of a tri-axial accelerometer is engaged in. Find out more.