Kevin is a Professor of Exercise Science in the School of Health Sciences, University of South Australia. He has about 30 years experience as an academic and has published about 200 research articles. He has worked with professional sporting organisations in Australian Football, Rugby Union and Rugby League, and Olympic water sports and also served on numerous national government and professional committees. Kevin and Lynda have produced a range of educational, sports and health-related software for high school students through to products for professionals, including the Science through Sport series, and HealthScreen Pro and MovementScreen Pro Apps on the Apple store. [email@example.com]
Lynda is a Senior Lecturer in the School of Health Sciences, Flinders University and teaches in health promotion, exercise science and paramedic studies. Lynda is a registered intensive care nurse who has practiced in both the USA and in Australia. Lynda also has an MPH and PhD in physical activity intervention strategies. In addition to software development, her current interests include trends in adult health and fitness, relationships between exercise and health outcomes, and mindfulness and stress modification in emergency services personnel. [firstname.lastname@example.org]
The EST has multiple analytical tools on approximately 40 screens. These are grouped within six modules covering:
1. Screening and health risk factor assessment
2. Fitness testing across the energy systems
3. Body composition analysis
4. Finding sports that match body size, shape and composition
5. Generating virtual populations to investigate relationships among behaviours, health biomarkers, and fitness
6. Analyzing blood biomarkers
The EST has two 'layers', or ways of using the program:
Using raw data collected on real people or creating a virtual person (VP) to further investigate.
Pre-exercise screening is part of the duty of care in exercise prescription. The screening module in the EST can be used for real clients to assist in managing medical conditions and in constructing appropriate programs for people beginning exercise. It also allows investigation of unlimited VP including cases, for example, the elderly and those in poorer health and fitness states, and elite-level athletes. This facilitates decision-making and cross-checking of these decisions in the safety of dealing with a VP rather than real clients.
Comprehensive ‘what-if’ functionality is embedded in the module to allow exploration of health risk factors, absolute risk of cardiovascular disease and life expectancy as behaviours and biomarkers are altered.
The Fitness testing module is structured to reflect a range of tests associated with each of the energy systems. The screens have both population-based norms (5-year age- and sex-specific groups) as well as a contemporary database of the best quality athletes reported in the scientific literature. There are printout options for each screen and numerous graphing and comparison functions.
The Body composition module allows the user to input anthropometry data and then explore outputs such as: comparisons against population norms, % body fat prediction, skinfold plots, somatotype, fractionation of body mass, and various types of reliability analyses to check skill competencies.
The Sport match module uses the inputs of a range of body measurements (size, shape and composition) and a probability function to determine the profile’s best fit among about 100 sports. The normative data are from published information of the highest quality athletes and the degree of ‘best fit’ is represented by an ‘overlap score’ out of 100. ‘What-if’ scenarios can be performed to modify inputs and explore the sports rankings thereby reinforcing body proportions and composition with sport functionality and athletic performance.
To facilitate investigation among the ~200 VP variables a ‘Virtual population’ function has been developed. This allows up to 5000 VP profiles to be generated per iteration and exported in excel files. These populations can be explored for patterns such as correlations or ageing trends, differences across sexes or, for example, when analysing different disease conditions, sedentariness and activity levels.
Users can enter data to determine percentile ranks for up to nine blood biomarkers, and to help reinforce units of measurement and risk factor threshold levels for key health indicators. Databases were constructed from national health surveys and large epidemiological studies across six countries and cover ages 18-75 yr.
GMT +10:30 hours
Norton, K. and L. Norton (2018).
Exercise Science Toolkit [Computer software].
Retrieved from http://exercisesciencetoolkit.com