Novel metrics for characterising detailed patterns of physical activity
ISPAH ePoster Library. Hillsdon M. 10/15/18; 225146; 525
Melvyn Hillsdon
Melvyn Hillsdon
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Abstract
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Abstract IntroductionLittle is known about how this activity should be taken despite evidence that different patterns of PA are required to reduce the risk of different diseases. The increasing use of accelerometers, that continuously record activity every second of every day for weeks at a time, mean that detailed characterisation of patterns of PA is now possible. Despite this most users of accelerometers still only derive measures of weekly volumes of PA in broad intensity categories. MethodsMethods will be presented that segment accelerometer data into clinically meaningful PA classifications according to type, frequency, duration, intensity and volume of every bout of recorded activity. In addition, metrics for characterising within and between day patterns of PA will be presented. ResultsAssociations between ‘traditional’ volume measures of PA and fitness will be presented and contrasted with associations between different patterns of acquiring of the same volume of PA and fitness in heart failure patients. ConclusionsIt is now possible to characterise detailed patterns of PA that go well beyond aggregate measures of weekly volumes and may provide new insights into the relationship between PA and health/disease. External funding details The work was partly funded via a PhD studentship from Activinsights.
Abstract IntroductionLittle is known about how this activity should be taken despite evidence that different patterns of PA are required to reduce the risk of different diseases. The increasing use of accelerometers, that continuously record activity every second of every day for weeks at a time, mean that detailed characterisation of patterns of PA is now possible. Despite this most users of accelerometers still only derive measures of weekly volumes of PA in broad intensity categories. MethodsMethods will be presented that segment accelerometer data into clinically meaningful PA classifications according to type, frequency, duration, intensity and volume of every bout of recorded activity. In addition, metrics for characterising within and between day patterns of PA will be presented. ResultsAssociations between ‘traditional’ volume measures of PA and fitness will be presented and contrasted with associations between different patterns of acquiring of the same volume of PA and fitness in heart failure patients. ConclusionsIt is now possible to characterise detailed patterns of PA that go well beyond aggregate measures of weekly volumes and may provide new insights into the relationship between PA and health/disease. External funding details The work was partly funded via a PhD studentship from Activinsights.
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