Conference Agenda
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Daily Overview |
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GR1_1: Abstract presentations: Physical Activity, Exercise and Healthy Ageing: Evidence, Interventions and New Perspectives Location: Glass room 1 Session Chair: Johan de Jong | |
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11:30am - 11:40am
Can accelerometers estimate activity energy expenditure in very old adults? A comparison with doubly labelled water 1: Center for Active and Healthy Ageing (CAHA), Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; 2: Research Unit OPEN, Odense University Hospital, Denmark; 3: Isotope Ratio Mass Spectrometry Core, Biotechnology Center, University of Wisconsin-Madison, Madison, WI, USA; 4: School of Medicine, Ulster University, Londonderry BT37 0QB, UK; 5: Research Unit for ORL - Head and Neck Surgery and Audiology, Odense University Hospital & University of Southern Denmark, Odense, Denmark; 6: Department of Social Medicine, CAPHRI Care and Public Health Research Institute, Maastricht University, The Netherlands; 7: Department of Health Sciences, Faculty of Science, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Introduction Accelerometers are widely used to quantify physical activity (PA), but despite the intuitive link between acceleration and energy expenditure, this association can be influenced by device placement, metric selection, and the inability to capture energy from low-acceleration activities such as sitting and standing. This study aimed to examine the association between accelerometer-derived PA from multiple placements and metrics with doubly labelled water (DLW)-measured activity energy expenditure (AEE), and whether accounting for postural energetics improves these associations. Method Ninety-eight community-dwelling older adults (75–90 years) participated. Participants wore accelerometers on the dominant wrist, thigh, hip, and lower back for 14 days, synchronized with DLW measurements. Resting metabolic rate and VO2 during sitting and standing were assessed by indirect calorimetry, and body composition by DEXA. Two accelerometer metrics were analyzed: ActiGraph counts per minute (AGVMC) and Euclidean Norm Minus One (ENMO). Results Weak but significant associations were observed between accelerometer-measured PA and DLW-measured AEE across the four positions and both metrics (R2=0.08-0.13), except for AGVMC from the wrist (R2=0.01). Adjusting for age, fat-free mass, and fat mass substantially improved the models (R2=0.37–0.45), while accounting for postural energetics (energy from sitting and standing) did not further enhance the associations. Conclusion Accelerometer-derived PA modestly predicts AEE in very old adults but is insufficient as a standalone measure. Device placement and metric selection have limited influence on outputs, while adjustments for age and body composition substantially improve prediction. Accounting for postural energetics has minimal impact on the association. | |
