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Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial Accelerometers
Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden.
Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI), Health and Sport. Spenshult Research and Development Center, Halmstad, Sweden.ORCID iD: 0000-0003-4260-7399
National Research Centre for the Working Environment, Copenhagen, Denmark; Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 3, article id 904Article in journal (Refereed) Published
Abstract [en]

Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (-135,141) min in the development dataset and +6.5 (-145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
Basel: MDPI, 2021. Vol. 21, no 3, article id 904
Keywords [en]
ProPASS, accuracy, bedtime, daily activity, objective measurement, physical activity, physical behaviour, posture, sedentary behaviour
National Category
Occupational Health and Environmental Health
Identifiers
URN: urn:nbn:se:hh:diva-44199DOI: 10.3390/s21030904ISI: 000615488700001PubMedID: 33572815Scopus ID: 2-s2.0-85099967515OAI: oai:DiVA.org:hh-44199DiVA, id: diva2:1546509
Funder
Swedish Rheumatism Association
Note

Funding Agency

National Health and Medical Research Council of Australia (Grant Number2020-APP1180812)

Uppsala University

Regional research funds ALF Uppsala Academic Hospital

Available from: 2021-04-22 Created: 2021-04-22 Last updated: 2022-02-10Bibliographically approved

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Aili, Katarina

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