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Article

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm

by
Benjamin D. Maylor
1,*,
Charlotte L. Edwardson
1,2,
Paddy C. Dempsey
1,2,3,4,
Matthew R. Patterson
5,
Tatiana Plekhanova
1,
Tom Yates
1 and
Alex V. Rowlands
1,2
1
Assessment of Movement Behaviours Group (AMBer), Leicester Lifestyle and Health Research Group, Diabetes Research Centre, University of Leicester, Leicester LE1 7RH, UK
2
NIHR Leicester Biomedical Research Centre, Leicester LE5 4PW, UK
3
MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 1TN, UK
4
Baker Heart and Diabetes Institute, Melbourne 3004, Australia
5
The Realtime Building, Clonshaugh Business and Technology Park, Shimmer Research Ltd., D17 H262 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(24), 9984; https://doi.org/10.3390/s22249984
Submission received: 11 November 2022 / Revised: 11 December 2022 / Accepted: 15 December 2022 / Published: 18 December 2022
(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)
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Abstract

Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.
Keywords: physical activity; accelerometry; ambulatory measurement; algorithms physical activity; accelerometry; ambulatory measurement; algorithms

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MDPI and ACS Style

Maylor, B.D.; Edwardson, C.L.; Dempsey, P.C.; Patterson, M.R.; Plekhanova, T.; Yates, T.; Rowlands, A.V. Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm. Sensors 2022, 22, 9984. https://doi.org/10.3390/s22249984

AMA Style

Maylor BD, Edwardson CL, Dempsey PC, Patterson MR, Plekhanova T, Yates T, Rowlands AV. Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm. Sensors. 2022; 22(24):9984. https://doi.org/10.3390/s22249984

Chicago/Turabian Style

Maylor, Benjamin D., Charlotte L. Edwardson, Paddy C. Dempsey, Matthew R. Patterson, Tatiana Plekhanova, Tom Yates, and Alex V. Rowlands. 2022. "Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm" Sensors 22, no. 24: 9984. https://doi.org/10.3390/s22249984

APA Style

Maylor, B. D., Edwardson, C. L., Dempsey, P. C., Patterson, M. R., Plekhanova, T., Yates, T., & Rowlands, A. V. (2022). Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm. Sensors, 22(24), 9984. https://doi.org/10.3390/s22249984

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