Impact of Reduced Sampling Rate on Accelerometer-Based Physical Activity Monitoring and Machine Learning Activity Classification

in Journal for the Measurement of Physical Behaviour

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Scott SmallNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

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Sara KhalidNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

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Paula DhimanNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

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Shing ChanLi Ka Shing Centre for Health Information and Discovery, Big Data Institute, University of Oxford, Oxford, Oxfordshire, United Kingdom

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Dan JacksonOpen Lab, School of Computing, Newcastle University, Newcastle upon Tyne, Tyne and Wear, United Kingdom

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Aiden DohertyLi Ka Shing Centre for Health Information and Discovery, Big Data Institute, University of Oxford, Oxford, Oxfordshire, United Kingdom

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Andrew PriceNuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

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DOI:
https://doi.org/10.1123/jmpb.2020-0061
Keywords:
self-reported; ambulatory monitoring; sampling frequency; activities of daily living; accelerometry
First Published Online:
30 Sep 2021
In Print:
Volume 4: Issue 4
Page Range:
298–310
Restricted access

Purpose: Lowering the sampling rate of accelerometers in physical activity research can dramatically increase study monitoring periods through longer battery life; however, the effect of reduced sampling rate on activity metric validity is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behavior types. Methods: Healthy adults wore sets of two Axivity AX3 accelerometers on the dominant wrist and hip for 24 hr. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate to vigorous activity, and behavioral activities were calculated and processed using both linear and nearest neighbor resampling. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results: Of the 54 total participants, 45 contributed >20 hr of hip wear time and 51 contributed >20 hr of wrist wear time. Strong correlation was observed between 25- and 100-Hz sampling rates in overall activity measurement (r = .97–.99), yet consistently lower activity was observed in data collected at 25 Hz (3.1%–13.9%). Reduced sleep and light activity and increased sedentary time was classified in 25-Hz data by machine learning models. Discrepancies were greater when linear interpolation resampling was used in postprocessing. Conclusions: The 25- and 100-Hz accelerometer data are highly correlated with predictable differences, which can be accounted for in interstudy comparisons. Sampling rate and resampling methods should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.

Supplementary Materials

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