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Observational Study
. 2022 Jul 22;10(7):e38077.
doi: 10.2196/38077.

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Marta Karas #  1 , John Muschelli #  1 , Andrew Leroux  2 , Jacek K Urbanek  3 , Amal A Wanigatunga  4 , Jiawei Bai  1 , Ciprian M Crainiceanu  1 , Jennifer A Schrack  3   4
Affiliations
Observational Study

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Marta Karas et al. JMIR Mhealth Uhealth. .

Abstract

Background: Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures.

Objective: We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data.

Methods: High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data.

Results: Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published.

Conclusions: The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

Keywords: MIMS; accelerometry; actigraphy; activity counts; aging; digital health; health monitoring; health technology; monitor-independent movement summary; older adult population; physical activity; wearable computing; wearable device; wearable technology.

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Conflict of interest statement

Conflicts of Interest: CMC is a consultant for Bayer and Johnson and Johnson. Both these consulting contracts have been disclosed through the Johns Hopkins University Edisclose system. The current manuscript is not related to or influenced by any of these contracts.

Figures

Figure 1
Figure 1
Estimated minute-level mapping. A black solid line shows generalized additive model–fitted values of a measure (monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, activity intensity) given the activity count value. The points represent a subset of the data created by taking every 100th observations from all participant- and minute-specific observations; this subset is the same for all 4 plots. AC: activity count; AI: activity intensity; ENMO: Euclidean norm minus one; MAD: mean amplitude deviation; MIMS: monitor-independent movement summary.
Figure 2
Figure 2
Smoothed 24-hour median activity counts per minute for each age group: <60 years (green), 60-67 years (red), 68-74 years (blue), and ≥75 years (orange). Semitransparent thick colored lines represent results obtained with activity count; they are the same for all 4 plots. Solid thin colored lines represent results obtained with values mapped into activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, or activity intensity. AC: activity count; AI: activity intensity; ENMO: Euclidean norm minus one; MAD: mean amplitude deviation; MIMS: monitor-independent movement summary.
See this image and copyright information in PMC

References

    1. Karas M, Bai J, Strączkiewicz M, Harezlak J, Glynn NW, Harris T, Zipunnikov V, Crainiceanu C, Urbanek JK. Accelerometry data in health research: challenges and opportunities. Stat Biosci. 2019 Jul;11(2):210–237. doi: 10.1007/s12561-018-9227-2. http://europepmc.org/abstract/MED/31762829 - DOI - PMC - PubMed
    1. Kuo P, Schrack JA, Shardell MD, Levine M, Moore AZ, An Y, Elango P, Karikkineth A, Tanaka T, de Cabo R, Zukley LM, AlGhatrif M, Chia CW, Simonsick EM, Egan JM, Resnick SM, Ferrucci L. A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging. J Intern Med. 2020 Apr;287(4):373–394. doi: 10.1111/joim.13024. doi: 10.1111/joim.13024. - DOI - DOI - PMC - PubMed
    1. Neishabouri A, Nguyen J, Samuelsson J, Guthrie T, Biggs M, Wyatt J, Cross D, Karas M, Migueles JH, Khan S, Guo CC. Quantification of acceleration as activity counts in ActiGraph wearables. Research Square. doi: 10.21203/rs.3.rs-1370418/v1. Preprint posted online on February 22, 2022. - DOI - PMC - PubMed
    1. John D, Tang Q, Albinali F, Intille S. An open-source monitor-independent movement summary for accelerometer data processing. J Meas Phys Behav. 2019 Dec;2(4):268–281. doi: 10.1123/jmpb.2018-0068. - DOI - PMC - PubMed
    1. Troiano Richard P, McClain James J, Brychta Robert J, Chen Kong Y. Evolution of accelerometer methods for physical activity research. Br J Sports Med. 2014 Jul;48(13):1019–23. doi: 10.1136/bjsports-2014-093546. http://europepmc.org/abstract/MED/24782483 bjsports-2014-093546 - DOI - PMC - PubMed

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