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

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. We aimed to compare objective measures of physical activity to increase the generalizab...

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Vydané v:JMIR mHealth and uHealth Ročník 10; číslo 7; s. e38077
Hlavní autori: Karas, Marta, Muschelli, John, Leroux, Andrew, Urbanek, Jacek K, Wanigatunga, Amal A, Bai, Jiawei, Crainiceanu, Ciprian M, Schrack, Jennifer A
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Canada JMIR Publications 22.07.2022
Predmet:
Accelerometers
Accelerometry - statistics & numerical data
Aged
Aging
Aging - physiology
Alzheimer's disease
Data Analysis
Datasets
Exercise
Exercise - physiology
Female
Humans
Longitudinal Studies
Male
Multimedia
Original Paper
Principal components analysis
Quality control
Regression analysis
Retrospective Studies
Software
Time series
United States > US
Baltimore Maryland
older adult population
health monitoring
MIMS
monitor-independent movement summary
wearable technology
wearable computing
physical activity
wearable device
accelerometry
digital health
actigraphy
aging
health technology
activity counts
ISSN:2291-5222, 2291-5222
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Shrnutí: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. We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data. 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. 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. 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.
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ISSN:2291-5222
2291-5222
DOI:10.2196/38077