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Differences among physical activity actigraphy algorithms in three chronic illness populations.

Megan L Alder1, Carolyn H Still1, Kelly L Wierenga2

  • 1Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.

Chronic Illness
|November 14, 2022
PubMed
Summary

Algorithm choice significantly impacts physical activity (PA) and metabolic equivalent (METs) measurements in chronic illness populations. Researchers must consider these differences when comparing PA data across diverse patient groups.

Keywords:
Actigraphy algorithmschronic illness populationscut pointsmetabolic equivalentsphysical activity

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Area of Science:

  • Wearable sensor technology and data analysis in chronic disease management.
  • Biomedical engineering and health informatics applications.
  • Public health and exercise science research methodologies.

Background:

  • Accurate physical activity (PA) measurement is crucial for managing chronic illnesses.
  • Various algorithms exist to process wearable sensor data, but their comparability is not well-established across different conditions.
  • Understanding algorithm variability is key to reliable PA assessment in patient populations.

Purpose of the Study:

  • To compare the performance of different algorithms for calculating wear time (WT%), kilocalories, light PA, moderate-to-vigorous PA (MVPA), and metabolic equivalents (METs).
  • To assess algorithm differences in three chronic illness cohorts: people living with HIV (PLHIV), cardiac event recovery, and hypertension (HTN), as well as a combined sample.
  • To determine if algorithm selection influences PA and energy expenditure metrics in these populations.

Main Methods:

  • Utilized ActiGraph(TM) wGT3X-BT devices worn for at least 3 days by participants.
  • Collected data from 29 PLHIV, 27 cardiac recovery patients, and 15 HTN patients.
  • Employed analysis of variance (ANOVA) to compare two WT% algorithms and four kilocalorie, light PA, MVPA, and METs algorithms.

Main Results:

  • No significant differences were observed between algorithms for WT% or kilocalorie calculations across all groups.
  • Significant differences were found among algorithms for light PA and METs in all chronic illness populations and the combined sample (p < .001).
  • MVPA algorithms showed significant differences in PLHIV (p = .007) and the combined sample (p < .001), but not in cardiac (p = .064) or HTN (p = .200) groups.

Conclusions:

  • The choice of algorithm significantly influences the determination of physical activity (PA) and metabolic equivalents (METs).
  • Algorithm variability necessitates careful consideration when comparing PA data across diverse chronic illness populations.
  • Standardization or clear reporting of algorithms is essential for reproducible and comparable PA research in clinical settings.