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Updated: Oct 15, 2025

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
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Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

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Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review.

Anne Backes1, Tripti Gupta1, Susanne Schmitz2

  • 1Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.

Scandinavian Journal of Medicine & Science in Sports
|October 25, 2021
PubMed
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This review identifies wearable-specific indicators of physical activity (PA) behavior (WIPAB) from accelerometer data. Many WIPAB offer insights into PA patterns and health, but few are currently used in research.

Area of Science:

  • Human Movement Science
  • Biomedical Engineering
  • Public Health

Background:

  • Physical activity (PA) is a complex behavior requiring multidimensional assessment.
  • Objective PA measurement using accelerometers is common, but advanced analytical methods are underutilized.
  • Wearable-specific indicators of PA behavior (WIPAB) offer a more nuanced understanding of PA.

Purpose of the Study:

  • To map advanced analytical methods and their summary variables (WIPAB) for assessing PA behavior.
  • To investigate the strengths, limitations, and health associations of identified WIPAB.
  • To synthesize the current literature on WIPAB derived from accelerometer data.

Main Methods:

  • Scoping review of three databases (MEDLINE, Embase, Web of Science) from January 2010 to April 2020.
Keywords:
accelerometryalgorithmdata processingphysical activity patternwearable sensors

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  • Inclusion criteria: articles assessing PA behavior, using objective accelerometer measures, and investigating WIPAB.
  • Synthesis of 24 selected articles, presenting data in summary tables.

    • Main Results:

    • Thirteen distinct WIPAB were identified, categorized into activity intensity distribution, activity accumulation, and signal regularity.
    • Only five of the thirteen WIPAB have been applied to investigate PA behavior and health relationships.
    • These WIPAB may provide valuable information beyond conventional PA variables.

    Conclusions:

    • Advanced analytical methods yield WIPAB that capture complex PA behavior dimensions.
    • There is a gap in the utilization of identified WIPAB for health-related research.
    • Further research should explore the application and validation of diverse WIPAB for comprehensive PA assessment and health outcome prediction.