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Related Experiment Video

Updated: Sep 2, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

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A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study.

George Boateng1, Curtis L Petersen2, David Kotz2

  • 1Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland.

JMIR Aging
|August 10, 2022
PubMed
Summary
This summary is machine-generated.

A new smartwatch app accurately counts steps for older adults, aiding physical activity and mobility. This validated algorithm offers a reliable tool to encourage exercise and improve health outcomes in seniors.

Keywords:
appmHealthmachine learningmobile appmobile applicationmobile healtholder adultspedometerphysical activitysmartwatchstep countingstep trackinguHealthwalkingwearable

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Digital Health

Background:

  • Physical activity reduces mobility impairment and disability in older adults.
  • Wearable devices and machine learning can motivate seniors, but current algorithms lack accuracy and tailoring.
  • Existing step-counting algorithms are often proprietary, not optimized for older adults, and perform poorly in real-world settings.

Purpose of the Study:

  • To develop and validate a smartwatch step-counting application specifically for older adults.
  • To evaluate the app's algorithm performance in free-living conditions over an extended period.

Main Methods:

  • Developed a step-counting app with activity inference algorithms for an open-source wrist-worn device (Amulet).
  • Validated the algorithm in laboratory settings (n=20) and through multiple field studies (2-day, n=6; 12-week, n=16) comparing against video and Fitbit data.
  • Evaluated performance across various walking patterns and optimized algorithm cut-off values using correlation and error rates.

Main Results:

  • The step-counting algorithm demonstrated strong performance in lab and field settings.
  • In lab validation, Amulet steps showed good correlation (R²=0.5 for normal walking) with video counts, with a small average difference.
  • Field studies showed high association with Fitbit (R²=0.989 in 2-day study) and acceptable accuracy (R²=0.669 in 12-week study).

Conclusions:

  • Iterative algorithm development and real-world validation are crucial for monitoring systems.
  • The developed smartwatch app shows good performance for counting steps in older adults.
  • This validated app has the potential to promote physical activity and enhance health in the senior population.