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

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Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm

Marcin Straczkiewicz¹, Nancy L Keating^2,3, Embree Thompson⁴

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

|November 15, 2023

Summary

This summary is machine-generated.

This study validates an open-source smartphone step-counting method, showing it reliably estimates steps across various conditions and populations, offering a scalable alternative to commercial trackers.

Keywords:

accelerometer cancer open-source smartphone step count validation wearable

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Area of Science:

Biomedical Engineering
Digital Health
Wearable Technology

Background:

Step counts are vital for public health and clinical research.
Commercial activity trackers have limitations in reproducibility and scalability.
Smartphones offer a promising, accessible alternative for step-counting.

Purpose of the Study:

To evaluate an open-source smartphone step-counting method.
Validation was performed against cross-body, visually assessed, and commercial wearable data.
The study assessed performance under diverse measurement conditions.

Main Methods:

Utilized 8 independent datasets from smartphones and accelerometers.
Employed a previously published smartphone step-counting algorithm using raw accelerometer data.
Bland-Altman analysis calculated mean bias and limits of agreement.

Main Results:

Cross-body validation showed a mean bias of -7.2 steps (-0.5%).
Visually assessed validation yielded a mean bias of -0.4 steps (0.1%).
Commercial wearable validation (Fitbit) demonstrated a 3.4% difference (-67.1 steps).

Conclusions:

The open-source smartphone method provides reliable step counts.
Accuracy is consistent across different sensor locations and measurement scenarios.
The method is validated for both healthy adults and cancer patients.