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Updated: Aug 14, 2026

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Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Criterion Validity of a Consumer Wearable for Step Counting and Activity Intensity Classification in Adults With Lung
Rujul Singh1, Emma Fortune2,3, Macy K Tetrick4
1Grossman School of Medicine, New York University, 550 1st Ave, New York, NY, United States, 1 8594098094.
JMIR Formative Research
|August 12, 2026
Summary
The Fitbit Charge 6 underestimates steps in lung cancer patients during short walks and slow speeds, impacting exercise-oncology trial data. Accuracy improves with longer, faster walking bouts.
Area of Science:
- Exercise Oncology
- Biomedical Engineering
- Wearable Technology Validation
Background:
- Consumer wearable activity monitors are increasingly used in exercise-oncology trials.
- Their accuracy is unvalidated in lung cancer patients, where gait may challenge algorithms.
Purpose of the Study:
- Assess Fitbit Charge 6 validity against direct observation in lung cancer patients.
- Evaluate step-count agreement across durations and speeds.
- Compare active/sedentary minute classification and spurious step detection.
Main Methods:
- 14 lung cancer adults participated in an in-laboratory validation study.
- Fitbit Charge 6 worn during walking trials (variable duration/speed) and nonwalking tasks.
- Bland-Altman analysis, MAPE, ICCs, and confusion matrix used for validation.
Main Results:
- Fitbit undercounted steps, with poor relative agreement and low ICC.
- Undercounting was greatest at slow gait speeds (<0.6 m/s) and short bouts (5s).
- High sensitivity but low specificity for activity intensity misclassified sedentary minutes as active.
Conclusions:
- Fitbit Charge 6 shows meaningful error during short bouts and slow speeds common in lung cancer patients.
- Systematic overestimation of active minutes due to low specificity.
- Findings impact interpretation of exercise-oncology interventions using wearable data.

