This study compared heart rate (HR) measurements for the Fitbit Charge HR 2 (Fitbit) and the Apple Watch devices with HR measurements for electrocardiogram (ECG). Thirty young adults (15/15 ...females/males, age 23.5 ± 3.0 years) completed the Bruce Protocol. HR measurements were recorded from the ECG and both devices every minute. Average HR for each participant was calculated for very light, light, moderate, vigorous and very vigorous intensities based on ECG-measured HR. A concordance correlation coefficient (CCC) was calculated to examine the strength of the relationship between ECG measured HR and HR measured by each device. Relative error rates (RER) were also calculated to indicate the difference between each device and ECG. An equivalence test was conducted to examine the equivalence of HRs measured by devices and ECG. The Apple Watch showed lower RER (2.4-5.1%) compared with the Fitbit (3.9-13.5%) for all exercise intensities. For both devices, the strongest relationship with ECG-measured HR was found for very light PA with very high CCC (>.90) and equivalence. The strength of the relationship declined as exercise intensity increased for both devices. These findings indicate that the accuracy of real-time HR monitoring by the Apple Watch and Fitbit Charge HR2 is reduced as exercise intensity increases.
Objectives: In this study, we sought to determine the accuracy of energy expenditure (EE) esti- mation for the Fitbit Charge HR 2 (Fitbit) and the Apple Watch. Design: An observational study. ...Methods: Thirty young adults (15 men and 15 women, aged 23.5 ±
2.96 years) completed the Bruce treadmill protocol. We measured gross EE by a PARVO metabolic cart (MetCart) and concurrently estimated by the Fitbit and Apple Watch. We calculated concordance correlation coefficients (CCC, rc) and relative error rates to indicate the difference
between each device and the MetCart system. Results: For the Apple Watch and Fitbit, the relative error rate was 24.3%, 20.1% for the pooled sample, 18.6%, 24.2% for men, and 29.9%, 16.7% for women, respectively. The Apple Watch overestimated EE for women and underestimated EE for men;
the Fitbit underestimated EE for both. Moderate CCCs between estimated EEs and MetCart measured EEs were found for both Apple Watch (rc =0.65, 0.43, and 0.39 overall, men and women, respectively) and Fitbit (rc =0.53, 0.39, and 0.21 overall, men and women, respectively).
Conclusion: Neither device showed accurate results compared with EE measured by a MetCart. Users should consider these results when designing programs or personal training plans where physical activity EE is a key outcome assessed with a wearable device.
Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.
The aim of this study was to determine the direction and ...magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).
This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.
The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=-.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).
Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.
Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke.
This ...study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF.
A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF.
Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout W mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R
) was 0.685 and 0.057, respectively (P<.001 and .29, respectively).
In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.
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