Objective To assess the accuracy of recently commercialized wearable devices in heart rate (HR) measurement during cardiopulmonary exercise test (CPX) under gradual increase in exercise intensity, while wearable devices with HR monitors are reported to be less accurate in different exercise intensities.
Methods CPX was performed for patients with coronary artery disease (CAD). Twelve lead electrocardiograph (ECG) was the gold standard and Apple watch 7 (AW7), Galaxy watch 4 (GW4) and Bio Patch Mobicare 200 (MC200) were applied for comparison. Paired absolute difference (PAD), mean absolute percentage error (MAPE) and intraclass correlation coefficient (ICC) were evaluated for each device.
Results Forty-four participants with CAD were included. All the devices showed MAPE under 2% and ICC above 0.9 in rest, exercise and recovery phases (MC200=0.999, GW4=0.997, AW7=0.998). When comparing exercise and recovery phase, PAD of MC200 and AW7 in recovery phase were significantly bigger than PAD of exercise phase (p<0.05). Although not significant, PAD of GW4 tended to be bigger in recovery phase, too. Also, when stratified by HR 20, ICC of all the devices were highest under HR of 100, and ICC decreased as HR increased. However, except for ICC of GW4 at HR above 160 (=0.867), all ICCs exceeded 0.9 indicating excellent accuracy.
Conclusion The HR measurement of the devices validated in this study shows a high concordance with the ECG device, so CAD patients may benefit from the devices during high-intensity exercise under conditions where HR is measured reliably.
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Objective To assess the accuracies and validities of popular smart bands for heart rate (HR) measurement in cardiovascular disease (CVD) patients during a graded exercise test (GXT).
Methods Seventy-eight patients were randomly assigned to wear two different smart bands out of three possible choices: Samsung Galaxy Fit 2, Xiaomi Mi Band 5, or Partron PWB-250 on each wrist. A 12-lead exercise electrocardiogram (ECG) and patch-type single-lead ECG were used to assess the comparative HR accuracy of the smart bands. The HR was recorded during the GXT using the modified Bruce protocol.
Results The concordance correlation coefficients (rc) were calculated to provide a measure of agreement between each device and the ECG. In all conditions, the Mi Band 5 and Galaxy Fit 2’ correlations were rc>0.90, while the PWB-250 correlation was rc=0.58 at rest. When evaluating the accuracy according to the magnitude of HR, all smart bands performed well (rc>0.90) when the HR was below 100 but accuracy tended to decrease with higher HR values.
Conclusion This study showed that the three smart bands had a high level of accuracy for HR measurements during low-intensity exercise. However, during moderate-intensity and high-intensity exercise, all the three smart bands performed less accurately. Further studies are needed to find a more optimal smart band for HR measurement that can be used for precise HR monitoring during formal cardiac rehabilitation exercise training, including at high and maximal intensity (Clinical Trial Registration No. cris.nih.go.kr/KCT0007036).
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Methods Records of 18 disabled patients who visited the Department of Rehabilitation from May 2018 to June 2018 were selected. Patients’ gait patterns were analyzed using the IMU system with different assistive devices to determine the most appropriate device depending on the patient’s condition. Evaluation was performed using two or more devices, and the appropriate device was selected by comparing the 14 parameters of gait evaluation. The device showing measurements nearer or the nearest to the normative value was selected for rehabilitation.
Results The result of the gait evaluation in all 18 patients was analyzed using the IMU system. According to the records, the patients were evaluated using various assistive devices without consideration of defilades. Moreover, this gait analysis was effective in determining the most appropriate device for each patient. Increased gait cycle time and swing phase and decreased stance phase were observed in devices requiring significant assistance.
Conclusion The IMU-based gait analysis system is beneficial in evaluating gait in clinical fields. Specifically, it is useful in evaluating patients with gait disturbances who require assistive devices. Furthermore, it allows the establishment of an evidence-based decision for the most appropriate assistive walking devices for patients with gait disturbances.
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