Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study

Kroll, Ryan R.; Boyd, J. Gordon; Maslove, David

doi:10.2196/jmir.6025

Cited by 115 publications

(104 citation statements)

References 29 publications

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“…HR is nowadays measured by nurses who have congested schedules and therefore limited time to measure the HR of patients. HR-sensing devices may be a solution for this problem and can be useful in extending the reach of vital signs monitoring in- and outside hospitals, which is typically limited by constraints on human resources [4]. Nowadays, the use of wireless monitors for assessment of HR is a common component of health and fitness programs.…”

Section: Introductionmentioning

confidence: 99%

“…This results in different validation processes that not always reflect the veracious outcome of comparison. Validation can be done in two ways: (1) by comparing the HR [4,14] or (2) by comparing the ECG- derived R-R intervals (RRI) [15,16] and the PPG-derived peak-to-peak intervals (PPI) [17] as shown in Figure 3. The goal of this paper was to explore which of the two validation approaches is more suited and to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study

Vandenberk¹,

Stans²,

Mortelmans³

et al. 2017

JMIR Mhealth Uhealth

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BackgroundPhotoplethysmography (PPG) is a proven way to measure heart rate (HR). This technology is already available in smartphones, which allows measuring HR only by using the smartphone. Given the widespread availability of smartphones, this creates a scalable way to enable mobile HR monitoring. An essential precondition is that these technologies are as reliable and accurate as the current clinical (gold) standards. At this moment, there is no consensus on a gold standard method for the validation of HR apps. This results in different validation processes that do not always reflect the veracious outcome of comparison.ObjectiveThe aim of this paper was to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology.MethodsThe FibriCheck (Qompium) app was used in two separate prospective nonrandomized studies. In the first study, the HR of the FibriCheck app was consecutively compared with 2 different Food and Drug Administration (FDA)-cleared HR devices: the Nonin oximeter and the AliveCor Mobile ECG. In the second study, a next step in validation was performed by comparing the beat-to-beat intervals of the FibriCheck app to a synchronized ECG recording.ResultsIn the first study, the HR (BPM, beats per minute) of 88 random subjects consecutively measured with the 3 devices showed a correlation coefficient of .834 between FibriCheck and Nonin, .88 between FibriCheck and AliveCor, and .897 between Nonin and AliveCor. A single way analysis of variance (ANOVA; P=.61 was executed to test the hypothesis that there were no significant differences between the HRs as measured by the 3 devices. In the second study, 20,298 (ms) R-R intervals (RRI)–peak-to-peak intervals (PPI) from 229 subjects were analyzed. This resulted in a positive correlation (rs=.993, root mean square deviation [RMSE]=23.04 ms, and normalized root mean square error [NRMSE]=0.012) between the PPI from FibriCheck and the RRI from the wearable ECG. There was no significant difference (P=.92) between these intervals.ConclusionsOur findings suggest that the most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system, compared on the basis of beat-to-beat analysis. This approach could lead to more correct assessments of the accuracy of HR apps.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study

Vandenberk¹,

Stans²,

Mortelmans³

et al. 2017

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…However for recent wearable devices, only a limited number of studies investigated the accuracy of heart rate monitoring, often in specific conditions. In non-moving conditions, heart rate monitoring using a wrist-worn personal fitness tracker has been evaluated with patients in the intensive care unit [27]. The measured values were slightly lower than those derived from continuous electrocardiographic monitoring.…”

Section: Related Workmentioning

confidence: 99%

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Pessemier

Martens

2018

Multimed Tools Appl

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Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter.

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“…9 The Fitbit wearable device (Fitbit, Inc) has been studied in the critical care setting where heart rate has been found to have moderate accuracy in patients who are in sinus rhythm, and Fitbit sleep data correlated moderately with self-reported sleep. 10,11 To date, there have been limited studies on the accuracy of wearables in hospitalized medical patients.…”

Section: Introductionmentioning

confidence: 99%

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

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BackgroundWearable devices such as Fitbits may provide important insights about hospitalized patients that include data on low activity and poor sleep. Monitoring this information could spur interventions to improve mobility and sleep which may reduce the adverse effects associated with hospitalization. However, there is a lack of studies assessing the accuracy of wearables in hospitalized medical patients. The purpose of our study was to determine the accuracy of Fitbit heart rate, sleep and physical activity in hospitalized medical patients.MethodsWe conducted a prospective cohort feasibility study enrolling 50 medical inpatients at two hospitals providing them with a Fitbit Charge. Our main measures were Fitbit heart rate, sleep and activity data as well as nurse recorded heart rates, patient reported sleep, and nurse assessments of activity.ResultsOf the 50 patients who consented to the study, 47 patients wore the devices. Comparing pairs of heart rate data from Fitbit and nurse recorded vital signs for the same minute, there were 261 pairs available for comparison. The mean difference was 0.45 bpm (SD: 13.0, Pearson correlation: 0.68 P<0.001) and the 95% limits of agreement were -25 to 26 bpm. The association between the patient-reported sleep score and Fitbit total sleep duration was 0.19 (P=0.24) and between the self-reported hours of sleep and Fitbit total sleep duration was 0.21 (P=0.21). The correlation between nurse-recorded activity and Fitbit daily steps was 0.06 (P=0.52). ConclusionsFitbit heart rates correlated well with nurse-recorded heart rate but did not correlate well with nurse assessments of activity nor with patient self-assessment of sleep. This study highlights limitations of the accuracy of current wearable wrist-worn device algorithms in activity and sleep detection in patients in hospital. The findings call into question the validity of Fitbits for assessment of patient activity and sleep in the hospital setting and suggest that they should not be routinely used without further validation.Trial RegistrationClinicalTrials.gov NCT03646435

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Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study

Cited by 115 publications

References 29 publications

Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study

Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

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