Heart rate measures from the Apple Watch, Fitbit Charge HR 2, and electrocardiogram across different exercise intensities

Thomson, Elizabeth; Nuss, Kayla; Comstock, Ashley; Reinwald, Steven; Blake, Sophie; Pimentel, Richard E.; Tracy, Brian; Li, Kaigang

doi:10.1080/02640414.2018.1560644

Cited by 96 publications

(81 citation statements)

References 20 publications

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“…Error in specific activity types was greater but the FC2 was statistically equivalent in most activity types. A recent study reported that erratic movements and a greater HR were associated with an increased error in HR [56] and another concluded that the error was exacerbated with increasing exercise intensity [57]. In contrast, our results showed the highest error in the walking task, yet the greatest accuracy in the running and sedentary tasks.…”

Section: Discussioncontrasting

confidence: 85%

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

et al. 2019

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Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min −1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable.

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Section: Discussioncontrasting

confidence: 85%

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

et al. 2019

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“…Smartwatches are also increasingly incorporating these capabilities, such as electrocardiograms. Initial published studies have shown good accuracy in comparisons with gold‐standard ambulatory electrocardiograms, making them useful for screening of atrial fibrillation and other cardiac issues 99‐101 . Additional features in development are smartphone‐based spirometry, video plethysmography, and detection of seizures 102‐104 .…”

Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

114

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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“…Whether heart rate can accurately be measured with wearables depends on the used method. Chest straps are generally considered to provide an accurate indication of heart rate over a wide range of intensities, whereas optical (wrist-worn) heart rate monitors do generally only provide an accurate indication at slow to moderate running speeds (Lee & Gorelick, 2011;Stahl, An, Dinkel, Noble, & Lee, 2016;Støve, Haucke, Nymann, Sigurdsson, & Larsen, 2019;Thomson et al, 2019).Wrist-worn accelerometer-based estimates of heart rate have also been found to be accurate at low to moderate running speeds (Shcherbina et al, 2017). Other emerging technologies such as smart textiles are promising (J. W. Lee & Yun, 2017), but require further validation with larger samples.…”

Section: Heart Ratementioning

confidence: 99%

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

Hooren

Goudsmit

Restrepo

et al. 2019

Journal of Sports Sciences

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Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise realtime feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

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Heart rate measures from the Apple Watch, Fitbit Charge HR 2, and electrocardiogram across different exercise intensities

Cited by 96 publications

References 20 publications

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Innovations in research and clinical care using patient‐generated health data

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

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