Abstract
SummaryConsumer “Smartbands” can collect physiological parameters, such as heart rate (HR), continuously across the sleep–wake cycle. Nevertheless, the quality of HR data detected by such devices and their place in the research and clinical field is debatable, as they are rarely rigorously validated. The objective of the present study was to investigate the reliability of pulse photoplethysmographic detection by the Fitbit ChargeHR™ (FBCHR, Fitbit Inc.) in a natural setting of continuous recording across vigilance states. To fulfil this aim, concurrent portable polysomnographic (pPSG) and the Fitbit’s photoplethysmographic data were collected from a group of 25 healthy young adults, for ≥12 hr. The pPSG‐derived HR was automatically computed and visually verified for each 1‐min epoch, while the FBCHR HR measurements were downloaded from the application programming interface provided by the manufacturer. The FBCHR was generally accurate in estimating the HR, with a mean (SD) difference of −0.66 (0.04) beats/min (bpm) versus the pPSG‐derived HR reference, and an overall Pearson’s correlation coefficient (r) of 0.93 (average per participant r = 0.85 ± 0.11), regardless of vigilance state. The correlation coefficients were larger during all sleep phases (rapid eye movement, r = 0.9662; N1, r = 0.9918; N2, r = 0.9793; N3, r = 0.9849) than in wakefulness (r = 0.8432). Moreover, the correlation coefficient was lower for HRs of >100 bpm (r = 0.374) than for HRs of <100 bpm (r = 0.84). Consistently, Bland–Altman analysis supports the overall higher accuracy in the detection of HR during sleep. The relatively high accuracy of FBCHR pulse rate detection during sleep makes this device suitable for sleep‐related research applications in healthy participants, under free‐living conditions.
Highlights
The commercial share of wrist-worn “Smartbands” has grown rapidly in recent years
Their role has been investigated in oncology, where they were tested as a tool for the activity tracking in breast cancer survivors (Chung et al, 2019), and in emergency departments, where they were tested for the low-cost heart rate (HR) monitoring in critical patients (Dagan & Mechanic, 2020)
De Zambotti et al, (2016) investigated the quality of HR detection of the FBCHR in sleep and compared its performance to the PSG-based HR estimation. This experimental setting provides information on how FBCHR behaves under mostly sedentary conditions, as the data were collected in a controlled environment
Summary
The commercial share of wrist-worn “Smartbands” has grown rapidly in recent years. Such devices can collect physiological data in a user-friendly and minimally invasive way, making them suitable for daily activity tracking (Henriksen et al, 2018). The possibility to collect data in a non-invasive way accounts for the large spread of commercial Smartbands in sleep research and explains why there is a large amount of validation studies that address their accuracy (Henriksen et al, 2018). They have been used in population-based projects to investigate circadian rhythms and sleep (Brazendale et al, 2019; Dunker Svendsen et al, 2019; Guarnieri et al, 2020; Lee & Finkelstein, 2015) and in autonomic nervous system (Dobbs et al, 2019; Hernando et al, 2018). A temporal resolution of 5 min could not properly describe the sleep-related HR dynamics (Penzel et al, 2003; Zemaityte et al, 1986)
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