Heart rate detection by Fitbit ChargeHR^™: A validation study versus portable polysomnography

Abstract

Summary Consumer “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.

Prevalence of sleep apnea in children and adolescents in Colombia according to the national health registry 2017–2021

“…Some of the departments with the lowest prevalence have in common a low population density and less urban development. The graphic prevalence distribution could also show a concentration of sleep medicine services in principal and developed cities, which could hinder access to diagnosis of SA to patients residing far from city centers this could reflect on the importance of validating and including other strategies other than conventional polysomnography such as portable polysomnography [ 39 , 40 ], bio-impedance [ 41 ], heart rate variability monitoring [ 42 ] and peripheral arterial tonometry [ 43 ] as alternatives for children residing far from sleep medicine services. The fact that most of the patients belong to the contributory regime, may reflect inequity in access a sleep medicine services.…”

The feasibility of multimodality remote monitoring of maternal physiology during pregnancy

“…Smartwatch (Fitbit Inspire, Fitbit): Fitbit watches have been validated in peer-reviewed publications, including the accuracy of heart rate measurements across different physical activity intensity levels. [ 26 , 27 ]…”

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

“…Energy expenditure data were stored at a frequency of one data point per minute, while the HR data were stored at a frequency of one data point per second during physical exercise, one data point each five seconds otherwise. 31 The third-party platform returns a minute-by-minute wake-sleep staging and derives from this sleep staging a series of sleep parameters through DORMI, a proprietary deep-learning algorithm. 39 , 40 Among these parameters, six of them were used to train the algorithm: sleep efficiency (SE), defined as the ratio of total sleep time (TST) to time in bed; 41 TST itself; Sleep Fragmentation Index (SFI), defined as the total number of awakenings divided by the total sleep time; 42 Wake After Sleep Onset (WASO), defined as the period of wakefulness that occurs after a defined sleep onset; 43 number of awakenings (Naw) after sleep onset and the mean length of awakenings after sleep onset (mLaw).…”