Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep

Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.

doi:10.1080/07420528.2019.1596947

Cited by 54 publications

(44 citation statements)

References 22 publications

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“…A total of 29 studies (266 comparisons) examined wearable device heart rate measurements compared with reference standard criterion measures, including electrocardiography [ 22 , 23 , 38 - 40 , 43 , 44 , 54 , 61 , 62 , 67 - 70 , 142 , 162 , 176 ], Polar brand chest straps [ 20 , 21 , 24 - 28 , 58 , 63 , 71 , 163 ], and pulse oximetry [ 66 ], in controlled settings (a detailed list of the criterion measures used is presented in Multimedia Appendix 6 ). Of these, 24 studies recruited healthy adults (mean age 29.8 years, SD 10.5 years), four studies recruited adults living with limited mobility/chronic diseases (mean age 59.6 years, SD 9.0 years), and one study recruited children undergoing surgery (mean age 8.2 years, SD 3.1 years).…”

Section: Resultsmentioning

confidence: 99%

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Fuller

Colwell

Low

et al. 2020

JMIR Mhealth Uhealth

337

278

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Background Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. Objective The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Methods We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. Results We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusions Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

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Section: Resultsmentioning

confidence: 99%

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Fuller

Colwell

Low

et al. 2020

JMIR Mhealth Uhealth

337

278

View full text Add to dashboard Cite

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“…13 Fitbit devices have shown good agreement with polysomnography and ECGs in measuring sleep and heart rate during sleep, with average heart rate less than 1 bpm lower than that recorded by ECG. 14,15…”

Section: Implications Of All the Available Evidencementioning

confidence: 99%

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study

Radin

Wineinger

Topol

et al. 2020

The Lancet Digital Health

248

211

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Background Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. Methods We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. Findings We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13•3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0•12 (SD 0•07) over baseline models, corresponding to an improvement of 6•3-32•9%. Correlations of the final models with the CDC ILI rates ranged from 0•84 to 0•97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. Interpretation Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks.

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“…Early-generation Fitbit models only determined sleep parameters. However, subsequent modifications and refinements, including a scoring algorithm based collectively on body movement and heart rate variability (HRV), enable recent-generation Fitbit models—Fitbit Charge 2, Fitbit Charge 3, Fitbit Alta HR, Fitbit Versa, Fitbit Versa 2, Fitbit Blaze, Fitbit Inspire HR, and Fitbit Ionic—to estimate not only sleep parameters and stages [5], but wake- and sleep-time heart rate [6]. A 2019 survey found wearable technology to be the number one fitness trend worldwide [7].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

et al. 2019

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BackgroundWearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way.ObjectiveWe conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.MethodsIn adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria.ResultsThe search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy.ConclusionsSleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

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Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep

Cited by 54 publications

References 22 publications

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

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