Assessment of the Fitbit Charge 2 for monitoring heart rate.
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ABSTRACT:
SUBMITTER: Benedetto S
PROVIDER: S-EPMC5831032 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Assessment of the Fitbit Charge 2 for monitoring heart rate.
Benedetto Simone S Caldato Christian C Bazzan Elia E Greenwood Darren C DC Pensabene Virginia V Actis Paolo P
PloS one 20180228 2
Fitness trackers are devices or applications for monitoring and tracking fitness-related metrics such as distance walked or run, calorie consumption, quality of sleep and heart rate. Since accurate heart rate monitoring is essential in fitness training, the objective of this study was to assess the accuracy and precision of the Fitbit Charge 2 for measuring heart rate with respect to a gold standard electrocardiograph. Fifteen healthy participants were asked to ride a stationary bike for 10 minu ...[more]
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Project description:BackgroundMultisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation.ObjectiveThis study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work.MethodsA reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement.ResultsSleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm.ConclusionsWe conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.
| S-EPMC8527385 | biostudies-literature
Project description:Remote photoplethysmography (rPPG) allows contactless monitoring of human cardiac activity through a video camera. In this study, we assessed the accuracy and precision for heart rate measurements of the only consumer product available on the market, namely the FacereaderTM rPPG by Noldus, with respect to a gold standard electrocardiograph. Twenty-four healthy participants were asked to sit in front of a computer screen and alternate two periods of rest with two stress tests (i.e. Go/No-Go task), while their heart rate was simultaneously acquired for 20 minutes using the ECG criterion measure and the FacereaderTM rPPG. Results show that the FacereaderTM rPPG tends to overestimate lower heart rates and underestimate higher heart rates compared to the ECG. The Facereader™ rPPG revealed a mean bias of 9.8 bpm, the 95% limits of agreement (LoA) ranged from almost -30 up to +50 bpm. These results suggest that whilst the rPPG FacereaderTM technology has potential for contactless heart rate monitoring, its predictions are inaccurate for higher heart rates, with unacceptable precision across the entire range, rendering its estimates unreliable for monitoring individuals.
| S-EPMC6874325 | biostudies-literature
Project description: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.
| S-EPMC9286609 | biostudies-literature
Project description:BackgroundAccurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities.ObjectiveThe primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise.MethodsA total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error.ResultsOverall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of -1 beats·min-1 and limits of agreement of -20 to 19 beats·min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min-1 compared with Polar H10, with a limit of agreement of -46 to 33 beats·min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities.ConclusionsCompared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.
| S-EPMC8088863 | biostudies-literature
Project description:BackgroundPhysical activity is a critical component of health.ObjectiveWe implemented a pilot project to determine feasibility of embedding activity tracker data within routine primary care visits among family medicine residency patients. This study also examined whether physician monitoring of and feedback concerning tracker improves exercise compliance and overall health.MethodsTwenty-six participants received the FitBit Charge HR and were randomized into the self-monitoring (control) or intervention group for 12 weeks. The intervention group received weekly electronic health record (EHR) messages from their physician with feedback. The control group self-monitored activity.ResultsData demonstrates patient interest in sharing exercise data with primary providers, patient-generated exercise data can be embedded within visits to personalize recommendations, and patient-physician data sharing is logistically feasible via the EHR.ConclusionPhysicians can utilize activity trackers to potentially positively influence patients' exercise. This research supports the shift toward personalized medicine by facilitating conversations and additional studies about fitness data.
| S-EPMC8145580 | biostudies-literature
Project description:Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.
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Project description:BackgroundMobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.ObjectiveThis review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research.MethodsWe conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV).ResultsThe findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF.ConclusionsA number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.
| S-EPMC6396075 | biostudies-literature
Project description:BackgroundDespite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern.ObjectiveA systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages.MethodsPer the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings.ResultsThe search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP.ConclusionsThe findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.
| S-EPMC11004611 | biostudies-literature
Project description:We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.
| S-EPMC8255273 | biostudies-literature