Assessment of the Fitbit Charge 2 for monitoring heart rate.
ABSTRACT: 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 minutes and their heart rate was simultaneously recorded from each device. Results showed that the Fitbit Charge 2 underestimates the heart rate. Although the mean bias in measuring heart rate was a modest -5.9 bpm (95% CI: -6.1 to -5.6 bpm), the limits of agreement, which indicate the precision of individual measurements, between the Fitbit Charge 2 and criterion measure were wide (+16.8 to -28.5 bpm) indicating that an individual heart rate measure could plausibly be underestimated by almost 30 bpm.
Project description:PURPOSE:This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. METHODS:59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. RESULTS:Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs. CONCLUSIONS:Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.
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.
Project description:OBJECTIVE:To evaluate physical activity (PA) and sedentary time in subjects with knee osteoarthritis (OA) measured by the Fitbit Charge 2 (Fitbit) and a wrist-worn ActiGraph GT3X+ (AGW) compared to the hip-worn ActiGraph (AGH). DESIGN:We recruited a cohort of subjects with knee OA from rheumatology clinics. Subjects wore the AGH for four weeks, AGW for two weeks, and Fitbit for two weeks over a four-week study period. We collected accelerometer counts (ActiGraphs) and steps (ActiGraphs, Fitbit) and calculated time spent in sedentary, light, and moderate-to-vigorous activity. We used triaxial PA intensity count cut-points from the literature for ActiGraph and a stride length-based cadence algorithm to categorize Fitbit PA. We compared Fitbit wear times calculated from a step-based algorithm and a novel algorithm that incorporates steps and heart rate (HR). RESULTS:We enrolled 15 subjects (67% female, mean age 68 years). Relative to AGH, Fitbit, on average, overestimated steps by 39% and sedentary time by 37% and underestimated MVPA by 5 minutes. Relative to AGH, AGW overestimated steps 116%, underestimated sedentary time by 66%, and captured 281 additional MVPA minutes. The step-based wear time Fitbit algorithm captured 14% less wear time than the HR-based algorithm. CONCLUSIONS:Fitbit overestimates steps and underestimates MVPA in knee OA subjects. Cut-offs validated for AGW should be developed to support the use of AGW for PA assessment. The HR-based Fitbit algorithm captured more wear time than the step-based algorithm. These data provide critical insight for researchers planning to use commercially-available accelerometers in pragmatic studies.
Project description:BACKGROUND:Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients' asthma outcomes have not been established. OBJECTIVE:The objective of this study was to explore the association between self?reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI). METHODS:We conducted an 8?week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient?reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep?related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F?SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest. RESULTS:We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=-.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=-.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62). CONCLUSIONS:Our findings support the potential of using wrist-worn devices to continuously monitor two important factors-physical activity and sleep-associated with patients' asthma outcomes and to develop a personalized asthma management platform.
Project description: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.
Project description:BACKGROUND:Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown. OBJECTIVE:To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings. METHODS:41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis. RESULTS:The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph. CONCLUSION:Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.
Project description:<h4>Objectives</h4>To examine the validity and reliability of the Fitbit Flex against direct observation for measuring steps in the laboratory and against the Actigraph for step counts in free-living conditions and for moderate-to-vigorous physical activity (MVPA) and activity energy expenditure (AEE) overall.<h4>Methods</h4>Twenty-five adults (12 females, 13 males) wore a Fitbit Flex and an Actigraph GT3X+ during a laboratory based protocol (including walking, incline walking, running and stepping) and free-living conditions during a single day period to examine measurement of steps, AEE and MVPA. Twenty-four of the participants attended a second session using the same protocol.<h4>Results</h4>Intraclass correlations (ICC) for test-retest reliability of the Fitbit Flex were strong for walking (ICC = 0.57), moderate for stair stepping (ICC = 0.34), and weak for incline walking (ICC = 0.22) and jogging (ICC = 0.26). The Fitbit significantly undercounted walking steps in the laboratory (absolute proportional difference: 21.2%, 95%CI 13.0-29.4%), but it was more accurate, despite slightly over counting, for both jogging (6.4%, 95%CI 3.7-9.0%) and stair stepping (15.5%, 95%CI 10.1-20.9%). The Fitbit had higher coefficients of variation (Cv) for step counts compared to direct observation and the Actigraph. In free-living conditions, the average MVPA minutes were lower in the Fitbit (35.4 minutes) compared to the Actigraph (54.6 minutes), but AEE was greater from the Fitbit (808.1 calories) versus the Actigraph (538.9 calories). The coefficients of variation were similar for AEE for the Actigraph (Cv = 36.0) and Fitbit (Cv = 35.0), but lower in the Actigraph (Cv = 25.5) for MVPA against the Fitbit (Cv = 32.7).<h4>Conclusion</h4>The Fitbit Flex has moderate validity for measuring physical activity relative to direct observation and the Actigraph. Test-rest reliability of the Fitbit was dependant on activity type and had greater variation between sessions compared to the Actigraph. Physical activity surveillance studies using the Fitbit Flex should consider the potential effect of measurement reactivity and undercounting of steps.
Project description:PURPOSE:Women in alcohol treatment are more likely to relapse when in unpleasant, negative emotional states. Given the demonstrated benefits of exercise for decreasing depression, negative affect, and urges to drink, helping women engage in a lifestyle physical activity (LPA) intervention in early recovery may provide them a tool they can utilize "in the moment" in order to cope with negative emotional states and alcohol craving when relapse risk is highest. New digital fitness technologies (e.g., Fitbit activity monitor with web and mobile applications) may facilitate increases in physical activity (PA) through goal setting and self-monitoring. METHOD:We piloted a 12-week LPA+Fitbit intervention focused on strategically using bouts of PA to cope with affect and alcohol cravings to prevent relapse in 20 depressed women (mean age=39.5years) in alcohol treatment. RESULTS:Participants wore their Fitbit on 73% of days during the intervention period. An average of 9174 steps/day were taken on the days the Fitbit was worn. Participants completed 4.7 of the 6 scheduled phone PA counseling sessions (78%). Among women who completed the intervention (n=15), 44% remained abstinent throughout the entire course of treatment. On average, women were abstinent on 95% of days during the 12-week intervention. Participants reported an increase in using PA to cope with either negative affect or urges to drink from baseline to end of treatment (p<0.05). Further, participants reported high satisfaction with the LPA+Fitbit intervention and with the Fitbit tracker. CONCLUSIONS:Further research is needed to evaluate the LPA+Fitbit intervention in a more rigorous randomized controlled trial. If the LPA+Fitbit intervention proves to be helpful during early recovery, this simple, low-cost and easily transported intervention can provide a much-needed alternate coping strategy to help reduce relapse risk among women in alcohol treatment.
Project description:As the sensing capabilities of wearable devices improve, there is increasing interest in their application in medical settings. Capabilities such as heart rate monitoring may be useful in hospitalized patients as a means of enhancing routine monitoring or as part of an early warning system to detect clinical deterioration.To evaluate the accuracy of heart rate monitoring by a personal fitness tracker (PFT) among hospital inpatients.We conducted a prospective observational study of 50 stable patients in the intensive care unit who each completed 24 hours of heart rate monitoring using a wrist-worn PFT. Accuracy of heart rate recordings was compared with gold standard measurements derived from continuous electrocardiographic (cECG) monitoring. The accuracy of heart rates measured by pulse oximetry (Spo2.R) was also measured as a positive control.On a per-patient basis, PFT-derived heart rate values were slightly lower than those derived from cECG monitoring (average bias of -1.14 beats per minute [bpm], with limits of agreement of 24 bpm). By comparison, Spo2.R recordings produced more accurate values (average bias of +0.15 bpm, limits of agreement of 13 bpm, P<.001 as compared with PFT). Personal fitness tracker device performance was significantly better in patients in sinus rhythm than in those who were not (average bias -0.99 bpm vs -5.02 bpm, P=.02).Personal fitness tracker-derived heart rates were slightly lower than those derived from cECG monitoring in real-world testing and not as accurate as Spo2.R-derived heart rates. Performance was worse among patients who were not in sinus rhythm. Further clinical evaluation is indicated to see if PFTs can augment early warning systems in hospitals.ClinicalTrials.gov NCT02527408; https://clinicaltrials.gov/ct2/show/NCT02527408 (Archived by WebCite at http://www.webcitation.org/6kOFez3on).
Project description:BACKGROUND:Wrist-worn monitors claim to provide accurate measures of heart rate and energy expenditure. People wishing to lose weight use these devices to monitor energy balance, however the accuracy of these devices to measure such parameters has not been established. AIM:To determine the accuracy of four wrist-worn devices (Apple Watch, Fitbit Charge HR, Samsung Gear S and Mio Alpha) to measure heart rate and energy expenditure at rest and during exercise. METHODS:Twenty-two healthy volunteers (50% female; aged 24 ± 5.6 years) completed ~1-hr protocols involving supine and seated rest, walking and running on a treadmill and cycling on an ergometer. Data from the devices collected during the protocol were compared with reference methods: electrocardiography (heart rate) and indirect calorimetry (energy expenditure). RESULTS:None of the devices performed significantly better overall, however heart rate was consistently more accurate than energy expenditure across all four devices. Correlations between the devices and reference methods were moderate to strong for heart rate (0.67-0.95 [0.35 to 0.98]) and weak to strong for energy expenditure (0.16-0.86 [-0.25 to 0.95]). All devices underestimated both outcomes compared to reference methods. The percentage error for heart rate was small across the devices (range: 1-9%) but greater for energy expenditure (9-43%). Similarly, limits of agreement were considerably narrower for heart rate (ranging from -27.3 to 13.1 bpm) than energy expenditure (ranging from -266.7 to 65.7 kcals) across devices. CONCLUSION:These devices accurately measure heart rate. However, estimates of energy expenditure are poor and would have implications for people using these devices for weight loss.