Query: Fitbit Charge

title: 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.

title: A validation study of Fitbit Charge 2(tm) compared with polysomnography in adults
abstract: We evaluated the performance of a consumer multi sensory wristband (Fitbit Charge 2(tm) against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In lab PSG and Fitbit Charge 2(tm) data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19 61 years; 26 women; 25 Caucasian) Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, 15/h) in nine participants, who were analyzed separately from the main group (n 35) PSG and Fitbit Charge 2(tm) sleep data were compared using paired t tests, Bland Altman plots, and epoch by epoch (EBE) analysis. In the main group, Fitbit Charge 2(tm) showed 0.96 sensitivity (accuracy to detect sleep) 0.61 specificity (accuracy to detect wake) 0.81 accuracy in detecting N1+N2 sleep "light sleep" 0.49 accuracy in detecting N3 sleep "deep sleep" and 0.74 accuracy in detecting rapid eye movement (REM) sleep. Fitbit Charge 2(tm) significantly (p 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2(tm) outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland Altman agreement limits for all sleep measures. Fitbit Charge 2(tm) correctly identified 82% of PSG defined non REM REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2(tm) shows promise in detecting sleep wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2(tm) accuracy and reliability need to be further investigated in different settings (at home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders)

title: Comparative evaluation of heart rate based monitors: Apple Watch vs Fitbit Charge HR
abstract: The purpose of this investigation was to examine the validity of energy expenditure (EE) steps, and heart rate measured with the Apple Watch 1 and Fitbit Charge HR. Thirty nine healthy adults wore the two monitors while completing a semi structured activity protocol consisting of 20 minutes of sedentary activity, 25 minutes of aerobic exercise, and 25 minutes of light intensity physical activity. Criterion measures were obtained from an Oxycon Mobile for EE, a pedometer for steps, and a Polar heart rate strap worn on the chest for heart rate. For estimating whole trial EE, the mean absolute percent error (MAPE) from Fitbit Charge HR (32.9% was more than twice that of Apple Watch 1 (15.2% This trend was consistent for the individual conditions. Both monitors accurately assessed steps during aerobic activity (MAPEApple: 6.2% MAPEFitbit: 9.4% but overestimated steps in light physical activity. For heart rate, Fitbit Charge HR produced its smallest MAPE in sedentary behaviors (7.2% followed by aerobic exercise (8.4% and light activity (10.1% The Apple Watch 1 had stronger validity than the Fitbit Charge HR for assessing overall EE and steps during aerobic exercise. The Fitbit Charge HR provided heart rate estimates that were statistically equivalent to Polar monitor.

title: Security Analysis of Wearable Fitness Devices Fitbit
abstract: This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end to end basis from the device to the Fitbit web service.

title: Breaking Fitness Records Without Moving: Reverse Engineering and Spoofing Fitbit
abstract: Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health related data to vendors' cloud platforms, enabling users to analyze summary statistics on line and adjust their habits. Third parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end to end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.

title: Fitbit(r) An accurate and reliable device for wireless physical activity tracking.
abstract: A smart accelerometer named the Fitbit has recently been introduced in the consumer market as a physical activity monitor that can interface wirelessly with mobile phones and a manufacturer established website to allow consumers to track their physical activity in real time. The purpose of this study was to examine the validity and reliability of the Fitbit for measuring energy expenditure during treadmill walking and running relative to energy expenditure assessed by indirect calorimetry.  A total of 23 healthy adults (10 males, mean age: 30.6 7.9 years; mean BMI: 24.7 3.0 kg/m2) completed a four phase treadmill exercise protocol (6 min/phase) under laboratory conditions. The protocol consisted of walking at slow (1.9 mph) moderate (3.0 mph) and brisk (4.0 mph) paces; and jogging (5.2 mph) Participants were fitted with three hip based Fitbit One devices (two on right, one on left hip) and two wrist based Fitbit Flex devices (one on right and left wrist) Energy expenditure was

title: Validation of the Fitbit One activity monitor device during treadmill walking.
abstract: In order to quantify the effects of physical activity such as walking on chronic disease, accurate measurement of physical activity is needed. The objective of this study was to determine the validity and reliability of a new activity monitor, the Fitbit One, in a population of healthy adults.   Thirty healthy adults ambulated at 5 different speeds (0.90, 1.12, 1.33, 1.54, 1.78 m/s) on a treadmill while wearing three Fitbit One activity monitors (two on the hips and one in the pocket) The order of each speed condition was randomized. Fitbit One step count output was compared to observer counts and distance output was compared to the calibrated treadmill output. Two way repeated measures ANOVA, concordance correlation coefficients, and Bland and Altman plots were used to assess validity and intra class correlation coefficients (ICC) were used to assess reliability.  No significant differences were noted between Fitbit One step count outputs and observer counts, and concordance was substantial (0.97 1.00) Inter device reliability of the step count was high for all walking speeds (ICC 0.95) Percent relative error was less than 1.3% The distance output of the Fitbit One activity monitors was significantly different from the criterion values for each monitor at all speeds (P<0.001) and exhibited poor concordance (0.0 0.05) Inter device reliability was excellent for all treadmill speeds (ICC 0.90) Percent relative error was high (up to 39.6% CONCLUSIONS The Fitbit One activity monitors are valid and reliable devices for measuring step counts in healthy young adults. The distance output of the monitors is inaccurate and should be noted with caution.

title: Validation of the Fitbit wireless activity tracker for prediction of energy expenditure.
abstract: The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities.  Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion) Participants performed walking and running trials on a treadmill and a simulated free living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities.  Mean bias for estimated energy expenditure for all activities was 4.5 1.0 kcals/6 min (95% limits of agreement: 25.2 to 15.8 kcals/6 min) The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities.  The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

title: Validity of the Fitbit activity tracker for measuring steps in community dwelling older adults
abstract: Commercially available activity monitors, such as the Fitbit, may encourage physical activity. However, the accuracy of the Fitbit in older adults remains unknown. This study aimed to determine (1) the criterion validity of Fitbit step counts compared to visual count and ActiGraph accelerometer step counts and (2) the accuracy of ActiGraph step counts compared to visual count in community dwelling older people.  Thirty two community dwelling adults aged over 60 wore Fitbit and ActiGraph devices simultaneously during a 2 min walk test (2MWT) and then during waking hours over a 7 day period. A physiotherapist counted the steps taken during the 2MWT.  There was excellent agreement between Fitbit and visually counted steps (intraclass correlation coefficient (ICC2,1)=0.88, 95% CI 0.76 to 0.94) from the 2MWT, and good agreement between Fitbit and ActiGraph (ICC2,1=0.66, 95% CI 0.41 to 0.82) and between ActiGraph and visually counted steps (ICC2,1=0.60, 95% CI 0.33 to 0.79) There was excellent agreement between the Fitbit and ActiGraph in average steps/day over 7 days (ICC2,1=0.94, 95% CI 0.88 to 0.97) Percentage agreement was closest for Fitbit steps compared to visual count (mean 0% SD 4% and least for Fitbit average steps/day compared to the ActiGraph (mean 13% SD 25% Conclusions The Fitbit accurately tracked steps during the 2MWT, but the ActiGraph appeared to underestimate steps. There was strong agreement between Fitbit and ActiGraph counted steps. The Fitbit tracker is sufficiently accurate to be used among community dwelling older adults to monitor and give feedback on step counts.

title: Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking.
abstract: Increased physical activity can provide numerous health benefits. The relationship between physical activity and health assumes reliable activity measurements including step count and distance traveled. This study assessed step count and distance accuracy for Nike+ FuelBand, Jawbone UP 24, Fitbit One, Fitbit Flex, Fitbit Zip, Garmin Vivofit, Yamax CW 701, and Omron HJ 321 during level, upstairs, and downstairs walking in healthy adults.  Forty subjects walked on flat ground (400m) upstairs (176 steps) and downstairs (176 steps) and a subset of 10 subjects performed treadmill walking trials to assess the influence of walking speed on accuracy. Activity monitor measured step count and distance values were compared with actual step count (determined from video recordings) and distance to determine accuracy.  For level walking, step count errors in Yamax CW 701, Fitbit Zip, Fitbit One, Omron HJ 321, and Jawbone UP 24 were within 1% and distance errors in Fitbit Zip and Yamax CW 701 were within 5% Garmin Vivofit and Omron HJ 321 were the most accurate in estimating step count for stairs with errors less than 4% An important finding is that all activity monitors overestimated distance for stair walking by at least 45% CONCLUSION In general, there were not accuracy differences among activity monitors for stair walking. Accuracy did not change between moderate and fast walking speeds, though slow walking increased errors for some activity monitors. Nike+ FuelBand was the least accurate step count estimator during all walking tasks. Caution should be taken when interpreting step count and distance estimates for activities involving stairs.