Accuracy of Fitbit Charge 2 Worn At Different Wrist Locations During Exercise

Many newly released activity monitors use heart rate measured at the wrist to estimate exercise intensity, however, where the device is placed on the wrist may affect accuracy of the measurement. PURPOSE: To determine whether the Pure Pulse technology on the Fitbit Charge 2 will show different heart rate readings when placed on the recommended exercise position compared to the all-day wear position at various exercise intensities. METHODS: Thirty-five participants (MEAN ± SD; 22.0 ± 2.9yrs; 23.9 ± 2.6kg/m2; 18 male) consented to participate in a single visit where two Fitbit Charge 2 devices were placed on the non-dominant wrist. Fitbit A was placed 2-3 fingers above the wrist bone. Fitbit B was placed directly above the wrist bone. The treadmill was set at 3 mph with 0% grade. Participants remained at this speed for 4 minutes. Heart rate measurements were taken at the last 10 seconds of each stage from both Fitbits and a polar heart rate monitor (chest strap). The same procedure was followed for 5 and 6 mph. Statistical analyses were performed using IBM SPSS 23.0. A Two-way (speed x location) Repeated Measures ANOVA was used to examine mean differences. Pairwise comparisons with Bonferroni correction were used in post-hoc analysis. Pearson correlations and mean bias between polar heart rate monitor and activity monitors were also calculated for each speed. RESULTS: Repeated Measures ANOVA found significant differences between speeds (p\u3c0.01) and location (p\u3c0.01), but not for the interaction (p=0.234). Pairwise comparisons indicated significant differences between each speed (p\u3c0.01) and between the polar monitor and Fitbit B (p\u3c0.05), but not between the polar monitor and Fitbit A (p=0.608). Pearson correlations indicated strong correlations between each Fitbit and the polar monitor (r= .58-.91; all p\u3c0.01). Mean bias decreased as speed increased for Fitbit A (mean bias BPM ± SD; -1.1 ± 5.4; -1.9 ± 9.5; -0.4 ± 6.9; -0.3 ± 7.3 for resting, 3mph, 5mph, 6mph respectively) while mean bias for Fitbit B increased as speed increased (-2.8 ± 8.8; -3.1 ± 11.1; -3.9 ± 14.6; -6.7 ± 14.3 for resting, 3mph, 5mph, 6mph respectively). CONCLUSION: Wrist-worn heart rate monitors appear to provide values adequate for recreational use, however, following recommended guidelines on wear-position may impact heart rate readings

Accuracy of Wrist-worn Physical Activity Monitors to Measure Energy Expenditure

IIn recent years, the popularity and demand of physical activity monitors has drastically risen with the need and want to improve physical fitness. Newer devices worn on the wrist measure both heart rate and energy expenditure but the accuracy of these measurements is unclear. PURPOSE: To measure the accuracy of three separate wrist-worn activity monitors to estimate energy expenditure during structured periods of aerobic exercise. METHODS: Twelve men and three women (22 ± 3 years, 25 ± 3 kg/m2) consented to participate in this study. Three different physical activity monitors, TomTom Cardio (TT), Microsoft Band (MB), and Fitbit Surge (FB), were randomly assigned to either the left or right wrist of each participant. The instructions for the testing procedure were thoroughly explained to every participant at the start of each trial. The treadmill started at a speed of 2 mph and increased by 1 mph every three minutes up to a max speed of 6 mph. Energy expenditure was estimated through direct measurement of oxygen consumed and carbon dioxide produced through a metabolic cart (MC, Parvo Medics True One ®2400). The mean bias in energy expenditure between MC and each device was calculated. Pearson product-moment correlations and 95% equivalence testing were also calculated. Statistical significance was set at an alpha level of 0.05. RESULTS: The mean bias between the MC and devices at 2 mph varied from -1.9 ± 1.1 kcal/min (FB) to 0.7 ± 1.0 kcal/min (MB) while the mean bias at 6 mph varied from -1.7 ± 2.1 kcal/min (MB) to 5.2 ± 1.7 kcal/min (TT). For total energy expenditure, all devices were significantly correlated with the MC (FB: r=0.66, p=0.007; TomTom: r=0.77, p\u3c0.001; MB: r=0.59, p=0.02). The mean bias for total energy expenditure was -25 ± 16 kcal for the FB, 26 ± 13 kcal for the TT, and -11 ± 17 kcal for the MB. The equivalence zone for MC was 88 kcal to 108 kcal but 90% confidence intervals of devices did not fall within this zone. CONCLUSION: The wrist-worn physical activity monitors used in this study that measure heart rate and energy expenditure tend to either underestimate or overestimate total energy expenditure from treadmill walking and running

Validity of Daily Physical Activity Measurements of Fitbit Charge 2

Physical activity monitors collect continuous data to provide a summary of daily activity. The Fitbit Charge 2 monitors heart rate as well as steps, calories, and active minutes throughout the day. There is currently no research validating the Fitbit Charge 2 at measuring daily physical activity levels in a real life setting. PURPOSE: To compare measures of daily steps and active minutes of Fitbit Charge 2 with a research-grade accelerometer. METHODS: Sixteen active college students (Mean±SD; 23±4.9yrs; 16.43±10.19%fat; 9 male) consented to be part of the study. Participants wore an ActiGraph GT3X accelerometer and Fitbit Charge 2 concurrently for seven consecutive days. Both devices were programed with each participant’s information and the participants were instructed to perform their daily activities wearing both devices and only remove them to shower and to sleep. Data were considered valid when participants wore both devices for at least 10 hours on 4 or more days of the week. Steps and active minutes (moderate-vigorous physical activity) were recorded by each device. Mean bias was calculated by subtracting ActiGraph steps and active minutes from those obtained from the Fitbit Charge 2 for each day and an average daily mean bias was calculated using values from all seven days. Absolute percentage error was also calculated [100(|Fitbit Charge 2 - ActiGraph|)/ActiGraph] to indicate the overall 7-day difference between the Fitbit Charge 2 and ActiGraph. Pearson correlations and paired sample t-test were performed to compare Fitbit Charge 2 measurements with the corresponding ActiGraph measurements with significance considered at p\u3c0.05. RESULTS: The Fitbit Charge 2 overestimated steps by 2,451.3±2085.4 compared to the ActiGraph using the daily average steps over the seven days. This was 32.2±40.7% above the ActiGraph measurement. Average mean bias for daily active minutes was -52.1±58.9 with the Fitbit Charge 2 underestimating compared to the ActiGraph. Active minutes for the Fitbit Charge 2 were an average of 69±26.1% away from the ActiGraph. Steps for the Fitbit Charge 2 were significantly correlated to ActiGraph steps (r=0.575, p=0.02) while active minutes were not significantly correlated (r= -0.255, p=0.34). Paired sample t-test results showed a significant difference between the Fitbit Charge 2 steps and active minutes compared with the ActiGraph (p\u3c0.01 for both). CONCLUSION: The Fitbit Charge 2 may be useful for measuring steps in a free-living environment, however active minutes are significantly underestimated

Accuracy of Fitbit Charge 2 at Estimating VO2max, Calories, and Steps on a Treadmill

Current fitness activity trackers can account for steps, calories burned, heart rate, and distance traveled. A new feature has recently been introduced on the Fitbit Charge 2, “Cardio Fitness Level,” which is comparable to a VO2max score in that it allows consumers to be aware of their aerobic fitness level. PURPOSE: To assess the accuracy of the Fitbit Charge 2 at estimating VO2 score (“Cardio Fitness Level”), calories, and steps when compared to indirect calorimetry and video analyzed steps, respectively. METHODS: Twenty-two healthy adults (Mean±SD; 24.1±4.2yrs; 16.9±9.0%fat; 15 male) completed two separate visits. On the first visit, anthropometric measurements were taken followed by a 10-minute outdoor run. Participants ran for 10 minutes at their own pace on flat terrain as recommended by Fitbit to generate a Cardio Fitness score. On the second visit, participants came fasted, at least 8 hours, and completed a standardized VO2max protocol (Arizona State protocol) using a PARVO TrueOne2400 metabolic cart. The treadmill was set at 3mph for the first 3 minutes with 0% grade. Following the first stage, the speed was raised to the participant’s pre-selected speed (between 5-8mph) with 0% grade. After stage 2 the grade increased every minute by 1.5% and speed was kept constant until fatigue was reached. Calories and step counts from the Fitbits were correlated with the metabolic cart and tally counter respectively, using 2-tailed Pearson correlations. Significance was set at pRESULTS: Participants completed the VO2max test in an average of 11:05. Eight of the 22 estimated VO2max ranges given by Fitbit included the value given by the metabolic cart. Fitbit ranges for seven participants were below the metabolic cart values and the Fitbit ranges for the remaining seven participants were above the metabolic cart values. Calories were correlated between the Fitbit and metabolic cart (r = 0.874, pCONCLUSION: VO2 scores given by the Fitbit Charge 2 did not always match values given by the metabolic cart but may serve as a rough estimate of fitness level. Fitbit Charge 2 may also be useful in tracking calories and steps in a controlled setting, but results may differ in real world conditions

Validity of wrist-worn consumer products to measure heart rate and energy expenditure

Introduction: The ability to monitor physical activity throughout the day and during various activities continues to improve with the development of wrist-worn monitors. However, the accuracy of wrist-worn monitors to measure both heart rate and energy expenditure during physical activity is still unclear. The purpose of this study was to determine the accuracy of several popular wrist-worn monitors at measuring heart rate and energy expenditure. Methods: Participants wore the TomTom Cardio, Microsoft Band and Fitbit Surge on randomly assigned locations on each wrist. The maximum number of monitors per wrist was two. The criteria used for heart rate and energy expenditure were a three-lead electrocardiogram and indirect calorimetry using a metabolic cart. Participants exercised on a treadmill at 3.2, 4.8, 6.4, 8 and 9.7 km/h for 3 minutes at each speed, with no rest between speeds. Heart rate and energy expenditure were manually recorded every minute throughout the protocol. Results: Mean absolute percentage error for heart rate varied from 2.17 to 8.06% for the Fitbit Surge, from 1.01 to 7.49% for the TomTom Cardio and from 1.31 to 7.37% for the Microsoft Band. The mean absolute percentage error for energy expenditure varied from 25.4 to 61.8% for the Fitbit Surge, from 0.4 to 26.6% for the TomTom Cardio and from 1.8 to 9.4% for the Microsoft Band. Conclusion: Data from these devices may be useful in obtaining an estimate of heart rate for everyday activities and general exercise, but energy expenditure from these devices may be significantly over- or underestimated

Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting

Activity trackers are widely used to measure daily physical activity. Many devices have been shown to measure steps more accurately at higher intensities, however, it is also important to determine the accuracy of these new devices at measuring steps while walking at a pace similar to that used during most daily activities. PURPOSE: To assess the accuracy of 6 popular activity trackers at measuring steps while walking on a treadmill. METHODS: Twenty-six college students (Mean±SD; 22.1±3.7yrs; 25.1±4.0kg/m2; 13 male) walked 500 steps at 3mph on a treadmill while wearing 6 different activity trackers (Pedometer, Fitbit Blaze, Charge HR, Alta, Flex, Zip, One). The Charge HR was placed two fingers above the right wrist while the Flex was next to the wrist bone. The Blaze was placed two fingers above the left wrist while the Alta was next to the wrist bone. The Fitbit Zip and the One were aligned with the hipbone on the left and right waistband respectively. Steps were counted by a trained researcher using a hand tally counter. Missing values were replaced with the mean value for that device. Step counts were correlated between Fitbit devices and the pedometer and tally counter using Pearson correlations. Significance was set at p\u3c0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. Mean Absolute Percent Error (MAPE) values were also calculated for each device relative to the tally counter. RESULTS: Fitbit Zip and One were significantly correlated with the tally counter (r=0.50, p\u3c0.05; r=0.68, p\u3c0.01, respectively) while the other devices were not significantly correlated. Mean bias and MAPE values were as follows: Device (Mean Bias/MAPE) Pedometer (-0.2±39.2/3.8±6.8), Blaze (34.5±67.1/9.9±11.3), Charge HR (-12.6±61.5/7.0±10.3), Alta (-85.0±70.8/17.1±14.1), Flex (49.5±242.4/19.7±45.3), Zip (1.8±3.4/0.4±0.6), One (0.2±2.1/0.3±0.3). Fitbit Zip and One were within one half percent of actual steps while wrist-worn Fitbits ranged from 7.0-19.7% from actual step counts. CONCLUSION: Consistent with previous research, activity trackers worn at the waist provide the most accurate step counts compared to wrist-worn models. Differences found in wrist-worn models may result in significant over- or underestimation of activity levels when worn for long periods of time

Validity of Wrist-worn Physical Activity Monitors to Measure Heart Rate

Numerous physical activity monitors exist and are used to track and improve fitness levels. Due to the increasing popularity of these devices, newer products have been developed that measure heart rate (HR) at the wrist. Little is known about how accurate these devices are at measuring HR at the wrist and how they compare to each other. PURPOSE: To determine how accurately HR was measured by three different wrist-worn physical activity monitors. METHODS: Recreationally active men (n=9) and women (n=3) participated in this study. The average age and weight of participants was 22 ± 3 years and 73.9 ± 12 kg. TomTom Cardio (TT), Fitbit Surge (FB) and Microsoft Band (MB) physical activity monitors were used. The TT, FB, and MB were randomly assigned to the right or left wrist for each participant. The testing procedure included speeds of 2, 3, 4, 5, and 6 mph with each speed lasting three minutes. HR was measured by electrocardiography (ECG) using standard limb lead II and by the three different physical activity monitors. HR was recorded from each device every minute throughout the duration of the procedure. Pearson product moment correlations and bias between electrocardiography (ECG) and physical activity monitors with 95% limits of agreement (Bland-Altman analysis) were calculated. Repeated measures ANOVA [Speed x Device] were also calculated. Statistical significance was set at pRESULTS: At 2 mph and 3 mph, only TT HR was significantly correlated with ECG heart rate (r=0.693, p=0.012 and r=0.592, p=0.043). At 4 mph and 6 mph TT was significantly correlated with ECG (r=0.911, pCONCLUSION: With increasing speeds, physical activity monitors more accurately measure HR but individuals should be aware that these devices may overestimate HR during slower walking speeds

Comparison of Smartphone Pedometer Apps on a Treadmill versus Outdoors

Previous research has focused on the accuracy of smartphone pedometer apps in laboratory settings, however less information is available in outdoor (free living) environments. PURPOSE: Determine the accuracy of 5 smartphone apps at recording steps at a walking speed in a laboratory versus an outdoor setting. METHODS: Twenty-three healthy college students consented (Mean±SD; 22±3.8yrs; BMI 24.9±4.13kg/m2) to participate in 2 separate visits. During the first visit participants walked 500 steps at 3mph on a treadmill while wearing a pedometer and a smartphone placed in the pocket using 5 pedometer apps concurrently (Moves, Google Fit (G-Fit), Runtastic, Accupedo, S-Health). During the second visit, participants walked 400 meters at 3mph on a sidewalk outside. Actual steps for each visit were recorded using a hand tally counter device. Zero and negative values were replaced with the mean value for that trial. Statistical analyses were performed using IBM SPSS 23.0. Mean bias scores were calculated between the step count for each app and the respective tally count for each trial. Mean bias scores were correlated between trials for each app using Pearson correlations and significance was set at p\u3c0.05. Mean Absolute Percent Error (MAPE) values were also calculated for each app for both trials. RESULTS: G-Fit recorded 2 zero values and 2 negative values and Moves recorded 1 zero value. Mean bias scores were significantly correlated between the indoor and outdoor protocols for the pedometer (r=0.67, p\u3c0.01) and S-Health (r=0.46, p\u3c0.5). The remaining apps were not correlated between protocols. The outdoor protocol producing a greater mean bias for the outdoor protocol for G-Fit, Runtastic, and Accupedo (mean bias ± SD indoor, outdoor; -4.3±53.1, -19.3±120.0; -10.7±63.3, -33.4±118.7; 16.0±143.6, 79.0±75.0; respectively) and a greater mean bias for the indoor protocol for the pedometer, Moves, and S-Health (mean bias indoor, outdoor; -1.4±41.5, 0.0±34.1; -117.4±196.7, -42.2±209.6; 11.3±28.4, 0.0±58.7; respectively). MAPE was below 5% for the pedometer and S-Health for both trials. CONCLUSION: Apps with the lowest error in a controlled setting may be less affected when used in other settings, while apps with greater variation in a controlled setting may be affected when used in a different environment

Repetition Count Concurrent Validity of Various Garmin Wrist Watches During Light Circuit Resistance Training

Wearable technology and strength training with free weights are two of the top 5 fitness trends worldwide. However, minimal physiological research has been conducted on the two together and none have measured the accuracy of devices measuring repetition counts across exercises. PURPOSE: The purpose of this study was to determine the concurrent validity of four wrist-worn Garmin devices, Instinct (x2), Fenix 6 Pro, and Vivoactive 3, to record repetition counts while performing 4 different exercises during circuit resistance training. METHODS: Twenty participants (n=10 female, n=10 male; age: 23.2 ± 7.7 years) completed this study. Participants completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise and 30 seconds rest between exercises and 1-1.5 min rest between circuits. Mean absolute percent error (MAPE, ≤10%) and Lin’s Concordance Coefficient (CCC, ρ≥0.7) were used to validate the device’s repetitions counts in all exercises compared to the criterion reference manual count. Dependent T-tests determined differences (p≤0.05). RESULTS: No devices were considered valid (meeting both the threshold for MAPE and CCC) for measuring repetition counts during front squats (MAPE range: 3.0-18.5% and CCC range: 0.27-0.68, p value range: 0.00-0.94), reverse lunge (MAPE range: 44.5-67.0% and CCC range: 0.19-0.31, p value range: 0.00-0.28), push-ups (MAPE range: 12.5-67.5% and CCC range: 0.10-0.34, p value range: 0.07-0.83), and shoulder press (MAPE range: 18.0-51.0% and CCC range: 0.11-0.43, p value range: 0.00-0.79) exercises. CONCLUSION: The wearable wrist-worn devices were not considered accurate for repetition counts and thus manual counting should be utilized. People who strength train using free weights will need to wait for either improved repetition counting algorithms or increased sensitivity of devices before this measure can be obtained with confidence