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JPM Free Full- Text Accuracy in Wrist- Worn, Sensor- Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort 2. Devices. Following a comprehensive literature and online search, 4.

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Criteria for inclusion included: wrist- worn watch or band; continuous measurement of HR; stated battery life > 2. Eight devices met the criteria; Apple Watch; Basis Peak; e.

Pulse. 2; Fitbit Surge; Microsoft Band; MIO Alpha 2; Pulse. On; and Samsung Gear S2. Multiple e. Pulse. All devices were bought commercially and handled according to the manufacturer’s instructions. Data were extracted according to standard procedures described below.

Devices were tested in two phases. The first phase included the Apple Watch, Basis Peak, Fitbit Surge and Microsoft Band. The second phase included the MIO Alpha 2, Pulse. On and Samsung Gear S2. Healthy adult volunteers (age ≥1.

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Stanford University and local amateur sports clubs. From these interested volunteers, study participants were selected to maximize demographic diversity as measured by age, height, weight, body mass index (BMI), wrist circumference, and fitness level. In total, 6. 0 participants (2. Participant characteristics are presented in Table 1. Skin tone at the wrist was rated independently by two investigators using the Von Luschan Chromatic scale (1–3. Fitzpatrick skin tone scale (1–6) [1. Maximal oxygen uptake (VO2max) was measured with the Quark CPET (COSMED, Rome, Italy) by incremental tests in running (n = 3.

In the running test, the subject began the test running at 5. Watch One Point O Online Moviesdbz. Each minute, the speed was increased by 0.

Borg Rating of Perceived Exertion (RPE) scale [1. In order to complete the test within a 1. For subjects who performed the cycling test, initial resistance was set at 1.

W and increased by 2. W each minute until volitional exhaustion. As with the running test, subjects rated their perceived exertion on the Borg RPE scale at the end of each minute. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Institutional Review Board of Stanford University (protocol ID 3. Euan Ashley). All participants provided informed consent prior to the initiation of the study.

Device Data Collection. Data was collected according to manufacturers’ instructions or by making use of an Application Programming Interface (API). Apple Watch. All data from the Apple Watch was sent to the Apple Health app on the i. Phone, and exported from Apple Health in XML format for analysis. The Apple Health app provided heart rate, energy expenditure, and step count data sampled at one minute granularity.

For intense activity (running and max test), the sampling frequency was higher than once per minute. In cases where more than one measurement was collected each minute, the average measurement for the minute was utilized, since the minute average is the granularity for several of the other devices.

Basis Peak (Version 1)Minute- granularity data was downloaded directly from the Basis app. Fitbit Surge. The Fitbit Developer API was used to create an application for downloading data at minute- level granularity from the Fitbit Surge device [1. M2ip. Ol. Q6. KOH6n.

AO4. UMj. KYm. U0. AEa. Sipy. 0i). 2. Microsoft Band (Version 1)The mitmproxy software tool [1. Microsoft Band, following the technique outlined by J. Huang [1. 6]. Data packets transmitted by the Microsoft phone app were re- routed to an external server for aggregation and analysis.

Sampling granularity varied by activity and subject. In cases where multiple data samples were collected each minute, the last data sample for the minute was utilized in the analysis. Mio Alpha 2. The raw data from the Mio device is not accessible. However, static images of the heart rate over the duration of the activity are stored in the Mio phone app. The Web. Plot. Digitizer tool was utilized to trace over the heart rate images and to discretize the data to the minute level. Pulse. On. The Pulse. On Android application transmits raw data to a SQLite.

Android device. The SQLite. Three- second samples for the last minute of each activity state were averaged to generate heart rate and energy expenditure values for the activity state. Samsung Gear S2. Raw data from the Samsung Gear is not accessible to users. However, heart rate and step count over time are displayed as static images within the Samsung Gear App. The Web. Plot. Digitizer [1.

Statistical Analysis. Statistical analysis was performed separately for HR and EE.

The gas analysis data from indirect calorimetry (VO2 and VCO2) served as the gold standard measurement for calculations of EE (kcal/min). ECG data was used as the gold standard for HR (beats- per- minute; bpm). The percent error relative to the gold standard was calculated for HR and EE using the following formula. Error = (device measurement−gold standard)/gold standard. Two- way ANOVA with post- hoc Tukey honest significant difference (HSD) was performed to check for a difference between groups for categorical demographic covariates: sex (male/female), arm choice (right/left), device position along the wrist (anterior/posterior) and device error measurements in heart rate (Table S2) and energy expenditure (Table S3).

For the continuous demographic variables (age, BMI, Fitzpatrick skin tone, Von Luschan skin tone, VO2max, and wrist circumference), a Pearson correlation test was performed between the demographic variable and device error (Tables S4 and S5). This was done with the R “stats” package (version 3. A separate test was performed for each device, and p- values were adjusted with the Bonferroni correction for multiple testing. Principal component analysis was performed to identify outliers and to cluster devices by error profiles. Any subjects with missing data were excluded from the principal component analysis (PCA). A singular value decomposition (SVD) was computed over the activity error rates.

Variables were not centered, so as to find components of deviation about zero, and the loadings for each principal component were computed. Several regression approaches were applied to uncover associations in the dataset. The “lm” function from the “statistics” package in R was used to fit a linear regression model [1. The first principal component from the PCA analysis was the response variable; predictor variables included device, sex, age, BMI, Von Luschen skin tone, and VO2max. The correlated variables (height and weight correlated with BMI) and the Fitzpatrick skin tone measure (correlated with the Von Luschen skin tone measure) were excluded from the analysis. In a parallel approach, a general estimating equation [1.

BMI, skin tone, wrist circumference, and VO2max as predictor variables. Interaction terms between the predictor variables of sex and age, activity and device, and intensity and device were included in the analysis. The exchangeable correlation structure was applied to enable inclusion of potentially correlated predictor variables. Regression was performed with the “gee” package in R.

The device contrasts were computed relative to the Apple Watch, and the activity contrasts were computed relative to the sitting activity. The “pdredge” function from the “Mu.

MIn” package (version 1. R [2. 0] was used to select the optimal subset of predictor variables to regress on the error response. In a third regression technique, the root mean square error (from zero) was computed for each individual on each device. Regression was then performed with device type as the predictor variable, and the root mean square error values across subjects as the response variable.

The Apple Watch served as the base factor value. The effects for other devices served as contrasts with Apple. The “glm” function from the R statistics package was used to fit a gamma distribution.

Finally, a Bland–Altman analysis was performed using the “Bland. Altman. Leh” R package [2. Measurement error relative to gold standard was averaged across all devices for a subject.