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. 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. Watch Extracted Online' title='Watch Extracted Online' />Watch Extracted OnlineWatch Extracted OnlineAll devices were bought commercially and handled according to the manufacturers 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. 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 13. Fitzpatrick skin tone scale 16 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. Each minute, the speed was increased by 0. Borg Rating of Perceived Exertion RPE scale 1. Watch Extracted Online' title='Watch Extracted Online' />Watch Arabic TV Live Online. Arabic TV Online streaming on internet, and free. Deliver various specific programmes and offers a variety of entertainment, sports, and. After doing a bit of online poking around, Im declaring this NOT urban myth. The 2005 edition of industry handbook Fenarolis Flavor Ingredients reports the. Watch all of the Apple TV Aerial video screensavers. RANCHO MIRAGE, Calif., NEC Corporation of America NEC will establish a biometric first during this weeks ANA Inspiration at Mission Hills Country. 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. Directed by Nir Paniry. With Sasha Roiz, Jenny Mollen, Dominic Bogart, Richard Riehle. A scientist who has invented a technique to watch peoples memories finds. A study published by The New England Journal of Medicine and partially funded by the U. S. Food and Drug Administration shows that children in America are not smoking. Replacing Body Parts. Posted 02. 26. 11 NOVA scienceNOW Scientists are learning how to grow custommade body parts so they can be ready when youand your vital. Includes online Bible study, church history, ministry information, and special events. 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 1Minute 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 1The 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. Watch The Smurfs 2 Online Free HD. 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 kcalmin. 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 measurementgold standardgold 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 malefemale, arm choice rightleft, device position along the wrist anteriorposterior 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.