Validity of wearable fitness trackers on sleep measure

Wearable trackers that detect sleep offer users a way to track their sleep quality and patterns without the use of expensive equipment. Few studies have tested the validity of these trackers on sleep measure. PURPOSE: To examine the validity of the Actigraph GT9X (AG), SenseWear Mini Armband (SW), Basis Peak (BP), Fitbit Charge HR (FB), Jawbone UP3 (JU), and Garmin Vivosmart (GV) for estimating sleep variables as compared with a sleep diary. METHODS: 78 healthy individuals participated in the study. Group 1 (n= 38) and wore the AG, SW, BP, and FB or Group 2 (n = 40) and wore the AG, JU, and GV. Monitors were worn on the non-dominant arm for 3 nights and a sleep log was completed. Sleep variables were total sleep time (TST), time in bed (TIB), sleep efficiency (SE), and wake after sleep onset (WASO). Pearson correlation, mean absolute percentage errors (MAPE), equivalence testing, Bland-Altman plots, and ANOVA were used to assess validity compared with the diary. RESULTS: Overall, monitors that showed the greatest correlation with the sleep diary for TST were the JU and FB (effect size= 0.09 and 0.23, respectively). The greatest correlation with the sleep diary for TIB was seen with the SW, GV, and JU (effect size= 0.09, 0.16, and 0.07, respectively). SE and WASO showed very poor correlation with the log. Measures for equivalence testing confirmed the success of the JU, SW, FB, and GV for measureing TIB and TST. CONCLUSION: The FB, SW, JU, and GV could be valid measure of TST and TIB. The monitors are not valid regarding wake times during sleep. Further research is needed to validate these monitors with polysomnography

Comparison of Wearable Trackers’ Ability to Estimate Sleep

Tracking physical activity and sleep patterns using wearable trackers has become a current trend. However, little information exists about the comparability of wearable trackers measuring sleep. This study examined the comparability of wearable trackers for estimating sleep measurement with a sleep diary (SD) for three full nights. A convenience sample of 78 adults were recruited in this research with a mean age of 27.6... 11.0 years. Comparisons between wearable trackers and sleep outcomes were analyzed using the mean absolute percentage errors, Pearson correlations, Bland–Altman Plots, and equivalent testing. Trackers that showed the greatest equivalence with the SD for total sleep time were the Jawbone UP3 and Fitbit Charge Heart Rate (effect size = 0.09 and 0.23, respectively). The greatest equivalence with the SD for time in bed was seen with the SenseWear Armband, Garmin Vivosmart, and Jawbone UP3 (effect size = 0.09, 0.16, and 0.07, respectively). Some of the wearable trackers resulted in closer approximations to self-reported sleep outcomes than a previously sleep research-grade device, these trackers offer a lower-cost alternative to tracking sleep in healthy populations

Validity of Wearable Activity Monitors for Estimation of Resting Energy Expenditure in Adults

PURPOSE: The purpose of this study was to evaluate the validity of the Fitbit Flex (FF) and SenseWear Mini Armband (SWA) in REE estimates in adults. METHODS: Sixty healthy (26.4±5.7 yrs) males (n=30) and females (n=30) volunteered to participate in the study. The REE measurement was performed in the morning (i.e., 6:00-9:00am) after a 10-hour fast, following previously published guidelines. Estimates of REE from the FF and SWA monitors were obtained from the corresponding software and website. These REE estimates were compared to REE measured from open-circuit indirect calorimetry (IC) and estimated using the Institute of Medicine (IOM) and World Health Organization (WHO) prediction equations. RESULTS: Analyses of covariance (ANCOVA) showed no significant effects of gender for any of the comparisons with REE from IC; therefore, males and females were combined for all analyses. REE (kcals/day) from FF, SWA, IOM, and WHO were (means±SD): 1554.3±249.3,1587.1±247.7, 1528.0±213.0, and 1559.0±232.0, respectively. Mean absolute percentage errors were: 10.85±8.8%, 9.53±8.2%, 9.31±8.4%, and 10.8±8.7% for the FF, SWA, IOM, and WHO, respectively. Pearson correlation coefficients for the FF, SWA, IOM, and WHO in relation to IC were 0.635, 0.640, 0.657, and 0.683, respectively. No significant differences (p-values \u3c 0.05) were observed between the measured REE, FF, SWA, IOM, and WHO in REE estimates. CONCLUSION: The estimates of REE from the FF, SWA, IOM, and WHO equation were similar to measured REE. The relatively high accuracy of the FF and SWA in estimating REE suggests that they have great potential to be utilized in intervention and surveillance studies aimed at precisely estimating total daily energy expenditure

Comparison of Wearable Trackers’ Ability to Estimate Sleep

Tracking physical activity and sleep patterns using wearable trackers has become a current trend. However, little information exists about the comparability of wearable trackers measuring sleep. This study examined the comparability of wearable trackers for estimating sleep measurement with a sleep diary (SD) for three full nights. A convenience sample of 78 adults were recruited in this research with a mean age of 27.6 ± 11.0 years. Comparisons between wearable trackers and sleep outcomes were analyzed using the mean absolute percentage errors, Pearson correlations, Bland–Altman Plots, and equivalent testing. Trackers that showed the greatest equivalence with the SD for total sleep time were the Jawbone UP3 and Fitbit Charge Heart Rate (effect size = 0.09 and 0.23, respectively). The greatest equivalence with the SD for time in bed was seen with the SenseWear Armband, Garmin Vivosmart, and Jawbone UP3 (effect size = 0.09, 0.16, and 0.07, respectively). Some of the wearable trackers resulted in closer approximations to self-reported sleep outcomes than a previously sleep research-grade device, these trackers offer a lower-cost alternative to tracking sleep in healthy populations