Newly launched models also incorporate other streams of biosignals, such as heart rate to measure sleep stages. Most of the consumer wristbands rely on a similar mechanism of clinical actigraphy that infers wake and sleep cycles from limb movement. These devices are relatively affordable, easy to use, and ready to purchase in the consumer market. In recent years, consumer sleep-monitoring wristbands and associated mobile phone apps have created an effective way for individuals to understand personal sleep patterns or improve sleep quality in daily settings. Having enough restorative sleep is essential for physical and mental health. Introduction Importance of Consumer Sleep Tracking Devices Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.Ĭonclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. SE>90% ( P=.047) was associated with significant increase in measurement error.

Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%.

Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Results: Sleep data were collected from 23 participants. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Sleep stage transition probabilities were derived from sleep hypnograms. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. JMIR Bioinformatics and Biotechnology 16 articlesĮmail: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data.JMIR Biomedical Engineering 59 articles.

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Fit sleep monitor