Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, comprehensive validation studies are needed to analyze the performance and reliability of these devices in the recording of sleep parameters.
Objective: This study compared the performance of one of the best-selling activity wristbands, the Xiaomi Mi Band 5, against polysomnography in measuring sleep stages.
Methods: This study was carried out at a hospital in A Coruña, Spain. People who were participating in a polysomnography study at a sleep unit were recruited to wear a Xiaomi Mi Band 5 simultaneously for 1 night. The total sample consisted of 45 adults, 25 (56%) with sleep disorders (SDis) and 20 (44%) without SDis.
Results: Overall, the Xiaomi Mi Band 5 displayed 78% accuracy, 89% sensitivity, 35% specificity, and a Cohen κ value of 0.22. It significantly overestimated polysomnography total sleep time (P=.09), light sleep (N1+N2 stages of non-rapid eye movement [REM] sleep; P=.005), and deep sleep (N3 stage of non-REM sleep; P=.01). In addition, it underestimated polysomnography wake after sleep onset and REM sleep. Moreover, the Xiaomi Mi Band 5 performed better in people without sleep problems than in those with sleep problems, specifically in detecting total sleep time and deep sleep.
Conclusions: The Xiaomi Mi Band 5 can be potentially used to monitor sleep and to detect changes in sleep patterns, especially for people without sleep problems. However, additional studies are necessary with this activity wristband in people with different types of SDis.
Trial registration: ClinicalTrials.gov NCT04568408; https://clinicaltrials.gov/ct2/show/NCT04568408.
International registered report identifier (irrid): RR2-10.3390/ijerph18031106.
Keywords: Internet of Things; Xiaomi Mi Band 5; health promotion; occupation; polysomnography; sleep.
©Patricia Concheiro-Moscoso, Betania Groba, Diego Alvarez-Estevez, María del Carmen Miranda-Duro, Thais Pousada, Laura Nieto-Riveiro, Francisco Javier Mejuto-Muiño, Javier Pereira. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.05.2023.