Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy

• Published in: PloS one 2022-10, Vol.17 (10), p.e0275287-e0275287
• Main Authors: Dong, Xiaofang, Yang, Sen, Guo, Yuanli, Lv, Peihua, Wang, Min, Li, Yusheng
• Format: Article
• Language: English
• Subjects: Accuracy; Biology and Life Sciences; Classification; Computer and Information Sciences; Consumers; Diagnosis; Electrocardiography; Engineering and Technology; Eye movements; Heart rate; Insomnia; Laboratories; Latency; Medicine and Health Sciences; Parameter estimation; Parameter sensitivity; People and Places; Performance assessment; Rapid eye movement state; REM sleep; Risk factors; Sleep; Sleep (REM); Sleep and wakefulness; Sleep disorders; Tracking devices; Validation studies; Wakefulness; Wearable computers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0275287

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4.sup.TM : FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.