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Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography

Measuring sleep duration and early onset rapid eye movement sleep (REMS) is critical in the assessment of suspected central disorders of hypersomnolence (CDH). Current multi‐sensor activity trackers that integrate accelerometry and heart rate are purported to accurately quantify sleep time and REMS; however, their utility in suspected CDH has not been established. This investigation aimed to determine the ability of a current, multi‐sensor tracker, Fitbit Alta HR (FBA‐HR), to quantify and classify sleep in patients with suspected CDH relative to polysomnography (PSG). Forty‐nine patients (46 female; mean age, 30.3 ± 9.84 years) underwent ad libitum PSG with concurrent use of the FBA‐HR. FBA‐HR sleep variable quantification was assessed using Bland‐Altman analysis. FBA‐HR all sleep (AS), light sleep (LS; PSG N1 + N2), deep sleep (DS; PSG N3) and REMS classification was evaluated using epoch‐by‐epoch comparisons. FBA‐HR‐detected sleep‐onset rapid eye movement periods (SOREMPs) were compared against PSG SOMREMPs. FBA‐HR displayed significant overestimation of total sleep time (11.6 min), sleep efficiency (1.98%) and duration of deep sleep (18.2 min). FBA‐HR sensitivity and specificity were as follows: AS, 0.96, 0.58; LS, 0.73, 0.72;DS, 0.67, 0.92; REMS, 0.74, 0.93. The device failed to detect any nocturnal SOREMPs. Device performance did not differ appreciably among diagnostic subgroups. These results suggest FBA‐HR cannot replace EEG‐based measurements of sleep and wake in the diagnostic assessment of suspected CDH, and that improvements in device performance are required prior to adoption in clinical or research settings.