LAUSR

ABSTRACT We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).