A validation study of Fitbit Charge 2™ compared with polysomnography in adults

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).

KEYWORDS:

• Wearables
• validation
• multisensory
• actigraphy

Acknowledgments

We would like to thank our sleep lab staff Leonardo Rosas, Vanessa Alschuler, Yun Qi Lim, and Maureen Gil for their efforts in data collection, and our participants for their valuable time.

Declaration of Interest statement

This study was funded by Fitbit, Inc. The company provided epoch-by-epoch sleep data from the Fitbit devices, blinded from PSG results; Fitbit had no involvement in data analysis, interpretation, and scientific writing. The manuscript solely reflects the views of the authors. MdZ, FB, and IC have received research funding unrelated to this work from Ebb Therapeutics Inc., and International Flavors & Fragrances Inc.