Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

Abstract

Goal and aimsCompare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording.Focus technology: Fitbit Charge 3 and actigraphy.Reference technology: Polysomnography. SampleTwenty-one university students (10 females). DesignSimultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants’ homes. Core analyticsTotal sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. Additional analytics and exploratory analysesVariability of specificity and negative predictive value across subjects and across nights. Core outcomesFitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). Important additional outcomesFitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. Core conclusionThis study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.