Background: Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. Objective: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. Methods: In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. Results: The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. Conclusions: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Biological processes are organized in time as innate rhythms defined by the period (τ), phase (peak [Φ] and trough time), amplitude (A, peak-trough difference) and mean level. The human time structure in its entirety is comprised of ultradian (τ < 20 h), circadian (20 h > τ < 28 h) and infradian (τ > 28 h) bioperiodicities. The circadian time structure (CTS) of human beings, which is more complicated than in lower animals, is orchestrated and staged by a brain central multioscillator system that includes a prominent pacemaker – the suprachiasmatic nuclei of the hypothalamus. Additional pacemaker activities are provided by the pineal hormone melatonin, which circulates during the nighttime, and the left and right cerebral cortices. Under ordinary circumstances this system coordinates the τ and Φ of rhythms driven by subservient peripheral cell, tissue and organ clock networks. Cyclic environmental, feeding and social time cues synchronize the endogenous 24 h clocks and rhythms. Accordingly, processes and functions of the internal environment are integrated in time for maximum biological efficiency, and they are also organized and synchronized in time to the external environment to ensure optimal performance and response to challenge. Artificial light at night (ALAN) exposure can alter the CTS as can night work, which, like rapid transmeridian displacement by air travel, necessitates realignment of the Φ of the multitude of 24 h rhythms. In 2001, Stevens and Rea coined the phrase “circadian disruption” (CD) to label the CTS misalignment induced by ALAN and shift work (SW) as a potential pathologic mechanism of the increased risk for cancer and other medical conditions. Current concerns relating to the effects of ALAN exposure on the CTS motivated us to renew our long-standing interest in the possible role of CD in the etiopathology of common human diseases and patient care. A surprisingly large number of medical conditions involve CD: adrenal insufficiency; nocturia; sleep-time non-dipping and rising blood pressure 24 h patterns (nocturnal hypertension); delayed sleep phase syndrome, non-24 h sleep/wake disorder; recurrent hypersomnia; SW intolerance; delirium; peptic ulcer disease; kidney failure; depression; mania; bipolar disorder; Parkinson’s disease; Smith–Magenis syndrome; fatal familial insomnia syndrome; autism spectrum disorder; asthma; byssinosis; cancers; hand, foot and mouth disease; post-operative state; and ICU outcome. Poorly conceived medical interventions, for example nighttime dosing of synthetic corticosteroids and certain β-antagonists and cyclic nocturnal enteral or parenteral nutrition, plus lifestyle habits, including atypical eating times and chronic alcohol consumption, also can be causal of CD. Just as surprisingly are the many proven chronotherapeutic strategies available today to manage the CD of several of these medical conditions. In clinical medicine, CD seems to be a common, yet mostly unrecognized, pathologic mechanism of human disease as are the many effective chronotherapeutic interventions to remedy it.