SummaryConsumer activity trackers claiming to measure sleep/wake patterns are ubiquitous within clinical and consumer settings. However, validation of these devices in sleep disorder populations are lacking. We examined 1 night of sleep in 42 individuals with insomnia (mean = 49.14 ± 17.54 years) using polysomnography, a wrist actigraph (Actiwatch Spectrum Pro: AWS) and a consumer activity tracker (Fitbit Alta HR: FBA). Epoch‐by‐epoch analysis and Bland−Altman methods evaluated each device against polysomnography for sleep/wake detection, total sleep time, sleep efficiency, wake after sleep onset and sleep latency. FBA sleep stage classification of light sleep (N1 + N2), deep sleep (N3) and rapid eye movement was also compared with polysomnography. Compared with polysomnography, both activity trackers displayed high accuracy (81.12% versus 82.80%, AWS and FBA respectively; ns) and sensitivity (sleep detection; 96.66% versus 96.04%, respectively; ns) but low specificity (wake detection; 39.09% versus 44.76%, respectively; p = .037). Both trackers overestimated total sleep time and sleep efficiency, and underestimated sleep latency and wake after sleep onset. FBA demonstrated sleep stage sensitivity and specificity, respectively, of 79.39% and 58.77% (light), 49.04% and 95.54% (deep), 65.97% and 91.53% (rapid eye movement). Both devices were more accurate in detecting sleep than wake, with equivalent sensitivity, but statistically different specificity. FBA provided equivalent estimates as AWS for all traditional actigraphy sleep parameters. FBA also showed high specificity when identifying N3, and rapid eye movement, though sensitivity was modest. Thus, it underestimates these sleep stages and overestimates light sleep, demonstrating more shallow sleep than actually obtained. Whether FBA could serve as a low‐cost substitute for actigraphy in insomnia requires further investigation.

Sleep disturbance occurs early in Huntington's disease (HD). Consumer- and research-grade activity monitors may enable routine assessment of sleep disturbances in HD. We compared Actiwatch Spectrum Pro, Jawbone UP2 and Fitbit One to the gold standard, polysomnography, in four late presymptomatic and three early HD participants. Compared to polysomnography, all ambulatory monitors overestimated total sleep time by >60 minutes and sleep efficiency by ∼15%. Thus, for assessment of specific sleep parameters in HD, none of the activity monitors are sufficiently accurate to replace polysomnography, although they may be sufficient for estimating overall sleep-wake patterns. Larger sample replication is required.

Accurate assessment of sleep can be fundamental for monitoring, managing and evaluating treatment outcomes within diseases. A proliferation of consumer activity trackers gives easy access to objective sleep. We evaluated the performance of a commercial device (Fitbit Alta HR) relative to a research‐grade actigraph (Actiwatch Spectrum Pro) in measuring sleep before and after a cognitive behavioural intervention in insomnia disorder. Twenty‐five individuals with DSM‐5 insomnia disorder (M = 50.6 ± 15.9 years) wore Fitbit and Actiwatch and completed a sleep diary during an in‐laboratory polysomnogram, and for 1 week preceding and following seven weekly sessions of cognitive‐behavioural intervention for insomnia. Device performance was compared for sleep outcomes (total sleep time, sleep latency, sleep efficiency and wake after sleep onset). The analyses assessed (a) agreement between devices across days and pre‐ to post‐treatment, and (b) whether pre‐ to post‐treatment changes in sleep assessed by devices correlated with clinical measures of change. Devices generally did not significantly differ from each other on sleep variable estimates, either night to night, in response to sleep manipulation (pre‐ to post‐treatment) or in response to changes in environment (in the laboratory versus at home). Change in sleep measures across time from each device showed some correlation with common clinical measures of change in insomnia, but not insomnia diagnosis as a categorical variable. Overall, the Fitbit provides similar estimates of sleep outside the laboratory to a research grade actigraph. Despite the similarity between Fitbit and Actiwatch performance, the use of consumer technology is still in its infancy and caution should be taken in its interpretation.

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Activity monitoring devices claiming to measure sleep have increased in popularity within research and consumer settings. However, validation studies on many of these devices are lacking. We examined twenty-nine healthy adults (Mage = 32.4, SDage = 11.6) overnight using three activity monitors (Actiwatch Spectrum Pro standard and sensitive settings, Fitbit One, Jawbone UP2) and polysomnography. We evaluated agreement between each device and polysomnography on total sleep time (TST) and sleep efficiency (SE). Clinically meaningful limits of agreement were set at ±30 minutes for TST and ±5% for SE. Each activity monitor overestimated TST and SE compared to polysomnography, and except for Actiwatch Spectrum Pro in sensitive setting, exceeded clinically meaningful agreement limits. Using Actiwatch Spectrum Pro sensitive as the gold standard, both consumer devices overestimated TST and SE, but their mean errors were generally not clinically meaningful (except Fitbit One’s error for SE). Actiwatch Spectrum Pro in its sensitive setting may be a valid substitute for polysomnography when measuring TST and SE. Choice of consumer device as an actigraphy replacement will depend on context. Jawbone UP2 shows closer agreement with Actiwatch Spectrum Pro, though Fitbit One demonstrates less variation in TST and SE estimations.