Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

Abstract

BackgroundWearable 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.ObjectiveWe conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.MethodsIn 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.ResultsThe 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.ConclusionsSleep-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.

Actigraphic estimates of sleep and the sleep-wake rhythm, and 6-sulfatoxymelatonin levels in healthy Dutch children

“…On the other hand, our findings might also be explained by an overestimation of wakefulness during sleep (WASO) by newer actigraph devices, especially in young children (Meltzer et al 2012). Proper sleep assessment is a function of both the hardware and interpretative algorithm technologies (Haghayegh et al 2019(Haghayegh et al , 2020. A previous study in 13 older children (10-14 years) that used the same actigraph (ActiGraph wGT3X-BT, Pensacola, FL, USA) and algorithm (Sadeh) as we did also show an underestimation of TST by an average of 49 min, an overestimation of WASO by an average of 59 min, and hence an 11.6% lower SE, compared to the golden standard (PSG) (Quante et al 2018).…”

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

“…With respect to sleep, our results in general medicine patients (R = 0.19, P = 0.24) did not show correlation with self-reported sleep. While a systematic review of Fitbit-based sleep assessment found that later versions of devices similar to the model that we used correlated well with total sleep time and sleep e ciency as measured by gold standard polysomnography, 24 all studies were either in the participant home or a sleep lab, and none were in hospitalized patients. Indeed, our results were similar to results found in ICU patients (R = 0.33, P = 0.03) with self-reported sleep.…”

“…Ultimately, device algorithms will likely improve further; improvements in sleep stage detection have already been demonstrated in newer devices compared to the previous generation. 24 As existing algorithms for determining sleep and steps were not developed using data from hospitalized patients, it is not surprising that these devices are less accurate in the hospital setting. Further re nement of these devices in collaboration with device manufacturers is necessary.…”

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

“…Of note, a systematic review of eight studies looking at Fitbit-based sleep assessment found that later versions of devices similar to the model that we used correlated well with total sleep time and sleep e ciency as measured by gold standard polysomnography. 28 These Fitbit models showed a sensitivity of 0.95-0.96 and a speci city of 0.58-0.69 in detecting sleep periods. However, all studies were either in the participant home or a sleep lab, and none were in hospitalized patients.…”