An online comprehensive search of the following databases was performed: the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Database, Embase, MEDLINE, PsycINFO, PubMed, and Web of Science. The search was initially performed during July 2018 and again during July and October 2019 using the keyword Fitbit without language, publication date, or other filters.

Retrieved publications qualified for the systematic review if they (1) involved validity of sleep data of any marketed Fitbit model and (2) incorporated PSG, actigraphy, home EEG, sleep diary, or survey method as reference. Exclusion criteria included the following: (1) sample size of less than 5 participants, (2) review paper, (3) absent or inappropriate statistical analysis, and (4) duplicate publication of the same data and findings.

Citations were imported into the reference manager software Mendeley. After elimination of duplicate reports, one author (SH) screened titles and abstracts first to remove unrelated publications; thereafter, two authors (SH and SK) independently screened remaining publications for eligibility according to inclusion and exclusion criteria. Disagreements were resolved by discussion.

The following items were extracted in a systematic manner by one author (SH) and checked for accuracy by another author (SK): first author; year of publication; type of sleep tracker and comparator; number, sex, type, and age of participants; study site; number of nights of sleep assessment; bedtime; Fitbit mode setting; anatomical placement of tracker; and study outcomes relative to the denoted reference standard—the precision of measuring the parameters of total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE), as well as the sensitivity, specificity, and accuracy of detecting both sleep epochs and sleep stages.

A checklist, adapted from Downs and Black [15], was applied to evaluate each publication for quality and risk of bias of research methods, internal validity, reported outcomes, and generalizability.

For studies that compared Fitbit with PSG and provided quantitative data, raw Hedges g effect sizes of SOL, WASO, TST, and SE were calculated as the mean differences between average values provided by Fitbit and PSG divided by the standard deviation of PSG values multiplied by the Hedges correction factor [16]. A positive effect size infers lower values derived by Fitbit relative to those derived by PSG. Overall effect size and 95% prediction interval for each parameter per nonsleep-staging and sleep-staging Fitbit models were calculated using a random-effects model [17,18]. Forest plots were created by Microsoft Excel for Mac, version 16.25 (Microsoft Corporation). A threshold probability of 5% (P=.05) was selected as the basis for rejecting the null hypothesis, effect size equals zero. Effect size values of 0.2, 0.5, and 0.8 are considered small, medium, and large effects, respectively [19]. The null hypothesis stating that studies share a common effect size per sleep parameter was tested by calculating the Q statistic [17]. τ² represents the overall variation of true effect size, and I² represents the proportion of observed variance indicative of actual variation among studies [17]. I² values in the order of 25%, 50%, and 75% are considered small, medium, and large heterogenicity, respectively [20]. Comparisons between nonsleep-staging and sleep-staging Fitbit models were accomplished by random-effect subgroup analyses. As recommended, the threshold probability of 10% (P=.10) was the basis for testing the significance of heterogenicity and also for determining statistical significance of subgroup comparisons [20,21].

Figure 1 presents a visual summary of the selection and qualification of articles for review. A total of 3074 publications were retrieved though a search of databases performed in July 2018 and again in July and October 2019. An additional 11 publications were identified through other sources, primarily through the reference list of identified articles. After eliminating duplicate publications found in the multiple databases, 1426 articles remained for screening. Examination of individual titles and abstracts yielded 72 publications for full-text appraisal; however, after scrutinizing each of them according to the a priori inclusion and exclusion criteria, only 22 qualified for systematic review [22-43], with 8 of these reporting raw data to enable quantitative synthesis and meta-analyses.

Tables 1 and 2 present the extracted details of each qualifying study involving nonsleep-staging and sleep-staging Fitbit models. Participants were diverse: normal sleepers as well as persons diagnosed with periodic limb movement in sleep (PLMS) [28], obstructive sleep apnea, sleep-disordered breathing [30], central disorders of hypersomnolence [26], insomnia [31], and depression [25], and Huntington disease gene carriers [38]. Sample size varied substantially between investigations, from 7 to 63 (median 30) participants, with approximately 77% of them involving more than 20 individuals. Average age was less than 20 years in 6 of the 22 studies (27%) and over 50 years in 1 study (5%). Out of 22 studies, 10 (45%) were conducted in a sleep laboratory, 11 (50%) in the home environment, and 1 (5%) either at home or in a hotel, based on the participant’s preference.

Table S1 in Multimedia Appendix 1 summarizes the risk for bias of research methods, internal validity, reported outcomes, and generalizability of each qualifying study. Only 5 investigations out of 22 (23%) attempted to blind the sleep laboratory technicians from outcome measures [22,25,26,28,40]. A total of 13 of the qualifying 22 studies (59%) relied on PSG as reference to evaluate Fitbit performance [22,25-28,30,31,37-42]; the other 9 (41%) relied on a sleep log, actigraphy, or home EEG as reference. Since PSG is the gold standard for measurement of sleep stages and parameters, use of other methods of reference constitutes an additional potential source of bias. Half of the investigations (11/22, 50%) were performed in the participant’s home. Clock hour of bedtime was unspecified in 5 of the 22 studies (23%), and the dictated bedtime differed from the habitual one of participants in 2 laboratory studies (9%). Disparity between habitual versus mandated bedtime might have resulted in a greater- or lesser-than-usual amount of wake time while in bed and this is likely to have led to bias. Moreover, use of somewhat bulky sleep trackers as reference for studies conducted in one’s residence might be problematic because of improper donning of instrumentation and oversight by technicians. Most studies (16/22, 73%) followed the manufacturer’s recommendation that the Fitbit model be worn on the nondominant hand. However, 2 out of the 22 studies (9%) utilized a counterbalance experimental design for placement of the Fitbit device, with half of the participants wearing it on the right hand and the other half wearing it on the left hand. Finally, in 2 out of the 22 studies (9%), the Fitbit was worn on the left wrist; in 2 other studies (9%), the hand upon which the Fitbit was worn was unreported. Out of the 22 studies, 1 (5%) [28] did not report the SE for Fitbit but did report it for PSG; this may constitute selective reporting bias. We contacted the corresponding author of this investigation for the missing information, but we did not obtain a response.

No published study assessed performance of sleep-staging Fitbit models relative to actigraphy in estimating sleep parameters. In contrast, 7 studies investigated the accuracy of nonsleep-staging Fitbit models relative to actigraphy [23,25,29,32,33,39,40]; 1 of them involved two different actigraph devices [39], and another 1 included two different samples: good sleepers and poor sleepers [32]. Among the total of seven potential comparisons, Fitbit significantly overestimated TST in five of them (N=84) by 24.1-74 minutes, while in two comparisons (N=31), it was nonsignificantly overestimated. In a total of four comparisons (N=69), Fitbit significantly overestimated SE by 1.1%-7.0%. Among a total of five comparisons, Fitbit significantly underestimated WASO in four of them (N=65) by 16-32 minutes, while in one (N=10), it was nonsignificantly underestimated. Only 1 study (N=21) evaluated SOL, finding an 11.5-minute significant overestimation by Fitbit.

Two studies [25,38] simultaneously compared in the same cohort a nonsleep-staging Fitbit model plus actigraph against laboratory PSG. Relative to PSG, both the actigraph and Fitbit overestimated TST: Fitbit in one study by 88 minutes and in the other by 46 minutes; actigraph in one study by 74 minutes and in the other by 40.6 minutes. They also overestimated SE: Fitbit in one study by 17.4% and in the other by 8.1%; actigraph in one study by 14.8% and in the other by 7%. However, they underestimated WASO: Fitbit in one study by 39 minutes and in the other by 44 minutes; actigraph in one study by 20 minutes and in the other by 27 minutes. Actigraphy showed less bias than Fitbit. On the other hand, in the same two studies, Fitbit performed better than actigraphy in measuring SOL relative to PSG as reference: Fitbit bias in one study of 17 minutes and 2 minutes in the other; actigraph bias in one study of 23 minutes and 14 minutes in the other. One of these studies also performed EBE analysis [25], finding approximately 1% higher sensitivity and accuracy in detecting sleep by Fitbit than by actigraphy relative to PSG.

A total of 3 studies investigated the extent of correlation between sleep parameters derived by a nonsleep-staging Fitbit model and a self-rated sleep diary [23,34]; 1 of them involved two different Fitbit models [24]. In the total of four potential comparisons (N=104), significant correlation, ranging between r=.55 and r=.71, was reported between TST measured by Fitbit versus sleep diary. A total of 1 study (N=38) found significant correlation between the two methods of sleep assessment for time in bed (TIB; r=.48) but poor correlation for WASO (r=.09) and SE (r=-.03). A total of 1 study (N=10) [36] investigated the extent of agreement between the sleep diary and sleep-staging Fitbit methods. It reported significant overestimation of WASO (13 minutes) and underestimation of SOL (5 minutes), TST (6 minutes), and SE (1.4%) by Fitbit in comparison to the sleep diary.

Only 1 study (N=60) [22] explored differences in accuracy of sleep-stage classification when new-generation Fitbit models were simultaneously worn on the dominant and nondominant hand. No between-hand difference was found in estimating sleep stages.

Early-generation nonsleep-staging, but not later-generation sleep-staging, Fitbit models allow the user to select either normal or sensitive mode to sense body movement to derive sleep parameters. A total of 3 studies evaluated, relative to PSG, performance of these Fitbit models when set to the sensitive mode [25,31,39]. In 2 of them, the sensitive mode significantly underestimated TST by 86.3 minutes (N=21) and 105 minutes (N=63), respectively, and underestimated SE by 16.0% (N=21) and 21% (N=63), respectively. The only study (N=21) that evaluated SOL found that the sensitive mode significantly overestimated it by 11.5 minutes. Across the 3 studies (N=134), differentiation of sleep from wake epochs by early-generation nonsleep-staging Fitbit models when set to the sensitive mode relative to PSG ranged from 0.66 to 0.78 in accuracy, from 0.64 to 0.78 in sensitivity, and from 0.79 to 0.89 in specificity.

A total of 3 studies evaluated interdevice reliability of nonsleep-staging Fitbit models. In 1 of them, 3 participants wore two Fitbit Classic bands on the same wrist, finding through EBE comparisons high interdevice reliability (96.5%-99.1%) [40]. Another study with 7 participants who wore two Fitbit Ultra devices on the same wrist also substantiated essentially equivalent between-device performance in estimating TST and SE; findings for other sleep parameters were not reported [39]. Finally, the remaining study involving 10 participants who simultaneously wore two Fitbit Alta devices on the wrist of their nondominant arm found no significant difference between the two devices in measuring WASO, but slight, yet statistically significant, difference (approximately 6 minutes) between them in measuring TST [32].

The quality and uses of personal monitoring technology are rapidly advancing, offering the promise of extensive improvement in medical literacy and health. An area of high interest today to consumers and health professionals is sleep quality because of its recognized importance to daytime cognitive and physical performance. A number of wrist-worn devices enable tracking of sleep parameters and stages. Some of the most popular ones are marketed by Fitbit, Inc; performance of several of its nonsleep-staging and sleep-staging models have been evaluated against laboratory PSG, home sleep trackers, or other methods. The objective of this systematic review was to comprehensively evaluate the worthiness of these consumer wristband devices in assessing sleep.

PSG is regarded as the gold standard for assessment of sleep parameters and stages; in comparison to PSG, nonsleep-staging Fitbit models overestimate TST and SE, underestimate WASO, but determine SOL equally well. Moreover, the amount of bias in estimating TST, SE, and WASO by such wristband models is not negligible. EBE analyses demonstrate, in comparison to laboratory PSG, the high accuracy and sensitivity of this type of Fitbit model in detecting sleep; however, the analyses demonstrate only modest specificity.

In 2007, the American Academy of Sleep Medicine certified wrist actigraphy for at-home evaluation of sleep patterns of both healthy adults and patients with certain suspected sleep disorders [44]. However, several studies have found actigraphy used in conjunction with any one of the four popular interpretative algorithms [2] overestimates sleep duration; although its sensitivity in detecting sleep is high (ie, between 0.87 and 0.99), its specificity is low (ie, between 0.28 and 0.67) [3]. This is the case because actigraphy and its interpretative algorithms tend to score epochs of quiet wakefulness as sleep [45]. Studies that compared nonsleep-staging Fitbit models with actigraphy [23,25,29,32,33,39,40] revealed that Fitbit overestimates both TST and SE and underestimates WASO. Only a single study [25] assessed SOL relative to actigraphy; it found that Fitbit significantly overestimated this sleep parameter, on average, by 12 minutes. The single study [25] that performed EBE analyses on the same participants who were simultaneously outfitted with nonsleep-staging Fitbit and actigraph devices reported only a minor (ie, approximately 1% higher) difference in sensitivity and accuracy by Fitbit. In this regard, nonsleep-staging Fitbit models enable user selection of either normal or sensitive mode to measure body movement to derive sleep parameters. The recommended normal mode only scores significant body movements while in bed as awake time. In contrast, the sensitive mode interprets nearly all such movements as awake or restless sleep time [46]. Studies that evaluated Fitbit devices relative to PSG when the devices were set to the sensitive and normal modes reported that the sensitive mode, compared to the normal mode, gave rise to notably higher bias in estimating SOL, SE, WASO, and TST; the sensitive mode also gave rise to lower accuracy and sensitivity but higher specificity. These findings confirm the company’s recommendation that the normal mode setting be used in most instances.

Fitbit introduced its sleep-staging feature in 2017, which is now incorporated into the Fitbit Charge 2, Fitbit Charge 3, Fitbit Alta HR, Fitbit Versa, Fitbit Versa 2, Fitbit Blaze, and Fitbit Ionic models. This feature relies on a combined body movement and HRV algorithm to identify and estimate time spent in individual sleep stages [5]. The Fitbit interpretative proprietary sleep-staging algorithm was derived using machine learning methods (ie, linear discriminant classifier) applied to three types of parameters—motion, HRV, and respiratory rate, with the last two calculated from heartbeat data sensed by photoplethysmography—measured during a sleep study of 60 normal sleepers, 18-60 years of age (mean 34, SD 10). These three groups of parameters led to an initial set of 180 features, which, through the method of recursive feature elimination, was reduced to 54 features. Subsequently, heartbeat-derived features were compared to movement-derived features and found to be of approximately equal importance in the overall classification and discrimination of sleep versus wake epochs [22]. Based on discussion with Fitbit, Inc, the same core hardware technology and software algorithm have been incorporated into all sleep-staging Fitbit models since their introduction in 2017, thereby making feasible valid performance comparisons across the published studies.

Thus far, only 3 qualifying studies investigated the performance of the newer-generation Fitbit hardware- and software-coupled technology relative to PSG [22,26,28]. Of these, 1 evaluated the performance of Fitbit for two different—normal sleeper and PLMS—cohorts, thereby increasing the number of possible comparisons to four, depending on the specific sleep parameters evaluated in individual trials. Although these comparisons showed that recent-generation Fitbit models display only moderate accuracy in detecting sleep stages, they were much better at estimating TST, SE, and WASO than were early-generation, nonsleep-staging Fitbit models. Overall, the amount of bias in estimating the majority of these sleep parameters by new-generation Fitbit models in reference to PSG was clinically negligible: less than 12 minutes in the three comparisons in which TST was determined, 1.98% in the single comparison in which SE was assessed [26], and less than 7 minutes in the three comparisons in which SOL was measured. Meta-analysis of the published data also substantiated the lack of a statistically significant difference between sleep-staging Fitbit models and PSG in measuring WASO, TST, and SE, and with effect sizes of differences in the range of small, even for SOL. EBE analyses conducted on the data from the same four comparisons also revealed high sensitivity (ie, between 0.95 and 0.96) and specificity (ie, between 0.58 and 0.69) in detecting sleep. These four comparison trials involving sleep-staging Fitbit models disclosed much higher specificity in detecting sleep than all of the nonsleep-staging Fitbit model comparison trials that reported specificity in the range of 0.10-0.52. Moreover, three of these four comparisons found no significant difference in WASO between methods of assessment. In contrast, five comparisons consisting of nonsleep-staging Fitbit models reported significant underestimation of WASO by 5.6-44 minutes. Collectively, these findings imply the body movement and HRV sensor and software algorithm technology of recent-generation sleep-staging Fitbit models, in comparison to the early-generation ones, more accurately detects wake epochs during intended sleep. These are very promising results, since the major drawback of body activity and movement-based trackers, such as actigraphs, is overestimation of sleep time and poor sensitivity of detecting WASO (ie, poor differentiation of wake from sleep epochs during spans of quiet bedtime activity) [45,47].

A concern about commercially available personal monitors is intra- and interdevice reliability. A total of 3 investigations [32,39,40] addressed this matter and they all reported acceptable consistency. The sole longitudinal (ie, 4 consecutive nights) investigation [29] found no systematic internight difference in TST and SE between Fitbit and actigraphy used as reference. The authors of this study concluded that Fitbit can be a useful device to assess trends in sleep quality, even though absolute values of some sleep parameters might be biased [29]. Of these studies, 1 reported a statistically significant, but nonetheless clinically nonsignificant, interdevice difference (approximately 6 minutes) in measuring TST [32].

Subjective self-report survey and diary methods, even though popular means of assessing sleep due to their ease of use and low cost, are of limited value. Self-report survey approaches depend on recall, which can be biased, especially when not restricted to recent, single point-in-time experiences. Sleep diary methods depend less on memory, but reported information may not be sufficiently accurate because of poor awareness of certain events, such as number or frequency of nighttime awakenings and precise time of falling asleep [48]. Studies that compared Fitbit models with sleep diary methods found a significant correlation between the two approaches for TIB and TST. Because of their simplicity and affordability, sleep survey and diary approaches are generally favored over actigraphy; however, the relatively inexpensive objective method of Fitbit models, along with their ease of use and better estimation of most sleep variables, may render them more appealing.

The findings of this systematic review pertaining to both nonsleep-staging and sleep-staging Fitbit models are based on our recent comprehensive search of databases for relevant published articles, which was repeated three different times during a 15-month span. The included research studies have certain limitations (eg, more than half of them were performed in a laboratory rather than a home setting, and participants were mostly young or middle-aged normal sleepers). Moreover, nonsignificant findings of studies with small sample sizes might be the consequence of insufficient statistical power. Furthermore, only 5 of the 22 qualifying published investigations involved Fitbit sleep-staging models. An additional limitation is a lack of published information as to the extent that advances in Fitbit hardware and/or software technology explain the described disparity in performance between the company’s different generations of models in deriving sleep parameters and stages. Based on our dialogue with company representatives, this disparity is due collectively to advances in sensor technology; improved fidelity of data acquisition; and incorporation of heart rate, HRV, and body movement into its sleep-study-validated proprietary interpretative algorithm. In spite of these limitations, findings of this review indicate that the advanced body movement and HRV method of recent-generation Fitbit wristband models seems appropriate to derive suitable estimates of sleep parameters and time spent in sleep. The findings further suggest that such Fitbit models may be useful for conduct of population-based sleep research, which, in the past, typically relied heavily or entirely upon subjective methods.

Fitbit models are marketed to the lay public to allow users to self-derive knowledge about their sleep quality, rather than as a substitute for standard clinical polysomnography. They appear useful for the study of the 24-hour sleep-wake pattern and for the determination of the duration, pattern, and quality of sleep, longitudinally, over many consecutive nights under normal living conditions. In this regard, individuals can benefit from information obtained by wristband trackers to improve sleep hygiene and sleep, itself. In certain cases, primary care and sleep specialists can at least gain superficial perspective on the sleep of patients. In spite of the fact that there has yet to be sufficient evaluation of recent-generation sleep-staging Fitbit models, findings of the few, thus far, published studies imply that their performance in differentiating wake from sleep epochs is better than that reported in the literature for actigraphy.