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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

Simple Summary: This study investigated the potential connection between working night shifts, sleep patterns, and the risk of thyroid cancer among nurses. The researchers followed over 114,000 women for 26 years and identified 588 cases of thyroid cancer. The study found that there was no link between working night shifts and thyroid cancer risk. However, it did suggest a possible association between frequently having difficulty falling or staying asleep and a higher risk of thyroid cancer. Among nurses who worked night shifts for over 10 years and experienced sleep difficulties most of the time, there was a modestly increased risk of thyroid cancer. In summary, the research suggests that sleep problems among those who work night shifts may be linked to an increased risk of thyroid cancer. However, more research is needed to confirm these findings and better understand the relationship between night shift work, sleep patterns, and thyroid cancer. Night shift work has been associated with breast, prostate, and colorectal cancer, but evidence on other types of cancer is limited. We prospectively evaluated the association of rotating night shift work, sleep duration, and sleep difficulty with thyroid cancer risk in the Nurses' Health Study 2 (NHS2). We assessed rotating night shift work duration (years) at baseline and throughout follow-up (1989–2015) and sleep characteristics in 2001. Cox proportional hazard models, adjusted for potential confounders, were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for (a) shift work duration, (b) sleep duration, and (c) difficulty falling or staying asleep. We stratified the analyses of night shift work by sleep duration and sleep difficulty. Over 26 years of follow-up, 588 incident cases were identified among 114,534 women in the NHS2 cohort. We observed no association between night shift work and the risk of thyroid cancer. Difficulty falling or staying asleep was suggestively associated with a higher incidence of thyroid cancer when reported sometimes (HR 1.26, 95% CI 0.95, 1.66) and all or most of the time (HR 1.35, 95% CI 1.00, 1.81). Night shift workers (10+ years) with sleep difficulty all or most of the time (HR 1.47; 0.58–3.73) or with >7 h of sleep duration (HR 2.17; 95% CI, 1.21–3.92) had a higher risk of thyroid cancer. We found modest evidence for an increased risk of thyroid cancer in relation to sleep difficulty, which was more pronounced among night shift workers. [ABSTRACT FROM AUTHOR]

Summary: The sleep–wake cycle is regulated by circadian Process C and homeostatic Process S. Selective thermal stimulation (STS) of the cervical spine region enhances glabrous skin blood flow (GSBF) and augments body heat dissipation to increase distal‐to‐proximal skin gradient (DPG) causing decrease of core body temperature (CBT), which can shorten sleep onset latency (SOL) and improve sleep quality. A total of 11 young healthy/normal sleeper males challenged to go to bed (lights‐off) 2 h earlier than usual were subjected in a randomised order to non‐consecutive treatment and control night‐time sleep sessions. The treatment night entailed activation of a dual‐temperature zone mattress with a cooler centre and warmer periphery plus STS pillow that applied mild heating to the cervical spinal skin for 30 min after lights‐off for sleep. During the first 30 min after lights‐off, GSBF (mean [standard error (SE)] Δ = 49.77 [19.13] perfusion units, p = 0.013) and DPG (mean [SE] Δ = 2.05 [0.62] °C, p = 0.005) were significantly higher and CBT (mean [SE] Δ = –0.15 [0.07] °C, p = 0.029) was significantly lower in the treatment than control night, while there was no significant difference in these variables during the 45 min prior to lights‐off (baseline). Moreover, SOL was significantly reduced (mean [SE] Δ = –48.6 [23.4] min, p = 0.032) and subjective sleep quality significantly better (p < 0.001) in the treatment than control night. In conclusion, the novel sleep facilitating system comprised of the STS pillow plus dual‐temperature zone mattress induced earlier increase in GSBF and DPG and earlier decline in CBT. This resulted in statistically significant shortened SOL and improved overall sleep quality, thereby reducing sleep pressure of Process S, even under the challenging investigative protocol requiring participants to go to sleep 2 h earlier than customary. [ABSTRACT FROM AUTHOR]

We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing 'Sadeh' IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions. [ABSTRACT FROM AUTHOR]

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs. [ABSTRACT FROM AUTHOR]