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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]

Thermoregulation is a process that is essential to the maintenance of life for all warm-blooded mammalian and avian species. It sustains a constant core body temperature in the face of a wide array of environmental thermal conditions and intensity of physical activities that generate internal heat. A primary component of thermoregulatory function is the movement of heat between the body core and the surface via the circulation of blood. The peripheral vasculature acts as a forced convection heat exchanger between blood and local peripheral tissues throughout the body enabling heat to be convected to the skin surface where is may be transferred to and from the environment via conduction, convection, radiation, and/or evaporation of water as local conditions dictate. Humans have evolved a particular vascular structure in glabrous (hairless) skin that is especially well suited for heat exchange. These vessels are called arteriovenous anastomoses (AVAs) and can vasodilate to large diameters and accommodate high flow rates. We report herein a new technology based on a physiological principle that enables simple and safe access to the thermoregulatory control system to allow manipulation of thermoregulatory function. The technology operates by applying a small amount of heating local to control tissue on the body surface overlying the cerebral spine that upregulates AVA perfusion. Under this action, heat exchangers can be applied to glabrous skin, preferably on the palms and soles, to alter the temperature of elevated blood flow prior to its return to the core. Therapeutic and prophylactic applications are discussed. [ABSTRACT FROM AUTHOR]

Background: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.Research Questions: (1) Does the actigraphy mode of operation -Proportional Integrating Measure (PIM) or Zero Crossing Mode (ZCM), responsive respectively to intensity and frequency of movements- impact sleep scoring; and (2) Can a better performing sleep scoring IA be developed by a deep learning approach combining PIM/ZCM data.Study Design and Methods: ZCM and PIM plus electroencephalographic (EEG) data of 40 healthy adults (17 female, mean age: 26.7 years) were obtained from a single in-home nighttime sleep study. Effect of mode of operation was first evaluated by applying several classic deep learning models to PIM only, ZCM only, and combined ZCM/PIM data. After, a novel deep learning model was developed incorporating combined ZCM/PIM data, and its performance was compared with existing Cole-Kripke, rescored Cole-Kripke, Sadeh, and UCSD IAs.Results: Relative to the EEG reference, ZCM/PIM combined mode produced higher agreement of scoring sleep/wake epochs than only ZCM or PIM modes. The proposed novel deep learning model showed 87.7% accuracy (0.2-1% higher than the other IAs), 94.1% sensitivity (0.7-4.3% lower than the other IAs), 64.0% specificity (9.9-21.5% higher than the other IAs), and 59.9% Kappa agreement (∼6.9-11.6% higher than other IAs) in detecting sleep epochs. The proposed deep learning model did not differ significantly from the reference EEG in estimating sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE). Amount of bias and minimum detectable change in estimating SOL, WASO, TST and SE by the deep learning model was smaller than other four IAs.Interpretation: The proposed novel deep learning algorithm simultaneously incorporating ZCM/PIM mode data performs significantly better in assessing sleep than existing conventional IAs. [ABSTRACT FROM AUTHOR]

The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland–Altman plot as a measure of agreement, and we provide formulae to calculate them along with illustrative examples for different types of study designs, specifically single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses. [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]

We compared performance of four popular interpretative algorithms (IAs), i.e., Cole–Kripke, Rescored Cole–Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of 'Rescoring Rules' improved performance of the Cole–Kripke IA. The Sadeh and Rescored Cole–Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen's Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen's kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention. [ABSTRACT FROM AUTHOR]

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland–Altman agreement range (2.67 bpm). The regression analysis of Bland–Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant "U" shape trend (P < 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60–80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults. [ABSTRACT FROM AUTHOR]

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]

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]

Background: We examined associations between dog ownership, morning dog walking and its timing and duration, and depression risk in female nurses, exploring effect modification by chronotype. We hypothesized that dog ownership and morning walking with the dog are associated with lower odds of depression, and that the latter is particularly beneficial for evening chronotypes by helping them to synchronize their biological clock with the solar system. Methods: 26,169 depression-free US women aged 53–72 from the Nurses' Health Study 2 (NHS2) were prospectively followed from 2017–2019. We used age- and multivariable-adjusted logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95%CIs) for depression according to dog ownership, and morning dog walking, duration, and timing. Results: Overall, there was no association between owning a dog (ORvs_no_pets = 1.12, 95%CI = 0.91–1.37), morning dog walking (ORvs_not = 0.87, 95%CI = 0.64–1.18), or the duration (OR>30min vs. ≤15mins = 0.68, 95%CI = 0.35–1.29) or timing of morning dog walks (ORafter9am vs. before7am = 1.06, 95%CI = 0.54–2.05) and depression. Chronotype of dog owners appeared to modify these associations. Compared to women of the same chronotype but without pets, dog owners with evening chronotypes had a significantly increased odds of depression (OR = 1.60, 95%CI = 1.12–2.29), whereas morning chronotypes did not (OR = 0.94, 95%CI = 0.71–1.23). Further, our data suggested that evening chronotypes benefited more from walking their dog themselves in the morning (OR = 0.75, 95%CI = 0.46–1.23, Pintx = 0.064;) than morning chronotypes. Conclusions: Overall, dog ownership was not associated with depression risk though it was increased among evening chronotypes. Walking their dog in the morning might help evening chronotypes to lower their odds of depression, though more data are needed to confirm this finding. [ABSTRACT FROM AUTHOR]

Only few longitudinal studies with high risk of bias have examined relationship between pets and adolescents' mental health. Our prospective cohort study followed depression-free US adolescents aged 12–18, enrolled in the Growing Up Today Study from pet ownership assessment in 1999 to possible occurrence of high depressive symptoms defined based on the McKnight Risk Factor Survey between 2001 and 2003. Propensity-score-adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were estimated using generalized estimating equation models. Among 9631 adolescents [42.4 % male, mean age 14.9 years (SD 1.6)], we found no association between pet ownership and risk of high depressive symptoms (OR any_pet = 1.14; 95%CI, 0.95–1.38). Stratified analyses revealed no evidence of effect modification by sex, but effect modification by maternal history of depression (depressed mothers OR any_pet = 0.83; 95 % CI: 0.58–1.19, non-depressed mothers OR any_pet = 1.27; 95 % CI: 1.02–1.58; P intx = 0.03), which differed further by children's sex. Effects were more pronounced among children with a history of childhood abuse (OR any_pet = 0.41 (0.14–1.15); P intx ≤0.03). No major differences by type of pet owned were observed in any of these analyses. Our sample is predominantly white and all are offspring of nurses with a similar academic background which could affect generalizability. Overall, we found no association between pet ownership and depression during adolescence, however subgroup analyses indicated some individuals may benefit from a pet. Future longitudinal studies with more detailed exposure assessments, including pet attachment are needed to further explore the potential of human-animal interaction on mental health. • Previous studies on pet ownership and depression risk are at high risk of bias. • Our data show that only some subgroups might benefit from pet ownership. • Studies with detailed exposure assessments, including pet attachment are needed. [ABSTRACT FROM AUTHOR]

The present research proposes an effective method to enhance the heat transport capability of conventional electronic coolers and improve their thermal management. Pulsating heat pipes (PHPs) are outstanding heat transfer devices in the field of electronic cooling. In the present study, two sets of open-loop pulsating heat pipes (OLPHPs) for two different magnetic nanofluids (with and without surfactant) were fabricated and their thermal performance was experimentally investigated. Effects of working fluid (water and two types of magnetic nanofluids), heating power, charging ratio, nanofluid concentration, inclination angle, application of a magnetic field, and magnet location are described. Experimental results showed that magnetic nanofluids can improve the thermal performance of OLPHPs. Application of a magnetic field reduces the thermal resistance of OLPHPs charged with magnetic nanofluids. The optimum charging ratio and nanofluid concentration are reported. The best thermal performance of OLPHPs was observed when oriented 67.5° relative to the horizontal axis. The variation in magnet location in OLPHPs charged with magnetic nanofluids resulted in a heat pipe with variable thermal performance at a fixed heating power, charging ratio, and orientation. OLPHPs that are charged with magnetic nanofluids provide a path toward considerable improvement in electronic cooling technology. [ABSTRACT FROM PUBLISHER]