Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study.

BACKGROUND: The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. OBJECTIVE: We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. METHODS: We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. RESULTS: During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. CONCLUSIONS: Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Strengths, weaknesses, opportunities and threats of using AI-enabled technology in sleep medicine: a commentary.

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine committee of the American Academy of Sleep Medicine (AASM) reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies three pivotal areas in sleep medicine which can benefit from AI technologies: clinical care, lifestyle management and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI enabled technologies and offers possible solutions.

Timing and duration of dog walking and dog owner's chronotype in relation to incident depression risk among middle to older-aged female nurses.

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.

Rotating Night Shift Work, Sleep, and Thyroid Cancer Risk in the Nurses' Health Study 2.

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.

Sleeping Difficulties, Sleep Duration, and Risk of Hypertension in Women.

BACKGROUND: Rates of poor sleep and hypertension are alarming worldwide. In this study, we investigate the association between sleeping difficulties and sleep duration with hypertension risk in women. METHODS: Sixty-six thousand one hundred twenty-two participants of the Nurses' Health Study 2, who were free of hypertension at baseline (2001), were followed prospectively for 16 years and incident hypertension assessed every 2 years. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for hypertension incidence associated with sleeping difficulties and sleep duration. RESULTS: During follow-up, we documented 25 987 incident cases of hypertension. After controlling for demographic and lifestyle risk factors, compared with women who slept 7 to 8 hours, women with shorter sleep duration had a significantly higher risk of hypertension (≤5 hours: HR, 1.10 [95% CI, 1.05-1.16]; 6 hours: HR, 1.07 [95% CI, 1.03-1.10]), whereas the risk for women with longer sleep duration was not statistically significant (9 hours: HR, 1.03 [95% CI, 0.97-1.10]; >9 hours: HR, 1.08 [95% CI, 0.94-1.23]). Compared with women rarely having difficulty falling or staying asleep, women sometimes or usually having these sleep difficulties had significantly higher risk of developing hypertension (HR, 1.14 [95% CI, 1.11-1.17] and 1.28 [95% CI, 1.22-1.35]; Ptrend<0.001). Early morning awakening was not associated with hypertension risk (Ptrend=0.722). There was no effect modification by night work or chronotype. CONCLUSIONS: Difficulty falling or staying asleep and short sleep duration were associated with higher risk of hypertension among women in our study. Screening for poor sleep could be useful in identifying people at higher risk for hypertension.

The association between night shift work and breast cancer risk in the Finnish twins cohort.

Breast cancer is highly prevalent yet a more complete understanding of the interplay between genes and probable environmental risk factors, such as night work, remains lagging. Using a discordant twin pair design, we examined the association between night shift work and breast cancer risk, controlling for familial confounding. Shift work pattern was prospectively assessed by mailed questionnaires among 5,781 female twins from the Older Finnish Twin Cohort. Over the study period (1990-2018), 407 incident breast cancer cases were recorded using the Finnish Cancer Registry. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for potential confounders. Within-pair co-twin analyses were employed in 57 pairs to account for potential familial confounding. Compared to women who worked days only, women with shift work that included night shifts had a 1.58-fold higher risk of breast cancer (HR = 1.58; 95%CI, 1.16-2.15, highest among the youngest women i.e. born 1950-1957, HR = 2.08; 95%CI, 1.32-3.28), whereas 2-shift workers not including night shifts, did not (HR = 0.84; 95%CI, 0.59-1.21). Women with longer sleep (average sleep duration > 8 h/night) appeared at greatest risk of breast cancer if they worked night shifts (HR = 2.91; 95%CI, 1.55-5.46; Pintx=0.32). Results did not vary by chronotype (Pintx=0.74). Co-twin analyses, though with limited power, suggested that night work may be associated with breast cancer risk independent of early environmental and genetic factors. These results confirm a previously described association between night shift work and breast cancer risk. Genetic influences only partially explain these associations.

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.

BACKGROUND: Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. OBJECTIVE: We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). METHODS: SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). RESULTS: The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. CONCLUSIONS: SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.

Rotating Night Shift Work and Bladder Cancer Risk in Women: Results of Two Prospective Cohort Studies.

Bladder cancer is the sixth most common cancer in the United States. Night shift work has previously been linked with cancer risk. Whether there is an association between rotating night shift work and bladder cancer in women has not been studied previously. Eligible participants in the Nurses' Health Study (NHS, n = 82,147, 1988-2016) and Nurses' Health Study II (NHSII, n = 113,630, 1989-2015) were prospectively followed and a total of 620 and 122 incident bladder cancer cases were documented during the follow-up of NHS and NHSII, respectively. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for bladder cancer incidence. We observed a significantly increased risk of bladder cancer among women with >5 years of night shift work history compared with women who never worked rotating night shifts in NHS (HR = 1.24; 95%CI = 1.01-1.54, p for trend = 0.06), but not in the pooled NHS and NHS II (HR = 1.18; 95%CI = 0.97-1.43, p for trend = 0.08). Secondary analyses stratified by smoking status showed no significant interaction (p = 0.89) between the duration of rotating night shift work and smoking status. In conclusion, our results did not provide strong evidence for an association between rotating night shift work and bladder cancer risk.

Recommended timing of medications that impact sleep and wakefulness: A review of the American Prescribers' Digital Reference.

An appreciable number of medicines have a recommended unique single time-of-day or asymmetrical or unequal-interval multiple-daily administration schedule. Many prescription and over-the-counter (OTC) products, according to administration time, can exert positive or negative impact on nighttime sleep and daytime wakefulness. Intuitively, medicines used to manage nighttime sleep and daytime wake disorders should be taken, respectively, at night before bedtime and morning after arising. However, some utilized for other medical conditions, if improperly timed, may compromise nocturnal sleep and diurnal attentiveness. We conducted a comprehensive review of the American Prescribers' Digital Reference, internet version of the Physician's Desk Reference, for the recommended scheduling of medications and OTC remedies that can impact sleep and wakefulness. The search revealed several hundred therapies of various classes -- α2-receptor agonists, antidepressants, barbiturates, central nervous system stimulants, benzodiazepines, dopamine agonists, dopamine norepinephrine reuptake inhibitors, selective norepinephrine reuptake inhibitors, eugeroics, γ-aminobutyric acid modulators, H1 and H3-receptor antagonists, melatonin analogues, OTC melatonin-containing products, non-benzodiazepine benzodiazepine-receptor agonists, dual orexin-receptor antagonists, and serotonin modulators -- that have a recommended unique dosing schedule. The tables and text of this article are intended to guide the proper scheduling of these medicines to optimize desired and/or minimize undesired effects.

Relationship between pet ownership and risk of high depressive symptoms in adolescence and young adulthood.

BACKGROUND: Only few longitudinal studies with high risk of bias have examined relationship between pets and adolescents' mental health. METHODS: 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. RESULTS: 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 (ORany_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 ORany_pet = 0.83; 95 % CI: 0.58-1.19, non-depressed mothers ORany_pet = 1.27; 95 % CI: 1.02-1.58; Pintx = 0.03), which differed further by children's sex. Effects were more pronounced among children with a history of childhood abuse (ORany_pet = 0.41 (0.14-1.15); Pintx ≤0.03). No major differences by type of pet owned were observed in any of these analyses. LIMITATIONS: Our sample is predominantly white and all are offspring of nurses with a similar academic background which could affect generalizability. CONCLUSIONS: 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.

Novel temperature-controlled sleep system to improve sleep: a proof-of-concept study.

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.

Bioheat Transfer Basis of Human Thermoregulation: Principles and Applications.

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.

The Circadian Rhythm of Thermoregulation Modulates both the Sleep/Wake Cycle and 24 h Pattern of Arterial Blood Pressure.

Borbély proposed an interacting two-component model of sleep regulation comprising a homeostatic Process S and a circadian Process C. The model has provided understanding of the association between core body temperature (CBT) as a key element of Process C that is deterministic of sleep onset and offset. However, it additionally provides a new perspective of the importance of the thermoregulatory mechanisms of Process C in modulating the circadian rhythm of arterial blood pressure (ABP). Herein, we examine the circadian physiology of thermoregulation, including at the end of the activity span the profound redistribution of cardiac output from the systemic circulation to the arteriovenous anastomoses of the glabrous skin that markedly enhances convective transfer of heat from the body to the environment to cause (i) decrease of the CBT as a pathway to sleep onset and (ii) attenuation of the asleep ABP mean and augmentation of the ABP decline (dipping) from the wake-time mean, in combination the strongest predictors of the risk for blood vessel and organ pathology and morbid and mortal cardiovascular disease events. We additionally review the means by which blood perfusion to the glabrous skin can be manipulated on demand by selective thermal stimulation, that is, mild warming, on the skin of the cervical spinal cord to intensify Process C as a way to facilitate sleep induction and promote healthy asleep ABP. © 2021 American Physiological Society. Compr Physiol 11:1-14, 2021.

Sleep problems during COVID-19 pandemic and its' association to psychological distress: A systematic review and meta-analysis.

BACKGROUND: The emerging novel coronavirus disease 2019 (COVID-19) has become one of the leading cause of deaths worldwide in 2020. The present systematic review and meta-analysis estimated the magnitude of sleep problems during the COVID-19 pandemic and its relationship with psychological distress. METHODS: Five academic databases (Scopus, PubMed Central, ProQuest, ISI Web of Knowledge, and Embase) were searched. Observational studies including case-control studies and cross-sectional studies were included if relevant data relationships were reported (i.e., sleep assessed utilizing the Pittsburgh Sleep Quality Index or Insomnia Severity Index). All the studies were English, peer-reviewed papers published between December 2019 and February 2021. PROSPERO registration number: CRD42020181644. FINDINGS: 168 cross-sectional, four case-control, and five longitudinal design papers comprising 345,270 participants from 39 countries were identified. The corrected pooled estimated prevalence of sleep problems were 31% among healthcare professionals, 18% among the general population, and 57% among COVID-19 patients (all p-values < 0.05). Sleep problems were associated with depression among healthcare professionals, the general population, and COVID-19 patients, with Fisher's Z scores of -0.28, -0.30, and -0.36, respectively. Sleep problems were positively (and moderately) associated with anxiety among healthcare professionals, the general population, and COVID-19 patients, with Fisher's z scores of 0.55, 0.48, and 0.49, respectively. INTERPRETATION: Sleep problems appear to have been common during the ongoing COVID-19 pandemic. Moreover, sleep problems were found to be associated with higher levels of psychological distress. With the use of effective programs treating sleep problems, psychological distress may be reduced. Vice versa, the use of effective programs treating psychological distress, sleep problems may be reduced. FUNDING: The present study received no funding.

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

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.

Application of deep learning to improve sleep scoring of wrist actigraphy.

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.

A comprehensive guideline for Bland-Altman and intra class correlation calculations to properly compare two methods of measurement and interpret findings.

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.

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages.

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.

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

BACKGROUND: Wearable 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. OBJECTIVE: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. METHODS: In 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. RESULTS: The 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. CONCLUSIONS: Sleep-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.