Dr Victoria Revell

Study Objectives: Portable electroencephalography (EEG) devices offer the potential for accurate quantification of sleep at home but have not been evaluated in relevant populations. We assessed the Dreem headband (DHB) and its automated sleep staging algorithm in 62 older adults [age (mean±SD) 70.5±6.7 years; 12 Alzheimer’s]. Methods: The accuracy of sleep measures, epoch-by-epoch staging, and the quality of EEG signals for quantitative EEG (qEEG) analysis were compared to standard polysomnography (PSG) in a sleep laboratory. Results: The DHB algorithm accurately estimated total sleep time (TST) and sleep efficiency (SEFF) with a Symmetric Mean Absolute Percentage Error (SMAPE)

Background: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. Objective: This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. Methods: Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. Results: The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: 16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P

To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. We collected 7 to 14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n=29), Emfit-QS (Emfit, n=17), a standard actigraphy device (Actiwatch Spectrum [AWS, n=34]) and a sleep diary. We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep diary assisted actigraphy (AWS|SD). Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (ICC: going to bed, getting out of bed, time in bed > 0.75) whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71±81.16 min; Emfit: +101.47±75.95 min) as did AWS-A (+46.95±67.26 min). The CSTs overestimated sleep efficiency (WSA: +9.19±14.26 %; Emfit: +9.41±11.05 %) whereas AWS-A estimate (-2.38±10.06 %) was accurate. About 65% (n=23) of participants reported daytime naps either in-bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-h sleep duration with an error of ≈10% compared to the sleep diary. CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency and naps before these CSTs can be fully utilized in field settings.

Introduction Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual and particularly so in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realise this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. Methods We evaluated the accuracy of heart rate and breathing rate measurements of three contactless technologies (two under-mattress trackers: Withings sleep analyser (WSA) and Emfit QS (Emfit) and a bedside radar: Somnofy) in a sleep laboratory environment and assessed their potential to capture vital signs (heart rate and breathing rate) in a real-world setting. Data were collected in 35 community dwelling older adults aged between 65 and 83 years (mean ± SD: 70.8 ± 4.9; 21 men) during a one-night clinical polysomnography (PSG) in a sleep laboratory, preceded by 7 to 14 days of data collection at-home. Several of the participants had health conditions including type-2 diabetes, hypertension, obesity, and arthritis and ≈49% (n = 17) had moderate to severe sleep apnea while ≈29% (n = 10) had periodic leg movement disorder. The under-mattress trackers provided estimates of both heart rate and breathing rate while the bedside radar provided only breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared to PSG electrocardiogram (ECG) derived heart rate (beats per minute, bpm) and respiratory inductance plethysmography thorax (RIP thorax) derived breathing rate (cycles per minute, cpm). We also evaluated breathing disturbance indices of snoring and the apnea-hypopnea index (AHI) available from the WSA. Results All three contactless technologies provided acceptable accuracy in estimating heart rate [mean absolute error (MAE) < 2.2 bpm and mean absolute percentage error (MAPE) < 5%] and breathing rate (MAE ≤ 1.6 cpm and MAPE < 12%) at 1 minute resolution. All three contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared to PSG estimates (R-squared: WSA Snore: 0.76, p < 0.001; WSA AHI: 0.59, p < 0.001). Conclusion Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community dwelling older adults at scale. They enable assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring which may provide insight into health trajectories.

Experimental inversion of circadian and behavioural rhythms by 12-hours adversely affects markers of metabolic health. We investigated the effects of a more modest 5-hour delay in behavioural cycles. Fourteen participants completed an 8-day in-patient laboratory protocol, with controlled sleep-wake opportunities, light-dark cycles, and diet. The 5-hour delay in behavioural cycles was induced by delaying sleep opportunity. We measured: melatonin to confirm central circadian phase; fasting markers and postprandial metabolism; energy expenditure; subjective sleepiness; and appetite, throughout the waking period. After the phase delay, there was slower gastric emptying at breakfast, lower fasting plasma glucose, higher postprandial plasma glucose and triglycerides, and lower thermic effect of feeding. Any changes were abolished or attenuated within 48-72 hours. These data extend our previous findings, which showed no time-of-day effect in healthy adults on daytime energy expenditure or thermic effect of feeding when accounting for circadian variation in resting metabolic rate.

Low indoor light in urban housing can dis-rupt health and wellbeing, especially in older adultswho experience reduced light sensitivity and sleep/cir-cadian disruptions with natural aging. While controlledstudies suggest that enhancing indoor lighting mayalleviate the negative effects of reduced light sensitiv-ity, evidence for this to be effective in the real world islacking. This study investigates the effects of two lightconditions on actigraphic rest-activity rhythms and sub-jective sleep in healthy older adults (≥ 60 years) livingat home. Two photon-matched lights were compared; acontrol white light (4000 K) and a blue-enriched whitelight (17000 K) at two different intensities (300–450 lxand 1100–1200 lx respectively). Participants (n = 36, 25female) completed an 11-week randomized, cross-overstudy, comprising 1 week of baseline, 3 weeks of self-administered light exposure (2 h in the morning and 2 hin the evening), and 2 weeks of washout for each lightcondition. Participants completed sleep diaries, worea wrist actigraph and a light sensor necklace, and col-lected urine to measure 6-sulphatoxymelatonin. Longerduration of morning blue-enriched light significantlyimproved rest-activity rhythm stability and decreasedsleep fragmentation. More time spent above 2500 lxincreased actigraphy amplitude, daytime activity, andadvanced bedtime. Evening light exposure, however,increased sleep latency and lowered sleep efficiency.Our findings show morning blue-enriched light is ben-eficial whereas evening light should be avoided. Optimaltiming of self-administered light interventions thus mayoffer a promising strategy to improve sleep and rest-activity rhythms in older adults in real-world settings.

Numerous hormones and genes exhibit diurnal 24-hr rhythms that can also be affected by sleep deprivation. Here we studied diurnal rhythms in DNA methylation under a 24-hr sleep/wake cycle and a subsequent 29 hr of continual wakefulness (1 night of sleep deprivation). Fifteen healthy men (19–35 years) spent 3 days/nights in a sleep laboratory: (1) adaptation; (2) baseline; (3) total sleep deprivation day/night. DNA methylation was analysed from peripheral blood leukocytes, collected every 3 hr for 45 hr (starting at 15:00 hours) during the baseline period and the total sleep deprivation period. Epigenome-wide DNA methylation variation was assessed with the Infinium MethylationEPIC v2.0 Beadchip kit. Rhythm analysis was performed separately for the baseline and the total sleep deprivation time-series data. Pairwise analysis between diurnal samples and sleep deprivation samples at the same timepoint was also carried out to detect differentially methylated positions related to sleep deprivation. Of all DNA methylation sites, 14% exhibited a diurnal rhythm in methylation on the baseline day/night that was altered by sleep deprivation. During sleep deprivation, the number of differentially methylated positions increased towards the end of the sleep deprivation period, with a dominating pattern of hypomethylation. Among differentially methylated positions, an enrichment of genes related to the FAS immune response pathway was detected. In conclusion, DNA methylation exhibits diurnal rhythmicity, and this time-of-day variation needs to be considered when studying DNA methylation as a biomarker in biomedical studies. In addition, the observed DNA methylation changes under wakefulness might serve as a mediator of sleep deprivation-related immune response alterations.

Summary There is an ongoing debate in the United Kingdom and in other countries about whether twice‐yearly changes into and out of Daylight Saving Time should be abolished. Opinions are divided about whether any abolition of Daylight Saving Time should result in permanent Standard Time, or year‐long Daylight Saving Time. The British Sleep Society concludes from the available scientific evidence that circadian and sleep health are affected negatively by enforced changes of clock time (especially in a forward direction) and positively by the availability of natural daylight during the morning. Thus, our recommendation is that the United Kingdom should abolish the twice‐yearly clock change and reinstate Standard Time throughout the year.

Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.

Sleep disturbances have been reported as one of the most common symptoms among people living with dementia (PLWD). Few technologies to longitudinally monitor sleep across the 24-h day are available. Here, we develop machine learning models that use multimodal data to accurately monitor sleep across the day and night.

Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure, social constraints or behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised sleep interventions. The timing of the human sleep-wake cycle has been modelled as an interaction between a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure, and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent, and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate we can describe individual sleep phenotypes in each of 34 older participants (65-83y) by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.Competing Interest StatementDJD and ACS are consultants to F. Hoffmann-La Roche, Ltd.Footnotes* This version of the manuscript has been revised to: - take account of referees comments - updated to correct minor errors, including typographical errors. The central messages and mathematical modelling remain unchanged.* https://github.com/anneskeldon/Homeostatic_circadian_light_model-factors_driving_sleep_phenotypes

Plasma biomarkers of dementia, including phosphorylated tau (p-tau217), offer promise as tools for diagnosis, stratification for clinical trials, monitoring disease progression, and assessing the success of interventions in those living with Alzheimer's disease. However, currently, it is unknown whether these dementia biomarker levels vary with the time of day, which could have implications for their clinical value. In two protocols, we studied 38 participants (70.8 ± 7.6 years; mean ± SD) in a 27-h laboratory protocol with either two samples taken 12 h apart or 3-hourly blood sampling for 24 h in the presence of a sleep-wake cycle. The study population comprised people living with mild Alzheimer's disease (PLWA, n = 8), partners/caregivers of PLWA (n = 6) and cognitively intact older adults (n = 24). Single-molecule array technology was used to measure phosphorylated tau (p-tau217) (ALZpath), amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42), glial fibrillary acidic protein, and neurofilament light (NfL) (Neuro 4-Plex E). Analysis with a linear mixed model (SAS, PROC MIXED) revealed a significant effect of time of day for p-tau217, Aβ40, Aβ42, and NfL, and a significant effect of participant group for p-tau217. For p-tau217, the lowest levels were observed in the morning upon waking and the highest values in the afternoon/early evening. The magnitude of the diurnal variation for p-tau217 was similar to the reported increase in p-tau217 over one year in amyloid-β-positive mild cognitively impaired people. Currently, the factors driving this diurnal variation are unknown and could be related to sleep, circadian mechanisms, activity, posture, or meals. Overall, this work implies that the time of day of sample collection may be relevant in the implementation and interpretation of plasma biomarkers in dementia research and care.

Abstract Background Sleep abnormalities are increasingly recognized as markers and potential drivers of neurodegenerative proteinopathies. However, the sleep phenotype of less common dementias remains poorly characterised. In particular, clinical experience suggests that patients with focal temporal lobe degeneration in the frontotemporal dementia spectrum can experience sometimes striking sleep disruption. We recently had the opportunity to corroborate this clinical impression in a small series of representative patients participating in a larger cohort study. Methods To date, we have studied seven patients and three healthy age‐matched controls. The patients’ diagnoses comprise behavioural variant frontotemporal dementia associated with focal right temporal lobe atrophy (n = 3), semantic dementia associated with focal left temporal lobe atrophy (n = 2), young onset Alzheimer’s disease and logopenic aphasia (each n = 1). No participant had a past history of a clinical sleep disorder. Detailed data on sleep patterns, adapted standard sleep questionnaires (Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale), overnight oximetry and neuropsychological test scores (covering domains of executive function, emotion processing, memory, and auditory perceptual learning) pre‐/post‐overnight sleep were collected with the assistance of patients’ caregivers. Results Two patients with right temporal lobe atrophy and one with left temporal lobe atrophy had marked sleep fragmentation, in one right temporal case accompanied by frequent sleep attacks, with increased overall time in bed and/or attempting to sleep. Epworth and Pittsburgh indices in these patients indicated significant daytime sleepiness and reduced sleep quality; none had evidence of significant sleep apnoea on overnight oximetry. Comparatively mild clinical sleep disturbances were reported in patients with other dementia diagnoses. However, there was evidence for reduced auditory perceptual learning benefit from overnight sleep relative to healthy older controls, particularly in the patients with underlying Alzheimer pathology. Conclusions These preliminary findings suggest that focal temporal lobe degeneration may be associated with clinically relevant sleep disruption, as part of the more pervasive derangement of homeostasis in these syndromes. Further work is indicated to define the hypnic phenotype in detail, with sleep electrophysiology and correlative behavioural, neuroimaging and laboratory data in larger patient cohorts.

Abstract Background Increased daytime napping and excessive sleepiness are associated with cognitive decline in older adults, especially in people living with dementia (PLWD) [1]. Subjective assessments of naps are burdensome and maybe unreliable in PLWD and hence there is a need for technologies that provide objective longitudinal assessment of the incidence and duration of naps. Here we compare two contactless sleep technologies (CSTs) against sleep diary and actigraphy for monitoring daytime napping in community dwelling non‐demented older adults. Method Two under‐mattress CSTs (Withings Sleep Analyser [WSA] and Emfit QS [Emfit]) along with actigraphy (Actiwatch Spectrum [AWS]) were deployed in the home of 17 older adults for a period of 14 days ( = 65 years; Mean Age ± SD = 72 ± 4.49; 6 Women). The ground truth nap information was collected using an extended consensus sleep diary that included additional information about naps (timing, duration, and location). We analyzed the agreement of the napping events and duration estimated by WSA, Emfit and AWS against the sleep diary reported events. Result The CSTs only detected in‐bed naps whilst the AWS detected both in‐bed and not‐in‐bed naps. Although all the compared devices detected spurious naps unreported in the sleep diary, it was highest in AWS (81% of total naps detected) followed by Emfit (63%) and WSA (16%) as shown in Figure 1. Among the CSTs, WSA accurately detected more in bed naps while registering less spurious naps compared to Emfit but had lower duration agreement to sleep diary (Figure 2). Further, when the contribution of daytime naps to 24‐h total sleep time was computed, the WSA estimate (12.8±6.1%) was closest to the sleep diary estimate (12.9±9.1%) followed by AWS (15.6±9.9%) and Emfit (17.2±11.1%). Conclusion CSTs, with their ability to provide both contextual location information and objective measures of napping, such as timing and duration, offer a reliable and unobtrusive alternative to traditional methods such as sleep diary and actigraphy for long‐term round‐the‐clock monitoring of sleep in older adults. References: [1] Li, P, Gao, L, Yu, L, et al. Daytime napping, and Alzheimer’s dementia: A potential bidirectional relationship. Alzheimer’s Dement. 2023; 19: 158– 168. https://doi.org/10.1002/alz.12636

Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.Methods and procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process.Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques.Clinical impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

Introduction Disturbances of sleep/wake behaviour are amongst the most disabling symptoms of dementia, leading to increased carers’ burden and institutionalisation. The lack of unobtrusive, low- burden technologies validated to monitor sleep in patients living with dementia (PLWD) has prevented longitudinal studies of nocturnal disturbances and their correlates. Aims To examine the effect of medication changes and clinical status on the intraindividual variation in sleep/wake behaviour in PLWD. Methods Using under-mattress pressure-sensing mat in 46 PLWD, we monitored sleep/wake behavioural metrics for 13,711 nights between 2019-2021. Machine learning and >3.6million nightly summaries from 13,671 individuals from the general population were used to detect abnormalities in PLWD’s nightly sleep/wake metrics and convert them to risk scores. Additionally, GP records were reviewed for each patient to determine whether medication changes and clinical events affected sleep parameters. Results PLWD’s went to bed earlier and rose later than sex- and age-matched controls. They had more nocturnal awakenings with longer out-of-bed durations. Notably, at the individual patient level, increased metric-specific risk scores were temporally related to changes in antipsychotics and antidepressants, and acute illness, including UTI, cardiac events, and depressive episodes. Conclusions Passive monitoring of sleep/wake behaviours is a promising way to identify novel markers of disease progression and evaluate the effectiveness of pharmaceutical interventions in patients with dementia.

OBJECTIVE: In an effort to enhance the efficiency, brightness, and contrast of light-emitting (LE) devices during the day, displays often generate substantial short-wavelength (blue-enriched) light emissions that can adversely affect sleep. We set out to verify the extent of such short-wavelength emissions, produced by a tablet (iPad Air), e-reader (Kindle Paperwhite 1st generation), and smartphone (iPhone 5s) and to determine the impact of strategies designed to reduce these light emissions. SETTING: University of Surrey dedicated chronobiology facility. METHODS: First, the spectral power of all the LE devices was assessed when displaying identical text. Second, we compared the text output with that of "Angry Birds" - a popular top 100 "App Store" game. Finally, we measured the impact of two strategies that attempt to reduce the output of short-wavelength light emissions. The first strategy employed an inexpensive commercially available pair of orange-tinted "blue-blocking" glasses. The second strategy tested an app designed to be "sleep-aware" whose designers deliberately attempted to reduce short-wavelength light emissions. RESULTS: All the LE devices shared very similar enhanced short-wavelength peaks when displaying text. This included the output from the backlit Kindle Paperwhite device. The spectra when comparing text to the Angry Birds game were also very similar, although the text emissions were higher intensity. Both the orange-tinted glasses and the "sleep-aware" app significantly reduced short-wavelength emissions. CONCLUSION: The LE devices tested were all bright and characterized by short-wavelength enriched emissions. Since this type of light is likely to cause the most disruption to sleep as it most effectively suppresses melatonin and increases alertness, there needs to be the recognition that at night-time "brighter and bluer" is not synonymous with "better." Ideally future software design could be better optimized when night-time use is anticipated, and hardware should allow an automatic "bedtime mode" that shifts blue and green light emissions to yellow and red as well as reduce backlight/light intensity.

Background The incidence of sleep disturbances increases with normal aging and is highly prevalent among people living with dementia (PLWD). To facilitate management and improvement of sleep quality in PLWD, validated unintrusive contactless technologies for long term objective monitoring of sleep are needed. Here we evaluate the ability of a contactless sleep tracker to accurately determine Time in Bed (TIB), Wake vs Sleep and Sleep stages (wake, light, deep, and REM sleep). Method We deployed the Emfit (Emfit QS), a contactless sleep tracker placed under the mattress. The Emfit uses ballistography to estimate respiration and heart rate and sleep stages. We collected data from 16 participants (Age: Mean‐72.12; SD‐4.6 years [6F:10M]) at home for a 14‐day period followed by a single overnight laboratory polysomnography (PSG) sleep assessment. The Emfit outputs a) timeseries at 30 s intervals (four sleep stages) and b) overnight summary sleep parameters. Sleep staging and sleep parameter estimation by Emfit was compared to, a) in‐lab gold standard PSG, and b) at‐home wristworn accelerometer (Actiwatch spectrum (AWS)) and sleep diary (SD) data. The epoch‐to‐epoch sleep staging concordance of Emfit was estimated over the total recording interval (∼10hrs) of the PSG for the laboratory session and between 1800hrs and 1200hrs for each SD entry for the home recordings. The concordance analysis for the sleep parameters, bed entry and exit times were performed using the summary data automatically generated by Emfit. Result The concordance between the four‐class sleep staging of the Emfit and PSG was poor (Figure 1). The two class (sleep/wake) analysis (Table 1) showed high sleep classification accuracy (sensitivity) but poor wake classification accuracy (specificity) compared to PSG. The sleep parameter estimates of Emfit also showed poor agreement with PSG (Figure 2). The home analysis indicated excellent accuracy for Time in Bed (TIB) (i.e., the bed entry and exit times) as registered by the SD (Table 2) and total sleep time (TST) for both sleep diary and AWS (Figure 3). Conclusion : The contactless sleep tracker provides accurate information about Time in Bed (TIB), but there is a lack of consensus of the sleep state classification with the PSG.

Conflicting evidence exists as to whether there are differences between males and females in circadian timing. The aim of the current study was to assess whether sex differences are present in the circadian regulation of melatonin and cortisol in plasma and urine matrices during a constant routine protocol. Thirty-two healthy individuals (16 females taking the oral contraceptive pill (OCP)), aged 23.8 ± 3.7 (mean ± SD) years, participated. Blood (hourly) and urine (4-hourly) samples were collected for measurement of plasma melatonin and cortisol, and urinary 6-sulfatoxymelatonin (aMT6s) and cortisol, respectively. Data from 28 individuals (14 females) showed no significant differences in the timing of plasma and urinary circadian phase markers between sexes. Females, however, exhibited significantly greater levels of plasma melatonin and cortisol than males (AUC melatonin: 937 ± 104 (mean ± SEM) vs. 642 ± 47 pg/ml.h; AUC cortisol: 13581 ± 1313 vs. 7340 ± 368 mmol/L.h). Females also exhibited a significantly higher amplitude rhythm in both hormones (melatonin: 43.8 ± 5.8 vs. 29.9 ± 2.3 pg/ml; cortisol: 241.7 ± 23.1 vs. 161.8 ± 15.9 mmol/L). Males excreted significantly more urinary cortisol than females during the CR (519.5 ± 63.8 vs. 349.2 ± 39.3 mol) but aMT6s levels did not differ between sexes. It was not possible to distinguish whether the elevated plasma melatonin and cortisol levels observed in females resulted from innate sex differences or the OCP affecting the synthetic and metabolic pathways of these hormones. The fact that the sex differences observed in total plasma concentrations for melatonin and cortisol were not reproduced in the urinary markers challenges their use as a proxy for plasma levels in circadian research, especially in OCP users.

The sleep/wake cycle is accompanied by changes in circulating numbers of immune cells. The goal of this study was to provide an in-depth characterization of diurnal rhythms in different blood cell populations and to investigate the effect of acute sleep deprivation on the immune system, as an indicator of the body's acute stress response.

Determining the time a biological trace was left at a scene of crime reflects a crucial aspect of forensic investigations as – if possible – it would permit testing the sample donor’s alibi directly from the trace evidence, helping to link (or not) the DNA-identified sample donor with the crime event. However, reliable and robust methodology is lacking thus far. In this study, we assessed the suitability of mRNA for the purpose of estimating blood deposition time, and its added value relative to melatonin and cortisol, two circadian hormones we previously introduced for this purpose. By analysing 21 candidate mRNA markers in blood samples from 12 individuals collected around the clock at 2 h intervals for 36 h under real-life, controlled conditions, we identified 11 mRNAs with statistically significant expression rhythms. We then used these 11 significantly rhythmic mRNA markers, with and without melatonin and cortisol also analysed in these samples, to establish statistical models for predicting day/night time categories. We found that although in general mRNA-based estimation of time categories was less accurate than hormone-based estimation, the use of three mRNA markers HSPA1B, MKNK2 and PER3 together with melatonin and cortisol generally enhanced the time prediction accuracy relative to the use of the two hormones alone. Our data best support a model that by using these five molecular biomarkers estimates three time categories, i.e. night/early morning, morning/noon, and afternoon/evening with prediction accuracies expressed as AUC values of 0.88, 0.88, and 0.95, respectively. For the first time, we demonstrate the value of mRNA for blood deposition timing and introduce a statistical model for estimating day/night time categories based on molecular biomarkers, which shall be further validated with additional samples in the future. Moreover, our work provides new leads for molecular approaches on time of death estimation using the significantly rhythmic mRNA markers established here.

Background Sleep disturbance is common among people living with dementia as well as their caregivers. Non‐contact video technology can be used to characterise such disturbances as well as quantifying sleep quality by measuring the number of sleep body positions (poses). Such an approach may be beneficial for home‐based longitudinal clinical monitoring of sleep pattern changes and disturbances at all stages of dementia. Here we present our pilot results of a personalised data‐driven method applied to video data for quantification of sleep disturbance comparing older and younger participants. Method Data were collected in two separate studies which included an overnight 10‐12 hour laboratory sleep recording from thirteen older (65‐80 years, 9 male:4 female) and eleven younger (18‐34 years, 7 male:4 female) participants in a dedicated sleep facility. A data‐driven analysis using Principal Component Analysis and k‐means clustering was applied to infrared video data extracted from a clinical polysomnography (PSG) system. The data‐driven analysis automatically determined statistically significant groupings or clusters of unique body poses for each individual. Pose number, number of pose transitions, pose duration, and pose transition duration were computed for each participant. Result The number of data‐driven poses in older and younger participants was remarkably similar with 15.2±3.9 and 15.8±1.8 (mean±SD) poses per participant, respectively. However, the older group had a higher number of pose transitions (33.0±8.82) compared to the younger group (23.9±6.83) (p = 0.03). A significant 20% difference (p = 0.03) in the average duration of each body position was observed, with 62.3±7.1 minutes and 78.9±15.9 minutes for the older and younger groups, respectively (see Figure 1 and Figure 2). Pose transition duration was 19.2±7.25 seconds and 14.6±4.32 seconds for the older and younger groups, respectively where they were not significantly different. Conclusion Although the number of body positions did not vary significantly between the two cohorts, the older group changed body position more frequently and it took them longer to do so. Data‐driven automated analysis of video‐based sleep monitoring holds significant promise for quantifying age‐related and inter‐individual differences in sleep behavior.

Nocturnal disturbance is frequently observed in dementia and is a major contributor to institutionalisation. Unobtrusive technology that can quantify sleep/wake and determine bed occupancy during the major nocturnal sleep episode may be beneficial for long-term clinical monitoring and the carer. Such technologies have, however, not been validated in older people. Here we assessed the performance of the Withings Sleep Mattress (WSM) in a heterogenous older population to ensure external validity.

Wearable heart rate monitors offer a cost-effective way of non-invasive, long-term monitoring of cardiac health. Validation of wearable technologies in an older populations is essential for evaluating their effectiveness during deployment in healthcare settings. To this end, we evaluated the validity of heart rate measures from a wearable device, Empatica E4, and compared them to the electrocardiography (ECG). We collected E4 data simultaneously with ECG in thirty-five older men and women during an overnight sleep recording in the laboratory. We propose a robust approach to resolve the missing inter-beat interval (IBI) data and improve the quality of E4 derived measures. We also evaluated the concordance of heart rate (HR) and heart rate variability (HRV) measures with ECG. The results demonstrate that the automatic E4 heart rate measures capture long-term HRV whilst the short-term metrics are affected by missing IBIs. Our approach provides an effective way to resolve the missing IBI issue of E4 and extracts reliable heart rate measures that are concordant with ECG. Clinical Relevance— This work discusses data quality challenges in heart rate data acquired by wearables and provides an efficient and reliable approach for extracting heart rate measures from the E4 wearable device and validates the metrics in older adults

Intrinsically photosensitive retinal ganglion cells (ipRGCs), whose photopigment melanopsin has a peak of sensitivity in the short wavelength range of the spectrum, constitute a common light input pathway to the olivary pretectal nucleus (OPN), the pupillary light reflex (PLR) regulatory centre, and to the suprachiasmatic nuclei (SCN), the major pacemaker of the circadian system. Thus, evaluating PLR under short wavelength light (λmax 500 nm) and creating an integrated PLR parameter, as a possible tool to indirectly assess the status of the circadian system, becomes of interest. Nine monochromatic, photon-matched light stimuli (300 s), in 10 nm increments from λmax 420 to 500 nm were administered to 15 healthy young participants (8 females), analyzing: i) the PLR; ii) wrist temperature (WT) and motor activity rhythms (WA), iii) light exposure (L) pattern and iv) diurnal preference (Horne- Östberg), sleep quality (Pittsburgh) and daytime sleepiness (Epworth). Linear correlations between the different PLR parameters and circadian status index obtained from WT, WA and L recordings and scores from questionnaires were calculated. In summary, we found markers of robust circadian rhythms, namely high stability, reduced fragmentation, high amplitude, phase advance and low internal desynchronization, were correlated with a reduced PLR to 460–490 nm wavelengths. Integrated circadian (CSI) and PLR (cp-PLR) parameters are proposed, that also showed an inverse correlation. These results demonstrate, for the first time, the existence of a close relationship between the circadian system robustness and the pupillary reflex response, two non-visual functions primarily under melanopsin-ipRGC input.

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low‐cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex‐printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self‐applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (“random forests”) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter‐individual variation in sleep parameters. The results demonstrate that machine‐learning‐based scoring of around‐the‐ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine‐learning‐based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine‐learning‐based scoring holds promise for large‐scale sleep studies.

Humans have largely supplanted natural light cycles with a variety of artificial light sources and schedules misaligned with day-night cycles. Circadian disruption has been linked to a number of disease processes, but the extent of circadian disruption among the population is unknown. We measured light exposure and wrist temperature among residents of an urban area for a full week during each of the four seasons, as well as light illuminance in nearby outdoor locations. Daily light exposure was significantly lower for individuals, compared to outdoor light sensors, for all four seasons. There was also little seasonal variation in the realized photoperiod experienced by individuals, with the only significant difference between winter and summer. We tested the hypothesis that differential light exposure impacts circadian phase timing, detected via the wrist temperature rhythm. To determine the influence of light exposure on circadian rhythms, we modeled the impact of morning, afternoon, and nighttime light exposure on the timing of the midline-estimating statistic of rhythm (MESOR). We found that morning light exposure and nighttime light exposure had a significant but opposing impact on MESOR timing. Our results demonstrate that nighttime light can shift/alter circadian rhythms to delay the morning transition from nighttime to daytime physiology, while morning light can lead to earlier onset. Our results demonstrate that circadian shifts and disruptions may be a more regular occurrence in the general population than is currently recognized. Significance Statement: Disruption of circadian rhythms has been linked to various diseases, but the prevalence of circadian disruption among the general population is unknown. Light plays a pivotal role in entraining circadian rhythms to the 24-hour day. Humans have largely supplanted natural light cycles with electrical lighting and through time spent indoors. We have shown that individuals experience a disconnect from natural light cycles, with low daytime light exposure, high levels of light-at-night, and minimal seasonal variation in light exposure. We identified measurable changes in the timing of wrist temperature rhythms as a function of differential light exposure during the morning and nighttime hours. Our findings suggest that circadian shifts, and potentially disruption, may be common in the general population.

The pupillary light reflex (PLR) is a neurological reflex driven by rods, cones, and melanopsin-containing retinal ganglion cells. Our aim was to achieve a more precise picture of the effects of 5-min duration monochromatic light stimuli, alone or in combination, on the human PLR, to determine its spectral sensitivity and to assess the importance of photon flux. Using pupillometry, the PLR was assessed in 13 participants (6 women) aged 27.2 ± 5.41 years (mean ± SD) during 5-min light stimuli of purple (437 nm), blue (479 nm), red (627 nm), and combinations of red+purple or red+blue light. In addition, nine 5-min, photon-matched light stimuli, ranging in 10 nm increments peaking between 420 and 500 nm were tested in 15 participants (8 women) aged 25.7 ± 8.90 years. Maximum pupil constriction, time to achieve this, constriction velocity, area under the curve (AUC) at short (0–60 s), and longer duration (240–300 s) light exposures, and 6-s post-illumination pupillary response (6-s PIPR) were assessed. Photoreceptor activation was estimated by mathematical modeling. The velocity of constriction was significantly faster with blue monochromatic light than with red or purple light. Within the blue light spectrum (between 420 and 500 nm), the velocity of constriction was significantly faster with the 480 nm light stimulus, while the slowest pupil constriction was observed with 430 nm light. Maximum pupil constriction was achieved with 470 nm light, and the greatest AUC0−60 and AUC240−300 was observed with 490 and 460 nm light, respectively. The 6-s PIPR was maximum after 490 nm light stimulus. Both the transient (AUC0−60) and sustained (AUC240−300) response was significantly correlated with melanopic activation. Higher photon fluxes for both purple and blue light produced greater amplitude sustained pupillary constriction. The findings confirm human PLR dependence on wavelength, monochromatic or bichromatic light and photon flux under 5-min duration light stimuli. Since the most rapid and high amplitude PLR occurred within the 460–490 nm light range (alone or combined), our results suggest that color discrimination should be studied under total or partial substitution of this blue light range (460–490 nm) by shorter wavelengths (~440 nm). Thus for nocturnal lighting, replacement of blue light with purple light might be a plausible solution to preserve color discrimination while minimizing melanopic activation.

The increased prevalence of circadian disruptions due to abnormal coupling between internal and external time makes the detection of circadian phase in humans by ambulatory recordings a compelling need. Here, we propose an accurate practical procedure to estimate circadian phase with the least possible burden for the subject, that is, without the restraints of a constant routine protocol or laboratory techniques such as melatonin quantification, both of which are standard procedures. In this validation study, subjects (N = 13) wore ambulatory monitoring devices, kept daily sleep diaries and went about their daily routine for 10 days. The devices measured skin temperature at wrist level (WT), motor activity and body position on the arm, and light exposure by means of a sensor placed on the chest. Dim light melatonin onset (DLMO) was used to compare and evaluate the accuracy of the ambulatory variables in assessing circadian phase. An evening increase in WT: WTOnset (WTOn) and "WT increase onset" (WTiO) was found to anticipate the evening increase in melatonin, while decreases in motor activity (Activity Offset or AcOff), body position (Position Offset (POff)), integrative TAP (a combination of WT, activity and body position) (TAPOffset or TAPOff) and an increase in declared sleep propensity were phase delayed with respect to DLMO. The phase markers obtained from subjective sleep (R = 0.811), WT (R = 0.756) and the composite variable TAP (R = 0.720) were highly and significantly correlated with DLMO. The findings strongly support a new method to calculate circadian phase based on WT (WTiO) that accurately predicts and shows a temporal association with DLMO. WTiO is especially recommended due to its simplicity and applicability to clinical use under conditions where knowing endogenous circadian phase is important, such as in cancer chronotherapy and light therapy.

Background More than 70% of people living with dementia (PLWD) experience sleep disturbances (e.g., early bedtime, long time in bed) even in the early stages of cognitive decline. Light interventions have been proposed as a promising non‐pharmacological approach to improve sleep timing, but current implementations are burdensome and not personalised. Quantitative tools for designing pragmatic light interventions for the home‐setting could benefit PLWD and their carers. Here, we report on a quantitative computational tool which combines readily collectable data with a new mathematical model to provide personalised advice on light interventions. Our focus is on designing low‐burden interventions requiring minimal changes to lifestyle or sleep‐wake routines. Method Eighteen older adults (65‐80 y) monitored their light exposure and sleep for a period of 7‐10 days at home using a wristworn monitor (Actiwatch Spectrum) and a sleep diary from which we calculated daily sleep timing and duration. We constructed a new mechanistic mathematical model for sleep timing that incorporates sleep homeostasis, circadian rhythmicity and light data (HCL). For each participant, we fitted the model to their sleep timing and duration data. We then used simulations to predict the effect of different light interventions on sleep timing and used optimisation to determine light exposure patterns that promote a target sleep onset time. Result Individuals varied in their sleep duration (07:18 ± 0:56 hh:mm; mean ± SD) and mid‐sleep timing (03:22 ± 01:00 hh:mm). We successfully retrieved ‘personal’ model parameters that accurately captured sleep duration and timing for 17 of the 18 participants. Our simulations indicated that the effect of a given light exposure pattern on sleep timing depends on the personalised model parameters, suggesting that an intervention that works for one person may not work for someone else. We were able to propose light exposure patterns requiring minimal behavioural change, facilitating adherence. Conclusion It is possible to extract individual physiological parameters for sleep‐wake regulation from data collected in the field. These individual parameters can be used to design personalised light interventions. Our fully‐documented code could be combined with passive sleep monitors and environmental light sensors to unobtrusively recommend personalised light exposure patterns in close‐to real time.

Understanding how metabolite levels change over the 24 hour day is of crucial importance for clinical and epidemiological studies. Additionally, the association between sleep deprivation and metabolic disorders such as diabetes and obesity requires investigation into the links between sleep and metabolism. Here, we characterise time-of-day variation and the effects of sleep deprivation on urinary metabolite profiles. Healthy male participants (n = 15) completed an in-laboratory study comprising one 24 h sleep/wake cycle prior to 24 h of continual wakefulness under highly controlled environmental conditions. Urine samples were collected over set 2-8 h intervals and analysed by (1)H NMR spectroscopy. Significant changes were observed with respect to both time of day and sleep deprivation. Of 32 identified metabolites, 7 (22%) exhibited cosine rhythmicity over at least one 24 h period; 5 exhibiting a cosine rhythm on both days. Eight metabolites significantly increased during sleep deprivation compared with sleep (taurine, formate, citrate, 3-indoxyl sulfate, carnitine, 3-hydroxyisobutyrate, TMAO and acetate) and 8 significantly decreased (dimethylamine, 4-DTA, creatinine, ascorbate, 2-hydroxyisobutyrate, allantoin, 4-DEA, 4-hydroxyphenylacetate). These data indicate that sampling time, the presence or absence of sleep and the response to sleep deprivation are highly relevant when identifying biomarkers in urinary metabolic profiling studies.

Ageing is associated with increased disturbances in the timing, duration, and quality of sleep. These disruptions may reflect changes in the circadian timing system and/or the sleep homeostat which are both necessary to produce consolidated sleep at an appropriate time. In addition, it is possible that age-related alterations in the detection and transmission of the photic signal responsible for synchronizing the circadian clock may play a role. Ageing is accompanied by many changes within the eye including alterations in pupil size, lens transmission, and number of photoreceptors. The observed increase in ocular lens density with age will diminish the transmission of short wavelength blue light to which the circadian system has been shown to be most sensitive, and may contribute, in part, to the observed increase in sleep disturbances in older people. We were the first group to test the hypothesis that non-visual responses to blue light would be impaired in older individuals. Our research has demonstrated that whilst acute non-visual effects of blue light are impaired with age, the light resetting effect appears unaltered. Future research should work towards optimizing the light environment for older people to promote good quality sleep and daytime functioning.

Although daily rhythms regulate multiple aspects of human physiology, rhythmic control of the metabolome remains poorly understood. The primary objective of this proof-of-concept study was identification of metabolites in human plasma that exhibit significant 24-h variation. This was assessed via an untargeted metabolomic approach using liquid chromatography-mass spectrometry (LC-MS). Eight lean, healthy, and unmedicated men, mean age 53.6 (SD ± 6.0) yrs, maintained a fixed sleep/wake schedule and dietary regime for 1 wk at home prior to an adaptation night and followed by a 25-h experimental session in the laboratory where the light/dark cycle, sleep/wake, posture, and calorific intake were strictly controlled. Plasma samples from each individual at selected time points were prepared using liquid-phase extraction followed by reverse-phase LC coupled to quadrupole time-of-flight MS analysis in positive ionization mode. Time-of-day variation in the metabolites was screened for using orthogonal partial least square discrimination between selected time points of 10:00 vs. 22:00 h, 16:00 vs. 04:00 h, and 07:00 (d 1) vs. 16:00 h, as well as repeated-measures analysis of variance with time as an independent variable. Subsequently, cosinor analysis was performed on all the sampled time points across the 24-h day to assess for significant daily variation. In this study, analytical variability, assessed using known internal standards, was low with coefficients of variation

[Display omitted] •Short-wavelength light during nighttime and over a long period (∼6h) can sustain inducing alertness, while long-wavelength (red) light at a shorter duration (∼1h) and lower intensity can almost produce this effect during the daytime until night.•EEG measurements of alertness reveal high activity at different times during exposure to short-wavelength and long-wavelength light, and this increase does not seem to associate with improving subjective and objective measures of alertness.•Rather than melanopic illuminance, L-cone-opic illuminance could be used to determine particular alerting effects of light, such as neural activity in the brain at specified times.•For producing alertness during nighttime, it is recommended to substitute light sources enriched at short-wavelength and middle-wavelength (420–555 nm) with long-wavelength (620 – 640 nm) light to avoid disrupting melatonin secretion. Light is detected in the eye by three classes of photoreceptors (rods, cones, and intrinsically photosensitive retinal ganglion cells (ipRGCs)) that are each optimized for a specific function and express a particular light-detecting photopigment. The significance of short-wavelength light and ipRGC's role in improving alertness has been well-established; however, few reviews have been undertaken to assess the other wavelengths' effects regarding timing and intensity. This study aims to evaluate the impact of different narrowband light wavelengths on subjective and objective alertness among the 36 studies included in this systematic review, 17 of which were meta-analyzed. Short-wavelength light (∼460 – 480 nm) significantly improves subjective alertness, cognitive function, and neurological brain activities at night, even for a sustained period (∼6h) (0.4 

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

[This corrects the article DOI: 10.2196/46338.].

Background Nocturnal disturbance is frequently observed in dementia and is a major contributor to institutionalisation. Unobtrusive technology that can quantify sleep/wake and determine bed occupancy during the major nocturnal sleep episode may be beneficial for long-term clinical monitoring and the carer. Such technologies have, however, not been validated in older people. Here we assessed the performance of the Withings Sleep Mattress (WSM) in a heterogenous older population to ensure external validity. Method Eighteen participants (65 – 80 years, 10M:8F) completed 7-12 days of sleep/wake monitoring at home prior to an overnight laboratory session. WSM performance was compared to gold-standard (laboratory polysomnography [PSG] with video) and silver standard (actiwatch [AWS] and sleep diary at home). WSM data were downloaded from a third party API and the minute-to-minute sleep/wake timeseries extracted and time-ordered to create a sleep profile. Discontinuities in the timeseries were labelled as ‘missing data’ events. Results Participants contributed 107 nights with WSM and PSG or AWS data. In the laboratory, the overall epoch to epoch agreement (accuracy) of sleep/wake detection of WSM compared to PSG was 0.71 (sensitivity 0.8; specificity 0.45) and to AWS was 0.74 (sensitivity 0.77; specificity 0.53). Visual inspection of video recordings demonstrated that 20 of 21 ‘missing data’ events were true ‘out of bed’ events. These events were always associated with an increase in activity (AWS). At home, all 97 WSM ‘missing data’ events that occurred within the major nocturnal sleep episode defined by sleep diary data, were associated with an increase in activity levels in the AWS data and 36 of these events were also associated with an increase in light levels, indicating that the participant had left the bed. In several participants, data recorded by the WSM during daytime coincided with reported naps in the sleep diary. Conclusion Although WSM cannot reliably distinguish between sleep and wake, the presence/absence of data in WSM seem to be an accurate representation of whether older people are in or out of bed (bed occupancy). Thus, in dementia, this contactless, low-burden technology may be able to provide information about nocturnal disturbances and daytime naps in bed.

Jet lag emerges after flights across time zones, and abates when the circadian clock re-entrains to the light/dark cycle of the destination time zone. During the days of re-entrainment there is substantial circadian misalignment, with sleep, wake and meals occurring at the wrong circadian times. This misalignment produces difficulty initiating or maintaining sleep, excessive daytime sleepiness, decrements in performance, and gastrointestinal distress. We review how to shift the circadian clock using appropriately timed light, dark for sleep, and melatonin. Using these circadian rhythm principles, we describe how to make schedules for travelers which can reduce and even prevent jet lag.

The effects of orexinergic peptides are diverse and are mediated by orexin-1 and orexin-2 receptors. Antagonists that target both receptors have been shown to promote sleep initiation and maintenance. Here, we investigated the role of the orexin-2 receptor in sleep regulation in a randomised, double-blind, placebo-controlled, three-period crossover clinical trial using two doses (20 and 50 mg) of a highly selective orexin-2 receptor antagonist (2-SORA) (JNJ-48816274). We used a phase advance model of sleep disruption where sleep initiation is scheduled in the circadian wake maintenance zone. We assessed objective and subjective sleep parameters, pharmacokinetic profiles and residual effects on cognitive performance in 18 healthy male participants without sleep disorders. The phase advance model alone (placebo condition) resulted in disruption of sleep at the beginning of the sleep period compared to baseline sleep (scheduled at habitual time). Compared to placebo, both doses of JNJ-48816274 significantly increased total sleep time, REM sleep duration and sleep efficiency, and reduced latency to persistent sleep, sleep onset latency, and REM latency. All night EEG spectral power density for both NREM and REM sleep were unaffected by either dose. Participants reported significantly better quality of sleep and feeling more refreshed upon awakening following JNJ-48816274 compared to placebo. No significant residual effects on objective performance measures were observed and the compound was well tolerated. In conclusion, the selective orexin-2 receptor antagonist JNJ-48816274 rapidly induced sleep when sleep was scheduled earlier in the circadian cycle and improved self-reported sleep quality without impact on waking performance.

Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.

The identification and investigation of novel clock-controlled genes (CCGs) has been conducted thus far mainly in model organisms such as nocturnal rodents, with limited information in humans. Here, we aimed to characterize daily and circadian expression rhythms of CCGs in human peripheral blood during a sleep/sleep deprivation (S/SD) study and a constant routine (CR) study. Blood expression levels of 9 candidate CCGs (SREBF1, TRIB1, USF1, THRA1, SIRT1, STAT3, CAPRIN1, MKNK2, and ROCK2), were measured across 48 h in 12 participants in the S/SD study and across 33 h in 12 participants in the CR study. Statistically significant rhythms in expression were observed for STAT3, SREBF1, TRIB1, and THRA1 in samples from both the S/SD and the CR studies, indicating that their rhythmicity is driven by the endogenous clock. The MKNK2 gene was significantly rhythmic in the S/SD but not the CR study, which implies its exogenously driven rhythmic expression. In addition, we confirmed the circadian expression of PER1, PER3, and REV-ERBα in the CR study samples, while BMAL1 and HSPA1B were not significantly rhythmic in the CR samples; all 5 genes previously showed significant expression in the S/SD study samples. Overall, our results demonstrate that rhythmic expression patterns of clock and selected clock-controlled genes in human blood cells are in part determined by exogenous factors (sleep and fasting state) and in part by the endogenous circadian timing system. Knowledge of the exogenous and endogenous regulation of gene expression rhythms is needed prior to the selection of potential candidate marker genes for future applications in medical and forensic settings.

Common cold sufferers frequently report sleep disruption during the symptomatic period of infections. We examined the effects of treatment with a topical aromatic pharmaceutical ointment (Vicks VapoRub®), on associated sleep disturbances. The effects of Vicks VapoRub® versus placebo (petrolatum ointment) on subjective and objective measured sleep parameters were assessed in an exploratory study of 100 common cold patients, in a randomized, single blind, controlled, two-arm, parallel design study. The primary efficacy variable was subjective sleep quality measured with the SQSQ (Subjective Quality of Sleep Questionnaire). Additional measures included, ease of falling asleep and depth of sleep (measured with a post-sleep Visual Analog Scale), total sleep time, sleep onset latency, activity score, percentage of sleep, sleep efficiency (measured with actigraphy and SQSQ) and sleep quality index measured with a modified Karolinska Sleep Diary (KSD). The primary endpoint, “How was the quality of your sleep last night?” showed a statistically significant difference in change from baseline in favour of VapoRub treatment (p = 0.0392) versus placebo. Positive effects of VapoRub versus placebo were also observed for “How refreshed did you feel upon waking up?” (p = 0.0122) (SQSQ), “Did you get enough sleep?” (p = 0.0036) (KSD), “How was it to get up?” (p = 0.0120) (KSD) and “Do you feel well-rested?” (p = 0.0125) (KSD). No statistically significant changes from baseline versus placebo were detected in the Actiwatch endpoints. Vicks VapoRub®when applied before retiring to bed can reduce subjective sleep disturbances during a common cold. The results of this exploratory study support the belief among patients that the use of VapoRub improves subjective sleep quality during common cold which was associated with more refreshing sleep.

Objectives: Experimental inversion of circadian and behavioural rhythms by 12-hours adversely effects markers of metabolic health. The objective of this work was to investigate the effects of a more modest 5-hour delay in behavioural cycles (as observed in e.g. trans-Atlantic or trans-continental jetlag) on circadian and metabolic markers. Methods: Fourteen participants completed an 8-day inpatient laboratory protocol, with controlled sleep-wake opportunities, light-dark cycles, meal timing and diet composition. The 5-hour delay in sleep/wake and mealtimes was induced by delaying sleep opportunity. We measured melatonin to assess central circadian phase; markers of postprandial metabolism; subjective sleepiness, and appetite. Results: Melatonin rhythms gradually adapted to the new behavioural cycle. In the 4-hours after the phase delay, there was slower gastric emptying at breakfast, lower fasting plasma glucose, higher postprandial plasma glucose and triglycerides, and lower thermic effect of feeding (p < 0.05). These changes were abolished or attenuated within 48–72 hours of the phase delay. Conclusions: Moderate (5-hour) circadian desynchrony only transiently disrupts metabolism. Occasional jetlag may therefore pose low metabolic health risk

Circadian rest-Activity Rhythm Disorders (CARDs) are common in patients with cancer, particularly in advanced disease. CARDs are associated with increased symptom burden, poorer quality of life, and shorter survival. Research and reporting practices lack standardization, and formal diagnostic criteria do not exist. This electronic Delphi (e-Delphi) study aimed to formulate international recommendations for the assessment and diagnosis of CARDs in patients with cancer. An international e-Delphi was performed using an online platform (Welphi). Round 1 developed statements regarding circadian rest-activity rhythms, diagnostic criteria, and assessment techniques. Rounds 2 and 3 involved participants rating their level of agreement with the statements and providing comments until consensus (defined internally as 67%) and stability between rounds were achieved. Recommendations were then created and distributed to participants for comments before being finalized. Sixteen participants from nine different clinical specialties and seven different countries, with 5-35 years of relevant research experience, were recruited, and thirteen participants completed all three rounds. Of the 164 generated statements, 66% achieved consensus, and responses were stable between the final two rounds. The e-Delphi resulted in international recommendations for assessing and diagnosing CARDs in patients with cancer. These recommendations should ensure standardized research and reporting practices in future studies.

Trace deposition timing reflects a novel concept in forensic molecular biology involving the use of rhythmic biomarkers for estimating the time within a 24-h day/night cycle a human biological sample was left at the crime scene, which in principle allows verifying a sample donor’s alibi. Previously, we introduced two circadian hormones for trace deposition timing and recently demonstrated that messenger RNA (mRNA) biomarkers significantly improve time prediction accuracy. Here, we investigate the suitability of metabolites measured using a targeted metabolomics approach, for trace deposition timing. Analysis of 171 plasma metabolites collected around the clock at 2-h intervals for 36 h from 12 male participants under controlled laboratory conditions identified 56 metabolites showing statistically significant oscillations, with peak times falling into three day/night time categories: morning/noon, afternoon/evening and night/early morning. Time prediction modelling identified 10 independently contributing metabolite biomarkers, which together achieved prediction accuracies expressed as AUC of 0.81, 0.86 and 0.90 for these three time categories respectively. Combining metabolites with previously established hormone and mRNA biomarkers in time prediction modelling resulted in an improved prediction accuracy reaching AUCs of 0.85, 0.89 and 0.96 respectively. The additional impact of metabolite biomarkers, however, was rather minor as the previously established model with melatonin, cortisol and three mRNA biomarkers achieved AUC values of 0.88, 0.88 and 0.95 for the same three time categories respectively. Nevertheless, the selected metabolites could become practically useful in scenarios where RNA marker information is unavailable such as due to RNA degradation. This is the first metabolomics study investigating circulating metabolites for trace deposition timing, and more work is needed to fully establish their usefulness for this forensic purpose.

Background Sleep disturbances are both risk factors for and symptoms of dementia. Current methods for assessing sleep disturbances are largely based on either polysomnography (PSG) which is costly and inconvenient, or self‐ or care‐giver reports which are prone to measurement error. Low‐cost methods to monitor sleep disturbances longitudinally and at scale can be useful for assessing symptom development. Here, we develop deep learning models that use multimodal variables (accelerometers and temperature) recorded by the AX3 to accurately identify sleep and wake epochs and derive sleep parameters. Method Eighteen men and women (65‐80y) participated in a sleep laboratory‐based study in which multiple devices for sleep monitoring were evaluated. PSGs were recorded over a 10‐h period and scored according to established criteria per 30 sec epochs. Tri‐axial accelerometers and temperature signals were captured with an Axivity AX3, at 100Hz and 1Hz, respectively, throughout a 19‐h period, including 10‐h concurrent PSG recording and 9‐h of wakefulness. We developed and evaluated a supervised deep learning algorithm to detect sleep and wake epochs and determine sleep parameters from the multimodal AX3 raw data. We validated our results with gold standard PSG measurements and compared our algorithm to the Biobank accelerometer analysis toolbox. Single modality (accelerometer or temperature) and multimodality (both signals) approaches were evaluated using the 3‐fold cross‐validation. Result The proposed deep learning model outperformed baseline models such as the Biobank accelerometer analysis toolbox and conventional machine learning classifiers (Random Forest and Support Vector Machine) by up to 25%. Using multimodal data improved sleep and wake classification performance (up to 18% higher) compared with the single modality. In terms of the sleep parameters, our approach boosted the accuracy of estimations by 11% on average compared to the Biobank accelerometer analysis toolbox. Conclusion In older adults without dementia, combining multimodal data from AX3 with deep learning methods allows satisfactory quantification of sleep and wakefulness. This approach holds promise for monitoring sleep behaviour and deriving accurate sleep parameters objectively and longitudinally from a low‐cost wearable sensor. A limitation of our current study is that the participants were healthy older adults: future work will focus on people living with dementia.

MAPK pathway activation is frequently observed in human malignancies, including melanoma, and is associated with sensitivity to MEK inhibition and changes in cellular metabolism. Using quantitative mass spectrometry-based metabolomics, we identified in preclinical models 21 plasma metabolites including amino acids, propionylcarnitine, phosphatidylcholines and sphingomyelins that were significantly altered in two B-RAF mutant melanoma xenografts and that were reversed following a single dose of the potent and selective MEK inhibitor RO4987655. Treatment of non-tumour bearing animals and mice bearing the PTEN null U87MG human glioblastoma xenograft elicited plasma changes only in amino acids and propionylcarnitine. In patients with advanced melanoma treated with RO4987655, on-treatment changes of amino acids were observed in patients with disease progression and not in responders. In contrast, changes in phosphatidylcholines and sphingomyelins were observed in responders. Furthermore, pre-treatment levels of 7 lipids identified in the preclinical screen were statistically significantly able to predict objective responses to RO4987655. The RO4987655 treatment-related changes were greater than baseline physiological variability in non-treated individuals. This study provides evidence of a translational exo-metabolomic plasma readout predictive of clinical efficacy together with pharmacodynamic utility following treatment with a signal transduction inhibitor.

Studying circadian rhythms in most human tissues is hampered by difficulty in collecting serial samples. Here we reveal circadian rhythms in the transcriptome and metabolic pathways of human white adipose tissue. Subcutaneous adipose tissue was taken from seven healthy males under highly controlled ‘constant routine’ conditions. Five biopsies per participant were taken at six-hourly intervals for microarray analysis and in silico integrative metabolic modelling. We identified 837 transcripts exhibiting circadian expression profiles (2% of 41619 transcript targeting probes on the array), with clear separation of transcripts peaking in the morning (258 probes) and evening (579 probes). There was only partial overlap of our rhythmic transcripts with published animal adipose and human blood transcriptome data. Morning-peaking transcripts associated with regulation of gene expression, nitrogen compound metabolism, and nucleic acid biology; evening-peaking transcripts associated with organic acid metabolism, cofactor metabolism and redox activity. In silico pathway analysis further indicated circadian regulation of lipid and nucleic acid metabolism; it also predicted circadian variation in key metabolic pathways such as the citric acid cycle and branched chain amino acid degradation. In summary, in vivo circadian rhythms exist in multiple adipose metabolic pathways, including those involved in lipid metabolism, and core aspects of cellular biochemistry.

Background People living with dementia (PLWD) often exhibit marked sleep disturbances. These cause substantial care challenges and may be causally related to dementia progression. Collecting ecologically valid data on sleep disturbance in naturalistic settings has been difficult. As a result, sleep assessments in PLWD are generally limited to short studies in sleep laboratories or data collection from wearables, where compliance is problematic. Here, we demonstrate how passive internet of things (IoT) sensors can be used to monitor the effects of dementia on nocturnal behaviour and physiology. Method Using the Withings under‐mattress pressure sensor, we validated bed occupancy and physiological measures in 35 older adults tested both at home and in the laboratory. We then examined data collected between 2019 and 2021 from the general population (N=13,663) and from a cohort of PLWD taking part in the UK DRI study of home monitoring for PLWD (N=46). More than 4 million unique bed mat observations were analysed. Result Arise time across all subjects was negatively correlated with time to bed (Fig.1a, r(13,617)=‐0.5, p

Light influences diverse aspects of human physiology and behaviour including neuroendocrine function, the circadian system and sleep. A role for melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) in driving such effects is well-established. However, rod and/or cone signals routed through ipRGCs could also influence 'non-visual' spectral sensitivity. In humans, this has been most extensively studied for acute, light-dependent, suppression of nocturnal melatonin production. Of the published action spectra for melatonin suppression, one demonstrates a spectral sensitivity consistent with that expected for melanopsin while our own (using briefer 30 min light exposures) displays very high sensitivity to short wavelength light, suggesting a contribution of S-cones. To clarify that possibility, six healthy young male participants were each exposed to 30 min of five irradiances of 415 nm monochromatic light (1 - 40 µW/cm ) across different nights. These data were then combined with the original action spectrum. The aggregated data are incompatible with the involvement of any single opsin and multi-opsin models based on the original action spectrum (including Circadian Stimulus) fail to predict the responses to 415 nm stimuli. Instead, the extended action spectrum can be most simply approximated by an ~2:1 combination of melanopsin and S-cone signals. Such a model also better describes the magnitude of melatonin suppression observed in other studies using an equivalent 30 min mono- or polychromatic light paradigm but not those using longer (90 min) light exposures. In sum, these data provide evidence for an initial S-cone contribution to melatonin suppression that rapidly decays under extended light exposure.

Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure along with social constraints and behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised interventions. The timing of the human sleep-wake cycle has been modelled as an interaction of a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate that individual sleep phenotypes in each of 34 older participants (65-83y) can be described by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights that different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.

Diurnal behavior in humans is governed by the period length of a circadian clock in the suprachiasmatic nuclei of the brain hypothalamus. Nevertheless, the cell-intrinsic mechanism of this clock is present in most cells of the body. We have shown previously that for individuals of extreme chronotype ("larks" and "owls"), clock properties measured in human fibroblasts correlated with extreme diurnal behavior.

This study investigated the impact of sleep deprivation on the human circadian system. Plasma melatonin and cortisol levels and leukocyte expression levels of 12 genes were examined over 48 h (sleep vs. no-sleep nights) in 12 young males (mean ± SD: 23 ± 5 yrs). During one night of total sleep deprivation, BMAL1 expression was suppressed, the heat shock gene HSPA1B expression was induced, and the amplitude of the melatonin rhythm increased, whereas other high-amplitude clock gene rhythms (e.g., PER1-3, REV-ERBα) remained unaffected. These data suggest that the core clock mechanism in peripheral oscillators is compromised during acute sleep deprivation.

Blue light sensitivity of melatonin suppression and subjective mood and alertness responses in humans is recognised as being melanopsin based. Observations that long wavelength (red) light can potentiate responses to subsequent short wavelength (blue) light have been attributed to the bistable nature of melanopsin whereby it forms stable associations with both 11-cis and alltrans isoforms of retinaldehyde and uses light to transition between these states. The current study examined the effect of concurrent administration of blue and red monochromatic light, as would occur in real-world white light, on acute melatonin suppression and subjective mood and alertness responses in humans. Young healthy males (18-35 years; n = 21) were studied in highly controlled laboratory sessions that included an individually timed 30 min light stimulus of blue (λmax 479 nm) or red (λmax 627 nm) monochromatic light at varying intensities (1013 - 1014 photons/cm2/s) presented, either alone or in combination, in a within-subject randomised design. Plasma melatonin levels and subjective mood and alertness were assessed at regular intervals relative to the light stimulus. Subjective alertness levels were elevated after light onset irrespective of light wavelength or irradiance. For melatonin suppression, a significant irradiance response was observed with blue light. Co-administration of red light, at any of the irradiances tested, did not significantly alter the response to blue light alone. Under the current experimental conditions the primary determinant of the melatonin suppression response was the irradiance of blue 479 nm light and this was unaffected by simultaneous red light administration.

Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array, which holds great potential for sleep-wake monitoring research. It is comfortable to wear, simple to apply, and minimally intrusive during sleep. Moreover, it can be combined with a smartphone-controlled miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive research. The present study aimed to explore the suitability of cEEGrid for sleep research, by testing whether cEEGrid data affords the signal quality and characteristics necessary for sleep stage scoring. In an accredited sleep laboratory, sleep data from cEEGrid and a standard PSG system were acquired simultaneously. Twenty participants were recorded for one extended nocturnal sleep opportunity. Fifteen data sets were scored manually. Sleep parameters relating to sleep maintenance and sleep architecture were then extracted and statistically assessed for signal quality and concordance. The findings suggest that the cEEGrid system is a viable and robust recording tool to capture sleep and wake EEG. Further research is needed to fully determine the suitability of cEEGrid for basic and applied research as well as sleep medicine.

Disruption to sleep and circadian rhythms can impact on metabolism. The study aimed to investigate the effect of acute sleep deprivation on plasma melatonin, cortisol and metabolites, to increase understanding of the metabolic pathways involved in sleep/wake regulation processes. Twelve healthy young female subjects remained in controlled laboratory conditions for ~92 h with respect to posture, meals and environment light (18:00‐23:00 h and 07:00‐09:00 h

A recent proof-of-concept pilot study proposed using microRNA (miRNA) markers for time of death determination. The markers – miRNA-142-5p and miRNA-541, were reported to show considerable expression differences in vitreous humor between individuals who died during the day or night. Here, we investigated whether these miRNA markers show the same diurnal expression pattern in blood, which would make them useful for estimating bloodstain deposition time to allow molecular alibi testing for forensic casework. We analyzed venous blood samples collected from 12 healthy individuals every 4 h during the 24 h day/night period under controlled sleep-laboratory conditions. MiRNA-142-5p normalized against miRNA-222 showed no statistically significant expression differences between blood samples collected during daytime and nighttime (one-way ANOVA p = 0.81), and also no statistically significant rhythmicity during the 24 h day/night period (cosine fit for all individuals p > 0.05, averaged data p = 0.932). MicroRNA-541 amplification in blood was above the 34-cycle threshold applied in the study, indicating too low quantities for obtaining reliable data. Overall, we conclude that the two miRNA markers previously suggested for time of death determination in vitreous humor are not suitable for estimating the deposition time of forensic bloodstains. Future studies may find out if miRNA markers with significant diurnal expression patterns can be identified and how useful they would be for forensic trace deposition timing.

Isolation from external time cues allows endogenous circadian rhythmicity to be demonstrated. In this study, also filmed as a television documentary, we assessed rhythmic changes in a healthy man time isolated in a bunker for 9 days/nights. During this period the lighting conditions were varied between: (1) self-selected light/dark cycle, (2) constant dim light, and (3) light/dark cycle with early wake up. A range of variables was assessed and related to the sleep-wake cycle, psychomotor and physical performance and clock-time estimation. This case study using modern non-invasive monitoring techniques emphasizes how different physiological circadian rhythms persist in temporal isolation under constant dim light conditions with different waveforms, free-running with a period (t) between 24 and 25 h. In addition, a significant correlation between time estimation and mid-sleep time, a proxy for circadian phase, was demonstrated.