Validation of a Device for the Ambulatory Monitoring of Sleep Patterns: A Pilot Study on Parkinson's Disease

Sleep and Circadian Health of Critical Survivors: A 12-Month Follow-Up Study

OBJECTIVES

To investigate the sleep and circadian health of critical survivors 12 months after hospital discharge and to evaluate a possible effect of the severity of the disease within this context.

DESIGN

Observational, prospective study.

SETTING

Single-center study.

PATIENTS

Two hundred sixty patients admitted to the ICU due to severe acute respiratory syndrome coronavirus 2 infection.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The cohort was composed of 260 patients (69.2% males), with a median (quartile 1-quartile 3) age of 61.5 years (52.0-67.0 yr). The median length of ICU stay was 11.0 days (6.00-21.8 d), where 56.2% of the patients required invasive mechanical ventilation (IMV). The Pittsburgh Sleep Quality Index (PSQI) revealed that 43.1% of the cohort presented poor sleep quality 12 months after hospital discharge. Actigraphy data indicated an influence of the disease severity on the fragmentation of the circadian rest-activity rhythm at the 3- and 6-month follow-ups, which was no longer significant in the long term. Still, the length of the ICU stay and the duration of IMV predicted a higher fragmentation of the rhythm at the 12-month follow-up with effect sizes (95% CI) of 0.248 (0.078-0.418) and 0.182 (0.005-0.359), respectively. Relevant associations between the PSQI and the Hospital Anxiety and Depression Scale (rho = 0.55, anxiety; rho = 0.5, depression) as well as between the fragmentation of the rhythm and the diffusing lung capacity for carbon monoxide (rho = -0.35) were observed at this time point.

CONCLUSIONS

Our findings reveal a great prevalence of critical survivors presenting poor sleep quality 12 months after hospital discharge. Actigraphy data indicated the persistence of circadian alterations and a possible impact of the disease severity on the fragmentation of the circadian rest-activity rhythm, which was attenuated at the 12-month follow-up. This altogether highlights the relevance of considering the sleep and circadian health of critical survivors in the long term.

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Sleep problems and circadian rhythm functioning in autistic children, autism with co-occurring attention deficit hyperactivity disorder, and typically developing children: A comparative study

LAY ABSTRACT

Sleep problems are common in autism spectrum disorder (ASD) and different factors can contribute to its occurrence in this population. Misalignment of the biological clock (our circadian system) has been described as one possible explanation. While there is a body of research on sleep problems, relatively less is known about circadian functioning and the specific population of autistic children with co-occurring attention deficit hyperactivity disorder (ADHD). Using an ambulatory circadian monitoring (ACM) system, which resembles a common watch, we gathered sleep parameters and the different rhythms obtained from measuring motor activity, light exposure and distal temperature in 87 autistic children and adolescents, 27 of whom were diagnosed with co-occurring ADHD, and 30 neurotypical children and adolescents as a comparison group. Autistic children and, especially, those with co-occurring ADHD showed greater motor activity during sleep which would be worth studying in future projects which could better define this restless sleep. Of note, we observed an atypical pattern of wrist temperature, with higher values in neurotypical children, followed by autistic children and, ultimately, those with co-occurring ADHD. Temperature is one of the most valuable factors evaluated here as it is closely connected to sleep-wakefulness and the hormone melatonin. Its special pattern during day and nighttime would support the hypothesis of an atypical secretion of melatonin in autistic individuals which would also link with the higher presence of sleep problems in this neurodevelopmental condition.

Discrepancies between self-report and objective sleep outcomes are associated with cognitive impairment and fatigue in people with multiple sclerosis and insomnia

OBJECTIVES

The primary aim of this study was to assess the degree to which discrepancies between self-reported and actigraphy-based measures of sleep are associated with specific demographic, disease characteristics, and clinical features in a sample of individuals with multiple sclerosis (MS) reporting clinically significant insomnia symptoms.

METHODS

Participants were 90 community-based participants with MS and insomnia. Measures included the Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory-Fast Screen (BDI-FS), Modified Fatigue Impact Scale (MFIS), and MS Neuropsychological Screening Questionnaire (MSNQ), and wrist actigraphy-derived sleep parameters. Discrepancy scores were calculated by subtracting actigraphy-derived values from PSQI-derived values for sleep latency (SL), total sleep time (TST), and sleep efficiency (SE).

RESULTS

Correlations between PSQI and actigraphy-derived values were weak. Significant discrepancies, with moderate-to-large effect sizes, were observed between PSQI and actigraphy for SL, TST, and SE, whereby the PSQI yielded longer SL, shorter TST, and less SE than actigraphy. MSNQ elevations correlated with greater PSQI-actigraphy discrepancies in SL and TST. MFIS elevations correlated with greater discrepancies in TST. Discrepancies were not significantly related to BDI-FS, gender, race, education level, or MS type.

CONCLUSIONS

Results emphasize the importance of assessing fatigue with sleep, and when feasible, inclusion of both self-report and actigraphy measures.

Use of remote monitoring and integrated platform for the evaluation of sleep quality in adult-onset idiopathic cervical dystonia

BACKGROUND

Up to 70% of individuals diagnosed with adult-onset idiopathic focal cervical dystonia (AOIFCD) report difficulties with sleep. Larger cohort studies using wrist-worn accelerometer devices have emerged as an alternative to smaller polysomnography studies, in order to evaluate sleep architecture.

METHODS

To measure activity during the sleep/wake cycle, individuals wore a consumer-grade wrist device (Garmin vivosmart 4) continuously over 7 days on their non-dominant wrist, while completing a daily sleep diary and standardised sleep and non-motor questionnaires via a dedicated app. Sleep measures were derived from the captured raw triaxial acceleration and heart rate values using previously published validated algorithms.

RESULTS

Data were collected from 50 individuals diagnosed with AOIFCD and 47 age- and sex-matched controls. Those with AOIFCD self-reported significantly higher levels of excessive daytime sleepiness (p = 0.04) and impaired sleep quality (p = 0.03), while accelerometer measurements found the AOIFCD cohort to have significantly longer total sleep times (p = 0.004) and time spent in NREM sleep (p = 0.009), compared to controls. Overall, there was limited agreement between wearable-derived sleep parameters, and self-reported sleep diary and visual analogue scale records.

DISCUSSION

This study shows the potential feasibility of using consumer-grade wearable devices in estimating sleep measures at scale in dystonia cohorts. Those diagnosed with AOIFCD were observed to have altered sleep architecture, notably longer total sleep time and NREM sleep, compared to controls. These findings suggest that previously reported disruptions to brainstem circuitry and serotonin neurotransmission may contribute to both motor and sleep pathophysiology.

Chronobiology of Parkinson's disease: Past, present and future

Parkinson's disease is a neurodegenerative disorder predominately affecting midbrain dopaminergic neurons that results in a broad range of motor and non-motor symptoms. Sleep complaints are among the most common non-motor symptoms, even in the prodromal period. Sleep alterations in Parkinson's disease patients may be associated with dysregulation of circadian rhythms, intrinsic 24-h cycles that control essential physiological functions, or with side effects from levodopa medication and physical and mental health challenges. The impact of circadian dysregulation on sleep disturbances in Parkinson's disease is not fully understood; as such, we review the systems, cellular and molecular mechanisms that may underlie circadian perturbations in Parkinson's disease. We also discuss the potential benefits of chronobiology-based personalized medicine in the management of Parkinson's disease both in terms of behavioural and pharmacological interventions. We propose that a fuller understanding of circadian clock function may shed important new light on the aetiology and symptomatology of the disease and may allow for improvements in the quality of life for the millions of people with Parkinson's disease.

Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.

Metabolic profiling of night shift work - The HORMONIT study

Mechanistic studies are needed to understand how rotating shift work perturbs metabolic processing. We collected plasma samples (n = 196) from 49 males, rotating car factory shift workers at the beginning and end of a night-shift (22:00-06:00 h) and day-shift (06:00 h-14:00 h). Samples underwent targeted LC-MS/MS metabolomics and concentrations of 130 metabolites were log2-transformed and pareto-scaled. An elastic net selected the most influential metabolites for linear mixed models examining within-person variation in metabolite levels at night-shift end (06:00 h) compared to day-shift start (06:00 h). Quantitative enrichment analysis explored differentially enriched biological pathways between sample time points. We included 20 metabolites (amino acids, biogenic amines, acylcarnitines, glycerophospholipids) in mixed models. Night-shift was associated with changes in concentrations of arginine (geometric mean ratio [GMR] 2.30, 95%CI 1.25, 4.23), glutamine (GMR 2.22, 95%CI 1.53, 3.24), kynurenine (GMR 3.22, 95%CI 1.05, 9.87), lysoPC18:2 (GMR 1.86, 95%CI 1.11, 3.11), lysoPC20:3 (GMR 2.48, 95%CI 1.05, 5.83), PCaa34:2 (GMR 2.27, 95%CI 1.16, 4.44), and PCae38:5 (GMR 1.66, 95%CI 1.02, 2.68). Tryptophan metabolism, glutathione metabolism, alanine metabolism, glycine and serine metabolism, and urea cycle were pathways differing between shifts. Night shift work was associated with changes in metabolites and the perturbation of metabolic and biochemical pathways related to a variety of health outcomes.

The Effect of a Sleep Intervention on Sleep Quality in Nursing Students: Study Protocol for a Randomized Controlled Trial

We develop a protocol for assessing the impact of an intervention aimed at improving sleep quality among university nursing students. The study is designed as a pilot randomized controlled trial to be applied during the 2022-23 academic year and is registered at Clinical Trials Gov website (NCT05273086). A total of 60 nursing students will be recruited from a Spanish university. They will be divided into two groups: (30) intervention group and (30) control group. The intervention group will attend two cognitive-behavioural therapy sleep programme sessions focused on knowledge of anatomical structures involved in sleep, chronotype, synchronization, and good sleeping habits. Subjective and objective sleep quality will be assessed before and after the intervention for both groups. In addition to sleep quality, socio-demographic parameters, physical activity, lifestyle habits, and anthropometric measures will be considered prior to intervention. Finally, a satisfaction questionnaire will be applied for posterior analysis. This study is an innovative, relevant intervention that aims to improve sleep quality among university nursing students. Both the approach and the use of objective and subjective validated outcome measurements are key features of this study.

Disruption of cellular immune response among male rotating night shift workers in Spain– The HORMONIT study

Introduction

Preliminary studies suggest that night shift work is associated with a desynchronization of rhythmic immune markers, possibly explaining the increased risk of infection, cardiometabolic disorders, and cancer in shift workers.

Methods

This study included 51 male rotating shift workers from a car industry in Barcelona, Spain, sampled twice toward the end of a 3-week night shift (22:00-06:00 h) and a 3-week day shift (06:00-14:00 h) rotation. We collected four blood samples per worker, at the start and end of each shift. We measured 27 cytokines, chemokines and growth factors in plasma samples by luminex using the Cytokine Human Magnetic 30-Plex Panel LHC6003M and applied linear mixed models to examine within-person associations between shift work and analytes’ concentrations, comparing samples taken at 06:00 h on a day and night shift. We also conducted a factor analysis using analyte concentrations from all 4 time points for each individual to identify common factors and determine if these factors were altered by shift work.

Results

We observed lower levels of 15 analytes in the night shift compared to the day shift including cytokines (pro-inflammatory TNF-α, IL-2R; anti-inflammatory IL1-RA; Th1 IL-2, Th2 IL-4 and Th17 Il-17), chemokines (IP-10, MIP-1α, MIP-1β, RANTES) and growth factors (EGF, G-CSF, HGF, VEGF, FGF). In a factor analysis, three factors were identified. The main factor (Factor 1), explaining 57% of the variance and including IL-1β, IL-12, IL-15, MIP-1α, MIP-1β, EGF and FGF; and another factor (Factor 3) explaining 10% of the variance and including the Th1 cytokine IL-12, were inversely associated with the night shift (coefficient: -0.17, 95%CI -0.32 to -0.01 and coefficient: -0.22, 95%CI -0.38, -0.06, for Factors 1 and 3, respectively). Our results indicate that night shift disrupts the levels of several immune markers, which could contribute to the increased risk of infections and cancer reported in night shift workers.

Conclusion

Night shift is associated with disruption of multiple immune response pathways.

Living at the Wrong Time: Effects of Unmatching Official Time in Portugal and Western Spain

Human circadian rhythmicity is subjected to the internal circadian clock, the sun and social clocks (official time, social/work schedules). The discrepancy among these clocks, as occurs when official time does not match its geographical time zone, may produce circadian disruption. Western Spain (GMT+1/+2) and Portugal (GMT0/+1) share similar longitudes (sun time) but have different official times. This provides a unique opportunity to evaluate the effects of official time on circadian rhythmicity and sleep in elderly and retired populations (with no remunerated duties presumed, although other social commitments may be present) at both locations. Although both populations slept enough for their age (7-8 h), circadian robustness (e.g., interdaily stability, relative amplitude) was greater in Portugal, especially during weekdays, while greater desynchronization (both body temperature vs. motor activity and body temperature vs. light exposure) tended to occur in the Spaniards. Once corrected by GMT0, meals took place later in Spain than in Portugal, especially as the day progresses, and a possible interplay between bed/meal timings and internal desynchronization was found. Our results point to the possible deleterious effect on circadian system robustness when official time is misaligned with its geographical time zone.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review

Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease.

Recent Findings

In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications.

Summary

This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.

Sleep Problems and Circadian Functioning in Children and Adolescents With Autism Spectrum Disorder

BACKGROUND

Sleep problems are a prevalent comorbidity in autism spectrum disorder (ASD) with a multifactorial basis in which circadian misalignment has been described.

METHODS

A cross-sectional study was conducted including 52 children and adolescents with ASD (9.85 ± 3.07) and 27 children and adolescent controls with normal intellectual functioning (8.81 ± 2.14). They were matched for age, sex, and body mass index, and all were drug-naïve. An ambulatory circadian monitoring device was used to record temperature and motor, body position, sleep, and light intensity.

RESULTS

Individuals with ASD presented longer sleep-onset latency, lower sleep efficiency, and decreased total sleep time and tended to be more sedentary and have less exposure to light. They also showed lower amplitude, low interdaily stability, and a different pattern of wrist temperature across the day, with a midpoint of sleep that did not concur with sleep midpoint indicated by the rest of circadian parameters.

CONCLUSIONS

The sleep problems observed in this sample resemble those reported previously, with the exception of nocturnal awakenings which did not show differences. The ambulatory circadian monitoring device enabled measurement of circadian parameters such as temperature which, until now, were scarcely described in children with ASD and could be used to better understand sleep and circadian system in ASD.

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BACKGROUND

Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep.

OBJECTIVE

This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest.

METHODS

The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each.

RESULTS

Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data.

CONCLUSIONS

We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.

Sleep Detection for Younger Adults, Healthy Older Adults, and Older Adults Living With Dementia Using Wrist Temperature and Actigraphy: Prototype Testing and Case Study Analysis

BACKGROUND

Sleep is essential for one's health and quality of life. Wearable technologies that use motion and temperature sensors have made it possible to self-monitor sleep. Although there is a growing body of research on sleep monitoring using wearable devices for healthy young-to-middle-aged adults, few studies have focused on older adults, including those living with dementia.

OBJECTIVE

This study aims to investigate the impact of age and dementia on sleep detection through movement and wrist temperature.

METHODS

A total of 10 younger adults, 10 healthy older adults, and 8 older adults living with dementia (OAWD) were recruited. Each participant wore a Mi Band 2 (accemetry-based sleep detection) and our custom-built wristband (actigraphy and wrist temperature) 24 hours a day for 2 weeks and was asked to keep a daily sleep journal. Sleep parameters detected by the Mi Band 2 were compared with sleep journals, and visual analysis of actigraphy and temperature data was performed.

RESULTS

The absolute differences in sleep onset and offset between the sleep journals and Mi Band 2 were 39 (SD 51) minutes and 31 (SD 52) minutes for younger adults, 49 (SD 58) minutes and 33 (SD 58) minutes for older adults, and 253 (SD 104) minutes and 161 (SD 94) minutes for OAWD. The Mi Band 2 was unable to accurately detect sleep in 3 healthy older adults and all OAWDs. The average sleep and wake temperature difference of OAWD (1.26 °C, SD 0.82 °C) was significantly lower than that of healthy older adults (2.04 °C, SD 0.70 °C) and healthy younger adults (2.48 °C, SD 0.88 °C). Actigraphy data showed that older adults had more movement during sleep compared with younger adults and that this trend appears to increase for those with dementia.

CONCLUSIONS

The Mi Band 2 did not accurately detect sleep in older adults who had greater levels of nighttime movement. As more nighttime movement appears to be a phenomenon that increases in prevalence with age and even more so with dementia, further research needs to be conducted with a larger sample size and greater diversity of commercially available wearable devices to explore these trends more conclusively. All participants, including older adults and OAWD, had a distinct sleep and wake wrist temperature contrast, which suggests that wrist temperature could be leveraged to create more robust and broadly applicable sleep detection algorithms.

Evening types have social jet lag and metabolic alterations in school-age children

Chronotype has been mostly assessed with subjective scales. Objective assessment has been undertaken with actigraphy, although problems may occur in classifying chronotype. The aims of the study were to assess chronotype in school-age children using a novel integrative measurement (TAP) derived from non-invasive assessments of wrist temperature (T) physical activity (A) and body position (P) and to explore associations between chronotype, sleep disturbances, and metabolic components. Four-hundred-thirty-two children of 8-12 years were recruited from a Mediterranean area of Spain. Measurements were: (a) Chronotype objectively (7-day-rhythms of TAP) and subjectively measured (Munich-chronotype-self-reported questionnaire); (b) sleep rhythms and light exposition; (c) 7-day-diaries of food intake; (d) anthropometry and metabolic parameters; (e) academic scores. TAP acrophase was able to assess eveningness. As compared to more morning-types, more evening-types displayed lower amplitude in temperature rhythms, increased physical activity in the evening, delayed sleep and midpoint of intake and had more frequent social jet lag (P < 0.05). More evening-types had higher light intensity at 2 h before sleep and lower melatonin values (01:00 h). Eveningness associated with higher BMI and metabolic risk (higher values of insulin, glucose, triglycerides and cholesterol). Evening-types presented better grades in art. In conclusion, more evening-types, as objectively assessed, presented sleep alterations, social jet lag, obesity and higher metabolic risk.

Detection of factors influencing circadian rhythms on Intensive Care inpatients and hospitalization: Protocol for an observational study

AIMS

Identify the prevalence of circadian health disorders in ICU and hospitalized patients. Stablish the relationship of the circadian system with light, temperature, noise and nursing activities.

BACKGROUND

Commonly, self-assessment through subjective questionnaires is used for research about sleep quality. However, more reliable and valid measures should be applied objectively to find out the real prevalence of the problem. There is a negative relationship between circadian rhythms and hospitalization. In this study, we will analyse sleep patterns and hospital environments to implement measures to improve the quality of care related to sleep.

DESIGN

Descriptive observational study. It is estimated that 975 patients from 13 hospitals need to be recruited from ICU and hospitalization units. The sample should meet the following criteria: Patients over 18 admitted to ICU and medical units, length of stay between 96-148 hr, with no visual, hearing or moderate-severe cognitive impairment. Variables: Sleep variables are collected with an electronic device (named Kronowise® and Kronobed®), circadian and infrared light exposure, environmental noise, temperature, unit structural characteristics, nursing care (from 0 to 6 a.m.) and, characteristics of hospitalization period. The study, registered on Clinical Trials, initiated in December 2019 and it will continue up to December 2022.

DISCUSSION

Using objective and subjective measures of sleep and circadian rhythms, this study will shed light on the factors that negatively affect the hospitalized patients' sleep quality and circadian health. The ultimate goal is to design hospital guidelines to minimize the adverse effects on the dependent variables studied. Arguably, these guidelines will contribute to reducing the risk of these alterations and it will also serve to improve the nursing activities.

CONCLUSION

We expect to obtain adequate results for the creation of a protocol to improve the circadian health, quality of care and health outcomes related to sleep in patients.

Shedding Light on Nocturnal Movements in Parkinson's Disease: Evidence from Wearable Technologies

In Parkinson’s disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

Living Without Temporal Cues: A Case Study

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 (τ) between 24 and 25 h. In addition, a significant correlation between time estimation and mid-sleep time, a proxy for circadian phase, was demonstrated.

Application of Machine Learning Methods to Ambulatory Circadian Monitoring (ACM) for Discriminating Sleep and Circadian Disorders

The present study proposes a classification model for the differential diagnosis of primary insomnia (PI) and delayed sleep phase disorder (DSPD), applying machine learning methods to circadian parameters obtained from ambulatory circadian monitoring (ACM). Nineteen healthy controls and 242 patients (PI = 184; DSPD = 58) were selected for a retrospective and non-interventional study from an anonymized Circadian Health Database (https://kronowizard.um.es/). ACM records wrist temperature (T), motor activity (A), body position (P), and environmental light exposure (L) rhythms during a whole week. Sleep was inferred from the integrated variable TAP (from temperature, activity, and position). Non-parametric analyses of TAP and estimated sleep yielded indexes of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and a global circadian function index (CFI). Mid-sleep and mid-wake times were estimated from the central time of TAP-L5 (five consecutive hours of lowest values) and TAP-M10 (10 consecutive hours of maximum values), respectively. The most discriminative parameters, determined by ANOVA, Chi-squared, and information gain criteria analysis, were employed to build a decision tree, using machine learning. This model differentiated between healthy controls, DSPD and three insomnia subgroups (compatible with onset, maintenance and mild insomnia), with accuracy, sensitivity, and AUC >85%. In conclusion, circadian parameters can be reliably and objectively used to discriminate and characterize different sleep and circadian disorders, such as DSPD and OI, which are commonly confounded, and between different subtypes of PI. Our findings highlight the importance of considering circadian rhythm assessment in sleep medicine.

Assessing Chronotypes by Ambulatory Circadian Monitoring

In order to develop objective indexes for chronotype identification by means of direct measurement of circadian rhythms, 159 undergraduate students were recruited as volunteers and instructed to wear ambulatory circadian monitoring (ACM) sensors that continuously gathered information on the individual's environmental light and temperature exposure, wrist temperature, body position, activity, and the integrated TAP (temperature, activity, and position) variable for 7 consecutive days under regular free-living conditions. Among all the proposed indexes, the night phase marker (NPM) of the TAP variable was the best suited to discriminate among chronotypes, due to its relationship with the Munich ChronoType Questionnaire (β = 0.531; p < 0.001). The NPM of TAP allowed subjects to be classified as early- (E-type, 20%), neither- (N-type, 60%), and late-types (L-type, 20%), each of which had its own characteristics. In terms of light exposure, while all subjects had short exposure times to bright light (>100 lux), with a daily average of 93.84 ± 5.72 min, the earlier chronotypes were exposed to brighter days and darker nights compared to the later chronotypes. Furthermore, the earlier chronotypes were associated with higher stability and day-night contrast, along with an earlier phase, which could be the cause or consequence of the light exposure habits. Overall, these data support the use of ACM for chronotype identification and for evaluation under free living conditions, using objective markers.