Multidimensional Circadian Monitoring by Wearable Biosensors in Parkinson's Disease

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.

Urban environment influences on stress, autonomic reactivity and circadian rhythm: protocol for an ambulatory study of mental health and sleep

Introduction

Converging evidence suggests that urban living is associated with an increased likelihood of developing mental health and sleep problems. Although these aspects have been investigated in separate streams of research, stress, autonomic reactivity and circadian misalignment can be hypothesized to play a prominent role in the causal pathways underlining the complex relationship between the urban environment and these two health dimensions. This study aims at quantifying the momentary impact of environmental stressors on increased autonomic reactivity and circadian rhythm, and thereby on mood and anxiety symptoms and sleep quality in the context of everyday urban living.

Method

The present article reports the protocol for a feasibility study that aims at assessing the daily environmental and mobility exposures of 40 participants from the urban area of Jerusalem over 7 days. Every participant will carry a set of wearable sensors while being tracked through space and time with GPS receivers. Skin conductance and heart rate variability will be tracked to monitor participants' stress responses and autonomic reactivity, whereas electroencephalographic signal will be used for sleep quality tracking. Light exposure, actigraphy and skin temperature will be used for ambulatory circadian monitoring. Geographically explicit ecological momentary assessment (GEMA) will be used to assess participants' perception of the environment, mood and anxiety symptoms, sleep quality and vitality. For each outcome variable (sleep quality and mental health), hierarchical mixed models including random effects at the individual level will be used. In a separate analysis, to control for potential unobserved individual-level confounders, a fixed effect at the individual level will be specified for case-crossover analyses (comparing each participant to oneself).

Conclusion

Recent developments in wearable sensing methods, as employed in our study or with even more advanced methods reviewed in the Discussion, make it possible to gather information on the functioning of neuro-endocrine and circadian systems in a real-world context as a way to investigate the complex interactions between environmental exposures, behavior and health. Our work aims to provide evidence on the health effects of urban stressors and circadian disruptors to inspire potential interventions, municipal policies and urban planning schemes aimed at addressing those factors.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data

Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.

Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification

Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.

Evaluation of Nocturnal Symptoms in Chinese Parkinson's Disease Patients Based on the PDSS-2 Scale: A Multicenter Cross-Sectional Study

BACKGROUND

Nocturnal symptoms have a significant effect on the quality of life in Parkinson's disease (PD) patients.

OBJECTIVE

This study aimed to investigate the prevalence and associated factors of nocturnal symptoms in Chinese PD patients.

METHODS

This multicenter cross-sectional study included 1,500 patients with primary PD from 18 centers in China was carried out between February 2019 and February 2020. Questionnaires including Parkinson's disease sleep scale 2 (PDSS-2), Parkinson's disease questionnaire 8 (PDQ-8), Beck depression inventory (BDI), and generalized anxiety disorder scale 7 (GAD-7) were used to assess nocturnal symptoms, quality of life, depression, and anxiety.

RESULTS

Among 1,500 Chinese PD patients, 576 (38.4%) reported nocturnal symptoms. Of them, 59.2% were older than 65 years. The PDQ-8 total score was higher in patients with nocturnal symptoms (p < 0.01). Moderate and severe depression was reported more often in patients with nocturnal symptoms (p < 0.01), and the occurrence and severity of anxiety were higher as well (p < 0.01). Longer disease duration and higher Hoehn-Yahr (HY) stage were independently associated with nocturnal symptoms (p < 0.01). Education level, depression, disease course, HY stage, and nocturnal symptoms were related to the quality of life in Chinese PD patients (p < 0.01).

CONCLUSION

Our study found that 38.4% of Chinese PD patients have nocturnal symptoms, even in early and mid-stage PD. Nocturnal symptoms were associated with worse quality of life and higher incidences of depression and anxiety. Nocturnal symptoms should be included in the assessment and care plan, especially in patients with longer disease courses and higher HY stages.

Phase Response Curve to Light under Ambulatory Conditions: A Pilot Study for Potential Application to Daylight Saving Time Transitions

Several studies have investigated the relationship between daylight saving time (DST) and sleep alterations, psychiatric disorders, cardiovascular events and traffic accidents. However, very few have monitored participants while maintaining their usual lifestyle before and after DST. Considering that DST transitions modify human behavior and, therefore, people's light exposure patterns, the aim of this study was to investigate the potential effects of DST on circadian variables, considering sleep and, for the first time, the human phase response curve to light. To accomplish this, eight healthy adults (33 ± 11 years old, mean ± SD) were recruited to monitor multivariable circadian markers and light exposure by means of a wearable ambulatory monitoring device: Kronowise^®. The following night phase markers were calculated: midpoints of the five consecutive hours of maximum wrist temperature (TM5) and the five consecutive hours of minimum time in movement (TL5), sleep onset and offset, as well as sleep duration and light intensity. TM5 for wrist temperature was set as circadian time 0 h, and the balance between advances and delays considering the phase response curve to light was calculated individually before and after both DST transitions. To assess internal desynchronization, the possible shift in TM5 for wrist temperature and TL5 for time in movement were compared. Our results indicate that the transition to DST seems to force the circadian system to produce a phase advance to adapt to the new time. However, the synchronizing signals provided by natural and personal light exposure are not in line with such an advance, which results in internal desynchronization and the need for longer synchronization times. On the contrary, the transition back to ST, which implies a phase delay, is characterized by a faster adaptation and maintenance of internal synchronization, despite the fact that exposure to natural light would favor a phase advance. Considering the pilot nature of this study, further research is needed with higher sample sizes.

Chronodisruption and diet associated with increased cardiometabolic risk in coronary heart disease patients: the CORDIOPREV study

Individuals with evening chronotypes are prone to suffer chronodisruption and display worse lifestyle habits than morning-types, exhibiting higher cardiovascular diseases (CVD). However, it is unknown whether CVD patients, who are evening chronotypes, have higher cardiometabolic risk than morning-types. This study explored whether individual chronotypes were associated with cardiometabolic risk in patients from the CORDIOPREV study (n = 857). We also investigated whether potential associations were moderated by long-term consumption of two healthy diets (Mediterranean and Low-fat diets). This population was classified into chronotypes using the Morningness-Eveningness Questionnaire. Seven-day daily rhythms in wrist temperature (T), rest-activity (A) and position (P) were recorded in a subset of patients (n = 168), and an integrative variable TAP was determined. Metabolic Syndrome (MetS) was determined at baseline, and metabolic and inflammation markers were measured at baseline and yearly during the 4 years of follow-up. Differences in several lifestyle factors were analyzed according to chronotype. At all times, evening-types had higher triglycerides, C-reactive protein and homocysteine and lower high density lipoprotein cholesterol than morning-types (P < 0.05). Evening-types had a higher prevalence of MetS (OR 1.58 IC 95% [1.10 - 2.28], P = 0.01). Moreover, they were more sedentary, displayed less and delayed physical activity and ate and slept later. In addition, evening-types had lower amplitude, greater fragmentation, lower robustness and less stable circadian pattern at TAP (P < 0.01), all related to a less healthy circadian pattern. In conclusion, evening-types with CVD had higher cardiometabolic risk and less robust circadian-related rhythms than morning-types, regardless of the nutritional intervention.

Associations of actigraphy derived rest activity patterns and circadian phase with clinical symptoms and polysomnographic parameters in chronic insomnia disorders

We explored the associations of actigraphy-derived rest-activity patterns and circadian phase parameters with clinical symptoms and level 1 polysomnography (PSG) results in patients with chronic insomnia to evaluate the clinical implications of actigraphy-derived parameters for PSG interpretation. Seventy-five participants underwent actigraphy assessments and level 1 PSG. Exploratory correlation analyses between parameters derived from actigraphy, PSG, and clinical assessments were performed. First, participants were classified into two groups based on rest-activity pattern variables; group differences were investigated following covariate adjustment. Participants with poorer rest-activity patterns on actigraphy (low inter-day stability and high intra-daily variability) exhibited higher insomnia severity index scores than participants with better rest-activity patterns. No between-group differences in PSG parameters were observed. Second, participants were classified into two groups based on circadian phase variables. Late-phase participants (least active 5-h and most active 10-h onset times) exhibited higher insomnia severity scores, longer sleep and rapid eye movement latency, and lower apnea-hypopnea index than early-phase participants. These associations remained significant even after adjusting for potential covariates. Some actigraphy-derived rest-activity patterns and circadian phase parameters were significantly associated with clinical symptoms and PSG results, suggesting their possible adjunctive role in deriving plans for PSG lights-off time and assessing the possible insomnia pathophysiology.

Moving Forward from the COVID-19 Pandemic: Needed Changes in Movement Disorders Care and Research

Purpose of Review

The COVID-19 pandemic has dramatically affected the health and well-being of individuals with movement disorders. This manuscript reviews these effects, discusses pandemic-related changes in clinical care and research, and suggests improvements to care and research models.

Recent Findings

During the on-going COVID-19 pandemic, individuals with movement disorders have experienced worsening of symptoms, likely due to decreased access to care, loss of social connection, and decreased physical activity. Through telemedicine, care has moved out of the clinic and into the home. Clinical research has also been significantly disrupted, and there has been a shift to decentralized approaches. The pandemic has highlighted disparities in access to care and representation in research.

Summary

We must now translate these experiences into better care and research models with a focus on equitable integration of telemedicine, better support of patients and caregivers, the development of meaningful digital endpoints, and optimization of decentralized research designs.

Domotics, Smart Homes, and Parkinson's Disease

Technology has an increasing presence and role in the management of Parkinson’s disease. Whether embraced or rebuffed by patients and clinicians, this is an undoubtedly growing area. Wearable sensors have received most of the attention so far. This review will focus on technology integrated into the home setting; from fixed sensors to automated appliances, which are able to capture information and have the potential to respond in an unsupervised manner. Domotics also have the potential to provide ‘real world’ context to kinematic data and therapeutic opportunities to tackle challenging motor and non-motor symptoms. Together with wearable technology, domotics have the ability to gather long-term data and record discrete events, changing the model of the cross-sectional outpatient assessment. As clinicians, our ultimate goal is to maximise quality of life, promote autonomy, and personalise care. In these respects, domotics may play an essential role in the coming years.

Neurodegenerative disorders management: state-of-art and prospects of nano-biotechnology

Neurodegenerative disorders (NDs) are highly prevalent among the aging population. It affects primarily the central nervous system (CNS) but the effects are also observed in the peripheral nervous system. Neural degeneration is a progressive loss of structure and function of neurons, which may ultimately involve cell death. Such patients suffer from debilitating memory loss and altered motor coordination which bring up non-affordable and unavoidable socio-economic burdens. Due to the unavailability of specific therapeutics and diagnostics, the necessity to control or manage NDs raised the demand to investigate and develop efficient alternative approaches. Keeping trends and advancements in view, this report describes both state-of-the-art and challenges in nano-biotechnology-based approaches to manage NDs, toward personalized healthcare management. Sincere efforts are being made to customize nano-theragnostics to control: therapeutic cargo packaging, delivery to the brain, nanomedicine of higher efficacy, deep brain stimulation, implanted stimulation, and managing brain cell functioning. These advancements are useful to design future therapy based on the severity of the patient's neurodegenerative disease. However, we observe a lack of knowledge shared among scientists of a variety of expertise to explore this multi-disciplinary research field for NDs management. Consequently, this review will provide a guideline platform that will be useful in developing novel smart nano-therapies by considering the aspects and advantages of nano-biotechnology to manage NDs in a personalized manner. Nano-biotechnology-based approaches have been proposed as effective and affordable alternatives at the clinical level due to recent advancements in nanotechnology-assisted theragnostics, targeted delivery, higher efficacy, and minimal side effects.

Taking the time for our bodies: How wearables can be used to assess circadian physiology

Consumer wearables, such as smart watches, are a promising tool for monitoring circadian health in "real world" settings. Bowman et al. demonstrate that circadian signals can be accurately captured through heart rate data obtained from wearables, opening up new possibilities for population-level studies on heart rate and circadian rhythm.

Sweating Out the Circadian Rhythm: A Technical Review

Circadian dysfunction or dysregulation is associated with many chronic morbidities. Current state-of-art technologies do not provide an accurate estimation of the extent of disease affliction. Recent advances call for using wearables for improving management and diagnosis of circadian related disorders. Sweat contains an abundance of relevant biomarkers like cortisol, DHEA, and so forth, which could be leveraged toward tracking the user's chronobiology. In this article, we provide a review of the key developments in the field of wearable sensors for circadian technologies. We highlight the value of using sweat along with portable electronics toward developing state-of-the-art platforms for efficient diagnosis and management of chronic conditions. Finally, we discuss challenges and opportunities for using wearable sweat sensors for circadian diagnosis and disease management.

What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology

Worldwide, it is estimated that millions of individuals suffer from a neurological disorder which can be the result of head injuries, ischaemic events such as a stroke, or neurodegenerative disorders such as Parkinson's disease (PD) and multiple sclerosis (MS). Problems with mobility and hemiparesis are common for these patients, making daily life, social factors and independence heavily affected. Current therapies aimed at improving such conditions are often tedious in nature, with patients often losing vital motivation and positive outlook towards their rehabilitation. The interest in the use of digital technology in neuro-rehabilitation has skyrocketed in the past decade. To gain insight, a systematic review of the literature in the field was conducting following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines for three categories: stroke, Parkinson's disease and multiple sclerosis. It was found that the majority of the literature (84%) was in favour of the use of digital technologies in the management of neurological dysfunction; with some papers taking a "neutral" or "against" standpoint. It was found that the use of technologies such as virtual reality (VR), robotics, wearable sensors and telehealth was highly accepted by patients, helped to improve function, reduced anxiety and make therapy more accessible to patients living in more remote areas. The most successful therapies were those that used a combination of conventional therapies and new digital technologies.

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.

Smart diagnostics devices through artificial intelligence and mechanobiological approaches

The present work illustrates the promising intervention of smart diagnostics devices through artificial intelligence (AI) and mechanobiological approaches in health care practices. The artificial intelligence and mechanobiological approaches in diagnostics widen the scope for point of care techniques for the timely revealing of diseases by understanding the biomechanical properties of the tissue of interest. Smart diagnostic device senses the physical parameters due to change in mechanical, biological, and luidic properties of the cells and to control these changes, supply the necessary drugs immediately using AI techniques. The latest techniques like sweat diagnostics to measure the overall health, Photoplethysmography (PPG) for real-time monitoring of pulse waveform by capturing the reflected signal due to blood pulsation), Micro-electromechanical systems (MEMS) and Nano-electromechanical systems (NEMS) smart devices to detect disease at its early stage, lab-on-chip and organ-on-chip technologies, Ambulatory Circadian Monitoring device (ACM), a wrist-worn device for Parkinson's disease have been discussed. The recent and futuristic smart diagnostics tool/techniques like emotion recognition by applying machine learning algorithms, atomic force microscopy that measures the fibrinogen and erythrocytes binding force, smartphone-based retinal image analyser system, image-based computational modeling for various neurological disorders, cardiovascular diseases, tuberculosis, predicting and preventing of Zika virus, optimal drugs and doses for HIV using AI, etc. have been reviewed. The objective of this review is to examine smart diagnostics devices based on artificial intelligence and mechanobiological approaches, with their medical applications in healthcare. This review determines that smart diagnostics devices have potential applications in healthcare, but more research work will be essential for prospective accomplishments of this technology.

O. Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with "epidermal electronics", including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

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.

Circadian disruption: What do we actually mean?

The circadian system regulates physiology and behavior. Acute challenges to the system, such as those experienced when traveling across time zones, will eventually result in re-synchronization to local environmental time cues, but this re-synchronization is oftentimes accompanied by adverse short-term consequences. When such challenges are experienced chronically, adaptation may not be achieved, as for example in the case of rotating night shift workers. The transient and chronic disturbance of the circadian system is most frequently referred to as "circadian disruption", but many other terms have been proposed and used to refer to similar situations. It is now beyond doubt that the circadian system contributes to health and disease, emphasizing the need for clear terminology when describing challenges to the circadian system and their consequences. The goal of this review is to provide an overview of the terms used to describe disruption of the circadian system, discuss proposed quantifications of disruption in experimental and observational settings with a focus on human research, and highlight limitations and challenges of currently available tools. For circadian research to advance as a translational science, clear, operationalizable, and scalable quantifications of circadian disruption are key, as they will enable improved assessment and reproducibility of results, ideally ranging from mechanistic settings, including animal research, to large-scale randomized clinical trials.

Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment

BACKGROUND

The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities.

OBJECTIVE

This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used.

METHODS

Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants.

RESULTS

65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living.

CONCLUSIONS

Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.

Postural impairments in Parkinson's disease are not associated with changes in circadian rhythms changes

Parkinson's disease (PD) is a progressive neurodegenerative disease, with a worldwide incidence of 1% in individuals >60 years of age. Its primary characteristics include postural impairments and changes in circadian rhythms. The authors investigated the association between postural impairment and changes in circadian rhythms in 24 PD subjects diagnosed with stages 1 to 3 on the Hoehn-Yard (HY) scale and regularly used dopaminergic medication for at least 1 year (experimental group - EG) and 24 healthy elderly individuals without a history of neurological impairment as the control group (CG). Static balance tests using a force plate were performed, and activity/rest rhythm, according to the relative amplitude of L5 and M10 values, was monitored for seven consecutive days using actimetry. The results indicated differences in posturographic indicators of mediolateral displacement (ML) [EG, 4.71 ± 0.85 mm; CG, 2.79 ± 0.53 mm (p < .0001)] and anteroposterior displacement of the center of pressure (COP) [EG, 5.61 ± 2.43 mm; CG, 8.23 ± 1.72 mm (< 0.0001)], ML velocity of the COP [EG, 2.39 ± 0.83 mm/s; CG, 1.40 ± 0.18 mm/s (p < .0001)], and total distance of the COP in the tandem stance condition [EG, 227.6 ± 75 mm; CG, 53.4 ± 6.1 mm (p < .0001)] between the EG and CG. There was no correlation between relative amplitude and posturographic data for the EG. Postural impairments were verified in comparing the EG and CG; however, there was no association between posturographic indicators and activity/rest rhythm.

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.

Smart Lighting Clinical Testbed Pilot Study on Circadian Phase Advancement

OBJECTIVE

Lighting is a strong synchronizer for circadian rhythms, which in turn drives a wide range of biological functions. The objective of our work is a) to construct a clinical in-patient testbed with smartİ lighting, and b) evaluate its feasibility for use in future clinical studies.

METHODS

A feedback capable, variable spectrum lighting system was installed at the University of New Mexico Hospital. The system consists of variable spectrum lighting troffers, color sensors, occupancy sensors, and computing and communication infrastructure. We conducted a pilot study to demonstrate proof of principle, that 1) this new technology is capable of providing continuous lighting and sensing in an active clinical environment, 2) subject recruitment and retention is feasible for round-the-clock, multi-day studies, and 3) current techniques for circadian regulation can be deployed in this unique testbed. Unlike light box studies, only troffer-based lighting was used, and both lighting intensity and spectral content were varied.

RESULTS

The hardware and software functioned seamlessly to gather biometric data and provide the desired lighting. Salivary samples that measure dim-light melatonin onset showed phase advancement for all three subjects.

CONCLUSION

We executed a five-day circadian rhythm study that varied intensity, spectrum, and timing of lighting as proof-of-concept or future clinical studies with troffer-based, variable spectrum lighting. Clinical Impact: The ability to perform circadian rhythm experiments in more realistic environments that do not overly constrain the subject is important for translating lighting research into practice, as well as for further research on the health impacts of lighting.

Determining Light Intensity, Timing and Type of Visible and Circadian Light From an Ambulatory Circadian Monitoring Device

During last decades, the way of life in modern societies has deeply modified the temporal adjustment of the circadian system, mainly due to the inappropriate use of artificial lighting and the high prevalence of social jet-lag. Therefore, it becomes necessary to design non-invasive and practical tools to monitor circadian marker rhythms but also its main synchronizer, the light-dark cycle under free-living conditions. The aim of this work was to improve the ambulatory circadian monitoring device (ACM, Kronowise^®) capabilities by developing an algorithm that allows to determine light intensity, timing and circadian light stimulation by differentiating between full visible, infrared and circadian light, as well as to discriminate between different light sources (natural and artificial with low and high infrared composition) in subjects under free living conditions. The ACM device is provided with three light sensors: (i) a wide-spectrum sensor (380–1100 nm); (ii) an infrared sensor (700–1100 nm) and (iii) a sensor equipped with a blue filter that mimics the sensitivity curve of the melanopsin photopigment and the melatonin light suppression curve. To calibrate the ACM device, different commercial light sources and sunlight were measured at four different standardized distances with both a spectroradiometer (SPR) and the ACM device. CIE S 026/E:2018 (2018), toolbox software was used to calculate the melanopic stimulation from data recorded by SPR. Although correlation between raw data of luminance measured by ACM and SPR was strong for both full spectrum (r = 0.946, p < 0.0001) and circadian channel (r = 0.902, p < 0.0001), even stronger correlations were obtained when light sources were clustered in three groups: natural, infrared-rich artificial light and infrared-poor artificial light, and their corresponding linear correlations with SPR were considered (r = 0.997, p < 0.0001 and r = 0.998, p < 0.0001, respectively). Our results show that the ACM device provided with three light sensors and the algorithm developed here allow an accurate detection of light type, intensity and timing for full visible and circadian light, with simultaneous monitoring of several circadian marker rhythms that will open the possibility to explore light synchronization in population groups while they maintain their normal lifestyle.

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

The development of wearable devices has increase interest in the use of ambulatory methods to detect sleep disorders more objectively than those permitted by subjective scales evaluating sleep quality, while subjects maintain their usual lifestyle. This study aims to validate an ambulatory circadian monitoring (ACM) device for the detection of sleep and wake states and apply it to the evaluation of sleep quality in patients with Parkinson disease (PD). A polysomnographic validation study was conducted on a group of patients with different sleep disorders in a preliminary phase, followed by a pilot study to apply this methodology to PD patients. The ACM device makes it possible to estimate the main sleep parameters very accurately, as demonstrated by: (a) the lack of significant differences between the mean values detected by PSG and ACM in time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and time awake after sleep onset (WASO); (b) the slope of the correlation lines between the parameters estimated by the two procedures, very close to 1, which demonstrates the linearity of the predictions; (c) the low bias value in the estimates obtained through ACM. Sleep in PD is associated with lower distal skin temperature, efficiency and overall sleep time; greater WASO, activity during sleep and duration of naps and a worse circadian function index. In summary, the ACM device has proven to be clinically useful to evaluate sleep in an objective manner, thanks to the integrated management of different complementary variables, having advantages over conventional actigraphy.

Chronotherapies for Parkinson's disease

Parkinson's disease (PD) is the second-most common progressive neurodegenerative disorder. Although the clinical diagnosis of PD is still based on its cardinal motor dysfunctions, several non-motor symptoms (NMS) have been established as integral part of the disease. Unlike motor disorders, development of therapies against NMS are still challenging and remain a critical unmet clinical need. During the last decade, several studies have characterised the molecular, physiological and behavioural alterations of the circadian system in PD patients. As a consequence, and given the ubiquitous nature of circadian rhythms in the entire organism, the biological clock has emerged as a potential therapeutic target to ease suffering from both motor and NMS in PD patients. Here we discuss the emerging field of using bright light, physical exercise and melatonin as chronotherapeutic tools to alleviate motor disorders, sleep/wake alterations, anxiety and depression in PD patients. We also highlight the potential of these readily available therapies to improve the general quality of life and wellbeing of PD patients. Finally, we provide specific data- and mechanisms-driven recommendations that might help improve the therapeutic benefit of light and physical exercise in PD patients.

Sleep problems in adults with autism spectrum disorder and intellectual disability

Sleep problems (SP) are recognized as a common comorbid condition in autism spectrum disorder (ASD) and can influence core autism symptoms and mental and physical health. SPs can be lifelong and have been reported that adults on the autistic spectrum with and without intellectual disability (ID) present SPs (longer sleep latency, frequent night awakenings, and circadian rhythm sleep-wake disorders). A prospective, objective sleep study was conducted in 41 adults with ASD (33 ± 6 years old) and ID and 51 typically developing adults (33 ± 5 years old) using ambulatory circadian monitoring (ACM) recording wrist temperature, motor activity, body position, sleep, and light intensity. The findings indicated that individuals with ASD presented sleep difficulties including low sleep efficiency, prolonged sleep latency and increased number and length of night awakenings, together with daily sedentary behavior, and increased nocturnal activity. Furthermore, indications of an advanced sleep-wake phase disorder were found in these autistic adults. Examining sleep and markers of the circadian system showed significant differences between adults with ASD and ID and an age-matched, healthy adult population. The sleep disturbances described for this sample of adults with ASD and ID are similar to those of already described for adults with ASD without ID; their relationship with intellectual ability should be further studied. Improving knowledge of sleep patterns in ASD adults with ID might help to designed targeted interventions to improve their functioning and reduce family stress. Autism Research 2019, 12: 66-79. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: SPs are very frequent in autism from childhood to adulthood. We recorded sleep with a watch-like device in adults with autism and ID and compared sleep patterns with nonautistic volunteers. Results showed poorer sleep conditions in adults with autism (increased sleep latency and number/length of night awakenings) that resulted in decreased sleep efficiency. Increasing knowledge of the SPs in adults on the autism spectrum will allow to improve their and their families' quality of life.