As winter approaches and daylight saving time has ended, many people are bracing themselves for shorter days, colder weather and what's often dismissed as the "winter blues ." But these seasonal shifts are more than a passing inconvenience, and can disrupt people's energy, moods and daily routines . Seasonal affective disorder (SAD) is a condition that heightens depressive symptoms during the fall and winter months, while the "winter blues" refers to a milder, temporary dip in mood. Although the exact cause of SAD remains unclear, it's thought to be linked to reduced exposure to natural light during the fall and winter, which can disrupt our circadian rhythm. Lower light levels affect brain chemistry by reducing serotonin -- a neurotransmitter that regulates mood, sleep and appetite -- while keeping melatonin elevated during daylight hours, leading to sleepiness and fatigue.

Do you need more sleep in fall and winter? Less sunlight, colder weather, and diet changes make us sleepier--and that's OK. Winter mornings make staying under the covers feel impossible to resist. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a crisp, fall day in mid-November, and though your calendar is filled with evening get-togethers and morning runs, you're feeling sluggish.

Is sleeping outside good for you? Science has a clear answer. A night under the stars can reset your body's clock, reduce stress, and more--but it might not benefit everyone. Sleeping outdoors may help reduce stress and reset your body's internal clock. Breakthroughs, discoveries, and DIY tips sent every weekday.

Sorry, no time policy will make winter days longer. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a hot (yet also sleepy) debate that ignites twice a year in the United States: Why are we still changing the clocks? The "spring forward" every March can feel particularly volatile, with research linking that loss of a precious hour of sleep to more heart attacks and fatal car accidents . Now, a new study published today in the journal indicates that sticking with standard time may improve health.

Background: Circadian desynchrony characterized by the misalignment between an individual's internal biological rhythms and external environmental cues, significantly affects various physiological processes and health outcomes. Quantifying circadian desynchrony often requires prolonged and frequent monitoring, and currently, an easy tool for this purpose is missing. Additionally, its association with the incidence of delirium has not been clearly explored. Methods: A prospective observational study was carried out in intensive care units (ICU) of a tertiary hospital. Circadian transcriptomics of blood monocytes from 86 individuals were collected on two consecutive days, although a second sample could not be obtained from all participants. Using two public datasets comprised of healthy volunteers, we replicated a model for determining internal circadian time. We developed an approach to quantify circadian desynchrony by comparing internal circadian time and external blood collection time. We applied the model and quantified circadian desynchrony index among ICU patients, and investigated its association with the incidence of delirium. Results: The replicated model for determining internal circadian time achieved comparable high accuracy. The quantified circadian desynchrony index was significantly higher among critically ill ICU patients compared to healthy subjects, with values of 10.03 hours vs 2.50-2.95 hours (p < 0.001). Most ICU patients had a circadian desynchrony index greater than 9 hours. Additionally, the index was lower in patients whose blood samples were drawn after 3pm, with values of 5.00 hours compared to 10.01-10.90 hours in other groups (p < 0.001)...

Circadian rhythms regulate the physiology and behavior of humans and animals. Despite advancements in understanding these rhythms and predicting circadian phases at the transcriptional level, predicting circadian phases from proteomic data remains elusive. This challenge is largely due to the scarcity of time labels in proteomic datasets, which are often characterized by small sample sizes, high dimensionality, and significant noise. Furthermore, existing methods for predicting circadian phases from transcriptomic data typically rely on prior knowledge of known rhythmic genes, making them unsuitable for proteomic datasets. To address this gap, we developed a novel computational method using unsupervised deep learning techniques to predict circadian sample phases from proteomic data without requiring time labels or prior knowledge of proteins or genes. Our model involves a two-stage training process optimized for robust circadian phase prediction: an initial greedy one-layer-at-a-time pre-training which generates informative initial parameters followed by fine-tuning. During fine-tuning, a specialized loss function guides the model to align protein expression levels with circadian patterns, enabling it to accurately capture the underlying rhythmic structure within the data. We tested our method on both time-labeled and unlabeled proteomic data. For labeled data, we compared our predictions to the known time labels, achieving high accuracy, while for unlabeled human datasets, including postmortem brain regions and urine samples, we explored circadian disruptions. Notably, our analysis identified disruptions in rhythmic proteins between Alzheimer's disease and control subjects across these samples.

Wearable devices are increasingly used as tools for biomedical research, as the continuous stream of behavioral and physiological data they collect can provide insights about our health in everyday contexts. Long-term tracking, defined in the timescale of months of year, can provide insights of patterns and changes as indicators of health changes. These insights can make medicine and healthcare more predictive, preventive, personalized, and participative (The 4P's). However, the challenges in modeling, understanding and processing longitudinal data are a significant barrier to their adoption in research studies and clinical settings. In this paper, we review and discuss three models used to make sense of longitudinal data: routines, rhythms and stability metrics. We present the challenges associated with the processing and analysis of longitudinal wearable sensor data, with a special focus on how to handle the different temporal dynamics at various granularities. We then discuss current limitations and identify directions for future work. This review is essential to the advancement of computational modeling and analysis of longitudinal sensor data for pervasive healthcare.

We tested the performance of reservoir computing (RC) in predicting the dynamics of a certain non-autonomous dynamical system. Specifically, we considered a van del Pol oscillator subjected to periodic external force with frequent phase shifts. The reservoir computer, which was trained and optimized with simulation data generated for a particular phase shift, was designed to predict the oscillation dynamics under periodic external forces with different phase shifts. The results suggest that if the training data have some complexity, it is possible to quantitatively predict the oscillation dynamics exposed to different phase shifts. The setting of this study was motivated by the problem of predicting the state of the circadian rhythm of shift workers and designing a better shift work schedule for each individual. Our results suggest that RC could be exploited for such applications.

To investigate whether a deep learning model can detect Covid-19 from disruptions in the human body's physiological (heart rate) and rest-activity rhythms (rhythmic dysregulation) caused by the SARS-CoV-2 virus. We propose CovidRhythm, a novel Gated Recurrent Unit (GRU) Network with Multi-Head Self-Attention (MHSA) that combines sensor and rhythmic features extracted from heart rate and activity (steps) data gathered passively using consumer-grade smart wearable to predict Covid-19. A total of 39 features were extracted (standard deviation, mean, min/max/avg length of sedentary and active bouts) from wearable sensor data. Biobehavioral rhythms were modeled using nine parameters (mesor, amplitude, acrophase, and intra-daily variability). These features were then input to CovidRhythm for predicting Covid-19 in the incubation phase (one day before biological symptoms manifest). A combination of sensor and biobehavioral rhythm features achieved the highest AUC-ROC of 0.79 [Sensitivity = 0.69, Specificity=0.89, F$_{0.1}$ = 0.76], outperforming prior approaches in discriminating Covid-positive patients from healthy controls using 24 hours of historical wearable physiological. Rhythmic features were the most predictive of Covid-19 infection when utilized either alone or in conjunction with sensor features. Sensor features predicted healthy subjects best. Circadian rest-activity rhythms that combine 24h activity and sleep information were the most disrupted. CovidRhythm demonstrates that biobehavioral rhythms derived from consumer-grade wearable data can facilitate timely Covid-19 detection. To the best of our knowledge, our work is the first to detect Covid-19 using deep learning and biobehavioral rhythms features derived from consumer-grade wearable data.

Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep disturbances and mental health deterioration are closely related, as mental health condition worsening regularly entails shifts in the patients' circadian rhythms. Therefore, Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles and to detect behavioural changes among them. Moreover, mobile passively sensed data captured from smartphones, thanks to these devices' ubiquity, constitute an excellent alternative to profile patients' biorhythm. In this work, we aim to identify major sleep episodes based on passively sensed data. To do so, a Heterogeneous Hidden Markov Model is proposed to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by clinically tested wearables, proving the effectiveness of the proposed approach.