Circadian Phase Prediction From Non-Intrusive and Ambulatory Physiological Data

Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management

Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.

Wearable Personal Core Body Temperature Measurement Considering Individual Differences and Dynamic Tissue Blood Perfusion

Accurate and continuous measurement of the human core body temperature by a wearable device is of great significance for human health care and disease monitoring. The current wearable thermometers ignore the physiological differences between individuals and the role of blood perfusion in thermoregulation, resulting in insufficient accuracy and limitations in terms of the measurement sites. This study proposed a novel personal model for measuring core body temperature by taking dynamic tissue blood perfusion and individual differences into consideration. The technique facilitates possible accurate core body temperature measurements from the skin surface of the wrist and forehead. First, the personal core body temperature model was established based on the thermal equilibrium between the human body and the measurement device, in which the tissue blood perfusion changes dynamically with tissue temperature. Then, the parameters of the personal model that imply individual physiological differences were obtained based on personal data collected daily. The results show that with the developed personal model, the accuracy of the measured body temperature from the wrist is close to that of the forehead model. The wrist model and the forehead model have a mean absolute error of 0.297 (SD=0.078) C and 0.224 (SD=0.071) C, respectively, which meets the accuracy and robustness requirements of practical applications. The personal models significantly improve the accuracy compared with that of the group model, especially for the wrist model.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.