Electrical activity in the heart exhibits 24-hour rhythmicity, and potentially fatal arrhythmias are more likely to occur at specific times of day. Here, we demonstrate that circadian clocks within the brain and heart set daily rhythms in sinoatrial (SA) and atrioventricular (AV) node activity, and impose a time-of–day dependent susceptibility to ventricular arrhythmia. Critically, the balance of circadian inputs from the autonomic nervous system and cardiomyocyte clock to the SA and AV nodes differ, and this renders the cardiac conduction system sensitive to decoupling during abrupt shifts in behavioural routine and sleep-wake timing. Our findings reveal a functional segregation of circadian control across the heart’s conduction system and inherent susceptibility to arrhythmia.

Night shift work increases risk of metabolic disorders, particularly obesity and insulin resistance. While the underlying mechanisms are unknown, evidence points to misalignment of peripheral oscillators causing metabolic disturbances. A pathway conveying such misalignment may involve exosome-based intercellular communication. Fourteen volunteers were assigned to a simulated day shift (DS) or night shift (NS) condition. After 3 days on the simulated shift schedule, blood samples were collected during a 24-h constant routine protocol. Exosomes were isolated from the plasma samples from each of the blood draws. Exosomes were added to naïve differentiated adipocytes, and insulin-induced pAkt/Akt expression changes were assessed. ChIP-Seq analyses for BMAL1 protein, mRNA microarrays and exosomal miRNA arrays combined with bioinformatics and functional effects of agomirs and antagomirs targeting miRNAs in NS and DS exosomal cargo were examined. Human adipocytes treated with exosomes from the NS condition showed altered Akt phosphorylation responses to insulin in comparison to those treated with exosomes from the DS condition. BMAL1 ChIP-Seq of exosome-treated adipocytes showed 42,037 binding sites in the DS condition and 5538 sites in the NS condition, with a large proportion of BMAL1 targets including genes encoding for metabolic regulators. A significant and restricted miRNA exosomal signature emerged after exposure to the NS condition. Among the exosomal miRNAs regulated differentially after 3 days of simulated NS versus DS, proof-of-concept validation of circadian misalignment signaling was demonstrated with hsa-mir-3614-5p. Exosomes from the NS condition markedly altered expression of key genes related to circadian rhythm in several cultured cell types, including adipocytes, myocytes, and hepatocytes, along with significant changes in 29 genes and downstream gene network interactions. Our results indicate that a simulated NS schedule leads to changes in exosomal cargo in the circulation. These changes promote reduction of insulin sensitivity of adipocytes in vitro and alter the expression of core clock genes in peripheral tissues. Circulating exosomal miRNAs may play an important role in metabolic dysfunction in NS workers by serving as messengers of circadian misalignment to peripheral tissues.

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Wearable sensor-based devices are increasingly applied in free-living and clinical settings to collect fine-grained, objective data about activity and sleep behavior. The manufacturers of these devices provide proprietary software that labels the sensor data at specified time intervals with activity and sleep information. If the device wearer has a health condition affecting their movement, such as a stroke, these labels and their values can vary greatly from manufacturer to manufacturer. Consequently, generating outcome predictions based on data collected from patients attending inpatient rehabilitation wearing different sensor devices can be challenging, which hampers usefulness of these data for patient care decisions. In this paper, we present a data-driven approach to combining datasets collected from different device manufacturers. With the ability to combine datasets, we merge data from three different device manufacturers to form a larger dataset of time series data collected from 44 patients receiving inpatient therapy services. To gain insights into the recovery process, we use this dataset to build models that predict a patient's next day physical activity duration and next night sleep duration. Using our data-driven approach and the combined dataset, we obtained a normalized root mean square error prediction of 9.11% for daytime physical activity and 11.18% for nighttime sleep duration. Our sleep result is comparable to the accuracy we achieved using the manufacturer's sleep labels (12.26%). Our device-independent predictions are suitable for both point-of-care and remote monitoring applications to provide information to clinicians for customizing therapy services and potentially decreasing recovery time.

Background: Falls are the leading cause of hospitalization and death among older adults; therefore, the ability to predict fall risk among older adults is critical. Several performancebased outcome measures exist to assess fall risk. Psychological factors are also associated with fall risk yet can be difficult to assess and are often overlooked. In this study, we investigated whether the Short Form 36 Item Health Survey (SF-36v2®), a measure of healthrelated quality of life (HRQOL), predicted future falls in a sample of community-dwelling older adults. A secondary purpose was to examine relationships between the SF-36v2® and balance performance. Methods: Forty-three community-dwelling older adults completed the SF-36v2® and 3 wellestablished measures of balance performance: Berg Balance Scale, Tinetti Mobility Assessment, and Timed Up and Go. After testing, the number of future falls were recorded for 12 months. Results: The SF-36v2® physical component summary (PCS) scores predicted future fall status at 12 months. The PCS was correlated with all three measures of balance performance, in which higher PCS scores were associated with better balance. The mental component summary scores were not associated with balance performance.

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