Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Which suggestions for behavioral modifications, based on mathematical models, are most likely to be followed in the real world? We address this question in the context of human circadian rhythms. Jet lag is a consequence of the misalignment of the body’s internal circadian (~24-hour) clock during an adjustment to a new schedule. Light is the clock’s primary synchronizer. Previous research has used mathematical models to compute light schedules that shift the circadian clock to a new time zone as quickly as possible. How users adjust their behavior when provided with these optimal schedules remains an open question. Here, we report data collected by wearables from more than 100 travelers as they cross time zones using a smartphone app, Entrain. We find that people rarely follow the optimal schedules generated through mathematical modeling entirely, but travelers who better followed the optimal schedules reported more positive moods after their trips. Using the data collected, we improve the optimal schedule predictions to accommodate real-world constraints. We also develop a scheduling algorithm that allows for the computation of approximately optimal schedules "on-the-fly" in response to disruptions. User burnout may not be critically important as long as the first parts of a schedule are followed. These results represent a crucial improvement in making the theoretical results of past work viable for practical use and show how theoretical predictions based on known human physiology can be efficiently used in real-world settings.

Purpose: Recent studies show that glioblastoma (GBM) is more sensitive to temozolomide (TMZ) in the morning. In cells, inhibiting O6-Methylguanine-DNA-Methyltransferase ( MGMT ) abolished time-dependent TMZ efficacy, suggesting that circadian regulation of this DNA repair enzyme underlies daily TMZ sensitivity. Here, we tested the hypotheses that MGMT promoter methylation and protein abundance vary with time-of-day in GBM, resulting in daily rhythms in TMZ efficacy. Methods: We assessed daily rhythms in MGMT promoter methylation in GBM in vitro and retrospectively analyzed MGMT methylation status in human GBM biopsies collected at different times of day. Next, we measured MGMT and BMAL1 protein abundances in GBM cells collected at four-hour intervals. To understand the therapeutic implications of circadian variations in MGMT, we incorporated its daily rhythms into an in vitro mathematical model capturing interactions between MGMT, TMZ, and GBM DNA. Results: We found daily rhythms in MGMT promoter methylation and protein levels in GBM in vitro, and in patient biopsies peaking at midday. Further, MGMT protein levels peaked at CT4, corresponding to the time of maximal TMZ efficacy in vitro. When we incorporated cell-intrinsic circadian rhythms in MGMT protein into a mathematical model for GBM chemotherapy, we found that dosing when daily MGMT levels peaked and began to decline produced maximum DNA damage. Conclusion: Our findings suggest that the likelihood of diagnosis of MGMT promoter methylation may vary with time of biopsy in GBM. Furthermore, theoretical modeling predicts that efforts to deliver TMZ after the daily peak of MGMT activity, with exact time being dose-dependent, may significantly enhance its therapeutic efficacy. Graphical

Oliva Walch, Franco Tavella, Jamie M Zeitzer, Renske Lok; Beyond phase shifting: Targeting circadian amplitude for light interventions in humans, Sleep, ,

High-grade gliomas, like glioblastoma multiforme (GBM), are the most common malignant brain tumors in adults and are treated with the chemotherapy drug temozolomide (TMZ). In humans, a retrospective analysis of patients’ overall survival suggests that morning dosing may confer a benefit over evening dosing. Circadian variation in O6-methylguanine-DNA methyltransferase (MGMT) gene expression and promoter methylation has been implicated in increased tumor cell sensitivity to TMZ in the morning. Although patient compliance with timed oral administration of TMZ was high in a prospective trial, it is not known whether differences in daily sleep patterns of patients impact the biological time of drug administration or overall survival. Using wrist actigraphy collected from 10 high-grade glioma patients, we quantified the moment of oral TMZ delivery in terms of wall clock time and internal biological time during the months after surgical tumor resection. We found that variation of daily rhythms within and between individuals caused dosing times to vary more in their internal biological time than wall clock time so that, for example, some doses taken by patients assigned for the evening (2000 h) were closer to the patient’s internal biological morning. We conclude that wrist actigraphy provides a reliable and non-invasive estimate of personal circadian time that could improve efficacy and precision of TMZ delivery. These findings may inform personalized circadian medicine and optimized times for TMZ delivery in the clinic.