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Results

1. We suggest that clinicians use actigraphy to estimate sleep parameters in adult patients with insomnia disorder.(Conditional) 2. We suggest that clinicians use actigraphy in the assessment of pediatric patients with insomnia disorder.(Conditional) 3. We suggest that clinicians use actigraphy in the assessment of adult patients with circadian rhythm sleep-wake disorder.(Conditional) 4. We suggest that clinicians use actigraphy in the assessment of pediatric patients with circadian rhythm sleep-wake disorder.(Conditional) 5. We suggest that clinicians use actigraphy integrated with home sleep apnea test devices to estimate total sleep time during recording (in the absence of alternative objective measurements of total sleep time) in adult patients suspected of sleep-disordered breathing.(Conditional) 6. We suggest that clinicians use actigraphy to monitor total sleep time prior to testing with the Multiple Sleep Latency Test in adult and pediatric patients with suspected central disorders of hypersomnolence.(Conditional) 7. We suggest that clinicians use actigraphy to estimate total sleep time in adult patients with suspected insufficient sleep syndrome.(Conditional) 8. We recommend that clinicians not use actigraphy in place of electromyography for the diagnosis of periodic limb movement disorder in adult and pediatric patients.(Strong)

Conclusion:

Support (If Any)

Full Text

A134SLEEP, Volume 42, Abstract Supplement, 2019 A. Basic and Translational Sleep Science XV. Instrumentation and Methodology creation. Using this data, we compared the contributions of mul-tiple features (motion, heart rate, and" clock proxy"--a stand-in for circadian drive) to classifier performance across several algorithms. Results: Best performance was achieved using a neural net trained on motion, heart rate, and" clock proxy" features. To assess generalizability of our results, we tested the models trained on Apple Watch data with data from the Multi-ethnic Study of Atherosclerosis (MESA) and were able to meet or exceed past sleep staging algorithm performance. Conclusion: This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with various methods to improve accuracy of sleep and sleep stage prediction. Additionally, we demonstrate the rele-vance of including estimated circadian phase as an input to im-prove sleep and sleep stage estimation. All code used in this project is being made open source. Support (If Any): This work is supported by an Exercise Sports Science Initiative grant from the University of Michigan. 0327 RELATION BETWEEN SPEED OF WAKE-SLEEP TRANSITIONS AND WAKE TIME IN POLYSOMNOGRAMS. Magdy Younes1, Eleni Giannouli2 1Internal Medicine, University of Manitoba, Winnipeg, MB, Canada, 2Sleep Disorders Center, University of Manitoba, Winnipeg, MB, Canada. Introduction: The immediate neurophysiological mechanism for excessive wake time (W) during polysomnography is unknown. Switching from wakefulness to stable sleep involves progression from full wakefulness to dozing, to light sleep to progressively deeper sleep. We hypothesized that the rate at which sleep depth increases may be an important determinant of W since the slower the rate, the longer the brain lingers in a highly arousable state, increasing the probability that the process is reversed soon after it starts. Methods: We determined rate of progression of sleep depth at wake-sleep transitions using the Odds-Ratio-Product (ORP), a validated continuous index of sleep depth ranging from 0 (deep sleep) to 2.5 (full wakefulness). Probability of spontaneous arousal occurring …