RestEaze: An Emerging Technology to Characterize Leg Movements During Sleep

Several sleep disorders are characterized by periodic leg movements during sleep including Restless Leg Syndrome, and can indicate disrupted sleep in otherwise healthy individuals. Current technologies to measure periodic leg movements during sleep are limited. Polysomnography and some home sleep tests use surface electromyography to measure electrical activity from the anterior tibilias muscle. Actigraphy uses 3-axis accelerometers to measure movement of the ankle. Electromyography misses periodic leg movements that involve other leg muscles and is obtrusive because of the wires needed to carry the signal. Actigraphy based devices require large amplitude movements of the ankle to detect leg movements (missing the significant number of more subtle leg movements) and can be worn in multiple configurations precluding precision measurement. These limitations have contributed to their lack of adoption as a standard of care for several sleep disorders. In this study, we develop the RestEaze sleep assessment tool as an ankle-worn wearable device that combines capacitive sensors and a 6-axis inertial measurement unit to precisely measure periodic leg movements during sleep. This unique combination of sensors and the form-factor of the device addresses current limitations of periodic leg movements during sleep measurement techniques. Pilot data collected shows high correlation with polysomnography across a heterogeneous participant sample and high usability ratings. RestEaze shows promise in providing ecologically valid, longitudinal measures of leg movements that will be useful for clinicians, researchers, and patients to better understand sleep.

A direct comparison of wrist actigraphy and sleep diaries in measuring sleep in healthy adults.

Measuring sleep and sleep quality is an important diagnostic and monitoring tool, and a number of different methods for measurement have been developed over the last half-century. Two prevalent methods include wrist actigraphy and sleep diaries. Both methods can be applied in different circumstances, but both have strengths and weaknesses. This study aimed to identify the extent to which there is congruence in the scores achieved by each method of sleep measurement. Sixty-eight respondents were asked to wear a wrist actigraphy and complete a sleep diary over the course of five days. There was a significant difference between the mean scores achieved using each measurement method, with actigraphy scores indicating lower total sleep time (TST) than diaries. However, this difference was not consistently present when the scores were compared on a day by day basis. Participant adherence is likely to fluctuate over the course of a sleep study and may undermine the accuracy of sleep diaries.

Prospective cohort study to evaluate the accuracy of sleep measurement by consumer-grade smart devices compared with polysomnography in a sleep disorders population

ObjectivesConsumer-grade smart devices are now commonly used by the public to measure waking activity and sleep. However, the ability of these devices to accurately measure sleep in clinical populations warrants more examination. The aim of the present study was to assess the accuracy of three consumer-grade sleep monitors compared with gold standard polysomnography (PSG).DesignA prospective cohort study was performed.SettingAdults undergoing PSG for investigation of a suspected sleep disorder.Participants54 sleep-clinic patients were assessed using three consumer-grade sleep monitors (Jawbone UP3, ResMed S+ and Beddit) in addition to PSG.OutcomesJawbone UP3, ResMed S+ and Beddit were compared with gold standard in-laboratory PSG on four major sleep parameters—total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO) and sleep efficiency (SE).ResultsThe accelerometer Jawbone UP3 was found to overestimate TST by 28 min (limits of agreement, LOA=−100.23 to 157.37), with reasonable agreement compared with gold standard for TST, WASO and SE. The doppler radar ResMed S+ device underestimated TST by 34 min (LOA=−257.06 to 188.34) and had poor absolute agreement compared with PSG for TST, SOL and SE. The mattress device, Beddit underestimated TST by 53 min (LOA=−238.79 to 132) on average and poor reliability compared with PSG for all measures except TST. High device synchronisation failure occurred, with 20% of recordings incomplete due to Bluetooth drop out and recording loss.ConclusionPoor to moderate agreement was found between PSG and each of the tested devices, however, Jawbone UP3 had relatively better absolute agreement than other devices in sleep measurements compared with PSG. Consumer grade devices assessed do not have strong enough agreement with gold standard measurement to replace clinical evaluation and PSG sleep testing. The models tested here have been superseded and newer models may have increase accuracy and thus potentially powerful patient engagement tools for long-term sleep measurement.

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

Using mobile wearables to establish sleep bioprofiles in newly diagnosed multiple myeloma (MM) patients.

8040 Background: Passive monitoring using wearables can objectively measure sleep over extended time periods. MM patients (PTs) are susceptible to fluctuating sleep patterns due to pain and dexamethasone (dex) treatment. In this prospective study, we remotely monitored sleep patterns on 40 newly diagnosed MM (NDMM) PTs while administering electronic PT reported outcome (ePRO) surveys. The study aim was to establish sleep bioprofiles during therapy and correlate with ePROs. Methods: Eligible PTs for the study had untreated NDMM and assigned to either Cohort A – PTs < 65 years or Cohort B – PTs ≥ 65 years. PTs were remotely monitored for sleep 1-7 days at baseline [BL] and continuously up to 6 therapy cycles. PTs completed ePRO surveys (EORTC - QLQC30 and MY20) at BL and after each cycle. Sleep data and completed ePRO surveys were synced to Medidata Rave through Sensorlink technology. Associations between sleep measurement trends and QLQC30 scores were estimated using a linear mixed model with a random intercept. Results: Between Feb 2017 - Sep 2019, 40 PTs (21 M and 19 F) were enrolled with 20 in cohort A (mean 54 yrs, 41-64) and 20 in cohort B (mean 71 yrs, 65-82). Regimens included KRd 14(35%), RVd 12(30%), Dara-KRd 8(20%), VCd 5(12.5%), and Rd 1(2.5%). Sleep data was compiled among 23/40 (57.5%) PTs. BL mean sleep was 578.9 min/24 hr for Cohort A vs. 544.9 min/24 hr for Cohort B (p = 0.41, 95% CI -51.5, 119.5). Overall median sleep trends changed for cohort A by -6.3 min/24 hr per cycle (p = 0.09) and for cohort B by +0.8 min/24 hr per cycle (p = 0.88). EPRO data trends include global health +1.5 score/cycle (p = 0.01, 95% CI 0.31, 3.1), physical +2.16 score/cycle (p < 0.001, 95% CI 1.26, 3.07), insomnia -1.6 score/cycle (p = 0.09, 95% CI [-3.47, 0.26]), role functioning +2.8 score/cycle (p = 0.001, 95% CI 1.15, 4.46), emotional +0.3 score/cycle (p = 0.6, 95% CI -0.73, 1.32), cognitive -0.36 score/cycle (p = 0.44, 95% CI -1.29,0.56), and fatigue -0.36 score/cycle (p = 0.4, 95% CI -1.65, 0.93). No association between sleep measurements and ePRO were detected. Difference in sleep on dex days compared to all other days during the sample cycle period for cohort A was 81.4 min/24 hr (p = 0.004, 95% CI 26, 135) and for cohort B was 37.4 min/24 hr (p = 0.35, 95% CI -41, 115). Conclusions: Our study provides insight into wearable sleep monitoring in NDMM. Overall sleep trends in both cohorts do not demonstrate significant gains or losses, and these trends fit with HRQOL ePRO insomnia responses. Upon further examination, we demonstrate objective differences (younger PTs) in intra-cyclic sleep measurements on dex days compared to other cycle days (less sleep by > 1 hr). For older patients, less variation in sleep profiles was detected during dex days, possibly due to higher levels of fatigue or longer sleep duration. Sleep is an integral part of well-being in the cancer patient. Future studies should continue to characterize sleep patterns as it relates to HRQOL.

324 Naturalistic Characterization of Sleep in Chronic Insomnia Using a Non-Contact Sleep Measurement Device

Introduction Individuals with insomnia report poor sleep quality and non-restorative sleep, and often exhibit irregular sleep patterns over days and weeks. First night effects and logistical challenges make it difficult to measure these sleep characteristics in the laboratory. Also, sensitivity to sleep disruption from obtrusive measurement devices confounds sleep measurements in people with insomnia in their naturalistic setting. Non-contact sleep measurement devices have the potential to address these issues and enable ecologically valid, longitudinal characterization of sleep in individuals with insomnia. Here we use a non-contact device – the SleepScore Max (SleepScore Labs) – to assess the sleep of individuals with chronic insomnia, compared to healthy sleeper controls, in their home setting. Methods As part of a larger study, 13 individuals with chronic insomnia (ages 25-60y, 7 males) and 8 healthy sleeper controls (ages 21-46y, 6 females) participated in an at-home sleep monitoring study. Enrollment criteria included an age range of 18-65y and, for the insomnia group, ICSD-3 criteria for chronic insomnia with no other clinically relevant illness. Participants used the non-contact sleep measurement device to record their sleep periods each night for 8 weeks. Sleep measurements were analyzed for group differences in both means (characterizing sleep overall) and within-subject standard deviations (characterizing sleep variability across nights), using mixed-effects regression controlling for systematic between-subject differences. Results Based on the non-contact sleep measurements, individuals with chronic insomnia exhibited greater variability in bedtime, time in bed, total sleep time, sleep latency, total wake time across time in bed, wakefulness after sleep onset, sleep interruptions, and estimated light sleep, compared to healthy sleeper controls (all F&gt;5.7, P&lt;0.05). No significant differences were found for group averages and for variability in estimated deep and REM sleep. Conclusion In this group of individuals with chronic insomnia, a non-contact device used to characterize sleep naturalistically captured enhanced variability across nights in multiple aspects of sleep stereotypical of sleep disturbances in chronic insomnia, differentiating the sample statistically significantly from healthy sleeper controls. Support (if any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.

094 Bedtime Hunger Predicts Reduced Sleep Efficiency That Night in Users of a Consumer Sleep Measurement Device

Introduction It has long been suspected that bedtime hunger can potentially disturb sleep. The neural circuits that control sleep are now known to receive signals of appetite and energy balance via hypocretin/orexin neurons from the lateral hypothalamus. But there remains need to specifically identify how, and under what conditions, appetite mechanisms affect human sleep. This study documents the relationship between bedtime hunger and subsequent sleep efficiency in users of a consumer sleep measurement device. Methods The Zeo headband (sold to the public during 2009–2013) used detected electrical potentials to periodically calculate the most probable stage of sleep or wake. Users uploaded sleep records online and could track nightly conditions — including bedtime hunger — on provided rating scales. De-identified summary data from these nightly records were aggregated into a research registry. We extracted the sleep records with bedtime hunger ratings and analyzed them using multilevel modeling to identify within-person and between-person relationships between bedtime hunger and sleep efficiency. We decomposed bedtime hunger ratings into person-mean hunger and nightly hunger, the difference between each night’s hunger rating and the person’s mean. Results 4,284 nightly sleep records with a bedtime hunger rating were provided by 183 people (age 19–77, 68% male, mean: 23 records/person). Sleep efficiency was not related to person-mean hunger (p=0.26), but was inversely related to nightly hunger that night (p=0.01). The model predicted a within-person difference in sleep efficiency between nights with high, versus low, nightly hunger that varied across the people in the sample (mean [range]: -2.4 [-12.5-1.6] percentage points) and correlated positively with typical sleep efficiency (r=0.73, p=0.00) and negatively with unexplained variability in sleep efficiency (r=-0.47, p=0.00). Conclusion For the people in this dataset, on average, going to bed hungrier than usual predicted reduced sleep efficiency that night. The effect was strongest in people who tend toward low and variable sleep efficiency. This finding strongly suggests that bedtime hunger can indeed disturb sleep, especially in poor sleepers. Further research is needed to determine who is most affected and to understand implications, such as for weight management, eating disorders, food insecurity, or sleep-supporting foods and dietary practices. Support (if any) USDA-NIFA-AFRI and NIH-NCATS