Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

GOAL AND AIMS: Commonly used actigraphy algorithms are designed to operate within a known in-bed interval. However, in free-living scenarios this interval is often unknown. We trained and evaluated a sleep/wake classifier that operates on actigraphy over ∼24-hour intervals, without knowledge of in-bed timing. FOCUS TECHNOLOGY: Actigraphy counts from ActiWatch Spectrum devices. REFERENCE TECHNOLOGY: Sleep staging derived from polysomnography, supplemented by observation of wakefulness outside of the staged interval. Classifications from the Oakley actigraphy algorithm were additionally used as performance reference. SAMPLE: Adults, sleeping in either a home or laboratory environment. DESIGN: Machine learning was used to train and evaluate a sleep/wake classifier in a supervised learning paradigm. The classifier is a temporal convolutional network, a form of deep neural network. CORE ANALYTICS: Performance was evaluated across ∼24 hours, and additionally restricted to only in-bed intervals, both in terms of epoch-by-epoch performance, and the discrepancy of summary statistics within the intervals. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Performance of the trained model applied to the Multi-Ethnic Study of Atherosclerosis dataset. CORE OUTCOMES: Over ∼24 hours, the temporal convolutional network classifier produced the same or better performance as the Oakley classifier on all measures tested. When restricting analysis to the in-bed interval, the temporal convolutional network remained favorable on several metrics. IMPORTANT SUPPLEMENTAL OUTCOMES: Performance decreased on the Multi-Ethnic Study of Atherosclerosis dataset, especially when restricting analysis to the in-bed interval. CORE CONCLUSION: A classifier using data labeled over ∼24-hour intervals allows for the continuous classification of sleep/wake without knowledge of in-bed intervals. Further development should focus on improving generalization performance.

A non-pharmacological multi-modal therapy to improve sleep and cognition and reduce mild cognitive impairment risk: Design and methodology of a randomized clinical trial.

Aging populations are at increased risk of sleep deficiencies (e.g., insomnia) that are associated with a variety of chronic health risks, including Alzheimer's disease and related dementias (ADRD). Insomnia medications carry additional risk, including increased drowsiness and falls, as well as polypharmacy risks. The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth is one way to increase access, particularly for older adults, but to date telehealth has been typically limited to simple videoconferencing portals. While these portals have been shown to be non-inferior to in-person treatment, it is plausible that telehealth could be significantly improved. This work describes a protocol designed to evaluate whether a clinician-patient dashboard inclusive of several user-friendly features (e.g., patterns of sleep data from ambulatory devices, guided relaxation resources, and reminders to complete in-home CBTi practice) could improve CBTi outcomes for middle- to older-aged adults (N = 100). Participants were randomly assigned to one of three telehealth interventions delivered through 6-weekly sessions: (1) CBTi augmented with a clinician-patient dashboard, smartphone application, and integrated smart devices; (2) standard CBTi (i.e., active comparator); or (3) sleep hygiene education (i.e., active control). All participants were assessed at screening, pre-study evaluation, baseline, throughout treatment, and at 1-week post-treatment. The primary outcome is the Insomnia Severity Index. Secondary and exploratory outcomes span sleep diary, actiwatch and Apple watch assessed sleep parameters (e.g., efficiency, duration, timing, variability), psychosocial correlates (e.g., fatigue, depression, stress), cognitive performance, treatment adherence, and neurodegenerative and systemic inflammatory biomarkers.

Enhancing Slow Oscillations and Increasing N3 Sleep Proportion with Supervised, Non-Phase-Locked Pink Noise and Other Non-Standard Auditory Stimulation During NREM Sleep.

PURPOSE: In non-rapid eye movement (NREM) stage 3 sleep (N3), phase-locked pink noise auditory stimulation can amplify slow oscillatory activity (0.5-1 Hz). Open-loop pink noise auditory stimulation can amplify slow oscillatory and delta frequency activity (0.5-4 Hz). We assessed the ability of pink noise and other sounds to elicit delta power, slow oscillatory power, and N3 sleep. PARTICIPANTS AND METHODS: Participants (n = 8) underwent four consecutive inpatient nights in a within-participants design, starting with a habituation night. A registered polysomnographic technologist live-scored sleep stage and administered stimuli on randomized counterbalanced Enhancing and Disruptive nights, with a preceding Habituation night (night 1) and an intervening Sham night (night 3). A variety of non-phase-locked pink noise stimuli were used on Enhancing night during NREM; on Disruptive night, environmental sounds were used throughout sleep to induce frequent auditory-evoked arousals. RESULTS: Total sleep time did not differ between conditions. Percentage of N3 was higher in the Enhancing condition, and lower in the Disruptive condition, versus Sham. Standard 0.8 Hz pink noise elicited low-frequency power more effectively than other pink noise, but was not the most effective stimulus. Both pink noise on the "Enhancing" night and sounds intended to Disrupt sleep administered on the "Disruptive" night increased momentary delta and slow-wave activity (ie, during stimulation versus the immediate pre-stimulation period). Disruptive auditory stimulation degraded sleep with frequent arousals and increased next-day vigilance lapses versus Sham despite preserved sleep duration and momentary increases in delta and slow-wave activity. CONCLUSION: These findings emphasize sound features of interest in ecologically valid, translational auditory intervention to increase restorative sleep. Preserving sleep continuity should be a primary consideration if auditory stimulation is used to enhance slow-wave activity.

Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography.

STUDY OBJECTIVES: Multisensor wearable consumer devices allowing the collection of multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home environment, are increasingly ubiquitous. However, the validity of such devices for sleep assessment has not been directly compared to alternatives such as wrist actigraphy or polysomnography (PSG). METHODS: Eight participants each completed four nights in a sleep laboratory, equipped with PSG and several wearable devices. Registered polysomnographic technologist-scored PSG served as ground truth for sleep-wake state. Wearable devices providing sleep-wake classification data were compared to PSG at both an epoch-by-epoch and night level. Data from multisensor wearables (Apple Watch and Oura Ring) were compared to data available from electrocardiography and a triaxial wrist actigraph to evaluate the quality and utility of heart rate and motion data. Machine learning methods were used to train and test sleep-wake classifiers, using data from consumer wearables. The quality of classifications derived from devices was compared. RESULTS: For epoch-by-epoch sleep-wake performance, research devices ranged in d' between 1.771 and 1.874, with sensitivity between 0.912 and 0.982, and specificity between 0.366 and 0.647. Data from multisensor wearables were strongly correlated at an epoch-by-epoch level with reference data sources. Classifiers developed from the multisensor wearable data ranged in d' between 1.827 and 2.347, with sensitivity between 0.883 and 0.977, and specificity between 0.407 and 0.821. CONCLUSIONS: Data from multisensor consumer wearables are strongly correlated with reference devices at the epoch level and can be used to develop epoch-by-epoch models of sleep-wake rivaling existing research devices.

Examining the Role of Task Requirements in the Magnitude of the Vigilance Decrement.

The vigilance decrement in sustained attention tasks is a prevalent example of cognitive fatigue in the literature. A critical challenge for current theories is to account for differences in the magnitude of the vigilance decrement across tasks that involve memory (successive tasks) and those that do not (simultaneous tasks). The empirical results described in this paper examine this issue by comparing performance, including eye movement data, between successive and simultaneous tasks that require multiple fixations to encode the stimulus for each trial. The findings show that differences in the magnitude of the vigilance decrement between successive and simultaneous tasks were observed only when a response deadline was imposed in the analysis of reaction times. This suggests that memory requirements did not exacerbate the deleterious impacts of time on task on the ability to accurately identify the critical stimuli. At the same time, eye tracking data collected during the study provided evidence for disruptions in cognitive processing that manifested as increased delays between fixations on stimulus elements and between encoding the second stimulus element and responding. These delays were particularly pronounced in later stages of encoding and responding. The similarity of the findings for both tasks suggests that the vigilance decrement may arise from common mechanisms in both cases. Differences in the magnitude of the decrement arise as a function of how degraded cognitive processing interacts with differences in the information processing requirements and other task characteristics. The findings are consistent with recent accounts of the vigilance decrement, which integrate features of prior theoretical perspectives.

Validation of sleep-2-Peak: A smartphone application that can detect fatigue-related changes in reaction times during sleep deprivation.

Despite its high sensitivity and validity in the context of sleep loss, the Psychomotor Vigilance Test (PVT) could be improved. The aim of the present study was to validate a new smartphone PVT-type application called sleep-2-Peak (s2P) by determining its ability to assess fatigue-related changes in alertness in a context of extended wakefulness. Short 3-min versions of s2P and of the classic PVT were administered at every even hour during a 35-h total sleep deprivation protocol. In addition, subjective measures of sleepiness were collected. The outcomes on these tests were then compared using Pearson product-moment correlations, t tests, and repeated measures within-groups analyses of variance. The results showed that both tests significantly correlated on all outcome variables, that both significantly distinguished between the alert and sleepy states in the same individual, and that both varied similarly through the sleep deprivation protocol as sleep loss accumulated. All outcome variables on both tests also correlated significantly with the subjective measures of sleepiness. These results suggest that a 3-min version of s2P is a valid tool for differentiating alert from sleepy states and is as sensitive as the PVT for tracking fatigue-related changes during extended wakefulness and sleep loss. Unlike the PVT, s2P does not provide feedback to subjects on each trial. We discuss how this feature of s2P raises the possibility that the performance results measured by s2P could be less impacted by motivational confounds, giving this tool added value in particular clinical and/or research settings.

Situation awareness recovery.

OBJECTIVE: We describe a novel concept, situation awareness recovery (SAR), and we identify perceptual and cognitive processes that characterize SAR. BACKGROUND: Situation awareness (SA) is typically described in terms of perceiving relevant elements of the environment, comprehending how those elements are integrated into a meaningful whole, and projecting that meaning into the future. Yet SA fluctuates during the time course of a task making it important to understand the process by which SA is recovered after it is degraded. METHOD: We investigated SAR using different types of interruptions to degrade SA. In Experiment I, participants watched short videos of an operator performing a supervisory control task, and then the participants were either interrupted or not interrupted, after which SA was assessed using a questionnaire. In Experiment 2, participants performed a supervisory control task in which they guided vehicles to their respective targets and either experienced an interruption, during which they performed a visual search task in a different panel, or were not interrupted. RESULTS: The SAR processes we identified included shorter fixation durations, increased number of objects scanned, longer resumption lags, and a greater likelihood of refixating on objects that were previously looked at. CONCLUSIONS: We interpret these findings in terms of the memory-for-goals model, which suggests that SAR consists of increased scanning in order to compensate for decay, and previously viewed cues act as associative primes that reactivate memory traces of goals and plans.