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Automated Processing of Big Data in Sleep Medicine
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Sara Mariani, Shaun M. Purcell, Susan Redline
The field of sleep medicine is a rich and diverse one, bringing together a variety of medical specialties and scientific disciplines, including neuroscience, pulmonology, psychiatry, epidemiology, genetics, public health, and others. The fundamental means of characterizing sleep and sleep disorders is through PSG, the simultaneous collection of multiple types of physiological data during sleep. Typically, data are collected that characterize brain activity, cardiac function, breathing and oxygen levels, and body and limb movements. PSG has traditionally been performed in specialized facilities (called “sleep laboratories”), where individuals undergo monitoring with EEG, electrooculography (EOG; measuring eye movements), chin electromyogram (EMG), ECG, respiration, and limb movement sensors. Other devices and technologies are also commonly used, such as home sleep apnea testing devices (HSATs), and actigraphy. HSATs are used for diagnosing sleep apnea. Typical HSAT signals are nasal airflow, oral airflow, respiratory effort, and oximetry. Actigraphy, typically recorded using a wrist accelerometer, is used to provide a noninvasive approach for estimating sleep time, latency, and quality over multiple days and nights of monitoring. Other data sources relevant to the practice of sleep medicine include those on patient-reported outcomes from questionnaires, adherence records from treatment devices, clinical information obtained by direct measurement or extracted from electronic medical records, output from “wearables,” and genomic and biomarker data [11].
Operator Fatigue: Implications for Human–Machine Interaction
Published in Guy A. Boy, The Handbook of Human-Machine Interaction, 2017
Philippa Gander, Curt Graeber, Gregory Belenky
A second widely-used sleep monitoring methodology is actigraphy, in which the operator wears a watch-sized device that continuously monitors and records movement of the non-dominant wrist (an actiwatch). Minute-by-minute total activity counts, recorded for weeks to months, can be downloaded to a computer and scored for sleep or wakefulness, using an algorithm validated against polysomnography. This methodology is less intrusive and cheaper than polysomnography and gives objective data on sleep patterns over long periods of time, although it does not reveal the internal structure of sleep and does not yield reliable measures of sleep quality (36). Factors that currently limit the usability of actigraphy as a Level 2 defense include the cost of devices and the time and expertise required to score the activity records (although considerably less than is required for manual scoring of polysomnography). The person being monitored also needs to keep a sleep diary that is subsequently used to identify which sections of the actigraphy record should be analyzed as sleep periods, and periods when the actiwatch was not being worn.
Anti-fatigue Strategies for Shift Lag and Jet Lag
Published in John A. Caldwell, J. Lynn Caldwell, Fatigue in Aviation, 2016
John A. Caldwell, J. Lynn Caldwell
Also, it is now possible to augment the accuracy of the fatigue predictions generated from models such as SAFTE™ via the use of sleep data obtained from wrist-worn activity monitors. Validated wrist activity monitors overcome the disadvantage of estimating rather than actually measuring operator sleep and this, of course, improves the accuracy of model-based fatigue-risk calculations. Although actigraphy is not fail-safe because it cannot accurately detect relaxed (movement-free) wakefulness or microsleeps (that is, lapses into sleep that last for 30 seconds or less), it is far better at tracking bedtimes, wakeup times, and sleep times than are subjective sleep logs or software-based sleep estimation routines. Actigraphically measured sleep histories can provide a solid indication of risk levels for operational fatigue attributable to sleep loss and disrupted sleep/wake cycles.
Do objective data support the claim that problematic smartphone use has a clinically meaningful impact upon adolescent sleep duration?
Published in Behaviour & Information Technology, 2022
Saoirse Mac Cárthaigh, John Perry, Claire Griffin
Commercial sleep tracking devices use the same technology as research-grade actigraphy (i.e. writs-mounted accelerometers). In addition, some models detect sleep or wake states by assessing changes in heart rate using technology known as optical plethysmography (Grandner and Rosenberger 2019). Over the past few years, validation research has begun to support the use of these affordable commercial sleep tracking devices (e.g. de Zambotti et al. 2016; de Zambotti et al. 2018; Degroote et al. 2020; Dickinson, Cazier, and Cech 2016; El-Amrawy and Nounou 2015; Henriksen et al. 2020; Kang et al. 2017; Lee et al. 2017; Lerner et al. 2018; Liang and Martell 2018; Montgomery-Downs, Insana, and Bond 2012; Pesonen and Kuula 2018; Stone et al. 2020; Tedesco et al. 2019). While commercial sleep trackers do not yet validly or reliably measure sleep staging (e.g. Lee et al. 2019; Liang and Martell 2018), they have comparable validity to gold-standard sleep measurement approaches on sleep parameters such as total sleep time and time in bed (e.g. de Zambotti et al. 2016; Kang et al. 2017). The use of these affordable devices could help to address the sample size limitations of previous research on the relationship between sleep and PSU.
Restricting short-wavelength light in the evening to improve sleep in recreational athletes – A pilot study
Published in European Journal of Sport Science, 2019
Melanie Knufinke, Lennart Fittkau-Koch, Els I. S. Møst, Michiel A. J. Kompier, Arne Nieuwenhuys
Objective sleep estimates were collected using an actigraph (Actiwatch 2, Philips Respironics, Murrysville, USA), that was continuously worn around the non-dominant wrist and only detached during training or when being in contact with water. Activity and photonic light was sampled in 60 s bins. The primary measure of interest was sleep onset latency (min), and secondary measures were wake after sleep onset (min), fragmentation index (%), total sleep time (h:min), and sleep efficiency (%). Actigraphy data were analysed using Respironics Actiware 5 (Philips Respironics, Murrysville, USA) and processed in accordance with the guidelines formulated by the Society of Behavioural Sleep Medicine (SBSM) (Ancoli-Israel et al., 2015). Data were visually inspected and excluded when activity counts and light values indicated detachment of the sensor. In all other cases, rest intervals were manually set when (i) event markers identified bed- and rise time, or – in case event markers were missing – when (ii) light and activity was absent. If light and activity values were ambiguous, (iii) diary entries were used to set rest intervals. The default setting (10-minutes immobility parameter) was used to identify sleep onset and sleep offset. Epochs were scored as wake if activity counts were above 40 (medium sleep-wake threshold).
The effect of poor sleep and occupational demands on driving safety in medical residents
Published in Traffic Injury Prevention, 2018
Benjamin McManus, Karen Heaton, Sylvie Mrug, John Porterfield, Mark Shall, Despina Stavrinos
Thirty-two medical residents (Mage = 28.56 years, SD =2.18) wore actigraphy watches continuously over the study period (M = 8.47 days, SD =3.04) that provided objective estimates of sleep duration and sleep quality. The actigraphy watches were calibrated to each participant’s age, gender, height, and weight and contained a 3-axis accelerometer to estimate movement and sleep continuously (Welk et al. 2004). During the study period, the medical residents completed 3 appointments: (1) Off day; (2) preduty; and (3) postduty. The pre- and postduty driving appointments occurred on the same day, with the duty shift occurring between them. The order of the off-day and on-day was randomized. At each appointment, the medical residents provided salivary cortisol, a well-validated biomarker of stress measurement (Dickerson and Kemeny 2004; Gaab et al. 2005), where higher cortisol levels indicated higher stress (Walker et al. 2011), and drove in a state-of-the-art driving simulator, shown in Figure 1. The simulated driving scenario was a 16-min nighttime drive with a scenario resembling the local region. Each of the 3 drives contained a hazard requiring a response by the driver to avoid a collision.