Evaluation Instruments for Sleep Disorders: A Brief History of Polysomnography and Sleep Medicine

Haba‐Rubio, José; Krieger, J

doi:10.1007/978-94-007-5470-6_2

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…This was used as a mapping tool for the detailed recording of sleep and wakefulness. The advancements in designs of circadian technologies is inextricably linked to the invention of polysomnography. , Another widely used circadian monitoring technique, actigraphy, started out as a crude wrist-worn mechanical sensor in the 1950s. With the addition of sophisticated technologies like piezoelectric sensors, lithium batteries, and virtual data storage, the data collected by actigraphy became accurate and reliable.…”

Section: History Of Circadian Monitoringmentioning

confidence: 99%

Sweating Out the Circadian Rhythm: A Technical Review

et al. 2021

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Circadian dysfunction or dysregulation is associated with many chronic morbidities. Current state-of-art technologies do not provide an accurate estimation of the extent of disease affliction. Recent advances call for using wearables for improving management and diagnosis of circadian related disorders. Sweat contains an abundance of relevant biomarkers like cortisol, DHEA, and so forth, which could be leveraged toward tracking the user's chronobiology. In this article, we provide a review of the key developments in the field of wearable sensors for circadian technologies. We highlight the value of using sweat along with portable electronics toward developing state-of-the-art platforms for efficient diagnosis and management of chronic conditions. Finally, we discuss challenges and opportunities for using wearable sweat sensors for circadian diagnosis and disease management.

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Section: History Of Circadian Monitoringmentioning

confidence: 99%

Sweating Out the Circadian Rhythm: A Technical Review

et al. 2021

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“…Formal sleep study in the medical field or Sleep medicine is done through Polysomnography [5] which is a diagnostic tool in sleep medicine done in Sleep Labs. However, nowadays, there is a surge of Directto-consumer trackers and devices popularizing and 1 bringing perhaps limited but still usefull sleep analysis tools to the masses [6,7].…”

Section: Related Workmentioning

confidence: 99%

Sleep Activity Recognition Using Binary Motion Sensors

El-Khadiri

Corona²,

Rose³

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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Early detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect potential sleep disturbances of the monitored senior residents. We use an unsupervised inference method based on actigraphy data generated by ambient motion sensors scattered around the senior's apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates.

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“…A number of sleep assessment methods have been developed, such as paper sleep diaries regularly utilized in clinical practice to subjectively measure sleep patterns [14]. The standard methods to objectively measure sleep variables such as total sleep time (TST, total number of undisturbed minutes that a person is asleep), sleep efficiency (percent of time asleep without fragmentation or restlessness), and stages of sleep began with polysomnography (PSG) throughout the 1960s and 1970s [15], actigraphy in 1988 for the United States military [16], and finally the integration of accelerometer technology in smartphones during recent years.…”

Section: Introductionmentioning

confidence: 99%

Objective measurement of sleep by smartphone application: comparison with actigraphy and relation to self-reported sleep

Maynard

Appleman

Cronin‐Golomb

et al. 2021

Exploration of Targeted Anti-Tumor Therapy

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Aim: Smartphone technology is increasingly used by the public to assess sleep. Specific features of some sleep-tracking applications are comparable to actigraphy in objectively monitoring sleep. The clinical utility of smartphone apps should be investigated further to increase access to convenient means of monitoring sleep. Methods: Smartphone and subjective sleep measures were administered to 29 community-dwelling healthy adults [aged 20-67, Mean (M) = 26.8; 18 women, 11 men], and actigraphy to 19 of them. Total sleep time (TST) and sleep efficiency were measured with actigraphy and the Sleep Time app (Azumio Inc.). Sleep diaries captured subjective TST and sleep efficiency, and the Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index provided self-report data. An exit questionnaire was administered to examine app feasibility and likelihood of future use. Results: The app significantly overestimated TST when compared to actigraphy. There was no significant difference in sleep efficiency between methodologies. There was also no significant difference between TST recorded through the app and through sleep diaries. Participants’ self-reported ease of use of the smartphone app positively correlated with likelihood of future use. Conclusions: Based on the current findings, future research is needed to investigate the utility and feasibility of multiple smartphone applications in monitoring sleep in healthy and clinical populations.

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Evaluation Instruments for Sleep Disorders: A Brief History of Polysomnography and Sleep Medicine

Cited by 7 publications

References 22 publications

Sweating Out the Circadian Rhythm: A Technical Review

Sweating Out the Circadian Rhythm: A Technical Review

Sleep Activity Recognition Using Binary Motion Sensors

Objective measurement of sleep by smartphone application: comparison with actigraphy and relation to self-reported sleep

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