<i>Sleep and Big Data</i>: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions—an era for<i>Sleep-Omics</i>?

Redline, Susan; Purcell, Shaun

doi:10.1093/sleep/zsab107

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…Together, a range of new and emerging devices could routinely generate large volumes of sleep measurements over extended periods. This approach will require evidence to support clinical use and value, and software tools to assist clinicians to assess, analyze, and interpret sleep-omics (Redline and Purcell, 2021). To increase the uptake of new technologies in research and clinical settings, greater communication between sleep medicine experts and device manufacturers is needed.…”

Section: A New Way Of Thinking: Top-down Sleep Signal Features Based On Underlying Neurobiology Rather Than Bottom-up Measurement Convenimentioning

confidence: 99%

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

et al. 2021

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Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

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Section: A New Way Of Thinking: Top-down Sleep Signal Features Based On Underlying Neurobiology Rather Than Bottom-up Measurement Convenimentioning

confidence: 99%

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

et al. 2021

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“…Sleep health is a public health priority, according to the Center for Disease Control's Healthy People 2020 Report [21]. However, sleep disturbance has become a very common disease and is increasing, especially in the elderly [22].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Information and Communication Technology (ICT) Interventions in Elderly’s Sleep Disturbances: A Systematic Review and Meta-Analysis

Lee

2021

Sensors

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Sleep is a crucial factor for human health and is closely related to quality of life. Sleep disturbances constitute a health problem that should be solved, especially when it affects the elderly. This study aims to examine the effectiveness of information and communication technologies (ICT) interventions in managing sleep disturbances in the elderly. The study used a systematic review of three databases: Ovid-Medline, Ovid-EMBASE, and the Cochrane library database for papers published till 15 April 2021. Two authors independently selected and screened relevant studies based on predefined inclusion criteria. The meta-analysis of randomized controlled trials (RCTs) was carried out using Review Manager 5.4. Two authors independently screened the titles and abstracts of 4297 studies considering both inclusion and exclusion criteria. The complete texts of 47 articles were then evaluated, 31 articles were excluded, and finally, 16 articles were selected. Our meta-analysis showed that the cognitive-behavioral therapy for insomnia (CBT-I) group had a significantly reduced Insomnia Severity Index (ISI) compared to the control group (−4.81 [−5.56, −4.06], p < 0.00001, I2 = 83%) in RCTs, with a significant reduction in ISI (3.47 [1.58, 5.35], p = 0.0003) found in quasi-experimental studies. A significant improvement was found in total sleep time in the CBT-I group compared to the control group (29.24 [15.41, 43.07], p <0.0001) in RCTs, while the CBT-I group showed significantly reduced wake time after sleep onset compared to the control group (−20.50 [−26.60, −14.41], p < 0.00001). In addition, a significant reduction in depression was found in the CBT-I group compared to the control group (−2.11 [−2.85, −1.37], p < 0.00001, I2 = 0%) in RCTs. The quality of life–mental component score (5.75 [1.64, 9.87], p = 0.006, I2 = 0%) and the quality of life–physical component score (5.19 [0.76, 9.62], p = 0.02, I2 = 0%) showed significant improvement in the CBT-I group compared to the control group. ICT interventions showed positive effects on sleep disturbances of the elderly, specifically confirming the positive effect on depression and quality of life as well as the indicators directly related to sleep such as ISI and quality of sleep. Thus, the application of ICT in the healthcare sector will be greater in the future, with changes in the nursing education and practice guidelines so that nurses can play a pivotal role in promoting health behaviors such as sleep-related quality of life and daily activities of the elderly.

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“…The development of common frameworks for storing and processing BioMeTs data will be a key in facilitating "big data" analyses of light dosimetry, and other types of data collected via digital sensors. [103][104][105] How to handle non-wear periods and quality control?. The assessment of rest-activity cycles with actigraphy is often paired with the use of a sleep diary, [106][107][108][109][110][111] sometimes indicating non-wear periods which can then be masked in later analysis steps.…”

Section: How Comparable Are Data From Different Groups and Populations?mentioning

confidence: 99%

“…The development of common frameworks for storing and processing BioMeTs data will be a key in facilitating “big data” analyses of light dosimetry, and other types of data collected via digital sensors. 103 – 105 …”

Section: Introductionmentioning

confidence: 99%

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers

et al. 2022

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Background Light exposure is an important driver and modulator of human physiology, behavior and overall health, including the biological clock, sleep-wake cycles, mood and alertness. Light can also be used as a directed intervention, e.g., in the form of light therapy in seasonal affective disorder (SAD), jetlag prevention and treatment, or to treat circadian disorders. Recently, a system of quantities and units related to the physiological effects of light was standardized by the International Commission on Illumination (CIE S 026/E:2018). At the same time, biometric monitoring technologies (BioMeTs) to capture personalized light exposure were developed. However, because there are currently no standard approaches to evaluate the digital dosimeters, the need to provide a firm framework for the characterization, calibration, and reporting for these digital sensors is urgent. Objective This article provides such a framework by applying the principles of verification, analytic validation and clinical validation (V3) as a state-of-the-art approach for tools and standards in digital medicine to light dosimetry. Results This article describes opportunities for the use of digital dosimeters for basic research, for monitoring light exposure, and for measuring adherence in both clinical and non-clinical populations to light-based interventions in clinical trials.

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Sleep and Big Data: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions—an era forSleep-Omics?

Cited by 15 publications

References 14 publications

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Effectiveness of Information and Communication Technology (ICT) Interventions in Elderly’s Sleep Disturbances: A Systematic Review and Meta-Analysis

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers

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