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The future of sleep health: a data-driven revolution in sleep science and medicine.

ABSTRACT: In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

SUBMITTER: Perez-Pozuelo I

PROVIDER: S-EPMC7089984 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The future of sleep health: a data-driven revolution in sleep science and medicine.

Perez-Pozuelo Ignacio I Zhai Bing B Palotti Joao J Mall Raghvendra R Aupetit Michaël M Garcia-Gomez Juan M JM Taheri Shahrad S Guan Yu Y Fernandez-Luque Luis L

NPJ digital medicine 20200323

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of ...[more]

PMID: 32219183

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Project description:BackgroundThe burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects.MethodsWe aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA.ResultsThe theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients' and physicians' decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk.ConclusionThis theoretical model introduces future HT management as an understandable way of conceiving patients' futures with a view to positively (or negatively) changing their behaviour. The model's ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

Project description:Study objectivesWe evaluated factors associated with the presence of sleep disturbance in patients with stroke and whether sleep disturbance predicted change in other patient-reported domains of health over time.MethodsObservational cohort study of 2,190 patients with stroke seen in a cerebrovascular clinic February 17, 2015 to July 5, 2017 who completed patient-reported outcome measures (PROMs) at ≥ 1 visit, including the Patient Health Questionnaire-9 depression screen, Quality of Life in Neurological Disorders (NeuroQoL) cognitive function and Patient-Reported Outcomes Information Measurement System (PROMIS) sleep disturbance, fatigue, anxiety, social role satisfaction, physical function, and pain interference. Separate multivariable models were constructed with PROMIS sleep disturbance as the dependent variable. Covariates included clinical and demographic variables, the Patient Health Questionnaire-9 depression screen scored on the PROMIS metric, and 1 of the 6 other PROMs. Among the 476 patients with ≥ 2 visits, linear regression models were constructed to determine the association of baseline sleep disturbance with follow-up PROMs after adjustment for the corresponding baseline PROMIS/NeuroQol scores, depression score, and clinical variables.ResultsYounger age and all PROM scores were associated with sleep disturbance; depression had the greatest association among PROMs (beta estimate 0.53, 95% confidence interval, 0.49, 0.57), physical function had the least (-0.06, 95% confidence interval, -0.11, -0.01). Baseline PROMIS sleep disturbance score was associated with worse adjusted follow-up scores for depression, fatigue, social role satisfaction, and physical function.ConclusionsYounger age and all 8 PROMs were associated with sleep disturbance in patients with stroke. The degree of sleep disturbance predicted future patient-reported outcomes for multiple domains of health. Further study is warranted to determine if interventions shown to improve sleep symptoms will also improve other outcomes.

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The future of sleep health: a data-driven revolution in sleep science and medicine.

Publications

The future of sleep health: a data-driven revolution in sleep science and medicine.

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