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Garbarino S, Bragazzi NL. Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine. J Pers Med 2024; 14:598. [PMID: 38929819 PMCID: PMC11204813 DOI: 10.3390/jpm14060598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/11/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.
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Affiliation(s)
- Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences (DINOGMI), University of Genoa, 16126 Genoa, Italy;
- Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Human Nutrition Unit (HNU), Department of Food and Drugs, University of Parma, 43125 Parma, Italy
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2
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Garbarino S, Bragazzi NL. Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4. J Sleep Res 2024:e14210. [PMID: 38577714 DOI: 10.1111/jsr.14210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024]
Abstract
This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.
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Affiliation(s)
- Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal, Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Nicola Luigi Bragazzi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal, Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Human Nutrition Unit (HNU), Department of Food and Drugs, University of Parma, Parma, Italy
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3
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Vennard H, Buchan E, Davies P, Gibson N, Lowe D, Langley R. Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"? Eur Respir Rev 2024; 33:240041. [PMID: 38925792 PMCID: PMC11216690 DOI: 10.1183/16000617.0041-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/16/2024] [Indexed: 06/28/2024] Open
Abstract
Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.
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Affiliation(s)
- Hannah Vennard
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Elise Buchan
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Philip Davies
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Neil Gibson
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - David Lowe
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ross Langley
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
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Wang Y, Genon S, Dong D, Zhou F, Li C, Yu D, Yuan K, He Q, Qiu J, Feng T, Chen H, Lei X. Covariance patterns between sleep health domains and distributed intrinsic functional connectivity. Nat Commun 2023; 14:7133. [PMID: 37932259 PMCID: PMC10628193 DOI: 10.1038/s41467-023-42945-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
Sleep health is both conceptually and operationally a composite concept containing multiple domains of sleep. In line with this, high dependence and interaction across different domains of sleep health encourage a transition in sleep health research from categorical to dimensional approaches that integrate neuroscience and sleep health. Here, we seek to identify the covariance patterns between multiple sleep health domains and distributed intrinsic functional connectivity by applying a multivariate approach (partial least squares). This multivariate analysis reveals a composite sleep health dimension co-varying with connectivity patterns involving the attentional and thalamic networks and which appear relevant at the neuromolecular level. These findings are further replicated and generalized to several unseen independent datasets. Critically, the identified sleep-health related connectome shows diagnostic potential for insomnia disorder. These results together delineate a potential brain connectome biomarker for sleep health with high potential for clinical translation.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chenyu Li
- Sleep Center, Department of Brain Disease, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Dahua Yu
- Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China.
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Ujma PP, Bódizs R, Dresler M, Simor P, Purcell S, Stone KL, Yaffe K, Redline S. Multivariate prediction of cognitive performance from the sleep electroencephalogram. Neuroimage 2023; 279:120319. [PMID: 37574121 PMCID: PMC10661862 DOI: 10.1016/j.neuroimage.2023.120319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/06/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.
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Affiliation(s)
- Péter P Ujma
- Semmelweis University, Institute of Behavioural Sciences, Budapest, Hungary.
| | - Róbert Bódizs
- Semmelweis University, Institute of Behavioural Sciences, Budapest, Hungary
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Péter Simor
- Institute of Psychology, Eötvös Loránd University, Budapest, Hungary
| | - Shaun Purcell
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Harvard University, USA
| | - Katie L Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Kristine Yaffe
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA; Department of Psychiatry, University of California, San Francisco, California, USA; Department of Neurology, University of California, San Francisco, California, USA; San Francisco VA Medical Center, San Francisco, California, USA
| | - Susan Redline
- Brigham and Women's Hospital, Harvard University, Boston, MA, USA
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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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Spitschan M, Smolders K, Vandendriessche B, Bent B, Bakker JP, Rodriguez-Chavez IR, Vetter C. Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers. Digit Health 2022; 8:20552076221144858. [PMID: 36601285 PMCID: PMC9806438 DOI: 10.1177/20552076221144858] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/25/2022] [Indexed: 12/27/2022] Open
Abstract
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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Affiliation(s)
- Manuel Spitschan
- Translational Sensory & Circadian Neuroscience, Max Planck
Institute for Biological Cybernetics, Tübingen, Germany
- Chronobiology & Health, TUM Department of Sport and Health
Sciences (TUM SG), Technical University of
Munich, Munich, Germany
- TUM Institute for Advanced Study (TUM-IAS), Technical University of
Munich, Garching, Germany
| | - Karin Smolders
- Human-Technology Interaction Group, Eindhoven University of
Technology, Eindhoven, The Netherlands
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve
University, Cleveland, OH, USA
| | | | | | | | - Céline Vetter
- Department of Integrative Physiology, University of Colorado
Boulder, Boulder, CO, USA
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Spitschan M, Santhi N. Individual differences and diversity in human physiological responses to light. EBioMedicine 2022; 75:103640. [PMID: 35027334 PMCID: PMC8808156 DOI: 10.1016/j.ebiom.2021.103640] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/18/2021] [Accepted: 10/06/2021] [Indexed: 02/01/2023] Open
Abstract
Exposure to light affects our physiology and behaviour through a pathway connecting the retina to the circadian pacemaker in the hypothalamus - the suprachiasmatic nucleus (SCN). Recent research has identified significant individual differences in the non-visual effects of light,mediated by this pathway. Here, we discuss the fundamentals and individual differences in the non-visual effects of light. We propose a set of actions to improve our evidence database to be more diverse: understanding systematic bias in the evidence base, dedicated efforts to recruit more diverse participants, routine deposition and sharing of data, and development of data standards and reporting guidelines.
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Affiliation(s)
- Manuel Spitschan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany; Department of Experimental Psychology, University of Oxford, United Kingdom.
| | - Nayantara Santhi
- Department of Psychology, Northumbria University, United Kingdom.
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Lechat B, Scott H, Naik G, Hansen K, Nguyen DP, Vakulin A, Catcheside P, Eckert DJ. New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences. Front Neurosci 2021; 15:751730. [PMID: 34690688 PMCID: PMC8530106 DOI: 10.3389/fnins.2021.751730] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/16/2021] [Indexed: 01/07/2023] Open
Abstract
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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Affiliation(s)
- Bastien Lechat
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Hannah Scott
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Ganesh Naik
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Kristy Hansen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Duc Phuc Nguyen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Andrew Vakulin
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Peter Catcheside
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Danny J Eckert
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
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Lee S, Yu S. Effectiveness of Information and Communication Technology (ICT) Interventions in Elderly's Sleep Disturbances: A Systematic Review and Meta-Analysis. SENSORS 2021; 21:s21186003. [PMID: 34577212 PMCID: PMC8468949 DOI: 10.3390/s21186003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/03/2021] [Accepted: 09/04/2021] [Indexed: 11/17/2022]
Abstract
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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Affiliation(s)
- Seonheui Lee
- Department of Nursing Science, College of Nursing, Gachon University, Incheon 21936, Korea;
| | - Soyoung Yu
- College of Nursing, CHA University, Pocheon 11160, Gyeonggido, Korea
- Correspondence: ; Tel.: +82-31-727-8886
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