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Holfinger S, Schutte-Rodin S, Ratnasoma D, Chiang AA, Baron K, Deak M, Jerkins E, Baughn J, Gipson K, Gruber R, Miller JN, Paruthi S, Shah S, Bandyopadhyay A, on behalf of the American Academy of Sleep Medicine Emerging Technology Committee. Evolving trends in novel sleep tracking and sleep testing technology publications between 2020 and 2022. J Clin Sleep Med 2025; 21:891-905. [PMID: 39789983 PMCID: PMC12048328 DOI: 10.5664/jcsm.11562] [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: 01/08/2025] [Accepted: 01/08/2025] [Indexed: 01/12/2025]
Abstract
STUDY OBJECTIVES To update sleep medicine providers regarding (1) published research on the uses and performance of novel sleep tracking and testing technologies, (2) the use of artificial intelligence to acquire and process sleep data, and (3) research trends and gaps regarding the development and/or evaluation of these technologies. METHODS Medline and Embase electronic databases were searched for studies utilizing screening and diagnostic sleep technologies, published between 2020 and 2022 in journals focusing on human sleep. Studies' quality was determined based on the Study Design criteria of The Oxford Center for Evidence-Based Medicine Levels of Evidence. RESULTS Ninety-six of 3,849 articles were included. Most studies were adult performance evaluation (validation) studies, often comparing a novel technology to polysomnography. Sleep tracker publications tended to be Unites States-based, nonindustry-funded, performance studies on healthy adults using non-Food and Drug Administration-cleared technologies. Sleep apnea testing technologies were more frequently industry-funded and Food and Drug Administration-cleared. All studied technologies utilized software with an algorithm and/or artificial intelligence. Few studies used randomized control designs, or accounted for recruitment/attrition biases associated with participants' age, race/ethnicity, or comorbid health conditions. CONCLUSIONS Evidence-based publications have not kept pace with the proliferation and landscape of consumer and clinical sleep technologies. Due to the variance in technologies used within sleep research, careful review of the software used within studies is recommended. Future publications may fill identified gaps by including underrepresented populations, maintaining independence from industry, and through rigorous study design. CITATION Holfinger S, Schutte-Rodin S, Ratnasoma D, et al. Evolving trends in novel sleep tracking and sleep testing technology publications between 2020 and 2022. J Clin Sleep Med. 2025;21(5):891-905.
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Affiliation(s)
| | - Sharon Schutte-Rodin
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | | | - Ambrose A. Chiang
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve University, Cleveland, Ohio
| | | | - Maryann Deak
- Evernorth Health Services, Boston, Massachusetts
| | - Evin Jerkins
- Ohio University Heritage College of Osteopathic Medicine, Dublin, Ohio
| | | | - Kevin Gipson
- Harvard Medical School, Mass General Hospital for Children, Boston, Massachusetts
| | | | | | - Shalini Paruthi
- Saint Louis University School of Medicine, St. Louis, Missouri
| | - Sachin Shah
- Indiana University Health North Hospital, Indiana University Health Methodist Hospital, Indianapolis, Indiana
| | | | - on behalf of the American Academy of Sleep Medicine Emerging Technology Committee
- The Ohio State University, Columbus, Ohio
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- First Physicians Group, Sarasota, Florida
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve University, Cleveland, Ohio
- University of Utah, Salt Lake City, Utah
- Evernorth Health Services, Boston, Massachusetts
- Ohio University Heritage College of Osteopathic Medicine, Dublin, Ohio
- Mayo Clinic, Rochester, Minnesota
- Harvard Medical School, Mass General Hospital for Children, Boston, Massachusetts
- McGill University, Montreal, Québec, Canada
- University of Nebraska Medical Center, Omaha, Nebraska
- Saint Louis University School of Medicine, St. Louis, Missouri
- Indiana University Health North Hospital, Indiana University Health Methodist Hospital, Indianapolis, Indiana
- Indiana University School of Medicine, Indianapolis, Indiana
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2
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Cepni AB, Burkart S, Zhu X, White J, Finnegan O, Nelakuditi S, Beets M, Brown III D, Pate R, Welk G, de Zambotti M, Ghosal R, Wang Y, Armstrong B, Adams E, van Hees V, Glenn Weaver R. Evaluating the performance of open-source and proprietary processing of actigraphy sleep estimation in children with suspected sleep disorders: a comparison with polysomnography. Sleep 2025; 48:zsae267. [PMID: 39560378 PMCID: PMC11985398 DOI: 10.1093/sleep/zsae267] [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: 09/04/2024] [Revised: 11/13/2024] [Indexed: 11/20/2024] Open
Abstract
STUDY OBJECTIVES Evaluate the performance of actigraphy-based open-source and proprietary sleep algorithms compared to polysomnography in children with suspected sleep disorders. METHODS In a sleep clinic, 110 children (5-12 years, 54% female, 50% black, 82% with sleep disorders) wore wrist-placed ActiGraph GT9X during overnight polysomnography. Actigraphy data were scored as sleep or wake using open-source GGIR and proprietary ActiLife software. Discrepancy and epoch-by-epoch analyses were conducted to assess agreement between algorithms and polysomnography, along with equivalence testing. RESULTS The open-source vanHees2015 algorithm showed good accuracy (79.5% ± 12.0%), sensitivity (81.1% ± 13.5%), and specificity (66.0% ± 23.8%) for sleep detection but was outperformed by the proprietary ActiLife algorithms. The magnitude and trend of bias for total sleep time (TST), sleep efficiency (SE), sleep onset latency, and wake after sleep onset were similar between algorithms. TST and SE were statistically equivalent for the Cole-Kripke (Actilife) and vanHees2015 algorithms compared to the Sadeh (Actilife) algorithm. The Cole-Kripke (ActiLife) demonstrated higher sensitivity (90.5%) to detect sleep but lower specificity (61.2%) than Cole-Kripke (GGIR) (sensitivity: 62.7%, specificity: 79.9%). Sadeh and Cole-Kripke estimated sleep outcomes were not statistically equivalent between implementations in ActiLife and GGIR. CONCLUSIONS The open-source vanHees2015 algorithm performed well but slightly worse than the proprietary ActiLife algorithms in children. The open-source nature vanHees2015 makes it ideal for clinical pediatric use. Implementation of the Sadeh and Cole-Kripke algorithms in the proprietary ActiLife and open-source GGIR software yield different sleep estimates, so comparisons between studies using these different implementations should be avoided.
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Affiliation(s)
- Aliye B Cepni
- Health and Human Performance Department, University of Houston, Houston, TX, USA
| | - Sarah Burkart
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Xuanxuan Zhu
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - James White
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Olivia Finnegan
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Srihari Nelakuditi
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Michael Beets
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - David Brown III
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Russell Pate
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Gregory Welk
- Department of Kinesiology, Iowa State University, Ames, IA, USA
| | | | - Rahul Ghosal
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Yuan Wang
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Bridget Armstrong
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Elizabeth Adams
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | | | - R Glenn Weaver
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
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3
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Hale L, Buxton OM, Applegate A, Redline S. The Past, Present, and Future of Sleep Health. Sleep Health 2025; 11:123-125. [PMID: 40274321 DOI: 10.1016/j.sleh.2025.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2025]
Affiliation(s)
- Lauren Hale
- Renaissance School of Medicine, Stony Brook University
| | | | | | - Susan Redline
- Brigham and Women's Hospital, Harvard TH Chan School of Public Health.
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Martinot JB, Le-Dong NN, Malhotra A, Pépin JL. Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements. J Prosthodont 2025; 34:10-25. [PMID: 39676388 PMCID: PMC12003084 DOI: 10.1111/jopr.14003] [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: 04/30/2024] [Accepted: 11/22/2024] [Indexed: 12/17/2024] Open
Abstract
PURPOSE This review aims to highlight the pivotal role of the mandibular jaw movement (MJM) signal in advancing artificial intelligence (AI)-powered technologies for diagnosing obstructive sleep apnea (OSA). METHODS A scoping review was conducted to evaluate various aspects of the MJM signal and their contribution to improving signal proficiency for users. RESULTS The comprehensive literature analysis is structured into four key sections, each addressing factors essential to signal proficiency. These factors include (1) the comprehensiveness of research, development, and application of MJM-based technology; (2) the physiological significance of the MJM signal for various clinical tasks; (3) the technical transparency; and (4) the interpretability of the MJM signal. Comparisons with the photoplethysmography (PPG) signal are made where applicable. CONCLUSIONS Proficiency in biosignal interpretation is essential for the success of AI-driven diagnostic tools and for maximizing the clinical benefits through enhanced physiological insight. Through rigorous research ensuring an enhanced understanding of the signal and its extensive validation, the MJM signal sets a new benchmark for the development of AI-driven diagnostic solutions in OSA diagnosis.
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Affiliation(s)
- Jean-Benoit Martinot
- Sleep Laboratory, CHU Université catholique de Louvain (UCL), Namur Site Sainte-Elisabeth, Namur, Belgium
- Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium
| | | | - Atul Malhotra
- University of California San Diego, La Jolla, California, USA
| | - Jean-Louis Pépin
- HP2 Laboratory, Inserm U1300, Grenoble Alpes University, Grenoble, France
- EFCR Laboratory, Grenoble Alpes University Hospital, Grenoble, France
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Das JN, Ji L, Shen Y, Kumara S, Buxton OM, Chow SM. Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting. Sleep Health 2025; 11:166-173. [PMID: 39788836 DOI: 10.1016/j.sleh.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 08/28/2024] [Accepted: 10/06/2024] [Indexed: 01/12/2025]
Abstract
GOAL AND AIMS One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes. FOCUS TECHNOLOGY Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics). REFERENCE TECHNOLOGY The built-in nonwear sensor as "ground truth" to classify nonwear periods using other data, mimicking features of Actiwatch 2. SAMPLE Data were collected over 1week from employed adults (n = 853). DESIGN Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows. CORE ANALYTICS The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features. CORE OUTCOMES The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms. IMPORTANT SUPPLEMENTAL OUTCOMES The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified. CORE CONCLUSION Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.
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Affiliation(s)
- Jyotirmoy Nirupam Das
- Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.
| | - Linying Ji
- Department of Psychology, Montana State University, Bozeman, Montana, USA
| | - Yuqi Shen
- Biobehavioral Health Department, The Pennsylvania State University, State College, Pennsylvania, USA
| | - Soundar Kumara
- Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Orfeu M Buxton
- Department of Biobehavioral Health Department, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Sy-Miin Chow
- Department of Human and Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA
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van der Aar JF, van Gilst MM, van den Ende DA, Fonseca P, van Wetten BNJ, Janssen HCJP, Peri E, Overeem S. Optimizing wearable single-channel electroencephalography sleep staging in a heterogeneous sleep-disordered population using transfer learning. J Clin Sleep Med 2025; 21:315-323. [PMID: 39347545 PMCID: PMC11789263 DOI: 10.5664/jcsm.11380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 10/01/2024]
Abstract
STUDY OBJECTIVES Although various wearable electroencephalography devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy participants. A major barrier for applying automated wearable electroencephalography sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as a strategy to overcome limited data availability and optimize automated single-channel electroencephalography sleep staging in people with sleep disorders. METHODS We acquired 52 single-channel frontopolar headband electroencephalography recordings from a heterogeneous sleep-disordered population with concurrent polysomnography (PSG). We compared 3 model training strategies: "pretraining" (ie, training on a larger dataset of 901 conventional PSGs), "training-from-scratch" (ie, training on wearable headband recordings), and "fine-tuning" (ie, training on conventional PSGs, followed by training on headband recordings). Performance was evaluated on all headband recordings using 10-fold cross-validation. RESULTS Highest performance for 5-stage classification was achieved with fine-tuning (κ = .778), significantly higher than with pretraining (κ = .769) and with training-from-scratch (κ = .733). No significant differences or systematic biases were observed with clinically relevant sleep parameters derived from PSG. All sleep disorder categories showed comparable performance. CONCLUSIONS This study emphasizes the importance of leveraging larger available datasets through deep transfer learning to optimize performance with limited data availability. Findings indicate strong similarity in data characteristics between conventional PSG and headband recordings. Altogether, results suggest the combination of the headband, classification model, and training methodology can be viable for sleep monitoring in the heterogeneous clinical population. CITATION van der Aar JF, van Gilst MM, van den Ende DA, et al. Optimizing wearable single-channel electroencephalography sleep staging in a heterogeneous sleep-disordered population using transfer learning. J Clin Sleep Med. 2025;21(2):315-323.
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Affiliation(s)
- Jaap F. van der Aar
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Sleep and Respiratory Care, Philips, Eindhoven, The Netherlands
| | - Merel M. van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Center for Sleep Medicine Kempenhaeghe, Heeze, The Netherlands
| | - Daan A. van den Ende
- Philips Innovation & Strategy, Department of Innovation Engineering, Philips, Eindhoven, The Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Sleep and Respiratory Care, Philips, Eindhoven, The Netherlands
| | | | | | - Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Center for Sleep Medicine Kempenhaeghe, Heeze, The Netherlands
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Palombini LDO, Assis M, Drager LF, Mello LILD, Pires GN, Zancanella E, Santos-Silva R. 2024 Position Statement on the Use of Different Diagnostic Methods for Sleep Disorders in Adults - Brazilian Sleep Association. Sleep Sci 2024; 17:e476-e492. [PMID: 39698173 PMCID: PMC11651843 DOI: 10.1055/s-0044-1800887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Accepted: 11/14/2024] [Indexed: 12/20/2024] Open
Abstract
Introduction The current document represents the official position of Associação Brasileira do Sono (ABS; Brazilian Sleep Association) on the application of different sleep studies and provides specific recommendations for the use of different types of polysomnography (PSG) and respiratory polygraphy. Materials and Methods The present document was based on existing guidelines. The steering committee discussed its findings and developed recommendations and contraindications, which were refined in discussions with the advisory committee. Adaptations were made based on professional experience, pathophysiological knowledge, and theoretical reasoning, especially to cover topics not discussed in previous guidelines or to adapt recommendations to the context and current practices in Brazil. Results A total of 55 recommendations were made, covering the following domains: professional requirements for the requisition and interpretation of sleep studies ( n = 7); eligibility for different sleep studies ( n = 9); diagnosis of sleep-disordered breathing (SDB; n = 5); diagnosis of SDB in special conditions ( n = 3); diagnosis of SDB in association with other sleep disorders and comorbidities ( n = 3); sleep studies on the follow-up of patients with SDB ( n = 9); sleep studies for positive air pressure titration ( n = 3); diagnosis of other sleep disorders ( n = 10); and sleep studies on other conditions ( n = 6). Conclusion The selection of the type of sleep study should be made carefully, considering resource constraints, clinical suspicion of moderate or severe obstructive sleep apnea (OSA), and individual patient needs, among other factors. It is crucial that health professionals receive appropriate training and board certification in sleep science, thus being able to determine the most suitable diagnostic method, understand their indications and limitations, and assure an accurate diagnosis for each patient.
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Affiliation(s)
| | - Márcia Assis
- Associação Brasileira do Sono, São Paulo, SP, Brazil
- Sleep Clinic of Curitiba, Hospital São Lucas, Curitiba, PR, Brazil
| | - Luciano Ferreira Drager
- Associação Brasileira do Sono, São Paulo, SP, Brazil
- Hypertension Clinical Unit, Instituto do Coração (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Nephrology Discipline, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Gabriel Natan Pires
- Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Edilson Zancanella
- Associação Brasileira de Medicina do Sono, São Paulo, SP, Brazil
- Universidade Estadual de Campinas (Unicamp), Campinas, SP, Brazil
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Ahrens E, Jennum P, Duun-Henriksen J, Djurhuus B, Homøe P, Kjær TW, Hemmsen MC. Automatic sleep staging based on 24/7 EEG SubQ (UNEEG medical) data displays strong agreement with polysomnography in healthy adults. Sleep Health 2024; 10:612-620. [PMID: 39406630 DOI: 10.1016/j.sleh.2024.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 07/08/2024] [Accepted: 08/25/2024] [Indexed: 12/08/2024]
Abstract
GOAL AND AIMS Performance evaluation of automatic sleep staging on two-channel subcutaneous electroencephalography. FOCUS TECHNOLOGY UNEEG medical's 24/7 electroencephalography SubQ (the SubQ device) with deep learning model U-SleepSQ. REFERENCE METHOD/TECHNOLOGY Manually scored hypnograms from polysomnographic recordings. SAMPLE Twenty-two healthy adults with 1-6 recordings per participant. The clinical study was registered at ClinicalTrials.gov with the identifier NCT04513743. DESIGN Fine-tuning of U-Sleep in 11-fold cross-participant validation on 22 healthy adults. The resultant model was called U-SleepSQ. CORE ANALYTICS Bland-Altman analysis of sleep parameters. Advanced multiclass model performance metrics: stage-specific accuracy, specificity, sensitivity, kappa (κ), and F1 score. Additionally, Cohen's κ coefficient and macro F1 score. Longitudinal and participant-level performance evaluation. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Exploration of model confidence quantification. Performance vs. age, sex, body mass index, SubQ implantation hemisphere, normalized entropy, transition index, and scores from the following three questionnaires: Morningness-Eveningness Questionnaire, World Health Organization's 5-item Well-being Index, and Major Depression Inventory. CORE OUTCOMES There was a strong agreement between the focus and reference method/technology. IMPORTANT SUPPLEMENTAL OUTCOMES The confidence score was a promising metric for estimating the reliability of each hypnogram classified by the system. CORE CONCLUSION The U-SleepSQ model classified hypnograms for healthy participants soon after implantation and longitudinally with a strong agreement with the gold standard of manually scored polysomnographics, exhibiting negligible temporal variation.
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Affiliation(s)
- Esben Ahrens
- Data Science, T&W Engineering A/S, Lillerød, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen N, Denmark.
| | - Poul Jennum
- Department of Clinical Medicine, University of Copenhagen, Copenhagen N, Denmark; Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark
| | | | - Bjarki Djurhuus
- Department of Clinical Medicine, University of Copenhagen, Copenhagen N, Denmark; Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Preben Homøe
- Department of Clinical Medicine, University of Copenhagen, Copenhagen N, Denmark; Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Troels W Kjær
- Product & Science Department, UNEEG medical A/S, Lillerød, Denmark
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Wester M, Lebek S. Breathless Nights and Cardiac Frights-How Snoring Is Breaking Hearts. Biomedicines 2024; 12:2695. [PMID: 39767602 PMCID: PMC11674012 DOI: 10.3390/biomedicines12122695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Accepted: 11/23/2024] [Indexed: 01/11/2025] Open
Abstract
While your nightly symphony may be testing your loved one's patience, it could also be giving your own heart reasons to complain [...].
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Affiliation(s)
| | - Simon Lebek
- Department of Internal Medicine II, University Hospital Regensburg, 93053 Regensburg, Germany;
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10
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Chiang AA, Jerkins E, Holfinger S, Schutte-Rodin S, Chandrakantan A, Mong L, Glinka S, Khosla S. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med 2024; 20:1823-1838. [PMID: 39132687 PMCID: PMC11530974 DOI: 10.5664/jcsm.11290] [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: 03/25/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/13/2024]
Abstract
STUDY OBJECTIVES From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature. METHODS We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible. RESULTS In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized. CONCLUSIONS In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies. CITATION Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med. 2024;20(11):1823-1838.
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Affiliation(s)
- Ambrose A. Chiang
- Sleep Medicine Section, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio
- Department of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Evin Jerkins
- Department of Primary Care, Ohio University Heritage College of Osteopathic Medicine, Dublin, Ohio
- Medical Director, Fairfield Medical Sleep Center, Lancaster, Ohio
| | - Steven Holfinger
- Division of Pulmonary, Critical Care, and Sleep Medicine, Ohio State University, Columbus, Ohio
| | - Sharon Schutte-Rodin
- Division of Sleep Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Arvind Chandrakantan
- Department of Anesthesiology & Pediatrics, Texas Children’s Hospital and Baylor College of Medicine, Houston, Texas
| | - Laura Mong
- Fairfield Medical Center, Lancaster, Ohio
| | - Steve Glinka
- MedBridge Healthcare, Greenville, South Carolina
| | - Seema Khosla
- North Dakoda Center for Sleep, Fargo, North Dakoda
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11
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Yuan H, Hill EA, Kyle SD, Doherty A. A systematic review of the performance of actigraphy in measuring sleep stages. J Sleep Res 2024; 33:e14143. [PMID: 38384163 DOI: 10.1111/jsr.14143] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024]
Abstract
The accuracy of actigraphy for sleep staging is assumed to be poor, but examination is limited. This systematic review aimed to assess the performance of actigraphy in sleep stage classification of adults. A systematic search was performed using MEDLINE, Web of Science, Google Scholar, and Embase databases. We identified eight studies that compared sleep architecture estimates between wrist-worn actigraphy and polysomnography. Large heterogeneity was found with respect to how sleep stages were grouped, and the choice of metrics used to evaluate performance. Quantitative synthesis was not possible, so we performed a narrative synthesis of the literature. From the limited number of studies, we found that actigraphy-based sleep staging had some ability to classify different sleep stages compared with polysomnography.
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Affiliation(s)
- Hang Yuan
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elizabeth A Hill
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, University of Oxford, Oxford, UK
| | - Simon D Kyle
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, University of Oxford, Oxford, UK
| | - Aiden Doherty
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Pires GN, Arnardóttir ES, Bailly S, McNicholas WT. Guidelines for the development, performance evaluation and validation of new sleep technologies (DEVSleepTech guidelines) - a protocol for a Delphi consensus study. J Sleep Res 2024; 33:e14163. [PMID: 38351277 DOI: 10.1111/jsr.14163] [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: 09/16/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 10/18/2024]
Abstract
New sleep technologies are being developed, refined and delivered at a fast pace. However, there are serious concerns about the validation and accuracy of new sleep-related technologies being made available, as many of them, especially consumer-sleep technologies, have not been tested in comparison with gold-standard methods or have been approved by health regulatory agencies. The importance of proper validation and performance evaluation of new sleep technologies has already been discussed in previous studies and some recommendations have already been published, but most of them do not employ standardized methodology and are not able to cover all aspects of new sleep technologies. The current protocol describes the methods of a Delphi consensus study to create guidelines for the development, performance evaluation and validation of new sleep devices and technologies. The resulting recommendations are not intended to be used as a quality assessment tool to evaluate individual articles, but rather to evaluate the overall procedures, studies and experiments performed to develop, evaluate performance and validate new technologies. We hope these guidelines can be helpful for researchers who work with new sleep technologies on the appraisal of their reliability and validation, for companies who are working on the development and refinement of new sleep technologies, and by regulatory agencies to evaluate new technologies that are looking for registration, approval or inclusion on health systems.
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Affiliation(s)
- Gabriel Natan Pires
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Sleep Institute, São Paulo, Brazil
- European Sleep Research Society (ESRS), Regensburg, Germany
| | - Erna S Arnardóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland
- Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Sébastien Bailly
- Grenoble Alpes University, Inserm U1300, Grenoble Alpes University Hospital, Grenoble, France
| | - Walter T McNicholas
- School of Medicine and the Conway Research Institute, University College Dublin, Dublin, Ireland
- Department of Respiratory and Sleep Medicine, St Vincent's Hospital Group, Dublin, Ireland
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Martynowicz H, Michalek‐Zrabkowska M, Gac P, Blaszczyk B, Fulek M, Frosztega W, Wojakowska A, Poreba R, Mazur G, Wieckiewicz M. Performance evaluation of portable respiratory polygraphy for assessing sleep bruxism in adults. J Oral Rehabil 2024; 51:1862-1871. [DOI: 10.1111/joor.13733] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/29/2024] [Indexed: 01/03/2025]
Abstract
AbstractBackgroundPolysomnography (PSG) is the gold standard for sleep bruxism (SB) assessment, it is expensive, not widely accessible, and time‐consuming.ObjectiveGiven the increasing prevalence of SB, there is a growing need for an alternative, readily available, reliable and cost‐effective diagnostic method. This study aimed to evaluate the diagnostic validity of portable respiratory polygraphy (PRPG) compared with PSG for SB diagnosis.MethodsOne hundred and three subjects underwent simultaneous examinations using PRPG (NOX T3, NOX Medical) and PSG (NOX A1, NOX Medical) in a sleep laboratory.ResultsThe mean Bruxism Episodes Index (BEI) measured by PRPG was 4.70 ± 3.98, whereas PSG yielded a mean BEI of 3.79 ± 3.08. The sensitivity for detecting sleep bruxism (BEI >2) by PRPG was 48.3%, with a specificity of 81.2%. The positive predictive value was estimated at 51.9%, and the negative predictive value at 78.9%. However, when distinguishing between mild bruxism (BEI >2 < 4) and severe bruxism (BEI >4), PRPG demonstrated a sensitivity of 77.8% and 68.3% and a specificity of 48.6% and 71.4%, respectively.ConclusionPolysomnography continues to be the SB diagnostic gold standard tool, as the sensitivity and specificity of PRPG are significantly lower when compared with PSG. Nevertheless, PRPG could serve as an alternative tool for SB screening or diagnosis, despite its limitations. Furthermore, our data indicate that comorbidities such as sleep apnea and sleep quality do not influence the diagnostic accuracy of PSG, suggesting its potential as a screening instrument in individuals with other sleep disorders.
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Affiliation(s)
- Helena Martynowicz
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Monika Michalek‐Zrabkowska
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Pawel Gac
- Division of Environmental Health and Occupational Medicine, Department of Population Health Wroclaw Medical University Wroclaw Poland
| | - Bartlomiej Blaszczyk
- Student Research Club No K133, Faculty of Medicine Wroclaw Medical University Wroclaw Poland
| | - Michal Fulek
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Weronika Frosztega
- Student Research Club No K133, Faculty of Medicine Wroclaw Medical University Wroclaw Poland
| | - Anna Wojakowska
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Rafal Poreba
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Grzegorz Mazur
- Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology Wroclaw Medical University Wroclaw Poland
| | - Mieszko Wieckiewicz
- Department of Experimental Dentistry Wroclaw Medical University Wroclaw Poland
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Shiao YH, Yu CC, Yeh YC. Validation of Downloadable Mobile Snore Applications by Polysomnography (PSG). Nat Sci Sleep 2024; 16:489-501. [PMID: 38800087 PMCID: PMC11127649 DOI: 10.2147/nss.s433351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 04/27/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose Obstructive sleep apnea (OSA) is a common breathing disorder during sleep that is associated with symptoms such as snoring, excessive daytime sleepiness, and breathing interruptions. Polysomnography (PSG) is the most reliable diagnostic test for OSA; however, its high cost and lengthy testing duration make it difficult to access for many patients. With the availability of free snore applications for home-monitoring, this study aimed to validate the top three ranked snore applications, namely SnoreLab (SL), Anti Snore Solution (ASS), and Sleep Cycle Alarm (SCA), using PSG. Patients and Methods Sixty participants underwent an overnight PSG while simultaneously using three identical smartphones with the tested apps to gather sleep and snoring data. Results The study discovered that all three applications were significantly correlated with the total recording time and snore counts of PSG, with ASS showing good agreement with snore counts. Furthermore, the Snore Score, Time Snoring of SL, and Sleep Quality of SCA had a significant correlation with the natural logarithm of apnea hypopnea index (lnAHI) of PSG. The Snore Score of SL and the Sleep Quality of SCA were shown to be useful for evaluating snore severity and for pre-diagnosing or predicting OSA above moderate levels. Conclusion These findings suggest that some parameters of free snore applications can be employed to monitor OSA progress, and future research could involve adjusted algorithms and larger-scale studies to further authenticate these downloadable snore and sleep applications.
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Affiliation(s)
- Yi-Hsien Shiao
- Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
- Graduate Institute of Natural Products, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chung-Chieh Yu
- Department of Chest, Critical Care, and Sleep Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
| | - Yuan-Chieh Yeh
- Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
- Program in Molecular Medicine, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
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15
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de Zambotti M, Goldstein C, Cook J, Menghini L, Altini M, Cheng P, Robillard R. State of the science and recommendations for using wearable technology in sleep and circadian research. Sleep 2024; 47:zsad325. [PMID: 38149978 DOI: 10.1093/sleep/zsad325] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.
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Affiliation(s)
- Massimiliano de Zambotti
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Lisa Health Inc., Oakland, CA, USA
| | - Cathy Goldstein
- Sleep Disorders Center, Department of Neurology, University of Michigan-Ann Arbor, Ann Arbor, MI, USA
| | - Jesse Cook
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Marco Altini
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Cheng
- Sleep Disorders and Research Center, Henry Ford Health, Detroit, MI, USA
| | - Rebecca Robillard
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
- Canadian Sleep Research Consortium, Canada
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16
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Della Monica C, Ravindran KKG, Atzori G, Lambert DJ, Rodriguez T, Mahvash-Mohammadi S, Bartsch U, Skeldon AC, Wells K, Hampshire A, Nilforooshan R, Hassanin H, The Uk Dementia Research Institute Care Research Amp Technology Research Group, Revell VL, Dijk DJ. A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community. Clocks Sleep 2024; 6:129-155. [PMID: 38534798 DOI: 10.3390/clockssleep6010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/12/2024] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.
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Affiliation(s)
- Ciro Della Monica
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Kiran K G Ravindran
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Damion J Lambert
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Thalia Rodriguez
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- School of Mathematics & Physics, University of Surrey, Guildford GU2 7XH, UK
| | - Sara Mahvash-Mohammadi
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
| | - Ullrich Bartsch
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Anne C Skeldon
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- School of Mathematics & Physics, University of Surrey, Guildford GU2 7XH, UK
| | - Kevin Wells
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College, London W12 0NN, UK
| | - Ramin Nilforooshan
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- Surrey and Borders Partnership NHS Foundation Trust Surrey, Chertsey KT16 9AU, UK
| | - Hana Hassanin
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
- Surrey Clinical Research Facility, University of Surrey, Guildford GU2 7XP, UK
- NIHR Royal Surrey CRF, Royal Surrey Foundation Trust, Guildford GU2 7XX, UK
| | | | - Victoria L Revell
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford GU2 7XP, UK
- UK Dementia Research Institute Care Research & Technology Centre (CR&T), Imperial College London and the University of Surrey, London W12 0NN, UK
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17
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Menghini L, Balducci C, de Zambotti M. Is it Time to Include Wearable Sleep Trackers in the Applied Psychologists' Toolbox? THE SPANISH JOURNAL OF PSYCHOLOGY 2024; 27:e8. [PMID: 38410074 DOI: 10.1017/sjp.2024.8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Wearable sleep trackers are increasingly used in applied psychology. Particularly, the recent boom in the fitness tracking industry has resulted in a number of relatively inexpensive consumer-oriented devices that further enlarge the potential applications of ambulatory sleep monitoring. While being largely positioned as wellness tools, wearable sleep trackers could be considered useful health devices supported by a growing number of independent peer-reviewed studies evaluating their accuracy. The inclusion of sensors that monitor cardiorespiratory physiology, diurnal activity data, and other environmental signals allows for a comprehensive and multidimensional approach to sleep health and its impact on psychological well-being. Moreover, the increasingly common combination of wearable trackers and experience sampling methods has the potential to uncover within-individual processes linking sleep to daily experiences, behaviors, and other psychosocial factors. Here, we provide a concise overview of the state-of-the-art, challenges, and opportunities of using wearable sleep-tracking technology in applied psychology. Specifically, we review key device profiles, capabilities, and limitations. By providing representative examples, we highlight how scholars and practitioners can fully exploit the potential of wearable sleep trackers while being aware of the most critical pitfalls characterizing these devices. Overall, consumer wearable sleep trackers are increasingly recognized as a valuable method to investigate, assess, and improve sleep health. Incorporating such devices in research and professional practice might significantly improve the quantity and quality of the collected information while opening the possibility of involving large samples over representative time periods. However, a rigorous and informed approach to their use is necessary.
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Affiliation(s)
- Luca Menghini
- Università di Trento (Italy)
- Università degli Studi di Padova (Italy)
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González DA, Wang D, Pollet E, Velarde A, Horn S, Coss P, Vaou O, Wang J, Li C, Seshadri S, Miao H, Gonzales MM. Performance of the Dreem 2 EEG headband, relative to polysomnography, for assessing sleep in Parkinson's disease. Sleep Health 2024; 10:24-30. [PMID: 38151377 DOI: 10.1016/j.sleh.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/20/2023] [Accepted: 11/22/2023] [Indexed: 12/29/2023]
Abstract
GOAL AND AIMS To pilot the feasibility and evaluate the performance of an EEG wearable for measuring sleep in individuals with Parkinson's disease. FOCUS TECHNOLOGY Dreem Headband, Version 2. REFERENCE TECHNOLOGY Polysomnography. SAMPLE Ten individuals with Parkinson's disease. DESIGN Individuals wore Dreem Headband during a single night of polysomnography. CORE ANALYTICS Comparison of summary metrics, bias, and epoch-by-epoch analysis. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Correlation of summary metrics with demographic and Parkinson's disease characteristics. CORE OUTCOMES Summary statistics showed Dreem Headband overestimated several sleep metrics, including total sleep, efficiency, deep sleep, and rapid eye movement sleep, with an exception in light sleep. Epoch-by-epoch analysis showed greater specificity than sensitivity, with adequate accuracy across sleep stages (0.55-0.82). IMPORTANT SUPPLEMENTAL OUTCOMES Greater Parkinson's disease duration and rapid eye movement behavior were associated with more wakefulness, and worse Parkinson's disease motor symptoms were associated with less deep sleep. CORE CONCLUSION The Dreem Headband performs similarly in Parkinson's disease as it did in non-Parkinson's disease samples and shows promise for improving access to sleep assessment in people with Parkinson's disease.
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Affiliation(s)
- David Andrés González
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA.
| | - Duo Wang
- Department of Statistics, Florida State University, Tallahassee, Florida, USA
| | - Erin Pollet
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Angel Velarde
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Sarah Horn
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Pablo Coss
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Okeanis Vaou
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, Florida, USA
| | - Chengdong Li
- College of Nursing, Florida State University, Tallahassee, Florida, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Hongyu Miao
- Department of Statistics, Florida State University, Tallahassee, Florida, USA; College of Nursing, Florida State University, Tallahassee, Florida, USA
| | - Mitzi M Gonzales
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
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Kainec KA, Caccavaro J, Barnes M, Hoff C, Berlin A, Spencer RMC. Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography. SENSORS (BASEL, SWITZERLAND) 2024; 24:635. [PMID: 38276327 PMCID: PMC10820351 DOI: 10.3390/s24020635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.
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Affiliation(s)
- Kyle A. Kainec
- Neuroscience & Behavior Program, French Hall, University of Massachusetts Amherst, 230 Stockbridge Road, Amherst, MA 01003, USA;
- Institute for Applied Life Sciences, Life Science Laboratories, University of Massachusetts Amherst, 240 Thatcher Road, Amherst, MA 01003, USA; (M.B.); (C.H.)
| | - Jamie Caccavaro
- Department of Psychological and Brain Sciences, Tobin Hall, University of Massachusetts Amherst, 135 Hicks Way, Amherst, MA 01003, USA
| | - Morgan Barnes
- Institute for Applied Life Sciences, Life Science Laboratories, University of Massachusetts Amherst, 240 Thatcher Road, Amherst, MA 01003, USA; (M.B.); (C.H.)
- Department of Psychological and Brain Sciences, Tobin Hall, University of Massachusetts Amherst, 135 Hicks Way, Amherst, MA 01003, USA
| | - Chloe Hoff
- Institute for Applied Life Sciences, Life Science Laboratories, University of Massachusetts Amherst, 240 Thatcher Road, Amherst, MA 01003, USA; (M.B.); (C.H.)
- Department of Psychological and Brain Sciences, Tobin Hall, University of Massachusetts Amherst, 135 Hicks Way, Amherst, MA 01003, USA
| | - Annika Berlin
- Institute for Applied Life Sciences, Life Science Laboratories, University of Massachusetts Amherst, 240 Thatcher Road, Amherst, MA 01003, USA; (M.B.); (C.H.)
- Department of Psychological and Brain Sciences, Tobin Hall, University of Massachusetts Amherst, 135 Hicks Way, Amherst, MA 01003, USA
| | - Rebecca M. C. Spencer
- Neuroscience & Behavior Program, French Hall, University of Massachusetts Amherst, 230 Stockbridge Road, Amherst, MA 01003, USA;
- Institute for Applied Life Sciences, Life Science Laboratories, University of Massachusetts Amherst, 240 Thatcher Road, Amherst, MA 01003, USA; (M.B.); (C.H.)
- Department of Psychological and Brain Sciences, Tobin Hall, University of Massachusetts Amherst, 135 Hicks Way, Amherst, MA 01003, USA
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20
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Schwartz LP, Devine JK, Choynowski J, Hursh SR. Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement. Sleep Health 2023:S2352-7218(23)00291-7. [PMID: 38151374 DOI: 10.1016/j.sleh.2023.11.009] [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: 06/12/2023] [Revised: 11/07/2023] [Accepted: 11/18/2023] [Indexed: 12/29/2023]
Abstract
OBJECTIVES Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate. METHODS Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology. RESULTS Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution. CONCLUSIONS Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.
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Affiliation(s)
- Lindsay P Schwartz
- Institutes for Beahvior Resources, Inc., Operational Fatigue and Performance Unit, Baltimore, Maryland, USA
| | - Jaime K Devine
- Institutes for Beahvior Resources, Inc., Operational Fatigue and Performance Unit, Baltimore, Maryland, USA.
| | - Jake Choynowski
- Institutes for Beahvior Resources, Inc., Operational Fatigue and Performance Unit, Baltimore, Maryland, USA
| | - Steven R Hursh
- Institutes for Beahvior Resources, Inc., Operational Fatigue and Performance Unit, Baltimore, Maryland, USA; Department of Psychiatry and Behavioral Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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21
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Benedetti D, Frati E, Kiss O, Yuksel D, Faraguna U, Hasler BP, Franzen PL, Clark DB, Baker FC, de Zambotti M. Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence. Sleep Health 2023; 9:910-924. [PMID: 37709595 PMCID: PMC11161906 DOI: 10.1016/j.sleh.2023.07.019] [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: 12/06/2022] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
GOAL AND AIMS To evaluate an automatic sleep scoring algorithm against manual polysomnography sleep scoring. FOCUS METHOD/TECHNOLOGY Yet Another Spindle Algorithm automatic sleep staging algorithm. REFERENCE METHOD/TECHNOLOGY Manual sleep scoring. SAMPLE 327 nights (151 healthy adolescents), from the NCANDA study. DESIGN Participants underwent one-to-three overnight polysomnography recordings, one consisting of an event-related-potential paradigm. CORE ANALYTICS Epoch by Epoch and discrepancy analyses (Bland Altman plots) were conducted on the overall sample. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Epoch by Epoch and discrepancy analysis were repeated separately on standard polysomnography nights and event-related potential nights. Regression models were estimated on age, sex, scorer, and site of recording, separately on standard polysomnography nights and event-related potential nights. CORE OUTCOMES The Yet Another Spindle Algorithm sleep scoring algorithm's average sensitivity of 93.04% for Wake, 87.67% for N2, 84.46% for N3, 86.02% for rapid-eye-movement, and 40.39% for N1. Specificity was 96.75% for Wake, 97.31% for N1, 88.87% for N2, 97.99% for N3, and 97.70% for rapid-eye-movement. The Matthews Correlation Coefficient was highest in rapid-eye-movement sleep (0.85) while lowest in N1 (0.39). Cohen's Kappa mirrored Matthews Correlation Coefficient results. In Bland-Altman plots, the bias between Yet Another Spindle Algorithm and human scoring showed proportionality to the manual scoring measurement size. IMPORTANT ADDITIONAL OUTCOMES Yet Another Spindle Algorithm performance was reduced in event-related-potential/polysomnography nights for N3 and rapid-eye-movement. According to the Matthews Correlation Coefficient, the Yet Another Spindle Algorithm performance was affected by younger age, male sex, recording sites, and scorers. CORE CONCLUSION Results support the use of Yet Another Spindle Algorithm to score adolescents' polysomnography sleep records, possibly with classification outcomes supervised by an expert scorer.
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Affiliation(s)
- Davide Benedetti
- Center for Health Sciences, SRI International, Menlo Park, California, USA; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy.
| | - Emma Frati
- Center for Health Sciences, SRI International, Menlo Park, California, USA; Columbia College, Columbia University, NYC, New York, USA
| | - Orsolya Kiss
- Center for Health Sciences, SRI International, Menlo Park, California, USA
| | - Dilara Yuksel
- Center for Health Sciences, SRI International, Menlo Park, California, USA
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Brant P Hasler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Peter L Franzen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Duncan B Clark
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, California, USA
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G Ravindran KK, Della Monica C, Atzori G, Lambert D, Hassanin H, Revell V, Dijk DJ. Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study. JMIR Mhealth Uhealth 2023; 11:e46338. [PMID: 37878360 PMCID: PMC10632916 DOI: 10.2196/46338] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/11/2023] [Accepted: 08/25/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. OBJECTIVE This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. METHODS Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. RESULTS The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). CONCLUSIONS These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.
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Affiliation(s)
- Kiran K G Ravindran
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Ciro Della Monica
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Damion Lambert
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Hana Hassanin
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
- Surrey Clinical Research Facility, School of Biosciences, Faculty of Health and Medical Sciences, Guildford, United Kingdom
- National Institute for Health Research - Royal Surrey Clinical Research Facility, Guildford, United Kingdom
| | - Victoria Revell
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
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Roberts DM, Schade MM, Master L, Honavar VG, Nahmod NG, Chang AM, Gartenberg D, Buxton OM. Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing. Sleep Health 2023; 9:596-610. [PMID: 37573208 PMCID: PMC11005467 DOI: 10.1016/j.sleh.2023.07.001] [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: 11/10/2022] [Revised: 06/05/2023] [Accepted: 07/02/2023] [Indexed: 08/14/2023]
Abstract
GOAL AND AIMS Commonly used actigraphy algorithms are designed to operate within a known in-bed interval. However, in free-living scenarios this interval is often unknown. We trained and evaluated a sleep/wake classifier that operates on actigraphy over ∼24-hour intervals, without knowledge of in-bed timing. FOCUS TECHNOLOGY Actigraphy counts from ActiWatch Spectrum devices. REFERENCE TECHNOLOGY Sleep staging derived from polysomnography, supplemented by observation of wakefulness outside of the staged interval. Classifications from the Oakley actigraphy algorithm were additionally used as performance reference. SAMPLE Adults, sleeping in either a home or laboratory environment. DESIGN Machine learning was used to train and evaluate a sleep/wake classifier in a supervised learning paradigm. The classifier is a temporal convolutional network, a form of deep neural network. CORE ANALYTICS Performance was evaluated across ∼24 hours, and additionally restricted to only in-bed intervals, both in terms of epoch-by-epoch performance, and the discrepancy of summary statistics within the intervals. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Performance of the trained model applied to the Multi-Ethnic Study of Atherosclerosis dataset. CORE OUTCOMES Over ∼24 hours, the temporal convolutional network classifier produced the same or better performance as the Oakley classifier on all measures tested. When restricting analysis to the in-bed interval, the temporal convolutional network remained favorable on several metrics. IMPORTANT SUPPLEMENTAL OUTCOMES Performance decreased on the Multi-Ethnic Study of Atherosclerosis dataset, especially when restricting analysis to the in-bed interval. CORE CONCLUSION A classifier using data labeled over ∼24-hour intervals allows for the continuous classification of sleep/wake without knowledge of in-bed intervals. Further development should focus on improving generalization performance.
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Affiliation(s)
- Daniel M Roberts
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA; Proactive Life, Inc, New York, New York, USA.
| | - Margeaux M Schade
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Lindsay Master
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Vasant G Honavar
- Faculty of Data Sciences, College of Information Science and Technology, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nicole G Nahmod
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Anne-Marie Chang
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
| | | | - Orfeu M Buxton
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA
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24
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Bandyopadhyay A, Bae C, Cheng H, Chiang A, Deak M, Seixas A, Singh J. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med 2023; 19:1823-1833. [PMID: 37394867 PMCID: PMC10545999 DOI: 10.5664/jcsm.10702] [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: 04/17/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/04/2023]
Abstract
Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions. CITATION Bandyopadhyay A, Bae C, Cheng H, et al. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med. 2023;19(10):1823-1833.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Charles Bae
- Division of Sleep Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hao Cheng
- Department of Pulmonary and Sleep Medicine, Miami VA Healthcare System, Miami, Florida
| | - Ambrose Chiang
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve University, Cleveland, Ohio
| | | | - Azizi Seixas
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Coral Gables, Florida
| | - Jaspal Singh
- Atrium Health Department of Medicine, Wake Forest School of Medicine, Charlotte, North Carolina
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25
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Bradicich M, Siciliano M, Donfrancesco F, Cherneva R, Ferraz B, Testelmans D, Sánchez-de-la-Torre M, Randerath W, Schiza S, Cruz J. Sleep and Breathing Conference highlights 2023: a summary by ERS Assembly 4. Breathe (Sheff) 2023; 19:230168. [PMID: 38020339 PMCID: PMC10644110 DOI: 10.1183/20734735.0168-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/08/2023] [Indexed: 12/01/2023] Open
Abstract
This paper presents some of the highlights of the Sleep and Breathing Conference 2023 https://bit.ly/46MxJml.
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Affiliation(s)
- Matteo Bradicich
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
- Department of Internal Medicine, Spital Zollikerberg, Zollikerberg, Switzerland
| | - Matteo Siciliano
- Università Cattolica del Sacro Cuore, Campus di Roma, Rome, Italy
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- These authors contributed equally
| | - Federico Donfrancesco
- Università Cattolica del Sacro Cuore, Campus di Roma, Rome, Italy
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- These authors contributed equally
| | - Radostina Cherneva
- Medical University, University Hospital “Ivan Rilski”, Respiratory Intensive Care Unit, Sofia, Bulgaria
- These authors contributed equally
| | - Beatriz Ferraz
- Pulmonology Department, Centro Hospitalar Universitário de Santo António, Porto, Portugal
- These authors contributed equally
| | - Dries Testelmans
- Department of Pneumology, University Hospitals Leuven, Leuven, Belgium
- These authors contributed equally
| | - Manuel Sánchez-de-la-Torre
- Respiratory Department, Hospital Universitari Arnau de Vilanova-Santa María, IRB Lleida, Lleida, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Winfried Randerath
- Institute of Pneumology, University Cologne, Bethanien Hospital, Solingen, Germany
- These authors contributed equally
| | - Sophia Schiza
- Department of Respiratory Medicine, School of Medicine, University of Crete, Heraklion, Greece
- These authors contributed equally
| | - Joana Cruz
- Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences (ESSLei), Polytechnic of Leiria, Leiria, Portugal
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Abstract
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas. Recent developments in computer science and artificial intelligence are nevertheless closing the gap. Technological advances are also opening new pathways for expanding our current understanding of the domain and its analysis.
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Affiliation(s)
- Diego Alvarez-Estevez
- Center for Information and Communications Technology Research (CITIC), Universidade da Coruña, 15071 A Coruña, Spain.
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27
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Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
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Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
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28
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Eylon G, Tikotzky L, Dinstein I. Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights. Sleep Health 2023; 9:407-416. [PMID: 37270397 DOI: 10.1016/j.sleh.2023.04.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 04/02/2023] [Accepted: 04/09/2023] [Indexed: 06/05/2023]
Abstract
GOAL AND AIMS Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography. SAMPLE Twenty-one university students (10 females). DESIGN Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes. CORE ANALYTICS Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Variability of specificity and negative predictive value across subjects and across nights. CORE OUTCOMES Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). IMPORTANT ADDITIONAL OUTCOMES Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. CORE CONCLUSION This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.
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Affiliation(s)
- Gal Eylon
- Cognitive and Brain Sciences Department, Ben Gurion University, Be'er Sheva, Israel; Azrieli National Centre for Autism and Neurodevelopment Research, Be'er Sheva, Israel.
| | - Liat Tikotzky
- Department of Psychology, Ben Gurion University, Be'er Sheva, Israel
| | - Ilan Dinstein
- Cognitive and Brain Sciences Department, Ben Gurion University, Be'er Sheva, Israel; Azrieli National Centre for Autism and Neurodevelopment Research, Be'er Sheva, Israel; Department of Psychology, Ben Gurion University, Be'er Sheva, Israel
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29
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Addison C, Grandner MA, Baron KG. Sleep medicine provider perceptions and attitudes regarding consumer sleep technology. J Clin Sleep Med 2023; 19:1457-1463. [PMID: 37086048 PMCID: PMC10394368 DOI: 10.5664/jcsm.10604] [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: 09/23/2022] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/23/2023]
Abstract
STUDY OBJECTIVES This study assessed perceptions and attitudes of sleep medicine providers regarding consumer sleep technology (CST). METHODS A convenience sample of n = 176 practicing sleep medicine and behavioral sleep medicine experts was obtained using social media and the American Academy of Sleep Medicine directory. Providers completed a questionnaire that assessed perceptions and attitudes about patient use of CST in the clinical setting. RESULTS The sample included both adult and pediatric psychologists, physicians, and advanced practice providers from a variety of health settings. Providers reported 36% (3%-95%) of patients used CST, and the most common devices seen by providers were wrist-worn devices followed by smartphone apps. The most common perceived patient motivations for frequent use were to measure sleep and self-discovery. Across sleep disorders, clinicians did not endorse frequent CST use; the highest reported use was for assisting patients in the completion of sleep diaries. Overall devices were rated as somewhat accurate and neutral regarding helpfulness. In qualitative responses, providers associated CST use with increased patient engagement but increased orthosomnia and misperceptions about sleep. CONCLUSIONS CST is frequently encountered in the sleep medicine clinic, and providers view CST as somewhat accurate but neither helpful nor unhelpful in clinical practice. Although providers viewed these devices as useful to drive patient engagement/awareness and track sleep patterns, providers also viewed them as a contributor to orthosomnia and misperceptions about sleep. CITATION Addison C, Grandner MA, Baron KG. Sleep medicine provider perceptions and attitudes regarding consumer sleep technology. J Clin Sleep Med. 2023;19(8):1457-1463.
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Affiliation(s)
- Conrad Addison
- Department of Internal Medicine, Division of Pulmonary Medicine, University of Utah, Salt Lake City, Utah
| | - Michael A. Grandner
- Sleep and Health Research Program, Department of Psychiatry, University of Arizona College of Medicine, Tucson, Arizona
| | - Kelly Glazer Baron
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah
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30
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Strumpf Z, Gu W, Tsai CW, Chen PL, Yeh E, Leung L, Cheung C, Wu IC, Strohl KP, Tsai T, Folz RJ, Chiang AA. Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea. Sleep Health 2023; 9:430-440. [PMID: 37380590 DOI: 10.1016/j.sleh.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/25/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
GOAL AND AIMS Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification. FOCUS TECHNOLOGY Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30. DESIGN Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule. CORE ANALYTICS Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix. CORE OUTCOMES The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep. CORE CONCLUSION Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.
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Affiliation(s)
- Zachary Strumpf
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Wenbo Gu
- Belun Technology Company Limited, Hong Kong; Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | | | | | - Eric Yeh
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | | | | | - I-Chen Wu
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kingman P Strohl
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Tiffany Tsai
- Case Western Reserve University, Cleveland, OH, USA
| | - Rodney J Folz
- Division of Pulmonary, Critical Care, and Sleep Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - Ambrose A Chiang
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
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Weaver RG, de Zambotti M, White J, Finnegan O, Nelakuditi S, Zhu X, Burkart S, Beets M, Brown D, Pate RR, Welk GJ, Ghosal R, Wang Y, Armstrong B, Adams EL, Reesor-Oyer L, Pfledderer C, Dugger R, Bastyr M, von Klinggraeff L, Parker H. Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions. Sleep Health 2023; 9:417-429. [PMID: 37391280 PMCID: PMC10524868 DOI: 10.1016/j.sleh.2023.04.005] [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: 01/06/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 07/02/2023]
Abstract
GOAL AND AIMS Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY Standard manual PSG sleep scoring. SAMPLE Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.
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Affiliation(s)
- R Glenn Weaver
- University of South Carolina, Columbia, South Carolina, USA.
| | | | - James White
- University of South Carolina, Columbia, South Carolina, USA
| | | | | | - Xuanxuan Zhu
- University of South Carolina, Columbia, South Carolina, USA
| | - Sarah Burkart
- University of South Carolina, Columbia, South Carolina, USA
| | - Michael Beets
- University of South Carolina, Columbia, South Carolina, USA
| | - David Brown
- University of South Carolina, Columbia, South Carolina, USA
| | - Russ R Pate
- University of South Carolina, Columbia, South Carolina, USA
| | | | - Rahul Ghosal
- University of South Carolina, Columbia, South Carolina, USA
| | - Yuan Wang
- University of South Carolina, Columbia, South Carolina, USA
| | | | | | | | | | | | - Meghan Bastyr
- University of South Carolina, Columbia, South Carolina, USA
| | | | - Hannah Parker
- University of South Carolina, Columbia, South Carolina, USA
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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Pires GN, Arnardóttir ES, Islind AS, Leppänen T, McNicholas WT. Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy. J Sleep Res 2023:e13819. [PMID: 36807680 DOI: 10.1111/jsr.13819] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 02/20/2023]
Abstract
There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.
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Affiliation(s)
- Gabriel Natan Pires
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.,European Sleep Research Society (ESRS), Regensburg, Germany
| | - Erna Sif Arnardóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Anna Sigridur Islind
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Walter T McNicholas
- Department of Respiratory and Sleep Medicine, St Vincent's Hospital Group, School of Medicine, University College Dublin, Dublin, Ireland
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Chinoy ED, Cuellar JA, Jameson JT, Markwald RR. Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep. Nat Sci Sleep 2023; 15:151-164. [PMID: 37032817 PMCID: PMC10075216 DOI: 10.2147/nss.s395732] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/20/2023] [Indexed: 04/11/2023] Open
Abstract
Purpose Previous studies have found that many commercial wearable devices can accurately track sleep-wake patterns in laboratory or home settings. However, nearly all previous studies tested devices under conditions with fixed time in bed (TIB) and during nighttime sleep episodes only. Despite its relevance to shift workers and others with irregular sleep schedules, it is largely unknown how devices track daytime sleep. Therefore, we tested the sleep-tracking performance of four commercial wearable devices during unrestricted home daytime sleep. Participants and Methods Participants were 16 healthy young adults (6 men, 10 women; 26.6 ± 4.6 years, mean ± SD) with habitual daytime sleep schedules. Participants slept at home for 1 week under unrestricted conditions (ie, self-selecting TIB) using a set of four commercial wearable devices and completed reference sleep logs. Wearables included the Fatigue Science ReadiBand, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Daytime sleep episode TIB biases and frequencies of missed and false-positive daytime sleep episodes were examined. Results TIB bias was low in general for all devices on most daytime sleep episodes, but some exhibited large biases (eg, >1 h). Total missed daytime sleep episodes were as follows: Fatigue Science: 3.6%; Fitbit: 4.8%; Oura: 6.0%; Polar: 37.3%. Missed episodes occurred most often when TIB was short (eg, naps <4 h). Conclusion When daytime sleep episodes were recorded, the devices generally exhibited similar performance for tracking TIB (ie, most episodes had low bias). However, the devices failed to detect some daytime episodes, which occurred most often when TIB was short, but varied across devices (especially Polar, which missed over one-third of episodes). Findings suggest that accurate daytime sleep tracking is largely achievable with commercial wearable devices. However, performance differences for missed recordings suggest that some devices vary in reliability (especially for naps), but improvements could likely be made with changes to algorithm sensitivities.
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Affiliation(s)
- Evan D Chinoy
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Joseph A Cuellar
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Jason T Jameson
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Rachel R Markwald
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Correspondence: Rachel R Markwald, Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA, 92106, USA, Tel +1 619 767 4494, Email
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35
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Baumert M, Cowie MR, Redline S, Mehra R, Arzt M, Pépin JL, Linz D. Sleep characterization with smart wearable devices: a call for standardization and consensus recommendations. Sleep 2022; 45:6652912. [PMID: 35913733 DOI: 10.1093/sleep/zsac183] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/06/2022] [Indexed: 12/14/2022] Open
Abstract
The general public increasingly adopts smart wearable devices to quantify sleep characteristics and dedicated devices for sleep assessment. The rapid evolution of technology has outpaced the ability to implement validation approaches and demonstrate relevant clinical applicability. There are untapped opportunities to validate and refine consumer devices in partnership with scientists in academic institutions, patients, and the private sector to allow effective integration into clinical management pathways and facilitate trust in adoption once reliability and validity have been demonstrated. We call for the formation of a working group involving stakeholders from academia, clinical care and industry to develop clear professional recommendations to facilitate appropriate and optimized clinical utilization of such technologies.
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Affiliation(s)
- Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, Australia
| | - Martin R Cowie
- School of Cardiovascular Medicine, Faculty of Medicine & Lifesciences, King's College London, London, UK.,Royal Brompton Hospital (Guy's & St Thomas' NHS Foundation Trust), London, UK
| | - Susan Redline
- Department of Medicine, Division of Sleep, Circadian Rhythm, and Neurobiology, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Division of Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Reena Mehra
- Sleep Disorders Research Program, Sleep Disorders Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Michael Arzt
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Jean-Louis Pépin
- HP2 Laboratory, INSERM U1300, Univ. Grenoble Alpes, and EFCR Laboratory, Grenoble Alpes University Hospital, Grenoble, France
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.,Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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36
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Goldstein C, de Zambotti M. Into the wild…the need for standardization and consensus recommendations to leverage consumer-facing sleep technologies. Sleep 2022; 45:6717905. [PMID: 36155805 DOI: 10.1093/sleep/zsac233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Cathy Goldstein
- University of Michigan, Department of Neurology, Sleep Disorder Center, Ann Arbor, MI, USA
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