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Frija J, Mullaert J, Abensur Vuillaume L, Grajoszex M, Wanono R, Benzaquen H, Kerzabi F, Geoffroy PA, Matrot B, Trioux T, Penzel T, d'Ortho MP. Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints. J Sleep Res 2024:e14235. [PMID: 38873908 DOI: 10.1111/jsr.14235] [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: 12/21/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/15/2024]
Abstract
Sleep trackers are used widely by patients with sleep complaints, however their metrological validation is often poor and relies on healthy subjects. We assessed the metrological validity of two commercially available sleep trackers (Withings Activité/Fitbit Alta HR) through a prospective observational monocentric study, in adult patients referred for polysomnography (PSG). We compared the total sleep time (TST), REM time, REM latency, nonREM1 + 2 time, nonREM3 time, and wake after sleep onset (WASO). We report absolute and relative errors, Bland-Altman representations, and a contingency table of times spent in sleep stages with respect to PSG. Sixty-five patients were included (final sample size 58 for Withings and 52 for Fitbit). Both devices gave a relatively accurate sleep start time with a median absolute error of 5 (IQR -43; 27) min for Withings and -2.0 (-12.5; 4.2) min for Fitbit but both overestimated TST. Withings tended to underestimate WASO with a median absolute error of -25.0 (-61.5; -8.5) min, while Fitbit tended to overestimate it (median absolute error 10 (-18; 43) min. Withings underestimated light sleep and overestimated deep sleep, while Fitbit overestimated light and REM sleep and underestimated deep sleep. The overall kappas for concordance of each epoch between PSG and devices were low: 0.12 (95%CI 0.117-0.121) for Withings and VPSG indications 0.07 (95%CI 0.067-0.071) for Fitbit, as well as kappas for each VPSG indication 0.07 (95%CI 0.067-0.071). Thus, commercially available sleep trackers are not reliable for sleep architecture in patients with sleep complaints/pathologies and should not replace actigraphy and/or PSG.
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Affiliation(s)
- Justine Frija
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, Paris, France
- Département de psychiatrie et d'addictologie, GHU Paris Nord, DMU Neurosciences, APHP, Hôpital Bichat Claude Bernard, Paris, France
| | - Jimmy Mullaert
- AP-HP, Hôpital Bichat, DEBRC, Paris, France
- Université de Paris, IAME, INSERM, Paris, France
| | | | - Mathieu Grajoszex
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
- Digital Medical Hub SAS, Assistance Publique Hôpitaux de Paris AP-HP, Hotel Dieu, Place du Parvis Notre Dame, Paris, France
| | - Ruben Wanono
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
| | - Hélène Benzaquen
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
| | - Fedja Kerzabi
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
| | - Pierre Alexis Geoffroy
- Université de Paris, NeuroDiderot, Inserm U1141, Paris, France
- Département de psychiatrie et d'addictologie, GHU Paris Nord, DMU Neurosciences, APHP, Hôpital Bichat Claude Bernard, Paris, France
| | - Boris Matrot
- Université de Paris, NeuroDiderot, Inserm U1141, Paris, France
| | | | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Marie-Pia d'Ortho
- Explorations Fonctionnelles et Centre du Sommeil- Département de Physiologie Clinique, APHP, Hôpital Bichat, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, Paris, France
- Digital Medical Hub SAS, Assistance Publique Hôpitaux de Paris AP-HP, Hotel Dieu, Place du Parvis Notre Dame, Paris, France
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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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Buimer HP, Siebelink NM, Gaasterland A, van Dam K, Smits A, Frederiks K, van der Poel A. Sleep-wake monitoring of people with intellectual disability: Examining the agreement of EMFIT QS and actigraphy. JOURNAL OF APPLIED RESEARCH IN INTELLECTUAL DISABILITIES 2023; 36:1276-1287. [PMID: 37489295 DOI: 10.1111/jar.13146] [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: 02/16/2023] [Revised: 05/23/2023] [Accepted: 07/06/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Gaining insight into sleep-wake patterns of persons with intellectual disabilities is commonly done using wrist actigraphy. For some people, contactless alternatives are needed. This study compares a contactless bed sensor with wrist actigraphy to monitor sleep-wake patterns of people with moderate to profound intellectual disabilities. METHOD Data were collected with EMFIT QS (activity and presence) and MotionWatch 8/Actiwatch 2 (activity, ambient light, and event marker/sleep diary) for 14 nights in 13 adults with moderate-profound intellectual disabilities residing in intramural care. RESULTS In a care-as-usual setting, EMFIT QS and actigraphy assessment show little agreement on sleep-wake variables. CONCLUSION Currently, EMFIT QS should not be considered an alternative to wrist actigraphy for sleep-wake monitoring. Further research is needed into assessing sleep-wake variables using (contactless) technological devices and how the data should be interpreted within the care context to achieve reliable and valid information on sleep-wake patterns of people with intellectual disabilities.
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Affiliation(s)
- Hendrik P Buimer
- Vilans, National Centre of Expertise for Long-term Care, Utrecht, The Netherlands
| | - Nienke M Siebelink
- Academy Het Dorp, Research & Advisory on Technology in Long-term Care, Arnhem, The Netherlands
| | | | - Kirstin van Dam
- Academy Het Dorp, Research & Advisory on Technology in Long-term Care, Arnhem, The Netherlands
| | | | - Kyra Frederiks
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Agnes van der Poel
- Academy Het Dorp, Research & Advisory on Technology in Long-term Care, Arnhem, The Netherlands
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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: 0] [Impact Index Per Article: 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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Ong JL, Baron KG. Contactless monitoring for the elderly: potential and pitfalls. Sleep 2023; 46:zsad227. [PMID: 37658741 PMCID: PMC10566232 DOI: 10.1093/sleep/zsad227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Indexed: 09/05/2023] Open
Affiliation(s)
- Ju Lynn Ong
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kelly Glazer Baron
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UTUSA
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Ravindran KKG, della Monica C, Atzori G, Lambert D, Hassanin H, Revell V, Dijk DJ. Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study. Sleep 2023; 46:zsad194. [PMID: 37471049 PMCID: PMC10566241 DOI: 10.1093/sleep/zsad194] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/16/2023] [Indexed: 07/21/2023] Open
Abstract
STUDY OBJECTIVES To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. METHODS We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). RESULTS Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. CONCLUSIONS CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.
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Affiliation(s)
- Kiran K G Ravindran
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Ciro della Monica
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Damion Lambert
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Hana Hassanin
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
- Surrey Clinical Research Facility, Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Victoria Revell
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
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Zhai H, Yan Y, He S, Zhao P, Zhang B. Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:4842. [PMID: 37430756 DOI: 10.3390/s23104842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs' performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.
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Affiliation(s)
- Huifang Zhai
- Faculty of Architecture and Urban Planning, Chongqing University, Chongqing 400044, China
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400044, China
| | - Yonghong Yan
- Faculty of Architecture and Urban Planning, Chongqing University, Chongqing 400044, China
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400044, China
| | - Siqi He
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Pinyong Zhao
- College of Mathematics and Statistics, Chongqing University, Chongqing 400044, China
| | - Bohan Zhang
- Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia
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Bender BF, Berry JA. Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2023; 5:e230002. [PMID: 37274061 PMCID: PMC10237513 DOI: 10.20900/agmr20230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.
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Affiliation(s)
| | - Jasmine A. Berry
- Robotics Institute, University of Michigan, College of Engineering, Ann Arbor, MI 48109, USA
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Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport. Sports (Basel) 2023; 11:sports11010014. [PMID: 36668718 PMCID: PMC9861232 DOI: 10.3390/sports11010014] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented 'smart pyjamas' through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete.
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Gonzales MM, Wang D, Pollet E, Velarde Á, Horn S, Coss P, Vaou O, Wang J, Li C, Seshadri S, Miao H, González DA. Pilot Study Results Assessing the Accuracy of a Ballistic Sleep Monitor Relative to Polysomnography in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2023; 13:1073-1076. [PMID: 37599538 PMCID: PMC10578264 DOI: 10.3233/jpd-230126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/02/2023] [Indexed: 08/22/2023]
Affiliation(s)
- Mitzi M. Gonzales
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Duo Wang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Erin Pollet
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Academy Diagnostics Sleep and EEG Center, 8215 Fredericksburg Road, San Antonio, TX, USA
| | - Ángel Velarde
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sarah Horn
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Pablo Coss
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Okeanis Vaou
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, FL, USA
| | - Chengdong Li
- College of Nursing, Florida State University, Tallahassee, FL, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Hongyu Miao
- Department of Statistics, Florida State University, Tallahassee, FL, USA
- College of Nursing, Florida State University, Tallahassee, FL, USA
| | - David Andrés González
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
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