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Santos IS, Echevarria P, Tovo-Rodrigues L, Matijasevich A, Domingues MR, Hallal PC. Are nocturnal awakenings at age 1 predictive of sleep duration and efficiency at age 6: Results from two birth cohorts. Sleep Med X 2024; 7:100105. [PMID: 38312370 PMCID: PMC10837084 DOI: 10.1016/j.sleepx.2024.100105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/06/2024] Open
Abstract
Objective To investigate the association of nighttime awakenings at 12 months with the duration and efficiency of nighttime sleep at 6 years of age. Methods Data from two population-based prospective studies (The Pelotas 2004 and The Pelotas 2015 Birth Cohorts) were used. Information on nighttime awakenings was provided by mothers during the 12-month follow-up interview. Infants who awakened >3 times after sleep onset at 12 months were considered frequent wakeners. Sleep duration and sleep efficiency were obtained by actigraphy at the 6-year follow-up. Children wore the device at the wrist of the non-dominant arm continuously for 3-7 days, including at least one weekend day. Unadjusted and adjusted beta coefficients were obtained by linear regression for each cohort separately. Results 2500 children from the 2004 and 2793 from the 2015 cohort had full information on nighttime awakenings at 12 months and actigraphy at 6 years and were analyzed. Prevalence of frequent wakeners was 6.3 % and 5.9 % in the 2004 and 2015 cohort, respectively. Mean bedtime and wake-up time at 6 years were, respectively, 23:23 and 08:41 h in the 2004 cohort, and 00:10 and 09:00 h int the 2015 cohort. Nighttime sleep lasted on average 7.54 and 7.24 h respectively in the 2004 and the 2015 cohort, and the sleep efficiency was 81.1 and 82.5 % respectively. In adjusted analyses, no associations were found between awakening at 12 months and sleep duration or sleep efficiency at 6 years of age. Conclusion In both cohorts sleep duration and efficiency were below the recommendation for school-age children (respectively 9-11 h and 85 %). There was no relationship between the number of nighttime awakenings at 12 months and sleep duration or efficiency at 6 years.
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Affiliation(s)
- Ina S. Santos
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Brazil
| | - Priscila Echevarria
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Brazil
| | | | - Alicia Matijasevich
- Departamento de Medicina Preventiva, Faculdade de Medicina FMUSP, Universidade de São Paulo, SP, Brazil
| | - Marlos R. Domingues
- Post-graduate Program in Physical Education, Federal University of Pelotas, Brazil
| | - Pedro C. Hallal
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Brazil
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Logacjov A, Skarpsno ES, Kongsvold A, Bach K, Mork PJ. A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information. Nat Sci Sleep 2024; 16:699-710. [PMID: 38863481 PMCID: PMC11164689 DOI: 10.2147/nss.s452799] [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: 12/15/2023] [Accepted: 05/02/2024] [Indexed: 06/13/2024] Open
Abstract
Purpose Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model. Patients and Methods Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17-70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model. Results Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR). Conclusion An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.
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Affiliation(s)
- Aleksej Logacjov
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Schjelderup Skarpsno
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, St. Olavs Hospital, Trondheim, Norway
| | - Atle Kongsvold
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kerstin Bach
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Paul Jarle Mork
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
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Vinod V, Saegner K, Maetzler W, Warmerdam E, Romijnders R, Beyer T, Göder R, Hansen C, Stürner K. Objectively assessed sleep quality parameters in Multiple Sclerosis at home: Association to disease, disease severity and physical activity. Sleep Med 2024; 118:71-77. [PMID: 38613859 DOI: 10.1016/j.sleep.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/12/2024] [Accepted: 03/16/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND Multiple Sclerosis (MS) is a chronic inflammatory autoimmune, neurodegenerative disease that affects regular mobility and leads predominantly to physical disability. Poor sleep quality, commonly reported in MS patients, impacts their physical activity (PA). Accelerometers monitor 24-h activity patterns, offering insights into disease progression in daily life. OBJECTIVE To test if the sleep quality variables of MS patients, as assessed with wrist-worn accelerometers, differ from those of controls and are associated with PA and disease severity variables. METHODS Seven-day raw accelerometer data collected from 40 MS patients and 24 controls was processed using an open-source GGIR package, from which variables of sleep quality (sleep efficiency, wake after sleep onset (WASO), sleep regularity index (SRI), intradaily variability (IV)) and PA (of different intensities: inactivity, light (LPA), moderate (MPA), vigorous (VPA)) were analyzed. The variables were compared between the two study groups and in MS patients, correlation tested associations among the variables of sleep quality, PA, and disease severity (assessed with the Expanded Disability Status Scale, EDSS). RESULTS Sleep efficiency was the only variable that differed significantly between MS patients and controls (lower in MS, p = 0.01). Both SRI (positively) and IV (negatively) correlated with the time spent in LPA and MPA. WASO correlated negatively with inactivity. CONCLUSION This is one of the few studies with a wrist-worn accelerometer that shows a difference in sleep efficiency between MS patients and controls and, in MS, an association of sleep quality variables with PA variables.
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Affiliation(s)
- Vaishali Vinod
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Karolina Saegner
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Elke Warmerdam
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Robbin Romijnders
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Thorben Beyer
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Robert Göder
- Department of Psychiatry and Psychotherapy, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany.
| | - Klarissa Stürner
- Department of Neurology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
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Yuan H, Plekhanova T, Walmsley R, Reynolds AC, Maddison KJ, Bucan M, Gehrman P, Rowlands A, Ray DW, Bennett D, McVeigh J, Straker L, Eastwood P, Kyle SD, Doherty A. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. NPJ Digit Med 2024; 7:86. [PMID: 38769347 PMCID: PMC11106264 DOI: 10.1038/s41746-024-01065-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/22/2024] [Indexed: 05/22/2024] Open
Abstract
Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
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Affiliation(s)
- Hang Yuan
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Rosemary Walmsley
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Amy C Reynolds
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Kathleen J Maddison
- Centre of Sleep Science, School of Human Sciences, University of Western Australia, Perth, WA, Australia
- West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip Gehrman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex Rowlands
- Diabetes Research Centre, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - David W Ray
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford Kavli Centre for Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Joanne McVeigh
- Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Leon Straker
- Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Peter Eastwood
- Health Futures Institute, Murdoch University, Murdoch, WA, Australia
| | - Simon D Kyle
- Sir Jules Thorn Sleep & Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Aiden Doherty
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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Saint-Maurice PF, Freeman JR, Russ D, Almeida JS, Shams-White MM, Patel S, Wolff-Hughes DL, Watts EL, Loftfield E, Hong HG, Moore SC, Matthews CE. Associations between actigraphy-measured sleep duration, continuity, and timing with mortality in the UK Biobank. Sleep 2024; 47:zsad312. [PMID: 38066693 PMCID: PMC10925955 DOI: 10.1093/sleep/zsad312] [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: 04/20/2023] [Revised: 11/23/2023] [Indexed: 01/12/2024] Open
Abstract
STUDY OBJECTIVES To examine the associations between sleep duration, continuity, timing, and mortality using actigraphy among adults. METHODS Data were from a cohort of 88 282 adults (40-69 years) in UK Biobank that wore a wrist-worn triaxial accelerometer for 7 days. Actigraphy data were processed to generate estimates of sleep duration and other sleep characteristics including wake after sleep onset (WASO), number of 5-minute awakenings, and midpoint for sleep onset/wake-up and the least active 5 hours (L5). Data were linked to mortality outcomes with follow-up to October 31, 2021. We implemented Cox models (hazard ratio, confidence intervals [HR, 95% CI]) to quantify sleep associations with mortality. Models were adjusted for demographics, lifestyle factors, and medical conditions. RESULTS Over an average of 6.8 years 2973 deaths occurred (1700 cancer, 586 CVD deaths). Overall sleep duration was significantly associated with risk for all-cause (p < 0.01), cancer (p < 0.01), and CVD (p = 0.03) mortality. For example, when compared to sleep durations of 7.0 hrs/d, durations of 5 hrs/d were associated with a 29% higher risk for all-cause mortality (HR: 1.29 [1.09, 1.52]). WASO and number of awakenings were not associated with mortality. Individuals with L5 early or late midpoints (<2:30 or ≥ 3:30) had a ~20% higher risk for all-cause mortality, compared to those with intermediate L5 midpoints (3:00-3:29; p ≤ 0.01; e.g. HR ≥ 3:30: 1.19 [1.07, 1.32]). CONCLUSIONS Shorter sleep duration and both early and late sleep timing were associated with a higher mortality risk. These findings reinforce the importance of public health efforts to promote healthy sleep patterns in adults.
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Affiliation(s)
- Pedro F Saint-Maurice
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Breast Unit, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Joshua R Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Russ
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Marissa M Shams-White
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shreya Patel
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, USA
| | - Dana L Wolff-Hughes
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eleanor L Watts
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hyokyoung G Hong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles E Matthews
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Henson J, Covenant A, Hall AP, Herring L, Rowlands AV, Yates T, Davies MJ. Waking Up to the Importance of Sleep in Type 2 Diabetes Management: A Narrative Review. Diabetes Care 2024; 47:331-343. [PMID: 38394635 DOI: 10.2337/dci23-0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/27/2023] [Indexed: 02/25/2024]
Abstract
For the first time, the latest American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines have incorporated a growing body of evidence linking health outcomes associated with type 2 diabetes to the movement behavior composition over the whole 24-h day. Of particular note, the importance of sleep as a key lifestyle component in the management of type 2 diabetes is promulgated and presented using three key constructs: quantity, quality, and timing (i.e., chronotype). In this narrative review we highlight some of the key evidence justifying the inclusion of sleep in the latest consensus guidelines by examining the associations of quantity, quality, and timing of sleep with measures of glycemia, cardiovascular disease risk, and mortality. We also consider potential mechanisms implicated in the association between sleep and type 2 diabetes and provide practical advice for health care professionals about initiating conversations pertaining to sleep in clinical care. In particular, we emphasize the importance of measuring sleep in a free-living environment and provide a summary of the different methodologies and targets. In summary, although the latest ADA/EASD consensus report highlights sleep as a central component in the management of type 2 diabetes, placing it, for the first time, on a level playing field with other lifestyle behaviors (e.g., physical activity and diet), the evidence base for improving sleep (beyond sleep disorders) in those living with type 2 diabetes is limited. This review should act as a timely reminder to incorporate sleep into clinical consultations, ongoing diabetes education, and future interventions.
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Affiliation(s)
- Joseph Henson
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
| | - Alix Covenant
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
| | - Andrew P Hall
- University Hospitals of Leicester NHS Trust, Leicester, U.K
- Hanning Sleep Laboratory, Leicester General Hospital, Leicester, U.K
| | - Louisa Herring
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
- University Hospitals of Leicester NHS Trust, Leicester, U.K
| | - Alex V Rowlands
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), UniSA Allied Health and Human Performance, University of South Australia, Adelaide, Australia
| | - Thomas Yates
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
| | - Melanie J Davies
- NIHR Leicester Biomedical Research Centre, Diabetes Research Centre, College of Life Sciences, University of Leicester, U.K
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Wendt A, Bielemann RM, Wehrmeister FC, Ricardo LIC, Müller WDA, Machado AKF, da Cruz MF, Bertoldi AD, Brage S, Ekelund U, Tovo-Rodrigues L, Crochemore-Silva I. Is rest-activity rhythm prospectively associated with all-cause mortality in older people regardless of sleep and physical activity level? The 'Como Vai?' Cohort study. PLoS One 2024; 19:e0298031. [PMID: 38363743 PMCID: PMC10871497 DOI: 10.1371/journal.pone.0298031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/16/2024] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVE This study aims to test the association of rest-activity rhythm (intradaily variability and interdaily stability) with all-cause mortality in an older adult cohort in Brazil. It also assesses whether the amount of time spent at each intensity level (i.e., physical activity and nocturnal sleep) interferes with this association. METHODS This cohort study started in 2014 with older adults (≥60 years). We investigated deaths from all causes that occurred until April 2017. Rest-activity rhythm variables were obtained using accelerometry at baseline. Intradaily variability indicates higher rhythm fragmentation, while interdaily stability indicates higher rhythm stability. Cox proportional-hazard models were used to test the associations controlling for confounders. RESULTS Among the 1451 older adults interviewed in 2014, 965 presented valid accelerometry data. During the follow-up period, 80 individuals died. After adjusting the analysis for sociodemographic, smoking, morbidity score, and number of medicines, an increase of one standard deviation in interdaily stability decreased 26% the risk of death. The adjustment for total sleep time and inactivity did not change this association. On the other hand, the association was no longer significant after adjusting for overall physical activity and moderate to vigorous physical activity. CONCLUSION Rest-activity rhythm pattern was not associated with mortality when physical activity was considered, possibly because this pattern could be driven by regular exercise. Promoting physical activity remains a relevant strategy to improve population health.
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Affiliation(s)
- Andrea Wendt
- Graduate Program in Health Technology, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil
| | - Renata Moraes Bielemann
- Post-Graduation Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
- School of Nutrition, Federal University of Pelotas, Pelotas, Brazil
| | | | - Luiza I. C. Ricardo
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | | | | | - Andréa D. Bertoldi
- Post-Graduation Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Soren Brage
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
- Department of Chronic diseases, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Inácio Crochemore-Silva
- Post-Graduation Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
- Post-Graduation Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
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8
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Yuan H, Plekhanova T, Walmsley R, Reynolds AC, Maddison KJ, Bucan M, Gehrman P, Rowlands A, Ray DW, Bennett D, McVeigh J, Straker L, Eastwood P, Kyle SD, Doherty A. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.07.23292251. [PMID: 37461532 PMCID: PMC10350137 DOI: 10.1101/2023.07.07.23292251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Background Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. Methods We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality. Findings After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77). Interpretation Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
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Affiliation(s)
- Hang Yuan
- Nuffield Department of Population Health, University of Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | | | - Rosemary Walmsley
- Nuffield Department of Population Health, University of Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Amy C Reynolds
- College of Medicine and Public Health, Flinders University, Australia
| | - Kathleen J Maddison
- Centre of Sleep Science, School of Human Sciences, University of Western Australia, Australia
- West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Australia
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, USA
| | - Philip Gehrman
- Department of Psychiatry, University of Pennsylvania, USA
| | - Alex Rowlands
- Diabetes Research Centre, University of Leicester, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, UK
| | - David W Ray
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Derrick Bennett
- Nuffield Department of Population Health, University of Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, UK
| | - Joanne McVeigh
- Curtin School of Allied Health, Curtin University, Australia
| | - Leon Straker
- Curtin School of Allied Health, Curtin University, Australia
| | | | - Simon D Kyle
- Sir Jules Thorn Sleep & Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Aiden Doherty
- Nuffield Department of Population Health, University of Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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9
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Shah VV, Brumbach BH, Pearson S, Vasilyev P, King E, Carlson-Kuhta P, Mancini M, Horak FB, Sowalsky K, McNames J, El-Gohary M. Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:2296. [PMID: 36850896 PMCID: PMC10003936 DOI: 10.3390/s23042296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/07/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland-Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test-retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland-Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | - Barbara H. Brumbach
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR 97201, USA
| | - Sean Pearson
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | - Paul Vasilyev
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | - Edward King
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | | | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | - Kristen Sowalsky
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
| | - James McNames
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, USA
| | - Mahmoud El-Gohary
- APDM Wearable Technologies-a Clario Company, Portland, OR 97201, USA
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