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Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. NPJ Digit Med 2024; 7:161. [PMID: 38890529 PMCID: PMC11189504 DOI: 10.1038/s41746-024-01151-3] [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: 11/02/2023] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Department of Behavioral and Social Sciences, Brown University, Providence, USA
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jürgen Bernard
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computer Science, University of Zurich, Zurich, Switzerland
| | | | - Viktor von Wyl
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
- Digital Society Initiative, University of Zurich, Zurich, Switzerland.
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
- Swiss School of Public Health (SSPH+), Zurich, Switzerland.
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Waqar S, Ghani Khan MU. Sleep stage prediction using multimodal body network and circadian rhythm. PeerJ Comput Sci 2024; 10:e1988. [PMID: 38686009 PMCID: PMC11057653 DOI: 10.7717/peerj-cs.1988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/21/2024] [Indexed: 05/02/2024]
Abstract
Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.
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Affiliation(s)
- Sahar Waqar
- Department of Computer Engineering, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
| | - Muhammad Usman Ghani Khan
- Department of Computer Science, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
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Kelly K, Kolbeinsson H, Blanck LM, Khan M, Kyriakakis R, Assifi MM, Wright GP, Chung M. Can we let our patients sleep in the hospital? A randomized controlled trial of a pragmatic sleep protocol in surgical oncology patients. J Surg Oncol 2024; 129:827-834. [PMID: 38115237 DOI: 10.1002/jso.27565] [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: 08/14/2023] [Revised: 11/07/2023] [Accepted: 12/04/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Postoperative inpatients experience increased stress due to pain and poor restorative sleep than non-surgical inpatients. OBJECTIVES AND METHODS A total of 101 patients, undergoing major oncologic surgery, were randomized to a postoperative sleep protocol (n = 50) or standard postoperative care (n = 51), between August 2020 and November 2021. The primary endpoint of the study was postoperative sleep time after major oncologic surgery. Sleep time and steps were measured using a Fitbit Charge 4®. RESULTS There was no statistically significant difference found in postoperative sleep time between the sleep protocol and standard group (median sleep time of 427 min vs. 402 min; p = 0.852, respectively). Major complication rates were similar in both groups (7.4% vs. 8.9%). Multivariate analysis found sex and Charlson Comorbidity Index to be significant factors affecting postoperative sleep time and step count. Postoperative delirium was only observed in the standard group, although this did not reach statistical significance. There were no in hospital mortalities. CONCLUSION The use of a sleep protocol was found to be safe in our study population. There was no statistical difference in postoperative sleep time or major complications. Institution of a more humane sleep protocol for postoperative inpatients should be considered.
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Affiliation(s)
- Kathrine Kelly
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
| | - Hordur Kolbeinsson
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
| | - Lauren M Blanck
- Department of Graduate Medical Education, Michigan State College of Human Medicine, Grand Rapids, Michigan, USA
| | - Mariam Khan
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
| | - Roxanne Kyriakakis
- Division of Colon and Rectal Surgery, Spectrum Health Colon and Rectal Fellowship, Grand Rapids, Michigan, USA
| | - M Mura Assifi
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
- Department of Graduate Medical Education, Michigan State College of Human Medicine, Grand Rapids, Michigan, USA
- Division of Surgical Oncology, Spectrum Health Medical Group, Grand Rapids, Michigan, USA
| | - G Paul Wright
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
- Department of Graduate Medical Education, Michigan State College of Human Medicine, Grand Rapids, Michigan, USA
- Division of Surgical Oncology, Spectrum Health Medical Group, Grand Rapids, Michigan, USA
| | - Mathew Chung
- Department of General Surgery, Spectrum Health/Michigan State University College of Human Medicine General, Grand Rapids, Michigan, USA
- Department of Graduate Medical Education, Michigan State College of Human Medicine, Grand Rapids, Michigan, USA
- Division of Surgical Oncology, Spectrum Health Medical Group, Grand Rapids, Michigan, USA
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Hassinger AB, Velez C, Wang J, Mador MJ, Wilding GE, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. J Clin Sleep Med 2024; 20:221-227. [PMID: 37767811 PMCID: PMC10835772 DOI: 10.5664/jcsm.10820] [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: 06/07/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023]
Abstract
STUDY OBJECTIVES Reduced sleep duration and work hour variability contribute to medical error and physician burnout. This study assesses the relationships between physician performance, burnout, and the dimensions of sleep beyond hours slept. METHODS This was an ancillary analysis of 3 years of data from an international prospective cohort study: the Intern Health Study. Actigraphy data from 3,654 intern physicians capturing sleep timing, regularity, efficiency, and duration were used individually and combined as a composite sleep health index to measure the association of multidimensional sleep patterns on self-reported medical errors and burnout. RESULTS From 2017-2019, interns' work hours decreased by 4 hours per week and total sleep time also decreased (6.7 to 5.99 hours), and sleep efficiency, timing, and regularity all worsened (all P < .05). In the 21.2% of participants who committed an error, there was no difference in sleep duration, timing, or regularity. Lower sleep efficiency was associated with higher odds of committing an error (P = .003) and higher burnout scores (P < .001). Although overall sleep quality was poor in the entire cohort, interns in the lowest quintile of sleep duration, regularity, and efficiency had higher burnout scores than those in the best quintile. CONCLUSIONS Sleep efficiency, not duration, was associated with increased self-reported medical errors and burnout in intern physicians. Overall sleep quality and duration worsened despite fewer hours worked. Future studies on physician burnout should measure all aspects of sleep health. CITATION Hassinger AB, Velez C, Wang J, Mador MJ, Wilding GE, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. J Clin Sleep Med. 2024;20(2):221-227.
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Affiliation(s)
- Amanda B. Hassinger
- Department of Pediatrics, Division of Pediatric Pulmonology and Sleep Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, John R. Oishei Children’s Hospital, Buffalo, New York
| | - Chiara Velez
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Monroe Carell Jr. Children’s Hospital at Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jia Wang
- Department of Biostatistics, University at Buffalo, Buffalo, New York
| | - M. Jeffery Mador
- Department of Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York
| | - Gregory E. Wilding
- Department of Biostatistics, University at Buffalo School of Public Health and Health Professions, Buffalo, New York
| | - Archana Mishra
- Department of Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York
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Jang H, Lee S, Son Y, Seo S, Baek Y, Mun S, Kim H, Kim I, Kim J. Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data. JMIR Mhealth Uhealth 2023; 11:e49144. [PMID: 37988148 PMCID: PMC10698662 DOI: 10.2196/49144] [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: 05/24/2023] [Revised: 09/11/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices. OBJECTIVE To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data. METHODS Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. RESULTS In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively. CONCLUSIONS The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.
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Affiliation(s)
- Hyunchul Jang
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Siwoo Lee
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Yunhee Son
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Sumin Seo
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Younghwa Baek
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Sujeong Mun
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Hoseok Kim
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Icktae Kim
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Junho Kim
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
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Carlson EJ, Wilckens KA, Wheeler ME. The Interactive Role of Sleep and Circadian Rhythms in Episodic Memory in Older Adults. J Gerontol A Biol Sci Med Sci 2023; 78:1844-1852. [PMID: 37167439 PMCID: PMC10562893 DOI: 10.1093/gerona/glad112] [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: 08/08/2022] [Indexed: 05/13/2023] Open
Abstract
Adequate sleep is essential for healthy physical, emotional, and cognitive functioning, including memory. However, sleep ability worsens with increasing age. Older adults on average have shorter sleep durations and more disrupted sleep compared with younger adults. Age-related sleep changes are thought to contribute to age-related deficits in episodic memory. Nonetheless, the nature of the relationship between sleep and episodic memory deficits in older adults is still unclear. Further complicating this relationship are age-related changes in circadian rhythms such as the shift in chronotype toward morningness and decreased circadian stability, which may influence memory abilities as well. Most sleep and cognitive aging studies do not account for circadian factors, making it unclear whether age-related and sleep-related episodic memory deficits are partly driven by interactions with circadian rhythms. This review will focus on age-related changes in sleep and circadian rhythms and evidence that these factors interact to affect episodic memory, specifically encoding and retrieval. Open questions, methodological considerations, and clinical implications for diagnosis and monitoring of age-related memory impairments are discussed.
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Affiliation(s)
- Elyse J Carlson
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Kristine A Wilckens
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Mark E Wheeler
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
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Stirling RE, Hidajat CM, Grayden DB, D’Souza WJ, Naim-Feil J, Dell KL, Schneider LD, Nurse E, Freestone D, Cook MJ, Karoly PJ. Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration. Brain 2023; 146:2803-2813. [PMID: 36511881 PMCID: PMC10316760 DOI: 10.1093/brain/awac476] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/24/2022] [Accepted: 11/26/2022] [Indexed: 08/21/2023] Open
Abstract
Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Cindy M Hidajat
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Wendyl J D’Souza
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Jodie Naim-Feil
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | | | - Ewan Nurse
- Research Department, Seer Medical, Melbourne 3000, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Dean Freestone
- Research Department, Seer Medical, Melbourne 3000, Australia
| | - Mark J Cook
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
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Hoang NH, Liang Z. Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review. JMIR Mhealth Uhealth 2023; 11:e42750. [PMID: 37379057 PMCID: PMC10365577 DOI: 10.2196/42750] [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: 09/16/2022] [Revised: 02/03/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data. OBJECTIVE This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic. METHODS Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included. RESULTS The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57%) were conducted in the United States, followed by Japan (3/14, 21%). Only a few of the publications (5/14, 36%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29%), sleep efficiency (4/14, 29%), sleep onset latency (4/14, 29%), and time at lights off (3/14, 21%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21%), regression analysis (3/14, 21%), and statistical tests or inferences (3/14, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7%) or anomaly detection (2/14, 14%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality. CONCLUSIONS This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.
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Affiliation(s)
- Nhung Huyen Hoang
- Graduate School of Engineering, Kyoto University of Advanced Science, Kyoto, Japan
| | - Zilu Liang
- Graduate School of Engineering, Kyoto University of Advanced Science, Kyoto, Japan
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Windrix C, Vandyck K, Tanaka KA, Butt AL. Night Float Rotations: Continued Questions With Few Answers. Anesth Analg 2023; 136:e41-e42. [PMID: 37205824 DOI: 10.1213/ane.0000000000006473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Affiliation(s)
- Casey Windrix
- Department of Anesthesiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma,
| | - Kofi Vandyck
- Department of Anesthesiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma,
| | - Kenichi A Tanaka
- Department of Surgery and Anesthesiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Amir L Butt
- Department of Anesthesiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
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Zhu Y, Stephenson C, Moghimi E, Jagayat J, Nikjoo N, Kumar A, Shirazi A, Patel C, Omrani M, Alavi N. Investigating the effectiveness of electronically delivered cognitive behavioural therapy (e-CBTi) compared to pharmaceutical interventions in treating insomnia: Protocol for a randomized controlled trial. PLoS One 2023; 18:e0285757. [PMID: 37192176 DOI: 10.1371/journal.pone.0285757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 04/26/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Insomnia is one of the most prevalent sleep disorders characterized by an inability to fall or stay asleep. Available treatments include pharmacotherapy and cognitive behavioural therapy for insomnia (CBTi). Although CBTi is the first-line treatment, it has limited availability. Therapist-guided electronic delivery of CBT for insomnia (e-CBTi) offers scalable solutions to enhance access to CBTi. While e-CBTi produces comparable outcomes to in-person CBTi, there is a lack of comparison to active pharmacotherapies. Therefore, direct comparisons between e-CBTi and trazodone, one of the most frequently prescribed medications for insomnia, is essential in establishing the effectiveness of this novel digital therapy in the health care system. OBJECTIVE The aim of this study is to compare the effectiveness of a therapist-guided electronically-delivered cognitive behavioural therapy (e-CBTi) program to trazodone in patients with insomnia. METHODS Patients (n = 60) will be randomly assigned to two groups: treatment as usual (TAU) + trazodone and TAU + e-CBTi for seven weeks. Each weekly sleep module will be delivered through the Online Psychotherapy Tool (OPTT), a secure, online mental health care delivery platform. Changes in insomnia symptoms will be evaluated throughout the study using clinically validated symptomatology questionnaires, Fitbits, and other behavioural variables. RESULTS Participant recruitment began in November 2021. To date, 18 participants have been recruited. Data collection is expected to conclude by December 2022 and analyses are expected to be completed by January 2023. CONCLUSIONS This comparative study will improve our understanding of the efficacy of therapist-guided e-CBTi in managing insomnia. These findings can be used to develop more accessible and effective treatment options and influence clinical practices for insomnia to further expand mental health care capacity in this population. TRIAL REGISTRATION ClinicalTrials.gov (NCT05125146).
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Affiliation(s)
- Yiran Zhu
- Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Callum Stephenson
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Elnaz Moghimi
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Jasleen Jagayat
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Niloofar Nikjoo
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Anchan Kumar
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Amirhossein Shirazi
- Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Charmy Patel
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Mohsen Omrani
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- OPTT Inc., Toronto, Ontario, Canada
| | - Nazanin Alavi
- Faculty of Health Sciences, Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
- Faculty of Health Sciences, Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
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Budig M, Stoohs R, Keiner M. Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239540. [PMID: 36502241 PMCID: PMC9741062 DOI: 10.3390/s22239540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 05/16/2023]
Abstract
Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
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Affiliation(s)
- Mario Budig
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
| | | | - Michael Keiner
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
- Correspondence:
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12
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Babaei N, Hannani N, Dabanloo NJ, Bahadori S. A Systematic Review of the Use of Commercial Wearable Activity Trackers for Monitoring Recovery in Individuals Undergoing Total Hip Replacement Surgery. CYBORG AND BIONIC SYSTEMS (WASHINGTON, D.C.) 2022; 2022:9794641. [PMID: 36751476 PMCID: PMC9636847 DOI: 10.34133/2022/9794641] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022]
Abstract
The innovation of wearable devices is advancing rapidly. Activity monitors can be used to improve the total hip replacement (THR) patients' recovery process and reduce costs. This systematic review assessed the body-worn accelerometers used in studies to enhance the rehabilitation process and monitor THR patients. Electronic databases such as Cochrane Database of Systematic Reviews library, CINAHL CompleteVR, Science Citation Index, and MedlineVR from January 2000 to January 2022 were searched. Due to inclusion criteria, fourteen eligible studies that utilised commercial wearable technology to monitor physical activity both before and after THR were identified. Their evidence quality was assessed with RoB 2.0 and ROBINS-I. This study demonstrates that wearable device technology might be feasible to predict, monitor, and detect physical activity following THR. They could be used as a motivational tool to increase patients' mobility and enhance the recovery process. Also, wearable activity monitors could provide a better insight into the individual's activity level in contrast to subjective self-reported questionnaires. However, they have some limitations, and further evidence is needed to establish this technology as the primary device in THR rehabilitation.
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Affiliation(s)
- Nasibeh Babaei
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Negin Hannani
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Shayan Bahadori
- Faculty of Health and Social Science, Bournemouth University, Bournemouth, UK
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13
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Ni CY, Hou GJ, Tang YY, Wang JJ, Chen WJ, Yang Y, Wang ZH, Zhou WP. Quantitative study of the effects of early standardized ambulation on sleep quality in patients after hepatectomy. Front Surg 2022; 9:941158. [PMID: 36211277 PMCID: PMC9545172 DOI: 10.3389/fsurg.2022.941158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundSleep quality has been always an important problem for patients after hepatectomy. The main purpose of the study is to investigate the effects of early ambulation on sleep quality in patients after liver resection via a quantitative study.MethodsPatients undergoing liver tumor resection were randomly divided into two groups, and the Pittsburgh Sleep Quality Index (PSQI) was used to assess the postoperative activities and sleep quality.ResultsPatients who started early ambulation after liver resection had significantly better sleep quality, faster recovery of gastrointestinal function and shorter lengths of postoperative hospital stay compared with the control group. And there was no significant difference in the incidence of postoperative complications between the two groups.ConclusionEarly standardized physical activities are feasible for patients after liver resection, which can significantly improve patient's sleep quality, reduce patient's pain and the nursing workload, and achieve rapid recovery.
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Affiliation(s)
- Chun-yan Ni
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- Suzhou Science / Technology Town Hospital, Suzhou, China
| | - Guo-jun Hou
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Ya-yuan Tang
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jing-jing Wang
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Wen-jun Chen
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yuan Yang
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- Correspondence: Yuan Yang Zhi-hong Wang Wei-ping Zhou
| | - Zhi-hong Wang
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- Correspondence: Yuan Yang Zhi-hong Wang Wei-ping Zhou
| | - Wei-ping Zhou
- Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- Correspondence: Yuan Yang Zhi-hong Wang Wei-ping Zhou
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14
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Kuosmanen E, Visuri A, Risto R, Hosio S. Comparing consumer grade sleep trackers for research purposes: A field study. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.971793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.
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15
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Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med 2022; 18:2189-2196. [PMID: 35686368 PMCID: PMC9435345 DOI: 10.5664/jcsm.10076] [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: 09/20/2021] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 11/13/2022]
Abstract
STUDY OBJECTIVES Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between US high school student-athletes and nonathletes during a semester of school and competition. METHODS Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years, and 38 nonathletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time, sleep efficiency, bedtimes, wake times, and steps counted. RESULTS Student-athletes and nonathletes did not differ in total sleep time (440.4 ± 46.4 vs 438.1 ± 41.7 min, P = .82) and sleep efficiency (93.6 ± 2.3 vs 92.9 ± 2.3%, P = .20). Fitbit data revealed that 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs 8,418 ± 2,489, P < .01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased total sleep time (P < .01). Earlier wake times were significantly correlated to increased steps per day (P < .01). CONCLUSIONS Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health. CITATION Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med. 2022;18(9):2189-2196.
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Affiliation(s)
- Corey T. Ungaro
- Gatorade Sports Science Institute, PepsiCo R&D, Barrington, Illinois
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16
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Hong J, Tran HH, Jung J, Jang H, Lee D, Yoon IY, Hong JK, Kim JW. End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices. Nat Sci Sleep 2022; 14:1187-1201. [PMID: 35783665 PMCID: PMC9241996 DOI: 10.2147/nss.s361270] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/03/2022] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. PATIENTS AND METHODS Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. RESULTS Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement. CONCLUSION The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.
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Affiliation(s)
- Joonki Hong
- Asleep Inc., Seoul, Korea.,Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | | | | | | | | | - In-Young Yoon
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.,Seoul National University College of Medicine, Seoul, Korea
| | - Jung Kyung Hong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.,Seoul National University College of Medicine, Seoul, Korea
| | - Jeong-Whun Kim
- Seoul National University College of Medicine, Seoul, Korea.,Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Seongnam, Korea
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17
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Liang Z. What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.774949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.
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18
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Elavsky S, Klocek A, Knapova L, Smahelova M, Smahel D, Cimler R, Kuhnova J. Feasibility of Real-time Behavior Monitoring Via Mobile Technology in Czech Adults Aged 50 Years and Above: 12-Week Study With Ecological Momentary Assessment. JMIR Aging 2021; 4:e15220. [PMID: 34757317 PMCID: PMC8663589 DOI: 10.2196/15220] [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: 11/04/2020] [Revised: 05/14/2021] [Accepted: 06/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background Czech older adults have lower rates of physical activity than the average population and lag behind in the use of digital technologies, compared with their peers from other European countries. Objective This study aims to assess the feasibility of intensive behavior monitoring through technology in Czech adults aged ≥50 years. Methods Participants (N=30; mean age 61.2 years, SD 6.8 years, range 50-74 years; 16/30, 53% male; 7/30, 23% retired) were monitored for 12 weeks while wearing a Fitbit Charge 2 monitor and completed three 8-day bursts of intensive data collection through surveys presented on a custom-made mobile app. Web-based surveys were also completed before and at the end of the 12-week period (along with poststudy focus groups) to evaluate participants’ perceptions of their experience in the study. Results All 30 participants completed the study. Across the three 8-day bursts, participants completed 1454 out of 1744 (83% compliance rate) surveys administered 3 times per day on a pseudorandom schedule, 451 out of 559 (81% compliance rate) end-of-day surveys, and 736 episodes of self-reported planned physical activity (with 29/736, 3.9% of the reports initiated but returned without data). The overall rating of using the mobile app and Fitbit was above average (74.5 out of 100 on the System Usability Scale). The majority reported that the Fitbit (27/30, 90%) and mobile app (25/30, 83%) were easy to use and rated their experience positively (25/30, 83%). Focus groups revealed that some surveys were missed owing to notifications not being noticed or that participants needed a longer time window for survey completion. Some found wearing the monitor in hot weather or at night uncomfortable, but overall, participants were highly motivated to complete the surveys and be compliant with the study procedures. Conclusions The use of a mobile survey app coupled with a wearable device appears feasible for use among Czech older adults. Participants in this study tolerated the intensive assessment schedule well, but lower compliance may be expected in studies of more diverse groups of older adults. Some difficulties were noted with the pairing and synchronization of devices on some types of smartphones, posing challenges for large-scale studies.
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Affiliation(s)
- Steriani Elavsky
- Department of Human Movement Studies, University of Ostrava, Ostrava, Czech Republic
| | - Adam Klocek
- Faculty of Social Studies, Masaryk University, Brno, Czech Republic
| | - Lenka Knapova
- Department of Human Movement Studies, University of Ostrava, Ostrava, Czech Republic
| | | | - David Smahel
- Faculty of Social Studies, Masaryk University, Interdisciplinary Research Team on Internet and Society, Brno, Czech Republic
| | - Richard Cimler
- Faculty of Science, University of Hradec Karlove, Hradec Kralove, Czech Republic
| | - Jitka Kuhnova
- Faculty of Science, University of Hradec Karlove, Hradec Kralove, Czech Republic
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19
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Rykov Y, Thach TQ, Bojic I, Christopoulos G, Car J. Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling. JMIR Mhealth Uhealth 2021; 9:e24872. [PMID: 34694233 PMCID: PMC8576601 DOI: 10.2196/24872] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 04/05/2021] [Accepted: 07/15/2021] [Indexed: 12/23/2022] Open
Abstract
Background Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. Objective The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. Methods This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. Results A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. Conclusions Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.
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Affiliation(s)
- Yuri Rykov
- Neuroglee Therapeutics, Singapore, Singapore
| | - Thuan-Quoc Thach
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China (Hong Kong)
| | - Iva Bojic
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - George Christopoulos
- Division of Leadership, Management and Organisation, Nanyang Business School, College of Business, Nanyang Technological University, Singapore, Singapore
| | - Josip Car
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
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20
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Stucky B, Clark I, Azza Y, Karlen W, Achermann P, Kleim B, Landolt HP. Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study. J Med Internet Res 2021; 23:e26476. [PMID: 34609317 PMCID: PMC8527385 DOI: 10.2196/26476] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/08/2021] [Accepted: 06/14/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. OBJECTIVE This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. METHODS A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. RESULTS Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. CONCLUSIONS We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.
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Affiliation(s)
- Benjamin Stucky
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Switzerland
| | - Ian Clark
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Yasmine Azza
- Department of Experimental Psychopathology and Psychotherapy, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, University of Zurich, Zurich, Switzerland
- Department of Psychiatry and Psychotherapy, Translational Psychiatry Unit, University of Lubeck, Lubeck, Germany
| | - Walter Karlen
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Switzerland
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Peter Achermann
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Switzerland
- The Key Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, University of Zurich, Zurich, Switzerland
| | - Birgit Kleim
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Switzerland
- Department of Experimental Psychopathology and Psychotherapy, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, University of Zurich, Zurich, Switzerland
| | - Hans-Peter Landolt
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Switzerland
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21
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Devine JK, Choynowski J, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco P, Hursh SR. Pilot Sleep Behavior across Time during Ultra-Long-Range Flights. Clocks Sleep 2021; 3:515-527. [PMID: 34698137 PMCID: PMC8544349 DOI: 10.3390/clockssleep3040036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/18/2021] [Indexed: 11/17/2022] Open
Abstract
Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.
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Affiliation(s)
- Jaime K. Devine
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (J.C.); (S.R.H.)
| | - Jake Choynowski
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (J.C.); (S.R.H.)
| | - Caio R. Garcia
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Audrey S. Simoes
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Marina R. Guelere
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Bruno de Godoy
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Diego S. Silva
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Philipe Pacheco
- Azul Linhas Aéreas Brasileiras, 06460-040 Sao Paulo, Brazil; (C.R.G.); (A.S.S.); (M.R.G.); (B.d.G.); (D.S.S.); (P.P.)
| | - Steven R. Hursh
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (J.C.); (S.R.H.)
- Institutes for Behavior Resources, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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22
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Leightley D, Lavelle G, White KM, Sun S, Matcham F, Ivan A, Oetzmann C, Penninx BWJH, Lamers F, Siddi S, Haro JM, Myin-Germeys I, Bruce S, Nica R, Wickersham A, Annas P, Mohr DC, Simblett S, Wykes T, Cummins N, Folarin AA, Conde P, Ranjan Y, Dobson RJB, Narayan VA, Hotopf M. Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology. BMC Psychiatry 2021; 21:435. [PMID: 34488697 PMCID: PMC8419819 DOI: 10.1186/s12888-021-03434-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown - pre-, during and post - in adults with a recent history of MDD across multiple centres. METHODS This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. RESULTS In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (- 12.16; 95% CI - 18.39 to - 5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. CONCLUSIONS We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid - 19 pandemic.
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Affiliation(s)
- Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Grace Lavelle
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Katie M. White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Josep Mario Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
- Romanian League for Mental Health, London, UK
| | - Alice Wickersham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - David C. Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, USA
| | - Sara Simblett
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Til Wykes
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Nicholas Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Augsburg, Germany
| | - Amos Akinola Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
| | | | - Mathew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
| | - On behalf of the RADAR-CNS Consortium
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
- Romanian League for Mental Health, London, UK
- H. Lundbeck A/S, Copenhagen, Denmark
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, USA
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Augsburg, Germany
- South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
- Janssen Research and Development, LLC, Titusville, NJ USA
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23
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de Vries H, Kamphuis W, Oldenhuis H, van der Schans C, Sanderman R. Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured with a Wearable and Smartphone App: a Within-Subject Design Using Continuous Monitoring. JMIR Cardio 2021; 5:e28731. [PMID: 34319877 PMCID: PMC8524333 DOI: 10.2196/28731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 12/11/2022] Open
Abstract
Background The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.
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Affiliation(s)
- Herman de Vries
- Professorship Personalized Digital Health, Hanze University of Applied Sciences, Zernikeplein 11, Groningen, NL.,Department of Human Behaviour & Training, TNO, Soesterberg, NL.,Department of Health Psychology, University Medical Center Groningen, Groningen, NL
| | - Wim Kamphuis
- Department of Human Behaviour & Training, TNO, Soesterberg, NL
| | - Hilbrand Oldenhuis
- Professorship Personalized Digital Health, Hanze University of Applied Sciences, Zernikeplein 11, Groningen, NL
| | - Cees van der Schans
- Department of Health Psychology, University Medical Center Groningen, Groningen, NL.,Department of Rehabilitation Medicine, University Medical Center Groningen, Groningen, NL.,Research Group Healthy Ageing Allied Health Care and Nursing, Hanze University of Applied Sciences, Groningen, NL
| | - Robbert Sanderman
- Department of Health Psychology, University Medical Center Groningen, Groningen, NL.,Department of Psychology, Health and Technology, University of Twente, Enschede, NL
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24
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Louzon PR, Andrews JL, Torres X, Pyles EC, Ali MH, Du Y, Devlin JW. Characterisation of ICU sleep by a commercially available activity tracker and its agreement with patient-perceived sleep quality. BMJ Open Respir Res 2021; 7:7/1/e000572. [PMID: 32332025 PMCID: PMC7204814 DOI: 10.1136/bmjresp-2020-000572] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/02/2020] [Accepted: 04/06/2020] [Indexed: 01/21/2023] Open
Abstract
Background A low-cost, quantitative method to evaluate sleep in the intensive care unit (ICU) that is both feasible for routine clinical practice and reliable does not yet exist. We characterised nocturnal ICU sleep using a commercially available activity tracker and evaluated agreement between tracker-derived sleep data and patient-perceived sleep quality. Patients and methods A prospective cohort study was performed in a 40-bed ICU at a community teaching hospital. An activity tracker (Fitbit Charge 2) was applied for up to 7 ICU days in English-speaking adults with an anticipated ICU stay ≥2 days and without mechanical ventilation, sleep apnoea, delirium, continuous sedation, contact isolation or recent anaesthesia. The Richards-Campbell Sleep Questionnaire (RCSQ) was administered each morning by a trained investigator. Results Available activity tracker-derived data for each ICU study night (20:00–09:00) (total sleep time (TST), number of awakenings (#AW), and time spent light sleep, deep sleep and rapid eye movement (REM) sleep) were downloaded and analysed. Across the 232 evaluated nights (76 patients), TST and RCSQ data were available for 232 (100%), #AW data for 180 (78%) and sleep stage data for 73 (31%). Agreement between TST (349±168 min) and RCSQ Score was moderate and significant (r=0.34; 95% CI 0.18 to 0.48). Agreement between #AW (median (IQR), 4 (2–9)) and RCSQ Score was negative and non-significant (r=−0.01; 95% CI −0.19 to 0.14). Agreement between time (min) spent in light (259 (182 to 328)), deep (43±29), and REM (47 (28–72)) sleep and RCSQ Score was moderate but non-significant (light (r=0.44, 95% CI −0.05 to 0.36); deep sleep (r=0.44, 95% CI −0.11 to 0.15) and REM sleep (r=0.44; 95% CI −0.21 to 0.21)). Conclusions A Fitbit Charge 2 when applied to non-intubated adults in an ICU consistently collects TST data but not #AW or sleep stage data at night. The TST moderately correlates with patient-perceived sleep quality; a correlation between either #AW or sleep stages and sleep quality was not found.
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Affiliation(s)
| | | | - Xavier Torres
- Department of Pharmacy, University of Chicago Medical Center, Chicago, Illinois, USA
| | - Eric C Pyles
- Department of Pharmacy, AdventHealth Orlando, Orlando, Florida, USA
| | - Mahmood H Ali
- Pulmonology, Central Florida Pulmonary Group PA, Orlando, Florida, USA
| | - Yuan Du
- Research Institute, AdventHealth Orlando, Orlando, Florida, USA
| | - John W Devlin
- School of Pharmacy, Northeastern University, Boston, Massachusetts, USA.,Division of Pulmonary, Critical Care and Sleep Medicine, Tufts Medical Center, Boston, Massachusetts, USA
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25
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Liang Z, Chapa-Martell MA. A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers. Front Digit Health 2021; 3:665946. [PMID: 34713139 PMCID: PMC8521802 DOI: 10.3389/fdgth.2021.665946] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/19/2021] [Indexed: 12/03/2022] Open
Abstract
Consumer wearable activity trackers, such as Fitbit are widely used in ubiquitous and longitudinal sleep monitoring in free-living environments. However, these devices are known to be inaccurate for measuring sleep stages. In this study, we develop and validate a novel approach that leverages the processed data readily available from consumer activity trackers (i.e., steps, heart rate, and sleep metrics) to predict sleep stages. The proposed approach adopts a selective correction strategy and consists of two levels of classifiers. The level-I classifier judges whether a Fitbit labeled sleep epoch is misclassified, and the level-II classifier re-classifies misclassified epochs into one of the four sleep stages (i.e., light sleep, deep sleep, REM sleep, and wakefulness). Best epoch-wise performance was achieved when support vector machine and gradient boosting decision tree (XGBoost) with up sampling were used, respectively at the level-I and level-II classification. The model achieved an overall per-epoch accuracy of 0.731 ± 0.119, Cohen's Kappa of 0.433 ± 0.212, and multi-class Matthew's correlation coefficient (MMCC) of 0.451 ± 0.214. Regarding the total duration of individual sleep stage, the mean normalized absolute bias (MAB) of this model was 0.469, which is a 23.9% reduction against the proprietary Fitbit algorithm. The model that combines support vector machine and XGBoost with down sampling achieved sub-optimal per-epoch accuracy of 0.704 ± 0.097, Cohen's Kappa of 0.427 ± 0.178, and MMCC of 0.439 ± 0.180. The sub-optimal model obtained a MAB of 0.179, a significantly reduction of 71.0% compared to the proprietary Fitbit algorithm. We highlight the challenges in machine learning based sleep stage prediction with consumer wearables, and suggest directions for future research.
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Affiliation(s)
- Zilu Liang
- Ubiquitous and Personal Computing Laboratory, Faculty of Engineering, Kyoto University of Advanced Science, Kyoto, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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26
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Zhang Y, Folarin AA, Sun S, Cummins N, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, White KM, Lamers F, Siddi S, Simblett S, Myin-Germeys I, Rintala A, Wykes T, Haro JM, Penninx BW, Narayan VA, Hotopf M, Dobson RJ. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study. JMIR Mhealth Uhealth 2021; 9:e24604. [PMID: 33843591 PMCID: PMC8076992 DOI: 10.2196/24604] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/07/2020] [Accepted: 02/03/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
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Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Rebecca Bendayan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Aki Rintala
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Til Wykes
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | | | - Matthew Hotopf
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Jb Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
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27
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Devine JK, Chinoy ED, Markwald RR, Schwartz LP, Hursh SR. Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation. SENSORS (BASEL, SWITZERLAND) 2020; 21:E76. [PMID: 33375557 PMCID: PMC7796293 DOI: 10.3390/s21010076] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 11/24/2022]
Abstract
Traditional measures of sleep or commercial wearables may not be ideal for use in operational environments. The Zulu watch is a commercial sleep-tracking device designed to collect longitudinal sleep data in real-world environments. Laboratory testing is the initial step towards validating a device for real-world sleep evaluation; therefore, the Zulu watch was tested against the gold-standard polysomnography (PSG) and actigraphy. Eight healthy, young adult participants wore a Zulu watch and Actiwatch simultaneously over a 3-day laboratory PSG sleep study. The accuracy, sensitivity, and specificity of epoch-by-epoch data were tested against PSG and actigraphy. Sleep summary statistics were compared using paired samples t-tests, intraclass correlation coefficients, and Bland-Altman plots. Compared with either PSG or actigraphy, both the accuracy and sensitivity for Zulu watch sleep-wake determination were >90%, while the specificity was low (~26% vs. PSG, ~33% vs. actigraphy). The accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. The Zulu watch showed mixed results but performed well in determining total sleep time, sleep efficiency, sleep onset, and final awakening in healthy adults compared with PSG or actigraphy. The next step will be to test the Zulu watch's ability to evaluate sleep in industrial operations.
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Affiliation(s)
- Jaime K. Devine
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (L.P.S.); (S.R.H.)
| | - Evan D. Chinoy
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA 92106, USA; (E.D.C.); (R.R.M.)
- Leidos, Inc., San Diego, CA 92106, USA
| | - Rachel R. Markwald
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA 92106, USA; (E.D.C.); (R.R.M.)
| | - Lindsay P. Schwartz
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (L.P.S.); (S.R.H.)
| | - Steven R. Hursh
- Institutes for Behavior Resources, Inc., Baltimore, MD 21218, USA; (L.P.S.); (S.R.H.)
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Mishra T, Wang M, Metwally AA, Bogu GK, Brooks AW, Bahmani A, Alavi A, Celli A, Higgs E, Dagan-Rosenfeld O, Fay B, Kirkpatrick S, Kellogg R, Gibson M, Wang T, Hunting EM, Mamic P, Ganz AB, Rolnik B, Li X, Snyder MP. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 2020; 4:1208-1220. [PMID: 33208926 PMCID: PMC9020268 DOI: 10.1038/s41551-020-00640-6] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/01/2020] [Indexed: 12/18/2022]
Abstract
Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.
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Affiliation(s)
- Tejaswini Mishra
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ahmed A Metwally
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Gireesh K Bogu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Amir Bahmani
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Arash Alavi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Emily Higgs
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Orit Dagan-Rosenfeld
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Bethany Fay
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Kirkpatrick
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan Kellogg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Michelle Gibson
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Tao Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Erika M Hunting
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Petra Mamic
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ariel B Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Benjamin Rolnik
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiao Li
- The Center for RNA Science and Therapeutics, Case Western University, Cleveland, OH, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
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29
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Rhee SY, Kim C, Shin DW, Steinhubl SR. Present and Future of Digital Health in Diabetes and Metabolic Disease. Diabetes Metab J 2020; 44:819-827. [PMID: 33389956 PMCID: PMC7801756 DOI: 10.4093/dmj.2020.0088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 08/18/2020] [Indexed: 12/19/2022] Open
Abstract
The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.
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Affiliation(s)
- Sang Youl Rhee
- Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea
- Department of Digital Health, Scripps Research Translational Institute, La Jolla, CA, USA
| | - Chiweon Kim
- Department of Internal Medicine, Seoul Wise Hospital, Uiwang, Korea
| | - Dong Wook Shin
- Department of Family Medicine/Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
| | - Steven R. Steinhubl
- Department of Digital Health, Scripps Research Translational Institute, La Jolla, CA, USA
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30
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Karas M, Marinsek N, Goldhahn J, Foschini L, Ramirez E, Clay I. Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables. Digit Biomark 2020; 4:73-86. [PMID: 33442582 DOI: 10.1159/000511531] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/11/2020] [Indexed: 12/20/2022] Open
Abstract
Introduction A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories. Methods For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (n = 355), tendon or ligament repair/reconstruction (n = 773), and knee or hip joint replacement (n = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time. Results The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual's baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available. Discussion Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.
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Affiliation(s)
- Marta Karas
- Evidation Health Inc., San Mateo, California, USA.,Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Jörg Goldhahn
- Institute of Translational Medicine, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule (ETH), Zurich, Switzerland
| | | | | | - Ieuan Clay
- Evidation Health Inc., San Mateo, California, USA
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31
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O'Mahony AM, Garvey JF, McNicholas WT. Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review. J Thorac Dis 2020; 12:5020-5038. [PMID: 33145074 PMCID: PMC7578472 DOI: 10.21037/jtd-sleep-2020-003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Obstructive sleep apnoea (OSA) is a growing and serious worldwide health problem with significant health and socioeconomic consequences. Current diagnostic testing strategies are limited by cost, access to resources and over reliance on one measure, namely the apnoea-hypopnoea frequency per hour (AHI). Recent evidence supports moving away from the AHI as the principle measure of OSA severity towards a more personalised approach to OSA diagnosis and treatment that includes phenotypic and biological traits. Novel advances in technology include the use of signals such as heart rate variability (HRV), oximetry and peripheral arterial tonometry (PAT) as alternative or additional measures. Ubiquitous use of smartphones and developments in wearable technology have also led to increased availability of applications and devices to facilitate home screening of at-risk populations, although current evidence indicates relatively poor accuracy in comparison with the traditional gold standard polysomnography (PSG). In this review, we evaluate the current strategies for diagnosing OSA in the context of their limitations, potential physiological targets as alternatives to AHI and the role of novel technology in OSA. We also evaluate the current evidence for using newer technologies in OSA diagnosis, the physiological targets such as smartphone applications and wearable technology. Future developments in OSA diagnosis and assessment will likely focus increasingly on systemic effects of sleep disordered breathing (SDB) such as changes in nocturnal oxygen and blood pressure (BP); and may also include other factors such as circulating biomarkers. These developments will likely require a re-evaluation of the diagnostic and grading criteria for clinically significant OSA.
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Affiliation(s)
- Anne M O'Mahony
- School of Medicine, University College Dublin, Dublin, Ireland
| | - John F Garvey
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Walter T McNicholas
- School of Medicine, University College Dublin, Dublin, Ireland.,First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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32
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Lunsford-Avery JR, Keller C, Kollins SH, Krystal AD, Jackson L, Engelhard MM. Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study. JMIR Mhealth Uhealth 2020; 8:e20590. [PMID: 33001035 PMCID: PMC7563632 DOI: 10.2196/20590] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 02/06/2023] Open
Abstract
Background Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. Objective The goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years. Methods A total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph. Results A total of 94.2% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8% (42/103) reported typical sleep while using the device, while 39.8% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device. Conclusions Wearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG–derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications. Trial Registration ClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762
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Affiliation(s)
- Jessica R Lunsford-Avery
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Casey Keller
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Scott H Kollins
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Andrew D Krystal
- Departments of Psychiatry and Neurology, University of California San Francisco School of Medicine, San Francisco, CA, United States
| | - Leah Jackson
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Matthew M Engelhard
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
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33
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Thota D. Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study. JMIR Form Res 2020; 4:e18086. [PMID: 32990631 PMCID: PMC7556371 DOI: 10.2196/18086] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/05/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022] Open
Abstract
Background Mental health disorders can disrupt a person’s sleep, resulting in lower quality of life. Early identification and referral to mental health services are critical for active duty service members returning from forward-deployed missions. Although technologies like wearable computing devices have the potential to help address this problem, research on the role of technologies like Fitbit in mental health services is in its infancy. Objective If Fitbit proves to be an appropriate clinical tool in a military setting, it could provide potential cost savings, improve clinician access to patient data, and create real-time treatment options for the greater active duty service member population. The purpose of this study was to determine if the Fitbit device can be used to identify indicators of mental health disorders by measuring the relationship between Fitbit sleep data, self-reported mood, and environmental contextual factors that may disrupt sleep. Methods This observational cohort study was conducted at the Madigan Army Medical Center. The study included 17 healthy adults who wore a Fitbit Flex for 2 weeks and completed a daily self-reported mood and sleep log. Daily Fitbit data were obtained for each participant. Contextual factors were collected with interim and postintervention surveys. This study had 3 specific aims: (1) Determine the correlation between daily Fitbit sleep data and daily self-reported sleep, (2) Determine the correlation between number of waking events and self-reported mood, and (3) Explore the qualitative relationships between Fitbit waking events and self-reported contextual factors for sleep. Results There was no significant difference in the scores for the pre-intevention Pittsburg Sleep Quality Index (PSQI; mean 5.88 points, SD 3.71 points) and postintervention PSQI (mean 5.33 points, SD 2.83 points). The Wilcoxon signed-ranks test showed that the difference between the pre-intervention PSQI and postintervention PSQI survey data was not statistically significant (Z=0.751, P=.05). The Spearman correlation between Fitbit sleep time and self-reported sleep time was moderate (r=0.643, P=.005). The Spearman correlation between number of waking events and self-reported mood was weak (r=0.354, P=.163). Top contextual factors disrupting sleep were “pain,” “noises,” and “worries.” A subanalysis of participants reporting “worries” found evidence of potential stress resilience and outliers in waking events. Conclusions Findings contribute valuable evidence on the strength of the Fitbit Flex device as a proxy that is consistent with self-reported sleep data. Mood data alone do not predict number of waking events. Mood and Fitbit data combined with further screening tools may be able to identify markers of underlying mental health disease.
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Affiliation(s)
- Darshan Thota
- Madigan Army Medical Center, Joint Base Lewis-McChord, WA, United States
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34
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Guillodo E, Lemey C, Simonnet M, Walter M, Baca-García E, Masetti V, Moga S, Larsen M, Ropars J, Berrouiguet S. Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review. JMIR Mhealth Uhealth 2020; 8:e10733. [PMID: 32234707 PMCID: PMC7160700 DOI: 10.2196/10733] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/04/2019] [Accepted: 10/22/2019] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. OBJECTIVE The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. METHODS We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. RESULTS The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. CONCLUSIONS This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring.
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Affiliation(s)
| | - Christophe Lemey
- IMT Atlantique, Lab-STICC, F-29238 Brest, Brest, France.,EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
| | | | - Michel Walter
- EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
| | | | | | | | - Mark Larsen
- Black Dog Institute, University of New South Wales, Sydney, Australia
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- Please see Acknowledgements for list of collaborators,
| | - Juliette Ropars
- Laboratoire de Traitement de l'Information Médicale, INSERM, UMR 1101, Brest, France.,Department of Child Neurology, University Hospital of Brest, Brest, France
| | - Sofian Berrouiguet
- IMT Atlantique, Lab-STICC, F-29238 Brest, Brest, France.,EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
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