Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.

[1]  W. Maetzler,et al.  Fatigue and Sleep Assessment using Digital Sleep Trackers: Insights from a Multi-Device Pilot Study , 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[2]  Kar Fye Alvin Lee,et al.  Lowering the Sampling Rate: Heart Rate Response during Cognitive Fatigue , 2022, Biosensors.

[3]  J. Hayano,et al.  Assessment of autonomic function by long-term heart rate variability: beyond the classical framework of LF and HF measurements , 2021, Journal of Physiological Anthropology.

[4]  I. Irurzun,et al.  The effect of age on the heart rate variability of healthy subjects , 2021, PloS one.

[5]  W. Ng,et al.  Fatigue in inflammatory rheumatic diseases: current knowledge and areas for future research , 2021, Nature Reviews Rheumatology.

[6]  L. Tarassenko,et al.  A Chest Patch for Continuous Vital Sign Monitoring: Clinical Validation Study During Movement and Controlled Hypoxia. , 2021, Journal of medical Internet research.

[7]  A. Petrėnas,et al.  Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device , 2021, Frontiers in Physiology.

[8]  Marco Altini,et al.  The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring , 2021, Sensors.

[9]  H. Girouard,et al.  Inflammation: a mediator between hypertension and neurodegenerative diseases. , 2021, American journal of hypertension.

[10]  Michael Bürgisser,et al.  Body Temperature Is Associated With Cognitive Performance in Older Adults With and Without Mild Cognitive Impairment: A Cross-sectional Analysis , 2021, Frontiers in Aging Neuroscience.

[11]  B. Arnrich,et al.  Longitudinal Autonomic Nervous System Measures Correlate With Stress and Ulcerative Colitis Disease Activity and Predict Flare. , 2020, Inflammatory bowel diseases.

[12]  Hongyu Luo,et al.  Assessment of Fatigue Using Wearable Sensors: A Pilot Study , 2020, Digital Biomarkers.

[13]  Emiliano Schena,et al.  The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise , 2020, Sensors.

[14]  Gabriel Nallathambi,et al.  An innovative hybrid approach for detection of pacemaker pulses at low sampling frequency , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[15]  Gabriel Nallathambi,et al.  Feasibility of Continuous Monitoring of Core Body Temperature Using Chest-worn Patch Sensor , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[16]  Ignacio Perez-Pozuelo,et al.  Making Sense of Sleep , 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[17]  Shangen Zhang,et al.  The effect of fatigue on brain connectivity networks , 2020 .

[18]  Matthew S. Goodwin,et al.  Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability , 2020, Molecular Autism.

[19]  Matthew S. Goodwin,et al.  Relationship Between Sleep and Behavior in Autism Spectrum Disorder: Exploring the Impact of Sleep Variability , 2020, Frontiers in Neuroscience.

[20]  R. M. Escorihuela,et al.  Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis , 2020, Journal of Translational Medicine.

[21]  F. Casellas,et al.  Prevalence and Factors Associated with Fatigue in Patients with Inflammatory Bowel Disease: A Multicenter Study. , 2019, Journal of Crohn's & colitis.

[22]  N. Rose,et al.  Fatigue, Sleep, and Autoimmune and Related Disorders , 2019, Front. Immunol..

[23]  B. Högl,et al.  Sleep in Parkinson’s disease , 2019, Neuropsychopharmacology.

[24]  J. Hayano,et al.  Pitfalls of assessment of autonomic function by heart rate variability , 2019, Journal of Physiological Anthropology.

[25]  H. Roschel,et al.  Chronotropic Incompetence and Reduced Heart Rate Recovery in Rheumatoid Arthritis , 2018, Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases.

[26]  A. Tessitore,et al.  Fatigue in Parkinson's disease: A systematic review and meta‐analysis , 2018, Movement disorders : official journal of the Movement Disorder Society.

[27]  Georgy L. Gimel'farb,et al.  Unobtrusive stress detection on the basis of smartphone usage data , 2018, Personal and Ubiquitous Computing.

[28]  Gabriel Nallathambi,et al.  Fully Disposable Wireless Patch Sensor for Continuous Remote Patient Monitoring , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[29]  J. Boissoneault,et al.  Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome , 2018, Brain Imaging and Behavior.

[30]  L. Quinn,et al.  Alterations in the metabolic and cardiorespiratory response to exercise in Huntington's Disease. , 2018, Parkinsonism & related disorders.

[31]  P. Pruszczyk,et al.  Attenuated post-exercise heart rate recovery in patients with systemic lupus erythematosus: the role of disease severity and beta-blocker treatment , 2018, Lupus.

[32]  H. Burgess,et al.  Sleep and Circadian Hygiene and Inflammatory Bowel Disease. , 2017, Gastroenterology clinics of North America.

[33]  C. Blanchard,et al.  Physical activity and sedentary behavior in patients with systemic lupus erythematosus and rheumatoid arthritis , 2017, Open access rheumatology : research and reviews.

[34]  Karim Chamari,et al.  Monitoring training load and fatigue in soccer players with physiological markers , 2017, Physiology & Behavior.

[35]  F. Shaffer,et al.  An Overview of Heart Rate Variability Metrics and Norms , 2017, Front. Public Health.

[36]  Samuele M. Marcora,et al.  Differential control of respiratory frequency and tidal volume during high‐intensity interval training , 2017, Experimental physiology.

[37]  S. Amir,et al.  Neurodegeneration and the Circadian Clock , 2017, Front. Aging Neurosci..

[38]  S. Qiu,et al.  Heart Rate Recovery and Risk of Cardiovascular Events and All‐Cause Mortality: A Meta‐Analysis of Prospective Cohort Studies , 2017, Journal of the American Heart Association.

[39]  J. Bakdash,et al.  Repeated Measures Correlation , 2017, Front. Psychol..

[40]  Vivek Jain,et al.  Respiratory rate variability in sleeping adults without obstructive sleep apnea , 2016, Physiological reports.

[41]  B. Galna,et al.  Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length , 2016, Journal of NeuroEngineering and Rehabilitation.

[42]  O. Poyrazoglu,et al.  Heart Rate Recovery Is Impaired in Patients with Inflammatory Bowel Diseases , 2016, Medical Principles and Practice.

[43]  Tom Kuusela,et al.  Methodological Aspects of Heart Rate Variability Analysis , 2016 .

[44]  K. Kräuchi,et al.  Daytime variation in ambient temperature affects skin temperatures and blood pressure: Ambulatory winter/summer comparison in healthy young women , 2015, Physiology & Behavior.

[45]  J. Thayer,et al.  A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability , 2015, Physiological measurement.

[46]  A. Peters,et al.  Short-Term Heart Rate Variability—Influence of Gender and Age in Healthy Subjects , 2015, PloS one.

[47]  Julien Penders,et al.  Personalizing energy expenditure estimation using physiological signals normalization during activities of daily living , 2014, Physiological measurement.

[48]  Meng Xiao,et al.  Sleep stages classification based on heart rate variability and random forest , 2013, Biomed. Signal Process. Control..

[49]  R. Moots,et al.  Health-related utility values of patients with primary Sjögren's syndrome and its predictors , 2013, Annals of the rheumatic diseases.

[50]  Roger M Enoka,et al.  Fatigue and fatigability in neurologic illnesses , 2013, Neurology.

[51]  Lewis Adams,et al.  The 6-minute walk test in outpatient cardiac rehabilitation: validity, reliability and responsiveness--a systematic review. , 2012, Physiotherapy.

[52]  Urban Wiklund,et al.  Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data , 2012, Biomedical engineering online.

[53]  Mirja A. Peltola Role of Editing of R–R Intervals in the Analysis of Heart Rate Variability , 2011, Front. Physio..

[54]  Hao Liu,et al.  Towards mental stress detection using wearable physiological sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[55]  Sarah Hewlett,et al.  Fatigue in rheumatoid arthritis: time for a conceptual model. , 2011, Rheumatology.

[56]  A. Oguzhan,et al.  Deterioration of Heart Rate Recovery Index in Patients with Systemic Lupus Erythematosus , 2010, The Journal of Rheumatology.

[57]  R. Roos,et al.  Sleep and circadian rhythm alterations correlate with depression and cognitive impairment in Huntington's disease. , 2010, Parkinsonism & related disorders.

[58]  T D Noakes,et al.  Heart rate recovery as a guide to monitor fatigue and predict changes in performance parameters , 2009, Scandinavian journal of medicine & science in sports.

[59]  O. Steichen,et al.  Respiratory rate: the neglected vital sign , 2008, The Medical journal of Australia.

[60]  K. Hillman,et al.  Respiratory rate: the neglected vital sign , 2008, The Medical journal of Australia.

[61]  K. Sykes,et al.  Validity of the 6-minute walk test for assessing heart rate recovery after an exercise-based cardiac rehabilitation programme , 2006 .

[62]  J. Cummings,et al.  The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment , 2005, Journal of the American Geriatrics Society.

[63]  S. Shiffman,et al.  Patient non-compliance with paper diaries , 2002, BMJ : British Medical Journal.

[64]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[65]  F. Abboud,et al.  Sympathetic-nerve activity during sleep in normal subjects. , 1993, The New England journal of medicine.

[66]  M. Mirmiran,et al.  Alterations in the circadian rest-activity rhythm in aging and Alzheimer's disease , 1990, Biological Psychiatry.

[67]  C. Singer,et al.  Hemodynamic responses to an exercise stress test in Parkinson's disease patients without orthostatic hypotension. , 2019, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[68]  Hirofumi Tanaka,et al.  Age-predicted maximal heart rate revisited. , 2001, Journal of the American College of Cardiology.