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

Emmi Antikainen; Haneen Njoum; Jennifer Kudelka; Diogo Branco; Rana Zia Ur Rehman; Victoria Macrae; Kristen Davies; Hanna Hildesheim; Kirsten Emmert; Ralf Reilmann; C Janneke van der Woude; Walter Maetzler; Wan-Fai Ng; Patricio O'Donnell; Geert Van Gassen; Frédéric Baribaud; Ioannis Pandis; Nikolay V Manyakov; Mark van Gils; Teemu Ahmaniemi; Meenakshi Chatterjee

doi:10.3389/fphys.2022.968185

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

Front Physiol. 2022 Nov 14:13:968185. doi: 10.3389/fphys.2022.968185. eCollection 2022.

Authors

Emmi Antikainen¹, Haneen Njoum², Jennifer Kudelka³, Diogo Branco⁴, Rana Zia Ur Rehman⁵, Victoria Macrae⁶, Kristen Davies⁵, Hanna Hildesheim³, Kirsten Emmert³, Ralf Reilmann^{7

8

9}, C Janneke van der Woude¹⁰, Walter Maetzler³, Wan-Fai Ng^{5

6}, Patricio O'Donnell¹¹, Geert Van Gassen¹², Frédéric Baribaud¹³, Ioannis Pandis¹⁴, Nikolay V Manyakov¹⁵, Mark van Gils¹⁶, Teemu Ahmaniemi¹, Meenakshi Chatterjee¹⁷

Affiliations

¹ VTT Technical Research Centre of Finland Ltd., Tampere, Finland.
² Sanofi R&D, Frankfurt, Germany.
³ Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.
⁴ LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
⁵ Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.
⁶ NIHR Newcastle Biomedical Research Centre and NIHR Newcastle Clinical Research Facility, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom.
⁷ George-Huntington-Institute, University of Münster, Münster, Germany.
⁸ Department of Clinical Radiology, University of Münster, Münster, Germany.
⁹ Department of Neurodegenerative Diseases and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
¹⁰ Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, Netherlands.
¹¹ Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, United States.
¹² Takeda Belgium, Zavente, Belgium.
¹³ Bristol Myers Squibb, New York, NY, United States.
¹⁴ Janssen Research & Development, London, United Kingdom.
¹⁵ Janssen Research & Development, Beerse, Belgium.
¹⁶ Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
¹⁷ Janssen Research & Development, Cambridge, MA, United States.

Abstract

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.

Keywords: biomedical signal analysis; chronic disease; continuous monitoring; fatigue; immune-mediated inflammatory disease; neurodegenerative diseases; sleep disturbance; wearabe sensors.

Copyright © 2022 Antikainen, Njoum, Kudelka, Branco, Rehman, Macrae, Davies, Hildesheim, Emmert, Reilmann, Janneke van der Woude, Maetzler, Ng, O’Donnell, Van Gassen, Baribaud, Pandis, Manyakov, van Gils, Ahmaniemi and Chatterjee.