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

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.

SUBMITTER: Antikainen E

PROVIDER: S-EPMC9702812 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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

Antikainen Emmi E Njoum Haneen H Kudelka Jennifer J Branco Diogo D Rehman Rana Zia Ur RZU Macrae Victoria V Davies Kristen K Hildesheim Hanna H Emmert Kirsten K Reilmann Ralf R Janneke van der Woude C C Maetzler Walter W Ng Wan-Fai WF O'Donnell Patricio P Van Gassen Geert G Baribaud Frédéric F Pandis Ioannis I Manyakov Nikolay V NV van Gils Mark M Ahmaniemi Teemu T Chatterjee Meenakshi M

Frontiers in physiology 20221114

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 <i>via</i> 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 ...[more]

PMID: 36452041

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Project description:The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms "fatigue," "drowsiness," "vigilance," or "alertness" in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (Tsk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

Project description:BackgroundImprovements in movement quality (ie, how well an individual moves) facilitate increases in movement quantity, subsequently improving general health and quality of life. Wearable technology offers a convenient, affordable means of measuring and assessing movement quality for the general population, while technology more broadly can provide constructive feedback through various modalities. Considering the perspectives of professionals involved in the development and implementation of technology helps translate user needs into effective strategies for the optimal application of consumer technologies to enhance movement quality.ObjectiveThis study aimed to obtain the opinions of wearable technology experts regarding the use of wearable devices to measure movement quality and provide feedback. A secondary objective was to determine potential strategies for integrating preferred assessment and feedback characteristics into a technology-based movement quality intervention for the general, recreationally active population.MethodsSemistructured interviews were conducted with 12 participants (age: mean 42, SD 9 years; 5 males) between August and September 2022 using a predetermined interview schedule. Participants were categorized based on their professional roles: commercial (n=4) and research and development (R&D; n=8). All participants had experience in the development or application of wearable technology for sports, exercise, and wellness. The verbatim interview transcripts were analyzed using reflexive thematic analysis in QSR NVivo (release 1.7), resulting in the identification of overarching themes and subthemes.ResultsThree main themes were generated as follows: (1) "Grab and Go," (2) "Adjust and Adapt," and (3) "Visualize and Feedback." Participants emphasized the importance of convenience to enhance user engagement when using wearables to collect movement data. However, it was suggested that users would tolerate minor inconveniences if the benefits were perceived as valuable. Simple, easily interpretable feedback was recommended to accommodate diverse audiences and aid understanding of their movement quality, while avoiding excessive detail was advised to prevent overload, which could deter users. Adaptability was endorsed to accommodate progressions in user movement quality, and customizable systems were advocated to offer variety, thereby increasing user interest and engagement. The findings indicate that visual feedback representative of the user (ie, an avatar) should be used, supplemented with concise text or audible instructions to form a comprehensive, multimodal feedback system.ConclusionsThe study provides insights from wearable technology experts on the use of consumer technologies for enhancing movement quality. The findings recommend the prioritization of user convenience and simplistic, multimodal feedback centered around visualizations, and an adaptable system suitable for a diverse audience. Emphasizing individualized feedback and user-centric design, this study provides valuable findings around the use of wearables and other consumer technologies to enhance movement quality among the general population. These findings, in conjunction with those of future research into user perspectives, should be applied in practical settings to evaluate their effectiveness in enhancing movement quality.

Project description:BACKGROUND: Chronic fatigue syndrome (CFS) is a disabling condition that affects approximately 800,000 adult Americans. The pathophysiology remains unknown and there are no diagnostic markers or characteristic physical signs or laboratory abnormalities. Most CFS patients complain of unrefreshing sleep and many of the postulated etiologies of CFS affect sleep. Conversely, many sleep disorders present similarly to CFS. Few studies characterizing sleep in unselected CFS subjects have been published and none have been performed in cases identified from population-based studies. METHODS: The study included 339 subjects (mean age 45.8 years, 77% female, 94.1% white) identified through telephone screen in a previously described population-based study of CFS in Wichita, Kansas. They completed questionnaires to assess fatigue and wellness and 2 self-administered sleep questionnaires. Scores for five of the six sleep factors (insomnia/hypersomnia, non-restorative sleep, excessive daytime somnolence, sleep apnea, and restlessness) in the Centre for Sleep and Chronobiology's Sleep Assessment Questionnaire (SAQ) were dichotomized based on threshold. The Epworth Sleepiness Scale score was used as a continuous variable. RESULTS: 81.4% of subjects had an abnormality in at least one SAQ sleep factor. Subjects with sleep factor abnormalities had significantly lower wellness scores but statistically unchanged fatigue severity scores compared to those without SAQ abnormality. CFS subjects had significantly increased risk of abnormal scores in the non-restorative (adjusted odds ratio [OR] = 28.1; 95% confidence interval [CI]= 7.4-107.0) and restlessness (OR = 16.0; 95% CI = 4.2-61.6) SAQ factors compared to non-fatigued, but not for factors of sleep apnea or excessive daytime somnolence. This is consistent with studies finding that, while fatigued, CFS subjects are not sleepy. A strong correlation (0.78) of Epworth score was found only for the excessive daytime somnolence factor. CONCLUSIONS: SAQ factors describe sleep abnormalities associated with CFS and provide more information than the Epworth score. Validation of these promising results will require formal polysomnographic sleep studies.

Dataset Information

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

Publications

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

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