Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study 2022 Antikainen et al

Andy

Andy

Retired committee member
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.

Open access, https://www.frontiersin.org/articles/10.3389/fphys.2022.968185/full
 
"Participants were excluded if they had certain comorbidities like major sleep disorders, chronic fatigue syndrome, respiratory, cardiovascular or metabolic disorders or physical traumas with hospitalization in the past 3 months, diagnosis of cancer in the past 3 years, major psychiatric disorders, suicidal attempt in the past 5 years or suicidal ideation in the past 6 months, substance or ethanol abuse or severe visual impairment."
 
Regarding that Newcastle Hospital article
Interesting that there is such as recognition of fatigue as being such a troublesome symptom.

Julia Newton is part of the Newcastle-upon-Tyne NHS, she ran (runs?) the fatigue Clinic there and seems to be seen as an expert on fatigue in that region. On paper, she would be the ideal person to collaborate with Prof Ng on investigating the use of wearables in ME/CFS. I'm not sure, though, about what her views about ME/CFS really are (she has at times seemed to be in favour of GET), and the fatigue clinic seems to leave people with ME/CFS to the Newcastle CFS service. That CFS service made some of the worst comments we saw in the NICE consultation process. Maybe people in that region know what is really going on there - it's rather confusing from here.

Anyway, perhaps someone could approach Prof Ng to see if he could do a project on monitoring using wearables in ME/CFS? A Professor in Rheumatology - perhaps you know him @Jonathan Edwards?

Excerpt:

"Newcastle Hospitals is leading the IDEA-FAST study, which aims to find effective ways to monitor symptoms in patients with chronic disease.

The study involved patients with neurodegenerative and inflammatory diseases, where fatigue can have a major impact on quality of life.

One of the common side effects of chronic diseases such as Parkinson’s disease, inflammatory bowel disease and rheumatoid arthritis are problems with fatigue and disturbed sleep. Among patients with these diseases, fatigue is often rated as one of the most disabling symptoms, affecting their daily activities and their quality of life.

Despite this, monitoring these symptoms often relies on simply asking patients to fill in a questionnaire about their experiences. This method can be affected by patients misremembering things and not having enough detail about the intensity of their fatigue throughout the day.

Small, wearable device
One way to provide more accurate and reliable results is for chronic disease patients to wear small devices to monitor these physiological signals throughout the day. In a paper published in Frontiers in Physiology, an international group of researchers used wearable devices to measure and record fatigue and sleep patterns in such patients to see how effective the technology could be.

The study is led by Prof. Fai Ng, honorary consultant rheumatologist at Newcastle Hospitals and professor of rheumatology in the Translational and Clinical Research Institute at Newcastle University.

Quality of life
Professor Ng said:

“There’s an increasing number of articles all pointing towards fatigue being one of the biggest, if not the biggest factor, leading to a loss of quality of life in these patients,” "
 
Andy said:
"Participants were excluded if they had certain comorbidities like major sleep disorders, chronic fatigue syndrome,
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That makes sense to me, as assessing fatigue caused by the specific conditions included would be complicated by someone also having CFS.

They seem to be basing their assumptions about how to measure fatigue with a heart rate monitor on other studies that found ME/CFS patients had lower heart rate variability, so they take that as a measure of fatigue. I'm not sure how accurate that is.
 
Hutan said:
Anyway, perhaps someone could approach Prof Ng to see if he could do a project on monitoring using wearables in ME/CFS? A Professor in Rheumatology - perhaps you know him @Jonathan Edwards?
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I am afraid I was not very inspired by the research approach of that group.
The emphasis seems to be just on measuring the amount of fatigue. I think we need to measure the character of the problem in different conditions - the time profiles etc.
 
Jonathan Edwards said:
I am afraid I was not very inspired by the research approach of that group.
The emphasis seems to be just on measuring the amount of fatigue. I think we need to measure the character of the problem in different conditions - the time profiles etc.
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What is that opinion based on? This paper, the IDEA-FAST project (which seems to have taken a broader approach to assessing the utility of wearables), or direct knowledge of what the group is doing? I assumed that this group probably played a role in NICE approving five devices for use in monitoring Parkinsons patients over time.

The presented data was obtained as a part of the IDEA-FAST project (The IDEA-FAST project consortium, 2020; Chen et al., 2022). Nine different candidate technologies measuring different modalities (activity trackers, ECG-sensors, sleep trackers) were explored in a feasibility study aiming to assess fatigue and sleep disorders. Additionally, the participants’ social activity, cognitive skills, and PROs were captured with smartphone applications. This paper focuses on the continuously measured physiological signals collected from the ECG-based VitalPatch sensor and the concurrently collected PROs. The digital measures from VitalPatch included heart rate (HR), R-to-R interval, respiratory rate (RR), skin temperature (skin T), number of steps, and posture. The first three are mainly derived from the ECG measurement and are the main focus of this study.
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The VitalPatch biosensor incorporates a single-lead ECG, a tri-axial accelerometer, and a thermistor. It records ECG at 125 Hz sampling frequency, with derived heart rate, R-to-R interval, and respiratory rate (partly derived from the accelerometer) sampled at 0.25 Hz. The accelerometer is used for step counting and posture detection at 1 Hz. The thermistor collects skin temperature at 0.25 Hz. The recorded data is encrypted and transferred with a latency in the order of seconds via a wireless connection to a cloud-based patient monitoring platform.
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I'm not sure about the 'normalisation' work done, adjusting data. But I think even this paper (one of a series from this IDEA-FAST project) is doing more than just measuring the amount of fatigue. They seem to be trying to find strong correlations between patient reported outcomes reported in nearly real time, and data collected by a wearable device. And a big part of this particular study seemed to be just showing the feasibility of wearables for data collection.

Fatigue and sleep disturbances reduce the quality of life and the activities of daily living. Digital measures collected with wearable devices could improve the objectivity and sensitivity of fatigue and sleep assessment, ultimately providing additional support for disease assessment and evaluation of new therapies. Wearable technologies could facilitate continuous monitoring outside the clinical setting without requiring active interaction from the patient. Moreover, digital measures in free-living settings may enable assessment that is more meaningful to the patient’s daily living. However, their potential for fatigue assessment have not been extensively studied, especially in the clinical context.

The results presented in this study suggest the feasibility of collecting reasonable quality physiological measures with a wearable biosensor on patients with chronic NDD and IMID diseases, as well as healthy controls. The median coverage was 77%–78% for all digital measures, with minimal variability across different cohorts. The coverage result implies high compliance to using the wearable biosensor. In contrast, only 91 among the 136 participants reported PROs at least three times during the study. Furthermore, in all the collected VitalPatch data, less than 0.5% of HR, RR, and R-to-R interval data and only 2.3% of skin temperature data needed to be cleaned out, indicating a sufficient data quality given the criteria used in this study.
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Inspecting the individual feature aggregates in Figures 7, 8 further imply the relevance of the digital measures. HR is relevantly associated with sleepiness, both in the healthy group and IMID patients. Interestingly, this association is not seen in the NDD group, suggesting that neurodegeneration breaks this association, e.g., by affecting the central autonomic nuclei and/or pathways. Significant associations between skin T and the dependent variables in the healthy and the IMID patients, but not in NDD patients in Figure 7, suggest a similar mechanism. These observations may be related to the circadian rhythm abnormalities in NDD patients reported in previous studies
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A research partner would ideally have access to wearables and to staff with experience in getting patients to use them accurately and in retrieving and perhaps cleaning the data. And access to a pipeline of funding. They don't necessarily have to be the ones coming up with what exactly is studied.
 
Hutan said:
What is that opinion based on?
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Interaction in relation to their rituximab for fatigue study, which I think was in primary biliary cirrhosis. There seemed to be an assumption that all fatigue was the same. Newton was explicitly saying that at the time.

I am unclear what Ng has to do with Chen and other people - sorry I am not clear of the case you are making in relation to whom.
 
The approach to fatigue and ME/CFS specifically in Newcastle seems to be variable, it probably depends enormously on which team you are talking about. There are definitely some problematic attitudes there, and I personally doubt that Julia Newton has done much to counter those attitudes. There is some evidence that she is contributing to them.
Rituximab for the treatment of fatigue in primary biliary cholangitis (formerly primary biliary cirrhosis): a randomised controlled trial
Regarding the rituximab for fatigue study that you mention, I see that Julia Newton is a co-author. Given that she has not clearly opposed BPS views of ME/CFS, I do think that it is best to keep her away from anything to do with ME/CFS.

But, this study is by different researchers. While I could perhaps pick holes in the clarity of the conclusions in this paper (I haven't read it closely enough to do that), at least they are applying technology to the question of fatigue description, paving the way for objective outcomes. As far as I can see, this particular paper is one result from an international team investigating wearables in chronic conditions. So, perhaps these people could be useful collaborators?
 
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