With the high occurrence rate of stroke survivor fatigue this research should also be done on stroke survivors. But with NO leadership and NO strategy nothing will be done because stroke medical 'professionals' don't have two functioning neurons to rub together!
At
least half of all stroke survivors experience fatigue Known since March 2017
Or is it 70%? Known since March 2015.
Or is it 40%? Known since September 2017.
Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 94 (2024)
Abstract
Background
Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study.
Methods
Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren’s syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning.
Results
Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis.
Conclusions
Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.
Background
Many people with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID) experience abnormal fatigue. For instance, abnormal fatigue has been reported in over 85% of those with systemic lupus erythematosus (SLE) [1, 2], 33% to 58% of those with Parkinson’s disease (PD) [3], 67% of people with primary Sjogren’s syndrome (PSS) [4], and over 41% of patients with rheumatoid arthritis (RA) showed clinically important levels of fatigue [5]. These symptoms can be debilitating for those who experience them and are key contributors to poor quality of life. Accordingly, a key goal of the IDEA-FAST consortium [6] is to explore and identify digital endpoints that provide reliable, objective, and sensitive evaluations of abnormal fatigue, which in turn will facilitate therapeutic development to alleviate these symptoms.
A common method for assessing fatigue is patient reported outcomes (PRO)s, where the patients answer questionnaires and diaries designed to record how the patient is feeling. This approach gives a snapshot view of fatigue, either during a one-off visit at the clinic or with regular at-home questionnaires. PROs, however, are subjective, susceptible to recall bias [7], and the measurement of granular changes over time requires high patient burden through repeated assessments. Furthermore, studies have shown that individuals who are sleep-deprived or are out of their circadian phase are prone to underestimating their fatigue-related impairments [8,9,10,11]. These issues are therefore worsened in cases where neurological functionality or sleep quality is impacted by an illness. Wearable devices may circumvent the pitfalls of PRO-based assessments since they could objectively and continuously monitor physiological changes related to physical and mental fatigue. Inertial measurement units (IMUs)—e.g., wearable devices comprising triaxial accelerometers and gyroscopes—are becoming an increasingly popular option for continuous remote monitoring, due to their affordability and ease of use in users’ natural environment. These recordings can be wirelessly transmitted to a device, such as a smartphone, where the signals can be processed, assessed, and reported to the user or clinicians.
Mobility (e.g., walking, gait) is considered as the 6th vital sign and represents an essential component of health and quality of life, being key to physical, mental, and social well-being [12]. Loss in mobility has been associated with morbidity, falls, dementia, cognitive decline, hospitalisations, mortality, and symptoms of chronic disorders [13,14,15,16]. As such, the current study will explore the relationship between gait characteristics and abnormal fatigue in people with NDD and IMID.
The current knowledge in the relationships between walking and abnormal fatigue in NDDs and IMIDs is very limited. Many studies exploring fatigue focus on muscle or exercise-induced fatigue in healthy participants with parameters such as accelerometer spectra [17], measures of acceleration, jerk, and posture [18,19,20,21], and temporal measures of gait (“micro” gait, e.g., gait speed, step time) [22, 23]. Most notably, the majority of existing literature has been conducted within in laboratory-based environment, where the participants are monitored whilst doing an instructed task. This includes the six-minute walking distance test (6-MWDT) to assess the impacts of fatigue on gait (exercise) capacity [24,25,26,27,28]; gait speed i.e., the ten-meter walking test (10-MWT) [24, 29,30,31,32]; an electronic walkway [33, 34]; and a predefined path for unaided walking [35]. We found three studies that explored fatigue in NDD or IMID specifically with free-living gait assessment. The first analysed physical activity over a seven-day period from a hip-worn tri-axial accelerometer in 123 participants with SLE [36]. Light and moderate/vigorous activity and moderate/vigorous activity periods > 10 min were identified from the accelerometer’s vector magnitude and compared to the participants’ Fatigue Severity Score (FSS). The second study investigated the impact of physical activity on non-motor symptoms with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) in 45 PD participants with a hip-worn accelerometer for at least three days to record daily step count and assess sedentary behaviour, light physical activity, and moderate-to-vigorous activity [37]. In the third, participants with multiple sclerosis (MS)—134 fatigued and 76 non-fatigued—wore an accelerometer above the dominant hip for seven days to record light and moderate-to-vigorous activity counts, whilst reporting on their fatigue severity with FSS [38].
In many cases, the exploration of gait measures is limited to characteristics such as gait speed [31, 39], and ambulatory activity (step count) [30]. Other studies were more focussed on gait, but only explored different activity levels based on step count [37], vector magnitude [36], and activity counts [38]. Another explored 6-MWT, dynamic activity, number of postural transitions, and walking bouts longer than ten seconds [28]. Three studies included a more comprehensive analysis of macro (reflecting activity) and micro (reflecting discrete gait outcomes) gait characteristics within a laboratory: one study assessed the associations of Multidimensional Fatigue Inventory (MFI) with measures of pace, rhythm, variability, asymmetry, and postural control [33]; one analysed the cycle time, stride length, swing time, and double support time and their variability (coefficient of variation (CoV)) [34]; and one compared gait duration (10-MWT), gait speed (m/s), cadence (steps/min), and stride length (m) to the Parkinson FSS [32]. Furthermore, the participants explored by the studies in the literature typically include only healthy subjects [33] or only one disease cohort such as PD [32, 37, 39], MS [35, 38], IBD [28], SLE [36], symptomatic knee osteoarthritis [31], fibromyalgia [34], and stroke survivors [30].
Therefore, in-depth analyses of the associations between real-world gait and abnormal fatigue in NND and IMID are currently lacking. The current study will analyse free-living data from several differing disease cohorts and will conduct an extensive exploration of various measures of gait and walking activity as a preliminary assessment of data collected by IDEA-FAST [6].
This study aims to:
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(i)
comprehensively explore the feasibility of using macro and micro gait characteristics from an IMU attached to the lower back to objectively identify PRO scores of physical and mental fatigue in NDD and IMID participants;
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(ii)
assess the associations of the macro and micro characteristics with selected PROs using a generalised linear mixed effect model and low vs. high fatigue binary classification performances of popular machine learning models;
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(iii)
explore the usefulness of the gait-model’s physiological feature groups using the associations from the linear mixed effect model.
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