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.

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.

Fatigue-Related Changes of Daily Function: Most Promising Measures for the Digital Age.

Background: Fatigue is a prominent symptom in many diseases and is strongly associated with impaired daily function. The measurement of daily function is currently almost always done with questionnaires, which are subjective and imprecise. With the recent advances of digital wearable technologies, novel approaches to evaluate daily function quantitatively and objectively in real-life conditions are increasingly possible. This also creates new possibilities to measure fatigue-related changes of daily function using such technologies. Summary: This review examines which digitally assessable parameters in immune-mediated inflammatory and neurodegenerative diseases may have the greatest potential to reflect fatigue-related changes of daily function. Key Messages: Results of a standardized analysis of the literature reporting about perception-, capacity-, and performance-evaluating assessment tools indicate that changes of the following parameters: physical activity, independence of daily living, social participation, working life, mental status, cognitive and aerobic capacity, and supervised and unsupervised mobility performance have the highest potential to reflect fatigue-related changes of daily function. These parameters thus hold the greatest potential for quantitatively measuring fatigue in representative diseases in real-life conditions, e.g., with digital wearable technologies. Furthermore, to the best of our knowledge, this is a new approach to analysing evidence for the design of performance-based digital assessment protocols in human research, which may stimulate further systematic research in this area.

Identification of Fatigue and Sleepiness in Immune and Neurodegenerative Disorders from Measures of Real-World Gait Variability.

Current assessments of fatigue and sleepiness rely on patient reported outcomes (PROs), which are subjective and prone to recall bias. The current study investigated the use of gait variability in the "real world" to identify patient fatigue and daytime sleepiness. Inertial measurement units were worn on the lower backs of 159 participants (117 with six different immune and neurodegenerative disorders and 42 healthy controls) for up to 20 days, whom completed regular PROs. To address walking bouts that were short and sparse, four feature groups were considered: sequence-independent variability (SIV), sequence-dependant variability (SDV), padded SDV (PSDV), and typical gait variability (TGV) measures. These gait variability measures were extracted from step, stride, stance, and swing time, step length, and step velocity. These different approaches were compared using correlations and four machine learning classifiers to separate low/high fatigue and sleepiness.Most balanced accuracies were above 50%, the highest was 57.04% from TGV measures. The strongest correlation was 0.262 from an SDV feature against sleepiness. Overall, TGV measures had lower correlations and classification accuracies.Identifying fatigue or sleepiness from gait variability is extremely complex and requires more investigation with a larger data set, but these measures have shown performances that could contribute to a larger feature set.Clinical relevance- Gait variability has been repeatedly used to assess fatigue in the lab. The current study, however, explores gait variability for fatigue and daytime sleepiness in real-world scenarios with multiple gait-impacted disorders.

Regulatory Qualification of a Cross-Disease Digital Measure: Benefits and Challenges from the Perspective of IMI Consortium IDEA-FAST.

Background: Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality, and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA. Summary: The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations, and the need for validation in both heterogeneous and homogeneous populations. Key Messages: EMA highlighted the challenges teams may encounter when developing a cross-disease measure, though benefits potentially include reduced resources for the technology developer and health authority, faster access to innovation across different therapeutic fields, and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.

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.

Fatigue and Sleep Assessment using Digital Sleep Trackers: Insights from a Multi-Device Pilot Study.

For the patient community with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID), fatigue and sleep disturbances stand out as two of the most common and disabling symptoms, which mightily impair patient's quality of life. Traditional questionnaire-oriented approaches to reflect such symptoms suffer from recall bias and poor sensitivity to change. By virtue of multiple sensing modalities at home, IDEA-FAST project aims to identify novel digital endpoints of fatigue and sleep disturbances, that are objective, reliable and sensitive to change. This article presents and discusses results from a pilot study of IDEA-FAST to evaluate the feasibility of capturing sleep and fatigue measures from three sleep trackers. Data collected from 143 participants (age range: 21-82) across 6 disease groups and healthy cohort for a period of 9 months, were investigated using our proposed sensor analytical pipeline. The overall performance reveals that the median coverage rate of sleep trackers ranged from 48.3% to 76.9%. Furthermore, the digital measures obtained from each device, indicated a higher association with sleep related patient reported outcomes (PROs) than fatigue related ones, when taking all participants into account.

Estimation of Beat-to-Beat Interval and Systolic Time Intervals Using Phono- and Seismocardiograms.

Systolic time intervals Pre-Ejection Period (PEP) and Left Ventricular Ejection Time (LVET) are widely used indicators of cardiac functions. While accurate assessment of them requires costly equipment such as echocardiography devices, a satisfactory estimation can be done by analyzing signals from simple accelerometer and microphone attached to human chest. This paper reports a study where heart rate and the systolic intervals were derived from phonocardiogram (PCG) and seismocardiogram (SCG) simultaneously. Both sensors, the microphone for PCG and the accelerometer for SCG were attached on the chest wall, close to sternum (PCG) and apex of the heart (SCG). The signals were acquired from 10 participants in a 33-minute laboratory protocol with synchronized ECG measurements. Both signals went through an identical processing path: band pass filtering, envelope extraction with Hilbert transformation and peak detection from the envelope signal. In heart rate estimation, PCG and SCG reached 84% and 93% accuracy, respectively. The systolic interval accuracy estimation was based on deviation analysis as the absolute reference values for PEP and LVET were not available. In PEP estimation, the average standard deviations during the rest periods of the protocol were 4 ms for PCG and 8 ms for SCG. In LVET estimation, the deviations were nearly 10 fold compared to PEP. However, the results show that both methods can be used for accurate heart rate estimation and with careful mechanical attachment also PEP can be accurately derived from both. Due to sharper envelope signal waveform, PEP estimation was more accurate with PCG than with SCG.

A chair based ballistocardiogram time interval measurement with cardiovascular provocations.

The objective of this study was to measure ballistocardiogram (BCG) based time intervals and compare them with systolic blood pressure values. Electrocardiogram (ECG) and BCG signals of six subjects sitting in a chair were measured with a ferroelectret film sensor. Time intervals between ECG R peak and BCG I and J waves were calculated to obtain RJ, RI and IJ intervals. The time intervals were modified with two cardiovascular provocations, controlled breathing and Valsalva maneuver. The controlled breathing changed all the time intervals (RJ, RI and IJ) whereas the Valsalva maneuver mainly caused variations in the RJ and RI intervals. The calculated time intervals were compared with reference arterial blood pressure values. Correlation coefficients of r = -0.61 and r = -0.78 were found between the RJ and RI time intervals and systolic blood pressure during Valsalva maneuver, respectively.

Variations of Heart Rate, Pulse Arrival Time and Blood Pressure in a Versatile Laboratory Protocol.

Many studies dealing with blood pressure modeling are evaluated based on a single type of provocation. This paper investigates widely used provocations such as controlled breathing, mental arithmetic and Stroop tests, Valsalva maneuver, cold pressor and muscle tension and combines them in a versatile laboratory protocol. The protocol was tested in an experiment where pulse arrival time (PAT) and heart rate (HR) were measured with chest ECG and finger PPG sensors and blood pressure (BP) with continuous fingercuff monitor. The experiment results show that mental tasks provoked HR, BP and PAT very little while cold pressor and muscle tension had strong impact in all parameters. Valsalva maneuver had strongest impact in HR and PAT but the effect was transient like. We also predicted systolic BP based on the PAT values. We selected nine points in the protocol to calculate linear prediction model for each subject and then fitted data points to the models. If only the calibration points are taken into account, the correlation between the predicted and measured systolic BP was 0.91. When all the data points are fed into model, correlation was 0.75.

Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation.

In this study, pulse arrival time (PAT) was measured using a simple measurement setup consisting of arm electrocardiogram (ECG) and finger photoplethysmogram (PPG). Four methods to calculate PAT from the measured signals were evaluated. PAT was calculated as the time delay between ECG R peak and one of the following points in the PPG waveform: peak (maximum value of PPG waveform), foot (minimum value of PPG waveform), dpeak (maximum value of the first derivative of PPG waveform) and ddpeak (maximum value of the second derivative of PPG waveform). In addition to PAT calculation, pulse period (PP) intervals based on the detected features were determined and compared to RR intervals derived from ECG signal. Based on the results obtained here, the most promising method to be used in PAT or PP calculation seems to be the dpeak detection method.

Pulse arrival time measurement with coffee provocation.

This study investigated the effect of coffee intake in pulse arrival time (PAT) and pulse wave velocity (PWV) measured with electrocardiogram (ECG) from arms and photoplethysmogram (PPG) from fingertip. In addition, correlation of PWV with blood pressure (BP) is analyzed. 30 healthy participants were recruited to two measurement sessions, one arranged before and another one after the coffee intake. During each session, ECG and PPG were measured continuously for six minutes and PAT values calculated from ECG R-peak to the maximum of the first derivative of the PPG pulse. In addition, blood pressure was measured twice during each session with cuff based method. Coffee intake had statistically significant influence on both systolic and diastolic blood pressure, but not on PAT or PWV. Correlation between systolic blood pressure and PWV was 0.44. Individual calibration, additional derivatives of ECG and PPG such as heart rate, pulse pressure, or waveform characteristics could improve the correlation.

Effect of dynamic vibrotactile feedback on the control of isometric finger force.

This study investigates how controllability of force is influenced by concurrent vibrotactile feedback that is generated proportionally to the applied force. Three different models to provide tactile feedback are introduced: amplitude and frequency modulation and granular synthesis. Then, an experiment investigating the effect of the feedback models on force control is reported. The tactile feedback conditions were compared to each other and to a condition with no feedback in a force repetition and a force hold tasks. In the force repetition task, all the feedback conditions yielded significantly better accuracy compared to no feedback condition. In the force hold task, there was no difference in the accuracy between the conditions including the no feedback condition. The results suggest that dynamic vibrotactile feedback assists the force control in force repetition tasks.

Design of Dynamic Vibrotactile Textures.

This paper describes a method for creating virtual textures without force feedback by using a simple motion sensor and a single vibrotactile actuator. It is based on wavetable synthesis driven by the user's hand movements. The output of the synthesis is rendered with the tactile actuator attached in a hand-held box together with the motion sensor. The method provides a solution for creating tangible properties for virtual objects which can be explored by pointing at them with the sensor-actuator device. The study introduces 12 virtual textures which were based on three different envelope ridge lengths, two spatial densities, and were either regularly or irregularly organized. To evaluate the role of each design parameter in the perception of the texture, a series of experiments was conducted. The perceived similarity was assessed in a pairwise comparison test and the outcome was analyzed by using multidimensional scaling. The analysis revealed that envelope ridge length and spatial density were distinguishable design parameters while regularity was not. The textures were also rated according to five attribute scales previously determined in the pilot experiment. The results show that ridge length and spatial density influence perceived roughness and flatness similarly as with real textures.