Matthew M. Engelhard

Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects

BACKGROUND The six-minute walk (6MW) is a common walking outcome in multiple sclerosis (MS) thought to measure fatigability in addition to overall walking disability. However, direct evidence of 6MW induced gait deterioration is limited by the difficulty of measuring qualitative changes in walking. OBJECTIVES This study aims to (1) define and validate a measure of fatigue-related gait deterioration based on data from body-worn sensors; and (2) use this measure to detect gait deterioration induced by the 6MW. METHODS Gait deterioration was assessed using the Warp Score, a measure of similarity between gait cycles based on dynamic time warping (DTW). Cycles from later minutes were compared to baseline cycles in 89 subjects with MS and 29 controls. Correlation, corrected (partial) correlation, and linear regression were used to quantify relationships to walking and fatigue outcomes. RESULTS Warp Scores rose between minute 3 and minute 6 in subjects with mild and moderate disability (p<0.001). Statistically significant correlations (p<0.001) to the MS walking scale (MSWS-12), modified fatigue impact scale (MFIS) physical subscale, and cerebellar and pyramidal functional system scores (FSS) were observed even after controlling for walking speed. Regression of MSWS-12 scores on Warp Scores and walking speed explained 73.9% of response variance. Correlations to individual MSWS-12 and MFIS items strongly suggest a relationship to fatigability. CONCLUSION The Warp Score has been validated in MS subjects as an objective measure of fatigue-related gait deterioration. Progressive changes to gait cycles induced by the 6MW often appeared in later minutes, supporting the importance of sustained walking in clinical assessment.

Remotely engaged: Lessons from remote monitoring in multiple sclerosis

OBJECTIVES Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being. METHODS Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months. RESULTS Approximately 87% of subjects completed wbPROs without assistance, and wbPROs strongly correlated with standard PROs (r>0.91). All wbPROs were completed less frequently in the second three months (p<0.05). Frequent wbPRO completion was significantly correlated with higher step on the Expanded Disability Status Scale (EDSS) (p=0.026). Nearly 52% of subjects reported improved understanding of their disease, and approximately 16% wanted individualized wbPRO content. Over half (63.9%) of perceived well-being variance was explained by MS symptoms, notably depression (rs=-0.459), fatigue (rs=-0.390), and pain (rs=-0.389). CONCLUSIONS wbPRO collection was feasible and reliable. More disabled subjects had higher completion rates, yet most subjects failed requirements in the second three months. Remote monitoring has potential to improve patient-centered care and communication between patient and provider, but tailored PRO content and other innovations are needed to combat declining adherence.

Correlations between inertial body sensor measures and clinical measures in multiple sclerosis

Gait assessment using inertial body sensors is becoming popular as an outcome measure in multiple sclerosis (MS) research, supplementing clinical observations and patient-reported outcomes with precise, objective measures. Although numerous research reports have demonstrated the performance of inertial measures in distinguishing healthy controls and MS subjects, the relationship between these measures and the impact of MS on gait impairment remains poorly understood. In contrast, although clinical evaluation has limited variability in scores, it is meaningful and interpretable for clinicians. Therefore, this paper investigates correlations between two inertial measures and three clinical measures of walking ability. The clinical measures are the MS Walking Scale (MSWS-12), the Expanded Disability Status Scale (EDSS), and the six minute walk (6MW) distance. The inertial measures are the double stance time to single stance time ratio (DST/SST) and the causality index, both of which have been proven effective in MS gait assessment in previous work. 28 MS subjects and 13 healthy controls were recruited from an MS outpatient clinic. Most correlations among measures were strong and significant. Experimental results suggested that combining all five measures may improve separability performance for tracking MS disease progression.

Relationship between kernel density function estimates of gait time series and clinical data

Multiple sclerosis (MS) is a neurological disorder which interrupts the communication between the brain and other parts of the body resulting in neurologic and physical and functional limitations. Gait deterioration is one of the most common problems and hence assessments of walking quality is a crucial part of MS diagnosis. In-clinic evaluations use physical examinations and an expanded disability status scale (EDSS) to label MS subjects into various disability groups such as mild, moderate, etc. Current research in MS focuses on leveraging the inertial data for accurate gait assessments to overcome the shortcomings of qualitative methods and enhancing the separability performance between MS and control subjects. However, MS symptoms vary among individuals. In [1], we showed that the inertial gait density estimates can be used to identify the multiple types of walk within each disability group. In this work, we show that the peak value of the inertial gait density estimate correlates significantly to distance covered in six minutes (r = −0.8028, p < 0.0001), making it clinically meaningful. The peak values also correlate with other related subjective data discussed in the paper. Thus the gait density of an MS subject can be evaluated to objectively assess the impact of MS on his/her functional capacities. We believe that we are supplementing existing information with a new, high-precision objective anchor to help reduce dependence on subjective and burdensome questionnaires.

Determining physiological significance of inertial gait features in multiple sclerosis

Gait impairment in Multiple Sclerosis (MS) can result from imbalance, physical fatigue, weakness, and other symptoms. Walking speed is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand the physiologic significance of two such features in MS. Both features are computed using the dynamic time warping (DTW) algorithm: the “DTW Score” is based on the usual DTW distance, and the “Warp Score” is based on the warping length. Using linear regression and stepwise regression models, the relationship between these features and several gait-related MS symptoms is analyzed. Results show that the DTW Score and Warp Score have distinct physiologic significance in MS compared to walking speed, and these features may also be useful for walking assessment in a wide range of clinical contexts.

Fatigue and fluid hydration status in multiple sclerosis: A hypothesis

Background: Fatigue is a prevalent and functionally disabling symptom for individuals living with multiple sclerosis (MS) which is poorly understood and multifactorial in etiology. Bladder dysfunction is another common MS symptom which limits social engagement and quality of life. To manage bladder issues, individuals with MS tend to limit their fluid intake, which may contribute to a low-hydration (LoH) state and fatigue. Objective: To evaluate the relationship between patient-reported MS fatigue, bladder dysfunction, and hydration status. Methods: We performed a prospective cross-sectional study in 50 women with MS. Participants submitted a random urine sample and completed several fatigue-related surveys. Using a urine specific gravity (USG) threshold of 1.015, we classified MS subjects into two groups: high-hydration (HiH) and LoH states. Results: LoH status was more common in MS subjects with bladder dysfunction. Statistically significant differences in self-reported Fatigue Performance Scale were observed between HiH and LoH subjects (p = 0.022). USG was significantly correlated with fatigue as measured by the MS Fatigue Severity Scale (FSS) score (r = 0.328, p = 0.020). Conclusion: Hydration status correlates with self-reported fatigue, with lower fatigue scores found in those with HiH status (USG < 1.015).

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The “DTW Score” is based on the usual DTW distance, and the “Warp Score” is based on the warping length. The third feature, based on kernel density estimation (KDE), is the “KDE Peak” value. Finally, the “Causality Index” is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.

Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features

Multiple sclerosis (MS) interrupts communication between the brain and other parts of the body causing functional deterioration. Gait impairment is a common finding in MS, one caused by several neurological symptoms. We perform an event-specific analysis to study the variable impact of MS on gait components. Our results show that the mid-swing to heel strike (HS) phase of a gait cycle is the most indicative of motor problems. We apply the Hilbert-Huang transform to inertial gait data, corresponding to this phase, to extract the spectral features and study their relationships with the patient-reported outcomes. A number of strong and statistically significant dependencies were found, many having to do with activities of daily living and MS walking scale, leading to the conclusion that the disturbance in mid-swing to HS is specific to deterioration in physical functions. Spearman correlations coefficients and adjusted R2 obtained using stepwise linear regression models are reported. We conclude that event-specific gait features can be used to quantify the precise impact of MS symptoms on gait phases and identify markers of balance, stability, or fall risk, etc. We believe that this information supplements on-going MS research and could be used to develop personalized disease-modifying therapies and exercises.

Adaptive symptom reporting for mobile patient-reported disability assessment

Mobile symptom reporting apps can conveniently gather health-related information at low cost from day to day, fundamentally altering the relationship between patients, health data, and care providers. However, current mobile systems face a difficult trade-off between the quality of the information they collect and the burden placed on patients. In this paper, we propose an algorithm for adaptive system reporting designed for mobile platforms. This algorithm uses personalization, domain-specific knowledge, and Bayesian reasoning to reduce the number of questions required for accurate disability assessment, substantially decreasing demands placed on the patient. Following development of the algorithm, we validate it retrospectively using responses to the 12-item multiple sclerosis walking scale collected from 31 subjects with multiple sclerosis. Trade-offs between accuracy and response quantity are explored in detail. In this dataset, a 42% reduction in the median number of patient prompts was achieved without causing a single clinically relevant estimation error. A 75% reduction was associated with 4.45% clinically relevant estimation error. Given these promising results, future work will focus on prospective validation in multiple sclerosis and other clinical populations.