Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

Robust Methods for Quantifying the Effect of a Continuous Exposure From Observational Data

A cornerstone of clinical medicine is intervening on a continuous exposure, such as titrating the dosage of a pharmaceutical or controlling a laboratory result. In clinical trials, continuous exposures are dichotomized into narrow ranges, excluding large portions of the realistic treatment scenarios. The existing computational methods for estimating the effect of continuous exposure rely on a set of strict assumptions. We introduce new methods that are more robust towards violations of these assumptions. Our methods are based on the key observation that changes of exposure in the clinical setting are often achieved gradually, so effect estimates must be "locally" robust in narrower exposure ranges. We compared our methods with several existing methods on three simulated studies with increasing complexity. We also applied the methods to data from 14 k sepsis patients at M Health Fairview to estimate the effect of antibiotic administration latency on prolonged hospital stay. The proposed methods achieve good performance in all simulation studies. When the assumptions were violated, the proposed methods had estimation errors of one half to one fifth of the state-of-the-art methods. Applying our methods to the sepsis cohort resulted in effect estimates consistent with clinical knowledge.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds

Spinal cord injury (SCI) patients suffer from diverse gait deficits depending on the severity of their injury. Gait assessments can objectively track the progress during rehabilitation and support clinical decision making, but a comprehensive gait analysis requires far more complex setups and time-consuming protocols that are not feasible in the daily clinical routine. As using inertial sensors for mobile gait analysis has started to gain ground, this work aimed to develop a sensor-based gait analysis for the specific population of SCI patients that measures the spatio-temporal parameters of typical gait laboratories for day-to-day clinical applications. The proposed algorithm uses shank-mounted inertial sensors and personalized thresholds to detect steps and gait events according to the individual gait profiles. The method was validated in nine SCI patients and 17 healthy controls walking on an instrumented treadmill while wearing reflective markers for motion capture used as a gold standard. The sensor-based algorithm (i) performed similarly well for the two cohorts and (ii) is robust enough to cover the diverse gait deficits of SCI patients, from slow (0.3 m/s) to preferred walking speeds.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

Walking endurance in multiple sclerosis: Meta-analysis of six-minute walk test performance

BACKGROUND

The 6-minute walk (6 MW) is the most commonly applied measure of endurance walking capacity in persons with multiple sclerosis (MS); however, we are not aware of a quantitative synthesis of 6 MW performance in MS.

RESEARCH QUESTION

We undertook a meta-analysis quantifying the overall magnitude of difference in 6 MW performance between MS and healthy controls (HCs), and then within MS as a function of disability status. We further examined possible moderator variables of 6 MW performance.

METHODS

The systematic search was conducted for articles that included the 6 MW in persons with MS and involved comparison groups (i.e., HCs or MS disability subgroups (i.e., mild vs moderate-to-severe disability status)). The mean and standard deviation of the distance traveled during the 6 MW as well as sample sizes were entered into Comprehensive Meta-Analysis software and we estimated the overall effect size (Cohen's d) using a random effects model and examined categorical variables as possible moderators (e.g., instruction protocol, provision of encouragement, method of distance measurement, and course description).

RESULTS

Thirty-four studies met inclusion criteria with a total sample size of 3204 persons (MS: 2683; HC: 521) yielding 42 total comparisons. Persons with MS walked a shorter distance than HCs (mean difference = -177.2 ± 19.1 m) with a large effect size (d = - 1.87). Persons with mild disability walked further than those with moderate-to-severe disability (mean difference = 185.19 ± 9.2 m) with a large effect (d = 1.83). The categorical variables of provision of encouragement and course layout moderated the effect of MS and course layout moderated the effect of disability status on 6 MW performance.

SIGNIFICANCE

This meta-analysis of 6 MW performance defines mean difference in 6 MW performance in MS compared with HCs and provides an estimate of the disease-related effect of MS on endurance walking capacity for application within clinical research and practice.

Novel technology for mobility and balance tracking in patients with multiple sclerosis: a systematic review

INTRODUCTION

Mobility and balance impairments in patients with multiple sclerosis (MS) are major factors for decreased quality of life. Novel sensing technologies have great potential to efficiently capture subtle changes in mobility and balance performance, and thus improve current practices by providing an easy-to-implement, objective, and continuous functional tracking in MS population. Areas covered: This review details the collective findings of novel technology utilization in mobility and balance tracking in patients with MS. Thirty-three were systematically identified and included in this review. Pertinent methodological features (participant demographics, sensing technology, study aims, functional assessment protocols, and outcome measures) were extracted from each article. The construct validity, reliability, clinical relevance, and discriminative ability of sensor-based assessment in the MS population were summarized. Expert commentary: Sensor-based balance and mobility assessment are valid in comparison with reference standard techniques and are reliable to measure performance in the MS population. Sensor-based measures are also associated with validated clinical outcomes and are sensitive to functional deficits in individuals with MS. Such technologies may greatly improve the likelihood of detecting mobility and balance dysfunctions in real-world environments, thus allowing healthcare professionals to monitor interventions and manage disease progression precisely and efficiently Abbreviations: PwMS: Patients with Multiple Sclerosis; BBS: Berg Balance Scale; DGI: Dynamic Gait Index; ABC: Activity-specific Balance Confidence; T25FW: Timed 25 Foot Walk; 6MWT: 6 minute walk test; TUG: Timed Up and Go test; EO: Eyes Open; EC: Eyes Closed; ICC: Intraclass Correlation Coefficient; EDSS: Expanded Disability Status Scale; MFIS: Modified Fatigue Impact Scale; MSWS: Multiple Sclerosis Walking Scale; MSIS: Mutliple Sclerosis Impact Scale; PPA: Physiological Profile Assessment; HC: Healthy Control; AP: Anterior-posterior direction; ML: Mediolateral direction.

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.