Background: Aerobic training has the potential to restore function, stimulate brain repair, and reduce inflammation in people with Multiple Sclerosis (MS). However, disability, fatigue, and heat sensitivity are major barriers to exercise for people with MS. We aimed to determine the feasibility of conducting vigorous harness-supported treadmill training in a room cooled to 16°C (10 weeks; 3times/week) and examine the longer-term effects on markers of function, brain repair, and inflammation among those using ambulatory aids.Methods: Ten participants (9 females) aged 29 to 74 years with an Expanded Disability Status Scale ranging from 6 to 7 underwent training (40 to 65% heart rate reserve) starting at 80% self-selected walking speed. Feasibility of conducting vigorous training was assessed using a checklist, which included attendance rates, number of missed appointments, reasons for not attending, adverse events, safety hazards during training, reasons for dropout, tolerance to training load, subjective reporting of symptom worsening during and after exercise, and physiological responses to exercise. Functional outcomes were assessed before, after, and 3 months after training. Walking ability was measured using Timed 25 Foot Walk test and on an instrumented walkway at both fast and self-selected speeds. Fatigue was measured using fatigue/energy/vitality sub-scale of 36-Item Short-Form (SF-36) Health Survey, Fatigue Severity Scale, modified Fatigue Impact Scale. Aerobic fitness (maximal oxygen consumption) was measured using maximal graded exercise test (GXT). Quality-of-life was measured using SF-36 Health Survey. Serum levels of neurotrophin (brain-derived neurotrophic factor) and cytokine (interleukin-6) were assessed before and after GXT. Results: Eight of the ten participants completed training (attendance rates ≥ 80%). No adverse events were observed. Fast walking speed (cm/s), gait quality (double-support (%)) while walking at self-selected speed, fatigue (modified Fatigue Impact Scale), fitness (maximal workload achieved during GXT), and quality-of-life (physical functioning sub-scale of SF-36) improved significantly after training, and improvements were sustained after 3months. Improvements in fitness (maximal respiratory exchange ratio and maximal oxygen consumption during GXT) were associated with increased brain-derived neurotrophic factor and decreased interleukin-6. Conclusion: Vigorous cool room training is feasible and can potentially improve walking, fatigue, fitness, and quality-of-life among people with moderate to severe MS-related disability.

Mental disorders are prevalent among public safety personnel (PSP) yet many people working across public safety professions appear reluctant to seek care for mental health-related concerns. Given the prevalence and impact of compromised mental health on these populations, finding ways to increase use of psychological support for police staff and officers is necessary. We conducted an interview and focus groups (n= 9) with police service members (n= 33) to examine the barriers police officers (n= 25) and communicators (n= 8) report facing when seeking treatment, and their suggestions for improving access to treatment. We identified three main barriers: stigma, worries about confidentiality, and occupationspecific experience with people in the community who present in mental distress. Three suggestions emerged from our participants that may improve current mental health support, namely, ensuring confidentiality, easy-to-use electronic resources, and access to occupation-specific content. We discuss the implications of our results with suggestions for policy and practice.

Background Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. Results Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. Conclusions The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).

Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS.

Objective: Physical fitness and preserved cognitive function may provide neuroprotection in multiple sclerosis (MS), but few studies have examined their role in symptom progression over time. Dual-task paradigms can be useful to detect subtle impairment among people with MS in early stages of the disease. The present study investigated whether higher aerobic fitness or greater cognitive function could predict performance in dual-task walking 1–2 years later among people with mild or no MS-related walking impairment. Method: Participants (n = 50) performed dual-task walking (walking while serially subtracting 7’s from 100), completed the Montreal Cognitive Assessment (MoCA), the Symbol Digit Modalities Test (SDMT), and a fitness test (VO2max). They were tested at two time points (T1 and T2), approximately 1 year apart. Walking speed, MoCA, SDMT, and VO2max at baseline (T1) were examined as predictors of dual-task walking speed at T2. Results: MoCA (higher score), but not SDMT or fitness, was significantly correlated with percentage decrease in dual-task walking and was a significant predictor of dual-task-walking speed at T2, accounting for additional 6.1% of its variance. Cognitive impairment (MoCA < 26) at baseline corresponded to a 12 cm/s unit decrease in dual-task-walking speed at T2. Conclusions: Our results provide longitudinal evidence that better cognitive function, specifically global MoCA score, may protect against decline in dual-task walking ability over the years.