Marcus D'Souza

MS quality of life, depression, and fatigue improve after mindfulness training: a randomized trial.

Objective: Health-related quality of life (HRQOL) is often much reduced among individuals with multiple sclerosis (MS), and incidences of depression, fatigue, and anxiety are high. We examined effects of a mindfulness-based intervention (MBI) compared to usual care (UC) upon HRQOL, depression, and fatigue among adults with relapsing-remitting or secondary progressive MS. Methods: A total of 150 patients were randomly assigned to the intervention (n = 76) or to UC (n = 74). MBI consisted of a structured 8-week program of mindfulness training. Assessments were made at baseline, postintervention, and 6 months follow-up. Primary outcomes included disease-specific and disease-aspecific HRQOL, depression, and fatigue. Anxiety, personal goal attainment, and adherence to homework were secondary outcomes. Results: Attrition was low in the intervention group (5%) and attendance rate high (92%). Employing intention-to-treat analysis, MBI, compared with UC, improved nonphysical dimensions of primary outcomes at postintervention and follow-up (p < 0.002); effect sizes, 0.4–0.9 posttreatment and 0.3–0.5 at follow-up. When analyses were repeated among subgroups with clinically relevant levels of preintervention depression, fatigue, or anxiety, postintervention and follow-up effects remained significant and effect sizes were larger than for the total sample. Conclusions: In addition to evidence of improved HRQOL and well-being, these findings demonstrate broad feasibility and acceptance of, as well as satisfaction and adherence with, a program of mindfulness training for patients with MS. The results may also have treatment implications for other chronic disorders that diminish HRQOL. Classification of evidence: This trial provides Class III evidence that MBI compared with UC improved HRQOL, fatigue, and depression up to 6 months postintervention.

Dimethyl fumarate for multiple sclerosis

Importance of the field: One of the disadvantages of currently available disease-modifying drugs (DMDs) for multiple sclerosis (MS) is their parenteral administration. Moreover, efficacy is only partial. Most patients treated with first-line DMDs do not remain relapse-free. There is a need for new oral drugs that are more effective than currently available compounds. Innovative oral drugs with new mechanisms of action showed promising results in clinical trials. One of these emerging drugs is BG00012 (BG-12), a fumaric acid ester (FAE). Its active agent, dimethyl fumarate had first been included in FAE treatments for psoriasis. Areas covered in this review: Results that highlight the potential role of BG-12 in MS treatment. We focus on findings of experimental studies and current results of clinical studies with FAE in MS. What the reader will gain: An overview of the immunomodulatory and neuroprotective effects of FAE, their effect in animal models of MS and their short-term efficacy and safety profile in a Phase IIb clinical trial. Take home message: BG-12 is a promising emerging treatment for relapsing–remitting MS, combining anti-inflammatory and possibly clinically relevant neuroprotective effects with the convenience of oral administration. However, the future role of BG-12 in treatment of MS will have to be determined after the completion of ongoing Phase III studies.

Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision

Background Sensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis (MS) using depth-sensing computer vision. It aims to provide a more consistent and finer-grained measurement of motor dysfunction than currently possible. Objective To test the usability and acceptability of ASSESS MS with health professionals and patients with MS. Methods A prospective, mixed-methods study was carried out at 3 centers. After a 1-hour training session, a convenience sample of 12 health professionals (6 neurologists and 6 nurses) used ASSESS MS to capture recordings of standardized movements performed by 51 volunteer patients. Metrics for effectiveness, efficiency, and acceptability were defined and used to analyze data captured by ASSESS MS, video recordings of each examination, feedback questionnaires, and follow-up interviews. Results All health professionals were able to complete recordings using ASSESS MS, achieving high levels of standardization on 3 of 4 metrics (movement performance, lateral positioning, and clear camera view but not distance positioning). Results were unaffected by patients’ level of physical or cognitive disability. ASSESS MS was perceived as easy to use by both patients and health professionals with high scores on the Likert-scale questions and positive interview commentary. ASSESS MS was highly acceptable to patients on all dimensions considered, including attitudes to future use, interaction (with health professionals), and overall perceptions of ASSESS MS. Health professionals also accepted ASSESS MS, but with greater ambivalence arising from the need to alter patient interaction styles. There was little variation in results across participating centers, and no differences between neurologists and nurses. Conclusions In typical clinical settings, ASSESS MS is usable and acceptable to both patients and health professionals, generating data of a quality suitable for clinical analysis. An iterative design process appears to have been successful in accounting for factors that permit ASSESS MS to be used by a range of health professionals in new settings with minimal training. The study shows the potential of shifting ubiquitous sensing technologies from research into the clinic through a design approach that gives appropriate attention to the clinic environment.

Setwise Comparison: Consistent, Scalable, Continuum Labels for Computer Vision

A growing number of domains, including affect recognition and movement analysis, require a single, real number ground truth label capturing some property of a video clip. We term this the provision of continuum labels. Unfortunately, there is often an uncacceptable trade-off between label consistency and the efficiency of the labelling process with current tools. We present a novel interaction technique, setwise comparison, which leverages the intrinsic human capability for consistent relative judgements and the TrueSkill algorithm to solve this problem. We describe SorTable, a system demonstrating this technique. We conducted a real-world study where clinicians labelled videos of patients with multiple sclerosis for the ASSESS MS computer vision system. In assessing the efficiency-consistency trade-off of setwise versus pairwise comparison, we demonstrated that not only is setwise comparison more efficient, but it also elicits more consistent labels. We further consider how our findings relate to the interactive machine learning literature.