The relationship between cognition, education, and employment in multiple sclerosis patients

Dual-task difficulties as a risk factor for unemployment in people with multiple sclerosis

BACKGROUND

No study has investigated the impact of dual-tasking difficulties as a risk factor for unemployment in people with multiple sclerosis (pwMS). The aim was to examine the influence of dual-task performance on employment status and work difficulties and to identify the predictors of employment status in pwMS.

METHODS

Eighty-four pwMS, including 42 employed and 42 unemployed, participated in the study. Dual-task difficulties were assessed using the Dual-task Impact on Daily-living Activities-Questionnaire (DIDA-Q), while dual-task performance was evaluated through the 30-second Walk Test and Nine-Hole Peg Test, incorporating a cognitive task. Walking and cognitive function were also measured.

RESULTS

Employed pwMS had better scores in walking, cognitive function, single and dual-task performance than unemployed pwMS (p < .05). Lower scores in walking (odds ratio [OR] = 1.81, p < .001) and upper extremity-related (OR = 1.44, p = .019) dual-task performance and higher scores in the cognitive subscale of the DIDA-Q questionnaire (OR = 1.20, p = .037) were significantly associated with higher odds of being unemployed. Among employed pwMS, DIDA-Q subscales showed moderate-to-strong correlations with MSWSDQ-23 scores. The other variables showed weak-to-moderate correlations with subscale and total scores of MSWSDQ-23.

CONCLUSION

Cognitive function, as opposed to motor function, has been found to be a significant predictor of unemployment in pwMS.

The Parkinson's disease waiting room of the future: measurements, not magazines

Utilizing technology to precisely quantify Parkinson's disease motor symptoms has evolved over the past 50 years from single point in time assessments using traditional biomechanical approaches to continuous monitoring of performance with wearables. Despite advances in the precision, usability, availability and affordability of technology, the "gold standard" for assessing Parkinson's motor symptoms continues to be a subjective clinical assessment as none of these technologies have been fully integrated into routine clinical care of Parkinson's disease patients. To facilitate the integration of technology into routine clinical care, the Develop with Clinical Intent (DCI) model was created. The DCI model takes a unique approach to the development and integration of technology into clinical practice by focusing on the clinical problem to be solved by technology rather than focusing on the technology and then contemplating how it could be integrated into clinical care. The DCI model was successfully used to develop the Parkinson's disease Waiting Room of the Future (WROTF) within the Center for Neurological Restoration at the Cleveland Clinic. Within the WROTF, Parkinson's disease patients complete the self-directed PD-Optimize application on an iPad. The PD-Optimize platform contains cognitive and motor assessments to quantify PD symptoms that are difficult and time-consuming to evaluate clinically. PD-Optimize is completed by the patient prior to their medical appointment and the results are immediately integrated into the electronic health record for discussion with the movement disorder neurologist. Insights from the clinical use of PD-Optimize has spurred the development of a virtual reality technology to evaluate instrumental activities of daily living in PD patients. This new technology will undergo rigorous assessment and validation as dictated by the DCI model. The DCI model is intended to serve as a health enablement roadmap to formalize and accelerate the process of bringing the advantages of cutting-edge technology to those who could benefit the most: the patient.