Measurement
Stroke
Technology (e.g. robotics, assistive technology, mHealth)
Oral Presentation
Alex Wong, PhD, DPhil
Assistant Professor
Washington University School of Medicine
St. Louis, Missouri
Objective:
The application of mobile health (mHealth) to obtain data pertinent to disease diagnosis, prevention, and management makes it possible to monitor and provide interventions for persons with physical and neurological disabilities. The author will discuss using mHealth to advance the precise measurement of processes and outcomes in stroke rehabilitation. In particular, he will share research: (1) examining the feasibility and validity of smartphone-based ecological momentary assessment (EMA) to assess daily functioning and other behavioral factors in adults after stroke, and (2) examining real-time relationships between self-appraised performance in daily activities and post-stroke symptoms.
A longitudinal study involving laboratory-based assessments and EMA surveys via a mobile application five times a day for 14 days.
Home and community settings
Forty-eight participants (mean age 61.8; 41.7% female) who were at least 3 months post-stroke.
Not applicable.
EMA included self-appraisals of performance in daily activities and post-stroke symptoms. Laboratory-based measures included assessments of cognitive, mood, motor, and functional capacity.
Excellent EMA adherence (81.93%) was found, resulting in an overall total of 2753 EMA data points. Higher scores from standard measures of instrumental activities of daily living (IADLs) correlated with more time spent in EMA-measured IADLs (r = 0.35 to 0.49, ps < 0.05). Lower self-appraisal of performance in daily activities was concurrently associated with higher fatigue (B = -0.804; 95% CI: -1.09 to -0.51) and lack of interest (B = -0.497; 95% CI: -0.75 to -0.24), and increased fatigue preceded decreased performance in daily activities on the next EMA survey (B: -0.481; 95% CI:-0.87 to -0.09).
Findings support EMA via smartphone as a feasible data collection method in adults after stroke and provide new insights into how post-stroke symptoms dynamically relate to daily function. Incorporating EMA methods may enhance the precision of assessment, which could have the potential to transform the development of personalized interventions to improve outcomes.
