Original Article
Functional measurement post-stroke via mobile application and body-worn sensor technology
Abstract
Background: Long-term management of individuals post-stroke is essential due to the resultant chronic disability and risk for recurrent stroke. Mobile health technology shows increasing promise to provide cost-effective monitoring and support systems for the patient, caregiver, and healthcare team. Ideally, such systems will include stroke management adherence support, mechanisms to link patients and caregivers to resources, and secure longitudinal data collection with archive that are employed to optimize recovery. However, healthcare providers and computer science application developers must first collaborate to identify meaningful measures and develop methods to reliably gather such data remotely via mobile systems.
Methods: mStroke is a mobile health system composed of two sensors and a mobile application designed to support optimal recovery for stroke survivors. Using the World Health Organization’s International Classification of Functioning, Disability and Health model (ICF model), the authors identified 4 measures that are commonly used in the clinic and developed the mobile application features to support remote data collection: National Institutes of Health Stroke Scale (NIHSS) items 5 and 6 (Motor Arm and Leg function), Functional Reach Test (FRT), and 10 Meter Walk Test (10MWT). At a local inpatient rehabilitation facility, each measure was executed with 35 stroke survivors through simultaneous scoring by the mStroke system and standardized clinical assessment. Correlation coefficients were calculated for clinician versus mStroke system scoring.
Results: All four clinical measures significantly correlated with mStroke system app scoring: NIHSS Motor Arm—0.839, P<0.001; NIHSS Motor Leg—0.736, P<0.001; FRT—0.630, P<0.01; 10MWT—0.994, P<0.001.
Conclusions: Results should be approached with caution as significant data skew was present for NIHSS Motor Arm and Motor Leg tests and the FRT results are not strong enough for broad translation. However, positive findings were demonstrated that support further investment in development, refinement, and testing of mobile health systems to provide clinically meaningful remote measurement via mobile technology. The ICF model was a helpful framework for guiding clinician and application developer collaboration and identifying meaningful features for app development.
Methods: mStroke is a mobile health system composed of two sensors and a mobile application designed to support optimal recovery for stroke survivors. Using the World Health Organization’s International Classification of Functioning, Disability and Health model (ICF model), the authors identified 4 measures that are commonly used in the clinic and developed the mobile application features to support remote data collection: National Institutes of Health Stroke Scale (NIHSS) items 5 and 6 (Motor Arm and Leg function), Functional Reach Test (FRT), and 10 Meter Walk Test (10MWT). At a local inpatient rehabilitation facility, each measure was executed with 35 stroke survivors through simultaneous scoring by the mStroke system and standardized clinical assessment. Correlation coefficients were calculated for clinician versus mStroke system scoring.
Results: All four clinical measures significantly correlated with mStroke system app scoring: NIHSS Motor Arm—0.839, P<0.001; NIHSS Motor Leg—0.736, P<0.001; FRT—0.630, P<0.01; 10MWT—0.994, P<0.001.
Conclusions: Results should be approached with caution as significant data skew was present for NIHSS Motor Arm and Motor Leg tests and the FRT results are not strong enough for broad translation. However, positive findings were demonstrated that support further investment in development, refinement, and testing of mobile health systems to provide clinically meaningful remote measurement via mobile technology. The ICF model was a helpful framework for guiding clinician and application developer collaboration and identifying meaningful features for app development.
