Creating a Mobile App to Assess Fall Risk in Older Adults with Multiple Sclerosis

At A Glance

A research group within Georgia State University (GSU)’s Department of Physical Therapy is committed to advancing research on fall risk among individuals with Multiple Sclerosis (MS). This group devised a specific protocol measure individual fall risk factors based on a series of physical measurements and patient-reported factors. In a key innovation, the team at GSU encapsulated this protocol into an Android mobile app that collected physical measurements using the device’s accelerometer and timers, along with a battery of risk assessment questions. 

Together with GenUI

Based on feedback from people with MS, the GSU team asked GenUI to review and redevelop the Android app they had created in-house to improve usability for people with MS, improve reliability and performance, and add risk factors that the previous version lacked. We began our collaboration with Dr. Katherine Hsieh, leading the protocol study, to understand her vision for both the app and the study in detail. We then audited the existing source code and user interface to be sure we had a clear understanding of the current approach, including both its benefits and limitations. 

Building MSafe

MSafe is a mobile app for fall risk assessment created with one primary objective in mind: to leverage mobile technology to measure fall risk factors specific for older adults with MS. The app includes various assessment modules aimed at capturing physical, cognitive, and environmental data directly from MS patients:

• Questionnaires for Demographics, Environmental and Behavioral Data: The app collects information on home safety, demographics, medication use, and balance confidence, offering a comprehensive view of internal and external factors that contribute to fall risk.

  The images above show the Questionnaire section. These questions provide insights into their home environment, giving researchers a complete picture of the various elements that could affect their safety and stability.

• Physical Assessments: We integrated tests such as gait speed, sit-to-stand, and standing balance tests that utilize the phone’s accelerometer to monitor postural stability and motion.
  

  The images above show the user interface for physical assessments. This data is captured using the phone’s accelerometer. 

• Cognitive Evaluations: The app features cognitive tests like reaction time and processing speed assessments to measure brain function and its impact on physical stability.

  Here, the cognitive evaluation tests capture the patient’s performance on tasks that measure reaction times and cognitive processing speeds, providing insights into how cognitive function relates to fall risk.

• Fall Risk Score results: A results screen displays fall risk across categories using color-coded gauge charts.

Collaborative Implementation and Data Integration

• Collaborative Development: Working closely with GSU’s team, we adopted a structured approach that included regular demos and iterative feedback sessions, ensuring that the app’s functionality aligned perfectly with the research requirements.
• Focus on Usability: To ensure the app could be easily used by older adults with MS, we employed user-centered design principles. We will also continuously refine the interface based on real user experiences to make it accessible and intuitive.
• Data Export and Research Integration: A key feature of the app is its ability to seamlessly export collected data in CSV format, making it ready for immediate analysis by GSU’s research team. This ensures that the data gathered from various fall risk factors can be used effectively for ongoing studies.

Realizing the Research Potential

Dr. Katherine Hsieh, Assistant Professor in Physical Therapy at GSU, highlights the importance of this solution in transforming the way fall risk data is collected and utilized:

“Falls are very common in people with MS, and MS symptoms fluctuate over time. Leveraging a mobile health app to measure risk factors can help implement fall prevention strategies early on to prevent future falls.”

Data-Driven Impact for Future Research

• Enhanced Research Capabilities: With a continuous flow of data collected directly from patients, GSU’s research team can now analyze trends and correlations that contribute to better fall risk assessments.
• Improving Intervention Strategies: By integrating physical, cognitive, and environmental data, the app enables researchers to develop personalized intervention plans aimed at reducing fall risk and improving patient outcomes.
• Scalable Research Infrastructure: The modular design of the app allows GSU to expand its research scope, adding new assessment criteria and data points as their research evolves.

The Path Forward

Building on the success of this project, we are excited to support Katherine and her team at GSU in further refining the app’s capabilities to accommodate more comprehensive data collection.