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Usability of a Fall Risk mHealth App for People With Multiple Sclerosis: Mixed Methods Study
Katherine Hsieh1,2, Jason Fanning3, Mikaela Frechette1
1Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
Background:
Multiple sclerosis (MS) is a chronic, neurodegenerative disease that causes a range of motor, sensory, and cognitive symptoms. Due to these symptoms, people with MS are at a high risk for falls, fall-related injuries, and reductions in quality of life. There is no cure for MS, and managing symptoms and disease progression is important to maintain a high quality of life. Mobile health (mHealth) apps are commonly used by people with MS to help manage their health. However, there are limited health apps for people with MS designed to evaluate fall risk. A fall risk app can increase access to fall risk assessments and improve self-management. When designing mHealth apps, a user-centered approach is critical for improving use and adoption.
Objective:
The purpose of this study is to undergo a user-centered approach to test and refine the usability of the app through an iterative design process.
Methods:
The fall risk app Steady-MS is an extension of Steady, a fall risk app for older adults. Steady-MS consists of 2 components: a 25-item questionnaire about demographics and MS symptoms and 5 standing balance tasks. Data from the questionnaire and balance tasks were inputted into an algorithm to compute a fall risk score. Two iterations of semistructured interviews (n=5 participants per iteration) were performed to evaluate usability. People with MS used Steady-MS on a smartphone, thinking out loud. Interviews were recorded, transcribed, and developed into codes and themes. People with MS also completed the System Usability Scale.
Results:
A total of 3 themes were identified: intuitive navigation, efficiency of use, and perceived value. Overall, the participants found Steady-MS efficient to use and useful to learn their fall risk score. There were challenges related to cognitive overload during the balance tasks. Modifications were made, and after the second iteration, people with MS reported that the app was intuitive and efficient. Average System Usability Scale scores were 95.5 in both iterations, representing excellent usability.
Conclusions:
Steady-MS is the first mHealth app for people with MS to assess their overall risk of falling and is usable by a subset of people with MS. People with MS found Steady-MS to be usable and useful for understanding their risk of falling. When developing future mHealth apps for people with MS, it is important to prevent cognitive overload through simple and clear instructions and present scores that are understood and interpreted correctly through visuals and text. These findings underscore the importance of user-centered design and provide a foundation for the future development of tools to assess and prevent scalable falls for people with MS. Future steps include understanding the validity of the fall risk algorithm and evaluating the clinical utility of the app.
Insights
The Steady-MS app helps people with multiple sclerosis (MS) assess fall risk using a user-centered design. This mobile health tool is usable and valuable for understanding fall risk, with future steps to validate its algorithm.
Area of Science:
- Neurology
- Digital Health
- Rehabilitation Science
Background:
- Multiple sclerosis (MS) is a chronic neurodegenerative disease leading to significant fall risk and reduced quality of life.
- Mobile health (mHealth) apps are increasingly used by individuals with MS for health management.
- Limited availability of mHealth apps specifically designed for fall risk assessment in MS populations.
Purpose of the Study:
- To apply a user-centered approach to iteratively test and refine the usability of the Steady-MS fall risk application.
- To enhance the adoption and utility of mHealth tools for individuals with MS.
Main Methods:
- The Steady-MS app, an extension of the Steady app for older adults, incorporates a questionnaire and balance tasks to compute fall risk.
- Two iterative cycles of semistructured interviews with participants with MS were conducted, involving think-aloud protocols.
- Usability was evaluated using the System Usability Scale (SUS) and qualitative thematic analysis of transcribed interviews.
Main Results:
- Three key themes emerged: intuitive navigation, efficiency, and perceived value of the app.
- Participants found Steady-MS efficient and valuable for assessing fall risk, despite initial cognitive overload challenges during balance tasks.
- Post-modification, participants reported high usability, with average SUS scores of 95.5, indicating excellent usability.
Conclusions:
- Steady-MS is the first mHealth app designed for fall risk assessment in people with MS and has demonstrated usability within this population.
- User-centered design is crucial for developing effective mHealth tools, emphasizing clear instructions and interpretable results through visuals and text.
- Future research will focus on validating the fall risk algorithm and evaluating the clinical utility of Steady-MS for scalable fall prevention in MS.
