Usability of a Fall Risk mHealth App for People With Multiple Sclerosis: Mixed Methods Study

• Published in: JMIR human factors 2021-03, Vol.8 (1), p.e25604-e25604
• Main Authors: Hsieh, Katherine, Fanning, Jason, Frechette, Mikaela, Sosnoff, Jacob
• Format: Article
• Language: English
• Subjects: Algorithms; Balance; Chronic illnesses; Design; Falls; Health risks; Injuries; Mixed methods research; Multiple sclerosis; Original Paper; Posture; Questionnaires; Risk assessment; Risk factors; Usability
• ISSN: 2292-9495; 2292-9495
• DOI: 10.2196/25604

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. 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. 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. 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. 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. Th