Design and development of a gait training system for Parkinson's disease

Background Rhythmic Auditory Stimulation (RAS) is an effective technique to improve gait and reduce freezing episodes for Persons with Parkinson's Disease (PwPD). The BeatHealth system, which comprises a mobile application, gait sensors, and a website, exploits the potential of the RAS technique. This paper describes the tools used for co-designing and evaluating the system and discusses the results and conclusions. Methods Personas, interviews, use cases, and ethnographic observations were used to define the functional requirements of the system. Low fidelity prototypes were created for iterative and incremental evaluation with end-users. Field trials were also performed with the final system. The process followed a user centered design methodology defined for this project with the aim of building a useful, usable, and easy-to-use system. Results Functional requirements of the system were produced as a result of the initial exploration phase. Building upon these, mock-ups for the BeatHealth system were created. The mobile application was iterated twice, with the second version of it achieving a rating of 75 when assessed by participants through the System Usability Scale (SUS). After another iteration field trials were performed and the mobile application was rated with an average 78.6 using SUS. Participants rated two website mock-ups, one for health professionals and another for end-users, as good except from minor issues related to visual design (e.g. font size), which were resolved in the final version. Conclusion The high ratings obtained in the evaluation of the BeatHealth system demonstrate the benefit of applying a user centered design methodology which involves stakeholders from the very beginning. Other important lessons were learned through the process of design and development of the system, such as the importance of motivational aspects, the techniques which work best, and the extra care that has to be taken when evaluating non-functional mock-ups with end users.