Study Rationale:
People with Parkinson disease often take small, slow and unsteady steps, which makes it hard to get around and lowers their quality of life. While focusing on walking can help a little, it’s difficult to keep up this effort all the time in everyday life. Helping people with Parkinson disease walk better is an important goal. One promising approach is walking to music, which can help people walk more smoothly and easily, without needing to think so hard about each step. But for this to work well, the music’s beat must match how a person naturally walks. In this study, we will test a new personalized music technology that does just that. It measures each person’s walking pattern and then plays music with a matching tempo. Over time, the music can adjust to help improve how a person walks.
Hypothesis:
We believe that walking with personalized music technology for three months will help people with Parkinson disease walk better. We think the music will help them take smoother, more stable steps with less thinking, which could lead to improved walking quality of life.
Study Design:
We plan to enroll 162 people with Parkinson disease in this study. Participants will walk in their community for 30 minutes, four times a week, for six months using this personalized music system. We’ll track changes in walking ability before, during, and after the program. For some participants, we’ll also use portable brain imaging to see how much mental effort walking takes.
Impact on Diagnosis/Treatment of Parkinson’s disease:
There aren’t many effective treatments to improve walking in Parkinson disease. We believe that this personalized music-based approach could help people walk better—with longer, faster, and more stable steps—with less thinking. This could lead to better mobility and quality of life.
Next Steps for Development:
If the program works, people with Parkinson disease could use this personalized music technology in their everyday lives, at home or out in the community, to support safer, more confident walking.