![]() ![]() ![]() Device magnitude of acceleration was significant in 12 of 14 tests ( $$85.7\%$$ 85.7 % ), regardless of test type. Significant features were also identified using decision tree classification. Decision tree classification of sensor-based features allowed for the discrimination of PD from healthy controls with an accuracy of $$92.6\%$$ 92.6 %, and early and advanced stages of PD with an accuracy of $$73.7\%$$ 73.7 % compared to the current gold standard tools. In this study, 75 participants ( $$n = 50$$ n = 50 PD $$n = 25$$ n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multifunction), functional movement assessments (e.g., Berg Balance Scale), and standardized health questionnaires (e.g., PDQ-39). Further, this work aims to compare perceived versus sensor-based neurocognitive abilities. The purpose of this preliminary work was to use ML classification to assess the benefits and relevance of neurocognitive features both tablet-based assessments and self-reported metrics, as they relate to Parkinson’s Disease (PD) and its stages. Their account demonstrates the value of the collaboration to research itself, but also the broader (often unexpected) benefits that can emerge when a collaboration has space and time to flourish.Īs digital health technology becomes more pervasive, machine learning (ML) provides a robust way to analyze and interpret the myriad of collected features. Challenges are also considered, including authorship of research articles and anonymity. They consider key ingredients for successful collaboration, including shared curiosity, open-mindedness and trust, as well as the importance of informal discussion and space. Further benefits were realised through co-teaching undergraduate students about Parkinson’s, establishing a broader culture of PPI within the research lab and sharing their expertise of PPI more broadly. They then worked together on the steering group for a research project about Parkinson’s and imitation, which led to co-designing interventions using imitation and imagination of movements to improve movements, including a dance class. Initially, working together helped to communicate the purpose of the research to a lay audience and to make lab-based testing sessions for people with Parkinson’s as comfortable as possible. They describe how the partnership arose, was established and evolved to produce unexpected benefits to the research and more broadly. This article is a personal reflection on a long-standing patient and public involvement (PPI) partnership between a person with Parkinson’s and a cognitive neuroscience researcher. ![]()
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