Official websites use. Share sensitive information only on official, secure websites. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients.
We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups in terms of standard medical test targets. Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.
Keywords: PD, MS, stroke, patient reported outcomes, wearables, quantitative motor analysis, sleep analysis, mood estimation. Recent studies acknowledge the burden that neurological disorders have on the lives of people experiencing them, as well as on the societies and economic systems in which they live [ 1 ]. At the same time, according to the World Health Organization, there is a shortage of 4. The need arises to put in comprehensive efforts to establish policies, financing resources and improvements in healthcare services for patients of neurological diseases [ 3 , 4 ].
This includes empowering the healthcare providers in providing their services in the most informed manner, by being easily, and in a timely manner, aware of changes in the health status of patients. In the case of brain disease research, technological advances and efforts towards the digital transformation of health provisioning services have shown particular promise [ 5 , 6 , 7 ].
Data analytics tools and machine learning ML methods can provide clinically actionable information that can complement or even empower medical recommendations [ 8 , 9 ].
