DTx Algorithms for Personalized Parkinson's Disease Treatment and Medication Plan Optimization

Résumé succinct

The study is aimed at developing Digital Therapeutics (DTx) algorithms for personalized PD treatment and medication plan optimization, based on Real World Data (RWD) collected from patients via digital mobile app and wearable sensors. The study design is observational/noninterventional, prospective, single-arm, aimed at collecting data from wearable sensors for validation of symptom detection algorithms, through (1) a supervised in-clinic motor assessment, performed using validated clinical scales (Visit 3, Visit 4), and (2) an unsupervised, home-based, 6-month (Visit 3 to Visit 4) data collection from wearable devices (passive monitoring) for algorithm cross-validation using patient reported outcomes (PROMs) and remote clinical assessments. The devices used in the study will be a commercial smartwatch (Garmin Vivosmart 5) for inertial data collection and a digital application through which subjects will report PROMs via a digital symptom diary. Screening visits (Visit 1 and Visit 2) will be conducted prior to enrollment to verify eligibility criteria through clinical assessments, the subjects' symptom diary, and by assessing adherence to the use of the study tools provided (i.e., mobile application and smartwatch).