Objective: The project focuses on integrating a mobile application (mPower) into the Luxembourg Parkinson’s cohort (HELP-PD) to monitor frequency and degree of variation in symptoms of PD, to identify potential sources and modulators of variation and to evaluate how symptoms are correlated with these modulators across patients.

Background: During the last years, there has been a rapid evolution in novel technologies, i.e. device-assisted registrations of PD symptoms. This enables the longitudinal registration of relevant and objective features related to disease stage and progression. Although these technologies provide an objective, time- and cost-effective approach, the validation and correlation of sensor-based data with standardized clinical assessments in large cohorts of patients remains a major need to translate into clinical decision support.

Methods: Within this project we integrate the mPower application into the longitudinal cohort programme for patients with parkinsonism in Luxembourg and the Greater Region (HELP-PD), that includes a total of 47 screening instruments for motor and non-motor functions in PD. The mPower app includes a traditional survey-based approach with more granular and precise data gleaned from the sensors of the mobile device related to passive or task-based assessments.

Results: We further developed the mPower app to account for the multilingual specificities of Luxembourg and allow for the participation of patients fluent in German, French or English. Outreach strategies to engage patients and integrate stakeholders were established that include a multiprofessional clinical steering committee and the involvement of patient associations. A novel database infrastructure was generated within HELP-PD, that integrates epidemiological and clinical data obtained from annual visits of study participants with unique longitudinal data derived from mPower for a wide variety of analyses.

Conclusions: The implementation of novel technologies allows for a more direct participation of patients and strengthen their autonomy for future research. The dramatic increase in high quality data characterizing PD at different levels enables novel strategies for patient stratification and identification of markers for therapeutic outcome, which can be translated into clinical decision support.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/implementation-of-a-mobile-application-in-the-luxembourg-parkinsons-study-for-identification-and-validation-of-disease-stage-and-variation/