Transforming Parkinson's Care With Predictive Algorithms
TechHealthPD
Revolutionizing Patient Care and Lifestyle Through Predictive Algorithms in Parkinson's Disease
1 other identifier
observational
200
1 country
1
Brief Summary
Lifestyle interventions can alleviate Parkinson's Disease (PD) symptoms and delay disease progression. The novelty of this project lies in the development of an innovative smart platform that utilizes artificial intelligence (AI) and predictive models to offer a groundbreaking solution that not only prevents disease progression but also significantly improves the well-being of patients with PD. For this, the technological smart platform will encompass 50 clinical variables, and a comprehensive range of other 50 supplementary variables, validated in a database with more than 1500 patients. The smart platform will include a user-friendly interface with different user profiles and a scalable back end with AI-based monitoring and prediction modules. This will offer primary prevention, early detection, and ongoing monitoring by specialized medical professionals at home and in hospitals.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Sep 2026
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 23, 2024
CompletedFirst Posted
Study publicly available on registry
January 1, 2025
CompletedStudy Start
First participant enrolled
September 1, 2026
ExpectedPrimary Completion
Last participant's last visit for primary outcome
September 1, 2026
Study Completion
Last participant's last visit for all outcomes
September 1, 2028
December 1, 2025
November 1, 2025
Same day
December 23, 2024
November 27, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
The MDS-UPDRS was developed to evaluate various aspects of Parkinson's disease including non-motor and motor experiences of daily living and motor complications. The MDS-UPDRS has a total score range of 0 to 236, with individual parts scoring between 0 to 52 (Parts I and II), 0 to 108 (Part III), and 0 to 24 (Part IV), assessing both motor and non-motor aspects of Parkinson's disease. A higher score indicates greater severity of Parkinson's disease symptoms.
From enrollment (app download) to 1 year
Secondary Outcomes (1)
Montreal Cognitive Assessment (MoCA)
At enrollment (app download) and after 1 year
Study Arms (1)
Lifestyle intervention app
The group of patients includes individuals diagnosed with Parkinson's Disease (PD). This cohort encompasses a range of disease stages, severities, and demographic characteristics.
Interventions
A technological smart platform (app) that integrates diets, physical activity programs, sleep habits and other lifestyle interventions
Eligibility Criteria
Patients diagnosed with Parkinson's disease
You may qualify if:
- Patients with Parkinson's disease
You may not qualify if:
- N/A
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University Ramon Llulllead
- University of Deustocollaborator
- Hospital Universitari Vall d'Hebron Research Institutecollaborator
Study Sites (1)
Ramon Llull University
Barcelona, 08022, Spain
Related Publications (2)
Magana JC, Deus CM, Baldellou L, Avellanet M, Gea-Rodriguez E, Enriquez-Calzada S, Laguna A, Martinez-Vicente M, Hernandez-Vara J, Gine-Garriga M, Pereira SP, Montane J. Investigating the impact of physical activity on mitochondrial function in Parkinson's disease (PARKEX): Study protocol for A randomized controlled clinical trial. PLoS One. 2023 Nov 22;18(11):e0293774. doi: 10.1371/journal.pone.0293774. eCollection 2023.
PMID: 37992028RESULTJossa-Bastidas O, Zahia S, Fuente-Vidal A, Sanchez Ferez N, Roda Noguera O, Montane J, Garcia-Zapirain B. Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach. Int J Environ Res Public Health. 2021 Oct 14;18(20):10769. doi: 10.3390/ijerph182010769.
PMID: 34682515RESULT
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- ECOLOGIC OR COMMUNITY
- Time Perspective
- PROSPECTIVE
- Target Duration
- 1 Year
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
December 23, 2024
First Posted
January 1, 2025
Study Start (Estimated)
September 1, 2026
Primary Completion (Estimated)
September 1, 2026
Study Completion (Estimated)
September 1, 2028
Last Updated
December 1, 2025
Record last verified: 2025-11