Clinical trial NCT06755645: Transforming Parkinson's Care with Predictive Algorithms
Official title: Revolutionizing Patient Care and Lifestyle Through Predictive Algorithms in Parkinson's Disease
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Parkinson
Keywords: healthy habits
physical activity
artificial intelligence
smart platform
Lead sponsor: University Ramon Llull
Sponsor class: OTHER
Collaborators: name: University of Deusto
name: Hospital Universitari Vall d'Hebron Research Institute
First posted: 2025-01-01
Enrollment: 200
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: ECOLOGIC_OR_COMMUNITY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients diagnosed with Parkinson's disease

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.

Detailed description: Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder and the most common disorder that affects motor coordination, affecting 2% of the population above 65 years old and 4% in the population above 80 years old. Patients diagnosed with PD suffer with severe motor and non-motor symptoms. Typical motor symptoms include resting tremor or rigidity, slowness in voluntary movements called bradykinesia, difficulty in pronunciation when speaking, while non-motor symptoms could be sleep, cognitive and autonomic function disorders. PD not only disrupts motor function but also significantly influences the quality of life, sleep patterns, cognitive aspects, and mood of those living with the condition. While there is no cure for PD, certain treatments such as medication, surgery, and interdisciplinary approaches can effectively address and manage PD-associated symptoms. Given the diversity of treatments and the diverse impact of motor and non-motor symptoms on daily functioning, there's a growing need for more comprehensive interventions. Numerous studies have explored the impact of lifestyle interventions, such as physical activity (PA) on various aspects of PD, suggesting that patients with PD might benefit from PA in a number of ways, from general improvements in health to disease-specific effects and potentially, disease-modifying outcomes. To this aim, we conducted a randomized double-blind clinical trial conducted to study the effects of PA on mitochondrial function in skin fibroblasts in patients with PD (NCT05963425). This was an intervention study, with twenty-four patients, that were randomly assigned into three groups (8 patients/group). The first group performed basic physical training (BPT) based on strength and resistance, the second group performed BPT combined with functional exercises (BPTFE) with cognitive and motor training, and the third group did not carry out any activity program, serving as control. The PA interventions lasted 60 minutes, with a frequency of 3 times a week, during 16 weeks. The main objective was to assess the effects of PA on mitochondrial function, and its effects on a wide variety of clinical factors, such as motor function, quality of life, sleep, cognitive aspects, and mood.

Building on the holistic insights from the previous study, this project sets out to create a technological smart platform, utilizing artificial intelligence (AI), to foster healthy habits and optimal clinical management among PD population. Technological platforms emerge as instrumental tools in transforming the landscape of PD management. These platforms can facilitate continuous monitoring and tailored interventions that integrate, for example, lifestyle-embedded exercise programs. However, while technological interventions have shown promise in promoting healthy habits, there is limited research on the integration of AI for personalized interventions in the context of PD. Models developed to date have several key limitations including use of only one single feature (i.e. motor symptoms), model assumptions that patients follow a fixed progression, or not accounting for both positive and negative effects of symptomatic therapies for PD. Herein, we address the current limitations and propose a holistic approach based on an AI model that provides professionals, patients and carers with a smart platform capable of promoting healthy habits and optimizing clinical aspects. Herein, we recognize the importance of PA along with other healthy habits for PD patients by incorporating more than 50 other variables from the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a comprehensive clinical tool used to assess both motor and non-motor symptoms in PD patients, divided into four parts that evaluate daily living experiences, motor examination, and therapy-related motor complications. Importantly, the data collected will be validated with the Parkinson's Progression Markers Initiative (PPMI), a landmark study with more than 1500 patients designed to identify biomarkers of PD progression, enhancing the reliability and applicability of our findings. The successful execution of this project will culminate in an integrated solution that combines web and mobile interfaces, enhancing the overall accessibility and effectiveness of PD diagnostic and monitoring capabilities. The proposed AI-based smart platform has significant social implications, primarily in improving the quality of life for PD patients by addressing both motor and non-motor symptoms. It fosters a more comprehensive and personalized approach to PD management, moving beyond the limitations of current treatment methods, which often focus on singular aspects of the disease. By incorporating a wide range of variables from a well-established clinical database, the platform aims to deliver tailored interventions that promote healthy habits and optimize clinical outcomes. Additionally, the platform's use of AI for continuous monitoring and adaptive interventions will empower patients and caregivers, providing real-time support and reducing the burden on healthcare systems. This represents a shift towards more patient-centered, proactive care, potentially improving disease progression management and quality of life for the aging population affected by PD. In conclusion, the comprehensive approach seeks to provide valuable insights into PD progression and support the implementation of healthy habits in clinical practice.

Eligibility criteria: Inclusion Criteria:

* Patients with Parkinson's disease

Exclusion Criteria:

* N/A

Primary outcomes:
measure: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
timeFrame: From enrollment (app download) to 1 year
Secondary outcomes:
measure: Montreal Cognitive Assessment (MoCA)
timeFrame: At enrollment (app download) and after 1 year
Other outcomes:
Locations:
city: Barcelona
country: Spain
facility: Ramon Llull University
Study officials:
References:
citation: 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.
citation: Jossa-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.