By Dr Maria Gabriella Ceravolo
.
Parkinson’s disease (PD) is widely recognised as a complex and progressive neurological condition. Tremor, rigidity, and bradykinesia are often the symptoms most people associate with the disease, yet they represent only part of the picture. Increasingly, research shows that disability in Parkinson’s develops through a far more complex process. Motor impairment interacts with non-motor symptoms, comorbidities, and environmental factors, shaping how the disease affects everyday life.
Recent research conducted within the PREPARE project illustrates how large clinical datasets and functioning-oriented frameworks can help capture this complexity. The aim is not simply to observe disease progression, but to move towards predictive and more personalised rehabilitation strategies.
At UNIVPM, researchers explored how disability evolves in Parkinson’s disease by combining longitudinal clinical data with the ClinFIT Generic-30, a tool derived from the World Health Organization’s International Classification of Functioning, Disability and Health (ICF). Unlike many traditional clinical assessments, ClinFIT evaluates functioning across multiple domains rather than focusing solely on disease severity.
This shift changes how we understand progression in Parkinson’s disease. Instead of looking only at symptoms, clinicians can examine how a person’s ability to function, participate, and engage with daily life changes over time.
MOVING BEYOND MOTOR SYMPTOMS
Clinical monitoring of Parkinson’s disease has traditionally focused on motor symptoms. Instruments such as the Unified Parkinson’s Disease Rating Scale (UPDRS) remain essential tools for assessing disease severity. However, they capture only part of what patients experience.
Parkinson’s affects much more than movement. Non-motor symptoms such as depression, fatigue, sleep disturbances, and autonomic dysfunction can significantly shape a person’s daily functioning. In many cases, these symptoms have a profound impact on quality of life.
The ISPRM study confirms that non-motor symptoms progress over time and contribute independently to disability progression. In other words, worsening disability cannot be explained by motor impairment alone.
THE CLINFIT FRAMEWORK: MEASURING WHAT MATTERS
A central element of this research is the ClinFIT Generic-30, a clinical assessment tool built on the ICF framework developed by the World Health Organization.
Rather than concentrating exclusively on disease severity, ClinFIT evaluates functioning across several interconnected dimensions:
- Body functions such as muscle power, energy levels, and fatigue
- Activities, including mobility and self-care
- Participation, which refers to involvement in social roles and everyday routines
- Environmental factors that influence a person’s ability to function
This multidimensional approach allows clinicians to describe functioning in a consistent and patient-centred way. It also creates a standardised structure for analysing functioning data across large clinical datasets.
LEARNING FROM LONGITUDINAL DATA
The study analysed data from 262 individuals with Parkinson’s disease, followed over a period of approximately six years. Functioning was measured at three different moments: baseline, three years, and six years. This longitudinal design made it possible to observe how disability evolves over time rather than relying on single-moment assessments. Four key patterns emerged:
1. Progressive functional decline
ClinFIT scores showed gradual worsening across several domains of functioning. The most affected areas included mobility, self-care, domestic activities, and social participation.
2. Disease severity remains a key driver
The Hoehn and Yahr stage, a widely used measure of Parkinson’s disease severity, remained the strongest predictor of functional decline.
3. Non-motor symptoms play a major role
Symptoms such as depression, anxiety, apathy, fatigue, daytime sleepiness, and urinary dysfunction were significant independent predictors of worsening disability.
4. High variability between individuals
Not all patients followed the same trajectory. Some experienced rapid functional decline, while others maintained relatively stable functioning for years.
This variability highlights a central challenge in Parkinson’s disease management. No two patients experience the disease in exactly the same way.
FROM MONITORING TO PREDICTION
The PREPARE project provides the broader research framework that makes these analyses possible. PREPARE is a Horizon Europe initiative that brings together multiple partners and datasets to develop predictive models for rehabilitation outcomes.
A key innovation of the project is its federated data approach, in which patient data remain within their original institutions while machine-learning models are trained across multiple datasets. This structure preserves privacy while enabling large-scale analysis of real-world clinical data.
Within this context, ClinFIT becomes more than just a clinical assessment tool. It becomes a structured source of functioning data that can feed predictive models capable of:
- Identifying early markers of disability progression
- Detecting transitions between disease stages
- Characterizing individualized progression trajectories
Rather than simply relying on population averages, predictive models may allow clinicians to anticipate how a specific patient’s functioning will evolve.
TOWARD PERSONALISED REHABILITATION
The ultimate goal of this research is to enable precision rehabilitation.
Parkinson’s disease management increasingly emphasizes lifestyle interventions, rehabilitation programs, and patient engagement. However, rehabilitation strategies are often designed around generalized treatment pathways rather than individual trajectories.
By integrating ClinFIT-based functioning data with large clinical datasets, predictive models could help clinicians:
- Stratify patients according to risk of functional decline
- Tailor rehabilitation intensity and timing
- Identify individuals who may benefit from early intervention
- Monitor outcomes in real-world settings
This shift, from reactive management to proactive prediction, has the potential to transform the way rehabilitation is delivered.
CHALLENGES AHEAD
Despite the promise of predictive rehabilitation, several challenges remain. First, predictive models must be interpretable and clinically meaningful. Clinicians need to understand how models generate predictions in order to integrate them into decision-making. Second, healthcare systems must adopt standardized data frameworks, such as the OMOP Common Data Model used in the PREPARE project, to ensure that datasets from different institutions can be analyzed together.
A NEW PERSPECTIVE ON PARKINSON’S DISEASE
The integration of functioning-based assessment tools such as ClinFIT with large-scale clinical datasets marks an important step forward in Parkinson’s disease research.
Rather than focusing exclusively on disease severity, clinicians and researchers can now examine how individuals function in their daily lives, how that functioning evolves over time, and how those changes might be anticipated.
The findings from this PREPARE-based research highlight a timely message: Disability progression in Parkinson’s disease is multidimensional, highly variable, and strongly influenced by non-motor symptoms.
Recognising this complexity allows clinicians and researchers to move beyond one-size-fits-all care and toward rehabilitation strategies that reflect the lived realities of people with Parkinson’s disease.
.
Posted by: Nayara Güércio (Trilateral Research)