Functioning is a fundamental dimension of health, yet it remains underrepresented in clinical systems, epidemiological models, and health policy. Our group develops and applies novel statistical and machine learning methods to operationalize functioning in a way that is clinically meaningful, scalable, and relevant for multiple levels of the health system. A central aim is to advance the concept of a Functioning Indicator to support decision-making across:

•    Micro (Clinical Practice): We develop clinical prediction models that inform individualized rehabilitation plans, guide return-to-function expectations, and support shared decision-making between patients and clinicians.
•    Meso (Clinical Decision Support Systems): We integrate functioning data into clinical decision support systems (CDSS) that assist healthcare providers in evaluating risk, allocating resources, and optimizing care pathways in rehabilitation settings.
•    Macro (Population Level): We contribute to the development of functioning-based indicators that complement traditional population health measures, enabling a more comprehensive assessment of health system performance and disability trends.

Our research is grounded in modeling functioning progression, capturing complex data structures from longitudinal records, and identifying patterns of interaction between disease progression and functioning loss or recovery. These methods are applied to large-scale RWD sources, including electronic health records, rehabilitation databases, and international registries.
 

Optimizing rehabilitation is essential to achieving meaningful improvements in functioning and well-being. Our group conducts applied research to identify the most effective and timely interventions, focusing on evidence that can directly inform practice. Key questions addressed in this stream of work include:

•    Which interventions yield the largest improvements in functioning?
•    Which patient subgroups benefit most from specific treatments?
•    What are the most relevant and modifiable factors influencing outcomes?
•    When should interventions be delivered for maximum effectiveness?
•    How do outcomes vary by rehabilitation setting (e.g., inpatient, outpatient, community)?

We use predictive modeling and comparative effectiveness research to generate RWE that informs care across the continuum of rehabilitation, from acute to long-term recovery. These insights are used to support clinical guideline development, resource allocation, and patient-centered care planning.
 

Spinal cord injury (SCI) is a central focus of our work at Swiss Paraplegic Research. SCI presents a complex, lifelong condition that profoundly impacts functioning. Our group investigates:

•    Functioning trajectories following SCI, with an emphasis on recovery patterns and long-term outcomes.
•    Interactions between disease progression and functional recovery, including secondary complications and comorbidities.
•    Modifiable factors associated with improved rehabilitation outcomes, such as psychosocial support, early intervention, and adaptive technology use.
•    International comparisons of outcomes and care models, using harmonized datasets to explore cross-country differences and inform global best practices.

This work contributes to individualized care planning and supports broader efforts to improve well-being and inclusion for individuals living with SCI.
 

In the area of sports rehabilitation, our group applies functioning epidemiology to athletic populations, with particular interest in injury prevention and return-to-play strategies. Our research addresses:

•    Epidemiology of injuries in both professional and youth athletes.
•    Risk prediction models for injury occurrence and reinjury.
•    Prognostic factors for recovery duration and functional performance.
•    Translation of evidence from rehabilitation and public health to real-world sports settings.
•    Functioning in children and youth, considering developmental and environmental contexts.

Our collaborations with elite sports organizations, including FC Barcelona, support a two-way exchange between clinical research and sports practice. We aim to bridge the gap between performance goals and health outcomes through data-driven, evidence-based approaches.

Across all pillars, our research emphasizes the use of real-world data, the development of clinical prediction models, and the implementation of decision support systems. These efforts are aligned with broader public health goals, including more inclusive and efficient health systems and the integration of functioning as a core metric in health policy.

By building an evidence base around functioning, we aim to support a shift toward person-centered care, responsive rehabilitation practices, and more meaningful health system performance indicators. Our work ultimately contributes to improving the lives of individuals living with health conditions and impairments by providing them with better tools, more precise care, and stronger systems of support.
 

In addition to our core research agenda, our group engages in several interdisciplinary projects that apply functioning epidemiology, real-world data analysis, and health systems thinking to broader health challenges. These projects reflect our interest in the complex interactions between biological, behavioral, and environmental factors, and their implications for clinical care and public health policy.

Circadian Rhythms and Fertility    
In collaboration with clinical and academic partners, we are investigating the role of circadian rhythms and social jetlag in fertility outcomes. This project explores how biological timing misalignments, driven by work schedules, light exposure, and behavioral routines, may influence reproductive health. Using data from fertility clinics and digital health platforms, we analyze chronotype patterns, treatment responses, and time-to-pregnancy metrics. The goal is to develop personalized guidance and intervention strategies for individuals undergoing assisted reproduction, including in vitro fertilization (IVF).

Vaccination and Preventive Health Policies
Our group contributes to the evaluation of vaccination programs and preventive health interventions, particularly through the use of observational data and quasi-experimental designs. We analyze uptake, effectiveness, and safety across different demographic groups and health systems, with special attention to vulnerable or under-vaccinated populations. This work supports policy decisions at the national and international levels, aiming to improve the design and equity of public health campaigns.

Pharmacoepidemiology and Drug Safety
Building on prior experience in biostatistics and real-world evidence generation, we continue to engage in pharmacoepidemiology projects. These include the use of longitudinal health data to assess drug utilization patterns, adherence, effectiveness, and adverse event risks. Our methodological focus includes propensity score methods, target trial emulation, and causal inference frameworks to reduce bias in non-randomized studies. These projects often intersect with our work on functioning and rehabilitation by exploring medication-related barriers or facilitators to recovery and participation.

These additional research streams enrich our core agenda by incorporating biological, behavioral, and policy-level perspectives. They exemplify our commitment to interdisciplinary collaboration and to producing high-quality, policy-relevant evidence that improves health across life stages and populations.