Speaker
Description
Background. Managing patients with multiple chronic conditions is a major challenge in modern health systems, particularly when exercise and lifestyle interventions are delivered in real-world settings. Robust statistical and machine-learning models require carefully designed data structures that capture the complexity of patients’ trajectories, comorbidities, and treatment exposures. In this abstract, we describe the design and implementation of a longitudinal data platform embedded in a real-world exercise rehabilitation center to support exercise-based management and advanced biostatistical and data-science research for this population.
Methods. The platform integrates routine data from an exercise-oriented rehabilitation program in which assessments are collected to tailor and monitor exercise prescriptions for adults with diverse musculoskeletal, cardiometabolic, and other chronic conditions. We constructed an event-based, key–value data model with three linked components: (i) a profile table containing demographics, socioeconomic factors, work characteristics, and baseline medical history; (ii) a longitudinal timeline of clinical, sleep, pain, training, hydrotherapy, and referral events; and (iii) detailed body-composition measurements from bioimpedance analyzers. All components are linked through a unique user identifier and harmonised time stamps. We implemented systematic coding of comorbidities and treatments, parsing of complex fields, quality-control rules (range checks, internal consistency, soft-delete flags), and de-identification procedures. We then show how this structure can be reshaped into patient-level and time-indexed analytical datasets suitable for joint longitudinal models, multistate and survival analyses, and machine-learning pipelines.
Results. The current registry comprises several thousand patients with tens of thousands of clinical and training events and a large subset with repeated body-composition assessments. The platform allows reconstruction of individual trajectories of pain, sleep quality, anthropometry, and exercise exposure, while retaining detailed information on multiple chronic conditions and referrals. We illustrate how derived features such as cumulative exercise dose, changes in visceral fat level, and multimorbidity indices can be obtained from the platform and prepared for future statistical modelling and prediction tasks.
Conclusions. This work demonstrates how a purpose-built, event-based longitudinal data platform can bridge everyday clinical exercise practice and advanced biostatistical or machine-learning methods in the management of patients with multiple conditions. By explicitly designing the data structure around future modelling needs, the registry provides a scalable foundation for studies on prognosis, treatment response, and personalized exercise prescriptions
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