CAML LISBOA
The Lisbon Academic Medical Center (Centro Académico de Medicina de Lisboa-CAML) is an innovative academic centre and a consortium of three institutions: one of the main portuguese hospitals – North Lisbon University Hospital Center (Centro Hospitalar Universitário Lisboa Norte-CHULN), a Medical School – Lisbon School of Medicine (Faculdade de Medicina da Universidade de Lisboa-FMUL) – and a biomedical research institute of excellence – Instituto de Medicina Molecular João Lobo Antunes (IMM).
Patients involved in the study
The recruitment phase for the BRAINTEASER clinical study has successfully concluded, enrolling 51 ALS patients. The next step focuses on monitoring these participants over time to collect longitudinal data. This will provide valuable insights into the progression of ALS and enable a comprehensive evaluation of the interventions implemented during the study.
Key takeaways from the BRAINTEASER observational study
The study has offered several critical lessons. High adherence to the study protocol has demonstrated the feasibility and acceptability of the interventions. Remote monitoring has emerged as a particularly valuable tool, offering potential benefits for managing ALS. Both patients and caregivers have expressed satisfaction with the BRAINTEASER model and its associated interventions, reinforcing its relevance and impact in clinical settings.
Patient and caregiver perspectives on the BRAINTEASER model
Patients and their caregivers have responded positively to the BRAINTEASER model. The remote monitoring capabilities, personalised care approach, and improved communication provided by the model are highly appreciated. The model has been empowering, helping to raise awareness of environmental factors such as air quality and the importance of physical activity, while also fostering a supportive network for patients and caregivers alike.
Future plans for BRAINTEASER
With the conclusion of the BRAINTEASER project, efforts will focus on building upon its promising findings. The predictive capabilities of the AI models developed during the study will be tested in clinical settings to enhance early detection and intervention for ALS progression. Remote monitoring strategies will be further refined and integrated into everyday clinical practice to optimise patient care and improve overall outcomes. These initiatives aim to ensure that the advancements made during the project will continue to benefit patients and clinicians in the years to come.