Monitor to Move - Development of real-world monitoring of movement behavior and brain plasticity-based home training for adolescents and young adults with cerebral palsy

Adolescence and young adulthood are critical periods for individuals with cerebral palsy (CP), during which functional decline may occur despite the non-progressive nature of the condition. The mechanisms underlying this decline remain poorly understood, partly due to limited methods for monitoring real-world movement behavior.This PhD thesis investigates how movement behavior can be objectively monitored and enhanced in adolescents and young adults with CP. The thesis comprises four studies: two peer-reviewed publications, one submitted manuscript, and one ongoing study.Study I developed and validated a convolutional neural network (CNN) using multiple inertial measurement units (IMUs) to classify 24-hour real-world movement behavior across severity levels of CP, achieving high classification accuracy. Study II evaluated the acceptability, technical reliability, and discriminant validity of this monitoring approach over multiple days, demonstrating high user acceptance and safety, while identifying technical challenges requiring further optimization.Study III synthesized evidence on the effects of extrinsic feedback on functioning in neurological populations through a systematic review and meta-analysis, revealing moderate to high benefits in stroke survivors but inconclusive evidence for individuals with CP. Building on these findings, Study IV presents a protocol for a randomized controlled trial evaluating a home-based, feedback-driven intervention to improve real-world movement behavior and functional outcomes in adolescents and young adults with CP.Overall, the findings suggest that real-world monitoring of movement behavior is feasible, acceptable, valid, and safe in this population, offering promising directions for scalable and personalized interventions to improve long-term outcomes in CP.