Credibility of digital health predictors of human movement

Human mobility is a critical indicator of health, impacted by complex physiological systems. Disruptions to these systems can lead to reduced mobility, with severe consequences including loss of independence and increased mortality. This not only affects individuals but also poses societal and healthcare challenges. To address this, innovative solutions are needed, leveraging technologies like wearable sensors and computational simulations. These tools offer deep insights into human biomechanics, allowing for long-term monitoring and analysis of mobility parameters, even in pathological conditions. Innovations in wearable sensors enable real-world mobility monitoring, while computational models, particularly musculoskeletal dynamics models, predict human body behaviour, facilitating personalised treatment plans. However, ensuring the credibility of these technologies is paramount, requiring rigorous testing against established standards before clinical use. This PhD thesis aimed to investigate the credibility assessment of two models: analytics software for wearable sensor data and musculoskeletal dynamics models for identifying the primary cause for the loss of muscle force (i.e., dynapenia), mirroring the two distinct projects (i.e., the Mobilise-D and the ForceLoss projects, respectively). Both projects targeted conditions affecting mobility, necessitating credibility assessments for drug development and clinical decision-making. The Mobilise-D project, funded by EU, aimed to qualify mobility-related parameters extracted from wearable sensors as biomarkers for drug development. Engagements with regulatory authorities provided valuable feedback, guiding the qualification process. Conversely, the ForceLoss project developed a new framework combining experimental measurements and computational simulations for the differential diagnosis of dynapenia on osteoarthritic patients. In addition to credibility assessments, the thesis aimed to lower barriers by sharing experimental data and regulatory insights.