Mobilise-D: Exploring mobility data to support health monitoring

Mobilise-D was a 5-year, IMI-funded project that produced validated and accepted digital mobility outcomes to monitor the daily life gait of people with various mobility problems, aiming to improve follow-up and personalised care.

Mobilise-D aimed to develop validated sensor-algorithm solutions to monitor mobility in real-world environments, ultimately enhancing therapeutic development and regulatory processes for clinical trials. A technical validation study ensured algorithm accuracy in both laboratory and real-world settings within the various medical conditions. A clinical validation study ensured that the outcomes of the technical study provided reliable, real-world mobility measurements essential for regulatory approval. Both studies enrolled over 2,000 participants, generating substantial datasets linking mobility changes to health status and treatment outcomes.

Exploration of Multivariate Heterogeneous Data

Part of the technical validation study required to identify which among the existing algorithms were the most effective for assessing mobility from these sensors, relative to specific diseases. The data involved in the ranking consists of heterogeneous multivariate data in need of exploration. Such data complexity for exploration purposes consisted our first data visualisation challenge.

We proposed a tool that enables the exploration of multivariate heterogeneous data by combining the strengths of Parallel Coordinates and Parallel Sets. The approach was validated through a usability evaluation, which confirms that the presented design is as efficient as other existing tools while offering additional features for correlation analysis.

Parallel Assemblies Plot (PAP), a visualisation metaphor enabling the identification of a correlation between variables disease, period and type of bout and a correlation between the type of bout and bout time.

The work was further extended to a fully functional dashboard, allowing the exploration of any multivariate heterogeneous datasets.

Demonstration of a fully functional dashboard for exploring multivariate heterogeneous datasets. This tool integrates PAP to facilitate correlation analysis, interactive filtering, and detailed data inspection.

Enhancing Patient-Centred Data Visualisation

Mobilise-D relied on patients’ participation whose engagement and involvement were mandatory for the success of the project. The project investigated how to give these patients access to their data considering accessibility and sharing restrictions.

We examined the challenges of patient-centred mobility data visualisation, drawing insights from the Mobilise-D initiative. We identified key obstacles, including contextual gaps and interpretation challenges, and proposed a structured recommendation framework.

Team

and members of the Mobilise-D consortium (see authors of the publications).

Partners

Funding

This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this page reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

Publications

  • Lumsdon, J., Wilson, C., Alcock, L., Becker, C., Benvenuti, F., Bonci, T., Van Den Brande, K., Brittain, G., Brown, P., Buckley, E., Caruso, M., Caulfield, B., Cereatti, A., Delgado-Ortiz, L., Del DIn, S., Evers, J., Garcia-Aymerich, J., Gaßner, H., Gur Arieh, T., … Cantu, A. (2025). Co-creating the visualisation of digital mobility outcomes: A Delphi-type process with patients. JMIR Formative Research. https://doi.org/10.2196/68782.

  • Kirk, C., Küderle, A., Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Soltani, A., Gazit, E., Salis, F., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., … Van Gelder, L. (2024). Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Scientific Reports, 14(1), 1754. https://doi.org/10.1038/s41598-024-51766-5

  • Keogh, A., Mc Ardle, R., Diaconu, M. G., Ammour, N., Arnera, V., Balzani, F., Brittain, G., Buckley, E., Buttery, S., Cantu, A., Corriol-Rohou, S., Delgado-Ortiz, L., Duysens, J., Forman-Hardy, T., Gur-Arieh, T., Hamerlijnck, D., Linnell, J., Leocani, L., McQuillan, T., … Mobilise-D consortium. (2023). Mobilizing patient and public involvement in the development of real-world digital technology solutions: Tutorial. Journal of Medical Internet Research, 25, e44206. https://doi.org/10.2196/44206

  • Kirk, C., Kuederle, A., Mico-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Soltani, A., Gazit, E., Salis, F., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., … Din, S. D. (2023). Estimating real-world walking speed from a single wearable device: Analytical pipeline, results and lessons learnt from the Mobilise-D technical validation study. https://doi.org/10.21203/rs.3.rs-2965670/v1

  • Romijnders, R., Salis, F., Hansen, C., Küderle, A., Paraschiv-Ionescu, A., Cereatti, A., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Bonci, T., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., Chiari, L., D’Ascanio, I., … Maetzler, W. (2023). Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Frontiers in Neurology, 14, 1247532. https://doi.org/10.3389/fneur.2023.1247532

  • Cantu, A., Micó-Amigo, M. E., Del Din, S., & Fernstad, S. J. (2023). Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: Application to digital mobility outcomes. 2023 IEEE 16th Pacific Visualization Symposium (PacificVis), 21–30. https://doi.org/10.1109/PacificVis56936.2023.00010

  • Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Kirk, C., Soltani, A., Küderle, A., Gazit, E., Salis, F., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., … for the Mobilise-D consortium. (2023). Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. Journal of NeuroEngineering and Rehabilitation, 20(1), 78. https://doi.org/10.1186/s12984-023-01198-5