2026-04-14T02:42:50Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/706462025-06-11T00:51:13Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Lumsdon, Jack author Delgado Ortiz, Laura author García Aymerich, Judith author Cantu, Alma author 2025-06-10T06:14:40Z 2025-06-10T06:14:40Z 2025 Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients; however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients' data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. Methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or proximal femoral fracture. The consensus was not reached for any visualization reviewed in round 2 or 3. Feedback was generally positive, and some participants reported that they were able to understand the visualization and interpret what the visualization represented. Conclusions: Through the feedback provided and existing data visualization principles, we developed recommendations for future visualizations of mobility- and health-related data. Visualizations should be readable by ensuring that large and clear fonts are used and should be friendly for people with vision impairments, such as color blindness. Patients have a strong understanding of their own condition and its variability; hence, adding additional factors into visualizations is recommended to better reflect the nuances of a condition. Ensuring that outcomes and visualizations are meaningful requires close collaboration with patients throughout the development process. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU; grant agreement 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). The content of the current publication reflects the authors’ view, and neither IMI nor the European Union, EFPIA, nor any associated partners are responsible for any use that may be made of the information contained herein. HG was supported by the Fraunhofer Internal Programs (grants Attract 044–602140 and 044–602150). HG also received institutional funding from the Deutsche Forschungsgemeinschaft (German Research Foundation)—SFB 1483—Project-ID 442419336, EmpkinS. LR, AY, and SDD were also supported by the IMI2 JU project IDEA-FAST (grant agreement 853981). LR, AY, and SDD were supported by the National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre based at The Newcastle upon Tyne Hospital National Health Service (NHS) Foundation Trust, Newcastle University, and the Cumbria, Northumberland, and Tyne and Wear NHS Foundation Trust and the NIHR/Wellcome Trust Clinical Research Facility infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust. LR received funding from the NIHR Senior Investigator Awards (2020-2024 and 2024-2028). EB was supported by the NIHR Sheffield Biomedical-Research Centre and Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or the Department of Health and Social Care. http://hdl.handle.net/10230/70646 Cocreation Data visualization Digital mobility outcomes Mobility Wearable devices Cocreating the visualization of digital mobility outcomes: Delphi-type process with patients