An Exercise-Based Precision Medicine Tool and Smartphone App for Managing Achilles Tendinopathy (the 'PhysViz' System): User-Centered Development Study
Bibliographic record
Abstract
BACKGROUND: People with Achilles tendinopathy (AT) experience persistent pain that can limit engagement with daily occupations and negatively impact mental health. Current therapeutic exercise approaches vary in success, with many people experiencing reinjury, leading to a cycle of chronic tendinopathy often lasting years. High-magnitude precision loading may help people exit this feedback cycle, but applying these principles clinically is challenging. OBJECTIVE: This user-centered design case study aims to provide an overview on how the PhysViz (a prototype for a novel remote rehabilitation intervention for AT management) was developed and evaluated following the development phase of the Framework for Accelerated and Systematic Technology-Based Intervention Development and Evaluation Research (FASTER). METHODS: The development process engaged a multidisciplinary team comprising people with AT experiences, clinicians, and engineers. It followed the 5 stages within the FASTER development phase: empathize, define, ideate, prototype, and test. The PhysViz development and evaluation were informed by needs assessments, surveys, literature reviews, validation studies, case studies, roundtable discussions, and usability testing (some of which have been published previously). The FASTER systematically guided the integration of evidence-based features and behavior change theory. RESULTS: By using the FASTER and ensuring that the PhysViz system was underpinned by diverse stakeholder needs, this work resulted in the development of a working prototype for both the PhysViz physical exercise tool and the accompanying PhysViz software package (mobile app and web application). A variety of study designs informed user-desired features that were integrated into the PhysViz prototype, including real-time biofeedback in the form of precision load monitoring, customizable exercise programs, and pain tracking. In addition, clinicians can visualize client data longitudinally and make changes to client exercise prescriptions remotely based on objective data. The identified areas for improvement, such as upgrading the user interface and user experience and expanding clinical applications, provide valuable insights for future PhysViz iterations. Further research is warranted to assess the long-term efficacy and feasibility of the PhysViz in diverse clinical settings and its potential to improve AT symptoms. CONCLUSIONS: Being one of the first technology development initiatives guided by the FASTER, this study exemplifies a systematic and multidisciplinary approach to creating a remote rehabilitation intervention. By incorporating stakeholder feedback and evidence-based features, the PhysViz addresses key challenges in AT rehabilitation, offering a novel solution for precision loading and therapeutic exercise engagement. Positive feedback from users and clinicians underscores the potential impact of the PhysViz in improving AT management outcomes. The PhysViz serves as a model for technology-based intervention development, with potential implications for other tendinopathies and remote rehabilitation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".