Using a Web-Based App to Deliver Rehabilitation Strategies to Persons With Chronic Conditions: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: The global rise in the incidence of chronic conditions and aging is associated with increased disability. Physiotherapists and occupational therapists can mitigate the resulting burden on the health care system with their expertise in optimizing function. Rehabilitation self-management strategies can assist people with chronic conditions to accept, adjust, and manage different aspects of their daily functioning. Interventions delivered using technology have the potential to increase the accessibility, availability, and affordability of rehabilitation self-management support and services. OBJECTIVE: This study aims to describe the development and usability evaluation of iamable, a web-based app created to provide rehabilitation self-management support for people with chronic conditions. METHODS: The development and evaluation of iamable were undertaken in several phases. We used user-centered design principles and an iterative process that included consultations with rehabilitation experts; developed a prototype; and conducted usability tests, heuristic evaluations, and a focus group analysis. RESULTS: The iamable app was developed to provide rehabilitation self-management strategies in the areas of exercise, fall prevention, fatigue management, pain management, physical activity, and stress management. We engaged adults aged ≥45 years with at least one chronic condition (N=11) in usability testing. They identified navigation and the understanding of instructions as the primary issues for end users. During the heuristic evaluation, clinicians (N=6) recommended that some areas of app content should be more succinct and that help should be more readily available. The focus group provided input to help guide clinical simulation testing, including strategies for selecting patients and overcoming barriers to implementation. CONCLUSIONS: We engaged end users and clinicians in the development and evaluation of the iamable app in an effort to create a web-based tool that was useful to therapists and their patients. By addressing usability issues, we were able to ensure that patients had access to rehabilitation strategies that could be used to help them better manage their health. Our app also provides therapists with a platform that they can trust to empower their patients to be more active in the management of chronic conditions. This paper provides a resource that can be used by others to develop and evaluate web-based health apps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".