Usability of Self-Management for Amputee Rehabilitation using Technology (SMART): An online self-management program for users with lower limb loss
Bibliographic record
Abstract
BACKGROUND: Individuals with lower limb loss (LLL) need education to adapt to their amputation. Self-management programs provide education and supportive skills to manage health-related physical and psychological challenges. eHealth technologies, such as online platforms, are increasing access to educational resources. We designed an online self-management program for individuals with LLL called Self-Management for Amputee Rehabilitation using Technology (SMART); however, before evaluating its efficacy, we wanted to understand its appropriateness in the target population. OBJECTIVE: To assess the usability of SMART among individuals with LLL. STUDY DESIGN: The study used a concurrent and retrospective think-aloud process. METHODS: Individuals with LLL, aged 18 years or older (n = 9), reviewed the modules during an online video conferencing session with an assessor. SMART included four stakeholder-informed modules with 18 total sections. Participants were asked to think aloud while completing 11 SMART tasks, such as entering SMART, goal setting, finding skin care, and reading the content of 10 sections, including limb care, diet, fatigue, and energy. The interviews were transcribed verbatim and analyzed using directed content analysis. RESULTS: The median age was 58 (range: 30-69) years. Overall, SMART was perceived as straightforward, easy to navigate, and an accessible resource for education and skills. Difficulties were identified with navigation (e.g. skipping the "Foot care for diabetes" section), presentation (e.g. unclear audio), and language (e.g. pistoning and contracture). CONCLUSIONS: SMART was redesigned to address the usability issues. The next step is to explore the perceived usefulness of SMART for content and intention to use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".