Abstract 15626: Visualizing Self-reported Symptom Status Can Support Self-management for Heart Failure Patients
Bibliographic record
Abstract
Introduction: Symptom self-management is important in heart failure (HF) but challenging given the high prevalence of associated cognitive impairment. Visualizations may support symptom self-management by improving recognition of meaningful changes in symptoms over time. The purpose of this study was to evaluate whether visualizations of self-reported symptom status were associated with recognition of worsening symptoms (risk perception) and intention to act on worsening symptoms (behavioral intention) . Methods: We recruited hospitalized, English-speaking adults with HF from 2 inpatient cardiac units at an urban academic medical center. A professional designer developed 4 visualizations of simulated changes in self-reported symptoms (e.g., fatigue; Figure 1 ) using best practices for displaying health information to adults with cognitive impairment. Using the participants’ favorite visualization of the 4, we evaluated risk perception and behavioral intention using validated scales. We also collected demographics, health literacy, and cognitive status using the Montreal Cognitive Assessment (MoCA). Results: Participants (n=40) had an average age of 61.3 years (±12.5) and were 22% female, 52% White, and 38% Latino. More than half (55%) had low health literacy. Most (88%) had mild/moderate cognitive impairment (MoCA score < 26). The favorite visualization (selected by 42%) was the number line. Regarding risk perception, 70% reported it was very/extremely likely their HF was getting worse and 54% reported they were very/extremely worried about their HF getting worse based on the visualization. Regarding behavioral intention, most (82%) were very/extremely likely to act based on the visualization. Conclusion: Visualizations of symptoms over time communicated risk to most patients who in turn reported being more likely to act, and may be an effective tool to support symptom self-management among HF patients with mild/moderate cognitive impairment.
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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) | 0.000 | 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".