Goal setting among older adults starting mobile health cardiac rehabilitation in the <scp>RESILIENT</scp> trial
Bibliographic record
Abstract
BACKGROUND: There is growing recognition that healthcare should align with individuals' health priorities; however, these priorities remain undefined, especially among older adults. The Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting (RESILIENT) trial, designed to test the efficacy of mobile health cardiac rehabilitation (mHealth-CR) in an older cohort, also measures the attainment of participant-defined health outcome goals as a prespecified secondary endpoint. This study aimed to characterize the health priorities of older adults with ischemic heart disease (IHD) using goal attainment scaling-a technique for measuring individualized goal achievement-in a sample of 100 RESILIENT participants. METHODS: The ongoing RESILIENT trial randomizes patients aged ≥65 years with IHD (defined as hospitalization for acute coronary syndrome and/or coronary revascularization), to receive mHealth-CR or usual care. For the current study, we qualitatively coded baseline goal attainment scales from randomly selected batches of 20 participants to identify participants' cardiac rehabilitation outcome goals and their perceptions of barriers and action plans for goal attainment. We used a deductive framework (i.e., 4 value categories from Patient Priorities Care) and inductive approaches to code and analyze interviews until thematic saturation. RESULTS: This sample of 100 older adults set diverse health outcome goals. Most (54.6%) prioritized physical activity, fewer (17.1%) identified symptom management, fewer still (13.7%) prioritized health metrics, mostly comprised of weight loss goals (10.3%), and the fewest (<4%) were related to clinical metrics such as reducing cholesterol or preventing hospital readmission. Participants anticipated extrinsic (access to places to exercise, time) and intrinsic (non-cardiac pain, motivation) barriers. Action plans detailed strategies for exercise, motivation, accountability, and overcoming time constraints. CONCLUSIONS: Using goal attainment scaling, we elicited specific and measurable goals among older adults with IHD beginning cardiac rehabilitation. Priorities were predominantly functional, diverging from clinical metrics emphasized by clinicians and healthcare systems.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".