Home-Based, Peer-Led Chronic Illness Self-Management Training: Findings From a 1-Year Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: Studies suggest peer-led self-management training improves chronic illness outcomes by enhancing illness management self-efficacy. Limitations of most studies, however, include use of multiple outcome measures without predesignated primary outcomes and lack of randomized follow-up beyond 6 months. We conducted a 1-year randomized controlled trial of Homing in on Health (HIOH), a Chronic Disease Self-Management Program variant, addressing these limitations. METHODS: We randomized outpatients (N = 415) aged 40 years and older and who had 1 or more of 6 common chronic illnesses, plus functional impairment, to HIOH delivered in homes or by telephone for 6 weeks or to usual care. Primary outcomes were the Medical Outcomes Study 36-ltem short-form health survey's physical component (PCS-36) and mental component (MCS-36) summary scores. Secondary outcomes included the EuroQol EQ-5D and visual analog scale (EQ VAS), hospitalizations, and health care expenditures. RESULTS: Compared with usual care, HIOH delivered in the home led to significantly higher illness management self-efficacy at 6 weeks (effect size = 0.27; 95% CI, 0.10-0.43) and at 6 months (0.17; 95% CI, 0.01-0.33), but not at 1 year. In-home HIOH had no significant effects on PCS-36 or MCS-36 scores and led to improvement in only 1 secondary outcome, the EQ VAS (1-year effect size = 0.40; CI, 0.14-0.66). HIOH delivered by telephone had no significant effects on any outcomes. CONCLUSIONS: Despite leading to improvements in self-efficacy comparable to those in other CDSMP studies, in-home HIOH had a limited sustained effect on only 1 secondary health status measure and no effect on utilization. These findings question the cost-effectiveness of peer-led illness self-management training from the health system perspective.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".