Relationships Among Symptoms, Self-Care, and Quality of Life in Individuals With Multimorbidity
Bibliographic record
Abstract
Abstract Multimorbidity is a major concern for aging individuals with heart failure (HF), increasing hospitalization, mortality, and healthcare costs. However, relationships among symptoms, self-care, and quality of life (QoL) remain underexplored. This study analyzed baseline survey data from a cohort study (2022–2023) at a university-affiliated hospital. Participants were aged ≥50 years with HF and at least one additional chronic condition. Socio-demographic and clinical data were collected; symptoms were assessed using the Edmonton Symptom Assessment Scale, QoL via the EuroQoL-5D-5L, and self-care using the Self-care of Heart Failure Index. A cross-sectional regression analysis was performed on a sample of 353 participants, with a mean (±SD) age of 70 (±9.5) years. Among them, 277 (78.5%) identified themselves as their primary caregiver. In the unadjusted analysis, all nine symptoms, self-care maintenance, and self-care confidence were significantly associated with QoL. In the adjusted model, after controlling for age, gender, social support and including all symptoms and self-care domains, pain (β = -0.035, p<.001), shortness of breath (β = -0.011, p<.05), well-being (β = -0.018, p <.01), self-care maintenance (β = 0.004, p<.001), and self-care monitoring (β = -0.001, p<.05) remained significant for QoL. These findings highlight the negative association of symptoms on QoL in those with multimorbidity, while self-care maintenance has positive association with QoL. As multimorbidity becomes increasingly prevalent with aging, further research is needed to better understand the complex relationships among symptoms, self-care, and QoL to inform the development and implementation of effective, tailored multimorbidity management strategies.
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
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".