Effectiveness of Interventions to Improve Malnutrition Among Older Adults Living with Frailty Who Are Discharged from the Acute Setting: A Systematic Review
Bibliographic record
Abstract
BACKGROUND & AIM: Malnutrition and frailty are prevalent among older adults following discharge from acute care, including emergency departments. This transition period presents a critical window for targeted nutrition interventions. This systematic review synthesises evidence on the effectiveness of nutrition interventions for malnourished, frail older adults and incorporates analyses of stakeholders' perspectives, including those of patients, caregivers, and healthcare professionals. By integrating clinical outcomes with stakeholder experiences, the review aims to identify strategies that can optimise nutritional care and support recovery in the post-acute setting. METHODS: Searches were conducted in Scopus, CINAHL, EBSCO, EMBASE, and PubMed for randomised controlled trials (RCTs) of nutrition interventions in participants ≥65 years living with frailty and identified as malnourished on discharge from acute care. The primary outcome was assessing the effects of nutrition interventions on malnutrition, nutrition status, physical function and frailty, food intake, and quality of life. Secondary outcomes were hospital readmission and mortality. The quality of studies was assessed using the Cochrane Risk of Bias Tool (V2). RESULTS: Five RCTs with 551 participants were included. Nutrition interventions, including counselling, oral nutrition supplements, and multidisciplinary strategies, improved dietary intake, weight, frailty, physical function, BMI, and quality of life in older adults post-discharge. Some studies also reported reduced hospital stays, readmissions, and mortality. However, none explored stakeholder perspectives, highlighting a gap in person-centred transitional care design. CONCLUSION: This systematic review highlights a critical gap in evidence for nutrition interventions targeting frail older adults at hospital discharge. While short-term benefits were observed, long-term sustainability and real-world feasibility remain uncertain. The absence of stakeholder involvement further limits person-centred design. These findings underscore the need for integrated nutrition care pathways that embed effective interventions into transitional care models.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".