Increasing Fluid Intake and Reducing Dehydration Risk in Older People Living in Long-Term Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy of interventions and environmental factors on increasing fluid intake or reducing dehydration risk in older people living in long-term care facilities. DESIGN: Systematic review of intervention and observational studies. DATA SOURCES: Thirteen electronic databases were searched from inception until September 2013 in all languages. References of included papers and reviews were checked. ELIGIBILITY CRITERIA: Intervention and observational studies investigating modifiable factors to increase fluid intake and/or reduce dehydration risk in older people (≥65 years) living in long-term care facilities who could drink orally. REVIEW METHODS: Two reviewers independently screened, selected, abstracted data, and assessed risk of bias from included studies; narrative synthesis was performed. RESULTS: A total of 4328 titles and abstracts were identified, 325 full-text articles were obtained and 23 were included in the review. Nineteen intervention and 4 observational studies from 7 countries investigated factors at the resident, institutional, or policy level. Overall, the studies were at high risk of bias due to selection and attrition bias and lack of valid outcome measures of fluid intake and dehydration assessment. Reported findings from 6 of the 9 intervention studies investigating the effect of multicomponent strategies on fluid intake or dehydration described a positive effect. Components included greater choice and availability of beverages, increased staff awareness, and increased staff assistance with drinking and toileting. Implementation of the US Resident Assessment Instrument reduced dehydration prevalence from 3% to 1%, P = .01. Two smaller studies reported positive effects: one on fluid intake in 9 men with Alzheimer disease using high-contrast red cups, the other involved supplementing 13 mildly dehydrated residents with oral hydration solution over 5 days to reduce dehydration. Modifications to the dining environment, advice to residents, presentation of beverages, and mode of delivery (straw vs beaker; prethickened drinks vs those thickened at the bedside) were inconclusive. Two large observational studies with good internal validity investigated effects of ownership; in Canada, for-profit ownership was associated with increased hospital admissions for dehydration; no difference was seen in dehydration prevalence between US for-profit and not-for-profit homes, although chain facilities were associated with lower odds of dehydration. This US study did not suggest any effect of staffing levels on dehydration prevalence. CONCLUSIONS: A wide range of interventions and exposures were identified, but the efficacy of many strategies remains unproven due to the high risk of bias present in many studies. Reducing dehydration prevalence in long-term care facilities is likely to require multiple strategies involving policymakers, management, and care staff, but these require further investigation using more robust study methodologies. The review protocol was registered with the International Prospective Register of Systematic Reviews (http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012003100).
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".