The effect of blue dishware versus white dishware on food intake and eating challenges among residents living with dementia: a crossover trial
Bibliographic record
Abstract
OBJECTIVE: Residents living with dementia (RLWD) often experience changes in their visual perception, which could reduce food intake. Inadequate food intake is known to cause malnutrition, which increases the risk of hospitalization, morbidity, and mortality. This study evaluated the effectiveness of using blue dishware compared to white dishware to improve food intake and mitigate eating challenges among 18 RLWD (mean age 84.6 ± 7.9 years, 72.2% female). RESULTS: A within-within person crossover design determined differences in food intake and eating challenges between blue and white dishware conditions. Five participants responded to the blue dishware and increased their average food intake by ≥ 10%. Responders were not different from non-responders in terms of demographic or health characteristics. The proportion of eating challenges experienced was not significantly different between the blue and white dishware conditions. Percent food intake was significantly greater at lunch (83.5 ± 19.0%) compared to dinner (75.8 ± 22.1%; p < 0.0001), regardless of dishware condition. However, there were no significant differences for food intake between the dishware conditions, even after matching food choices. Promoting food intake and reducing eating challenges in RLWD likely needs multi-component interventions targeting meal quality, meal access, and mealtime experience. Trial registration ClincialTrials.gov Identifier: NCT04298788. Retrospectively registered: 6 March 2020, https://clinicaltrials.gov/ct2/show/NCT04298788?term=NCT04298788&draw=2&rank=1 .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".