Nurse-led coaching to improve dietary protein intake and reduce the risk of sarcopenia in middle-aged women
Bibliographic record
Abstract
Background and objective: An adequate dietary protein intake is critical to preventing sarcopenia, a condition characterized by reductions in muscle mass, strength, and function. The objectives of this investigation were to determine the effectiveness and how telephone-based diet coaching by a nurse practitioner contributed to improving dietary protein intakes in middle-aged women.Methods: Middle-aged women were recruited, and those randomized to receive nutrition education (including a protein prescription) and weekly diet coaching (focused on improving protein intake) provided weekly responses to three semi-structured interview questions. Qualitative content analysis was used to examine the responses to these questions. Dietary protein intake was analyzed at baseline and the end of the 12-week study from three 24-hour diet recalls at each time point using diet-analysis software and repeated measures analysis of variance.Results: Coached participants (n = 25) significantly increased dietary protein intake (55.3 ± 10.3 g at baseline to 83.7 ± 14.5 g/day at the end of the study); 19 of the 25 participants (76%) met their recommended dietary protein prescription by the study’s end. Three themes “Identifying Opportunities for Behavior Change”, “Beneficial Behavior Changes”, and “Tailoring Individual Interventions” were identified as a result of the coaching and led to the overarching theme “Empowered by Knowledge, Successful by Support” depicting how coaching contributed to the behavior changes.Conclusions: Nurse-led coaching is an effective approach enabling middle-aged women to improve dietary protein intake. These improvements are especially important in reducing the risk for the development of sarcopenia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".