Current Practices of Irish Dietitians Assessing and Monitoring Muscle Strength, Mass and Function: A Cross‐Sectional Survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: Measuring muscle mass, strength, and function is vital in nutritional assessment, offering valuable insights into overall health, including nutritional adequacy, metabolic function and physical well-being. Nonetheless, the use of these measures for nutritional assessment and monitoring in dietetic practice is not widely implemented, and gaps in care remain. This study aimed to explore Irish dietitians' current nutritional assessment practices related to muscle health. METHODS: A cross-sectional descriptive 29-item online survey was adapted and distributed via link in email and social media to state-registered dietitians (N = 1340) in Ireland between 21 September 2023 and 26 October 2023. Data were analysed descriptively. RESULTS: The majority of dietitians (84/85) agreed that musculature was important in the assessment of nutritional status, with 80% (n = 56/70) reporting the integration of at least one assessment of muscle health into their clinical practice. Handgrip strength (HGS) was viewed as the most important (95.7%; n = 67/70), frequently applied (64.3%; n = 45/70) and most useful for monitoring muscle health (77.1%; n = 54/70). Regardless, the frequency of muscle health assessment in routine practice was low. The muscle health assessments that are routinely ( > once/week) measured include body weight (82.9%; n = 58/70), BMI (81.4%; n = 57/70), HGS (25.7%; n = 18/70) and the Timed up and go test, chair stand test or short physical performance battery (10%; n = 7/70). The main barriers to muscle health assessment were 'lack of training/application experience' (61.4%, n = 43/70) and 'lack of device availability' (58.5%, n = 41/70). CONCLUSION: This study provides insights into the application of muscle health assessments within nutritional assessment among Irish dietitians. Results indicate a gap between the recognised value of muscle health and its use in nutritional assessment. Despite an almost unanimous agreement on the importance of musculature, challenges such as insufficient training and lack of equipment hinder the widespread implementation of muscle health assessment as a standard component of nutritional assessment. These findings emphasise the need for further practical education and measures to improve the availability of equipment to bridge this gap and optimise nutritional care.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".