Handgrip strength: Should repeated measurements be performed in both hands?
Bibliographic record
Abstract
AIM: We aimed to determine whether both hands should be tested for handgrip strength and whether it is necessary to perform repeated measurements in each hand. METHODS: The data were obtained from the Korea National Health and Nutrition Examination Survey conducted from 2014 to 2018. The participants performed three trials of handgrip strength measurement for each hand alternately, with 60-s rest between the trials. From this pool of data, we included 23 901 participants aged ≥19 years who had completed surveys on the handgrip strength test, and obtained their medical history. RESULTS: The dominant hand had a significantly stronger handgrip strength than the non-dominant hand (32.75 ± 0.10 vs. 30.95 ± 0.09 kg, P < 0.001); however, 26.4% of the subjects had stronger handgrip strength in the non-dominant hand. During the three repeated measurements, the handgrip strength gradually increased; however, the mean difference between the trials (0.579 and 0.104 kg) was below the noninferiority threshold. In older adults, however, the mean difference in the handgrip strength between the first and the second trial was higher than the noninferiority threshold. CONCLUSIONS: While the handgrip strength gradually increased during three repeated measurements, the difference was clinically important only in older adults. Hence, we suggest that the handgrip strength should be measured in both hands and at least twice in older adults, whereas a single attempt provides a maximal value in younger adults. Geriatr Gerontol Int 2021; 21: 426-432.
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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 | 0.054 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".