GripAble: Interrater reliability and normative grip strength of UK population
Bibliographic record
Abstract
BACKGROUND: Hand grip strength is an established indicator of individual health status and is used as a biomarker for predicting mortality, disability, and disease risks. GripAble hand grip dynamometer offers a modernized approach to measuring grip strength with its digital and high-accuracy measurement system. PURPOSE: This study aimed to (1) assess the interrater reliability of maximum grip strength (MGS) measurement and (2) establish GripAble's own gender-, age group- and hand-stratified normative MGS reference values of the adult UK population. STUDY DESIGN: Cross-sectional study design. METHODS: Interrater reliability among three raters assessing 30 participants across diverse age groups was measured using the intraclass correlation. In the second study, 11 investigators gathered MGS data from 907 participants across diverse age groups and gender. The average, standard deviation, minimum, median, maximum, and percentiles of MGS were computed for each gender, age group, and hand (L/R). The relationship between MGS and age was examined using quantile regression analysis. Additionally, generalized linear model regression analysis was conducted to explore the influence of participants' demographics (gender, hand [L/R], hand length, hand circumference, age, weight, and height) on MGS. RESULTS: MGS measurements between raters showed excellent agreement (ICC(2,1) = 0.991, 95% confidence interval [0.98, 1.0]). The MGS and age relationship follows a curvilinear pattern, reaching a peak median MGS values of up to 20 kg between 30 and 49 years for females and up to 35 kg between 30 and 59 years for males. Subsequently, MGS declined as age advanced. Gender and hand (L/R) emerged as the primary factors influencing MGS, followed by hand length, hand circumference, age, weight, and height. CONCLUSIONS: The presented normative MGS reference values can be used for interpreting MGS measurements obtained from adults in the United Kingdom using GripAble. This study, along with previous studies on GripAble devices, confirms GripAble as a reliable and valid tool for measuring MGS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".