Investigating the grip forces exerted by individuals with and without hand arthritis while swinging a golf club with the use of a new wearable sensor technology
Bibliographic record
Abstract
Hand arthritis is the leading cause of disability in individuals over the age of 50, causing impairments in grip strength and range of motion. Golf is often recommended to patients with hand arthritis as a low-impact sport to maintain a healthy lifestyle. As such, numerous “arthritic” golf grips have been marketed, but lack quantitative measures to justify their use. The objective of this study was to quantify the differences in total applied grip force in golfers with/without hand arthritis using several types of golf grips. Twenty-seven participants (17 without and 10 with hand arthritis) were evaluated swinging mid-iron clubs with 12 different golf grip designs (9 standard and 3 “arthritic”). The trail hand thumb, index, middle, and ring finger applied grip forces were measured using the wireless FingerTPS system. Finger grip configuration (finger joint angles) of the thumb and index were measured using the Dartfish Movement Analysis Software paired with the newly developed Grip Configuration Model to obtain grip range of motion. Results indicated that golfers with hand arthritis had a significant deficit of 45% golf grip strength (P = 0.02). In addition, individuals with hand arthritis exhibited larger forces in 11 out of 12 golf grips tested when compared with their maximum golf grip strength. Despite how these grips are marketed, there are no “savings” in finger force or grip configuration when using the “arthritic” designed golf grips. Therefore, these grips may not be beneficial for patients with hand arthritis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".