期刊
ERGONOMICS
卷 53, 期 1, 页码 109-129出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00140130903389068
关键词
holding time; fatigue; isometric; muscle; references; elbow; knee; shoulder; ankle; trunk; grip
资金
- National Institutes for Health [K12 HD055931, 1K01AR056134]
- NRSA [F31 AR056175]
- Foundation for Physical Therapy
- United States Council for Automotive Research (USCAR), Dearborn,
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [K12HD055931] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [F31AR056175, K01AR056134] Funding Source: NIH RePORTER
Static task intensity-endurance time (ET) relationships (e.g. Rohmert's curve) were first reported decades ago. However, a comprehensive meta-analysis to compare experimentally-observed ETs across bodily regions has not been reported. We performed a systematic literature review of ETs for static contractions, developed joint-specific power and exponential models of the intensity-ET relationships, and compared these models between each joint (ankle, trunk, hand/grip, elbow, knee, and shoulder) and the pooled data (generalised curve). 194 publications were found, representing a total of 369 data points. The power model provided the best fit to the experimental data. Significant intensity-dependent ET differences were predicted between each pair of joints. Overall, the ankle was most fatigue-resistant, followed by the trunk, hand/grip, elbow, knee and finally the shoulder was most fatigable. We conclude ET varies systematically between joints, in some cases with large effect sizes. Thus, a single generalised ET model does not adequately represent fatigue across joints. Statement of Relevance: Rohmert curves have been used in ergonomic analyses of fatigue, as there are limited tools available to accurately predict force decrements. This study provides updated endurance time-intensity curves using a large meta-analysis of fatigue data. Specific models derived for five distinct joint regions should further increase prediction accuracy.
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