Kieser, J, Langford, M, Stover, E, Tomkinson, GR, Clark, BC, Cawthon, PM, and McGrath, R. Absolute agreement between subjective hand squeeze and objective handgrip strength in adults. J Strength Cond Res 39(1): 16-23, 2025-Despite handgrip strength (HGS) being considered a convenient muscle strength assessment, HGS lacks routine measurement in sports medicine and healthcare settings because barriers such as time and lack of instrumentation may exist. Alternatives to circumvent these barriers should be sought. This study examined the absolute agreement of a subjective estimate of strength capacity on objectively measured HGS in adults aged 18-84 years. We also evaluated the test-retest reliability of an electronic handgrip dynamometer as a secondary purpose. There were 4 trained interviewers (i.e., assessors) who were assigned completely at random to subject laboratory visits occurring on 2 separate days. Trained interviewers carefully positioned their fingers into the hand of each subject before asking them to squeeze their fingers with maximal effort, and interviewers estimated each subject's HGS. An electronic handgrip dynamometer objectively measured HGS. Intraclass correlation coefficients (ICCs) were used for the analyses. The analytic sample included 100 subjects (age: 48.0 ± 20.2 years; 61% women). Poor agreement between mean objective HGS and averaged subjective hand squeeze was observed (ICC: 0.47; 95% confidence interval [CI]: 0.40-0.53). However, there was moderate agreement between dynamometer-derived maximal HGS and the most accurate HGS estimate (ICC: 0.75; CI: 0.65-0.86). An excellent test-retest reliability was found for mean (ICC: 0.97; CI: 0.95-0.98) and maximal HGS with the electronic dynamometer (ICC: 0.97; CI: 0.96-0.98). Trained interviewers performing subjective hand squeezes can approximate objective HGS with adequate accuracy, which could be useful when time and handgrip dynamometry access are lacking. Expanded interviewer training and testing may help with implementation.
Publications
2025
2024
Humans adapt their locomotion seamlessly in response to changes in the body or the environment. It is unclear how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controller's performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons - capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.