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The present study examines whether nonactive older adults are more dependent on visual information when executing aiming movements and whether age-related declines in proprioception play a mediating role herein. Young (N = 40) and older adults (N = 38) were divided into physically active and non-active subgroups based on self-reported sports participation levels. In experiment 1, participants executed wrist-aiming movements with and without visual feedback. In experiment 2, passive proprioceptive acuity was assessed using wrist motion detection and position matching tests. Results showed similar aiming accuracy across age groups both with and without visual feedback, but older adults exhibited longer movement times, prolonged homing-in phase, and made more corrective submovements. Passive proprioceptive acuity was significantly affected by physical activity level and age, with participants in the active group scoring better than their non-active peers. However, these declines did not predict performance changes on the aiming task. Taken together, our observations suggest that decline in proprioceptive acuity did not predict performance changes on the aiming task and older adults were able to compensate for their decreased motion and position sense when allowed sufficient time. In line with these observations, we proposed that older adults are able to compensate for their decline in proprioception by increasing their reliance on predictive models.
The present study examines whether nonactive older adults are more dependent on visual information when executing aiming movements and whether age-related declines in proprioception play a mediating role herein. Young (N = 40) and older adults (N = 38) were divided into physically active and non-active subgroups based on self-reported sports participation levels. In experiment 1, participants executed wrist-aiming movements with and without visual feedback. In experiment 2, passive proprioceptive acuity was assessed using wrist motion detection and position matching tests. Results showed similar aiming accuracy across age groups both with and without visual feedback, but older adults exhibited longer movement times, prolonged homing-in phase, and made more corrective submovements. Passive proprioceptive acuity was significantly affected by physical activity level and age, with participants in the active group scoring better than their non-active peers. However, these declines did not predict performance changes on the aiming task. Taken together, our observations suggest that decline in proprioceptive acuity did not predict performance changes on the aiming task and older adults were able to compensate for their decreased motion and position sense when allowed sufficient time. In line with these observations, we proposed that older adults are able to compensate for their decline in proprioception by increasing their reliance on predictive models.
This study aimed to better characterize age-related differences in dexterity by using an integrative approach where movement times and kinematics were measured for both hands. Forty-five young (age 19-31) and 55 healthy older adults (age 60-88) were evaluated during unimanual and bimanual performance of the Purdue Pegboard Test. Gender effects were also assessed. From video-recorded data, movement times and kinematics were obtained for reaching, grasping, transport, and inserting. Results showed that older adults had longer movement times for grasping and inserting with the right hand, and across all movements with the left hand. Kinematic differences were found in path length, linear, and angular velocity. The patterns of slowing were similar in unimanual and bimanual tasks. Gender effects showed more slowing in older males than older females. Age differences in dexterity not only comprise slowing of movements but also kinematic alterations. The importance of gender in hand function was demonstrated.
We investigated age-related differences in motor behavior under different task contexts of isometric force control. The tasks involved rapid force production and force maintenance, either separately or in combination. For the combined context, we used Fitts-like tasks, in which we scaled either the force level (D manipulation, i.e., manipulation of the amplitude of the force to be produced) or the tolerance range (W manipulation, i.e., manipulation of the target width in which force is allowed to fluctuate). We studied two age groups and analyzed mainly variables that quantify behavioral variability (SD), information processing (signal-to-noise ratio and efficiency functions), and age-related slowing (slowing ratio). For rapid force control, age-related differences were more pronounced when preplanned processes were primarily involved, that is, in the rapid force production and Fitts-D manipulation tasks. Further, older adults were comparable to the younger adults in terms of end-point variability at the cost of being slower and more variable in timing. For force maintenance control, requiring mainly online control, age-related differences were the most visible in the stabilized phase of Fitts-D manipulation, followed by Fitts-W manipulation for SD, and then the force maintenance task. In sum, our findings reveal an age-related reorganization of how preplanned and online control processes are combined under different force control contexts. Indeed, both behavioral slowing and the overreliance on online control processes seem to be dependent on the task. In this respect, beyond the study of force control, we show the interest of investigating age effects using functionally different tasks.
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