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The Use of Neuro-cognitive Strategies to Power Generalization Effect After Short-term Postural Training for Older Adults
Sponsor study identifier
interventional
150
1 country
1
Study Start
August 1, 2019
Actual (reported)Primary Completion
December 30, 2022
Actual (reported)Study Completion
December 30, 2022
Actual (reported)Generalization refers to skill transfer under various working spaces following motor practice. The extent of generalization effect links causal to in-depth recognition of error properties during motor practice. Idiom says "imperfect practice makes perfect". It could be beneficial for the elderly to gain superior capacity of balance transfer skill under the short-term productive failure learning environments. In contrast to traditional visual feedback that uses error avoidance training to optimize target balance task, the present 3-year proposal is to propose three potential neuro-cognitive strategies to improve motor skill transfer following stabilometer training. The strategies are expected to enhance opportunities of error experience and motor exploration via modified visual feedback, underlying facilitations of attentional resource and error-related neural networks. In the first year, the neuro-cognitive strategy for balance practice is progressive augmentation of visual error size to improve balance skill transfer. In the second year, the neuro-cognitive strategy for balance practice is visual feedback with virtual uncertainness of motor goal. In the third year, the neuro-cognitive strategy for balance practice is stroboscopic vision. EEG and central of pressure will be processed with non-linear approaches. Graph theory will characterize EEG functional connectivity and brain network efficiency regarding to brain mechanisms for practice-related leaning transfer. Trajectories of central of pressure will be analyzed with stabilogram diffusion analysis to reveal behavior mechanisms for practice-related variations in feedback and feedforward process for error corrections.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 30, 2022
Actual (reported)Click on a node to explore related trials.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
August 1, 2019
Actual (reported)October 20, 2019
Actual (reported)October 24, 2019
Actual (reported)December 30, 2022
Actual (reported)December 30, 2022
Actual (reported)EEG graph analysis
Graph theory will characterize EEG functional connectivity and brain network efficiency regarding to brain mechanisms for practice-related leaning transfer.
through study completion, an average of 1 year
stabilogram diffusion analysis of central of pressure
through study completion, an average of 1 year
root mean sqaure error of stabilometer
through study completion, an average of 1 year
Day1(Pretest): 1. stabilometer stance, 3 times/1 min 2. air pillow stance, 3 time/30 sec 3. poture-supraposture dual task, 3 times/1 min Day2 to Day5: 1. Exp.group:27 times of posture tracking with high visual error gain. 2. Control group:27 times of posture training with normal visual error gain. Day6 (Posttest): 1. stabilometer stance, 3 times/1 min 2. air pillow stance, 3 time/30 sec 3. poture-supraposture dual task, 3 times/1 min
Day1(Pretest): 1. stabilometer stance, 3 times/1 min 2. air pillow stance, 3 time/30 sec 3. poture-supraposture dual task, 3 times/1 min Day2 to Day5: 1. Exp.group A:27 times of posture training with posture tracking signal-to-noise ratio=2:1. 2. Exp.group B:27 times of posture training with posture tracking signal-to-noise ratio=4:1. 3. Control group:27 times of posture training with normal visual error gain. Day6(Posttest): 1. stabilometer stance, 3 times/1 min 2. air pillow stance, 3 time/30 sec 3. poture-supraposture dual task, 3 times/1 min
The strategies are expected to enhance opportunities of error experience and motor exploration via modified visual feedback, underlying facilitations of attentional resource and error-related neural networks.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
National Chen Kong University Hospital
Taian, Eastern District, 701, Taiwan
Hwang Ing-Shiou, Phd
NCKU, Institute of Allied Health Sciences
150 participants (actual enrollment reported by the registry).
First Submitted
October 20, 2019
First Posted
October 24, 2019
Study Start
August 1, 2019
Actual (reported)Day1 (Pretest): 1. stabilometer stance with wearing flash glasses (low frequency with opaque ratio 50%), 4 times/45 sec 2. stabilometer stance with wearing flash glasses (high frequency with opaque ratio 50%), 4 times/45 sec. 3. stabilometer stance with wearing flash glasses (clear), 4 times/45 sec 4. air pillow stance, 8 times/1 min Day2 (training section): 1. Exp. group:Posture tracking with wearing flash glasses (low frequency with opaque ratio 50%), 12 times/45 sec. 2. Control group:Posture tracking with wearing flash glasses (clear), 12 times/45 sec. Day3 (Posttest): 1. stabilometer stance with wearing flash glasses (low frequency with opaque ratio 50%), 4 times/45 sec 2. stabilometer stance with wearing flash glasses (high frequency with opaque ratio 50%), 4 times/45 sec 3. stabilometer stance with wearing flash glasses (clear), 4 times/45 sec 4. air pillow stance, 8 times/1 min
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Primary Completion
December 30, 2022
Actual (reported)Study Completion
December 30, 2022
Actual (reported)Last Updated
September 19, 2024
Record last verified: 2024-09
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Source: ClinicalTrials.gov.
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