Periodic Reporting for period 1 - ICOME (Individualised COgnitive and Motor learning for the Elderly)
Okres sprawozdawczy: 2020-05-01 do 2022-04-30
Motor sequence learning in the elderly is crucial for improvement and maintenance of their motor function. Current motor learning programmes tend to neglect individual cognitive and motor differences, resulting in significantly varied improvements in motor function. The original goal of the Marie Skłodowska-Curie Actions (MSCA) Individualised COgnitive and Motor learning for the Elderly (ICOME) project was to explore an efficient method that provides a personalised approach to motor sequence learning in the elderly.
The work here is important as the EU in particularly is an aging society. More than 50% of the population is projected to be >65 years old by the year 2070 and the elderly have vastly different physical and mental capacities. Despite this, training approaches to maintain function has been largely fixed with no considerations of individual statuses. With the advent of greater computing power, there should be a several approaches to help plan and implement programs in an individualised manner. The approach in this project is therefore to pilot an individualised method, learning from the data and in combination with cognitive approaches plus training type modifications to increase motor learning gains. With this we can therefore provide one of the many ways that could help increase functional capacity in the elderly.
The main scientific objectives and goals were to: (1) Development of a theoretical paradigm and propose testable hypotheses; (2) Uncover the cortical oscillatory dynamics of learning expertise in motor learning of the elderly; (3) Pilot test an individualised approach to increase motor learning gains in the elderly.
The work here is important as the EU in particularly is an aging society. More than 50% of the population is projected to be >65 years old by the year 2070 and the elderly have vastly different physical and mental capacities. Despite this, training approaches to maintain function has been largely fixed with no considerations of individual statuses. With the advent of greater computing power, there should be a several approaches to help plan and implement programs in an individualised manner. The approach in this project is therefore to pilot an individualised method, learning from the data and in combination with cognitive approaches plus training type modifications to increase motor learning gains. With this we can therefore provide one of the many ways that could help increase functional capacity in the elderly.
The main scientific objectives and goals were to: (1) Development of a theoretical paradigm and propose testable hypotheses; (2) Uncover the cortical oscillatory dynamics of learning expertise in motor learning of the elderly; (3) Pilot test an individualised approach to increase motor learning gains in the elderly.
The work performed here was disrupted by the COVID-19 pandemic between 2020 to 2023. This seriously affected the ability to recruit the elderly meaningfully. Hence, the original goals and were only tested on younger adults but with the focus on individualisation. Despite this, the progressive Work Plans (WPs) have cumulated to push the field and understanding of motor learning mechanisms much further.
In WP1, the goal was to design a hypothesis framework for which one could work with to predict the influence of individual differences and predict training requirements. The unique contribution is the individual electrophysiological perspectives combined with a predictive framework on the effects of different combinations of mental and physical training on those individual factors. This is different from literature reviews that already exists in various forms. The work was disseminated as a conference abstract and is currently a preprint accessible to the public:
Chan, R. W., Van der Lubbe, R. H., Immink, M. A., & Verwey, W. B. (2021). Individualized COgnitive and Motor Learning for the Elderly (ICOME): A Guiding Framework for Enhancing Motor Learning Performance. Journal of Sport and Exercise Psychology, 43(S1), S25-S26. https://doi.org/10.1123/jsep.2021-0103
Chan, R. W., Van der Lubbe, R. H., Immink, M. A., & Verwey, W. B. (Preprint). Individualised COgnitive and Motor learning for the Elderly (ICOME): A guiding framework for enhancing motor learning performance. https://doi.org/10.31234/osf.io/dhb9g
In WP2, the Fellow found two different aspects of electroencephalography (EEG) data that are novel in the field of motor learning. During motor learning, a significant rise in Theta power in the middle of executing a sequence was found. This seemed to coincide with a learning mechanism called concatenation and likely index the emergence of a motor chunk. In addition, there is a rise in Beta power following the completion of a sequence was also found to correlate with learning. This is significant as it the first time that oscillatory activity could be used as an index of learning expertise that could also be easily implemented in the elderly. The work was disseminated as a conference abstract and the manuscript is currently in preparation:
Chan, R.W. Van der Lubbe, R. H. J., & Verwey, W. B. (2021). Beta Time-Frequency Dynamics of Expertise Development From Motor Sequence Learning. International Journal of Psychophysiology, 168, S157-S158. https://doi.org/10.1016/j.ijpsycho.2021.07.442
Chan, R. W., Verwey, W. B., Titsing, D. & Van der Lubbe, R. H. J. (In prep.). Cortical oscillatory predictors of motor sequence learning.
In WP3, the Fellow tested a novel approach to measure motor learning using portable motion capture technology in the form portable Inertial Measurement Unit sensors (Xsens). An established keyboard-based motor learning paradigm called the Discrete Sequence Production Task was changed to a whole body version and different modifications to the motor learning program using cognitive training and progressive chunk training was also tested. The main scientific insight from WP3 was the shift in testing paradigms away from keyboard-based testing to overall more whole-body type movements combined with motion capture. The work was disseminated as a manuscript that is currently under review.
Chan, R. W., Wiechmann, E., & Verwey, W. B. (Under review). Motor Sequencing Learning from Dance Step: A whole-body version of the Discrete Sequence Production Task.
With regards to learning enhancement – 2 experiments found learning benefits from using progressive chunking and meditation. These were also part of the proposed program for the elderly and the results support that similar gains in the elderly is also possible. Two manuscripts are currently in preparation in relation to the results:
Chan, R. W., Veith, L., & Verwey, W. B. (In prep.). Enhancements of learning resulting from progressive chunk training. University of Twente.
Chan, R. W., Bertalis, N., Immink, M. A., & Verwey, W. B. (In prep.). Cognitive enhancement effects arising from different meditations on motor sequence learning. University of Twente.
In WP1, the goal was to design a hypothesis framework for which one could work with to predict the influence of individual differences and predict training requirements. The unique contribution is the individual electrophysiological perspectives combined with a predictive framework on the effects of different combinations of mental and physical training on those individual factors. This is different from literature reviews that already exists in various forms. The work was disseminated as a conference abstract and is currently a preprint accessible to the public:
Chan, R. W., Van der Lubbe, R. H., Immink, M. A., & Verwey, W. B. (2021). Individualized COgnitive and Motor Learning for the Elderly (ICOME): A Guiding Framework for Enhancing Motor Learning Performance. Journal of Sport and Exercise Psychology, 43(S1), S25-S26. https://doi.org/10.1123/jsep.2021-0103
Chan, R. W., Van der Lubbe, R. H., Immink, M. A., & Verwey, W. B. (Preprint). Individualised COgnitive and Motor learning for the Elderly (ICOME): A guiding framework for enhancing motor learning performance. https://doi.org/10.31234/osf.io/dhb9g
In WP2, the Fellow found two different aspects of electroencephalography (EEG) data that are novel in the field of motor learning. During motor learning, a significant rise in Theta power in the middle of executing a sequence was found. This seemed to coincide with a learning mechanism called concatenation and likely index the emergence of a motor chunk. In addition, there is a rise in Beta power following the completion of a sequence was also found to correlate with learning. This is significant as it the first time that oscillatory activity could be used as an index of learning expertise that could also be easily implemented in the elderly. The work was disseminated as a conference abstract and the manuscript is currently in preparation:
Chan, R.W. Van der Lubbe, R. H. J., & Verwey, W. B. (2021). Beta Time-Frequency Dynamics of Expertise Development From Motor Sequence Learning. International Journal of Psychophysiology, 168, S157-S158. https://doi.org/10.1016/j.ijpsycho.2021.07.442
Chan, R. W., Verwey, W. B., Titsing, D. & Van der Lubbe, R. H. J. (In prep.). Cortical oscillatory predictors of motor sequence learning.
In WP3, the Fellow tested a novel approach to measure motor learning using portable motion capture technology in the form portable Inertial Measurement Unit sensors (Xsens). An established keyboard-based motor learning paradigm called the Discrete Sequence Production Task was changed to a whole body version and different modifications to the motor learning program using cognitive training and progressive chunk training was also tested. The main scientific insight from WP3 was the shift in testing paradigms away from keyboard-based testing to overall more whole-body type movements combined with motion capture. The work was disseminated as a manuscript that is currently under review.
Chan, R. W., Wiechmann, E., & Verwey, W. B. (Under review). Motor Sequencing Learning from Dance Step: A whole-body version of the Discrete Sequence Production Task.
With regards to learning enhancement – 2 experiments found learning benefits from using progressive chunking and meditation. These were also part of the proposed program for the elderly and the results support that similar gains in the elderly is also possible. Two manuscripts are currently in preparation in relation to the results:
Chan, R. W., Veith, L., & Verwey, W. B. (In prep.). Enhancements of learning resulting from progressive chunk training. University of Twente.
Chan, R. W., Bertalis, N., Immink, M. A., & Verwey, W. B. (In prep.). Cognitive enhancement effects arising from different meditations on motor sequence learning. University of Twente.
The results thus far are all unique and have not been discovered by the field. We expect these results to further push the field in terms of (1) Individual status prior to learning; (2) (2) Individualisation of training approaches; (2) Consideration of cognitive and training modifications to enhance learning.
In addition, the project has utilised a multimodal approach in the collection of data: behavioural, self-report, EEG and motion capture. This is ambitious as the large amount of data requires advanced analysis techniques to progress the field beyond the state of the art. Yet, this approach is needed to discover new breakthroughs in the dynamics of motor learning in both the elderly or the normal functions adults in general.
In terms of the wider societal implications, this is the beginning of making a strong case of developing an individualised approach to managing the health of the elderly. The Fellow envisions that such multimodal approaches would become a new norm in the testing, training and rehabilitation of elder adults with the incorporation of AI technologies that can quickly plan and design individualised training programs.
These kinds of development will also allow for hospitals, rehabilitation centres and policy makers to consider and adopt one of the many possible approaches in the management of elderly motor function and possibly allow for alleviation of some resource pressure in the EU public health system.
In addition, the project has utilised a multimodal approach in the collection of data: behavioural, self-report, EEG and motion capture. This is ambitious as the large amount of data requires advanced analysis techniques to progress the field beyond the state of the art. Yet, this approach is needed to discover new breakthroughs in the dynamics of motor learning in both the elderly or the normal functions adults in general.
In terms of the wider societal implications, this is the beginning of making a strong case of developing an individualised approach to managing the health of the elderly. The Fellow envisions that such multimodal approaches would become a new norm in the testing, training and rehabilitation of elder adults with the incorporation of AI technologies that can quickly plan and design individualised training programs.
These kinds of development will also allow for hospitals, rehabilitation centres and policy makers to consider and adopt one of the many possible approaches in the management of elderly motor function and possibly allow for alleviation of some resource pressure in the EU public health system.