Individualized COgnitive and Motor Learning for the Elderly (ICOME): A Guiding Framework for Enhancing Motor Learning Performance

In the near future, elder adults aged >65 will make up more than 50% of the
population in developed countries. It is therefore increasingly important
that elder adults maintain the ability to retain and relearn motor functions as
this can facilitate an active quality of life for increased health and wellbeing.
Current health systems typically rely on a ‘one-size-fits all’ for motor
learning due to limited resources despite wide-ranging differences in
physiology, cognitive and motor capacities in the ageing population. We
hypothesize that, compared to generic programs, increased benefits are
possible when implementing an individualized learning approach, which we
call Individualized COgnitive and Motor learning for the Elderly (ICOME).
Firstly, to unpack motor sequence learning and related phenomenon such as
motor chunks and concatenation, we outline how ICOME is grounded in
prominent theoretical sequence learning models like the Cognitive framework
for Sequential Motor Behavior (Verwey et al., 2015). Cognitive
control is an important consideration that generic programs often fail to
consider, which we think meditation as a cognitive practice can offer
additional enhancement benefits. To monitor changes in cognitive control,
we review the use of event-related synchronization/desynchronization
(ERS/ERD), a form of frequency decomposition in electroencephalography
during motor sequence learning. We specifically target changes in Alpha
bandwidth (μ/ mu) of 8 – 13 Hz and Beta bandwidth of 15 – 30 Hz in the
ERS/ERD, that are most relevant for changes in cortical activity over the
motor cortices during sequence execution. Lastly, we unify the topics with
modelling predictions across behavioral and cortical measures to test the
effectiveness of the ICOME approach. For example, we predict that using
the ICOME approach will result in greater reductions of Beta ERD (pre and
post movement) across learning modelled against reaction time reductions,
compared to using a generic motor learning approach. Funding source: This
research project has received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-
Curie grant agreement No. 898286 for Russell W. Chan.