i Anna P Wolf University of Tasmania A Thesis Submitted in Partial Fulfillment of the Requirements

412 Pages·2016·2.96 MB·English

by  Anna Wolf

IDENTIFICATION AND PREDICTION OF INTER-INDIVIDUAL DIFFERENCES
IN COGNITIVE TRAINING TRAJECTORIES:
A GROWTH MIXTURE MODELLING APPROACH

Anna P Wolf
University of Tasmania

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy (Clinical Psychology) in the School of Psychology.
June 2015

All rights reserved. This thesis may not be reproduced in whole or in part, by
photocopy or other means, without the permission of the author.

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ABSTRACT
There is emerging evidence of inter-individual differences in cognitive training
responsiveness. Conventional statistics do not adequately address heterogeneity and
longitudinal performance trajectories.
Generalised growth mixture modelling (GGMM; Muthén, 2004) was utilised to
identify and predict heterogeneous longitudinal cognitive performance trajectories
following training. Specific and generalised effects of training were examined.
Baseline characteristics such as age, sex and proxies for cognitive reserve were also
explored as predictors of trajectories.
Data from 315 community-dwelling older adults (age 55–85 years) from the Active
Cognitive Enhancement (ACE) Program training study were analysed. Short-term
(VM) and long-term verbal memory (LTVM) and executive functioning (EF) were
tested using the Rey Auditory Verbal Learning Test (RAVLT) and the CogState Ltd
Groton Maze Learning Test (GMLT) at baseline and at 3-, 6- and 12-month follow-
ups.
Generalised growth mixture modelling demonstrated High, Moderate, and Low
performance classes for memory performance. High and Low classes were identified
for executive function. Also identified were demonstrable performance trajectory
gains in the trained individuals of the Low class for executive function, those
performing at a low normative level at baseline (Cohen’s d = 2.23). These results
offer a novel contribution to the literature.
Gains by those trained in the Low performing VM and LTVM classes’ performance
trajectories were also shown (Cohen’s d = 4.48 and 1.38, respectively). However, the
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experimental participants were compared to a small number of controls (n = 2) thus
no meaningful training effects on memory were identified. The GGMM models
therefore demonstrated that the multidomain ACE cognitive training program
produced some generalised cognitive improvement in healthy older adults, albeit to
limited extent.
Age and estimated premorbid IQ (a proxy for cognitive reserve) predicted Low EF
performance trajectories compared to High class performances. Trained individuals
were more likely to be older and have lower levels of estimated pre-morbid IQ.
Individuals who demonstrated executive function performance gains were less likely
to demonstrate verbal memory trajectory gains. These findings suggest distinct
responses to training in different cognitive domains and/or distinctive inter-individual
responses to elements of the multi-domain training program. Caution with
interpretation of GGMM labels and predictive factors identified is necessary, given
their relativity to the cohort. With this approach, current theories including
compensation, magnification, ‘Use It or Lose It’, plasticity, flexibility, and cognitive
reserve are supported. Application of GGMM can also further facilitate development
of individually tailored and cost effective cognitive training programs.
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DECLARATION
This thesis contains no material which has been accepted for a degree or diploma by
the University or any other institution, and to the best of the my knowledge and belief
no material previously published or written by another person except where due
acknowledgement is made in the text of the thesis, nor does the thesis contain any
material that infringes copyright.
Signed: Date: 12/1/2016
STATEMENT OF AUTHORITY OF ACCESS
This thesis may be made available for loan and limited copying in accordance with
the Copyright Act of 1968.
Signed: Date: 12/1/2016
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ACKNOWLEDGEMENTS
Firstly, I’d like to thank the Australian Research Council (ARC), in collaboration with
Alzheimer’s Australia, Tasmania (AATas), Department of Health and Human
Services (DHHS), and the School of Psychology, University of Tasmania (UTas) for
their generous funding support to implement this ambitious research project.
To my primary supervisor, Jeff, thank you for taking me on as one of your students
and your encouragement to do a PhD. Thank you for sharing with me some of your
immense academic wisdom. I am so grateful for your instrumental assistance in
seeing me through the inevitable (multiple) hurdles along the way. Thank you also for
your fantastic anecdotes about university life, research and your travels, which always
made supervision such a pleasure.
To my co-supervisor Rapson, thank you for your enthusiasm when coming on board
the research team, and for introducing me to, and assisting me with implementing
growth mixture modelling. Without this assistance, this PhD could not have come
about.
To my second co-supervisor Mathew, thank you for your assistance with the study’s
development, and your clinical expertise and guidance during the running of the
program. I’m also very appreciative of your thorough and useful feedback about the
final document.
To Sarah, thank you for not only being instrumental in the development and
implementation of the ACE project as a whole, but for your professional support and
your caring friendship.
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To Malcolm, for being such a committed ACE developer, facilitator and instigator of
those much needed coffee meetings!
To Avril, thank you for your tireless research assistant work and your generous
hospitality in Tasmania.
To Amy, for your fantastic editing assistance. In particular thank you for being so
thorough and generous with your time and effort.
To all those who participated in the research study, as well as all the volunteers who
devoted their time to the ACE project – thank you all for your integral contributions,
enthusiasm and commitment.
To my treasured friends and family both near and far. I love you all dearly. You kept
me going! I can’t wait to have loads more spare time now to continue the fun!
To Anastasia, thank you for your friendship, thoughtfulness and crazy ways. You do
make me laugh!
To Leonie, thanks for all the fun times, “walkie talkies”, and of course for our
continuing friendship for all these years.
To Dani - Wifey! Thank you for sharing the “nanna rug” time, and for being such a
honest and caring friend.
To Sinead, thank you for our fantastic long chats at Uni, Alzheimer’s, cafes and via
Skype! I look forward to more!
To Vandita, for your friendship in both Tassie and Sydney. Just hearing your voice
instantaneously fills my heart with joy!
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To Eddy, for welcoming me so openly to Hobart, for your assistance with surviving
the “smelly boys” and for continuing to share your life with me.
To Dave, Buddy, thank you for always being such a loving brother! I miss you now
you’re in Canada, but I’m so happy you’re so happy over there. I know you’ll always
be there for me.
To DiDi, thank you for your open ear and being such a nurturing aunty.
To Alan, thanks for our stimulating chats about movies, politics and for sharing your
amazing cooking and incredible travel photos!
Joan for being such a kind, caring and immensely compassionate person. I am so
grateful for all your unyielding support and understanding. I’m also so thankful for
you sharing your home with me and for our family dinners!
Finally to my extraordinary parents, Penny and John. Words cannot fully encapsulate
how much gratitude I have for your undying love, support, dear friendship and
mentorship all the way through this long journey. Mum, Dad, what can I say? I simply
couldn’t have done it without you.
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