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CASE: iReadMore: optimization and personalization of a reading therapy app for patients with central alexia

Alexander Leff, Ashley Peacock, NeuroDiversity

  1. Aims
  • To optimize the usability and clinical effectiveness of iReadMore in the context of a ‘rollout’ trial
  • To develop patient-specific therapy algorithms
  • To develop a predictive algorithm from completed patients’ data that will provide estimates of likely effect sizes that incoming patients can reasonably expect to achieve
  1. Background and rationale

iReadMore is a reading therapy app for adult aphasic patients. It contains diagnostic, therapeutic (practice-based) and outcome components.

Central alexia is the commonest type of acquired reading disorder in adults. It is usually encountered as part of a generalized language disorder known as aphasia. Stroke is the leading cause of aphasia but head-injury, brain tumours and dementia also figure prominently. In the UK alone, there are more than 350,000 people with aphasia; around 2/3rds of these will have central alexia. While standard speech and language therapy (SALT) improves outcomes in patients with aphasia, the ‘dose’ needed to achieve this (>100 hours) is ~10 times more than the average aphasic patient current receives on the NHS.

We have recently completed a phase II trial of iReadMore therapy in a group of patients with central alexia. The results (which are in revision with the journal Brain) demonstrate that the therapy works well on trained items. After an average of 35 hours therapy, patients improved their reading accuracy by 9%. The standardized effect size was “large” (Cohen’s d = 1.38) Reaction times also significantly improved.

We now plan to ‘rollout’ iReadMore therapy for suitable patients to use who have internet access. We wish to optimize the therapy and will do so by testing a series of hypotheses, all aimed at improving the usability and/or clinical effectiveness of iReadMore. We have ethical approval in place.

3. Potential themes to be explored by the student in their PhD:

 

1)         Gamification: The student will explore a range of gamification options with suitable patients in focus groups. Using qualitative methodologies, the student will select one or more gaming options to develop in an iterative manner which will be tested with a new set of patients in subsequent focus groups.

2)         Adaptive interface: The student will carry out quantitative analysis of users’ interactions with the graphical user interface. This information will guide iterative adaptations to improve the usability and acceptability of iReadMore. This type of analysis, while common in the games development industry, has not been applied to patient groups yet.

3)         Tailoring therapy: As patients work their way through the therapy items, we collect large numbers of trials (many thousands per subject). Using novel Bayesian modelling techniques, the student will explore which patterns of item exposure that lead to the most efficient gains in reading ability (improved accuracy and reduced exposure times). These analyses will then be used to optimise future versions of iReadMore.

4)         Predicting individual patient’s responses to therapy: In the roll-out phase the student will be able to collect data on a large number of patients. Using a variety of statistical modelling techniques, the student will investigate how well we can predict individual patients’ responses to iReadMore given their baseline data (by comparing them to previous patients with similar baseline profiles). This will allow us to provide incoming users with information on how hard they are going to have to work to make certain predicted gains.

Personal specification

  1. Essential
    1. Passion for Health technology.
    2. Experience with at least one programming language / tool such as: matlab, octave, R, Python, Java.
    3. Fundamental knowledge in machine learning, data science or statistics.
    4. Degree in computer science, mathematics, or science equivalent (e.g. psychology).

     

    Preferable

    1. Experience with app development, ideally Android app development
    2. Experience with database software such as mysql
    3. Experience with the Java programming language
    4. Interest in gamification and how to use games to increase
    5. Understanding of user demographic and challenges in designing for challenging user groups
    6. Understanding of using tools such as Git
    7. Experience with evaluating interfaces with users

    The scientific team includes several members and local collaborators who have the necessary statistical and mathematical skills to successfully guide the student in these analyses.    Plan of investigation

iReadMore webpage: http://www.ucl.ac.uk/aphasialab/apps/ireadmore.html

Woodhead ZVJ, Ong Y-H & Leff AP

Web-based therapy for hemianopic alexia is syndrome-specific

BMJ Innovations 2015; 1(3): 88-95

Ong Y-H, Jacquin-Courtois S, Gorgoraptis N, Bays PM, Husain M & Leff AP.

Eye-Search: a web-based therapy that improves visual search in hemianopia

Annals of Clinical and Translational Neurology 2014; 2(1): 74-78