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Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time
Yuanying Pang1, Ankita Singh2, Shayok Chakraborty2
1School of Information, Florida State University, Tallahassee, Florida, United States of America.
Plos One
|October 2, 2024
Summary
Machine learning accurately predicts adherence to gamified cognitive training. Game performance, not baseline traits, is key, with early engagement and balanced difficulty crucial for sustained participation in older adults.
Area of Science:
- Cognitive science
- Machine learning
- Gerontology
Background:
- Adherence to cognitive training is crucial for efficacy.
- Gamified interventions offer potential for sustained engagement.
- Predicting adherence is vital for optimizing training programs.
Purpose of the Study:
- Develop a machine learning model to predict adherence to gamified cognitive training.
- Identify key game performance indicators and baseline measures for adherence prediction.
- Evaluate ensemble models combining baseline and performance data for long-term adherence prediction.
Main Methods:
- Employed machine learning algorithms (logistic regression, SVM, random forests) to predict adherence.
- Utilized game performance metrics from the first two weeks and baseline characteristics as predictors.
- Validated model robustness and generalizability using five-fold cross-validation.
Main Results:
- Game performance metrics significantly outperformed baseline characteristics in predicting adherence.
- "Supply Run," "Ante Up," and "Sentry Duty" were key games for adherence prediction.
- Early high achievement negatively correlated with sustained adherence; session frequency positively correlated.
Conclusions:
- Game performance data offers valuable insights for predicting and promoting adherence.
- Tailored gamified interventions can foster long-term cognitive training adherence in aging populations.
- Findings inform just-in-time strategies to enhance engagement in cognitive training.
