Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy.
<h4>Background and objectives</h4>This study aims to develop a machine learning-based approach to predict adherence to gamified cognitive training using a variety of baseline measures (demographic, attitudinal, and cognitive abilities) as well as game performance data. We aimed to: (1) i...
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Public Library of Science (PLoS)
2024-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0311279 |
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| author | Yuanying Pang Ankita Singh Shayok Chakraborty Neil Charness Walter R Boot Zhe He |
| author_facet | Yuanying Pang Ankita Singh Shayok Chakraborty Neil Charness Walter R Boot Zhe He |
| author_sort | Yuanying Pang |
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| description | <h4>Background and objectives</h4>This study aims to develop a machine learning-based approach to predict adherence to gamified cognitive training using a variety of baseline measures (demographic, attitudinal, and cognitive abilities) as well as game performance data. We aimed to: (1) identify the cognitive games with the strongest adherence prediction and their key performance indicators; (2) compare baseline characteristics and game performance indicators for adherence prediction, and (3) test ensemble models that use baseline characteristics and game performance data to predict adherence over ten weeks.<h4>Research design and method</h4>Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. Predictors included game performance metrics in the first two weeks and baseline measures. These models' robustness and generalizability were tested through five-fold cross-validation.<h4>Results</h4>The findings indicated that game performance measures were superior to baseline characteristics in predicting adherence. Notably, the games "Supply Run," "Ante Up," and "Sentry Duty" emerged as significant adherence predictors. Key performance indicators included the highest level achieved, total game sessions played, and overall gameplay proportion. A notable finding was the negative correlation between initial high achievement levels and sustained adherence, suggesting that maintaining a balanced difficulty level is crucial for long-term engagement. Conversely, a positive correlation between the number of sessions played and adherence highlighted the importance of early active involvement.<h4>Discussion and implications</h4>The insights from this research inform just-in-time strategies to promote adherence to cognitive training programs, catering to the needs and abilities of the aging population. It also underscores the potential of tailored, gamified interventions to foster long-term adherence to cognitive training. |
| format | Article |
| id | doaj-art-6941a52bf702423bbee2fcbaa22d7048 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-6941a52bf702423bbee2fcbaa22d70482025-08-20T02:58:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011910e031127910.1371/journal.pone.0311279Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy.Yuanying PangAnkita SinghShayok ChakrabortyNeil CharnessWalter R BootZhe He<h4>Background and objectives</h4>This study aims to develop a machine learning-based approach to predict adherence to gamified cognitive training using a variety of baseline measures (demographic, attitudinal, and cognitive abilities) as well as game performance data. We aimed to: (1) identify the cognitive games with the strongest adherence prediction and their key performance indicators; (2) compare baseline characteristics and game performance indicators for adherence prediction, and (3) test ensemble models that use baseline characteristics and game performance data to predict adherence over ten weeks.<h4>Research design and method</h4>Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. Predictors included game performance metrics in the first two weeks and baseline measures. These models' robustness and generalizability were tested through five-fold cross-validation.<h4>Results</h4>The findings indicated that game performance measures were superior to baseline characteristics in predicting adherence. Notably, the games "Supply Run," "Ante Up," and "Sentry Duty" emerged as significant adherence predictors. Key performance indicators included the highest level achieved, total game sessions played, and overall gameplay proportion. A notable finding was the negative correlation between initial high achievement levels and sustained adherence, suggesting that maintaining a balanced difficulty level is crucial for long-term engagement. Conversely, a positive correlation between the number of sessions played and adherence highlighted the importance of early active involvement.<h4>Discussion and implications</h4>The insights from this research inform just-in-time strategies to promote adherence to cognitive training programs, catering to the needs and abilities of the aging population. It also underscores the potential of tailored, gamified interventions to foster long-term adherence to cognitive training.https://doi.org/10.1371/journal.pone.0311279 |
| spellingShingle | Yuanying Pang Ankita Singh Shayok Chakraborty Neil Charness Walter R Boot Zhe He Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. PLoS ONE |
| title | Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. |
| title_full | Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. |
| title_fullStr | Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. |
| title_full_unstemmed | Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. |
| title_short | Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. |
| title_sort | predicting adherence to gamified cognitive training using early phase game performance data towards a just in time adherence promotion strategy |
| url | https://doi.org/10.1371/journal.pone.0311279 |
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