Pang Yuanying, Singh Ankita, Chakraborty Shayok, Charness Neil, Boot Walter R, He Zhe
School of Information, Florida State University, Tallahassee, Florida, United States of America.
Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America.
PLoS One. 2024 Oct 2;19(10):e0311279. doi: 10.1371/journal.pone.0311279. eCollection 2024.
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.
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.
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.
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.
本研究旨在开发一种基于机器学习的方法，利用各种基线测量指标（人口统计学、态度和认知能力）以及游戏表现数据来预测对游戏化认知训练的依从性。我们的目标是：（1）确定依从性预测最强的认知游戏及其关键绩效指标；（2）比较用于依从性预测的基线特征和游戏表现指标；（3）测试使用基线特征和游戏表现数据来预测十周内依从性的集成模型。
我们使用包括逻辑回归、岭回归、支持向量机、分类树和随机森林在内的机器学习算法，预测第3周至第12周的依从性。预测指标包括前两周的游戏表现指标和基线测量指标。通过五折交叉验证测试了这些模型的稳健性和通用性。
研究结果表明，在预测依从性方面，游戏表现指标优于基线特征。值得注意的是，“补给行动”“押注”和“哨兵任务”这几款游戏成为了显著的依从性预测指标。关键绩效指标包括达到的最高级别、玩过的游戏总次数以及整体游戏时长比例。一个值得注意的发现是，初始高成就水平与持续依从性之间存在负相关，这表明保持平衡的难度水平对长期参与至关重要。相反，玩游戏的次数与依从性之间的正相关突出了早期积极参与的重要性。
本研究的见解为促进对认知训练项目的依从性提供了适时策略，满足了老年人群的需求和能力。它还强调了量身定制的游戏化干预措施在促进长期坚持认知训练方面的潜力。
