Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants’ adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants’ overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

充分的坚持是任何干预措施成功的必要条件，包括旨在减轻与年龄相关的认知衰退的计算机化认知训练。定制的提示系统有望促进依从性和促进干预成功。然而，开发能够及时自适应提醒的依从性支持系统需要了解预测依从性的因素，特别是即将发生的依从性失效。在这项研究中，我们构建了机器学习模型，使用从之前的认知训练干预中收集的数据来预测参与者在不同级别（总体和每周）的依从性。然后，我们构建了机器学习模型来使用各种基线测量（人口统计、态度和认知能力变量）来预测依从性，以及深度学习模型来使用从前一周的训练交互中得出的变量来预测下周的依从性。具有选定基线变量的逻辑回归模型能够以中等准确度预测总体依从性（AUROC：0.71），而一些循环神经网络模型能够基于每日交互以高精度预测每周依从性（AUROC：0.84-0.86）。对机器学习模型的事后解释的分析表明，一般自我效能、客观记忆测量和技术自我效能最能预测参与者的整体依从性，而训练时间、参加的课程和游戏结果则可预测参与者的整体依从性。下周的坚持。基于机器学习的方法表明，个体差异特征和之前的干预互动都为预测依从性提供了有用的信息，这些见解可以提供关于依从性支持策略的目标对象以及何时提供支持的初步线索。这些信息将为基于技术的及时依从性支持系统的开发提供信息。