A Machine-Learning Based Approach for Predicting Older Adults? Adherence to Technology-Based Cognitive Training
A Machine-Learning Based Approach for Predicting Older Adults? Adherence to Technology-Based Cognitive Training
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DOI：
10.1016/j.ipm.2022.103034
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发表时间：
2022-07-21
影响因子：
8.6
通讯作者：
Boot, Walter R.
中科院分区：
文献类型：
--
作者：
He, Zhe;Tian, Shubo;Boot, Walter R.
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能力下降的计算机化认知训练。量身定制的提示系统有望促进依从性和促进干预的成功。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，我们建立了机器学习模型，使用从以前的认知训练干预中收集的数据来预测参与者在不同水平（总体和每周）的依从性。然后，我们建立了机器学习模型，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，使用来自前一周培训互动的变量来预测下周的依从性。选择基线变量的Logistic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相互作用预测每周依从性（AUROC: 0.84-0.86）。对机器学习模型的事后解释分析显示，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、玩的次数和游戏结果可以预测下周的依从性。基于机器学习的方法表明，个体差异特征和先前干预的相互作用都为预测依从性提供了有用的信息，这些见解可以为依从性支持策略的目标人群以及何时提供支持提供初步线索。这些信息将为基于技术的及时遵守支持系统的开发提供信息。
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 re-quires 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 in-teractions 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, ses-sions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous inter-vention 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.