A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training. Issue 5 (September 2022)

• Record Type: Journal Article
• Authors: He, Zhe; Tian, Shubo; Singh, Ankita; Chakraborty, Shayok; Zhang, Shenghao; Lustria, Mia Liza A.; Charness, Neil; Roque, Nelson A.; Harrell, Erin R.; Boot, Walter R.
• Is Part Of: Information processing & management. Volume 59:Issue 5(2022)
• Publication Date: 2022-09
• Subjects: Cognitive training -- Machine learning -- Adherence prediction -- Just-in-time intervention; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103034
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We build accurate machine learning models to predict participants' adherence to a technology-based cognitive training program. We identify factors that are predictive of a participant's overall adherence and weekly adherence to the training program. The resulting prediction models can inform the development of just-in-time adaptive reminder systems to promote training adherence. Abstract: 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.