Journal Article

Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning

He, Zhe, Chakraborty, Shayok, Charness, Neil, Zhang, Shenghao, Singh, Ankita, Pang, Yuanying, Lustria, Mia Liza, Subedi, Ronast, Boot, Walter

2024

Overview

Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging. The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults. Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults' daily adherence to cognitive training programs. Our results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values. Our findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.

• Publisher: JMIR Publications
• Subject: Adaptation / Aged / Aged, 80 and over / Aging / Algorithms / Big Data Analytics for Elder Care / Cognition & reasoning / Cognitive ability / Cognitive Dysfunction - psychology / Cognitive Dysfunction - therapy / Cognitive Training / Cognitive Training for the Elderly / Consent / Datasets / Deep Learning / Dementia / Dementia and Cognitive Decline / Dementia disorders / Engagement with and Adherence to Digital Health Interventions, Law of Attrition / Female / Games / Health Services Research and Health Care Utilization in Older Patients / Humans / Intervention / Machine Learning / Male / mHealth for Treatment Adherence / Neural networks / Older people / Original Paper / Patient Compliance - psychology / Quality of life / Quality of Life - psychology / Review boards / Training / User Training and Skills / Well being