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

Background: Cognitive impairment and dementia pose a significant challenge to the aging population, impacting thewell-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strainon health care and economic systems. While computerized cognitive training programs have demonstrated some promise inaddressing cognitive decline, adherence to these interventions can be challenging. Objective: The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailoredadherence support systems to promote engagement with cognitive training among older adults. Methods: Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levelsamong older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities andpredict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limitedtraining data for each participant, by using data from other participants with similar playing patterns. Time series data wereconverted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of ourknowledge, this is the first effort to use DA techniques to predict older adults' daily adherence to cognitive training programs. Results: 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. Conclusions: Our findings highlight that deep learning and DA techniques can aid in the development of adherence supportsystems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, andwell-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancingthe quality of life of individuals at risk for cognitive impairments. This research informs the development of more effectiveinterventions, benefiting individuals and society by improving conditions associated with aging.