Singh Ankita, Chakraborty Shayok, He Zhe, Pang Yuanying, Zhang Shenghao, Subedi Ronast, Lustria Mia Liza, Charness Neil, Boot Walter
Department of Computer Science, Florida State University, Tallahassee, FL, United States.
School of Information, Florida State University, Tallahassee, FL, United States.
JMIR Aging. 2024 Sep 16;7:e53793. doi: 10.2196/53793.
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.
认知障碍和痴呆对老年人群构成了重大挑战，影响着受影响个体的幸福感、生活质量和自主性。随着人口老龄化，这将给医疗保健和经济系统带来巨大压力。虽然计算机化认知训练项目在应对认知衰退方面已显示出一些前景，但坚持这些干预措施可能具有挑战性。
本研究的目的是提高预测依从性失误的准确性，最终开发量身定制的依从性支持系统，以促进老年人参与认知训练。
使用之前进行的两项认知训练干预研究的数据来预测老年参与者的依从水平。利用深度卷积神经网络的特征学习能力，根据过去的行为预测依从模式。通过使用来自其他具有相似游戏模式参与者的数据，采用域适应（DA）来应对每个参与者训练数据有限的挑战。使用格拉姆角场将时间序列数据转换为图像格式，以便在域适应期间对参与者进行聚类。据我们所知，这是首次尝试使用域适应技术来预测老年人对认知训练项目的日常依从性。
我们的结果证明了深度神经网络和域适应在预测依从性失误方面的前景和潜力。在所有三项研究中，使用两个独立的数据集，域适应始终产生最佳的准确率值。
我们的研究结果表明，深度学习和域适应技术有助于开发用于计算机化认知训练以及其他旨在改善健康、认知和幸福感的干预措施的依从性支持系统。这些技术可以提高参与度并最大化此类干预措施的益处，最终提高有认知障碍风险个体的生活质量。这项研究为开发更有效的干预措施提供了信息，通过改善与衰老相关的状况使个人和社会受益。
