A Machine-Learning Based Approach for Predicting Older Adults? Adherence to Technology-Based Cognitive Training

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dc.contributor.authorHe, Zhe
dc.contributor.authorTian, Shubo
dc.contributor.authorSingh, Ankita
dc.contributor.authorChakraborty, Shayok
dc.contributor.authorZhang, Shenghao
dc.contributor.authorLustria, Mia Liza A.
dc.contributor.authorCharness, Neil
dc.contributor.authorRoque, Nelson A.
dc.contributor.authorHarrell, Erin R.
dc.contributor.authorBoot, Walter R.
dc.contributor.otherFlorida State University
dc.contributor.otherUniversity of Central Florida
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T19:31:06Z
dc.date.available2023-09-28T19:31:06Z
dc.date.issued2022
dc.description.abstractAdequate 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.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationHe, Z., Tian, S., Singh, A., Chakraborty, S., Zhang, S., Lustria, M. L. A., Charness, N., Roque, N. A., Harrell, E. R., & Boot, W. R. (2022). A Machine-Learning Based Approach for Predicting Older Adults’ Adherence to Technology-Based Cognitive Training. In Information Processing & Management (Vol. 59, Issue 5, p. 103034). Elsevier BV. https://doi.org/10.1016/j.ipm.2022.103034
dc.identifier.doi10.1016/j.ipm.2022.103034
dc.identifier.orcidhttps://orcid.org/0000-0001-6415-1439
dc.identifier.orcidhttps://orcid.org/0000-0002-3870-1975
dc.identifier.orcidhttps://orcid.org/0009-0003-7085-7552
dc.identifier.orcidhttps://orcid.org/0000-0003-3608-0244
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11354
dc.languageEnglish
dc.language.isoen_US
dc.publisherElsevier
dc.subjectCognitive training
dc.subjectMachine learning
dc.subjectAdherence prediction
dc.subjectJust -in -time intervention
dc.subjectEFFICACY
dc.subjectIMPACT
dc.subjectINTERVENTIONS
dc.subjectQUESTIONNAIRE
dc.subjectPROFICIENCY
dc.subjectPROGRAMS
dc.subjectOUTCOMES
dc.subjectPEOPLE
dc.subjectComputer Science, Information Systems
dc.subjectInformation Science & Library Science
dc.titleA Machine-Learning Based Approach for Predicting Older Adults? Adherence to Technology-Based Cognitive Trainingen_US
dc.typeArticle
dc.typetext

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