Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning
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.
Authors
- Ankita Singh (ORCID: https://orcid.org/0009-0003-7085-7552)
- Neil Charness (ORCID: https://orcid.org/0000-0002-1002-3439)
- Mia Liza A. Lustria (ORCID: https://orcid.org/0000-0002-1144-2985)
- Y J Pang (ORCID: https://orcid.org/0009-0008-4262-1186)
- Walter R. Boot (ORCID: https://orcid.org/0000-0003-1047-5467)
- Zhe He (ORCID: https://orcid.org/0000-0003-3608-0244)
- Ronast Subedi (ORCID: https://orcid.org/0000-0002-7569-724X)
- Shenghao Zhang (ORCID: https://orcid.org/0000-0002-3870-1975)
- Shayok Chakraborty (ORCID: https://orcid.org/0000-0001-6378-8286)
Institutions
- Florida State University (US)
- Cornell University (US)
- Weill Cornell Medicine (US)
Publication Details
- Journal
- JMIR Aging
- Published
- 2024-09-16
- DOI
- https://doi.org/10.2196/53793
- Citations
- 4
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.54
Funders
- Florida State University
- National Institutes of Health
- National Center for Advancing Translational Sciences