Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults Using Source-Free Domain Adaptation: Algorithm Development and Validation
Our findings indicate that deep learning and SFDA techniques can be useful in the development of adherence support systems for computerized cognitive training, aimed at improving the health and well-being of older adults.
Authors
- 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
- 2025-12-18
- DOI
- https://doi.org/10.2196/79123
- Primary Topic
- Technology Use by Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 0.00