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Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults Using Source-Free Domain

Ronast Subedi¹, Shayok Chakraborty¹, Zhe He^2,3

¹Department of Computer Science, Florida State University, Tallahassee, FL, United States.

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces a new method using deep learning and source-free domain adaptation (SFDA) to predict adherence to cognitive training in older adults, enhancing support systems for better cognitive health.

Keywords:

AI adherence prediction artificial intelligence cognitive training data privacy source-free domain adaptation

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Area of Science:

Gerontology
Artificial Intelligence
Cognitive Science

Background:

Cognitive decline is a significant global challenge in aging populations.
Cognitive training shows promise but requires consistent adherence for effectiveness.
Sustaining adherence to cognitive interventions is a major hurdle.

Purpose of the Study:

To improve the prediction of adherence patterns in older adults undergoing cognitive training.
To develop personalized support systems to enhance adherence and cognitive outcomes.
To address data limitations in predictive modeling for cognitive training adherence.

Main Methods:

Employed source-free domain adaptation (SFDA) to predict adherence without direct access to external datasets.
Utilized deep learning models trained on previously conducted cognitive studies.
Pioneered the application of SFDA for predicting daily adherence in older adults' cognitive training.

Main Results:

Deep learning models combined with SFDA accurately predicted adherence lapses in cognitive training.
The approach effectively addressed data privacy concerns by not requiring access to external datasets.
Demonstrated efficacy using data from three prior cognitive training intervention studies.

Conclusions:

Deep learning and SFDA are valuable tools for creating adherence support systems for computerized cognitive training.
These techniques can help improve the health and well-being of older adults through better cognitive training adherence.
The study highlights a privacy-preserving method for enhancing engagement in digital health interventions.