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A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills

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

Zhe He^1,2, Shubo Tian³, Ankita Singh⁴

¹School of Information, Florida State University, Tallahassee, Florida USA.

Information Processing & Management

|August 1, 2022

Summary

Machine learning accurately predicts adherence to cognitive training interventions. Models identify individuals likely to lapse, enabling timely support for better outcomes in cognitive health programs.

Keywords:

Adherence prediction Cognitive training Just-in-time intervention Machine learning

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Published on: September 20, 2020

Area of Science:

Gerontology and Cognitive Science
Computational Neuroscience
Human-Computer Interaction

Background:

Adherence is crucial for the success of interventions, particularly computerized cognitive training for age-related cognitive decline.
Effective adherence support systems, such as just-in-time adaptive reminders, require understanding predictors of adherence lapses.
Previous research highlights the potential of tailored prompting systems to enhance intervention engagement and outcomes.

Purpose of the Study:

To develop and evaluate machine learning models for predicting adherence to computerized cognitive training.
To identify baseline individual difference characteristics and in-session interaction variables that predict adherence.
To inform the development of adaptive, technology-based adherence support systems.

Main Methods:

Utilized data from a prior cognitive training intervention to build machine learning models.
Employed logistic regression with baseline variables (demographic, attitudinal, cognitive) to predict overall adherence.
Developed recurrent neural network models using daily interaction data to predict weekly adherence.

Main Results:

Logistic regression models predicted overall adherence with moderate accuracy (AUROC: 0.71).
Recurrent neural network models achieved high accuracy in predicting weekly adherence (AUROC: 0.84–0.86).
Key predictors included self-efficacy, memory measures, training time, sessions played, and game outcomes.

Conclusions:

Machine learning effectively predicts adherence using both individual characteristics and intervention interaction data.
Insights gained can guide targeted and timely interventions to improve adherence.
Findings support the development of intelligent, just-in-time adherence support systems for cognitive interventions.