Exploring the role of engagement and adherence in chatbot-based cognitive training for older adults: memory function and mental health outcomes

Yuyoung Kim, Yoonyoung Kang, Bori Kim, Jinwoo Kim and Geon Ha Kim

Behaviour and Information Technology, 2025, vol. 44, issue 10, 2405-2417

Abstract: Objectives: This study aimed to assess the impact of cognitive training (CT) chatbots on older adults’ memory function and mental health. Specifically, it focused on the effects of engagement and adherence. Methods: A CT chatbot developed for this study incorporates motivational interviewing strategies and personalisation features to enhance engagement and adherence. Thirty-two participants (M = 73.3 years, SD = 5.85) used it for 90 days. Measures included Delayed Matching to Sample (DMS) and Paired Associated Learning (PAL) for memory, and depression and anxiety scales. Multiple regression analysis was used to identify the influence of engagement and adherence on these outcomes. Results: Engagement and adherence statistics were significant, with the percentage of days that the participants logged into the chatbot of 86.78%. Improved memory function was observed (p = 0.01); higher engagement was associated with better DMS scores (p = 0.001) and was linked to lower anxiety levels (p = 0.033), while greater adherence was correlated with reduced depression (p = 0.014). Conclusion: This study highlights the importance of engagement and adherence in enhancing CT chatbots’ effects on memory, depression, and anxiety among older adults. These insights suggest optimising chatbot-based cognitive training should focus on strategies that improve engagement and adherence.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0144929X.2024.2362406 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tbitxx:v:44:y:2025:i:10:p:2405-2417

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tbit20

DOI: 10.1080/0144929X.2024.2362406

Access Statistics for this article

Behaviour and Information Technology is currently edited by Dr Panos P Markopoulos

More articles in Behaviour and Information Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().