JMIR Publications

ABSTRACT

Background:

Storytelling intervention has demonstrated significant potential in improving emotional well-being, cognitive function, and quality of life for older adults. However, its effectiveness is often limited by the challenges of processing disorganized and redundant life stories, which impose substantial cognitive demands on caregivers. Although storytelling intervention is a well-established therapeutic approach, current practices depend heavily on manual narrative organization, restricting both the scalability and consistency of treatment delivery. Prior research has primarily focused on validating the clinical outcomes of storytelling intervention, with insufficient attention given to technological solutions that could enhance narrative processing while preserving therapeutic integrity. Digital approaches to life story structuring remain underexplored, despite their potential to amplify storytelling intervention benefits by reducing cognitive load and improving recall accuracy.

Objective:

This study aims to enhance cognitive reminiscence therapy (CRT) by developing Story Mosaic, a collaborative computing system designed to (1) automatically organize fragmented life stories into structured timelines, (2) retain clinically relevant contextual details during compression, and (3) optimize recall processes for improved therapeutic outcomes. The goal is to reduce manual intervention costs while increasing treatment efficacy through AI-driven narrative structuring.

Methods:

We have designed a novel method, CARE-ET, which combines a temporal attention mechanism with graph-based event relationship modeling, and developed the Story Mosaic system based on this method as its core algorithm. The system uses multi-feature extraction technology to capture event clues from oral histories; it prioritizes the six elements of events through a hierarchical attention mechanism; and it uses adaptive compression algorithms to reduce redundancy while maintaining narrative continuity. The system validation employed a comprehensive mixed-methods approach comprising: (1) quantitative performance evaluation of the CARE-ET algorithm, (2) qualitative assessment of cognitive load differences between AI-assisted and manual organization methods, and (3) usability evaluation of the Story Mosaic system - both conducted through qualitative interviews with 10 caregivers.

Results:

The proposed CARE-ET algorithm outperforms the baseline in both narrative flow and temporal accuracy. Caregiver interviews revealed that the structured life stories exhibited strong readability and comprehensibility, reducing work-related stress and cognitive load. System usability evaluations achieved an A- grade based on standardized metrics. Qualitative feedback from participants highlighted enhanced confidence in memory retrieval during therapy sessions. These findings collectively indicate that the Story Mosaic system based on the CART-ET method is helpful in enhancing CRT. Future research should investigate longitudinal effects on cognitive preservation and explore integration with existing dementia care protocols. This work establishes a critical foundation for intelligent assistive technologies in geriatric mental health interventions.

Conclusions:

The proposed method enables structured extraction of representative event summaries, transforming disorganized life stories into actionable data for caregiver-supported elderly wellbeing interventions.