Remindful: Designing Reminder Systems for Caregiver Interpretation in Dementia Care

2026-04-21

AI Summary

This work proposes Remindful, a human-centered, context-aware reminder platform designed for in-home dementia care that transforms conventional unidirectional prompting systems into an explanatory assistive infrastructure. By integrating alerts, summaries, and retrospective review features—developed through caregiver interviews, input from lived-experience advisors, and in-situ household deployments—the platform unifies behavioral sensing with interpretive support. Findings from field evaluations demonstrate that Remindful enhances caregivers’ peace of mind, improves household coordination, and deepens understanding of daily routines. The study further reveals that reminder data interpretation is highly context-dependent, significantly influenced by family engagement levels, attribution of prompts, and mismatches between system expectations and actual household practices.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Digital reminder systems are widely used in dementia care to support everyday tasks, but they are typically designed for one-way prompting rather than helping caregivers interpret engagement over time. We present Remindful, a caregiver-informed reminder platform that extends task prompting with caregiver-facing alerts, summaries, and review features to support awareness in home-based dementia care. Drawing on formative caregiver interviews, lived-experience advisor input, and in-home deployments with two caregiver-PLwD dyads, we examine how reminder-based caregiver awareness functions in practice. Our findings show that reminder systems can support caregiver reassurance, household coordination, and awareness of routines over time, but that reminder interaction data is highly context-dependent. Household participation, prompt attribution, routine mismatch, accessibility barriers, and technical failures all shaped what reminder logs could reasonably mean. We argue that reminder systems should not be treated as neutral behavioral sensors, but designed as assistive infrastructures for caregiver interpretation that preserve uncertainty and support contextual sensemaking in real homes.

Problem

• dementia care
• reminder systems
• caregiver interpretation
• contextual sensemaking
• assistive technology

Innovation

• caregiver-aware reminder systems
• contextual sensemaking
• dementia care technology
• assistive infrastructure
• behavioral interpretation