Exploring home-based reminder systems for daily routine support: usability, data insights, and future directions - Alyssia A. Sanchez; Joy Lai; Alexander Tighe; Alex Mihailidis (Gerontechnology)
Background: This study evaluates 'Remindful,' a home-based reminder system designed to support people living with dementia (PLwDs) and their caregivers, assessing its usability and the effectiveness of its integration into daily routines. Research Aim: The study aims to refine and evaluate the usability of the 'Remindful' system, collect and analyze interaction data to optimize its functionality, and provide actionable insights that can inform the development of future digital reminder systems. The objectives focus on assessing the usability of the Remindful system to meet the practical needs of PLwDs and caregivers, demonstrating the feasibility of using collected data to optimize reminder delivery and identify usage patterns, and utilizing the findings to enhance future digital reminder systems by focusing on optimizing reminder timing and detecting anomalies. Methods: Data were collected by logging reminder interactions from two dyads of PLwDs and caregivers to assess system usability in real-world settings. Data-driven techniques, including predictive modeling and anomaly detection, were applied to explore opportunities for optimizing reminder timing and identifying unusual usage patterns. Results: The study analyzed the system's ease of use, user satisfaction, and the effectiveness of reminder acknowledgment across various home locations. The findings suggest that Remindful is effective in improving daily task management, though challenges in accessibility and reminder customization were identified. Conclusion: The results emphasize the need for continuous user feedback in refining cognitive support technologies (CSTs) and demonstrate the system's potential in everyday settings. Future research should focus on broader testing to ensure adaptability and effectiveness across diverse populations.