Indicators and Models for Frailty Prediction using Passive Lifelogging in Community-Dwelling Older Adults: A Scoping Review

Research Overview: Scoping Review of Passive Lifelogging for Frailty Prediction in Older Adults 1. Purpose of the Research (Definition) The objective of this scoping review is to systematically map the current landscape of research utilizing non-intrusive, passive lifelogging technologies to predict or identify frailty in community-dwelling older adults. Unlike wearable-based studies, this review focuses exclusively on ambient and contact-free sensing methods to establish the state-of-the-art in "invisible" health monitoring and to identify critical research gaps in the functional assessment of the elderly. 2. Methodological Approach (Explanation) By adhering to the PRISMA-ScR guidelines and the JBI methodology, this study explores how daily activity patterns—captured through PIR sensors, smart plugs, and ambient environmental sensors—correlate with frailty indicators. The review categorizes identified digital biomarkers according to their functional domains and examines the various machine learning architectures employed to process these high-frequency, longitudinal datasets. This process serves to validate the reliability of passive sensing as a stand-alone or primary indicator of physiological decline. 3. Expected Results (Example) The findings will provide a comprehensive taxonomy of passive sensors and a structured mapping of lifelogging variables (e.g., nocturnal activity, gait speed, and kitchen usage) to frailty phenotypes. Technologically, it will highlight the most effective sensor combinations and algorithmic approaches. Clinically, it will demonstrate the potential for continuous monitoring without user burden. Strategically, these results will justify the selection of input features for the subsequent development of a robust frailty prediction model.