A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoods

TL;DR

This paper introduces GEOFRAIL, a comprehensive longitudinal geospatial multimodal dataset tracking frail older adults post-hospital discharge, enabling analysis of recovery influenced by health, mobility, and neighborhood factors.

Contribution

It provides a novel, detailed dataset combining sensor data, clinical assessments, and geospatial information to study recovery trajectories in frail older adults.

Findings

Data validation shows internal consistency across measures.

Baseline machine learning models characterize recovery trajectories.

Dataset enables analysis of neighborhood effects on recovery.

Abstract

Frailty in older adults is associated with increased vulnerability to functional decline, reduced mobility, social isolation, and challenges during the transition from hospital to community living. These factors are associated with rehospitalization and may adversely influence recovery. Neighborhood environments can further shape recovery trajectories by affecting mobility opportunities, social engagement, and access to community resources. Multimodal sensing technologies combined with data-driven analytical approaches offer the potential to continuously monitor these multidimensional factors in real-world settings. This Data Descriptor presents GEOFRAIL, a longitudinal geospatial multimodal dataset collected from community-dwelling frail older adults following hospital discharge. The dataset is organized into interconnected tables capturing participant demographics, features derived from multimodal sensors, biweekly clinical assessments of frailty, physical function, and social isolation, and temporal location records linked to neighborhood amenities, crime rates, and census-based socioeconomic indicators. Data were collected over an eight-week post-discharge period using standardized pipelines with privacy-preserving spatial aggregation. Technical validation demonstrates internal consistency across geospatial, sensor-derived, and clinical measures and reports baseline performance of machine learning models for characterizing recovery trajectories.