老年人跌倒风险早期预警与干预效果评估数据

本数据集基于杨陵区社区康养监测体系，整合可穿戴设备、电子健康档案、居家环境评估及干预随访等多源数据，经深度加工形成跌倒风险多维评估‑干预响应‑效果验证闭环数据产品。原始数据涵盖人口学基线、慢病用药、步态传感器时序信号、夜间行为日志、居家危险因素评估及干预记录。数据集创造性构建“静态基线—动态生理—行为环境—干预历史”四级风险标签体系，将干预措施数字化并关联随访结局，形成干预效果量化分级。最终每条记录凝练为包含风险得分、风险等级、干预方案及效果评估的结构化“健康预警报告”，实现了从分散监测数据到主动健康决策的本质跃升，为老年跌倒防控提供了可度量、可追踪、可优化的数据基础设施。

This dataset is developed based on the community health and wellness monitoring system in Yangling District, integrating multi-source data including wearable devices, electronic health records, home environment assessment, and intervention follow-up, and forming a closed-loop data product for multi-dimensional fall risk assessment, intervention response and effect validation through in-depth processing. Raw data covers demographic baselines, chronic disease medication records, temporal signals from gait sensors, nighttime behavior logs, home risk factor assessments, and intervention records. The dataset creatively establishes a four-tier risk labeling system categorized as "static baseline, dynamic physiology, behavioral environment, and intervention history", digitizes intervention measures, correlates them with follow-up outcomes, and enables quantitative grading of intervention efficacy. Ultimately, each record is condensed into a structured "health alert report" that includes risk score, risk level, intervention plan, and effect evaluation. This represents a substantial leap from scattered monitoring data to proactive health decision-making, providing a measurable, traceable, and optimizable data infrastructure for fall prevention among older adults.

搜集汇总

背景与挑战

背景概述

该数据集基于社区康养监测体系，整合可穿戴设备、健康档案、环境评估等多源数据，构建了从静态基线到干预历史的四级风险标签体系，实现跌倒风险的早期预警。通过将干预措施数字化并关联随访结局，数据集形成结构化健康预警报告，量化干预效果，支持主动健康决策。它为老年跌倒防控提供了可度量、可追踪、可优化的数据基础设施，助力风险防控和效果评估。