Seminerio Emanuele, Morganti Wanda, Barbagelata Marina, Sabharwal Sanket Rajeev, Ghisio Simone, Prete Camilla, Senesi Barbara, Dini Simone, Custureri Romina, Galliani Simonetta, Morelli Simona, Puleo Gianluca, Berutti-Bergotto Carlo, Camurri Antonio, Pilotto Alberto
Geriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Department of Informatics, Bioengineering, Robotics and System Engineering, Casa Paganini-InfoMus Research Center, University of Genova, Genoa, Italy.
Sci Rep. 2024 Dec 4;14(1):30232. doi: 10.1038/s41598-024-80061-6.
An interconnected system employing Kinect Azure and Fitbit Sense for continuous and non-intrusive data collection was used in the PRO-HOME protected discharge program, aiming at monitoring functional and clinical parameters in hospitalized older patients at different risks of frailty. The present study shows the findings on 30 older patients included in the PRO-HOME project. The Fitbit Sense recorded the mean daily and hourly number of steps, mean daily walked distance, and time spent inactive. Moreover, Kinect infrared camera captured gait speed and daily mean latero-lateral (body sway) and antero-posterior oscillations (lean-in). Patients underwent a standard Comprehensive Geriatric Assessment (CGA) to compute the Multidimensional Prognostic Index (MPI), including basic and instrumental activities of daily living (ADL, IADL), cognition (Short Portable Mental Status Questionnaire, SPMSQ) and nutrition, risk of pressure sores (Exton-Smith Scale, ESS), comorbidity, number of drugs and cohabitation status. Significant correlations between the mean hourly number of steps and MPI (p = 0.022), IADL (p = 0.013), SPMSQ (p = 0.006), ESS (p = 0.009), and both mean and maximum automated gait speed (p = 0.046 and p = 0.048) were found. Automated gait speed was also correlated with mean walked distance per day (p = 0.007) and lean-in (p = 0.047). Domotic technological monitoring through Fitbit Sense and Kinect Azure provides information on multidimensional frailty, including mobility and cognitive and functional status, in older people.
在PRO-HOME保护性出院计划中，使用了一个采用Kinect Azure和Fitbit Sense进行连续且非侵入性数据收集的互联系统，旨在监测不同脆弱风险的住院老年患者的功能和临床参数。本研究展示了PRO-HOME项目中30名老年患者的研究结果。Fitbit Sense记录了每日和每小时的平均步数、每日平均步行距离以及久坐时间。此外，Kinect红外摄像头捕捉了步态速度以及每日平均左右（身体摇摆）和前后摆动（前倾）。患者接受了标准的综合老年评估（CGA）以计算多维预后指数（MPI），包括基本和工具性日常生活活动（ADL、IADL）、认知（简短便携式精神状态问卷，SPMSQ）和营养、压疮风险（埃克斯顿-史密斯量表，ESS）、合并症、药物数量和同居状态。发现每小时平均步数与MPI（p = 0.022）、IADL（p = 0.013）、SPMSQ（p = 0.006）、ESS（p = 0.009）以及平均和最大自动步态速度（p = 0.046和p = 0.048）之间存在显著相关性。自动步态速度还与每日平均步行距离（p = 0.007）和前倾（p = 0.047）相关。通过Fitbit Sense和Kinect Azure进行的智能家居技术监测可提供有关老年人多维脆弱性的信息，包括 mobility、认知和功能状态。
