WANDERING-- BACKROUND AND PROXIMAL FACTORS

这个应用程序是三个交互式项目中的一个。他们共同的长期目标是，针对痴呆症患者中出现的一系列通常被称为“破坏性或激动性”的行为，制定基于理论、经过经验检验的干预措施。本项目关注流浪和非攻击性身体行为（NAPB），旨在建立这些行为的解释模型。与此互动集中的其他项目一起，其他目标是建立一个适用于“破坏性，激动性”行为范围的解释性模型（此处称为需求驱动，痴呆损害行为（ndb）），并对随附提案中讨论的流浪，NAPB和ndb开发的初步模型进行进一步测试。一个描述性的调查设计，建议使用288名老年痴呆症居民从估计15个随机选择的养老院的整群样本。直接观察、神经心理学和人格测量、生理需求状态指标，以及物理环境的光、声和温度读数，以及社会环境的选定方面，将用于捕捉被认为在产生游荡、NAPB和其他ndb中起作用的背景和近端因素。多水平回归建模技术将用于识别影响流浪和NAPB的背景和近端变量。由此产生的流浪和NAPB的初步模型将在本集附带的项目中获得的独立样本中进一步测试。此外，来自交互项目中常见的一组核心背景因素和近端因素的数据将结合起来，再次使用多层回归建模技术，开发一个总体上影响ndb生产的因素模型。

This application is one in a trio of interactive projects. Together they have the long-term goal of developing theoretically-based, empirically-tested interventions for a range of behaviors commonly termed "disruptive or agitated" that occur among persons with dementia. This project focuses on wandering and non-aggressive physical behaviors (NAPB) with the aim of building explanatory models of these behaviors. With other projects in this interactive set, additional aims are to build an explanatory model applicable to the range of "disruptive, agitated" behaviors (herein termed need-driven, dementia-compromised behaviors (NDBs)), and to conduct further testing of the preliminary models developed for wandering, NAPB, and NDBs addressed in accompanying proposals. A descriptive survey design is proposed using a cluster sample of 288 residents with dementia from an estimated 15 randomly-chosen nursing homes. Direct observation, neuropsychological and personality measures, and indicators of physiological need states, along with light, sound, and temperature readings of the physical environment and selected aspects of the social environment will be used to capture background and proximal factors thought to operate in producing wandering, NAPB, and other NDBs. Multilevel regression modeling techniques will be used to identify background and proximal variables affecting wandering and NAPB. The resulting preliminary models of wandering and NAPB will be further tested in an independent sample obtained from the accompanying projects in this set. Further, data from a core set of background and proximal factors common across interactive projects will be combined to develop a model of factors operative in producing NDBs in general, again using Multilevel regression modeling techniques.