WANDERING-- BACKROUND AND PROXIMAL FACTORS

这个应用程序是三个交互项目中的一个。他们共同的长期目标是为痴呆症患者的一系列行为开发基于理论、经过经验测试的干预措施，这些行为通常被称为“颠覆性或激动性”。该项目关注游荡和非攻击性身体行为(NAPB)，目的是建立这些行为的解释性模型。对于这个互动集合中的其他项目，更多的目标是建立一个适用于各种“破坏性、激动型”行为(这里称为需求驱动、痴呆症折衷行为(NDB))的解释性模型，并对附带提案中涉及的游荡、NAPB和NDB开发的初步模型进行进一步测试。一项描述性调查设计被提出，使用来自大约15个随机选择的疗养院的288名痴呆症患者的整群样本。直接观察、神经心理和人格测量以及生理需求状态的指标，以及物理环境和社会环境选定方面的光、声和温度读数，将被用来捕捉被认为在产生流浪、NAPB和其他NDB中起作用的背景和邻近因素。将使用多水平回归建模技术来确定影响游荡和NAPB的背景和近端变量。由此产生的漫游和NAPB的初步模型将在从本套附带项目中获得的独立样本中进一步测试。此外，还将利用多级回归建模技术，将互动项目中常见的一组核心背景因素和近邻因素的数据结合起来，以开发在一般情况下产生国家数据库的可操作因素模型。

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.