Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB).
Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB).
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DOI：
10.3389/fpsyg.2018.00690
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发表时间：
2018
影响因子：
3.8
通讯作者：
Barakova E
中科院分区：
文献类型：
--
作者：
Perugia G;van Berkel R;Díaz-Boladeras M;Català-Mallofré A;Rauterberg M;Barakova E
参与活动对痴呆症患者至关重要。最先进的评估技术完全依赖于行为观察来衡量痴呆症的参与度。这些技术要么太笼统，无法掌握参与如何通过行为自然表达，要么太复杂，无法追溯到整体参与状态。我们进行了一项纵向研究，以开发一个编码系统的承诺相关的行为，可以解决这些问题，并创建一个基于证据的参与模型，使这样的编码系统的意义。14名患有轻度至中度痴呆症的老年人参加了这项研究。他们参与了两项活动：基于游戏的认知刺激和基于机器人的自由游戏。编码系统是以混合方法开发的：人种学和拉班启发。首先，我们开发了两个行为图来详细描述参与者在这两个活动中的行为。然后，我们使用拉班运动分析（LMA），以确定一个共同的结构，在两个民族的行为，并统一在一个独特的编码系统。编码系统的评分员间可靠性（IRR）被证明是非常好的认知游戏（Kappa = 0.78）和机器人玩（Kappa = 0.74）。从视频的评分，我们开发了一个基于证据的参与模型。这是基于最常见的身体部位组织模式（即，身体各部分在运动中连接的方式）。通过参考文献，每个模式都被赋予了参与的含义。该模型进行了测试，使用结构方程模型（SEM）。它实现了一个很好的拟合优度和所有假设的变量之间的关系是显着的。我们将我们开发的编码系统称为行为学和拉班启发的参与编码系统（ELICSE），并将该模型称为参与相关行为的循证模型（EMODEB）。据我们所知，ELICSE和EMODEB构成了痴呆症行为相关行为的第一个形式化，描述了行为如何随着时间的推移而展开，以及它在参与方面意味着什么。
Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.
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DOI：
10.1177/153331750602100109
发表时间：
2006-01-01
期刊：
American journal of Alzheimer's disease and other dementias
影响因子：
--
作者：
Kolanowski, Ann;Buettner, Linda;Yu, Fang
通讯作者：
Yu, Fang
影响因子：
2.8
作者：
Benveniste, S.;Jouvelot, P.;Pequignot, R.
通讯作者：
Pequignot, R.
影响因子：
7.2
作者：
Cohen-Mansfield, Jiska;Marx, Marcia S.;Dakheel-Ali, Maha
通讯作者：
Dakheel-Ali, Maha
影响因子：
7.2
作者：
Cohen-Mansfield, Jiska;Dakheel-Ali, Maha;Marx, Marcia S.
通讯作者：
Marx, Marcia S.
影响因子：
2.3
作者：
Jones, Cindy;Sung, Billy;Moyle, Wendy
通讯作者：
Moyle, Wendy