Czarnuch Stephen, Ricciardelli Rose, Mihailidis Alex
Department of Electrical and Computer Engineering and Discipline of Emergency Medicine, Memorial University, S.J. Carew Building, St. John's, NL, A1B 3X5, Canada.
Department of Sociology, Memorial University, Arts and Administration Building, St. John's, NL, A1C 5S7, Canada.
BMC Geriatr. 2016 Jul 20;16:143. doi: 10.1186/s12877-016-0314-2.
The population of people with dementia is not homogeneous. People with dementia exhibit a wide range of needs, each characterized by diverse factors including age, sex, ethnicity, and place of residence. These needs and characterizing factors may influence the applicability, and ultimately the acceptance, of assistive technologies developed to support the independence of people with dementia. Accordingly, predicting the needs of users before developing the technologies may increase the applicability and acceptance of assistive technologies. Current methods of prediction rely on the difficult collection of subjective, potentially invasive information. We propose a method of prediction that uses objective, unobtrusive, easy to collect information to help inform the development of assistive technologies.
We develop a set of models that can predict the level of independence of people with dementia during 20 activities of daily living using simple, objective information. Using data collected from a Canadian survey conducted with caregivers of people with dementia, we create an ordered logistic regression model for each of the twenty daily tasks in the Bristol ADL scale.
Data collected from 430 Canadian caregivers of people with dementia were analyzed to reveal: most care recipients were mothers or husbands, married, living in private housing with their caregivers, English-speaking, Canadian born, clinically diagnosed with dementia 1 to 6 years prior to the study, and were dependent on their caregiver. Next, we developed models that use 13 factors to predict a person with dementia's ability to complete the 20 Bristol activities of daily living independently. The 13 factors include caregiver relation, age, marital status, place of residence, language, housing type, proximity to caregiver, service use, informal primary caregiver, diagnosis of Alzheimer's disease or dementia, time since diagnosis, and level of dependence on caregiver. The resulting models predicted the aggregate level of independence correctly for 88 of 100 total responses categories, marginally for nine, and incorrectly for three.
Objective, easy to collect information can predict caregiver-reported level of task independence for a person with dementia. Knowledge of task independence can then inform the development of assistive technologies for people with dementia, improving their applicability and acceptance.
痴呆症患者群体并非同质化。痴呆症患者表现出广泛的需求，每种需求都由包括年龄、性别、种族和居住地点等多种因素所决定。这些需求和特征因素可能会影响为支持痴呆症患者独立生活而开发的辅助技术的适用性，最终影响其接受度。因此，在开发技术之前预测用户需求可能会提高辅助技术的适用性和接受度。目前的预测方法依赖于收集主观的、可能具有侵入性的信息，这很困难。我们提出一种预测方法，该方法使用客观的、不引人注意的、易于收集的信息来为辅助技术的开发提供参考。
我们开发了一组模型，这些模型可以使用简单的客观信息预测痴呆症患者在20项日常生活活动中的独立程度。利用从加拿大对痴呆症患者护理人员进行的一项调查中收集的数据，我们为布里斯托尔日常生活活动量表中的20项日常任务分别创建了一个有序逻辑回归模型。
对从430名加拿大痴呆症患者护理人员那里收集的数据进行分析后发现：大多数受照顾者是母亲或丈夫，已婚，与他们的护理人员住在私人住宅中，说英语，出生在加拿大，在研究前1至6年被临床诊断为痴呆症，并且依赖于他们的护理人员。接下来，我们开发了一些模型，这些模型使用13个因素来预测痴呆症患者独立完成布里斯托尔20项日常生活活动的能力。这13个因素包括护理人员关系、年龄、婚姻状况、居住地点、语言、住房类型、与护理人员的距离、服务使用情况、非正式主要护理人员、阿尔茨海默病或痴呆症的诊断、诊断后的时间以及对护理人员的依赖程度。最终得到的模型在总共100个反应类别中，有88个正确预测了独立程度的总体水平，9个预测接近正确，3个预测错误。
客观的、易于收集的信息可以预测护理人员报告的痴呆症患者的任务独立程度。了解任务独立程度随后可为痴呆症患者辅助技术的开发提供参考，提高其适用性和接受度。
