Development Project 1

随着年龄的增长，功能独立性的丧失对老年人的生活质量和 给整个社会造成了巨大的社会和经济负担。残疾预防已经成为一种 老年学和老年医学的主要焦点和以行动能力为代表的早期功能衰退 自OAIC成立以来，残疾一直是我们的重点。然而，残疾预防研究一直是 受到与测量有关的问题的阻碍。例如，大多数评估工具的目标是“残疾”或困难 具有非常基本的一组任务，而不是跨越广泛的任务需求的能力。这导致了天花板 影响，在某些情况下，导致对所设计的干预措施的影响不敏感的措施 以增强功能。此外，对残疾的关注，而不是对“能力”的关注，意味着我们对 一大部分老年人口“功能正常”；即，那些没有报告有困难的人 基本功能。此外，目前使用的大多数行动不便自我报告措施都要求 受试者要对明显简单的任务的隐含含义做出复杂的判断 描述如走2-3个街区。然而，有许多上下文因素在 做出这样的判断。例如，这是室内还是室外？有山吗？我必须走多快？是 有路障还是交通堵塞？这些模糊性都会增加测量的方差，并可能限制以下能力 比较不同人口(例如，农村和城市人口)之间的结果。 为了解决这些限制，我们建议创建并验证创新的基础设施 提供评估多媒体增强的、计算机化的移动功能和残疾的能力 自适应测试(CAT)环境，我们称之为M-CAT。增强的多媒体组件 广泛使用动画视频剪辑。动画有三个目的：第一，它消除了潜力 由于演员的性别、种族、年龄或经验等特征而产生的判断偏差。 其次，它规范了项目解释。受访者查看任务的实际需求，没有 不再需要对项目内容做出隐含的判断。例如，当询问关于攀岩的问题时 一段楼梯，我们可以提出标准化的速度、步数、光照条件和 有或没有扶手。第三，动画使我们能够创造出范围广泛的渐进式 通过操作任务的视觉呈现方式在受控设置中实现困难任务，这是 提供更精细的能力区分的方法，并使我们能够避免天花板和下限 效果。我们能够捕捉到所有感知到的移动能力，从拄着拐杖走路到 在崎岖的地形上慢跑和行走。 评估环境的另一个组成部分CAT是基于计算机的评估 允许为每个受访者定制项目(调查问卷)的技术。这个 计算机给每个人的回答打分，然后确定最好的问题 对该个人进行管理，以有效地确定其功能能力。有了这种改进的能力， 快速“放大”适合每个人的项目，我们可以：(A)制定一种具有 大量项目(例如，&gt；100)，因为个人仅根据以下条件回答这些问题的子集 他们的初始反应，(B)减少反应负担，(C)评估老年人的行动能力 (D)提高测量的精确度。我们还预计， 在M-CAT上的分数与行动障碍的实际表现测量之间的相关性将更高 由于这项测量技术提高了精度，因此比广泛使用的测量方法更精确。

The loss of functional independence with aging has profound effects on older adults' quality of life and results in substantial social and economic burden to society at large. Disability prevention has emerged as a major focus of gerontology and geriatric medicine and an early functional decline represented by mobility disability has been the focus of our OAIC since its inception. However, disability prevention research has been hindered by measurement-related issues. For example, most assessment tools target "disability" or difficulty with a very basic set of tasks rather than "ability" across a broad range of task demands. This leads to ceiling effects and, in some instances, results in measures insensitive to the effects of interventions that are designed to enhance function. Also, the focus on disability rather .than "ability" has meant that we know very little about the large segment of the older population that is "well-functioning"; i.e., those who do not report difficulties with basic functioning. Furthermore, most currently used self-report measures of mobility disability require participants to make complex judgments about the implicit meaning of apparently straightforward task descriptions such as walking 2-3 blocks. Yet, there are a number of contextual factors that are important in making such judgments. For example, is this inside or outside? Are there hills? How fast must I walk? Are there curbs or traffic? These ambiguities both add to the measure's variance and may limit the ability to compare results between different populations (e.g., rural and urban populations). To address these limitations, we propose to create and validate an innovative infrastructure that provides the capacity to assess mobility function and disability in a multimedia enhanced, and Computerized Adaptive Testing (CAT) environment, a measure we call M-CAT. The enhanced multimedia component makes extensive use of animation video clips.. Animation serves three purposes: First, it removes potential biases in judgments that may arise from characteristics such as the sex, race, age or experience of the actor. Second, it standardizes item interpretation. Respondents view the actual demands of the task and are no longer required to make implicit judgments regarding item content. For example, when asking about climbing a flight of stairs, we can present the task standardizing the speed, number of steps, light conditions and the presence or absence of handrails. And third, animation enables us to create a broad range of progressively difficult tasks in controlled settings by manipulating how the task is visually presented, an innovation in methodology that provides much finer discrimination of abilities and enables us to avoid ceiling and floor effects. We are able to capture the entire range of perceived ability of mobility from walking with a cane to jogging and walking over uneven terrain. The other component of the assessment environment, CAT, is a computer-based assessment technology that allows the customization of items (questionnaires) for each individual respondent. The computer scores every response from an individual and determines the best question to subsequently administer to that individual to efficiently determine his/her functional ability. With this improved ability to quickly "zoom in" on items that are appropriate for each individual, we can: (a) develop a measure that has a large number of items (e.g., >100) because individuals only respond to a subset of these questions based on their initial responses, (b) reduce response burden, (c) assess the mobility of older adults with a single instrument across all levels of ability, and (d) gain precision in measurement. We also expect that the correlation between scores on the M-CAT and actual performance measures of mobility disability will be higher than with widely-used measures due the enhanced precision of this measurement technology.