Stuttering in the real world: Quantifying variability to improve measurement reliability and validity

现实世界中的口吃：量化变异性以提高测量的可靠性和有效性

• 批准号: 10217095
• 负责人: J. Scott Yaruss
• 金额: $ 43.55万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10217095
• 关键词: Address; Affect; Behavior; Characteristics; Clinic; Clinical; Clinical Research; Communication; Data; Databases; Diagnosis; Disease; Evaluation; Exhibits; Family; Goals; Individual; Intervention; Laboratories; Life; Linguistics; Measurement; Measures; Methods; Modeling; Motor; Paper; Patient Self-Report; Persons; Phenotype; Prevalence; Procedures; Quality of life; Reading; Recovery; Research; Research Personnel; Resources; Sampling; Severities; Social Interaction; Speech; Structure; Stuttering; Testing; Thinking; Time; Treatment outcome; Uncertainty; United States; Validity and Reliability; Work; base; clinical decision-making; decision research; experience; improved; relating to nervous system

Project Summary (30 lines) Stuttering is a highly variable condition: People exhibit different amounts of stuttering behavior in different tasks, when talking to different people, in different settings, and at different times. Although variability is widely acknowledged, most assessments of stuttering behavior, whether conducted in a clinical setting or for research, involve brief speech samples collected in a small number of settings that do not reflect natural communication (e.g., monologuing or reading aloud). Unfortunately, we do not know if these clinic/laboratory measures are representative of people's speaking experiences in the real world. Ample evidence suggests that they are not, but the vast majority of clinical work and research on stuttering still does not account for this variability. This raises questions about the validity of existing research that relies on measures that may not be reflective of people's true stuttering behavior. In this project, we seek to overcome these uncertainties about the validity of stuttering measurement. We will collect the largest-ever samples of the everyday speech of people who stutter through continuous recording of conversational interactions over a 7-day period. This will allow us to examine the full range of stuttering behaviors that people exhibit across situations and over time. For Aim 1, we will use these unprecedentedly large speech samples to test whether current practices of collecting brief speech samples in clinical or laboratory settings are adequate. Pilot data and prior studies suggests that a broader range of measures will be needed in order to better reflect people's true stuttering behaviors. For Aim 2, we will develop a measurement model for stuttering using latent variable modeling to support new methods for evaluating stuttering that are more reflective of real-world variability. For Aim 3, we will use structural regression modeling to explore the ways in which key personal characteristics, such as the ways in which a person responds to stuttering, contribute to the experience of variability. For Aim 4, we will conduct a mixed-methods study to explore how variability affects the adverse impact that stuttering has on people's lives, with the ultimate goal of improving the assessment of stuttering so that we can reduce adverse impact and enhance quality of life for people who stutter. Overall Impact: Our findings will increase our understanding of the factors that contribute to the variability of stuttering and provide needed guidance for improving assessment of stuttering in clinical and laboratory settings. We will make our carefully transcribed, annotated speech samples available through FluencyBank, so that other researchers can use our data to answer many unanswered questions about stuttering in the real world. Our work has the potential to disrupt current thinking about stuttering measurement while simultaneously providing a solution about how researchers and clinicians can better assess stuttering to gather more representative measures of people's fluency in their daily lives.

项目摘要（30行） 口吃是一个高度可变的条件：人们在不同的情况下表现出不同数量的口吃行为 任务，与不同的人交谈，在不同的设置和不同的时间。虽然可变性是 广泛认可的大多数关于口吃行为的评估，无论是在临床环境中进行还是 对于研究，涉及在少数不反映的少数设置中收集的简短语音样本 自然沟通（例如，大声或大声朗读）。不幸的是，我们不知道是否 诊所/实验室措施代表着人们在现实世界中的讲话经历。充足 有证据表明它们不是，但是绝大多数临床工作和关于口吃的研究仍然 不考虑这种变异性。这就提出了有关现有研究的有效性的疑问 依靠可能不反映人们真正口吃行为的措施。 在这个项目中，我们试图克服有关切口测量的有效性的这些不确定性。 我们将收集口吃的人的日常演讲中最大的样本 在7天的时间内连续记录对话互动。这将使我们能够 检查人们在情况和随着时间的流逝中表现出的各种口吃行为。 对于AIM 1，我们将使用这些前所未有的大型语音样本来测试 在临床或实验室环境中收集简短的语音样本是足够的。试点数据和先前的研究 建议将需要采取更广泛的措施，以更好地反映人们的真正口吃 行为。对于AIM 2，我们将开发一个使用潜在变量建模的测量模型 支持新方法评估口吃，这些方法更反映现实世界的可变性。目的 3，我们将使用结构回归建模来探索关键个人特征的方式， 例如，一个人对口吃的反应方式，有助于可变性的经验。为了 AIM 4，我们将进行一项混合方法研究，以探讨变异性如何影响不良影响 口吃对人们的生活产生了，最终的目的是改善口吃的评估 我们可以减少口吃的人的不利影响并提高生活质量。 总体影响：我们的发现将增加我们对有助于变异性的因素的理解 口吃，并提供了改善临床和实验室口吃评估的所需指导 设置。我们将通过精心抄录，带注释的语音样本可通过 流利银行，以便其他研究人员可以使用我们的数据来回答许多关于 在现实世界中口吃。我们的工作有可能破坏当前关于口吃的思考 测量同时提供有关研究人员和临床医生如何如何的解决方案 更好地评估口吃，以收集更多代表性的措施，以了解人们的日常生活中的流利性。