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） 口吃是一种高度可变的状况：人们在不同的环境中表现出不同程度的口吃行为 在不同的场合和不同的时间与不同的人交谈时，虽然可变性是 广泛承认，大多数对口吃行为的评估，无论是在临床环境中进行的， 为了研究，涉及在少数环境中收集的简短语音样本，这些样本并不反映 自然通信（例如，独白或阅读）。不幸的是，我们不知道这些 临床/实验室测量代表人们在真实的世界中的说话经验。充足 有证据表明，他们不是，但绝大多数的临床工作和研究口吃仍然 并不能解释这种变化。这就对现有研究的有效性提出了质疑， 依赖于可能无法反映人们真正口吃行为的措施。 在这个项目中，我们试图克服这些不确定性的有效性口吃测量。 我们将收集有史以来最大规模的口吃者的日常语言样本 连续记录7天内的对话互动。这将使我们能够 检查人们在不同情况下和随着时间的推移所表现出的各种口吃行为。 对于目标1，我们将使用这些前所未有的大语音样本来测试当前的 在临床或实验室环境中收集简短的语音样本是足够的。初步数据和先前研究 研究表明，为了更好地反映人们真正的口吃情况， 行为。对于目标2，我们将使用潜变量建模开发口吃的测量模型 支持评估口吃的新方法，这些方法更能反映现实世界的变化。为宗旨 3，我们将使用结构回归模型来探索关键个人特征， 例如一个人对口吃的反应方式，有助于可变性的体验。为 目标4，我们将进行一项混合方法研究，以探索可变性如何影响 口吃对人们生活的影响，最终目的是提高对口吃的评估， 我们可以减少口吃的负面影响，提高口吃者的生活质量。 总体影响：我们的研究结果将增加我们对导致变异性的因素的理解。 为改善口吃的临床和实验室评估提供必要的指导 设置.我们将通过以下方式提供我们精心转录的注释语音样本： FluencyBank，以便其他研究人员可以使用我们的数据来回答许多未回答的问题， 在真实的世界里口吃。我们的工作有可能颠覆目前对口吃的看法 测量，同时提供有关研究人员和临床医生如何 更好地评估口吃，以收集人们在日常生活中流利程度的更具代表性的衡量标准。