Modulating stimulus intensity to improve clinical outcomes in aphasia treatment

摘要 确定治疗的最佳强度对于设计和实施任何 失语症的治疗方案然而，治疗强度是一个复杂的结构和信息的变量 调整它仍然模糊和有限。神经科学和临床文献中报告的研究 支持强化治疗的需要，以诱导长期的神经可塑性变化，而认知 心理学文献表明，学习是最好的保持与分布式时间表。少数研究 研究了单字命名任务的剂量参数，但关于剂量的证据有限 参数的治疗，重点是训练生产更大的单位，如句子，甚至 连接的话语。一种临床上经常使用并有证据证明其有效性的方法是脚本 训练目前关于脚本训练的最佳剂量知之甚少（即，重复次数 随着时间的推移，脚本中的每个句子），这是促进最佳结果所必需的。 在这个建议中，我们进行了一个双因素随机对照试验的影响，调制刺激变量，具体来说， 刺激练习分布和刺激重复。我们使用一种基线脚本治疗， 支持其有效性，并允许操纵这些变量。确保独立性 和保真度，在受控的计算机环境（台式机和平板电脑）中提供治疗。为了避免临床医生- 相关变量，如专业知识和个性因素，可能会影响治疗，句子是 在治疗期间由具有高视觉语音清晰度和情感的拟人代理建模 表情 关于“最佳结果”，推广是任何治疗方法的最终目标。因此，我们认为， 主要结果是会话的一般化测量。次级措施解决短期问题 采集、长期维持和响应概括，用于评估超过基线的增益， 差异效应和相互作用。一种移动连接的无线可穿戴喉部传感器， 在家里和社区的谈话时间作为治疗效果和转移的衡量标准。为了隐私，它 不录制音频。 学习的结果和计算模型（泛化、短期习得和长期习得 维护）将提供新的证据，以填补目前科学认识的关键空白， 失语症治疗方法的有效性和临床应用。更一般地说，研究结果将有助于 告知临床实践和神经沟通障碍的治疗;虚拟临床医生指导 该提案提出的干预措施有可能减少昂贵的临床医生-客户时间， 需要长期康复。 .

Abstract Determining the optimal intensity of treatment is essential to the design and implementation of any treatment program for aphasia. Yet, treatment intensity is a complex construct and information on the variables modulating it remain ambiguous and limited. Studies reported in the neuroscience and clinical literature support the need for intensive treatment to induce long-term neuroplastic changes while the cognitive psychology literature suggests that learning is best maintained with distributed schedules. A few studies have looked at dose parameters for single word naming tasks, but there is limited evidence regarding dose parameters for treatments that focus on training the production of larger units, such as sentences or even connected discourse. One approach that is frequently used clinically and has evidence for its efficacy is script training. Little is currently known regarding the optimum dose of script training (i.e., the number of repetitions over time of each sentence within the script) that is required to promote the best outcomes. In this proposal we conduct a two-factor RCT on the effects of modulating stimulus variables, specifically stimulus practice distribution and stimulus repetition. We use a baseline script treatment that has experimental support regarding its efficacy, and that allows the manipulation of these variables. To ensure independence and fidelity, treatment is provided in a controlled computer environment (desktop and tablet). To avoid clinician- related variables such as expertise and personality factors that may influence treatment, sentences are modeled during treatment by an anthropomorphic agent with high visual speech intelligibility and affective expressions. With regard to “best outcomes”, generalization is the ultimate goal of any treatment approach. Therefore, the primary outcome is a generalization measure of conversation. Secondary measures address short-term acquisition, longer-term maintenance, and response generalization for assessing gain over baseline, differential effects, and interactions. A mobile-connected wireless wearable laryngeal sensor allows tracking of talk time at home and in the community as a measure of treatment effectiveness and transfer. For privacy, it does not record audio. Results and computational models of learning (generalization, short-term acquisition, and longer-term maintenance) will contribute new evidence to fill critical gaps in current scientific understanding regarding the effectiveness and clinical application of aphasia treatment approaches. More generally, findings will help to inform clinical practice and treatment of neurologic communication disorders; the virtual clinician guided intervention that the proposal develops has the potential to reduce costly clinician-client time otherwise required for long-term rehabilitation. .