Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia.
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使用基于计算机的抽认卡软件重新学习和保留与个人相关的单词。

DOI:
10.3389/fnhum.2016.00561
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发表时间:
2016
影响因子:
2.9
通讯作者:
Dickerson BC
Dickerson BC
中科院分区:
医学3区
文献类型:
--
作者:
Evans WS;Quimby M;Dickey MW;Dickerson BC

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尽管失范治疗通常专注于训练小的单词集,希望促进对未训练项目的泛化,但另一种选择是更有效地直接训练更大的单词集。目前的病例研究报告了一种新的治疗方案,用于治疗语义变异型原发性进行性失语症(svPPA)患者,其中患者被教导使用开源计算机程序(Anki)制作和练习与个人相关的单词的抽认卡。结果表明,患者能够重新学习并保留她学习的单词的大部分,长达20个月,即整个研究期间。在治疗结束时,她表现出良好的保留139个单词。虽然这只是总体研究的591个单词中的一个子集,但仍然远远超过了svPPA干预措施中通常针对的单词。此外,她表现出证据的泛化知觉不同的刺激在对抗命名和暂时的收益语义流畅性,表明有限的收益语义知识作为培训的结果。该病例代表了以患者为中心的治疗的成功范例,患者在家中独立使用基于计算机的干预。它还说明了在常规临床护理过程中从基于计算机的治疗中捕获的数据如何为激励进一步的治疗研究提供有价值的“基于实践的证据”。
Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable “practice-based evidence” for motivating further treatment research.
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