2022 Fiscal Year Final Research Report
Development of Applications of Cognitive Behavioral Therapy to Achieve Tailored Treatment
| Project/Area Number |
20K14215
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 10030:Clinical psychology-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | テーラーメイド / 認知行動療法 / プロセス・ベースド・セラピー / パーソナリティフィット / アプリケーション / 個別最適化 |
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| Outline of Final Research Achievements |
In this project, I developed a web application (T-CBT) that issues an account to each participant (client) and which allows for individualized online intervention; I then examined its effectiveness and compatibility with an individual’s personality (or symptoms). A series of studies showed that some treatment techniques are more effective than others depending on personality; that, even for short-term cases, online-based interventions are effective; that it is necessary to not only focus on the variability of symptoms, but on their meaning and function as well so as to achieve individual optimization; and that it is even effective for people with specific mental disorders. These results not only revealed the therapeutic effects of individualized online interventions, but also provide insight into how to apply big data analysis to clinical practice.
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| Free Research Field |
臨床心理学
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| Academic Significance and Societal Importance of the Research Achievements |
従来の認知行動療法の介入研究では，特定のプログラム（パッケージ）を作成して，その効果の平均値の変動で効果の有無を判断することが多かった。それに対して，本研究では個人最適化するためのツール（ウェブアプリ）を開発し，個々人のパーソナリティや症状によってどの程度効果が異なるかを検証した点は学術的な意義が大きい。このようなアプリケーションがネイティブアプリとして容易にダウンロード出来るようになれば，国内のメンタルヘルスで悩む人々に対して，個別に最適な心理療法を提供できるようになる。収集したビッグデータを機械学習にかけることで，その精度をより高めることができると考えている。
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