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Cognitive therapy, pioneered by Aaron T. Beck in the 1960s, is a structured approach to addressing psychological distress by focusing on the influence of thoughts on emotions and behaviors. All cognitive therapies involve the basic assumption that human beings have control over their feelings, and that how individuals feel about something depends on how they think about it. Unlike psychoanalytic methods that delve into unconscious processes or humanistic approaches emphasizing...
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Psychotherapy is a versatile, nonmedical approach aimed at helping individuals address emotional, behavioral, and interpersonal issues to enhance their overall well-being. It can involve one-on-one sessions, couples counseling, or small group discussions with a therapist. The therapeutic process includes various techniques such as open discussion, interpretation of thoughts and behaviors, active listening, positive reinforcement, and role modeling. Psychotherapy aims to support individuals in...
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Personalised & optimised therapy (POT) algorithm using five cognitive and behavioural skills for subthreshold

Toshi A Furukawa1, Hisashi Noma2, Aran Tajika3

  • 1Kyoto University Office of Institutional Advancement and Communications, Kyoto, Japan. furukawa@kuhp.kyoto-u.ac.jp.

NPJ Digital Medicine
|August 20, 2025
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Summary
This summary is machine-generated.

Personalized smartphone cognitive-behavioral therapy (CBT) significantly improved depression outcomes compared to standard care. Machine learning algorithms identified optimal CBT approaches for individual patients, enhancing treatment effectiveness.

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Area of Science:

  • Psychiatry
  • Digital Health
  • Machine Learning in Medicine

Background:

  • Personalizing psychotherapy can improve depression treatment efficacy.
  • Smartphone-based interventions offer scalable delivery methods for mental health support.

Purpose of the Study:

  • To evaluate the efficacy of personalized smartphone cognitive-behavioral therapy (CBT) for depression.
  • To develop and test a machine learning algorithm for recommending optimal CBT interventions.

Main Methods:

  • A randomized controlled trial involving 4,469 adults in Japan testing nine CBT interventions or a health information control.
  • Development of machine learning models to predict depression symptom changes (PHQ-9) and a personalized and optimized therapy (POT) algorithm.

Main Results:

  • All tested interventions demonstrated efficacy.
  • The POT algorithm, in a simulated comparison, showed a 35% greater improvement in PHQ-9 scores compared to the best group-average intervention over a health information control.

Conclusions:

  • Personalized and optimized therapy using a machine learning algorithm shows significant potential for enhancing depression treatment.
  • Further randomized trials are needed to confirm the external validity and applicability of the POT algorithm in real-world clinical settings.