Evidence map›Paper›PMID 40835713›Full record

ArticleNPJ digital medicine2025

Personalised & optimised therapy (POT) algorithm using five cognitive and behavioural skills for subthreshold depression.

Toshi A Furukawa, Hisashi Noma, Aran Tajika, Rie Toyomoto, Masatsugu Sakata, Yan Luo, Masaru Horikoshi, Tatsuo Akechi, Norito Kawakami, Takeo Nakayama and 6 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Frontiers in psychology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Toshi A Furukawa *Kyoto University Office of Institutional Advancement and Communications, Kyoto, Japan. furukawa@kuhp.kyoto-u.ac.jp.
Hisashi Noma *Department of Interdisciplinary Statistical Mathematics, The Institute of Statistical Mathematics, Tokyo, Japan.
Aran TajikaDepartment of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Rie ToyomotoDepartment of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Masatsugu SakataDepartment of Neurodevelopmental Disorders, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Yan LuoDepartment of Neurodevelopmental Disorders, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Masaru HorikoshiMusashino University, Tokyo, Japan.
Tatsuo AkechiDepartment of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Norito KawakamiDepartment of Digital Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takeo NakayamaDepartment of Health Informatics, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Naoki KondoDepartment of Social Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Shingo FukumaHuman Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
James M S WasonPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Wolfgang LutzDepartment of Psychology, Trier University, Trier, Germany.
Pim CuijpersDepartment of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Funding

Japan Agency for Medical Research and Development JP21de0107005
6 · The paper itself

Abstract

Personalising psychotherapies for depression may enhance their efficacy. We conducted a randomised controlled trial of smartphone cognitive-behavioural therapy (CBT) among 4,469 adults in Japan (RESiLIENT trial, UMIN-CTR UMIN000047124). Participants received one of nine CBT skills or combinations, or a health information control (HI), over six weeks. All interventions were found efficacious. We developed prescriptive models using machine learning to forecast changes on the Patient Health Questionnaire-9 (PHQ-9) at week 26 and created a personalised and optimised therapy (POT) algorithm that recommended the most suitable CBT for each participant. In a simulated randomised comparison, the effect of POTs over HI was a difference by -1.41 (95%CI: -1.91 to -0.90) points on the PHQ-9 corresponding with a standardised mean difference of -0.37 (-0.49 to -0.23), which was 35% greater than that of the group-average best intervention. A new randomized trial to confirm the external validity and applicability of the algorithm is warranted.

Identifiers

PMID40835713
PMCPMC12368086

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.