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

• Published in: NPJ digital medicine Vol. 8; no. 1; pp. 531 - 10
• Main Authors: Furukawa, Toshi A., Noma, Hisashi, Tajika, Aran, Toyomoto, Rie, Sakata, Masatsugu, Luo, Yan, Horikoshi, Masaru, Akechi, Tatsuo, Kawakami, Norito, Nakayama, Takeo, Kondo, Naoki, Fukuma, Shingo, Wason, James M. S., Kessler, Ronald C., Lutz, Wolfgang, Cuijpers, Pim
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477; 692/308; 692/699; Algorithms; Behavior modification; Behavior therapy; Biomedicine; Biotechnology; Generalized anxiety disorder; Insomnia; Intervention; Medicine; Medicine & Public Health; Mental depression; Patients; Precision medicine; Problem solving; Psychotherapy; Questionnaires; Smartphones
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-025-01906-6

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.