Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning

Meinlschmidt, Gunther and Tegethoff, Marion and Belardi, Angelo and Stalujanis, Esther and Oh, Minkyung and Jung, Eun Kyung and Kim, Hyun-Chul and Yoo, Seung-Schik and Lee, Jong-Hwan. (2019) Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning. Journal of affective disorders. epubaheadofrint.

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Official URL: https://edoc.unibas.ch/73931/

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Tailoring healthcare to patients' individual needs is a central goal of precision medicine. Combining smartphone-based interventions with machine learning approaches may help attaining this goal. The aim of our study was to explore the predictability of the success of smartphone-based psychotherapeutic micro-interventions in eliciting mood changes using machine learning.; Participants conducted daily smartphone-based psychotherapeutic micro-interventions, guided by short video clips, for 13 consecutive days. Participants chose one of four intervention techniques used in psychotherapeutic approaches. Mood changes were assessed using the Multidimensional Mood State Questionnaire. Micro-intervention success was predicted using random forest (RF) tree-based mixed-effects logistic regression models. Data from 27 participants were used, totaling 324 micro-interventions, randomly split 100 times into training and test samples, using within-subject and between-subject sampling.; Mood improved from pre- to post-intervention in 137 sessions (initial success-rate: 42.3%). The RF approach resulted in predictions of micro-intervention success significantly better than the initial success-rate within and between subjects (positive predictive value: 0.732 (95%-CI: 0.607; 0.820) and 0.698 (95%-CI: 0.564; 0.805), respectively). Prediction quality was highest using the RF approach within subjects (rand accuracy: 0.75 (95%-CI: 0.641; 0.840), Matthew's correlation coefficient: 0.483 (95%-CI: 0.323; 0.723)).; The RF approach does not allow firm conclusions about the exact contribution of each factor to the algorithm's predictions. We included a limited number of predictors and did not compare whether predictability differed between psychotherapeutic techniques.; Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.
Faculties and Departments:03 Faculty of Medicine > Bereich Medizinische Fächer (Klinik) > Psychosomatik > Psychosomatik (Schäfert)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Medizinische Fächer (Klinik) > Psychosomatik > Psychosomatik (Schäfert)
07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Klinische Psychologie und Epidemiologie (Lieb)
UniBasel Contributors:Meinlschmidt, Gunther
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:21 Aug 2020 12:43
Deposited On:21 Aug 2020 12:43

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