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

Gunther Meinlschmidt; Marion Tegethoff; Angelo Belardi; Esther Stalujanis; Minkyung Oh; Eun Kyung Jung; Hyun Chul Kim; Seung Schik Yoo; Jong Hwan Lee

doi:10.1016/j.jad.2019.11.071

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

Gunther Meinlschmidt, Marion Tegethoff, Angelo Belardi, Esther Stalujanis, Minkyung Oh, Eun Kyung Jung, Hyun Chul Kim, Seung Schik Yoo, Jong Hwan Lee

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

Background: 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. Methods: 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. Results: 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)). Limitations: 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. Conclusions: Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.

Original language	English
Pages (from-to)	430-437
Number of pages	8
Journal	Journal of Affective Disorders
Volume	264
DOIs	https://doi.org/10.1016/j.jad.2019.11.071
Publication status	Published - 2020 Mar 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364 ); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023 ); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450 ); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST ). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18 ); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsförderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328 ).

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsf?rderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328).

Publisher Copyright:
© 2019

Keywords

Binary classification
Ecological momentary intervention
Internet- and mobile-based intervention
Mental disorder
Mhealth
Supervised learning

ASJC Scopus subject areas

Clinical Psychology
Psychiatry and Mental health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.jad.2019.11.071

Cite this

@article{a8df383a8bc743c0a82bdb733c2530c4,

title = "Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning",

abstract = "Background: Tailoring healthcare to patients{\textquoteright} 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. Methods: 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. Results: 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)). Limitations: 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. Conclusions: Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.",

keywords = "Binary classification, Ecological momentary intervention, Internet- and mobile-based intervention, Mental disorder, Mhealth, Supervised learning",

author = "Gunther Meinlschmidt and Marion Tegethoff and Angelo Belardi and Esther Stalujanis and Minkyung Oh and Jung, {Eun Kyung} and Kim, {Hyun Chul} and Yoo, {Seung Schik} and Lee, {Jong Hwan}",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364 ); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023 ); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450 ); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST ). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18 ); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsf{\"o}rderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328 ). Funding Information: This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsf?rderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328). Publisher Copyright: {\textcopyright} 2019",

year = "2020",

month = mar,

day = "1",

doi = "10.1016/j.jad.2019.11.071",

language = "English",

volume = "264",

pages = "430--437",

journal = "Journal of Affective Disorders",

issn = "0165-0327",

publisher = "Elsevier",

}

TY - JOUR

T1 - Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning

AU - Meinlschmidt, Gunther

AU - Tegethoff, Marion

AU - Belardi, Angelo

AU - Stalujanis, Esther

AU - Oh, Minkyung

AU - Jung, Eun Kyung

AU - Kim, Hyun Chul

AU - Yoo, Seung Schik

AU - Lee, Jong Hwan

N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364 ); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023 ); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450 ); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST ). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18 ); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsförderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328 ). Funding Information: This work was supported by the National Research Foundation of Korea (NRF) within the Global Research Network Program (G.M., M.T., J.L., project no. 2013S1A2A2035364); the Swiss National Science Foundation (SNSF) (M.T., project no. PZ00P1_137023); the NRF grant, Ministry of Science and ICT (MSIT) of Korea (J.L., project no. NRF-2016M3C7A1914450); and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (J.L., project no. CAP-18-01-KIST). Further, GM received funding from the Stanley Thomas Johnson Stiftung & Gottfried und Julia Bangerter-Rhyner-Stiftung (projects no. PC_28/17 and PC_05/18); from the International Psychoanalytic University (IPU) Berlin; from Gesundheitsf?rderung Schweiz (project no. 18.191); and from the SNSF (project no. 1000014_135328). Publisher Copyright: © 2019

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Background: 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. Methods: 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. Results: 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)). Limitations: 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. Conclusions: Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.

AB - Background: 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. Methods: 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. Results: 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)). Limitations: 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. Conclusions: Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.

KW - Binary classification

KW - Ecological momentary intervention

KW - Internet- and mobile-based intervention

KW - Mental disorder

KW - Mhealth

KW - Supervised learning

UR - http://www.scopus.com/inward/record.url?scp=85075906633&partnerID=8YFLogxK

U2 - 10.1016/j.jad.2019.11.071

DO - 10.1016/j.jad.2019.11.071

M3 - Article

C2 - 31787419

AN - SCOPUS:85075906633

SN - 0165-0327

VL - 264

SP - 430

EP - 437

JO - Journal of Affective Disorders

JF - Journal of Affective Disorders

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this