dc.contributor.author
Hammelrath, Leona
dc.contributor.author
Brose, Annette
dc.contributor.author
Heinrich, Manuel
dc.contributor.author
Zagorscak, Pavle
dc.contributor.author
Burchert, Sebastian
dc.contributor.author
Langhammer, Till
dc.contributor.author
Knaevelsrud, Christine
dc.date.accessioned
2025-07-28T10:34:54Z
dc.date.available
2025-07-28T10:34:54Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/48414
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-48136
dc.description.abstract
Introduction
Cognitive behavioural therapy (CBT) serves as a first-line treatment for internalising disorders (ID), encompassing depressive, anxiety or obsessive-compulsive disorders. Nonetheless, a substantial proportion of patients do not experience sufficient symptom relief. Recent advances in wearable technology and smartphone integration enable new, ecologically valid approaches to capture dynamic processes in real time. By combining ecological momentary assessment (EMA) with passive sensing of behavioural and physiological information, this project seeks to track daily fluctuations in symptom-associated constructs like affect, emotion regulation (ER) and physical activity. Our central goal is to determine whether dynamic, multimodal markers derived from EMA and passive sensing can predict treatment non-response and illuminate key factors that drive or hinder therapeutic change.
Methods and analysis
PREACT-digital is a subproject of the Research Unit FOR 5187 (PREACT), a large multicentre observational study in four outpatient clinics. PREACT channels state-of-the-art machine learning techniques identify predictors of non-response to CBT in ID. The study is currently running and will end in June 2026. Patients seeking CBT at one of four participating outpatient clinics are invited to join PREACT-digital. They can take part in (1) a short version with a 14-day EMA and passive sensing phase prior to therapy, or (2) a long version in which the short version’s assessments are extended throughout the therapy. It is estimated that 468 patients take part in PREACT-digital, of which 350 opt for the long version of the study. Participants are provided with a smartwatch and a customised study app. We collect passive data on heart rate, physical activity, sleep and location patterns. EMA assessments cover affect, ER strategies, context and therapeutic agency. Primary outcomes on (non)-response are assessed after 20 therapy sessions and therapy end. We employ predictive and exploratory analyses. Predictive analyses focus on classification of non-response using basic algorithms (ie, logistic regression and gradient boosting) for straightforward interpretability and advanced methods (LSTM, DSEM) to capture complex temporal and hierarchical patterns. Exploratory analyses investigate mechanistic links, examine the interplay of variables over time and analyse change trajectories. Study findings will inform more personalised and ecologically valid approaches to CBT for ID.
Ethics and dissemination
The study has received ethical approval from the Institutional Ethics Committee of the Department of Psychology at Humboldt Universität zu Berlin (Approval No. 2021–01) and the Ethics Committee of Charité-Universitätsmedizin Berlin (Approval No. EA1/186/22). Written informed consent will be obtained from all participants prior to enrolment. Results will be disseminated through peer-reviewed journals and presentations at national and international conferences.
Trial registration number
DRKS00030915; OSF PREACT: http://osf.io/bcgax ; OSF PREACT-digital: https://osf.io/253nb .
en
dc.format.extent
9 Seiten
dc.rights
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Machine Learning
en
dc.subject
Digital Technology
en
dc.subject
Anxiety disorders
en
dc.subject
Depression & mood disorders
en
dc.subject.ddc
100 Philosophie und Psychologie::150 Psychologie::150 Psychologie
dc.title
PREACT-digital: study protocol for a longitudinal, observational multicentre study on digital phenotypes of non-response to cognitive behavioural therapy for internalising disorders
dc.type
Wissenschaftlicher Artikel
dc.date.updated
2025-07-10T15:28:58Z
dcterms.bibliographicCitation.articlenumber
e102392
dcterms.bibliographicCitation.doi
10.1136/bmjopen-2025-102392
dcterms.bibliographicCitation.journaltitle
BMJ Open
dcterms.bibliographicCitation.number
7
dcterms.bibliographicCitation.volume
15
dcterms.bibliographicCitation.url
https://doi.org/10.1136/bmjopen-2025-102392
refubium.affiliation
Erziehungswissenschaft und Psychologie
refubium.affiliation.other
Arbeitsbereich Klinisch-Psychologische Intervention

refubium.funding
Publikationsfonds FU
refubium.note.author
Gefördert aus Open-Access-Mitteln der Freien Universität Berlin.
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2044-6055
refubium.resourceType.provider
DeepGreen