CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.
Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .
Livrables
In order to provide effective communication channels in order to inform the wider public on relevant project strategies and outcomes, a professional project website as well as social media channels will be created by the PMO which represent the consortium and the project online.
'Midterm recruitment report' - Deliverable to be scheduled for the time point when 50% of the study population is expected to have been recruited. The report shall include an overview of recruited subjects by study site, potential recruiting problems and, if applicable, a detailed description of implemented and planned measures to compensate delays in the study subject recruitment.
Implementation Guide for interoperable data structures and interfaces (s’ouvre dans une nouvelle fenêtre)WP3 will provide an implementation guide covering data structures, interfaces and an overall systems architecture for the DMMH platform. Open internationally adopted standards will be leveraged to ensure a sustainable implementation.
Set of basic statistics for direct implementation and visualization (s’ouvre dans une nouvelle fenêtre)To obtain the basic statistics for visualization in WP2 and implemented in WP7 as well as to provide a solid baseline to which to compare the more advanced methods below WP4 will first focus on simple quick to compute statistically robust low standard error and easy to interpret statistics that describe distributional properties of the data and covariation between different feature dimensions These involve mean levels and variability of symptomscontextual factors over time as indicators of symptomcontext level and volatility and their mutual comparison to detect key personalized strengths and weaknesses Next they involve simple statistical time series tools for tracking symptoms and contextual factors and their correlations over time such as autoregressive movingaverage ARMA models or measures of mutual predictability Granger causality allowing to obtain basic insight into how symptomscontext variables cooccur or predict one another over time Finally to capture clinically significant moments of change or tipping points within the behavioural trajectories as they are unfolding across larger periods of time WP4 will use statistical change point detection techniques as developed in Leuven eg 138 and Mannheim eg 139 to detect reliable phase changes in mean levels as a function of for instance instalment of treatment or a particular change in treatment over time Methods for the correction of familywise error rate such as the HolmBonferroni procedure regularization techniques in model estimation and measures of outofsample prediction error will be adopted to minimize the risk of false positives to inform clinical decision making Outcomes from lowerlevel statistics and machine learning predictors will be summarized and delivered to the visualization platform in a clinically meaningful and accessible way
Report on Technology Context and Self-tracking Practices (s’ouvre dans une nouvelle fenêtre)This report will consist of two papers one paper that reports the full results of the survey conducted in Task 51 and one paper that reports qualitative and quantitative findings from Task 52
Consolidated descriptions of interventions and implementation strategies for each of the participating sites (s’ouvre dans une nouvelle fenêtre)Jointly with WP5 stakeholder engagement and other work packages ie WP24 WP6 we will first specify and optimize our strategies for implementation of the DMMH into practice The chosen implementation strategies will includea the DMMH information technology system which adheres to prevailing standards and regulations particularly regarding data protectionb an intervention manual consistent with the Template for Intervention Description and Replication Checklist training and support package for clinicians and services to facilitate the use of the DMMH with service usersc a wellbalanced package of tailored information counselling and reminders for service users to motivate and enable them to use the DMMHThese strategies will purposefully vary somewhat between the different clinical sites to address local requirements A detailed factual description of the DMMH intervention and implementation strategies as planned will be tailored to and optimized based on the requirements of each site Building on the work carried out in WP5 stakeholder engagement we will generate an a priori assessment of anticipated barriers and facilitators that influence implementation using the nonadoption abandonment scaleup spread and sustainability NASSS implementation science framework84 This has been specifically proposed for the implementation of novel technologies and will be used to optimize in close collaboration with WP5 the DMMH implementation strategies with regard to the 7 domains of this framework the condition or illness ie a mental disorder the technology the value proposition the adopter system comprising professional staff service users and informal caregivers the organizations the wider institutional and societal context and the interaction and mutual adaptation between all these domains over time Findings from the qualitative framework analysis method which has been designed to inform policy will directly inform tailoring of our implementation strategies to local requirements These will be further adopted and finalized based on findings from other work packages ie WP24 WP6 prior to the start of the cRCT
H - Requirement No. 12 (s’ouvre dans une nouvelle fenêtre)For each clinical study the following documentsinformation must be submitted as a deliverable in one package prior to enrolment of first study subject i Final version of study protocol as submitted to regulatorsethics committees ii Registration number of clinical study in a WHO or ICMJEapproved registry with the possibility to post results iii Approvals ethics committees and national competent authority if applicable required for invitationenrolment of first subject in at least one clinical centre
WP3 will provide a comprehensive data management plan based on the Horizon 2020 template DMP. The DMP will cover aspects of data acquisition, processing, quality control, data privacy aspects, metadata annotation, data deposition, licensing and usage policy according to the FAIR guiding principles. The DMP will provide as appendices an inventory of data elements processed during the project and a threat analysis regarding data privacy.
Machine learning models developed in the present context need to efficiently exploit the multivariate and multimodal time series structure, and at the same time be able to adapt to the individual user, to make single subject inferences (e.g., 140, 141). Here, WP4 will infer deep time series models (DTSMs) based on recurrent neural networks (RNNs) from the individual app-based time series, as these are particularly suited to learn dynamical models of single subject behaviour, can adapt over time, and can, once trained on the (multimodal) measurements, be simulated to forecast and examine effects of environmental factors and interventions 25-27, 142. The DTSMs will be used to identify interpretable behavioural contingencies underlying changes in the subjects’ trajectories. By analysing trained model parameters, WP4 will be able to infer which behavioural variables, or multimodal variable combinations, and external factors are most strongly connected with each other. WP4 will further receive input from WP5 regarding missing data in the form of indicator, categorical, or count variables which may contain information about the mental health status or individual patterns of interaction with the mobile device, and can be explicitly included as response variables in the subject models.
Publications
Auteurs:
Marelli, L., Stevens, M., Sharon, T., Van Hoyweghen, I., Boeckhout, M., Colussi, I., ... & Southerington, T.
Publié dans:
Health Policy, Numéro 104861, 2023, ISSN 0922-3444
Éditeur:
Elsevier
DOI:
10.1016/j.healthpol.2023.104861
Auteurs:
Kurilla, A., Dančík, D., Čavojská, N., Izáková, Ľ., Pečeňák, J., Hajdúk, M., Heretik, A.
Publié dans:
Psychiatria pre prax, Numéro 23(4), 2022, Page(s) 163-166, ISSN 1335-9584
Éditeur:
SOLEN Medical Education
Auteurs:
de Thurah L, Kiekens G, Sips R, Teixeira A, Kasanova Z, Myin-Germeys I
Publié dans:
Psychiatry Research, Numéro 324, 2023, Page(s) 115207, ISSN 0165-1781
Éditeur:
Elsevier BV
DOI:
10.1016/j.psychres.2023.115207
Auteurs:
Reininghaus, U., Schwannauer, M., Barne, I., Beames, J.R., Bonnier, R.A., Brenner, M., Dančík, D., De Allegri, M., Di Folco, S., Dusterwitz, D., Hajduk, M., Heretik, A., Pecenak, J., Gugel, J., Izakova, L., Katreniakova, Z., Kiekens, G., Koppe, G., Kurilla, A., Marelli, L., Nagyova, I., Nguyen, H., Schulte-Strathaus, J., Sotomayor-Enriquez, K., Uyttebroek, L., Weermeijer, J.D.M., Wolters, M., We
Publié dans:
BMC Psychiatry, 2024, ISSN 1471-244X
Éditeur:
BioMed Central
Auteurs:
Myin-Germeys, I., Schick, A., Ganslandt, T., Hajdúk, M., Heretik, A., Van Hoyweghen, I., ... Reininghaus, U.
Publié dans:
Psychological Medicine, 2024, ISSN 1469-8978
Éditeur:
Psychological Medicine
Auteurs:
Myin-Germeys, I.
Publié dans:
DUSUNEN ADAM-JOURNAL OF PSYCHIATRY AND NEUROLOGICAL SCIENCES, Numéro 36, 2023, Page(s) 1-3, ISSN 1018-8681
Éditeur:
Bakirkoy Research and Training Hospital for Psychiatry, Neurology and Neurosurgery
DOI:
10.14744/dajpns.2022.00200
Auteurs:
Myin-Germeys, I.
Publié dans:
Psychiatry Research, Numéro 319, 2023, Page(s) 114978, ISSN 0165-1781
Éditeur:
Elsevier BV
DOI:
10.1016/j.psychres.2022.114978
Auteurs:
Dančík, D., Hajdúk, M., Heretik, A.
Publié dans:
E-Psychologie, 2022, ISSN 1802-8853
Éditeur:
Czech-Moravian Psychological Society
DOI:
10.29364/epsy.448
Auteurs:
Lievevrouw, E., Marelli, L. & Van Hoyweghen, I.
Publié dans:
Social Science & Medicine, Numéro 346, 2024, Page(s) 116620, ISSN 0277-9536
Éditeur:
Pergamon Press Ltd.
DOI:
10.1016/j.socscimed.2024.116620
Auteurs:
Brandoburová, P., & Dančík, D.
Publié dans:
In P. Kulišťák (Ed.), 2023
Éditeur:
Klinická neuropsychologie v praxi
Auteurs:
Brenner, M., Koppe, G., & Durstewitz, D.
Publié dans:
2022, ISSN 2331-8422
Éditeur:
Cornell University
DOI:
10.48550/arxiv.2212.07892
Auteurs:
Fechtelpeter, J., Rauschenberg, C., Jamalabadi, H., Boecking, B., van Amelsvoort, T., Reininghaus, U., Durstewitz, D., & Koppe, G.
Publié dans:
2023
Éditeur:
PsyArXiv
DOI:
10.31234/osf.io/97teh
Auteurs:
de Thurah L, Kiekens G, Weermeijer J, Uyttebroek L, Wampers M, Bonnier RAM, Myin-Germeys I
Publié dans:
2024
Éditeur:
OSF Preprint
DOI:
10.31219/osf.io/fm8kt
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