Combining H1-Coil Deep Transcranial Magnetic Stimulation (dTMS) and App-guided CBT in Subjects With Major Depression Disorder (MDD)

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Updated on 5 October 2022


A naturalistic study design, in which dTMS patients will be randomized to get a free add-on CBT treatment. The dTMS procedure will include treatment as usual, and participants will use the app from post randomization (Pre-treatment is defined as measures from the first three days of treatment) to the end of dTMS treatment (Post-treatment which is defined as measures from after twenty TMS sessions over a minimum of four weeks), and for an additional three months of FU (FU).


Participants will be recruited from those attending dTMS clinics around the world (i.e.,naturalistic study design), and consenting individuals (in person consent done in the clinic on paper) will be given a random user code. This code will be specific for their version of the app, and the only link between their signature and the code will remain at the site for auditing purposes. Participants will undergo treatment as usual of dTMS and will use GGDE twice a day. Participants will be asked to complete demographic and clinical questionnaires via the GGDE app, and relevant clinical questionnaires will be re-administered following treatment and during FU. The initial and all following dTMS sessions will involve patients going through the stimulation procedure (The operator will record stimulation variables such as individual patient's intensity of stimulation and coil location into GGDE) followed by 5 min of GGDE use. The patient will indicate which statements (in that session) were most relevant and challenging to them, which will be followed by a pre-prepared psycho-education paragraph about the specific maladaptive belief addressed by the app that day. The patient will then be prompted to use the app one more time at home during the same day. Patient will continue similar use during FU, with two GGDE usage each day. The naturalistic design will allow the accumulation of large quantities of data in a short period of time, aiming to optimize GGDE using machine learning. Importantly, GGDE will allow the random allocation of users to different GGDE modules of the app (i.e., similar app versions with changes in specific parameters related to different depressive traits). One out of the ten modules used in this trial will include neutral (placebo) stimuli requiring the user to respond to plus/minus signs rather than MDD relevant statements.

Condition Data Collection for Facilitation of Machine Learning Algorithm for Personalized Treatment
Treatment CBT-based, mobile-delivered, intervention platform
Clinical Study IdentifierNCT04455646
Last Modified on5 October 2022

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