Continuous glucose monitoring (CGM) provides real-time glucose data for people with diabetes. However, detailed knowledge of its use in daily life remains limited. We aim to investigate the interaction between people with type 1 diabetes (T1D) and their CGM data and the impact of the interaction on glycaemia and diabetes distress.

This is a two-centre observational study of adults (n=500) with T1D using FreeStyle Libre 2. Over a period of 14 days, participants will continue their regular CGM use, record insulin doses and timing with smart insulin pens, track activity and sleep with an activity tracker, log all food intake in the LibreLink app and answer questions about quality of life and hypoglycaemia two times per day. Before the study period, the participants will complete a survey of 11 validated questionnaires assessing diabetes distress, hypoglycaemia awareness and other patient-reported outcomes (PROs). After the study period, the participants will complete two additional questionnaires assessing diabetes distress and health literacy.

The collected data will be used in two substudies with the overall aims of:

Substudy 1: to investigate how CGM is used in practice and the impact of the interaction on diabetes distress and glycaemia.

Substudy 2: to investigate whether and how CGM functions as a technological substitute for impaired awareness of hypoglycaemia, focusing on alarm data.

Endpoints will include CGM metrics, alarm data and PROs.

The Danish Data Protection Agency approved the study (P-2024–15985), and the regional committee on health research ethics has granted an ethical waiver (H-24014662). All participants have signed written informed consent forms before participating. The results will be published in an international peer-reviewed scientific journal by the study investigators and shared via www.clinicaltrials.gov. Participants who agreed to receive information about the study will be sent the results after publication.

ClinicalTrials.gov (NCT06453434).

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.

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.

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.

DRKS00030915; OSF PREACT: http://osf.io/bcgax; OSF PREACT-digital: https://osf.io/253nb.