Studies mental health and blood sugar patterns in people with diabetes
Follows adults with diabetes to track mental health and blood sugar over time.
Plain-language title generated from the registry record
Registry title: Precision Subclassification of Mental Health in Diabetes: Digital Twins for Precision Mental Health to Track Subgroups
Key facts
- Where
- Bad Mergentheim and Würzburg, GermanySee the 3 sites
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 80 years old?
Source: NCT07212075, who can take part
Where in Germany
- Diabetes Clinic Mergentheim (DCM)Bad Mergentheim, GermanyTaking people
- MVZ Diabetespraxis MergentheimBad Mergentheim, GermanyNo longer taking part
- MVZ DiaMedicum WürzburgWürzburg, GermanyNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Forschungsinstitut der Diabetes Akademie Mergentheim
- Trial code
- NCT07212075
- Record updated
- 4 December 2025
- Original description
Mental conditions and disorders (e.g. distress, depressive, anxiety, and eating disorders) are more prevalent in people with diabetes (PWD) and associated with reduced quality of life and impaired glycaemic outcomes. Evidence supports a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is assumed that subgroups exist that show different trajectories of glycaemia and mental health.Read full original descriptionShow less
Precision Subclassification of Mental Health in Diabetes: Digital Twins for Precision Mental Health to Track Subgroups
Mental conditions and disorders (e.g. distress, depressive, anxiety, and eating disorders) are more prevalent in people with diabetes (PWD) and associated with reduced quality of life and impaired glycaemic outcomes. Evidence supports a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is assumed that subgroups exist that show different trajectories of glycaemia and mental health.
Belonging to a particular subgroup may be linked with a higher risk of developing mental health problems compared to others. This suggests that it is possible to treat individuals in different subgroups in a manner that optimizes their treatment and can improve health outcomes. Accurate characterisation can inform more individualized care. This calls for a more personalised approach considering the idiosyncrasies of different subgroups.
Over 3 years, the investigators have established the basis of a precision mental health approach for diabetes using n-of-1 analyses. By utilizing combined ecological momentary assessment (EMA: repeated daily sampling of psychosocial factors in everyday life) and continuous glucose monitoring (CGM), intensive longitudinal data per person could be collected. This enables the analysis of individual associations between glycaemic parameters and psychosocial variables and identification of individual sources of diabetes distress in each person.
The objective of the present study is to use of the n-of-1 approach to identify subgroups of PWD who share common characteristics in the associations between glucose and psychosocial variables. The identified subgroups shall be used to develop a digital twin for precision mental health in diabetes. The digital twin serves as representation of a real person, allowing to make simulations and predictions of the course of mental health and glycaemia. These predictions can inform diabetes care and lead to more precise, personalised treatment decisions.
To achieve this, a longitudinal panel including over 1,400 PWD who continuously complete EMA and questionnaire surveys and measure glucose levels using CGM was developed. Over 1000 clinical interviews to diagnose mental disorders have been conducted to identify major mental health conditions and map mental outcomes. To identify subgroups and develop the digital twin, the sampling will be expanded aiming at a total of 1,809 PWD. Incidence and remission of mental disorders will be determined via repeated interviews.
The complex networks between clinical, metabolic, and psychosocial data will be analysed using machine learning, leading to new insights with the potential to shape future guidelines. These results will be used by the digital twin to predict courses of glycaemic control and mental health, translating the individual evidence into direct treatment suggestions.
Inclusion: 18 to 80 years of age; Diagnosis of type 1 diabetes or type 2 diabetes or other specific type of diabetes; Diabetes duration ≥ 1 year; Sufficient German language skills; Informed consent.
Exclusion: Significant cognitive impairment (e.g. dementia); Severe disorder or condition impacting the person's ability to participate in the study or likely to confound results (e.g. treated cancer, heart disease ≥ NYHA III, schizophrenia/psychotic disorder); Terminal illness; Inability to consent; Being bedridden.