Andreas Schmitt

Aims/hypothesis: The aims were to: (1) analyse the day-to-day mechanisms through which daily diabetes-specific negative emotional experiences (burdens) accumulate and potentially affect emotional well-being; and (2) identify the specific triggers of these dynamics in people with type 1 and type 2 diabetes. Methods: Data were derived from three prospective observational studies employing ecological momentary assessment for up to 17 days. Multilevel vector autoregression models were used to estimate within-person temporal networks of nine daily diabetes-specific burdens. Out-strength and in-strength indices quantified each variable’s role as a trigger (i.e. influencing others) or receiver (i.e. being influenced by others). Individual network density was computed to characterise how strongly daily burdens predicted one another. Logistic regression models tested whether these dynamic features predicted questionnaire-assessed incidence and remission of elevated diabetes distress and depressive symptoms at follow-up. Results: Among 741 participants (50.1% with type 2 diabetes, 50.7% male, aged [mean ± SD] 50.4±14.2 years, HbA _1c [mean ± SD] 65±18.6 mmol/mol [8.1±1.7%]), ‘feelings of guilt’ regarding suboptimal self-management emerged as the most significant trigger of other negative emotional experiences/burdens. In type 1 diabetes, greater ‘feelings of guilt’ on one day were temporally associated with increased levels of ‘feeling alone’ (β=0.09, p p =0.016), ‘feeling restricted’ (β=0.05, p =0.015) and ‘feeling burdened by hyperglycaemia’ (β=0.05, p =0.030) on the following day. In type 2 diabetes, ‘feelings of guilt’ functioned primarily as a receiver. Greater ‘feelings of guilt’ on one day were predicted by ‘feeling alone’ (β=−0.07, p =0.016) and ‘feeling burdened by hypoglycaemia’ (β=0.04, p =0.017) on the previous day. Higher individual network density predicted greater odds of incident elevated diabetes distress (OR 1.50, p =0.025) and depressive symptoms (OR 2.08, p <0.001), with ‘feelings of guilt’ and ‘feeling restricted’ as the most relevant predictors. Conclusions/interpretation: The results provide evidence of how daily diabetes-specific burdens can accumulate and potentially affect the course of emotional well-being. Feeling guilty regarding diabetes self-management may play a central role and represent an important clinical target as well as outcome. Trial registration: ClinicalTrials.gov NCT03811132, NCT04438018, NCT05548699.

Purpose of Review: Diabetes self-management is accompanied by time-varying emotional and motivational challenges that impact mental health. These day-to-day fluctuations can be assessed via ecological momentary assessment (EMA), that allows the repeated sampling of psychosocial variables in everyday life. The benefits of EMA over questionnaires mirror the benefits of continuous glucose monitoring (CGM) over HbA1c. We describe the insights generated by combining EMA and CGM and highlight its potential. Recent Findings: Research shows that glucose levels can influence subsequent mood, stress, cognitive functioning, and symptom reporting, with nocturnal hypoglycemia and overnight glucose being particularly relevant. Studies demonstrated the importance of differentiating between subjective person-interpreted and objectively sensor-assessed glucose levels. N-of-1 analyses revealed relevant intraindividual differences in the association between glycemic and psychosocial parameters. Summary: Combining EMA and CGM can enhance our understanding of the dynamic relationship between glycemic and psychosocial variables, supporting precision medicine approaches for mental health in diabetes.

Aims: Rapid changes in diabetes therapy combined with limitations of traditional methodological approaches challenge the field of psychosocial research to adequately capture the experiences of people with diabetes. This narrative review provides an overview of emerging qualitative and quantitative approaches that can advance the study of psychosocial aspects of diabetes. Methods: We searched PubMed and Google Scholar for English‐language articles regarding novel qualitative and quantitative methodologies. Results: Emerging qualitative methodologies aim to increase the transferability of lived experiences to other contexts and populations by employing novel ways to stimulate interactions and using digital tools. Culturally sensitive methods (e.g. yarning) and the use of pictures (e.g. photovoice) and storytelling methods (e.g. story completion) can capture more diverse experiences and sensitive topics while being able to minimise social desirability. Online qualitative surveys can increase the reach while artificial intelligence (AI) can be implemented in qualitative research protocols. Emerging quantitative methodologies aim to better understand dynamic within‐person processes. With repeated daily smartphone‐based assessments (e.g. ecological momentary assessment) and passive sensor‐based data collections (e.g. digital phenotyping), intensive longitudinal data can be collected that allow for n‐of‐1 trials, especially in combination with continuous glucose monitoring. Quantitative data can also be used to identify clusters/subgroups of people with shared experiences. Innovative digital twin technology and AI offer intriguing possibilities that can advance the field towards precision mental health care. Conclusions: Several innovative methodologies (will) enrich our understanding of psychosocial aspects in diabetes. To fully capitalise on these methodologies, co‐design and mixed methods approaches are necessary.

Aims/hypothesis: People with diabetes and depression show large heterogeneity in their response to depression treatment. This study aimed to identify biomarkers of subclinical inflammation that were associated with improvement of depressive symptoms in people with type 1 diabetes and type 2 diabetes. Methods: The prospective analysis combined data from three studies (DIAMOS, ECCE HOMO and DDCT). A total of 332 people with type 1 diabetes and 189 people with type 2 diabetes completed both the baseline and 1 year follow-up examinations. Depressive symptoms were assessed using the Center for Epidemiological Studies depression scale (CES-D). Associations between baseline serum levels of 76 biomarkers of inflammation and 1 year changes in depressive symptoms were estimated using multiple linear regression. Results: In people with type 2 diabetes, higher levels of 26 biomarkers were associated with greater reductions in depressive symptoms (β=0.128 to 0.255; pβ=−0.189 to −0.094; pp _interaction<0.05). The positive associations in type 2 diabetes were strongest for improvements in cognitive-affective and anhedonia symptoms, while the inverse associations in type 1 diabetes were strongest for improvements in somatic symptoms. Conclusions/interpretation: Higher baseline levels of multiple biomarkers of inflammation were associated with greater depression reduction in type 2 diabetes but lower depression reduction in type 1 diabetes. There were also diabetes type-specific differences in the associations with symptom clusters of depression. This suggests that different inflammation-related pathways may be relevant for the response to depression treatment in people with type 1 diabetes or type 2 diabetes. Graphical

Aims: Binge eating disorders (BED) are underdiagnosed in diabetes care, despite being the most common eating problem for diabetes patients. While diabetes‐specific screening for disordered eating behaviour is recommended, the only diabetes‐specific instrument available, Diabetes Eating Problem Survey‐Revised (DEPS‐R), focuses on type 1 diabetes and rapid‐acting insulin, limiting its use across diabetes types and treatment regimens. This study aimed to develop a non‐insulin version of the DEPS‐R and evaluate its screening performance for BED in people with type 1 and type 2 diabetes. Methods: The DEPS‐R was reduced to 10 non‐insulin‐specific items (DEPS‐10). As part of the ongoing pro‐mental study, 679 people with type 1 or type 2 diabetes completed the baseline survey and took part in diagnostic interviews to assess BED. The screening performance of the DEPS‐10 was tested via receiver operating characteristic (ROC) curve analysis and compared with DEPS‐R and food‐related items of the Problem Areas In Diabetes (PAID). Results: N = 24 participants (total = 3.5%; type 1 = 2.9%, type 2 = 4.3%) were diagnosed with a current BED. The DEPS‐10 performed well in screening for BED (area under the curve [AUC] = 0.92, p < 0.001) comparable with the DEPS‐R (AUC = 0.92, p < 0.001) and exceeded the performance of food‐related PAID items (AUC = 0.82, p < 0.001). A cut‐off score of ≥15 showed optimal sensitivity and specificity in BED screening. People who met the cut‐off had significantly higher BMI and HbA1c and more diabetes distress, depressive and anxiety symptoms. Conclusions: DEPS‐10 is a reliable screening instrument for BED. Its associations with glycaemic and mental health outcomes reflect its good construct validity comparable to DEPS‐R.

Aim: To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment. Materials and Methods: Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening). The symptom ratings were associated with the glucose levels for the previous 2 hours, measured with CGM. Linear mixed‐effects models were used, allowing for nested random effects and the conduct of N = 1 analysis of individual associations. Results: In total, 192 individuals with type 1 diabetes and 179 with type 2 diabetes completed 6380 app check‐ins. Four symptoms showed a significant negative association with glucose values, indicating higher ratings at lower glucose (speech difficulties, P = .003; coordination problems, P = .00005; confusion, P = .049; and food cravings, P = .0003). Four symptoms showed a significant positive association with glucose values, indicating higher scores at higher glucose (thirst, P = .0001; urination, P = .0003; taste disturbances, P = .021; and itching, P = .0120). There were also significant positive associations between microangiopathy and eight symptoms. Elevated depression and diabetes distress were associated with higher symptom scores. N = 1 analysis showed highly idiosyncratic associations between symptom reports and glucose levels. Conclusions: The N = 1 analysis facilitated the creation of personalized symptom profiles related to glucose levels with consideration of factors such as complications, gender, body mass index, depression and diabetes distress. This approach can enhance precision monitoring for diabetes symptoms in precision medicine.

Aims/hypothesis: Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up. Methods: In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up. Results: Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; pt=13.637; pt=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all pp1c (β=0.12; pN-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach. Graphical

Aims: To analyze if midterm improvement in diabetes distress can be explained by resilience, diabetes acceptance, and patient characteristics. Methods: N = 179 adults with type 1 diabetes were enrolled during their stay at a tertiary diabetes center (monocentric enrolment) and followed up over three months in a prospective, observational study (‘DIA-LINK1’). Improvement in diabetes distress was assessed as reduction in the Problem Areas in Diabetes Scale score from baseline to follow-up. Resilience (Resilience Scale-13), acceptance (Diabetes Acceptance Scale), and patient characteristics were analyzed as predictors of improvement in diabetes distress using hierarchical multiple regression. Results: Greater reductions in diabetes distress were significantly explained by lower diabetes acceptance at baseline (β = −0.34, p < 0.01), while resilience, diabetes complications, and other person-related variables were not significantly related to changes in diabetes distress (all p > 0.05). When change in diabetes acceptance from baseline to follow-up was added to the model, improved diabetes distress was explained by increasing diabetes acceptance (β = 0.41, p < 0.01) and a shorter duration of diabetes (β = -0.18, p = 0.03), while baseline diabetes acceptance was no longer significantly associated (β = −0.14, p > 0.05). Conclusions: Diabetes acceptance is inversely related to diabetes distress, and increasing acceptance explained greater improvement in diabetes distress. These findings suggest that increasing diabetes acceptance may facilitate the reduction of diabetes distress. Treatment approaches targeting acceptance might be useful for the mental healthcare of people with type 1 diabetes and clinically elevated diabetes distress.

Objective: Fear of diabetes complications (FDC) is a common source of emotional distress in people with diabetes across types and treatments and may affect health outcomes. To assess FDC, the Fear of Diabetes Complications Questionnaire (FDCQ) was developed. This study evaluates the FDCQ's German version in people with type 1 diabetes (T1D) and type 2 diabetes (T2D). Method: A German version of the FDCQ was developed and administered as part of four different studies sampling people with T1D and T2D. Measurement properties were evaluated across studies using factor analyses, reliability estimates, and associations of the measure within a network of variables. A cutoff criterion for elevated FDC was derived. A short form scale was also developed. Results: High reliability and validity were supported. FDC as measured by the FDCQ was independently associated with higher diabetes distress and depressive symptoms. A cut-off score for elevated FDC was set at = 30 in the 15-item FDCQ. Elevated FDCQ scores were detected in 36% of participants in secondary diabetes care and up to 46% of those in tertiary care. Conclusions: FDC is prevalent in people with T1D and T2D and associated with diabetes distress and depressive symptoms. The FDCQ is a reliable and valid tool for assessing FDC in research and practice. It may help identify persons in need of tailored education and care and monitor effects following treatment.