Laura Yvonne Klinker

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.

To develop and evaluate the German versions of the Diabetes Stigma Assessment Scales for type 1 (DSAS-1) and type 2 diabetes (DSAS-2). The questionnaires were translated using a standardized translation and back-translation procedure. Adults with type 1 (N=636) and type 2 diabetes (N=177) completed the DSAS-1 and DSAS-2 via an online panel. Factor analyses, internal consistency (Cronbach’s α), and correlations with convergent measures and discriminant constructs were conducted to assess reliability and validity. For the DSAS-1, the expected three-factor structure was confirmed; for the DSAS-2, a two-factor solution emerged, although a forced three-factor analysis supported the original structure with minor cross-loadings. Scale consistencies were excellent (DSAS-1: 0.94, DSAS-2: 0.95, and subscales: 0.86–0.93). Both scales showed good concurrent, convergent and discriminant validity. The DSAS-2 showed stronger associations with weight self-stigma and self-esteem than the DSAS-1, consistent with its focus on internalized stigma. Three in four persons (type 1 diabetes: 74% and type 2 diabetes: 72%) endorsed at least one stigma item, mostly related to “blame and judgement.” Diabetes stigma constitutes an important psychosocial problem. The German DSAS-1and DSAS-2 are reliable, valid instruments for assessing diabetes stigma. Blame and judgement appeared as the most common form of stigma experienced in this sample.

Aim: The potential implementation of early type 1 diabetes (T1D) detection pathways, encompassing autoantibody screening and longitudinal monitoring, raises important psychosocial considerations for ethical, person‐centred care. This review summarises evidence on the psychosocial impact of early T1D detection, identifying key evidence gaps and recommendations for integrating psychosocial support. Methods: A semi‐structured narrative review was conducted using PubMed‐indexed international peer‐reviewed literature, complemented by experiential insights from an interdisciplinary authorship team. Results: Broader Health screening literature emphasises that individuals' values, beliefs about chance, severity and controllability, alongside sociocultural context, shape decision making and coping. Early T1D detection introduces inherent uncertainty regarding timing and clinical progression. Despite a limited evidence base, largely focused on parents and lacking tailored person‐reported outcome measures (PROMs), studies suggest a transient increase in negative emotional responses (e.g., anxiety, depressive symptoms, distress). Individual variation appears linked to screening outcome, prior experiences with T1D and tolerance for uncertainty. Effective, stigma‐free communication and psychosocial support delivered by family‐oriented professionals can facilitate informed, autonomous decision making. Mental health specialists play a key role in developing behaviourally informed protocols for care and communication, training care teams and providing targeted support for families experiencing persistent distress. Community involvement in the design, testing and evaluation of these communication tools, models of care and development of PROMs are essential for acceptability and equity. Conclusion: Integrating psychological monitoring and care is a critical component of the early T1D detection pathway. Contextually relevant and co‐designed information, support strategies and PROMs are needed to help families make informed decisions and navigate uncertainties.

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: 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

Background: Previous qualitative studies on attitudes towards schistosomiasis demonstrated inconclusive results on the extent of stigma towards schistosomiasis in endemic communities around the world. The Explanatory Model Interview Catalogue–Community Stigma Scale (EMIC-CSS) has been used and validated for the assessment of public stigma across numerous countries in various health conditions. This study tested the performance of the scale in the context of stigma related to schistosomiasis in twelve communities in the three districts of Magu, Nyamagana and Ilemela in Mwanza region, Tanzania. Methodology/Principal findings: The 15-item-version of the EMC-CSS was first translated to Kiswahili language. The translation was discussed within the research team to retain the meaning of the items and implement cultural adaptations. Validation of the adapted EMIC-CSS scale was conducted following the framework of Herdman and Fox- Rushby. A pilot study with 41 participants from two communities provided the basis for testing the performance of each item and assessing the semantic and operational equivalence of the scales. In addition, eight qualitative focus group discussions (FGDs) were conducted to evaluate the conceptional equivalence of the EMIC-CSS. Finally, the performance of the adjusted scale was tested on 200 participants with a 50:50 male-female ratio from ten communities. The mean score of the EMIC-CSS M = 8.35 (SD = 6.63) shows clear indications for public stigma towards schistosomiasis. The EMIC-CSS demonstrated a good internal consistency with Cronbach’s alpha α = .857 and no floor and ceiling effects. Conclusion/Significance: The results demonstrate that the EMIC-CSS is a useful instrument in assessing public stigma towards schistosomiasis and allow a clear recommendation of the EMIC-CSS for schistosomiasis in the Tanzanian culture. However, future studies are additionally recommended to address specific aspects and forms of the disease and how they contribute to the development of stigma towards schistosomiasis.