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Article

The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study

by
Antonia Losonczi
1,2,*,
Csaba Kazinczi
1,3,
Flora Fehervari
4,
Mandorla Illenyi
4,
Zoltan Herold
5,
Dora Palmai
4,
Adrien Rigo
6,
Eva Varga
4,
Zsofia Maria Zemplenyi
4,
Agnes Vincze
4 and
Geza Nagy
4
1
Department of Psychiatry and Psychotherapy, Semmelweis University, H-1085 Budapest, Hungary
2
Behavioral Sciences Program, Semmelweis University Doctoral College, H-1085 Budapest, Hungary
3
Department of Neurology, University of Szeged, H-6725 Szeged, Hungary
4
Department of Internal Medicine and Hematology, Semmelweis University, H-1088 Budapest, Hungary
5
Division of Oncology, Department of Internal Medicine and Oncology, Semmelweis University, H-1083 Budapest, Hungary
6
Department of Personality and Health Psychology, Faculty of Education and Psychology, Eötvös Loránd University, H-1075 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Diabetology 2025, 6(3), 19; https://doi.org/10.3390/diabetology6030019
Submission received: 11 February 2025 / Revised: 2 March 2025 / Accepted: 4 March 2025 / Published: 11 March 2025
Browse Figure
Versions Notes

Abstract

:
Background: Diabetes-related distress (DD) significantly impacts self-management and quality of life (QoL) in individuals with type 1 diabetes (T1D). While previous research has established a strong link between DD and glycemic control in type 2 diabetes, the relationship remains less consistent in T1D. Additionally, continuous glucose monitoring (CGM) has been shown to improve glycemic outcomes, yet its effects on self-management and QoL are still debated. This study aimed to examine the relationship between DD, self-management efficacy (SME), and QoL in T1D, incorporating both physiological and behavioral indicators. Furthermore, differences between CGM-users and non-users were investigated. Methods: A cross-sectional study including 108 T1D patients was conducted. Participants completed several validated self-report measures, including the Diabetes Distress Scale (DDS), Diabetes Self-Management Questionnaire (DSMQ), and Audit of Diabetes-Dependent Quality of Life (ADDQoL-19). HbA1c levels and CGM usage were retrieved from medical records. Structural equation modeling (SEM) was used to examine the relationships between DD, self-management, and QoL. Results: Distress level (DDS) had a significant negative effect on SME (β = −0.47, p < 0.001), suggesting that higher distress levels are associated with lower self-management. In contrast, SME showed no significant impact on quality of life (β = 0.03, p = 0.779). However, the relationship between quality of life and distress was significant and negative (β = −0.37, p < 0.001), meaning that higher distress levels are linked to a lower quality of life. No significant differences in DD, SME, HbA1c, or QoL were found among CGM users and non-users. Conclusions: DD significantly impacts self-management and QoL in individuals with T1D. Therefore, incorporating PROs on DD and on behavioral aspects of self-management alongside HbA1c levels in clinical care is essential for optimizing treatment plans and improving physical health outcomes. While CGM technology facilitates glucose regulation, it does not inherently improve QoL, which is more closely linked to distress.

1. Introduction

Diabetes is a highly demanding chronic disease with significant health and economic consequences [1,2,3]. In 2021, diabetes affected roughly one in ten adults worldwide [1]. In Hungary, diabetes prevalence is on par with European estimates, at 9.1% versus 9.2% in Europe [4]. Type 1 diabetes (T1D) receives less research attention compared to other types of diabetes because it has a lower prevalence, making the estimated 8.4 million T1D population worldwide [2] less accessible for studies. Individuals with T1D must continuously adhere to a complex treatment plan to achieve optimal glycemic control and prevent complications [1,5]. Being largely self-managed, it can affect physical and mental health and quality of life (QoL) [6,7]. Therefore, a deeper comprehension of the underlying factors of self-management as well as the associated components is crucial.
Diabetes-related distress (DD) has been at the center of attention in the diabetes literature in the past decades [8,9]. In one of the most prominent cross-national studies, 44.6% of patients were found to experience high levels of diabetes-related distress (>40 score on the Problem Areas in Diabetes Scale) [10]. Among T1D patients, higher DD was correlated with low diabetes empowerment [11], low QoL [11], unhealthy diet [11], not being physically active [11], and poor glycemic control [8,11,12,13]. According to a systematic review, 20–30% of T1D patients might experience elevated DD that would deteriorate their glycemic control [14]. Additionally, DD might have a stronger association with glycemic control than other patient-reported outcomes (PROs) [15]. The component of DD that is found to have the strongest association with HbA1c and self-management behavior is regimen-related distress [8,11,15,16]. Most studies have examined only one aspect of self-management, either HbA1c levels or a questionnaire-based method, even though these two indicators of self-management do not always correlate. In clinical care, a more comprehensive assessment and early detection of DD are necessary to prevent diabetes burnout (DB) that might develop if DD remains untreated [17]. DB is a relatively new PRO in the diabetes literature, described as the result of a vicious cycle consisting of high levels of DD [18], feeling overwhelmed by the challenges of disease management [17], physical and/or emotional exhaustion [17,18,19], resulting in the deterioration of glycemic control [17,18]. Health-related quality of life (HRQoL) is an essential and well-researched component among PROs and an aspect when evaluating the impact of a chronic disease [20]. In several studies, higher QoL was associated with better glycemic control [6,7,21,22] among T1D patients. The relationship between lower DD levels and better QoL is mainly found among T2DM patients [23] or T1D and T2DM mixed sample populations [16].
Recent technological advancements necessitate further research to assess their impact on patients’ lives. As is well established, self-monitoring of blood glucose (SMBG) can be beneficial in preventing long-term complications and helpful during consultations [24]. Its recent alternatives, continuous glucose monitoring (CGM) systems, provide more precise and detailed information on glucose levels, and are proven to improve glycemic control [25]. Among T1D patients using CGM, an improvement of HbA1c levels [26,27,28,29], of diabetes-specific QoL [30], treatment satisfaction [26], elevated overall well-being [26], reductions in DD [27,29,30], improvement of hypoglycemic confidence [26,30], and self-efficacy [27] was found compared to SMBG users [30] or conventional treatment periods [26]. Others did not find significant changes in hypoglycemia rates, awareness [27], or more general distress levels [27]. Additionally, drawbacks such as the complexity of glucose management leading to overwhelming feelings were also identified [29]. In conclusion, there are inconsistent findings about the relationship between the CGM system usage and QoL; therefore, further research is needed to improve comprehension.
The primary objective of this study is to examine the link between DD and self-management in T1D, using a comprehensive indicator that includes physiological and behavioral aspects. The secondary objectives are to make a comparison between continuous glucose monitoring (CGM) and non-continuous glucose monitoring (NCGM) groups across the examined variables. We hypothesized that 1. diabetes distress would negatively predict diabetes self-management and quality of life; 2. more effective self-management would be associated with better quality of life; 3. DD influences quality of life not only directly but also indirectly through self-management; and 4. the use of CGM is associated with lower diabetes distress, better self-management, and higher quality of life.

2. Materials and Methods

This cross-sectional study was conducted among T1D patients at the Department of Internal Medicine and Hematology, Semmelweis University, Budapest, Hungary, between August 2024 and January 2025. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [31]. Patients older than 18 years, fluent Hungarian speakers, with a T1D diagnosis (for at least 3 months) who consented to participate were included in this study and were sent an online battery of surveys. Exclusion criteria were any cognitive decline that impairs the understanding and completion of the questionnaires, vision impairment, reading disability, frailty, and severe anemia or renal disease. The execution of the research had been permitted by the Medical Research Council (ethical approval number: BM/13016-3/2024; date of approval: 29 July 2024).
A total of 121 patients who met the inclusion criteria and had no exclusion factors were invited to complete the battery of online questionnaires. Of these, 108 participants successfully completed the questionnaire. No missing responses were recorded, as the online survey required all questions to be answered before proceeding.
The 17-item Diabetes Distress Scale (DDS, [9]) was selected to assess diabetes-related emotional distress due to its advantage over other questionnaires in distinguishing four different domains as subscales: Emotional Burden, Physician-Related Distress, Regimen-Related Distress, and Diabetes-Related Interpersonal Distress subscale [9]. Each item is rated on a 6-point Likert scale, ranging from 1 (not a problem) to 6 (a very serious problem), reflecting the degree of distress experienced over the past month. Higher scores indicate more significant distress. The DDS provides both total and individual subscale scores, facilitating a comprehensive understanding of specific distress areas [9]. DDS had been translated and validated in several languages and found a reliable tool to assess diabetes-related distress [11,16,32,33,34,35] and correlated with diabetic self-care and metabolic outcomes [11,33].
For the assessment of the quality of life, we used the Audit of Diabetes Dependent Quality of Life-19 (ADDQOL-19) [36], one of the most widely used self-reported QoL tools, considered to be a comprehensive and sensitive, 19-item questionnaire [37] appraising different domains of life (e.g., work, traveling, and social and sexual life), whilst imagining how life would be without diabetes. Respondents were also asked how important each domain is to their overall QoL, and they were offered an N/A option if a specific domain would be irrelevant to them, resulting in a weighted score for each domain and one for measuring overall QoL [36]. Each item is rated on a 5-point scale assessing the perceived impact of diabetes. The ADDQOL is the most translated and validated questionnaire assessing diabetes-specific QoL with the advantage of being sensitive to change and to subgroup differences [38]. The scale’s Cronbach’s alphas range falls between 0.85 and 0.93, indicating a good internal consistency [38,39,40].
The level of diabetes-related self-management was measured with the Diabetes Self-Management Questionnaire (DSMQ, [41]). DSMQ is designed to assess self-care activities directly influencing glycemic control in individuals with diabetes. DSMQ comprises 15 items categorized into four subscales (Glucose Management, Dietary Control, Physical Activity, and Healthcare Use). One additional item contributes solely to the total scale score. Each item is rated on a four-point Likert scale, reflecting the frequency of specific self-care behaviors. The subscale scores and a ‘Sum Scale’ representing overall self-management are calculated by summing relevant item scores and transforming them to a 0–10 scale, where higher scores indicate more effective self-care behaviors [41]. DSMQ is reported to be a high-quality responsive instrument [42], demonstrating strong reliability [43]. Additionally, it has been identified as a predictor of glycemic control as measured by HbA1C levels [44,45,46,47] and has been successfully validated in Hungarian [45].
In addition to the above-mentioned self-reported questionnaires, the data on whether or not CGM was used and glycated hemoglobin (HbA1c) values were obtained from patients’ medical records, all assessed within one month before this study.
Statistical analyses were performed using SPSS 24.0.0 (IBM SPSS Statistics, New York, NY, USA) and Jamovi version 2.3.28.0 (The Jamovi Project, 2023). For these analyses, p was considered a statistically significant criterion at <0.05. In addition to a descriptive analysis of the data, their normality was tested using the Shapiro–Wilk test. Between-group comparisons were performed using independent samples t-test (parametric) or Mann–Whitney U-test (non-parametric) and Chi-square tests. We also calculated the Cronbach’s alpha values of the scales used in the model.
Structural Equation Modeling (SEM) was used to investigate the relationships between variables. The statistical power of the SEM model was estimated using the web-based application pwrSEM [48], which uses Monte Carlo simulation to determine the probability of detecting significant effects. This approach simulates multiple data sets based on a given model structure and evaluates the proportion of significant estimates at α = 0.05. In the present case, 5000 simulations were performed with a sample size of 104, for which the minimum statistical power was set at the uniformly accepted value of 0.80 [48]. The models were developed using HbA1c values and the primary scales of the DSMQ, DDS, and ADDQoL19 questionnaires. Generalized least squares (GLS) estimation was employed for modeling, as the data deviated from normal distribution. In our study model, we defined the primary scale of HbA1c and DSMQ as a latent variable of ‘Self-Management Efficacy’ (SME), incorporating aspects of glycemic control and self-management behavior. The scaling of the latent variable was achieved by fixing its variance at 1.0 with standardized values. This approach ensures the identifiability of parameters while allowing the loadings of the indicators to be freely estimated. Treating the core scales of DDS and ADDQoL19 as manifest variables provides an opportunity to analyze distress to investigate its impact on self-management and quality of life. Model fit was assessed according to Hu and Bentler’s [49] recommendations: Standardized Root Mean Square Residual (SRMR) ≤ 0.08, Tucker–Lewis index (TLI) ≥ 0.95, Comparative Fit Index (CFI) ≥ 0.95, and Root Mean Square Error of Approximation (RMSEA) ≤ 0.06 (with the upper bound of 90% CI ≤ 0.08). Furthermore, ‘Estimate’ refers to the non-standardized coefficient representing the effect of a one-unit increase in the independent variable on the dependent variable, measured on the original scale of the variables used in the figures. The ‘β’ represents the standardized coefficient, which indicates the effect of one standard deviation increase in the independent variable on the dependent variable, facilitating comparisons of variables on different scales.

3. Results

3.1. Primary Outcomes and Sample Characteristics

This study involved 108 participants with diverse characteristics, including age, gender, diabetes duration, and sensor usage. Overall, 71% of participants used a continuous glucose monitoring (CGM) sensor, while 29% did not. Gender distribution and age were balanced across groups, with subtle variations in median age, and BMI observed between sensor users and non-users; however, diabetes duration showed a significant between-group difference. The median HbA1c levels were similar across groups. Regarding diabetes-related complications, 15% of participants had at least one complication, with differences in the prevalence of specific conditions such as retinopathy, macrovascular disease, and nephropathy between sensor users and non-users. There were no significant between-group differences (CGM user vs. non-user) in DDS, DSMQ main and subscales, ADDQoL19, and HbA1c. Furthermore, the results indicate high reliability for all the assessed psychometric scales. The DSMQ scale showed a Cronbach’s alpha of 0.83, indicating strong internal consistency. The DDS scale demonstrated excellent reliability with a Cronbach’s alpha of 0.94. The ADDQoL19 scales achieved Cronbach’s alpha values of 0.88, reflecting robust internal consistency. Details of the sample characteristics, including demographic, clinical, and complication-related data, are summarized in Table 1, while correlations between the used variables are in Table 2.
Age was positively correlated with overall diabetes self-management (DSMQ) and its subscales, including glucose management, dietary control, and physical activity. However, it was negatively correlated with HbA1c, suggesting that older age is associated with better glycemic control. Diabetes duration showed no significant correlation with HbA1c or other self-management variables. HbA1c was negatively correlated with diabetes self-management and all its subscales, particularly glucose management and dietary control. This indicates that better diabetes self-management is associated with lower HbA1c levels.
Diabetes distress (DDS) was negatively correlated with overall diabetes self-management, glucose management, and dietary control. Additionally, it was negatively associated with healthcare use, indicating that higher distress is related to poorer self-management and lower engagement with healthcare. Quality of life (ADDQoL19) was significantly correlated with dietary control and healthcare use but not with other subscales of DSMQ. However, it was strongly negatively correlated with diabetes distress, suggesting that greater distress is linked to lower diabetes-related quality of life.

3.2. Structural Equation Model of Diabetes Management

The model included 104 observations with 13 free parameters. The fit results were excellent, with a user model χ2 value of 0.73 (p = 0.393), indicating a good fit. Other fit indicators also supported the model fit, including SRMR (0.02) and RMSEA (0.00; 90% CI: 0.00−0.24). In addition, the other fit indicators were also excellent, including CFI (1.00) and TLI (1.03), confirming a strong relationship between the data and the model. In the analysis of parameter estimates, distress level (DDS) had a significant negative effect on the latent variable SME (β = −0.47, p < 0.001), suggesting that higher distress levels are associated with lower self-management. In contrast, SME showed no significant impact on quality of life (ADDQoL19) (β = 0.03, p = 0.779). However, the relationship between quality of life and distress was significant and negative (β = −0.37, p < 0.001), meaning that higher distress levels are linked to a lower quality of life. Regarding the measurement model, HbA1c had a significant negative loading on SME (β = −0.54, p < 0.001), while self-management (DSMQ) had a strong positive loading (β = 0.85, p < 0.001). Finally, the model also tested for indirect effects, such as the pathway (DDS → SME → ADDQoL19), which was non-significant (β = −0.02, p = 0.780). This indicates that the mediating role of SME between distress and quality of life is not relevant (Figure 1). Based on the post hoc power analysis, the latent variable SME was reliably estimated by its observed indicators (DSMQ: Power = 0.98; HbA1c: Power = 0.98). However, the structural path between SME and quality of life exhibited low power (0.06), indicating insufficient sensitivity to detect this effect. In contrast, the path from distress to SME demonstrated high power (0.92), as did the direct effect of distress on quality of life (0.89), suggesting strong and well-detectable relationships.

4. Discussion

4.1. Perspectives for Clinical Practice

The present study explored the relationship between diabetes-related distress (DD) and self-management in individuals with type 1 diabetes, utilizing a comprehensive self-management indicator that combined HbA1c levels and self-reported behaviors. Our findings demonstrated a significant negative association between DD and self-management efficacy (SME), indicating that higher levels of distress were linked to poorer self-management behaviors and glycemic control (Hypothesis 1/1). Additionally, quality of life (ADDQoL-19) was negatively associated with DD (Hypothesis 1/2), confirming that individuals experiencing greater distress reported a lower diabetes-specific quality of life. However, SME did not significantly mediate the relationship between DD and quality of life (Hypothesis 2), suggesting that factors beyond self-management efficacy may play a crucial role in determining diabetes-related quality of life (Hypothesis 3). No significant differences between continuous glucose monitoring (CGM) users and non-users regarding DD, self-management behaviors (DSMQ), HbA1c, and quality of life were found (Hypothesis 4). However, CGM users had significantly longer diabetes duration. The structural equation model (SEM) fits the data well, further supporting the validity of the proposed associations.
The relationship between diabetes-related distress and self-management has been well-established in patients with T2DM [50,51,52,53,54,55,56]. However, in the less studied T1D population, such a relationship is less consistent, and overlapping constructs such as diabetes burnout may further complicate the interpretation of findings [17,19,57,58].
Our result is consistent with previous cross-sectional and longitudinal findings that have confirmed that higher levels of DD are associated with higher HbA1c levels, and that DD may serve as a mediator between difficulties in blood glucose management and mental health indicators [5]. Previous studies have examined glucose control only concerning HbA1c levels. To acquire more precise data about self-management, we used a comprehensive indicator that incorporated the patient’s subjective self-management experience along with HbA1c levels. Our decision aligns with the growing trend that, besides biological markers, using PROs is essential for understanding the challenges of coping and living with diseases [59]. The importance of using a more comprehensive measure of self-management efficiency—incorporating both biological and self-reported self-management indicators simultaneously—is supported by the correlation analyses. HbA1c values show a weak-to-moderate association with the self-reported self-management variable (and its subscales), while behavioral self-management indicators (except for one subscale) are significantly related to diabetes distress. In contrast, diabetes distress correlates with HbA1c only at a trend level. Since different mechanisms may underline the relationship between diabetes distress (DD) and HbA1c, as well as between DD and behavioral self-management, and given that the latter is likely influenced by mechanisms that can be more easily modified through interventions, it is practically important to understand the pathways through which DD impairs self-regulation efficiency.
The relationship between higher DD and lower QoL is also consistent with previous findings [11,23]. According to our data, SME did not significantly mediate the relationship between DD and quality of life, suggesting that factors beyond self-management efficacy may be crucial in determining diabetes-related quality of life. Several factors may explain the lack of association between self-management efficiency and quality of life. Quality of life showed no correlation with HbA1c and was only marginally associated with the overall behavioral self-management indicator. Correlation analyses of the subscales suggest that quality of life is primarily linked to dietary control and healthcare use, indicating that these self-management activities may substantially impact patients’ daily lives. The absence of a relationship between HbA1c and well-being indicators has also been documented in some previous studies, which offer several possible explanations. One key argument is the limited role of HbA1c in determining QoL/well-being—as a biomarker of long-term blood glucose regulation, HbA1c does not necessarily capture the acute blood glucose fluctuations or hypoglycemic events that patients experience daily, which may have a more immediate and significant impact on QoL. Another drawback to HbA1c levels is that they are inherently imprecise with measuring actual glucose levels, since they are affected by multiple biological parameters, e.g., anemia or renal disease, and other factors like age, ethnicity, or type of medication [60]. Additionally, psychosocial factors such as depression [61], perceived social support [62,63], health anxiety [64], illness perception [65], fear of hypoglycemia [66], self-efficacy [67,68], and health literacy [69] might contribute to shaping quality of life. And as a matter of fact, DDS is an instrument that covers most of these constructs, e.g., fear of complications, illness perception, self-efficacy, social support, and exhaustion [9] and is found to be correlated with diabetic self-care and metabolic outcomes [11,33].
These factors may exert a greater influence on QoL than glycemic control itself, further explaining the weak or inconsistent associations between HbA1c and quality of life in individuals with diabetes [70,71].
An additional finding of this study is that, although CGM systems facilitate patients’ self-management, our data suggest that this technological advancement does not automatically improve quality of life, which is more closely associated with diabetes-related distress. Although several studies found improved glycemic control with CGM usage [26,28,29], the results are inconsistent. Device satisfaction appeared to be a significant factor in influencing adherence among patients using new technologies [72], potentially having an impact on distress levels, self-management, and QoL. Data also shows that CGM usage does not necessarily reduce distress levels [73]. Patients with a lower socioeconomic status or belonging to an ethnic minority group are less likely to benefit from CGM usage [74]. Impaired awareness of hypoglycemia (IAH) also seemed to be a significant factor elevating DD levels among sensor users [58]. Moreover, individual variability in self-management behaviors is also a factor to consider despite implementing technological advancements [75]. Implementing new technologies with structured education or a training program significantly improved glycemic control [76,77], acceptance, satisfaction, and motivation to use them [77]. Psychosocial factors such as depressive symptoms, different types of anxiety, illness acceptance, fear of hypoglycemia, fear of long-term complications, feelings of shame related to wearing a device, self-efficacy, locus of control could also affect efficacy of technological advancements. Moreover, regarding the possible permutations of these factors within an individual, the focus of psychotherapeutic interventions might be highly heterogenic from patient to patient. Thus, psychological assessment and individually tailored interventions could also enhance the benefits of advanced devices and improve self-management.
This study has several strengths that contribute to its scientific value. One of its key strengths is the comprehensive self-management assessment, which integrates physiological (HbA1c) and behavioral (DSMQ) indicators. Using a structural equation modeling (SEM) approach, this study provides a sophisticated statistical analysis, allowing for a nuanced understanding of the relationships between diabetes distress, self-management efficacy, and quality of life.

4.2. Limitations

Despite the strengths of this study, its limitations must also be acknowledged. The cross-sectional design prevents causal inferences. Additionally, this study relies on self-reported measures, which may introduce recall and social desirability biases, potentially affecting the accuracy of the reported self-management behaviors and perceived distress. Another limitation is the sample size (particularly the low sample size of the NCGM group), which, while sufficient for the conducted analyses, may limit statistical power in detecting smaller effects and interactions. We did not control several variables (e.g., depression, executive function development, health literacy, etc.), which may influence both self-management and quality of life. Finally, generalizability is a concern, as the sample was drawn from a single medical institution that provides a higher level of specialized care, and findings may not fully apply to broader populations or individuals receiving diabetes care in different healthcare settings. Future research should consider longitudinal designs and more prominent, more diverse samples to validate these findings further, and explore causal relationships between self-management, diabetes distress, and quality of life. Additionally, future studies should consider incorporating psychosocial variables such as depression, executive functioning, or health literacy. Since covering all the above-mentioned factors that could potentially influence self-management and quality of life is nearly unfeasible due to the length of questionnaires, focusing on the clarification of constructs might be advantageous in the future.

5. Conclusions

This study underscores the significant role of diabetes-related distress (DD) in self-management efficacy (SME) among individuals with type 1 diabetes (T1D). Our findings indicate that integrating both physiological (HbA1c levels) and behavioral (DSMQ) indicators provides a more nuanced understanding of diabetes self-care. In addition, considering that DDS is an instrument that covers multiple aspects of self-management, e.g., fear of complications, illness perception, self-efficacy, social support, exhaustion, along with distress [9], using it in different healthcare settings could provide valuable information regarding patients’ self-management. There is a global shortfall in personal care for individuals with diabetes or other chronic diseases, a challenge that was further exacerbated by the COVID-19 pandemic [78,79,80]. This highlights the need for stronger support systems to improve chronic disease management. Expanding the role of healthcare professionals, such as family and community nurses, is essential to bridging organizational gaps in primary care [80]. Additionally, integrating remote healthcare solutions can further enhance accessibility and continuity of care [78]. Our data further supports the implementation of easy-to-use, standardized instruments such as the DSMQ or DDS, which could significantly improve efficiency and support healthcare providers, even in complex settings like telemedicine, beyond just research purposes.

Author Contributions

Conceptualization, A.L., A.R., A.V. and G.N.; methodology, F.F., M.I., D.P. and Z.M.Z.; formal analysis, C.K.; investigation, A.L., F.F., M.I., Z.H., D.P., E.V., Z.M.Z. and A.V.; resources, E.V. and G.N.; data curation, A.L. and C.K.; writing—original draft preparation, A.L., C.K. and A.V.; writing—review and editing, F.F., M.I., Z.H., D.P., A.R., E.V., Z.M.Z. and G.N.; visualization, C.K. and A.V.; supervision, Z.H., A.R., A.V. and G.N.; project administration, A.V. and G.N.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Research Council (approval number: BM/13016-3/2024, date of approval: 29 July 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Diabetology 06 00019 g001
Figure 1. Structural equation model for the total sample. The model displays ‘Estimate’ values for paths and residuals for variables. Boxes represent manifest measurement variables, while ovals represent latent variables operationalized by manifest predictors. Abbreviations: TLI, Tucker–Lewis index; CFI, comparative fit index; SRMR, standardized root mean square residual; RMSEA, root mean square error of approximation. Significant paths are marked with * (p < 0.05).
Figure 1. Structural equation model for the total sample. The model displays ‘Estimate’ values for paths and residuals for variables. Boxes represent manifest measurement variables, while ovals represent latent variables operationalized by manifest predictors. Abbreviations: TLI, Tucker–Lewis index; CFI, comparative fit index; SRMR, standardized root mean square residual; RMSEA, root mean square error of approximation. Significant paths are marked with * (p < 0.05).
Diabetology 06 00019 g001
Table 1. Sample characteristics.
Table 1. Sample characteristics.
SampleCGM: +CGM: −Between-Group Significance
N = 108n = 77n = 31
Demographic variables
Gender (F/M/O) (%)52/47/153/46/148/52/00.711
Age (years) (±SD)32.06 (14.94)31.77 (14.19)32.81 (16.89)0.951
Diabetes-related variables
CGM (%)71
BMI (kg/m2) (±SD)23.90 (3.85)23.31 (3.98)22.68 (3.33)0.115
Diabetes duration (years) (±SD)13.16 (8.87)14.21 (9.36)10.55 (7.00)0.039 *
HbA1c (±SD)8.18 (1.70)8.24 (1.77)8.02 (1.53)0.752
Complication of disease (+) (%)1516130.723
Neuropathy (+) (%)6560.796
Nephropathy (+) (%)2130.502
Retinopathy (+) (%)7930.292
Macrovascular disease (+) (%)4500.196
Psychological variables
DDS (±SD)2.05 (0.99)2.04 (.91)2.06 (1.18)0.543
DSMQ (±SD)35.91 (6.60)36.16 (6.09)35.29 (7.79)0.839
DSMQ-GM (±SD)11.77 (2.66)11.99 (2.55)11.23 (2.88)0.204
DSMQ-DC (±SD)7.45 (2.27)7.29 (2.30)7.84 (2.21)0.258
DSMQ-PA (±SD)6.25 (2.47)6.39 (2.24)5.90 (2.96)0.686
DSMQ-HU (±SD)7.44 (2.08)7.49 (1.97)7.32 (2.37)0.672
ADDQoL19 (±SD)−1.59 (0.98)−1.66 (0.99)−1.44 (0.98)0.291
The mean values are shown in the table. Significant difference is marked with * (p < 0.05). Abbreviations: continuous glucose monitor = CGM; female = F; male = M; other = O; body mass index = BMI; diabetes distress scale = DDS; diabetes self-management questionnaire = DSMQ; glucose management = GM; dietary control = DC; physical activity = PA; healthcare use = HU; individualized measure of the impact of diabetes on quality of life = ADDQoL19.
Table 2. Correlation matrix.
Table 2. Correlation matrix.
AgeDiabetes DurationHbA1cDSMQDSMQ-GMDSMQ-DCDSMQ-PADSMQ-HUDDSADDQoL19
Agerho
df
Diabetes Durationrho−0.02
df106
HbA1crho−0.30 *0.15
df106106
DSMQrho0.39 *−0.12−0.44 *
df105105105
DSMQ-GMrho0.40 *−0.11−0.43 *0.77 *
df105105105105
DSMQ-DCrho0.39 *−0.00−0.33 *0.72 *0.40 *
df105105105105105
DSMQ-PArho0.19 *−0.09−0.24 *0.67 *0.33 *0.38 *
df105105105105105105
DSMQ-HUrho0.06−0.01−0.30 *0.58 *0.37 *0.20 *0.22 *
df105105105105105105105
DDSrho−0.120.190.19−0.41 *−0.26 *−0.43 *−0.14−0.31 *
df103103103103103103103103
ADDQoL19rho−0.19−0.060.010.18−0.020.22 *0.060.23 *−0.47 *
df102102102102102102102102102
The correlation matrix is shown in the table. Significant difference is marked with * (p < 0.05). Abbreviations: distress scale = DDS; diabetes self-management questionnaire = DSMQ; glucose management = GM; dietary control = DC; physical activity = PA; healthcare use = HU; individualized measure of the impact of diabetes on quality of life = ADDQoL19.
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Losonczi, A.; Kazinczi, C.; Fehervari, F.; Illenyi, M.; Herold, Z.; Palmai, D.; Rigo, A.; Varga, E.; Zemplenyi, Z.M.; Vincze, A.; et al. The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study. Diabetology 2025, 6, 19. https://doi.org/10.3390/diabetology6030019

AMA Style

Losonczi A, Kazinczi C, Fehervari F, Illenyi M, Herold Z, Palmai D, Rigo A, Varga E, Zemplenyi ZM, Vincze A, et al. The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study. Diabetology. 2025; 6(3):19. https://doi.org/10.3390/diabetology6030019

Chicago/Turabian Style

Losonczi, Antonia, Csaba Kazinczi, Flora Fehervari, Mandorla Illenyi, Zoltan Herold, Dora Palmai, Adrien Rigo, Eva Varga, Zsofia Maria Zemplenyi, Agnes Vincze, and et al. 2025. "The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study" Diabetology 6, no. 3: 19. https://doi.org/10.3390/diabetology6030019

APA Style

Losonczi, A., Kazinczi, C., Fehervari, F., Illenyi, M., Herold, Z., Palmai, D., Rigo, A., Varga, E., Zemplenyi, Z. M., Vincze, A., & Nagy, G. (2025). The Relationship Between Quality of Life, Diabetes Distress, and Metabolic Control in Hungarian Type 1 Diabetic Patients: A Cross-Sectional Study. Diabetology, 6(3), 19. https://doi.org/10.3390/diabetology6030019

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