{"title":"Clinical Correlates of Type 2 Diabetes Self-Management in Adults: A Cross-Sectional Study.","authors":"Paula-Alexandra Popovici, Andreea Diana Igna, Bianca-Lăcrimioara Petca, Timea Claudia Ghitea, Mihaela Simona Popoviciu","doi":"10.3390/diseases14090348","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Diabetes self-management is essential for type 2 diabetes care, but cross-sectional associations may be distorted when questionnaire scores are incorrectly derived or converted into arbitrary categories. We evaluated clinical and sociodemographic correlates of the continuous Diabetes Self-Management Questionnaire (DSMQ) total score in a secondary analysis of an outpatient clinical database.</p><p><strong>Methods: </strong>The analysis included 262 adults with type 2 diabetes recruited between 1 November 2025 and 1 May 2026. DSMQ total and subscale scores were independently recalculated from the 16 item-level responses using the published scoring algorithm. The primary analysis retained the DSMQ score as a continuous outcome. The database provided a participant-level count of documented glucose measurements > 250 mg/dL. This operational count was not treated as a standardized clinical event or severity category; individual measurement timestamps, participant-specific observation duration, and the total number of glucose measurements were unavailable.</p><p><strong>Results: </strong>The recalculated DSMQ score was 5.26 ± 2.35 (median 5.10; interquartile range 3.12-7.08). Among 261 participants with an interpretable sex code, 144 (55.2%) were women. In univariable analyses, lower DSMQ scores were concurrently associated with longer diabetes duration, a higher recorded severe-hyperglycemia count, higher HbA1c, and higher fasting glucose. In the adjusted model (<i>n</i> = 261), the recorded severe-hyperglycemia count remained negatively associated with DSMQ score (adjusted coefficient -0.412 points per recorded episode; 95% confidence interval -0.621 to -0.203; <i>p</i> < 0.001). Individualized versus hospital-based education was not independently associated with the corrected score. A beta-regression sensitivity analysis produced a concordant direction of association (coefficient -0.152 on the logit mean scale; <i>p</i> < 0.001). The linear model explained 17.2% of observed variance (adjusted R<sup>2</sup> = 0.136), with an optimism-corrected R<sup>2</sup> of 0.099.</p><p><strong>Conclusions: </strong>The recorded severe-hyperglycemia count was a concurrent correlate of lower DSMQ score. Because the study is cross-sectional and glucose-measurement counts were preaggregated without observation-time or monitoring-frequency data, the findings do not establish temporal direction, causality, or individual-level clinical prediction. Item-level score verification and continuous-outcome analysis materially changed the interpretation of the dataset.</p>","PeriodicalId":72832,"journal":{"name":"Diseases (Basel, Switzerland)","volume":"14 9","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605858/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diseases (Basel, Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/diseases14090348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Diabetes self-management is essential for type 2 diabetes care, but cross-sectional associations may be distorted when questionnaire scores are incorrectly derived or converted into arbitrary categories. We evaluated clinical and sociodemographic correlates of the continuous Diabetes Self-Management Questionnaire (DSMQ) total score in a secondary analysis of an outpatient clinical database.
Methods: The analysis included 262 adults with type 2 diabetes recruited between 1 November 2025 and 1 May 2026. DSMQ total and subscale scores were independently recalculated from the 16 item-level responses using the published scoring algorithm. The primary analysis retained the DSMQ score as a continuous outcome. The database provided a participant-level count of documented glucose measurements > 250 mg/dL. This operational count was not treated as a standardized clinical event or severity category; individual measurement timestamps, participant-specific observation duration, and the total number of glucose measurements were unavailable.
Results: The recalculated DSMQ score was 5.26 ± 2.35 (median 5.10; interquartile range 3.12-7.08). Among 261 participants with an interpretable sex code, 144 (55.2%) were women. In univariable analyses, lower DSMQ scores were concurrently associated with longer diabetes duration, a higher recorded severe-hyperglycemia count, higher HbA1c, and higher fasting glucose. In the adjusted model (n = 261), the recorded severe-hyperglycemia count remained negatively associated with DSMQ score (adjusted coefficient -0.412 points per recorded episode; 95% confidence interval -0.621 to -0.203; p < 0.001). Individualized versus hospital-based education was not independently associated with the corrected score. A beta-regression sensitivity analysis produced a concordant direction of association (coefficient -0.152 on the logit mean scale; p < 0.001). The linear model explained 17.2% of observed variance (adjusted R2 = 0.136), with an optimism-corrected R2 of 0.099.
Conclusions: The recorded severe-hyperglycemia count was a concurrent correlate of lower DSMQ score. Because the study is cross-sectional and glucose-measurement counts were preaggregated without observation-time or monitoring-frequency data, the findings do not establish temporal direction, causality, or individual-level clinical prediction. Item-level score verification and continuous-outcome analysis materially changed the interpretation of the dataset.