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      The Impact of Diabetes Self-Care, Healthy Lifestyle, Social Support, and Demographic Variables on Outcomes HbA1c in Patients With Type 2 Diabetes

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          Abstract

          Background:

          Controlling HbA1c can help reduce the symptoms and complications of diabetes. However, only about 25% of adults with diabetes achieve this diabetes care goal. It can be seen that diabetes requires more research investment and breakthroughs, as well as a more complete discussion of related factors that affect diabetes control, in order to better control the disease. This study explored the effects of diabetes self-care behavior, healthy lifestyle, diabetes symptoms, social support, demographic variables, and physical examination values on HbA1c levels in individuals with type 2 diabetes.

          Methods:

          This study used a cross-sectional design and recruited 305 subjects with type 2 diabetes at a medical center. Personal data were collected using a structured questionnaire and same-day outpatient medical records.

          Results:

          Multiple linear regression analysis identified significant predictors of HbA1c levels. These include insulin treatment ( P < .001), age ( P < .001), gender ( P < .001), diabetes duration ( P = .003), proteinuria ( P < .001), diabetes self-care behaviors ( P = .021), physical activity (running; P = .018), and spousal involvement in care ( P = .031). Female gender, insulin treatment, longer diabetes duration, spousal involvement in care, and the presence of proteinuria were positively associated with higher HbA1c levels. In contrast, higher age, better diabetes self-care behaviors, and regular physical activity (running) were associated with lower HbA1c levels. These findings underscore the importance of considering both demographic factors and lifestyle behaviors in the management of HbA1c levels.

          Conclusions:

          For reasons that hinder subjects from controlling HbA1c levels, individual self-management intervention programs should be provided to improve the effectiveness of subjects in controlling HbA1c levels. The care giver should be included in the educational program of diabetes management. Running exercise contributes to the control of HbA1c levels.

          Plain Language Summary

          HbA1c control and related factors

          Managing blood sugar levels (HbA1c) is crucial for people with type 2 diabetes to prevent complications. However, only about 25% of patients successfully meet this goal. This study examined how self-care behaviors, lifestyle choices, social support, and personal factors influence HbA1c levels.

          Researchers studied 305 people with type 2 diabetes using questionnaire and medical records. The results showed that several factors impact blood sugar control. Higher age, good self-care habits, and regular running were linked to better control (lower HbA1c). However, being female, using insulin, having diabetes for a long time, having kidney issues (proteinuria), and receiving care from a spouse were associated with higher HbA1c levels.

          The study suggests that diabetes care should include personalized self-management plans. Caregivers should also be involved in diabetes education, and running could be a helpful exercise for managing blood sugar levels.

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          Most cited references35

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          G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

          G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies.

            Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
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              Statistical Power Analysis for the Behavioral Sciences

              <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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                Author and article information

                Journal
                Clin Med Insights Endocrinol Diabetes
                Clin Med Insights Endocrinol Diabetes
                END
                spend
                Clinical Medicine Insights. Endocrinology and Diabetes
                SAGE Publications (Sage UK: London, England )
                1179-5514
                4 April 2025
                2025
                : 18
                : 11795514251331907
                Affiliations
                [1 ]Department of Internal Nursing, Kaohsiung Veterans General Hospital, Kaohsiung, Kaohsiung City, Taiwan
                [2 ]Department of Nursing, Kaohsiung Veterans General Hospital, Kaohsiung, Kaohsiung City, Taiwan
                [3 ]Meiho University, Neipu, Taiwan Province, Taiwan
                Author notes
                [*]Li-Ying Lin, Department of Nursing, Kaohsiung Veterans General Hospital, 386, Ta-Chung 1st. Road, Kaohsiung City 81362, Taiwan. Emails: llylin0@ 123456gmail.com ; llylin@ 123456vghks.gov.tw
                Author information
                https://orcid.org/0000-0002-2099-6437
                Article
                10.1177_11795514251331907
                10.1177/11795514251331907
                11970087
                cb8b8e57-18f4-4456-87b9-70ebd57cbec8
                © The Author(s) 2025

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 13 November 2024
                : 17 March 2025
                Categories
                Original Research
                Custom metadata
                January-December 2025
                ts1

                Endocrinology & Diabetes
                diabetes mellitus,type 2,glycated hemoglobin,self care,healthy lifestyle,clinical manifestations of diabetes

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