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Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study

OBJECTIVE: Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of...

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Autores principales: Heise, Marcus, Fink, Astrid, Baumert, Jens, Heidemann, Christin, Du, Yong, Frese, Thomas, Carmienke, Solveig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978380/
https://www.ncbi.nlm.nih.gov/pubmed/33740024
http://dx.doi.org/10.1371/journal.pone.0248992
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author Heise, Marcus
Fink, Astrid
Baumert, Jens
Heidemann, Christin
Du, Yong
Frese, Thomas
Carmienke, Solveig
author_facet Heise, Marcus
Fink, Astrid
Baumert, Jens
Heidemann, Christin
Du, Yong
Frese, Thomas
Carmienke, Solveig
author_sort Heise, Marcus
collection PubMed
description OBJECTIVE: Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of SMB among persons with diabetes. RESEARCH DESIGN AND METHODS: The present analysis includes data of 1,466 persons (age 18 to 99 years; 44.0% female; 56.0% male) with diabetes (type I and II) from the population-based study German Health Update 2014/2015 (GEDA 2014/2015-EHIS). We used latent class analysis in order to distinguish different patterns of self-management behaviours among persons with diabetes. The assessment of SMB was based on seven self-reported activities by respondents (dietary plan, diabetes-diary, diabetes health pass, self-assessment of blood glucose, self-examination of feet, retinopathy-screenings and assessment of HbA1c). Subsequent multinomial latent variable regressions identified factors that were associated with self-management behaviour. RESULTS: Latent class analysis suggested a distinction between three patterns of SMB. Based on modal posterior probabilities 42.8% of respondents showed an adherent pattern of diabetes self-management with above-average frequency in all seven indicators of SMB. 32.1% showed a nonadherent pattern with a below-average commitment in all seven forms of SMB. Another 25.1% were assigned to an ambivalent type, which showed to be adherent with regard to retinopathy screenings, foot examinations, and the assessment of HbA1c, yet nonadherent with regard to all other forms of SMB. In multivariable regression analyses, participation in Diabetes Self-Management Education programs (DSME) was the most important predictor of good self-management behaviour (marginal effect = 51.7 percentage points), followed by attentiveness towards one’s personal health (31.0 percentage points). Respondents with a duration of illness of less than 10 years (19.5 percentage points), employed respondents (7.5 percentage points), as well as respondents with a high socioeconomic status (24.7 percentage points) were more likely to show suboptimal forms of diabetes self-management. DISCUSSION: In the present nationwide population-based study, a large proportion of persons with diabetes showed suboptimal self-management behaviour. Participation in a DSME program was the strongest predictor of good self-management. Results underline the need for continual and consistent health education for patients with diabetes.
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spelling pubmed-79783802021-03-30 Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study Heise, Marcus Fink, Astrid Baumert, Jens Heidemann, Christin Du, Yong Frese, Thomas Carmienke, Solveig PLoS One Research Article OBJECTIVE: Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of SMB among persons with diabetes. RESEARCH DESIGN AND METHODS: The present analysis includes data of 1,466 persons (age 18 to 99 years; 44.0% female; 56.0% male) with diabetes (type I and II) from the population-based study German Health Update 2014/2015 (GEDA 2014/2015-EHIS). We used latent class analysis in order to distinguish different patterns of self-management behaviours among persons with diabetes. The assessment of SMB was based on seven self-reported activities by respondents (dietary plan, diabetes-diary, diabetes health pass, self-assessment of blood glucose, self-examination of feet, retinopathy-screenings and assessment of HbA1c). Subsequent multinomial latent variable regressions identified factors that were associated with self-management behaviour. RESULTS: Latent class analysis suggested a distinction between three patterns of SMB. Based on modal posterior probabilities 42.8% of respondents showed an adherent pattern of diabetes self-management with above-average frequency in all seven indicators of SMB. 32.1% showed a nonadherent pattern with a below-average commitment in all seven forms of SMB. Another 25.1% were assigned to an ambivalent type, which showed to be adherent with regard to retinopathy screenings, foot examinations, and the assessment of HbA1c, yet nonadherent with regard to all other forms of SMB. In multivariable regression analyses, participation in Diabetes Self-Management Education programs (DSME) was the most important predictor of good self-management behaviour (marginal effect = 51.7 percentage points), followed by attentiveness towards one’s personal health (31.0 percentage points). Respondents with a duration of illness of less than 10 years (19.5 percentage points), employed respondents (7.5 percentage points), as well as respondents with a high socioeconomic status (24.7 percentage points) were more likely to show suboptimal forms of diabetes self-management. DISCUSSION: In the present nationwide population-based study, a large proportion of persons with diabetes showed suboptimal self-management behaviour. Participation in a DSME program was the strongest predictor of good self-management. Results underline the need for continual and consistent health education for patients with diabetes. Public Library of Science 2021-03-19 /pmc/articles/PMC7978380/ /pubmed/33740024 http://dx.doi.org/10.1371/journal.pone.0248992 Text en © 2021 Heise et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Heise, Marcus
Fink, Astrid
Baumert, Jens
Heidemann, Christin
Du, Yong
Frese, Thomas
Carmienke, Solveig
Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title_full Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title_fullStr Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title_full_unstemmed Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title_short Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study
title_sort patterns and associated factors of diabetes self-management: results of a latent class analysis in a german population-based study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978380/
https://www.ncbi.nlm.nih.gov/pubmed/33740024
http://dx.doi.org/10.1371/journal.pone.0248992
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