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

Abstract

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.

Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis

“…They found that the typology from the study provides important insights for prevention strategies within diabetes health care. 30 A recent study identified three latent classes based on subtypes of dietary behavior and examined the association between class membership and cardiometabolic risk factors. The authors suggested the LCA-driven obesity phenotypes are helpful in assessing and managing obesity and metabolic syndrome by providing interventions tailored to the needs of participants in each class.…”

Behavioral Engagement and Activation Model Study (BEAMS): A latent class analysis of adopters and non-adopters of digital health technologies among people with Type 2 diabetes

“…Statistical methods such as latent class analysis (LCA) provide a useful approach for exploring underlying heterogeneity in a population by allowing researchers to examine whether and how multiple characteristics co-occur to form profiles [ 17 , 18 ]. While LCA has been used to explore patterns of diabetes self-management behavior [ 19 ] and behavioral risk factors for developing diabetes [ 18 ], to our knowledge, it has not been used to understand typologies of people who do and do not use DHTs. The co-occurrence of different factors could represent different profiles of DHT adopters and non-adopters, each of which may need different types of support to use the tools.…”