Structured diabetes self-management education and its association with perceived diabetes knowledge, information, and disease distress: Results of a nationwide population-based study.

OBJECTIVE: To evaluate, how participation in structured diabetes self-management education (DSME) programs is associated with perceived level of knowledge about diabetes, information needs, information sources and disease distress. METHODS: We included 796 ever- and 277 never-DSME participants of the population-based survey "Disease knowledge and information needs - Diabetes mellitus (2017)" from Germany. Data on perceived level of diabetes knowledge (12 items), information needs (11 items), information sources (13 items) and disease distress (2 indices) were collected. Multiple logistic regression analyses were used to examine the association of DSME-participation with these outcomes. RESULTS: DSME-participants showed a higher level of diabetes knowledge compared to never-DSME participants, particularly in aspects concerning diabetes in general (odds ratio 2.53; 95% confidence intervals 1.48-4.33), treatment (2.41; 1.36-4.26), acute complications (1.91; 1.07-3.41) and diabetes in everyday life (1.83; 1.04-3.22). DSME-participants showed higher information needs regarding late complications (1.51; 1.04-2.18) and acute complications (1.71; 1.71-2.48) than DSME never participants. DSME-participants more frequently consulted diabetologists (5.54; 3.56-8.60) and diabetes care specialists (5.62; 3.61-8.75) as information sources. DSME participation was not associated with disease distress. CONCLUSION: DSME is a valuable tool for improving individual knowledge about diabetes. However, DSME should focus more on psychosocial aspects to reduce the disease burden.

Mixture randomized item-response modeling: a smoking behavior validation study.

Misleading response behavior is expected in medical settings where incriminating behavior is negatively related to the recovery from a disease. In the present study, lung patients feel social and professional pressure concerning smoking and experience questions about smoking behavior as sensitive and tend to conceal embarrassing or threatening information. The randomized item-response survey method is expected to improve the accuracy of self-reports as individual item responses are masked and only randomized item responses are observed. We explored the validation of the randomized item-response technique in a unique experimental study. Therefore, we administered a new multi-item measure assessing smoking behavior by using a treatment-control design (randomized response (RR) or direct questioning). After the questionnaire, we administered a breath test by using a carbon monoxide (CO) monitor to determine the smoking status of the patient. We used the response data to measure the individual smoking behavior by using a mixture item-response model. It is shown that the detected smokers scored significantly higher in the RR condition compared with the directly questioned condition. We proposed a Bayesian latent variable framework to evaluate the diagnostic test accuracy of the questionnaire using the randomized-response technique, which is based on the posterior densities of the subject's smoking behavior scores together with the breath test measurements. For different diagnostic test thresholds, we obtained moderate posterior mean estimates of sensitivity and specificity by observing a limited number of discrete randomized item responses.