To estimate the associations between insufficient diabetes acceptance and relevant diabetes outcomes.
A total of 320 patients completed questionnaires on diabetes non-acceptance (the Acceptance and Action Diabetes Questionnaire), diabetes distress (the Problem Areas in Diabetes Scale), depressive mood (the Center for Epidemiologic Studies Depression Scale), coping with illness (the Freiburg Questionnaire of Coping with Illness), self-care activities (the Summary of Diabetes Self-Care Activities Measure) and quality of life (the Short Form-36 Health Questionnaire). A six-item version of the Acceptance and Action Diabetes Questionnaire showing good reliability and validity was established, and the associations between insufficient acceptance and clinical outcomes were estimated.
Higher diabetes non-acceptance correlated significantly with less active coping (-0.37), reduced self-care (-0.43) and higher HbA1c levels (0.31), higher diabetes distress (0.53) and more depressive symptoms (0.36). Correlations of diabetes non-acceptance with diabetes self-care/glycaemic control were significantly higher than were those of depressive mood or diabetes distress with these criteria.
Low diabetes acceptance is associated with impaired self-care and glycaemic control. Assessment of diabetes acceptance may facilitate the detection of patients at high risk and may present an essential target for treatments to improve diabetes control that is more relevant than elevated depressive mood or diabetes distress.
The aim of this paper is the scientific development of a maturity model concerning the digital transformation of companies within the manufacturing industry’s supply chain. The rather “broad” and dispersed “mega-trend” of digitalization is expected to play an increasingly important role for companies as well as for the (digital) supply chain of the future. Such a model comprises the objective of addressing fundamental components, complementary innovations and relevant terminologies, like smart products, Cyber-Physical Systems (CPS) and Big Data Analytics.
Scientific rigor is achieved through conducting grounded theory research and in-depth interviews as methods of data collection and evaluation.
Furthermore, relevant aspects concerning the development and construction of maturity models are discussed, before a suitable and scientifically elaborated maturity model concerning digitalization emerges from the course of investigation and its value for economic practice as well as for the scientific community is specified.