Thematic analysis was conducted using NVivo 12.Ī total of 56 HCPs participated in the study. All FGDs were audio-recorded and transcribed verbatim. We conducted focus group discussions (FGD) with purposively selected HCPs in Singapore. This study aims to explore factors influencing diabetes self-management in adult patients with diabetes from the perspectives of HCPs and their views of the value of mHealth application for diabetes self-management. However, literature has been largely limited to perspectives of patients within the context of a Western healthcare setting. The perspectives of healthcare professionals (HCPs) are pivotal to co-development of self-management strategies for patients with diabetes. The developed machine-learning model using four predictors had a good predictive ability to identify patients who failed to attend a follow-up visit for diabetes care after a screening program. The four selected predictors in the Lasso regression model were lower frequency of physician visits in the previous year, lower HbA1c levels, and negative history of antidyslipidemic or antihypertensive treatment. The Lasso regression model using four predictors had a better discrimination ability than the previously reported logistic regression model using 13 predictors (C-statistic: 0.71 vs. We identified 10,645 patients, including 5,450 patients who failed to attend follow-up visits for diabetes care. In the test set (remaining 20%), prediction performance was examined. In the training set (randomly selected 80% of the sample), we developed two models (previously reported logistic regression model and Lasso regression model). The candidate predictors were patient demographics, comorbidities, and medication history. We defined failure to attend a follow-up visit for diabetes care as no physician consultation during the 6 months after the screening. We conducted a retrospective cohort study of adults with newly screened diabetes at a national screening program using a large Japanese insurance claims database (JMDC, Tokyo, Japan). We aimed to develop a machine-learning model for predicting people’s failure to attend a follow-up visit. Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We identified two possible factors (delivery mode and patient characteristics) that may affect the effectiveness of nudge intervention. Nudging has shown potential in changing health behaviour of patients with diabetes in specific context. Of these, studies with adherence to medication, foot care practice and quality of life as targeted health behaviours were more likely to show a statistically significant outcome.Ĭonclusion. The targeted health behaviours identified were medication adherence, physical activity, diet, blood glucose monitoring, foot care, self-efficacy, HbA1C and quality of life. Studies on reminders and gamification were more likely to have a statistically significant outcome. Studies included utilized framing (n=5), reminders (n=10), gamification (n=2), social modelling (n=5) and social influence (n=16). An additional five studies were added through snowballing. We retrieved 11,494 studies from our searches and included 33. The conditions present in effective nudge interventions were assessed and reported. We summarized patient characteristics, the nudge intervention, according to nudging strategies, delivery modeand their outcomes.
We adopted a two-arm search strategy comprising the search of literature databases and snowballing using relevant search terms. Therefore, we aim to collate a list of nudge intervention and determine the context in which nudging is successful. Since nudging is a new concept, no review of literature on nudging diabetic patients into improving their health behaviour has been done. Diabetes is a chronic disease associated with a variety of complications, and nudgingmay be a potential solution to improve diabetes control.