Foot care intervention delivered by community health worker (CHWs): A scoping review.

Variations of Community Health Workers (CHWs) interventions in diabetes self-management education (DSME) have been reviewed by many studies. In contrast, specific interventions regarding foot care intervention (FCI) are scarce and need to be explored further as one preventive measure to reduce diabetic foot problems in the community. This scoping review aimed to identify, and report nature of FCIs and the core components of FCIs delivered by CHWs. The scoping review was undertaken using PRISMA Extension for Scoping Reviews (PRISMA-ScR). The following electronic databases were searched for articles from data first indicated date through December 2022: CINAHL, EMBASE, Cochrane, Scopus, Web of Science, Theses ProQuest, PubMed, google scholar and other sources by using search terms related to foot care, community health workers, and diabetes mellitus. Descriptive synthesis was used to summarise the data. Nine studies from 1644 were included. All studies found that CHWs provided DSME in general, and foot care education was included. There was no detailed description of the core components of the intervention on foot care. Although, all studies might not provide detailed data on how CHW provided FCIs; the CHW intervention is an undoubtedly vital strategy to promote and prevent foot problems in medically underserved communities.

Artificial neural network machine learning prediction of the smoking behavior and health risks perception of Indonesian health professionals.

Health professionals (HPs) can play an important role in influencing the smoking behavior of their patients and the implementation of smoke-free workplace policies. In some countries physicians and dentists may not have a no-smoking policy in place. Breathing in other people's tobacco smoke (second-hand smokers) increase the risk of smoking related diseases. Environmental Tobacco smoke ETS causes a similar range of diseases to active smoking, including various cancers, heart disease, stroke, and respiratory diseases. Little is known about the smoking-related attitudes and clinical practices of HPs in Indonesia. Evidence suggests that high smoking rates remain among male HPs; however, the risk perceptions and attitudes to smoking among Indonesian HPs have not been investigated using prediction model artificial neural networks. For this reason, we developed and validated an artificial neural network (ANN) to identify HPs with smoking behavior. The study population consisted of 240 HPs, including 108 (45%) physicians, and 132 (55%) dentists, with more female (n=159) than male participants (n=81) for both professions. Participants were randomly divided into two sets, the training (192) and test (48) sets. The input variables included gender, profession (doctor or dentist), knowledge regarding smoking-related diseases and awareness of smoking provided to their patients, smoke-free policy in place at their workplace, and smoking status. ANN was constructed with data from the training and selection sets and validated in the test set. The performance of ANN was simultaneously evaluated by discrimination and calibration. After the training, we completed the process using the test dataset with a multilayer perceptron network, determined by 36 input variables. Our results suggested that our final ANN concurrently had good precision (89%), accuracy (81%), sensitivity (85%), and area under the curve (AUC; 70%). ANN can be used as a promising tool for the prediction of smoking status based on health risk perceptions of HPs in Indonesia.