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Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

Background: Self-management support has been recognized as one of the most essential elements of the Chronic Care Model (CCM). Inspired by the CCM, the EMPOWER-SUSTAIN Global Cardiovascular Risks Self-Management Booklet© was developed to aid and sustain self-management among patients with metabolic syndrome (MetS) in primary care to prevent cardiovascular complications. However, the usability of this booklet among these patients is not known. Therefore, this study aimed to evaluate the usability of this self-management booklet and identify the factors associated with its usability among patients with MetS in primary care. Methods: This cross-sectional study was conducted among patients with MetS attending a university primary care clinic in Selangor, Malaysia. The usability score was measured using a previously translated and validated EMPOWER-SUSTAIN Usability Questionnaire (E-SUQ) with a score of > 68 indicating good usability. Multiple logistic regressions determined the factors associated with its usability. Results: A total of 391 patients participated in this study. More than half (61.4%) had a good usability score of > 68, with a mean (± SD) usability score of 72.8 (± 16.1). Participants with high education levels [secondary education (AOR 2.46, 95% CI 1.04, 5.83) and tertiary education (AOR 2.49, 95% CI 1.04, 5.96)], those who used the booklet at home weekly (AOR 2.94, 95% CI 1.63, 5.33) or daily (AOR 2.73, 95% CI 1.09, 6.85), and those who had social support to use the booklet (AOR 1.64, 95% CI 1.02, 2.64) were significantly associated with good usability of the booklet. Conclusions: The usability of the EMPOWER-SUSTAIN Global Cardiovascular Risks Self-Management Booklet© was good among patients with MetS in this primary care clinic, which supports its widespread use as a patient empowerment tool. The findings of this study also suggest that it is vital to encourage daily or weekly use of this booklet at home, with the support of family members. The focus should also be given to those with lower education to improve the usability of this booklet for this group of patients. [ABSTRACT FROM AUTHOR]

Introduction. Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with high-fat or high-carbohydrate diets. Generally, increased triglyceride levels occur in conjunction with other symptoms that are difficult to notice and recognize. Objectives. The study's goal was to develop and predict the model that could be used to explain the relationship between triglycerides and waist circumference, high-density lipoprotein (HDL), and hypertension status by determining the relationship between triglycerides and waist circumference, HDL, and hypertension status. This model was developed using qualitative predictor variables and incorporated data bootstrapping multilayer perceptron neural networks and fuzzy linear regression. Materials and procedures. This was a public health study that combined retrospective data analysis with methodology development. The medical records of patients who attended outpatient clinics at Hospital Universiti Sains Malaysia (USM) were collected and analyzed. This was to provide a more extensive illustration of the methods developed. Screening and selection of patient data were necessary following the inclusion and exclusion criteria. The patient's medical record was used to obtain triglycerides, high-density lipoprotein (HDL), waist circumference, and hypertension status. Due to the critical nature of the variable, it was chosen to aid the clinical expert. The R-Studio software was used to develop the associated syntax for the hybrid model, which would define the association between the examined variables. The purpose of this study is to create a technique for the clinical trial design that utilizes bootstrapping, Qualitative Predictor Variables (QPV), Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Fuzzy Regression (FR). All analyses were performed using the newly introduced R syntax. The research developed a fuzzy linear model that increased modelling performance by incorporating clinically significant factors and validated variables via Multilayer Perceptron (MLP). Conclusion. The proposed technique for modelling and prediction appeared to be the ideal combination of bootstrap, Multilayer Feed Forward (MLFF) neural network, and fuzzy linear regression. The created syntax is currently being evaluated and validated clinically. For modelling and prediction, the proposed technique looked to be the best, as it incorporated bootstrap, MLFF neural network, and fuzzy linear regression. The established syntax is now being utilized in the clinic to evaluate and validate the outcome. In terms of variable selection, modelling, and model validation, this strategy was superior to earlier approaches for fuzzy regression modelling. [ABSTRACT FROM AUTHOR]