METHODS: This cross-sectional study was conducted between 1 July and 31 December 2019 among patients in medical wards who had a blood glucose (BG) level of > 7.8 mmol/L and stayed in the wards for ≥ 24 h. We retrieved information on demographics, diabetes history and BG profiles. The definition of controlled glycaemic status is when ≥ 80% of BG readings were between 4.0 mmol/L and 10.0 mmol/L during the hospital stay.
RESULTS: The prevalence of inpatient hyperglycaemia was 55.2%. There were 841 patients who met the eligibility criteria; their mean age was 60 (13.8) years old. Most (79.4%) of the patients were Malay and 53.9% were male. There were 452 (53.7%) patients in the uncontrolled group. They were younger and admitted with more kidney complications compared to those in the controlled group. The median LOS for both groups was 3 (2) days. The uncontrolled group showed a higher percentage of readmission within 30 days (7.5% versus 4.6 %) and death during admission (3.3% versus 1.6 %) (P = 0.100 and P = 0.082).
CONCLUSION: The prevalence of inpatient hyperglycaemia was high. More than half of them had uncontrolled BG. Both groups had a similar average length of stay. The 30-day readmission rate and death during admission were higher in the uncontrolled group, although statistically not significant.
OBJECTIVE: We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.
METHODS: A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).
RESULTS: Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).
CONCLUSIONS: The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.