Methods: A pool of items was generated from a qualitative study, literature reviews, and expert reviews. Exploratory factor analysis (EFA) was performed on the original 40 items of the E-CIS and followed by 27 items for confirmatory factor analysis (CFA). A total of 470 elderly people with chronic constipation were involved.
Results: The mean age of the participants was 68.64 ± 6.57. Finally, only 22 items were indicated as appropriately representing the E-CIS, which were grouped into seven subscales: 'daily activities', 'treatment satisfaction', 'lack of control of bodily function', 'diet restriction', 'symptom intensity', 'anxiety' and 'preventive actions'. The scale was confirmed as valid (root mean square error of approximation (RMSEA) = 0.04, comparative fit index (CFI) = 0.961, Tucker-Lewis index (TLI) = 0.952 and chi-square/degree of freedom (chiSq/df) = 1.44) and reliable (Cronbach's alpha: 0.66-0.85, composite reliability (CR) = 0.699-0.851) to assess the impact of chronic constipation on the elderly's QoL.
Conclusions: The E-CIS is useful to measure the impact of chronic constipation on the elderly's QoL. A further test is needed to determine the validity and reliability of this scale in other elderly population.
METHODS AND RESULTS: Oral health assessment included dental caries and dental plaque maturity scores (DPMS) while the nutritional assessment included children's height-for-age Z-score (HAZ), body mass index-for-age Z-score (BAZ), mid-upper-arm circumference (MUAC), nutrient intake, cariogenic food frequency (CFF) and daily sugar exposure (DSE). Ninety-three CP children were recruited. The prevalence of caries was 81.7% (95% CI: 72.7%-88.3%). The median (IQR) of the DMFT and dft scores were 0.5(4.0) and 3.0(8.0), respectively. Most of the participants had acid-producing plaque (90.3%), severely stunted (81.4%), and 45% were severely thin with acute malnutrition. Intakes of calcium, iron, zinc, vitamin A, vitamin D and total fat were below 77% of the Recommended Nutrient Intakes for Malaysian children (RNI 2017). Nine types of cariogenic foods/drinks were consumed moderately, and DSE indicated that 45% of the children were at moderate risk of dental caries.
CONCLUSION: Untreated dental caries, severe stunting and thinness were prevalent, and cariogenic foods/drinks were consumed moderately suggesting a moderate risk of caries. Therefore, controlling cariogenic food intake is crucial, but monitoring daily nutrient intake is needed for the optimum growth of children with CP.
DESIGN: Data-based convergent mixed-method systematic review.
METHODS: Three electronic databases (Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature) will be used in the identification stage. The first search will use the search string for each database to identify relevant studies. The articles retrieved will be screened by year of publication, article type and language. Abstracts and full-text of selected studies will be screened for eligibility independently by a minimum of two reviewers. The reference lists will be manually screened to identify additional publications. The quality assessment will be conducted by two reviewers using the Mixed Methods Appraisal Tools. Quantitative and mixed-method studies will be transformed into qualitative. A thematic approach will be used to synthesize and report the data. Ethics approval and funding have been approved in April 2020.
DISCUSSION: This study will synthesize the types of challenges perceived by final-year undergraduate nursing students in different clinical learning environments across the country.
IMPACT: The proposed study findings will help nursing education stakeholders and faculty provide assistance to final-year nursing students in their transition year to become registered nurses.
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.
METHODS: Methods used included policy analysis of legal, policy and regulatory framework documents, and in-depth interviews with key informants from governmental and non-governmental organisations in two States of Malaysia.
RESULTS: The findings show that women's NGOs and health professionals were instrumental in the formulation and scaling-up of the OSCC policy. However, the subsequent breakdown of the NGO-health coalition negatively impacted on the long-term implementation of the policy, which lacked financial resources and clear policy guidance from the Ministry of Health.
CONCLUSION: The findings confirm that a clearly-defined partnership between NGOs and health staff can be very powerful for influencing the legal and policy environment in which health care services for intimate partner violence are developed. It is critical to gain high level support from the Ministry of Health in order to institutionalise the violence-response across the entire health care system. Without clear operational details and resources policy implementation cannot be fully ensured and taken to scale.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.