The global prevalence of obesity is increasing. Obesity is associated with many chronic diseases and health conditions. This study aims to estimate the current prevalence of obesity in Saudi Arabia and described the current national-level status of the association between obesity and various health conditions. This study is a nationwide cross-sectional survey conducted over phone-interviews in June 2020. In this study, a proportional quota-sampling technique was used to obtain equal distributions of participants, stratified by age and gender, across the 13 regions of Saudi Arabia. Weight and height were self-reported, and the obesity was determined as BMI ≥ 30. Logistic regression adjusted for age and gender was used for exploring current associations between obesity and health conditions. Of the 6239 participants contacted, 4709 participants responded and completed the interview with a response rate of 75.48%. Of them, 50.1% were female, the mean age was 36.4 ± 13.5 (Range: 18–90), and the median age was 36. The national weighted prevalence of obesity (BMI ≥ 30) was 24.7%, and the prevalence in the sample (unweighted) was 21.7%. Obesity was significantly associated with type 2 diabetes [Odd ratio, (OR) = 1.52], hypercholesterolemia (OR = 1.69), hypertension (OR = 1.61), lung diseases (OR = 1.69), rheumatoid arthritis (OR = 1.57), sleep apnea (OR = 1.82), colon diseases (OR = 1.31), and thyroid disorders (OR = 1.8). This study provides an update on the recent prevalence of obesity in Saudi Arabia. It also shows the variation in prevalence rates between different regions, which might be explored further. Although obesity shows a decreasing trend, almost one-quarter of this study sample were obese. Obesity is currently associated with many health conditions that can affect the individuals’ quality of life, impose stress on the healthcare system and impose an economic burden on the country. This evidence highlights the need for action to focus more on obesity in Saudi Arabia.

Introduction: There are large numbers of health-related applications (apps) available in various app stores for many smartphone devices. Consequently, increasing numbers of articles are attempting to evaluate the content and the quality of health-related smartphone apps for specific health conditions. The aim of this article is to describe and summarize the methodologies used to determine the quality of health-related apps targeting health consumers and to propose a set of criteria for evaluating the quality of smartphone health-related apps. Materials and Methods: In 2013, literature searches were performed using Medline and CINAHL, and we included all articles that had the aim of assessing the quality of health-related smartphone apps. The assessment method used in these studies was summarized and scored using a set of quality criteria developed for this study. Results: From 606 articles generated by the search, only 10 met the inclusion criteria. Based on our quality criteria, the mean score was 5.05 out of 8 (range, 2–7). Eighty percent of the studies did not identify the app store country in which the apps were found. Forty percent of the studies did not clearly mention whether they only had assessed the app description or had downloaded the app content for evaluation. Sixty percent of the studies did not provide a list of the apps they had evaluated. Overall, we identified six evaluation methodologies used to assess the quality of health-related apps described in Results. Conclusions: This article provides a summary of currently used methods for assessing the quality of smartphone health-related apps and proposes a set of criteria to enable future studies to consistently review health-related app quality in a standardized manner.

Background and objective Smartphone applications (apps) have the potential to be valuable self-help interventions for depression screening. However, information about their feasibility and effectiveness and the characteristics of app users is limited. The aim of this study is to explore the uptake, utilization, and characteristics of voluntary users of an app for depression screening. Methods This was a cross-sectional study of a free depression screening smartphone app that contains the demographics, patient health questionnaire (PHQ-9), brief anxiety test, personalized recommendation based on the participant's results, and links to depression-relevant websites. The free app was released globally via Apple's App Store. Participants aged 18 and older downloaded the study app and were recruited passively between September 2012 and January 2013. Findings 8241 participants from 66 countries had downloaded the app, with a response rate of 73.9%. While one quarter of the participants had a previous diagnosis of depression, the prevalence of participants with a higher risk of depression was 82.5% and 66.8% at PHQ-9 cut-off 11 and cut-off 15, respectively. Many of the participants had one or more physical comorbid conditions and suicidal ideation. The cut-off 11 (OR: 1.4; 95% CI 1.2 to 1.6), previous depression diagnosis (OR: 1.3; 95% CI1.2 to 1.5), and postgraduate educational level (OR: 1.2; 95% CI 1.0 to 1.5) were associated with completing the PHQ-9 questionnaire more than once. Conclusions Smartphone apps can be used to deliver a screening tool for depression across a large number of countries. Apps have the potential to play a significant role in disease screening, self-management, monitoring, and health education, particularly among younger adults.

Background: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R² statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results: The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males–59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data ( P < 0·006). Conclusions: The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet