The Effect of Adding Mirtazapine to Quetiapine on Reducing Agitation in Patients with Alzheimer's Disease.

Background: Alzheimer's disease (AD) is one of the most debilitating diseases in old age, associated with cognitive decline and behavioral symptoms. Objective: This study aimed to investigate the effect of adding mirtazapine to quetiapine in reducing agitation among patients with AD. Methods: Thirty-seven elderly patients (18 cases and 19 controls) with AD, diagnosed according to National Institute on Aging and Alzheimer's Association (NIA-AA) criteria, were enrolled at Nezam-Mafi Clinic. Inclusion criteria comprised a minimum of two years post-diagnosis, a Cohen-Mansfield Agitation and Aggression Questionnaire (CMAI) score above 45, and treatment with 100-150 mg of quetiapine. Patients were randomly assigned to receive mirtazapine (15 mg at night, increased to 30 mg at night after two weeks) or a placebo. Cognitive changes were assessed at weeks 0 and 6 using the Mini-Mental State Examination instrument. Furthermore, symptoms of agitation and aggression were evaluated using the CMAI questionnaire at weeks 4 and 6. Results: In this study, the mean duration of AD in the control group was 4.68 years, and in the case group, it was 5.05 years. Although the total agitation score showed no significant change at the end of the study compared to the control group, the rate of physical non-aggressive behavior showed a significant decrease (p <  0.05). Conclusions: According to this study, adding mirtazapine to the antipsychotic drug regimen may not be an effective treatment for agitation in AD patients.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%). CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data.

Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

Trends in the burden and determinants of hypertensive heart disease in the Eastern Mediterranean region, 1990-2019: an analysis of the Global Burden of Disease Study 2019.

Background: Hypertensive heart disease (HHD), one of the end-organ damage consequences of hypertension, is an important public health issue worldwide. Data on the HHD burden in the Eastern Mediterranean region (EMR) are scarce. We aimed to investigate the burden of HHD in the EMR, its member countries, and globally from 1990 to 2019. Methods: We used 2019 Global Burden of Disease (GBD) data to report the HHD age-standardised prevalence, disability adjusted life years (DALYs), years of life lost (YLLs), and mortality, as well as HHD risk factors attribution percent with their 95% uncertainty interval (UI). Global data are reported alongside EMR data, and its 22 respective countries. We compared the burden of HHD by socio-demographic index (SDI), sex, age groups, and countries. Findings: The age-standardised prevalence rate (per 100,000 population) of HHD was higher in the EMR (281.7; 95% UI: 204.5-383.4) in 2019, compared with the global prevalence (233.8; 95% UI: 170.5-312.9). The EMR age-standardised DALYs (per 100,000 population) for HHD in 2019 was 561.9 (361.0-704.1), compared with 268.2 (204.6-298.1) at the global level. There was an increase in HHD prevalence, reduction in mortality, and DALYs between 1990 and 2019 (4.01%, -7.6%, and -6.5%, respectively) in EMR. Among EMR countries, the highest versus lowest rates of age-standardised prevalence, mortality, and DALYs in 2019 [estimate (95% UI)] were in Jordan [561.62 (417.9-747.6)] versus Saudi Arabia [94.9 (69.5-129.0)]; Afghanistan [74.5 (23.7-112.3)] versus Saudi Arabia [4.3 (3.3-5.9)]; and Afghanistan [1374.1 (467.2-2020.7)] versus Qatar [87.11 (64.40-114.29)], respectively. Interpretation: HHD remains a significant problem in the EMR, with a higher burden than global levels. Serious efforts toward high-quality management and prevention are strongly recommended. Based on this study, our recommendation for the EMR is to adopt effective preventive strategies. For example, promoting healthy dietary patterns and prompt screening for undiagnosed HTN in public places, promoting regular blood pressure measurements at home, and creating community awareness about early detection of HTN. Funding: None.

Body composition predicts hypertension using machine learning methods: a cohort study.

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.

Effects of sulfur dioxide and particulate matter pollution on hospital admissions for hypertensive cardiovascular disease: A time series analysis.

Background and aims: Air pollution is a major environmental risk factor and the leading cause of disease burden with detrimental effects on cardiovascular systems. Cardiovascular diseases are predisposed by various risk factors, including hypertension, as the most important modifiable risk factor. However, there is a lack of sufficient data concerning the impact of air pollution on hypertension. We sought to study the associations of short-term exposure to Sulfur dioxide (SO2) and particulate matter (PM10) with the number of daily hospital admissions of hypertensive cardiovascular diseases (HCD). Methods: All hospitalized patients between March 2010 to March 2012 were recruited with the final diagnosis of HCD based on the International Classification of Diseases 10 (codes: I10-I15) from 15 hospitals in Isfahan, one of the most polluted cities in Iran. The 24-hour average concentrations of pollutants were obtained from 4 monitoring stations. In addition to single- and two-pollutant models, we used Negative Binomial and Poisson models with covariates of holidays, dew point, temperature, wind speed, and extracted latent factors of other pollutants controlling for multi-collinearity to examine the risk for hospital admissions for HCD affected by SO2 and PM10 exposures in the multi-pollutant model. Results: A total of 3132 hospitalized patients (63% female) with a mean (standard deviation) age of 64.96 (13.81) were incorporated in the study. The mean concentrations of SO2 and PM10 were 37.64 µg/m3 and 139.08 µg/m3, respectively. Our findings showed that a significantly increased risk of HCD-induced hospital admission was detected for a 10 µg/m3 increase in the 6-day and 3-day moving average of SO2 and PM10 concentrations in the multi-pollutant model with a percent change of 2.11% (95% confidence interval: 0.61 to 3.63%) and 1.19% (0.33 to 2.05%), respectively. This finding was robust in all models and did not vary by gender (for SO2 and PM10) and season (for SO2). However, people aged 35-64 and 18-34 years were vulnerable to SO2 and PM10 exposure-triggered HCD risk, respectively. Conclusions: This study supports the hypothesis of the association between short-term exposure to ambient SO2 and PM10 and the number of hospital admissions due to HCD.

Ambient carbon monoxide and cardiovascular-related hospital admissions: A time-series analysis.

Background and aims: Although several studies have investigated the association between air pollutants and cardiovascular diseases (CVDs) in recent years, a lack of evidence exists regarding carbon monoxide (CO) exposure, especially in the Eastern Mediterranean's polluted regions. In this study, we aimed to evaluate the short-term effect of CO exposure on daily CVD hospital admissions in Isfahan, a major city in Iran. Methods: Data were extracted from the CAPACITY study on daily CVD hospital admissions in Isfahan from March 2010 to March 2012. The 24-h mean CO concentrations were obtained from four local monitoring stations. In a time-series framework, the association between CO and daily hospitalizations for total and cause-specific CVDs in adults (ischemic heart disease (IHD), heart failure (HF), and cerebrovascular disease) was conducted using Poisson's (or negative binomial) regression, after adjusting for holidays, temperature, dew point, and wind speed, considering different lags and mean lags of CO. The robustness of the results was examined via two- and multiple-pollutant models. Stratified analysis was also conducted for age groups (18-64 and ≥65 years), sex, and seasons (cold and warm). Results: The current study incorporated a total of 24,335 hospitalized patients, (51.6%) male with a mean age of 61.9 ± 16.4 years. The mean CO concentration was 4.5 ± 2.3 mg/m³. For a 1 mg/m3 increase in CO, we found a significant association with the number of CVD hospitalizations. The largest adjusted percent change in HF cases was seen in lag0, 4.61% (2.23, 7.05), while that for total CVDs, IHD, and cerebrovascular diseases occurred in mean lag2-5, 2.31% (1.42, 3.22), 2.23% (1.04, 3.43), and 5.70% (3.59, 7.85), respectively. Results were found to be robust in two- and multiple-pollutant models. Although the associations changed for sex, age groups, and seasons, they remained significant for IHD and total CVD, except for the warm season, and for HF, except for the younger age group and cold seasons. Additionally, the exposure-response relationship curve of the CO concentrations with total and cause-specific CVD admissions showed non-linear relationships for IHD and total CVDs. Conclusions: Our results showed that exposure to CO contributed to an increase in the number of CVD hospitalizations. The associations were not independent of age groups, season, and sex.

Changes in the burden and underlying causes of heart failure in the Eastern Mediterranean Region, 1990-2019: An analysis of the Global Burden of Disease Study 2019.

Background: The burden of heart failure (HF) is high globally, but information on its burden in the Eastern Mediterranean Region (EMR) is limited. This study provides a systematic analysis of the burden and underlying causes of HF in the EMR, including at the country level, between 1990 and 2019. Methods: We used the 2019 Global Burden of Disease (GBD) data for estimates of prevalence, years lived with disability (YLDs), and underlying causes of HF in the EMR. Age-standardised prevalence, YLDs, and underlying causes of HF were compared by 5-year age groups (considering 15 years old and more), sex (male and female), and countries. Findings: In contrast with the decreasing trend of HF burden globally, EMR showed an increasing trend. Globally, the HF age-standardised prevalence and YLDs decreased by 7.06% (95% UI: -7.22%, -6.9%) and 6.82% (95% UI: -6.98%, -6.66%) respectively, from 1990 to 2019. The HF age-standardised prevalence and YLDs in the EMR in 2019 were 706.43 (95% UI: 558.22-887.87) and 63.46 (95% UI: 39.82-92.59) per 100,000 persons, representing an increase of 8.07% (95% UI: 7.9%, 8.24%) and 8.79% (95% UI: 8.61%, 8.97%) from 1990, respectively. Amongst EMR countries, the age-standardised prevalence and YLDs were highest in Kuwait, while Pakistan consistently had the lowest HF burden. The dramatic increase of the age-standardised prevalence and YLDs were seen in Oman (28.79%; 95% UI: 28.51%, 29.07% and 29.56%; 95% UI: 29.28%, 29.84%), while Bahrain witnessed a reduction over the period shown (-9.66%; 95% UI: -9.84%, -9.48% and-9.14%; 95% UI: -9.32%, -8.96%). There were significant country-specific differences in trends of HF burden from 1990 to 2019. Males had relatively higher rates than females in all age groups. Among all causes of HF in 2019, ischemic heart disease accounted for the highest age-standardised prevalence and YLDs, followed by hypertensive heart disease. Interpretation: The burden of HF in the EMR was higher than the global, with increasing age-standardised prevalence and YLDs in countries of the region. A more comprehensive approach is needed to prevent underlying causes and improve medical care to control the burden of HF in the region. Funding: None.

The effectiveness of psychoeducation in improving attitudes towards psychological disorders and internalized stigma in the family members of bipolar patients: A quasi-experimental study.

Family members of bipolar patients experience stigma following being labeled by others, which can disrupt their routine lives and result in social isolation. This study aims to evaluate the effectiveness of psychoeducation in improving the attitudes of bipolar patients' family members towards psychological disorders and internalized stigma. The present study is a quasi-experimental study with a pre- and post-test follow-up design, in which 74 individuals were selected by convenience sampling among families who had a member with a bipolar disorder who was referred to the largest psychiatric hospital in Iran in 2021. The participants were randomly divided into the experimental and control groups (n = 37 per group). The members of the experimental group then underwent eight 90-min sessions of psychoeducation. The control group also received psychoeducation at the end of the study. The results of analysis of covariance showed that psychoeducation reduced internalized stigma in the family members of bipolar patients and increased their positive attitudes towards psychological disorders compared with the control group (p < .01). On this basis, psychoeducation can be useful to reduce the internalized stigma of family members of bipolar patients and to increase their positive attitudes towards psychological disorders.

What is holding back glucometer use? -A comparative study of rural and urban India.

BACKGROUND: Self-monitoring of blood glucose (SMBG) is associated with better glycemic control. There have been significant technological advances in blood glucose monitoring in the recent past, but the wider acceptance of these technologies is still debatable. AIM: This study investigates the adoption of glucometers and the extent of the use of features in rural and urban India. METHODS: The study uses Bass Model to predict the diffusion of innovation (DOI). Mathematical modeling was used to determine the stage of adoption (using 5-stage of DOI) and the Chi-Square test was used for examining the relationship between depth of implementation (extent of use) and place of residence. RESULTS: The result of the study suggests that glucometer diffusion of innovation has passed the initial hurdle (chasm) in the urban population and is in the late majority. However, for rural respondents, the adoption of glucometers has just passed the chasm and falls under the early majority stage. The diffusion of innovation of combined rural and urban populations has just entered the late majority zone in 2022. The study suggests that a significant number of people with diabetes are still not using a glucometer and discusses the socio-economic issue related to this phenomenon. The utilization of the potential of a glucometer in SMBG falls under the localized shallow implementation category (not using advanced features). CONCLUSIONS: The study suggests that the adoption of glucometer has just passed the initial hurdle in rural India, where two-thirds of the Indian population lives. Furthermore, the adoption of advanced glucometers is low among users.

Effects of milk protein concentrate supplementation on metabolic parameters, adipocytokines and body composition in obese women under weight-loss diet: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity impairs metabolic function and increases the risk of cardiovascular disease and type 2 diabetes mellitus. Evidence suggests that high-protein diets help to increase weight loss and protect against weight gain. Milk protein concentrate (MPC) is a dairy product with a high protein content with a ratio of casein and whey protein similar to skim milk. This trial aims to evaluate the effect of MPC supplementation in obese women under a weight-loss diet. METHODS AND ANALYSIS: We will conduct a 2-month open-label, parallel-group, randomised controlled trial to determine the effect of MPC supplementation on levels of glycaemic and lipid profile, leptin, adiponectin, appetite, waist circumference, body mass index and body composition in 44 premenopausal obese women on a weight-loss diet. ETHICS AND DISSEMINATION: This protocol, approved by the Medical Ethics Committee of Ahvaz University of Medical Sciences, is in accordance with the Declaration of Helsinki (approval number: IR.AJUMS.REC.1399.795). The trial results will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: Iranian Registry of Clinical Trials (IRCT20201223049804N1).

Biological and practical considerations regarding circadian rhythm and mental health relationships among nurses working night shifts: a narrative review and recommendations.

BACKGROUND: Sleep is a vital physiological process regulated by the circadian clock and homeostatic mechanisms. Shift work is necessary to ensure continuity of healthcare provision. Worldwide, nurses work night shifts on a rotational or permanent basis. OBJECTIVE: To analyse the impact of circadian rhythm disruption due to night shift work on the mental wellbeing of nurses. METHODS: The literature search was conducted using PubMed and Scopus electronic databases. Selection criteria include studies published in English between 1997 and 2021 that examined the impact of night-shift work on the mental health of nurses. RESULTS: The searches generated a total of 22 records on the PubMed database, and 9 records on the Scopus database, and a total of 31 studies. 29 papers were identified after removal of duplicates. However, 29 articles were screened based on the review of titles and abstracts. 19 articles were identified for full-test review. Seven papers were included in this review. CONCLUSIONS: Disrupted circadian rhythms and poor sleep quality and quantity have been identified as two of the most significant elements in the long-term effects of night-shift work on nurses' mental health. Strategies and policies to promote workplace health may reduce the occurrence of mental health disorders among night-shift nurses, whether the shifts are rotational or permanent. Nursing supervisors and hospital administrators should consider developing new guidelines to minimize the negative impact of night shift rotations on mental health and the quality of life among nurses.

Randomised controlled trial of a web-based low carbohydrate diet intervention for adults with type 2 diabetes: the T2Diet study protocol.

INTRODUCTION: Type 2 diabetes (T2D) management frequently involves a multidisciplinary care team. However, standard care for patients with T2D is the central role of the general practice physician, and consists of routine appointments to monitor glycaemic status and overall health. Dietary modification is an essential component of T2D management. Evidence suggests that a low carbohydrate diet (LCD) provides better clinical outcomes for people with T2D compared with other diets. However, providing dietary support in face-to-face settings is challenged by issues of availability and accessibility. Provided in conjunction with standard care, digital interventions can help bridge this gap. The objective of this paper is to describe the protocol of a randomised controlled trial (RCT) of a web-based intervention that will evaluate the effectiveness of standard care plus web-based LCD intervention when compared with standard care only. METHODS AND ANALYSIS: In a two-arm parallel RCT, 100 adults with non-insulin-dependent T2D aged between 40 and 89 years will be randomised to either a theoretically informed 16-week automated web-based LCD intervention plus standard care or standard care only. LCD recommendations emphasise consuming nutrient-dense whole foods and encourage a daily carbohydrate goal of 50-100 g, with an objective of achieving 10% to <26% carbohydrates from total energy intake. Assessments will take place at baseline and 16 weeks. The primary outcome will be haemoglobin A1c. Additional data collected will include dietary intake, self-efficacy, weight and height, anti-diabetes medication and dosages, and diabetes-related comorbidities. Process evaluation will consist of a mixed-methods assessment of website engagement metrics, user experience and participants' perspectives. ETHICS AND DISSEMINATION: All study procedures have been approved by the Deakin University Human Research Ethics Committee (2020-349). Study findings will be disseminated widely through public, professional and academic presentation and publication. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12621000096853).

Mental health status of married women during COVID-19 pandemic in Bangladesh: A cross-sectional study.

AIM: The uncontrolled spread of COVID-19 has demanded unparalleled measures, from the imposition of quarantine to the declaration as a public health emergency of international concern. COVID-19 poses a severe threat to our day-to-day life as well as physical and mental health. This study explores mental health status among married women that remain understudied in Bangladesh during the COVID-19 pandemic. METHODOLOGY: A cross-sectional study was conducted among 597 married women via face-to-face interview, maintaining all safety protocols. A semi-structured questionnaire was assembled that included socio-demographics and the DASS-21 scale. Descriptive analysis and logistic regression were performed to examine the associations between variables. RESULT: Almost 35% of the respondents had stress, 20% had anxiety, and 44% had depression ranging from mild to extremely severe. Metropolitan city inhabitants, being housewives, higher educational status, number of children, financial condition, comorbidities, family members assistance in household activities, relocation during COVID-19, social media use, concern about family, infected family members, tendency to get COVID-19 updates had been found significant in multivariable and univariate regression analysis with depression, anxiety, and stress. CONCLUSION: In this study, we found high rates of stress, anxiety, and depression among the study participants. These findings provide us with an epidemiological picture of the mental health status of our target population that could be a key benchmark for identifying high-risk groups and developing policies as well. Results could also be used to formulate psychological interventions that might be helpful during the COVID-19 period and later.

Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review.

BACKGROUND: Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear. OBJECTIVE: This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible. METHODS: Databases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults; either developed or evaluated integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were adaptive or just-in-time adaptive; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results. RESULTS: A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA; 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study; P=.08), increased light PA (1 study; P=.002), walking steps (2 studies; P=.06 and P<.001), walking time (1 study; P=.02), moderate-to-vigorous PA (2 studies; P=.08 and P=.81), and nonwalking exercise time (1 study; P=.31). RCT studies showed increased walking steps (1 study; P=.003) and walking time (1 study; P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone's GPS, and 3 studies used wearable activity trackers. CONCLUSIONS: To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.

Yemen's triple emergency: Food crisis amid a civil war and COVID-19 pandemic.

Yemen has been termed as the world's worst humanitarian crisis by the United Nations. About 20.1 million (more than 50% of population) Yemenis are facing hunger and 10 million are severely food insecure according to reports by the World Food Programme. With the spread of COVID-19, the situation in Yemen has worsened and humanitarian aid from other countries has become the basis of life for hundreds of thousands of Yemenis after the threat of famine. Yemen is practically one of the poorest countries in the world. It has structural vulnerabilities that have developed over a protracted period of conflict and poor governance and more than 50% live in starving, they suffer for getting one meal a day. To prevent a total collapse of Yemen's food crises, the government and the international community should act now more decisively.

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review.

Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.

Barriers and facilitators to patient engagement in patient safety from patients and healthcare professionals' perspectives: A systematic review and meta-synthesis.

AIMS: To explore patients' and healthcare professionals' (HCPs) perceived barriers and facilitators to patient engagement in patient safety. METHODS: We conducted a systematic review and meta-synthesis from five computerized databases, including PubMed/MEDLINE, Embase, Web of Science, Scopus and PsycINFO, as well as grey literature and reference lists of included studies. Data were last searched in December 2019 with no limitation on the year of publication. Qualitative and Mix-methods studies that explored HCPs' and patients' perceptions of barriers and facilitators to patient engagement in patient safety were included. Two authors independently screened the titles and the abstracts of studies. Next, the full texts of the screened studies were reviewed by two authors. Potential discrepancies were resolved by consensus with a third author. The Mixed Methods Appraisal Tool was used for quality appraisal. Thematic analysis was used to synthesize results. RESULTS: Nineteen studies out of 2616 were included in this systematic review. Themes related to barriers included: patient unwillingness, HCPs' unwillingness, and inadequate infrastructures. Themes related to facilitators were: encouraging patients, sharing information with patients, establishing trustful relationship, establishing patient-centred care and improving organizational resources. CONCLUSION: Patients have an active role in improving their safety. Strategies are required to address barriers that hinder or prevent patient engagement and create capacity and facilitate action.