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Maple syrup urine disease (MSUD) is a metabolic disorder characterized by a difficulty to digest and process proteins necessary for growth. To monitor and maintain the ideal growth of children with MSUD, caregivers need to carefully control the consumption of harmful branched-chain amino acids (BCAAs). The dietary limits of amino acids for MSUD patients are recommended and controlled by pediatricians and metabolic dietitians according to age, height, weight, and the prevailing percentage of amino acids in the body. This study introduces an intelligent dietary tool called MSUD Baby Buddy for caregivers of MSUD patients that tracks the amino acids intake out of baby formulas for babies 0-6 months old. This tool aims to provide accurate recommendations of the appropriate daily intake of protein and BCAAs based on the patients' data, plasma BCAAs, and formula preferences. We use a knowledge-based system, including knowledge acquisition and verification, as well as knowledge management tool validation, and the ripple-down rules are employed for building the system. MSUD Baby Buddy can support the maintenance of adequate amino acid levels and increase awareness about the control of BCAAs. The average usability of MSUD Baby Buddy is 84.25, indicating that the tool is intuitive and may help caregivers to easily determine the recommended doses of formula based on patients' biometric data and preferred formula. On the other hand, interviews with metabolic dietitians revealed some drawbacks, which were addressed to further improve the tool. MSUD Baby Buddy is expected to help caregivers of MSUD patients to independently track nutrient intake and reduce the number of visits to the pediatrician and metabolic dietitian.

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BACKGROUND: As of 2022, people are getting better at learning how to coexist with the Covid-19 global pandemic. In Saudi Arabia, many attempts have been made to raise public health awareness. However, most health awareness campaigns are generic and might not influence the desired behavior among individuals. OBJECTIVES: This study aims to apply geospatial intelligence and user modeling to profile the districts of the city of Jeddah. This customized map can provide a baseline for a customized health awareness campaign that targets the locals of each district individually based on the virus spread level. METHODOLOGY: It is ongoing research, which has resulted in the creation of a health messages library in the first phase [1]. This paper focuses on a second phase of the research study, which aims to provide a customized baseline for this campaign by applying the geospatial artificial intelligence technique known as space-time cube (STC). STC was applied to create a local map of the Saudi city of Jeddah, representing three different profiles for the city's districts. The model is built using valid COVID-19 clinical data obtained from one of Jeddah's general hospitals. RESULTS AND IMPLICATIONS: When applied, STC displays three profiles for the districts of Jeddah city: high infection, moderate infection, and low infection. To assess the geo-intelligent map, a new instrument was created and validated. The usability and practicality of this map were quantitatively evaluated in a cross-sectional survey using the goal-question-metric measurement framework, and a total of 43 participants filled out the questionnaire. The results indicate that the geo-intelligent map is suitable for everyday use, as evidenced by the participants' responses. We argue that the developed instrument can also be used to assess any geo-intelligence map. This research provides a legitimate approach to customizing health awareness messages during pandemics.

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BACKGROUND AND OBJECTIVES: In this study, we assessed the potential impact of employee empowerment on health care workers' performance during the novel coronavirus SARS-CoV-2 (COVID-19) pandemic. In particular, we aimed to determine the empowerment practices that would have the greatest positive effect on employee performance. Understanding the relationship between performance and empowerment can help health care providers better manage worker stress during any global crisis. This understanding is crucial in guiding policies and interventions aimed at maintaining health care workers' psychological well-being and their overall performance. METHODS: This cross-sectional study evaluated the relationship between employee empowerment and performance, determining the best empowerment practices for health care leaders to utilize. Frontline health care workers (n = 100) selected using convenience and snowball sampling completed the survey between March 15 and 31, 2020. This is the period when the pandemic just started to accelerate in Saudi Arabia. We conducted Pearson's correlation analysis to assess whether there was a relationship between performance and health care workers' empowerment practice, and stepwise linear regression analysis to investigate the impact each of these empowerment practices on health care workers' performance. RESULTS: Our results indicate that health care workers' performance can be expected to increase the most through 2 empowerment practices: giving employees the discretion to change work processes and offering performance-based rewards (R2 = 0.301, P < .05). CONCLUSION: Our findings suggest that health care leaders must invest in these 2 practices to better equip frontline health care workers. During a global crisis, additional discretion granted to employees helps reduce their anxiety and burnout and hence empowers them with the flexibility to adapt to unforeseen circumstances and improve the quality of their interactions with health service recipients.

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For years, several countries have been concerned about how to dispose of unused pharmaceuticals that can endanger human health and the environment. Moreover, some people are in desperate need of medical attention and medications, but they lack the financial resources to obtain them. In Saudi Arabia, there are no take-back medicine programs, and there is no published research on how medications properly are disposed. The aim of this research is to use the power of artificial intelligence to assist in the proper management and disposal of expired and unused medications and to develop a prototype device for collecting medication by automatically classifying medications for proper disposal and donation. In this research, artificial intelligence technologies such as web-based expert systems, image recognition and classification algorithms, chatbots, and the internet of things are used to assist in a take-back medications program. In conclusion, the prototype design of a web-based expert system and the device reduced improper disposal risks by providing significant advice on the safe disposal of unwanted pharmaceuticals. By using an organized method of collecting expired medications, the benefits were made possible.

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BACKGROUND: Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease such as COVID-19. In Saudi Arabia, many attempts have been made to raise public awareness about COVID-19 infection and precautionary health measures. However, most of the health information delivered through the national dashboard and the COVID-19 awareness campaigns are generic and do not necessarily make the impact needed to be seen on individuals' behavior. Health messages need to be applicable and reverent to the individual in the audience. OBJECTIVE: In light of Fogg-Behavior model, this research aims to build and validate a behavior-change-based messaging campaign to promote precautionary health behavior in individuals during the COVID-19 pandemic. Intervention messages can then be targeted appropriately during the pandemic. METHODS: An initial library of 32 text-based and video-based messages were developed and validated based on Fogg behavior model for behavior change. Based on this model, three groups of messages were created to reflect the model's three theoretical concepts of motivation, ability and triggers. Each group of messages is designed to target different segment of the audience. The content of the messages was developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The validity of this content was evaluated by domain experts through the content validity index. RESULTS: Fogg-Behavior Model was used to segment the audience into three different groups based on their perceived ability and motivation. The three groups of messages designed for those groups were found relevant to Fogg theoretical concepts. Thirteen professional health care workers (n = 13) evaluated the content of the message libraries in Arabic and English. Thirty-two messages were found to have acceptable content validity (I-CVI = 0.87). CONCLUSIONS: This research introduced Fogg Behavior Model as a behavior change model to develop targeted messages for three groups of the audience based on their motivation and ability level toward maintaining precautionary behavior during the pandemic. This targeted awareness messaging campaign can be utilized by health authorities to raise individuals' awareness about the precautionary measures that should be taken, maintain these measures and hence help in reducing the number of positive cases in the city of Jeddah.

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BACKGROUND: Presently, dietary management approaches are mostly oriented toward using calorie-counting and diet-tracking tools that draw our attention away from the nutritional value of our food. To improve individuals' dietary behavior, primarily that of people with type 2 diabetes, a simple technique is needed to increase their understanding of the nutritional content of their food. OBJECTIVE: This study aimed to design, develop, and evaluate a customized nutrient-profiling tool called EasyNutrition. EasyNutrition was built to introduce the new concept of nutrient profiling by applying the Intelligent Nutrition Engine, an algorithm that we developed for ranking different food recipes based on their nutritional value. This study also aimed to investigate the efficacy of EasyNutrition in lowering glycated hemoglobin (HbA1c) levels and improving dietary habits among people with type 2 diabetes. METHODS: We evaluated the utility of EasyNutrition using design science research in three sequential stages. This paper has elaborated on the third stage to investigate the efficacy of EasyNutrition in managing type 2 diabetes. A quasi-experimental study was conducted in a diabetes treatment center (n=28). The intervention group utilized EasyNutrition over 3 months, whereas participants in the control group utilized the standard of care provided by the center. Dietary habits and HbA1c levels were measured to capture any change before and after experimenting with EasyNutrition. RESULTS: The intervention group (n=9) exhibited a statistically significant change between the pre- and postexposure results of their HbA1c (t9=2.427; P=.04). Their HbA1c dropped from 8.13 to 6.72. This provided preliminary evidence of the efficacy of using a customized nutrient-profiling app in reducing HbA1c for people with type 2 diabetes. CONCLUSIONS: This study adds to the evidence base that a nutrient-profiling strategy may be a modern adjunct to diabetes dietary management. In conjunction with reliable dietary education provided by a registered dietician, EasyNutrition may have some beneficial effects to improve the dietary habits of people with type 2 diabetes.

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Assistive technology (AT) involvement in therapeutic treatment has provided simple and efficient healthcare solutions to people. Within a short span of time, mobile health (mHealth) has grown rapidly for assisting people living with a chronic disorder. This research paper presents the comprehensive study to identify and review existing mHealth dementia applications (apps), and also synthesize the evidence of using these applications in assisting people with dementia including Alzheimer's disease (AD) and their caregivers. Six electronic databases searched with the purpose of finding literature-based evidence. The search yielded 2818 research articles, with 29 meeting quantified inclusion and exclusion criteria. Six groups and their associated sub-groups emerged from the literature. The main groups are (1) activities of daily living (ADL) based cognitive training, (2) monitoring, (3) dementia screening, (4) reminiscence and socialization, (5) tracking, and (6) caregiver support. Moreover, two commercial mobile application stores i.e., Apple App Store (iOS) and Google Play Store (Android) explored with the intention of identifying the advantages and disadvantages of existing commercially available dementia and AD healthcare apps. From 678 apps, a total of 38 mobile apps qualified as per defined exclusion and inclusion criteria. The shortlisted commercial apps generally targeted different aspects of dementia as identified in research articles. This comprehensive study determined the feasibility of using mobile Health based applications for dementia including AD individuals and their caregivers regardless of limited available research, and these apps have capability to incorporate a variety of strategies and resources to dementia community care.

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One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.

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Diet-related chronic diseases are on the rise. Current dietary management approaches are mostly calorie-counter tools that draw our attention away from the nutritional quality of our food choices. To improve consumers' dietary behavior, we need a simple technique to educate them about nutrition and increase their understanding of the nutritional quality of their food. This study aims to design a dietary tool to promote a nutrient-dense diet. To this end, we applied the concept of Nutrient Profiling to classify food recipes based on their nutritional quality, by developing the Intelligent Nutrition Engine. This engine undergirds our mobile-based application, Easy Nutrition, which was designed to enable users to find food recipes and understand their nutritional quality. To evaluate the usability and understandability of our approach, we piloted the prototype of Easy Nutrition on 24 consumers. The results indicate that our approach provides a sustainable avenue to help consumers manage their diets.

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