Multimodal hybrid reasoning methodology for personalized wellbeing services

FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis

The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.

Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach

Dance is a unique art with the human body movement as the main means, but dance is not limited to the human body movement itself. Like any art, dance is the product of human social behavior and a romantic behavior of human thoughts and emotions in the virtual world. Dances with different characteristics will also reflect different aesthetics, different cultural psychology, different living styles, and emotional trajectories of different times and different nationalities. People rely on the image of dance artists to develop and inherit the profound ideological connotation and philosophy of life. Viewers may form their own diversified and unique aesthetic characteristics. In the new era, in order to better promote the development, communication, and dissemination of dance art, it is very necessary to analyze and explore the connotation and aesthetic characteristics of dance art. Only through specific movements can the value and ideological connotation of works be expressed. Therefore, this paper comparatively analyzes dance movement aesthetic emotion based on deep learning. Experimentations are performed to systematically analyze the models from various perspectives. Findings of the evaluation show that CAP and CNN are effective models that can successfully extract high-level emotional features. The method proposes and effectively selects the best models among the five standard models based on key features and is, therefore, suitable in predicting the dancer's emotion and for the analysis of the dance movement in the future.

Evaluation of Application Effect of Self-Made Compression Cold Therapy in Postoperative Rehabilitation of Patients with Orthopedic Dyskinesia

With the accelerated aging of the population, orthopedic injuries have become more collective. Among them, the incidence of ankle fractures remains high. Surgery is an effective way to treat ankle fractures by utilizing special surgical site, complex anatomical structure, and specific surgical methods. With surgical approach, it is easy for basis postoperative blood loss, pain, swelling, and other problems. After surgery, most patients suffer from symptoms of fear, increased pain sensitivity, and excessive irrational concerns about physical movement or activity. Compression cold therapy combines cold therapy with air pressure therapy to ease local exudation, constrict blood vessels, improve circulation, relieve pain, and control inflammation through the effects of low temperature and pressure. Application during the rehabilitation period can prevent joint swelling, reduce muscle soreness, and promote the functional recovery of limbs, which provides an effective guarantee for postoperative rehabilitation of patients with orthopedic dyskinesia. Based on this, it is very important to evaluate the application and effect of self-made compression cold therapy in postoperative rehabilitation of patients with orthopedic dyskinesia. This work proposes a one-dimensional deep convolutional neural network-based method; DenseNet for analyzing the rehabilitation effect of patients with orthopedic dyskinesia after ankle fracture surgery. The approach is to evaluate the rehabilitation effect of self-made compression cold therapy from the perspectives of feature reuse, attention mechanism, and feature decoupling. Experiments on the dataset show that the proposed neural network has better efficacy evaluation performance. The proposed systematic assessment based on the emerging deep learning network has great significance in healthcare domain, particularly in assessing applicability, side effects, and noninvasiveness of treatment methods.

Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence

Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is the most basic and important aspect of a country's educational system. As a result, individuals pursuing a degree in preschool education take on a lot of responsibility. The preschool education handmade course has become an important component of preschool education due to its practicality and creativity. Preschool education major offers classes in traditional crafts such as paper cutting, paper dyeing, origami, paper three-dimensional modeling, and ornamental painting. The teaching methods for custom-made preschool education courses are always evolving with the progress of society. The question of how to assess the efficacy of unique teaching methodologies for handmade courses has become crucial. This study employs artificial intelligence to create a neural network for assessing the creativity of teaching approaches for handmade courses in preschool education. The following is the specific work: Firstly, the idea, as well as the benefits and drawbacks of genetic algorithms, is investigated. To build an improved genetic algorithm (IGA), the chromosome encoding, fitness function, and three operation options are enhanced. Secondly, by improving the genetic algorithm, the selection of weights and thresholds in the BP neural network model is improved, and a combined model (IGA-BP) is created by integrating the improved genetic algorithm with the BP network. Finally, rigorous and systematic tests confirm the work's efficacy and viability.

Analysis of the Effect of Applying Ultrasound-Guided Nerve Block Anesthesia to Fracture Patients in the Context of Internet-Based Blockchain

In the process of surgical treatment, the introduction of ultrasound technology to implement nerve block anesthesia can make the operations of patients with fractures under visualization and it can also significantly improve the anesthesia effect. With this technology, it is possible to minimize the anesthesia operation causing accidental injury and lay a good foundation for the smooth operation of surgical treatment. Blockchain technology is a new decentralized infrastructure and distributed computing paradigm. This technology has great development opportunities in the medical field and is expected to play an important role in the construction of Internet medical ecology. This study aims to investigate the effect of ultrasound-guided nerve block anesthesia on fracture treatment in the context of blockchain. This method has high application value and potential in medical data sharing, reducing treatment costs, improving the medical claims system, strengthening medical management, and optimizing medical decision-making using blockchain technology. This study also addresses the uniqueness and complexity of ultrasound-guided nerve block anesthesia itself and analyzes the effect of the proposed method. The analysis shows that using the internet-based blockchain ultrasound-guided subacromial nerve block anesthesia for fracture patients is effective, and the patient's vital signs are stable, and the block is effective.

Application of Wearable Sensors in the Treatment of Cervical Spondylosis Radiculopathy with Acupuncture

Research shows that cervical spondylosis radiculopathy (CSR) is the most common type of cervical spondylosis in clinic, and Chinese medicine treatment has obvious advantages, among which acupuncture therapy has received increasing attention. CSR has the characteristics of high incidence, long treatment time, and easy recurrence after treatment. In order to meet the different needs of different patients, this paper uses wearable sensors to collect patient dynamic data, extracts the action features of cervical spondylosis to design a scoring system, analyzes the input feature scores through a convolutional neural network (CNN) model, and then outputs personalized acupuncture treatment plan. The development status of wearable sensors at home and abroad is introduced, and the modules and functions of the wearable sensors are designed. The CNN network is used as the network model for classification and recognition. The experimental results show that the CNN model used in this paper has a high classification accuracy, achieving an accuracy of up to 97%, and can help produce an effective treatment plan. In order to determine whether the treatment plan output by the model is effective, each group of data is handed over to two cervical spondylosis experts for scoring, and then the final treatment plan is determined from 10 acupuncture plans. In our experiments, 9 out of 10 plans generated by the CNN model were the same as generated by the experts, which shows the effectiveness of the model.

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference (P < 0.05) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.

Effect of Carbon Dioxide on Bispectral Index of EEG under Intravenous Target-Controlled Anesthesia Based on Intelligent Medical Treatment

Laparoscopic surgery has the advantages of less trauma and quick recovery, and it is more and more favored by surgeons and patients in clinical practice. However, the impact of carbon dioxide pneumoperitoneum on the body during laparoscopic surgery has attracted the attention of many scholars. Pneumoperitoneum can cause increased cerebral blood flow and increased intracranial pressure, cerebral metabolic rate is highly correlated with blood carbon dioxide partial pressure, and cerebral metabolism without cardiopulmonary bypass is linearly correlated with the depth of anesthesia. Electroencephalographic (EEG) bispectral index (BIS) is a signal analysis method, which can directly measure the effect of drugs on the cerebral cortex and reflect the depth of anesthesia. Based on this, this study takes smart medical treatment as the background and uses the improved BP neural network as a tool to explore the effect of carbon dioxide on EEG bispectral index under intravenous target-controlled anesthesia. The main purpose is to observe the correlation between arterial blood carbon dioxide partial pressure and EEG bispectral index under propofol target-controlled anesthesia during retroperitoneal laparoscopic surgery. The experimental results show that the model proposed in this study can efficiently and accurately obtain the size of the influencing factors, which provides a clinical basis for the anesthesia management and anesthesia depth regulation of carbon dioxide pneumoperitoneum laparoscopic surgery.

Curative Effect of Foraminal Endoscopic Surgery and Efficacy of the Wearable Lumbar Spine Protection Equipment in the Treatment of Lumbar Disc Herniation

Lumbar disc herniation is a common and frequently-occurring disease in pain clinics. The incidence rate of affliction is increasing with every passing year. Besides the aged, young people also suffer from long-term pain, which not only affects their daily routines but may also lead to serious impairment. The causes of chronic low back and leg pain caused by lumbar disc herniation are mainly related to mechanical compression, the adhesion of epidural space, intervertebral space, and aseptic inflammatory reaction. The treatment of lumbar disc herniation should follow the principle of step-by-step treatment. An appropriate treatment scheme needs to be adopted according to the patient's condition. About 80% of patients received nonsurgical treatment to get relief from the pain symptoms. However, 10% to 15% of patients still need traditional open surgery. Spinal foraminal surgery is a new method for the treatment of lumbar disc herniation, lumbar surgery failure syndrome, and lumbar spinal stenosis. However, there are only scattered clinical reports on the efficacy of spinal foraminal surgery. Based on it, this paper proposes a method to explore the efficacy of spinal foraminal mirror surgery in the treatment of lumbar disc herniation. Besides, postoperative wearable lumbar protective equipment is proposed to ensure a seamless rehabilitation effect on the patients. Statistical analysis performed using a t-test revealed that there was a significant difference between the visual analog scales (VAS) scores of the two groups after 3 and 6 months of treatment (P < 0.05). The paper analyzes and summarizes the cases with definite and poor curative effects, which not only provides the basis for clinical practice but also paves the way to multicenter clinical research.

Health Recommender Systems: Systematic Review

Background

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

Objective

We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

Methods

We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

Results

Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.

Conclusions

There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

On computing critical factors based healthy behavior index for behavior assessment

OBJECTIVE

Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

MATERIALS AND METHODS

To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines.

RESULTS

The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants.

CONCLUSIONS

The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation.

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

Objective

Tailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential of recommender systems to support and enhance computer-tailored digital health interventions. The aim is threefold, to explore: (1) how recommender systems provide health recommendations, (2) to what extent recommender systems incorporate theoretical models and (3) how the use of recommender systems may enhance the usage of computer-tailored interventions.

Methods

A scoping review was conducted, using MEDLINE and ScienceDirect, to identify health recommender systems reported in studies between January 2007 and December 2017. Information was subsequently extracted to understand the potential benefits of recommender systems for computer-tailored digital health programs. Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form.

Results

A total of 26 articles were included for data extraction. General characteristics were reported, with eight studies reporting hybrid filtering. A description of how each recommender system provides a recommendation is described; the majority of recommender systems used messages as recommendation. We identified the potential effects of recommender systems on efficiency, effectiveness, trustworthiness and enjoyment of the digital health program.

Conclusions

Incorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience. This study illustrates how recommender systems, especially hybrid programs, may have the potential to bring tailored digital health forward.

User Models for Personalized Physical Activity Interventions: Scoping Review

Background

Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.

Objective

This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.

Methods

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.

Results

The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.

Conclusions

This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol

BACKGROUND

Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items-for instance, motivational messages aimed at smoking cessation-for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium.

METHODS

Patients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients' feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed.

DISCUSSION

This study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation.

TRIAL REGISTRATION

The trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review

BACKGROUND

Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population.

OBJECTIVE

This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, "Ensure healthy lives and promote well-being for all at all ages"), and to suggest possible reasons for these gaps as well as to propose some solutions.

METHODS

We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-language journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects-the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects-using a new multidisciplinary taxonomy.

RESULTS

Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not assess (cost)-effectiveness.

DISCUSSION

Health recommender systems may be further improved by using relevant behavior change strategies and by implementing essential characteristics of tailored interventions. In addition, many of the features required to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects were not reported in the studies.

CONCLUSIONS

The studies analyzed presented few evidence in support of the positive effects of using health recommender systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with electronic health records and the incorporation of health promotion theoretical factors and behavior change theories. This will render those studies more useful for policymakers since they will cover all aspects needed to determine their impact toward meeting SDG3.