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Objectives A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods We retrieved dates of non-moon and moon phases for eight years (1st January 2001–31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001–31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

Introduction India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.

Background People from lower and middle socioeconomic classes and vulnerable populations are among the worst affected by the COVID-19 pandemic, thus exacerbating disparities and the digital divide. Objective To draw a portrait of e-services as a digital approach to support digital health literacy in vulnerable populations amid the COVID-19 infodemic, and identify the barriers and facilitators for their implementation. Methods A scoping review was performed to gather published literature with a broad range of study designs and grey literature without exclusions based on country of publication. A search was created in Medline (Ovid) in March 2021 and translated to Medline, PsycINFO, Scopus and CINAHL with Full Text (EBSCOhost). The combined literature search generated 819 manuscripts. To be included, manuscripts had to be written in English, and present information on digital intervention(s) (e.g. social media) used to enable or increase digital health literacy among vulnerable populations during the COVID-19 pandemic (e.g. older adults, Indigenous people living on reserve). Results Five articles were included in the study. Various digital health literacy-enabling e-services have been implemented in different vulnerable populations. Identified e-services aimed to increase disease knowledge, digital health literacy and social media usage, help in coping with changes in routines and practices, decrease fear and anxiety, increase digital knowledge and skills, decrease health literacy barriers and increase technology acceptance in specific groups. Many facilitators of digital health literacy-enabling e-services implementation were identified in expectant mothers and their families, older adults and people with low-income. Barriers such as low literacy limited to no knowledge about the viruses, medium of contamination, treatment options played an important role in distracting and believing in misinformation and disinformation. Poor health literacy was the only barrier found, which may hinder the understanding of individual health needs, illness processes and treatments for people with HIV/AIDS. Conclusions The literature on the topic is scarce, sparse and immature. We did not find any literature on digital health literacy in Indigenous people, though we targeted this vulnerable population. Although only a few papers were included, two types of health conditions were covered by the literature on digital health literacy-enabling e-services, namely chronic conditions and conditions that are new to the patients. Digital health literacy can help improve prevention and adherence to a healthy lifestyle, improve capacity building and enable users to take the best advantage of the options available, thus strengthening the patient’s involvement in health decisions and empowerment, and finally improving health outcomes. Therefore, there is an urgent need to pursue research on digital health literacy and develop digital platforms to help solve current and future COVID-19-related health needs.

Background An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients’ symptoms remotely, prioritize the patients’ visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future.

BACKGROUND An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. OBJECTIVE In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. METHODS An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. RESULTS The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. CONCLUSIONS Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Background: Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. Methods: A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. Results: Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. Conclusion: The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.

Objective Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data. Materials and methods We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 Results Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03–1.06), dyspnea (OR, 2.56; 95% CI: 1.51–4.33), male sex (OR, 1.70; 95% CI: 1.10–2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17–4.16) and CKD (OR, 1.76; 95% CI: 1.01–3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33–0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76). Conclusions Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure., Highlights • Severe Covid-19 illness can be predicted with demographics, symptoms and medical history. • Increasing age, male sex, and presentation with dyspnea predicted severe illness. • Immunocompromised status and chronic kidney disease also predict severe illness. • Hyperlipidemia was protective in our multivariable model.

Implementing a reform in medical education requires students’ awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students’ perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

This document represents the assessment of the iHelp approaches by showing the proposed data collecting processing and modelling flow. This workflow’s final purpose is to provide to the clinicians a better insight of the Pancreatic Cancer disease. There are five pilot studies within the iHelp designed to help test and validate the personalized solution developed in this project by providing different types of data which is useful within the various aspects/scenarios of disease prevention. The five pilots involved in this project, provide the input data needed for the IHelp technical partners to analyse and compile smart models that can help with predicting this type of disease. Upon the completion of the 1st iteration of this report on D6.5 (“Data Collection and Pilot Assessments I”), adjustments and improvements will be made and will be reflected in the 2nd iteration of this deliverable, i.e., D6.6 (“Data Collection and Pilot Assessments II”). This document contains the description of data collection processes and procedures and the instruments used to evaluate these processes. It also describes the evaluation mechanisms of pilot studies. The data collection instruments section presents an overview of what the data collection strategy is for each pilot, how data will be formatted and distributed and what is the methodology for data ingestion into the iHelp platform from each pilot’s perspective. Pilots will present details about the measuring of the data quality by describing the evaluation instruments for data collection and assessment measures. Pilots will present details about the data quality and quantity and will describe how the data flow is managed. To ensure proper data flow and smooth ingestion of the data from the pilots to the iHelp platform, partners have identified potential risks that could appear, and the solutions that alleviate the impact of these potential issues.

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy.

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy.

Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...]

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital’s palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.

Mobile Health (mHealth) has a great potential to enhance the self-management of cancer patients and survivors. Our study aimed to perform a scoping review to evaluate the impact and trends of mobile application-based interventions on adherence and their effects on health outcomes among the cancer population. In addition, we aimed to develop a taxonomy of mobile-app-based interventions to assist app developers and healthcare researchers in creating future mHealth cancer care solutions. Relevant articles were screened from the online databases PubMed, EMBASE, and Scopus, spanning the time period from 1 January 2016 to 31 December 2022. Of the 4135 articles initially identified, 55 were finally selected for the review. In the selected studies, breast cancer was the focus of 20 studies (36%), while mixed cancers were the subject of 23 studies (42%). The studies revealed that the usage rate of mHealth was over 80% in 41 of the 55 studies, with factors such as guided supervision, personalized suggestions, theoretical intervention foundations, and wearable technology enhancing adherence and efficacy. However, cancer progression, technical challenges, and unfamiliarity with devices were common factors that led to dropouts. We also proposed a taxonomy based on diverse theoretical foundations of mHealth interventions, delivery methods, psycho-educational programs, and social platforms. We suggest that future research should investigate, improve, and verify this taxonomy classification to enhance the design and efficacy of mHealth interventions.

This deliverable summarizes the work that has been done in the score of Task 2.1 (“State of the art and requirement analysis”) until M12. This is the second version of a series of deliverables of this task, whose main objective is to specify the pilot scenarios, their involved datasets, and their corresponding user requirements, as well as the system and technical requirements that are being imposed by the platform. As mentioned in the previous version of this deliverable (i.e. D2.1 – State of the art & Requirement Analysis I), the purpose of these series is to track those requirements throughout the project and update them during the progress of the project. As a result, this second version depicts the enhanced (primary & secondary) data requirements that have been identified after the delivery of the first versions of the scientific reports and provides an updated version of some of the technical requirements of the iHelp platform. As mentioned in the first version of this deliverable the approach that has been followed is twofold: A top-down approach that is followed with respect to the user requirements that were collected by the use case providers themselves, after specifying the business goals and objectives of the use case, along with a concrete definition of the scenario. Moreover, a bottom-up approach is additionally complemented that aims to identify and analyse the technical requirements with respect to the technical work packages that are focusing on the platform technological needs. The result of this analysis is an updated list of measurable an unambiguous requirement that will drive the design of the overall architecture of the iHelp platform, focusing on serving all different needs of the various use cases of the project. As the project is still progressing, a final version of this deliverable will help the architecture designers of the platform and its software developers to adjust the overall architecture and its implementation accordingly, with respect to the principles of the agile methodology. Moreover, as our scope is to continuously keep track with the latest technological advances and how we can take advantage of them while developing our -a state-of-the-art analysis has been performed regarding the major technologies that are envisioned to be exploited. This deliverable is being released on M12 of the project and its main aim is to specify the scenarios of the use cases, which drove the user requirements based on their perspective, the updated version of the secondary data requirements and the technical requirements as foreseen by the technical partners that are being involved in the design of the overall architecture of the platform. A final version of this document will be released on M18.

Older adults tend to suffer from multi-morbidity, requiring complex treatment methodologies demanding poly-pharmacy. The increasing medication usage can tend towards the mismanagement of prescriptions and irregular or faulty administration. Thus, there arises an urgent need for a proper pill management system for these prescribed medicines. To tackle this grave concern, we propose a mobile, cost-effective, robust, and easy to use solution involving the extension to the human body-smartphones and conductive stickers. The technology utilizes a unique combination of touch-points on the smartphone screen to recognize the medication and give information regarding the proper usage and dosage and gives a reminder of the intake of the medicine. Our tool is comprised of two components—(1) the conductive ink stickers containing a unique combination of conductive inks to be applied to the pill container and (2) the mobile application utilizing touch-points generated by the conductive ink sticker to give information of the corresponding medicine. The following functionalities could be performed by the application-detection of pill container: providing essential information about pill container and dosage; keeping a count of pills already taken, to be taken and remaining pills; reordering the medication and reminding about the medicine intake at the correct designated time.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients.Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused widespread fear and stress. The pandemic has affected everyone, everywhere, and created systemic inequities, leaving no one behind. In India alone, more than 34,094,373 confirmed COVID-19 cases and 452,454 related deaths have been reported as of October 19, 2021. Around May 2021, the daily number of new COVID-19 cases crossed the 400,000 mark, seriously hampering the health care system. Despite the devastating situation, the public response was seen through their efforts to come forward with innovative ideas for potential ways to combat the pandemic, for instance, dealing with the shortage of oxygen cylinders and hospital bed availability. With increasing COVID-19 vaccination rates since September 2021, along with the diminishing number of daily new cases, the country is conducting preventive and preparatory measures for the third wave. In this article, we propose the pivotal role of public participation and digital solutions to re-establish our society and describe how Sustainable Development Goals (SDGs) can support eHealth initiatives and mitigate infodemics to tackle a postpandemic situation. This viewpoint reflects that the COVID-19 pandemic has featured a need to bring together research findings across disciplines, build greater coherence within the field, and be a driving force for multi-sectoral, cross-disciplinary collaboration. The article also highlights the various needs to develop digital solutions that can be applied to pandemic situations and be reprocessed to focus on other SDGs. Promoting the use of digital health care solutions to implement preventive measures can be enhanced by public empowerment and engagement. Wearable technologies can be efficiently used for remote monitoring or home-based care for patients with chronic conditions. Furthermore, the development and implementation of informational tools can aid the improvement of well-being and dissolve panic-ridden behaviors contributing toward infodemics. Thus, a call to action for an observatory of digital health initiatives on COVID-19 is required to share the main conclusions and lessons learned in terms of resilience, crisis mitigation, and preparedness.

Breast and prostate cancer patients may experience physical and psychological distress, and a possible decrease in sleep quality. Subjective and objective methods measure different aspects of sleep quality. Our study attempted to determine differences between objective and subjective measurements of sleep quality using bivariate and Pearson's correlation data analysis. Forty breast (

Due to the challenges and restrictions posed by COVID-19 pandemic, technology and digital solutions played an important role in the rendering of necessary healthcare services, notably in medical education and clinical care. The aim of this scoping review was to analyze and sum up the most recent developments in Virtual Reality (VR) use for therapeutic care and medical education, with a focus on training medical students and patients. We identified 3743 studies, of which 28 were ultimately selected for the review. The search strategy followed the most recent Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping review (PRISMA-ScR) guidelines. 11 studies (39.3%) in the field of medical education assessed different domains, such as knowledge, skills, attitudes, confidence, self-efficacy, and empathy. 17 studies (60.7%) focused on clinical care, particularly in the areas of mental health, and rehabilitation. Among these, 13 studies also investigated user experiences and feasibility in addition to clinical outcomes. Overall, the findings of our review reported considerable improvements in terms of medical education and clinical care. VR systems were also found to be safe, engaging, and beneficial by the studies’ participants. There were huge variations in studies with respect to the study designs, VR contents, devices, evaluation methods, and treatment periods. In the future, studies may focus on creating definitive guidelines that can help in improving patient care further. Hence, there is an urgent need for researchers to collaborate with the VR industry and healthcare professionals to foster a better understanding of contents and simulation development.

Many citizens nowadays cope with busy and dynamic lifestyles. Adopting or maintaining a healthy lifestyle to prevent chronic diseases or mental disorders is a core societal challenge. The current global pandemic has evidenced more than ever before the critical importance of engaging citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Recommender Systems for personalized health and well-being have lately become a research trend, particularly for food and physical activity recommendation. This paper presents F-EvoRecSys: an extension of an evolutionary algorithm-driven approach for "healthy bundle" well-being recommendations that incorporates a fuzzy inference engine aimed at improving physical activity recommendations based on users’ exercising habits. An experimental study demonstrates how this can lead to more diversified recommendations. The paper also discusses challenges and future directions for personalized well-being recommender systems under different perspectives.

IntroductionIndia reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021. This study aimed at developing artificial intelligence-based models to predict the risk of mucormycosis among the patients at the time of discharge from the hospital.MethodsThe dataset included 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree, and random forest, and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error.ResultsThe logistic regression, XGBoost, and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. This study also determined the top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed a positive impact on the risk of mucormycosis.ConclusionThe developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.

Background Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. Methods This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach’s alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson’s product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. Results In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. Conclusions Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology. Electronic supplementary material The online version of this article (10.1186/s12877-019-1218-8) contains supplementary material, which is available to authorized users.

Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.

The use of wearable devices (WDs) in healthcare monitoring and management has attracted increasing attention. A major problem is patients’ adherence and acceptance of WDs given that they are already experiencing a disease burden and treatment side effects. This scoping review explored the use of wrist-worn devices in the cancer population, with a special focus on adherence and clinical outcomes. Relevant articles focusing on the use of WDs in cancer care management were retrieved from PubMed, Scopus, and Embase from 1 January 2017 to 3 March 2022. Studies were independently screened and relevant information was extracted. We identified 752 studies, of which 38 met our inclusion criteria. Studies focused on mixed, breast, colorectal, lung, gastric, urothelial, skin, liver, and blood cancers. Adherence to WDs varied from 60% to 100%. The highest adherence was reported in the 12-week studies. Most studies focused on physical activity, sleep analysis, and heart vital signs. Of the 10 studies that described patient-reported outcomes using questionnaires and personal interviews, 8 indicated a positive correlation between the patient-reported and wearable outcomes. The definitions of the outcome measures and adherence varied across the studies. A better understanding of the intervention standards in terms of the clinical outcomes could improve adherence to wearables.

There are five pilot studies within the iHelp designed to help test and validate the personalised healthcare solution developed in the project. Pilot settings cover from community based (general population) to hospital based (patients). All pilots provide different aspects in relation to disease prevention, personalised health monitoring and decision support. More specifically, Pilot#1 (UNIMAN) focuses on pancreatic cancer risk prediction and risk mitigation. In the risk mitigation process, biomarkers will be assessed and be used as part of motivational change in health habits. Pilot#2 (FPG) focuses on predict outcomes and toxicity using real-world data (RWD) analysis approach. Data on Patient Reported Experience Measures (PREMs) and Patient-Reported Outcome Measures (PROMs) for pancreatic cancer patients with indication to radiotherapy will be systematically collected using a mobile application connected with Internet of Thing (IoT) devices. Pilot#3 (HDM) sets to investigate lifestyle choices on elevating the Risk Factors for Pancreatic Cancer. In addition, this pilot will employ case-control study design to explore new as well as established risk factors associate with pancreatic cancer. Pilot#4 (MUP) will also investigate factors associated with pancreatic risk to derive personalised recommendations and measures to raise awareness. Finally, Pilot#5 (TMU) will explore factors that predict high-risk individuals towards pancreatic and liver cancer for early-stage management of the disease and further explore the effect of digital therapeutics solutions among these individuals. This deliverable describes details of each pilot in more in-depth.

UNSTRUCTURED The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused widespread fear and stress. The pandemic has affected everyone, everywhere, and created systemic inequities, leaving no one behind. In India alone, more than 34,094,373 confirmed COVID-19 cases and 452,454 related deaths have been reported as of October 19, 2021. Around May 2021, the daily number of new COVID-19 cases crossed the 400,000 mark, seriously hampering the health care system. Despite the devastating situation, the public response was seen through their efforts to come forward with innovative ideas for potential ways to combat the pandemic, for instance, dealing with the shortage of oxygen cylinders and hospital bed availability. With increasing COVID-19 vaccination rates since September 2021, along with the diminishing number of daily new cases, the country is conducting preventive and preparatory measures for the third wave. In this article, we propose the pivotal role of public participation and digital solutions to re-establish our society and describe how Sustainable Development Goals (SDGs) can support eHealth initiatives and mitigate infodemics to tackle a postpandemic situation. This viewpoint reflects that the COVID-19 pandemic has featured a need to bring together research findings across disciplines, build greater coherence within the field, and be a driving force for multi-sectoral, cross-disciplinary collaboration. The article also highlights the various needs to develop digital solutions that can be applied to pandemic situations and be reprocessed to focus on other SDGs. Promoting the use of digital health care solutions to implement preventive measures can be enhanced by public empowerment and engagement. Wearable technologies can be efficiently used for remote monitoring or home-based care for patients with chronic conditions. Furthermore, the development and implementation of informational tools can aid the improvement of well-being and dissolve panic-ridden behaviors contributing toward infodemics. Thus, a call to action for an observatory of digital health initiatives on COVID-19 is required to share the main conclusions and lessons learned in terms of resilience, crisis mitigation, and preparedness.

[EN] Background: Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. Objective: This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. Methods: We first studied the users' needs and gathered users' graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. Results: We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m(2) (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities. Conclusions: The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives., Moreover, the authors acknowledge the funding support for this study provided by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies, 727560).

Taiwan’s unique circumstance as a contested state makes it imperative for the country to increase international support for and awareness of its embattled democracy at the hands of the authoritarian government in Beijing through positive public visibility. #TaiwanCanHelp, while perhaps similar to other forms of hashtag activism, is also very different because it also encompasses digitalization of diplomacy, in which the Taiwanese government seeks to raise international awareness of its struggle and gain goodwill through its medical equipment donations. The social influence of #TaiwanCanHelp has not been measured and examined extensively. Evaluation of hashtag use at a holistic level is imperative to the understanding of the extent of resonance across social media. This study explores the dynamics of the #TaiwanCanHelp hashtag on Twitter by quantifying the public response and uncovering the public opinions around #TaiwanCanHelp. A mixed-methods methodology incorporating time-series analysis, sentiment analysis, spatial analysis, and word clouds has been adopted. Our results reveal that #TaiwanCanHelp has resonated beyond Taiwan and received positive responses from diverse users across the globe. Our findings also suggest that the prominence of #TaiwanCanHelp rides on real-life events and is a successful attempt by the Taiwan government in generating international interest through digitalization of diplomacy to advance Taiwan’s interests.

Proton pump inhibitors (PPIs) are used for maintaining or improving gastric problems. Evidence from observational studies indicates that PPI therapy is associated with an increased risk of gastric cancer. However, the evidence for PPIs increasing the risk of gastric cancer is still being debated. Therefore, we aimed to investigate whether long-term PPI use is associated with an increased risk of gastric cancer. We systematically searched the relevant literature in electronic databases, including PubMed, EMBASE, Scopus, and Web of Science. The search and collection of eligible studies was between 1 January 2000 and 1 July 2021. Two independent authors were responsible for the study selection process, and they considered only observational studies that compared the risk of gastric cancer with PPI treatment. We extracted relevant information from selected studies, and assessed the quality using the Newcastle−Ottawa scale (NOS). Finally, we calculated overall risk ratios (RRs) with 95% confidence intervals (CIs) of gastric cancer in the group receiving PPI therapy and the control group. Thirteen observational studies, comprising 10,557 gastric cancer participants, were included. Compared with patients who did not take PPIs, the pooled RR for developing gastric cancer in patients receiving PPIs was 1.80 (95% CI, 1.46–2.22, p < 0.001). The overall risk of gastric cancer also increased in patients with gastroesophageal reflux disease (GERD), H. pylori treatment, and various adjusted factors. The findings were also consistent across several sensitivity analyses. PPI use is associated with an increased risk of gastric cancer in patients compared with those with no PPI treatment. The findings of this updated study could be used in making clinical decisions between physicians and patients about the initiation and continuation of PPI therapy, especially in patients at high risk of gastric cancer. Additionally, large randomized controlled trials are needed to determine whether PPIs are associated with a higher risk of gastric cancer.

BACKGROUND As the obesity rate continues to rise persistently, there is an urgent need to develop an effective weight-loss management strategy. Nowadays, the development of Artificial Intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy (AE). OBJECTIVE This study aims to design a chatbot with artificial empathic motivational support for weight loss, called “SlimMe,” and to investigate how people react with a diet bot. METHODS SlimMe infrastructure was built using Dialogflow as the Natural Language Processing (NLP) platform and LINE mobile messenger as the messaging platform. To simulate the artificial empathy responses, we proposed a text-based emotion analysis to recognize the user emotion. A preliminary evaluation was performed to investigate the user experience after 7-days simulation trials. RESULTS Our preliminary evaluation revealed that having an artificially empathic diet bot for weight loss management was a fun and interesting experience. The use of emoticons, stickers, and GIF images made the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1007 bot responses from 892 user input messages. Of these, 67.38% (601/1007) the chatbot generated an accurate response to a relevant user request, 21.19% (189/1007) inaccurate responses to a relevant request, 10.31% (92/1007) accurate response to an irrelevant request, and only 1.12% (10/1007) the chatbot does not answer. CONCLUSIONS We present the design of an artificially empathic diet bot as a friendly assistant to help users estimating their calorie intake and calories burned in more interactive and engaging ways. To our knowledge, this is the first chatbot designed with artificial empathy features and it looks very promising in promoting long-term weight management. However, since our study is still in the preliminary stage, through more user interactions and further enhancement on the data training and validation, it will improve the bot’s in-built knowledge-base and emotional intelligence-base. CLINICALTRIAL N/A

Due to the rising demands and pressing needs of global health, the healthcare industry has gone through constant flux and transformed over the time. By reforming and modernizing the healthcare services, delivery, as well as systems in the recent decade, digital health has become part and parcel of the current healthcare practices and is considered as the cornerstone of the participatory / personalized health. With the advent of smartphones, wearable devices, and tracking and sensing technologies, the concept of mHealth / connected health emerged as another influential and promising name in digital health technologies and played a key role in encouraging the healthy behavior and active lifestyle for patients as well as public. By putting the patients at the center of healthcare paradigm, digital health technologies have empowered them the opportunity to participate in shared decision-making based on their values and preferences and provided them the self-management options. Disruptive technologies, such as artificial intelligence applications in data mining and analytics using mobile and wearable devices, such as smartphones, smartwatches and wristbands, have opened the new avenues of exploration in the field of digital health. Keeping the above-mentioned fields in consideration, this book covers the theoretical aspects of digital health like methodologies, models, policies, regulations, opportunities and challenges in current healthcare practices first, and then reports the practical aspects of digital health approach using use cases, implementation, evaluation and sustainability of various mobile applications and wearable systems in the healthcare industry that can lead to the goal of participatory / personalized health for tomorrow’s healthcare.

Background Type 2 diabetes mellitus (T2DM) and hypertension (HTN), both non-communicable diseases, are leading causes of death globally, with more imbalances in lower middle-income countries. Furthermore, poor treatment and management are known to lead to intensified healthcare utilization and increased medical care costs and impose a significant societal burden, in these countries, including Indonesia. Predicting future clinical outcomes can determine the line of treatment and value of healthcare costs, while ensuring effective patient care. In this paper, we present the prediction of length of stay (LoS) and mortality among hospitalized patients at a tertiary referral hospital in Tasikmalaya, Indonesia, between 2016 and 2019. We also aimed to determine how socio-demographic characteristics, and T2DM- or HTN-related comorbidities affect inpatient LoS and mortality. Methods We analyzed insurance claims data of 4376 patients with T2DM or HTN hospitalized in the referral hospital. We used four prediction models based on machine-learning algorithms for LoS prediction, in relation to disease severity, physician-in-charge, room type, co-morbidities, and types of procedures performed. We used five classifiers based on multilayer perceptron (MLP) to predict inpatient mortality and compared them according to training time, testing time, and Area under Receiver Operative Curve (AUROC). Classifier accuracy measures, which included positive predictive value (PPV), negative predictive value (NPV), F-Measure, and recall, were used as performance evaluation methods. Results A Random forest best predicted inpatient LoS (R2, 0.70; root mean square error [RMSE], 1.96; mean absolute error [MAE], 0.935), and the gradient boosting regression model also performed similarly (R2, 0.69; RMSE, 1.96; MAE, 0.935). For inpatient mortality, best results were observed using MLP with back propagation (AUROC 0.899; 69.33 and 98.61 for PPV and NPV, respectively). The other classifiers, stochastic gradient descent with regression loss function, Huber, and random forest models all showed an average performance. Conclusions Linear regression model best predicted LoS and mortality was best predicted using MLP. Patients with primary diseases such as T2DM or HTN may have comorbidities that can prolong inpatient LoS. Physicians play an important role in disseminating health related information. These predictions could assist in the development of health policies and strategies that reduce disease burden in resource-limited settings.

BACKGROUND Older adults tend to suffer from multi-morbidity, requiring complex treatment methodologies demanding poly-pharmacy. The increasing medication usage can tend towards the mismanagement of prescriptions and irregular or faulty administration. Thus, there arises an urgent need for a proper pill management system for these prescribed medicines. The solutions offered by the current market seem to be sub-optimal; they either fail to be cost-effective or fail to provide much the required assistance. OBJECTIVE We propose a mobile, cost-effective, robust, and easy to use solution involving the extension to the human body-smartphones and pill bottles METHODS The technology utilizes a unique combination of touch-points on the smartphone screen to recognize the medication and give information regarding the proper usage and dosage. The building of the pill management system was conducted in two phases; first, the pill bottles were developed since these were to be detected and identified, followed by the mobile application, which did the whole pill management. RESULTS Our tool comprised of two components–(1) the conductive ink enabled pill bottle containing a unique combination of conductive inks and (2) the mobile application utilizing the touch-points generated by the conductive ink sticker to give information of the corresponding medicine. Whenever a pill bottle is placed on the mobile screen, the conductive ink imitates the finger touch effect creating a unique touch pattern which is detected on the mobile screen. It is analyzed by the mobile application, holding the database of the corresponding medicine assigned to that specific touch pattern, and the user is provided with the medicine and its dosage details. CONCLUSIONS Our smart pill bottle system presents a novel implementation of touchpoint technology. Further, being easy to use, affordable, and compatible with simple equipment, our system showed feasibility in implementation and usage.

The Digital Image and Communications in Medicine (DICOM) viewer is a very useful component in telemedicine applications. Owing to increased demand, adoption, and prospects of browser-based software in the recent past, web-based DICOM viewers have gained significant ground. There are myriad web-based DICOM viewers which are open source and are available free of cost as stand-alone applications. These freely available tools have rich functionality like the commercial ones. To find an optimal DICOM viewer for integration with a web-based telemedicine solution is quite a challenge, and no research has gone into assessing these freely available DICOM viewers. This research assessed a range of web-based, open-source, and freely available DICOM viewers from the perspective of their integration with the Indian National Telemedicine Solution (eSanjeevani). To introduce teleradiology module in eSanjeevani, a study is carried out to enable viewing of radiological images through DICOM viewer. eSanjeevani is being prepared for a national roll-out at 155,000 health and wellness centers across rural India by the Ministry of Health and Family Welfare (Government of India) under the Ayushman Bharat Scheme (the world’s largest health insurance scheme). In total, 13 free, open-source, and web-based DICOM viewers were identified for evaluation; however, only six were shortlisted as assessed. This study can serve as a one-stop source for researchers looking for a suitable DICOM viewer for their healthcare IT applications.

Background and ObjectiveChronic kidney disease (CKD) represent a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data, obtained from Taiwan's National Health Insurance Research Database, to forecast whether an individual will develop CKD within the next 6 or 12 months, and thus forecast the prevalence in the population.MethodsA total of 18,000 people with CKD and 72,000 people without CKD diagnosis along with the past two years of medication and comorbidity data matched by propensity score were used to build a predicting model. A series of approaches were tested, including Convoluted Neural Networks (CNN). 5-fold cross-validation was used to assess the performance metrics of the algorithms.ResultsBoth for the 6 month and 12-month models, the CNN approach performed best, with the AUROC of 0.957 and 0.954, respectively. The most prominent features in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides, angiotensins which had an impact on the progression of CKD.ConclusionsThe model proposed in this study can be a useful tool for the policy-makers helping them in predicting the trends of CKD in the population in the next 6 to 12 months. Information provided by this model can allow closely monitoring the people with risk, early detection of CKD, better allocation of resources, and patient-centric management

Highlights • A pandemic situation may increase public awareness to take necessary precautions. • Taiwan government encouraged the use of face-masks, sanitizers and following social-distancing as a part of prevention during COVID-19 outbreak. • This response may have contributed to a decline in other infectious diseases., Many communicable diseases may have contact, airborne, and/or droplets transmission. Following the COVID-19 outbreak, Taiwan government implemented the use of masks and sanitizer and other prevention measures, like social distancing for prevention. This public response may have likely contributed significantly to the decline in the outbreak of other infectious diseases.

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study. European Union's Horizon 2020 Research and Innovation Programme under Grant 681120