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BackgroundObesity 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. ObjectiveThis 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. MethodsWe 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. ResultsWe 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/m2 (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. ConclusionsThe 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.

IntroductionThe amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). ObjectiveThe aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. MethodsWe retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. ResultsThe interrater agreement of classification was moderate (Fleiss’ kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P

Neurorehabilitation (NR), a major component of neurosciences, is the process of restoring a patient's damaged/disorganized neurological function, through training, therapy, and education, while focusing on patient's independence and well-being. Since the advent of the COVID-19 pandemic, various applications of telecare and telehealth services surged drastically and became an integral part of current clinical practices. Tele-Neurorehabilitation (TNR) is one of such applications. When rehabilitation services were disrupted globally due to lockdown and travel restrictions, the importance of TNR was recognized, especially in developed, low, and middle-income countries. With exponential deployment of telehealth interventions in neurosciences, TNR has become a distinct stand-alone sub-specialty of neurosciences and telehealth. Digital technologies, such as wearables, robotics, and Virtual Reality (VR) have enabled TNR to improve the quality of patients' lives. Providing NR remotely using digital technologies and customized digital devices is now a reality, and likely to be the new norm soon. This article provides an overview of the needs, utilization, and deployment of TNR, and focuses on digital technology enablers of TNR in pre- and post-COVID-19 pandemic era. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak, declared a pandemic in March 2020, lacked specific treatments until vaccine development. Medication misinformation via media caused panic, self-prescription, and drug resistance. False propaganda led to shortages. This study analyzes Google Trends for hydroxychloroquine (HCQ), azithromycin, and BCG vaccine searches across six countries. US, Brazil, and India showed interest in HCQ, while Taiwan, Japan, and South Korea focused on BCG vaccine. This article aims to raise awareness of adverse drug reactions, cautioning against self-prescription, political assumptions, and social media during future emergencies. [ABSTRACT FROM AUTHOR]

Simple Summary: This population-based study, spanning 20 years, revealed trends regarding the incidence and mortality due to malignant neoplasm of the brain (MNB) in association with mobile phone usage in Taiwan. The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB. The compound annual growth rates further support these observations, highlighting consistent growth in mobile phone users and a corresponding increase in MNB incidences and deaths. While this study suggests a weak association between mobile phone users and MNB incidence and mortality, it is important to acknowledge that conclusive results cannot be drawn at this stage. Further investigation is required to obtain more definitive findings. Continued research in this area will contribute to better understanding of the potential risks and aid in the development of safer mobile phone usage practices in the future. (1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000–December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB. The compound annual growth rates further support these observations, highlighting consistent growth in mobile phone users and a corresponding increase in MNB incidences and deaths. (4) Conclusions: The results suggest a weaker association between the growing number of mobile phone users and the rising rates of MNB, and no significant correlation was observed between MNB incidences and deaths and mobile phone usage. Ultimately, it is important to acknowledge that conclusive results cannot be drawn at this stage and further investigation is required by considering various other confounding factors and potential risks to obtain more definitive findings and a clearer picture. [ABSTRACT FROM AUTHOR]

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Palliative care is a vital aspect of healthcare that aims to improve the quality of life for individuals battling life-threatening diseases, such as cancer. Our research delved into the potential of deep learning (DL) model approaches to predict survival outcomes for end-stage cancer patients. Furthermore, we compared the results of wearable-technology-based activity monitoring with traditional prognostic tools. Interestingly, we found that models trained using both clinical data and time series data demonstrated better performance than those trained solely with time series data. Our research findings are novel in the palliative care field since DL models are not typically employed for predicting survival outcomes. (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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]