Global mapping of interventions to improve quality of life using mind-body therapies during 1990-2018.

OBJECTIVES: Mind-body therapies (MBT) are a range of practices which improve well-being and have shown promising results in a variety of illness. To inform the application of MBT in health promotion, and intervention designs, we conducted a global review of publication growth and content analysis of studies examining the impacts of MBT on quality of life (QoL) of different patient groups. DESIGN: Data from 1990 to 2018 was collected from the Web of Science (WoS). They were analyzed with descriptive statistics (publication volume by year, citations, and countries). The development of research areas overtime was structured using Latent Dirichlet Allocation, and co-occurrence of keywords of titles, and abstracts. RESULTS: 3906 studies were obtained, with an exponential increase in recent years. Mindfulness, mental health, cancer, surgery, and QoL are common themes in the literature. Research has explored the efficacy, mechanism, and approaches to deliver MBT in both the general population and the patients. CONCLUSIONS: MBT has proven promising in a wide range of medical conditions, not only as a complementary therapy but also been incorporated into health services, especially for chronic diseases. By characterizing the trends in research productivity, and topics, we suggest robust adverse reporting, and guidelines for disease specific MBT should be improved. This global mapping of MBT studies also provides insight for future research, policy, and management direction.

Passive Smoking Exposure and Perceived Health Status in Children Seeking Pediatric Care Services at a Vietnamese Tertiary Hospital.

Understanding the predictors of health conditions and exposure to secondhand smoke among children is necessary to determine the severity of the issues and identify effective solutions. Despite the significant prevalence in smoking and child exposure to secondhand smoke, there have been only a few studies focusing on this area in Vietnam, and thus the current study aims to fill in this gap. The questionnaires of 435 children aged between 0 and 6 and their caregivers, who agreed to participate in the research, were collected at the Pediatric Department of Bach Mai hospital, Hanoi, in 2016. Multivariable logistic regression was employed to identify factors associated with perceived health status and exposure to secondhand smoke among children in the last 24 h and the last 7 days from the date of the survey. Our study found that 43% of the respondents had smokers in the family, and 46.4% of children were exposed to passive smoking in the last 7 days. Urban children were most frequently exposed to passive smoking at home and in public, whereas in the rural area, the home and relatives' houses were the most common places for exposure. Compared to children whose caregivers were farmers, children of non-government workers were more likely to be exposed to passive smoking in the last 7 days. Moreover, children in a family having smoking rules and no smokers were less likely to be exposed to passive smoking in the last 24 h and 7 days than those living in a family allowing smoking and having smokers. In conclusion, our study shows that the government needs to implement better public smoking monitoring and encourage caregivers to implement smoke-free households or smoking rules in their houses.

Prevalence and Sources of Second-Hand Smoking Exposure among Non-Smoking Pregnant Women in an Urban Setting of Vietnam.

Exposure to second-hand smoke (SHS) among non-smoking pregnant women can lead to adverse maternal and fetal outcomes. A cross-sectional study was performed from July to August 2016 among 432 pregnant women at Bach Mai Hospital, Hanoi, Vietnam, to assess the prevalence and sources of SHS exposure among non-smoking pregnant women. Socio-economic characteristics and information regarding SHS exposure of participants were collected. Multivariable logistic regression was employed to identify associated factors. Overall, 92.6% and 64.5% of pregnant women were exposed to SHS in their lifetime and in the last 30 days, respectively. Cafeterias and restaurants had the highest proportion of pregnant women exposed to SHS. Those who reported that "smoking is allowed at home" (OR = 3.18; 95%CI = 1.97-5.13); going to working place (OR = 1.86; 95%CI = 1.08-3.19), going to state authority offices (OR = 1.98; 95%CI = 1.15-3.41), and cafeterias (OR = 1.96; 95%CI = 1.22-3.16) had the highest risk of SHS exposure in the last 30 days. We have found a high proportion of SHS exposure among non-smoking pregnant women in Vietnam. This comes from a multitude of sources, including homes, workplaces, cafeterias, and restaurants. The data emphasises the need for further intervention to address this health issue.

Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study.

BACKGROUND: Artificial intelligence (AI)-based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. OBJECTIVE: This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. METHODS: We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. RESULTS: The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. CONCLUSIONS: The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis.

BACKGROUND: Artificial intelligence (AI)-based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner. OBJECTIVE: The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. METHODS: An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. RESULTS: From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices. CONCLUSIONS: The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis.

The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.

Changes in Substance Abuse and HIV Risk Behaviors over 12-Month Methadone Maintenance Treatment among Vietnamese Patients in Mountainous Provinces.

Methadone maintenance treatment (MMT) programs have been used worldwide to reduce the number of drug users and for HIV prevention; however, evidence of their effectiveness in mountainous areas is limited. This study aimed to identify changes in substance abuse and sexual practices among MMT patients after treatment in three Vietnamese mountainous provinces. A survey on risk behaviors was conducted among 300 drug users in six MMT clinics prior to and following one year of MMT. Cramér's effect size of changes was extrapolated to justify the magnitude of the intervention's effectiveness. A generalized estimation equation was used to find the factors associated with respondents' substance use and sexual risk behavior. While drug-related risk behaviors were significantly reduced, alcohol and sex-related behaviors remained risk factors for HIV in this group. Additionally, condom use was common among participants at both time points, but not among those having sex with sex workers. Socio-economic characteristics of ethnic, education, occupation, as well as drug use history influenced the possibility of engaging in drug use and/or sexual risk behavior following treatment. Further emphasis on managing these among MMT patients is required, potentially by providing integrated services including smoking and drinking counseling and condom use promotion in accordance with MMT.

Depressive Symptoms among Industrial Workers in Vietnam and Correlated Factors: A Multi-Site Survey.

Depressive disorders have been found to be a significant health issue among industrial workers, resulting from work-related factors and causing serious burdens for the workers as well as their employers. Literature on mental health problems of Vietnamese industrial workers has been limited, despite the rapid foreign investment-fueled industrialization process of the country. This study aimed to fill the gap in literature by examining the prevalence of depressive disorders and their potential associated factors among a cohort of Vietnamese industrial workers. A cross-sectional study was conducted in 3 industrial areas in Hanoi and Bac Ninh. A total of 289 workers agreed to participate in the study. Generalized linear mixed models were applied to identify associated factors with depression status of respondents. 38.6% of participants reported suffering depression. Factors found to be positively associated with the possibility of having depression and higher PHQ9 score were long working hours, suffering more health problems, and health hazards exposure at work. Meanwhile, being females, having more children, living with parents, engaging in smoking, and being local workers were found to correlate with lower likelihood of being depressed. The findings suggested the importance of regular health screening, work safety assurance, and social support outside of workplace on the mental health of the workers.

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study.

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008⁻2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors' keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer's, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products.

Understanding Risk Behaviors of Vietnamese Adults with Chronic Hepatitis B in an Urban Setting.

Cigarette smoking and alcohol consumption can be considered as risk factors that increase the progression of chronic liver disease. Meanwhile, unprotected sex is one of the main causes of hepatitis B infection. This study aimed to explore drinking, smoking, and risky sexual behaviors among people with chronic hepatitis B virus (HBV) in a Vietnamese urban setting, as well as investigating potential associated factors. A cross-sectional study was performed in October 2018 in Viet-Tiep Hospital, Hai Phong, Vietnam. A total of 298 patients who had been diagnosed with chronic hepatitis B reported their smoking status, alcohol use, and sexual risk behavior in the last 12 months. A multivariate logistic regression model was used to identify the associated factors. It was identified that 82.5% of participants never used alcohol. The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) positive result among male patients was 7.4% (0% in female patients). In addition, 14.5% of participants were current smokers and the mean number of cigarettes per day was 7.4 (SD = 3.4). It was found that 35.4% of male patients had sex with two or more sex partners. Furthermore, 66.7% and 74.1% of participants used condoms when having sex with casual partners/one-night stands and sex workers, respectively. There was a positive correlation between monthly drinking and currently smoking. White-collar workers were less likely to have multiple sex partners within the last 12 months. Our study highlights the need for integrating counseling sessions and educational programs with treatment services.