bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study
Authors: Yang Yujie , Zheng Jing , Du Zhenzhen , Li Ye , Cai Yunpeng 
Published: September 2021

Background
Stroke risk assessment is an important means of primary prevention, but the applicability of existing stroke risk assessment scales in the Chinese population has always been controversial. A prospective study is a common method of medical research, but it is time-consuming and labor-intensive. Medical big data has been demonstrated to promote disease risk factor discovery and prognosis, attracting broad research interest.

Objective
We aimed to establish a high-precision stroke risk prediction model for hypertensive patients based on historical electronic medical record data and machine learning algorithms.

Methods
Based on the Shenzhen Health Information Big Data Platform, a total of 57,671 patients were screened from 250,788 registered patients with hypertension, of whom 9421 had stroke onset during the 3-year follow-up. In addition to baseline characteristics and historical symptoms, we constructed some trend characteristics from multitemporal medical records. Stratified sampling according to gender ratio and age stratification was implemented to balance the positive and negative cases, and the final 19,953 samples were randomly divided into a training set and test set according to a ratio of 7:3. We used 4 machine learning algorithms for modeling, and the risk prediction performance was compared with the traditional risk scales. We also analyzed the nonlinear effect of continuous characteristics on stroke onset.

Results
The tree-based integration algorithm extreme gradient boosting achieved the optimal performance with an area under the receiver operating characteristic curve of 0.9220, surpassing the other 3 traditional machine learning algorithms. Compared with 2 traditional risk scales, the Framingham stroke risk profiles and the Chinese Multiprovincial Cohort Study, our proposed model achieved better performance on the independent validation set, and the area under the receiver operating characteristic value increased by 0.17. Further nonlinear effect analysis revealed the importance of multitemporal trend characteristics in stroke risk prediction, which will benefit the standardized management of hypertensive patients.

Conclusions
A high-precision 3-year stroke risk prediction model for hypertensive patients was established, and the model's performance was verified by comparing it with the traditional risk scales. Multitemporal trend characteristics played an important role in stroke onset, and thus the model could be deployed to electronic health record systems to assist in more pervasive, preemptive stroke risk screening, enabling higher efficiency of early disease prevention and intervention.
Reducing Dental Anxiety in Children Using a Mobile Health App: Usability and User Experience Study
Authors: del Carmen María del Carmen , Cagigas-Muñiz Daniel , García-Robles Rocío , Oprescu Andreea Madalina 
Published: September 2021

Background
Dentistry interventions cause common anxiety and fear problems in children (6-11 years), and according to scientific evidence, this causes a decrease in their quality of life. Therapies mediated by IT-based tools have been shown to positively influence children’s mood based on distraction as well as relaxing activities, but there is no evidence of their use to reduce dental anxiety in children.

Objective
The aim of this study was to answer the following research question: Does our new children-centered codesign methodology contribute to achieving a usable mobile-based product with a highly scored user experience?

Methods
A mobile health app was developed to reduce dental anxiety in children using rapid application development following the usage-centered design methodology. Structured interviews were conducted to test the usability and user experience of the app prototype among 40 children (n=20, 50%, boys and n=20, 50%, girls; age 6-11 years) using a children-adapted questionnaire and the 7-point Single Ease Question rating scale. The Smiley Faces Program—Revised questionnaire was used to assess the level of dental anxiety in participants.

Results
There were no significant differences between girls and boys. The task completion rate was 95% (n=19) for children aged 6-8 years (group 1) and 100% (n=20) for children aged 9-11 years (group 2). Group 1 found watching the relaxing video (task C) to be the easiest, followed by playing a video minigame (task B) and watching the narrative (task A). Group 2 found task C to be the easiest, followed by task A and then task B. The average time spent on the different types of tasks was similar in both age groups. Most of the children in both age groups were happy with the app and found it funny. All children thought that having the app in the waiting room during a dental visit would be useful.

Conclusions
The findings confirmed that the app is usable and provides an excellent user experience. Our children-adapted methodology contributes to achieving usable mobile-based products for children with a highly scored user experience.
Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis
Authors: Gong Jianxia , Sihag Vikrant , Kong Qingxia , Zhao Lindu 
Published: September 2021

Background
The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots.

Objective
The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis.

Methods
We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence.

Results
The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention.

Conclusions
Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”
Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing
Authors: Beliga Slobodan , Martinčić-Ipšić Sanda , Matešić Mihaela , Petrijevčanin Vuksanović Irena , Meštrović Ana 
Published: September 2021

Background
Online media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication.

Objective
The goal of this study was to perform a longitudinal analysis of the COVID-19–related content on online media based on natural language processing.

Methods
We collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period.

Results
The results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions.

Conclusions
Our study shows that online media have a prompt response to the pandemic with a large number of COVID-19–related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered.
Digital Instruments for Reporting of Gastrointestinal Symptoms in Clinical Trials: Comparison of End-of-Day Diaries Versus the Experience Sampling Method
Authors: Beckers Abraham B , Snijkers Johanna T W , Weerts Zsa Zsa R M , Vork Lisa , Klaassen Tim , Smeets Fabienne G M , Masclee Ad A M , Keszthelyi Daniel 
Published: September 2021

Background
Questionnaires are necessary tools for assessing symptoms of disorders of the brain-gut interaction in clinical trials. We previously reported on the excellent adherence to a smartphone app used as symptom diary in a randomized clinical trial on irritable bowel syndrome (IBS). Other sampling methods, such as the experience sampling method (ESM), are better equipped to measure symptom variability over time and provide useful information regarding possible symptom triggers, and they are free of ecological and recall bias. The high frequency of measurements, however, could limit the feasibility of ESM in clinical trials.

Objective
This study aimed to compare the adherence rates of a smartphone-based end-of-day diary and ESM for symptom assessment in IBS and functional dyspepsia (FD).

Methods
Data from 4 separate studies were included. Patients with IBS participated in a randomized controlled trial, which involved a smartphone end-of-day diary for a 2+8-week (pretreatment + treatment) period, and an observational study in which patients completed ESM assessments using a smartphone app for 1 week. Patients with FD participated in a randomized controlled trial, which involved a smartphone end-of-day diary for a 2+12-week (pretreatment + treatment) period, and an observational study in which patients completed ESM assessments using a smartphone app for 1 week. Adherence rates were compared between these 2 symptom sampling methods.

Results
In total, 25 patients with IBS and 15 patients with FD were included. Overall adherence rates for the end-of-day diaries were significantly higher than those for ESM (IBS: 92.7% vs 69.8%, FD: 90.1% vs 61.4%, respectively).

Conclusions
This study demonstrates excellent adherence rates for smartphone app–based end-of-day diaries as used in 2 separate clinical trials. Overall adherence rates for ESM were significantly lower, rendering it more suitable for intermittent sampling periods rather than continuous sampling during longer clinical trials.
Impact of the COVID-19 Pandemic on a Physician Group’s WhatsApp Chat: Qualitative Content Analysis
Authors: Abdel-Razig Sawsan , Anglade Pascale , Ibrahim Halah 
Published: September 2021

Background
Social media has emerged as an effective means of information sharing and community building among health professionals. The utility of these platforms is likely heightened during times of health system crises and global uncertainty. Studies have demonstrated that physicians’ social media platforms serve to bridge the gap of information between on-the-ground experiences of health care workers and emerging knowledge.

Objective
The primary aim of this study was to characterize the use of a physician WhatsApp (WhatsApp LLC) group chat during the early months of the COVID-19 pandemic.

Methods
Through the lens of the social network theory, we performed a qualitative content analysis of the posts of a women physician WhatsApp group located in the United Arab Emirates between February 1, 2020, and May 31, 2020, that is, during the initial surge of COVID-19 cases.

Results
There were 6101 posts during the study period, which reflected a 2.6-fold increase in platform use when compared with platform use in the year prior. A total of 8 themes and 9 subthemes were described. The top 3 uses of the platform were requests for information (posts: 2818/6101, 46.2%), member support and promotion (posts: 988/6101, 16.2%), and information sharing (posts: 896/6101, 14.7%). A substantial proportion of posts were related to COVID-19 (2653/6101, 43.5%), with the most popular theme being requests for logistical (nonmedical) information. Among posts containing COVID-19–related medical information, it was notable that two-thirds (571/868, 65.8%) of these posts were from public mass media or unverified sources.

Conclusions
Health crises can potentiate the use of social media platforms among physicians. This reflects physicians’ tendency to turn to these platforms for information sharing and community building purposes. However, important questions remain regarding the accuracy and credibility of the information shared. Our findings suggest that the training of physicians in social media practices and information dissemination may be needed.
Telemedicine in Intensive Care Units: Scoping Review
Authors: Guinemer Camille , Boeker Martin , Fürstenau Daniel , Poncette Akira-Sebastian , Weiss Björn , Mörgeli Rudolf , Balzer Felix 
Published: September 2021

Background
The role of telemedicine in intensive care has been increasing steadily. Tele–intensive care unit (ICU) interventions are varied and can be used in different levels of treatment, often with direct implications for the intensive care processes. Although a substantial body of primary and secondary literature has been published on the topic, there is a need for broadening the understanding of the organizational factors influencing the effectiveness of telemedical interventions in the ICU.

Objective
This scoping review aims to provide a map of existing evidence on tele-ICU interventions, focusing on the analysis of the implementation context and identifying areas for further technological research.

Methods
A research protocol outlining the method has been published in JMIR Research Protocols. This review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A core research team was assembled to provide feedback and discuss findings.

Results
A total of 3019 results were retrieved. After screening, 25 studies were included in the final analysis. We were able to characterize the context of tele-ICU studies and identify three use cases for tele-ICU interventions. The first use case is extending coverage, which describes interventions aimed at extending the availability of intensive care capabilities. The second use case is improving compliance, which includes interventions targeted at improving patient safety, intensive care best practices, and quality of care. The third use case, facilitating transfer, describes telemedicine interventions targeted toward the management of patient transfers to or from the ICU.

Conclusions
The benefits of tele-ICU interventions have been well documented for centralized systems aimed at extending critical care capabilities in a community setting and improving care compliance in tertiary hospitals. No strong evidence has been found on the reduction of patient transfers following tele-ICU intervention.

International Registered Report Identifier (IRRID)
RR2-10.2196/19695
Use of a Guided Imagery Mobile App (See Me Serene) to Reduce COVID-19–Related Stress: Pilot Feasibility Study
Authors: Gordon Judith S , Sbarra David , Armin Julie , Pace Thaddeus W W , Gniady Chris , Barraza Yessenya 
Published: September 2021

Background
The SARS-CoV-2 pandemic has led to concerns about mental health resulting from regional and national lockdowns, social isolation, job loss, and concern about disease exposure.

Objective
We describe results of the pilot feasibility study of the See Me Serene mHealth app. The app provides users with immersive, vivid, nature experiences to reduce stress and anxiety related to COVID-19 and other isolation. The goals of the study were to develop the See Me Serene app and test the feasibility and acceptability of study procedures, and explore the potential impact of the app on stress and anxiety.

Methods
We developed and tested the See Me Serene app and our study procedures for feasibility, and gathered preliminary data with a goal of 100 participants. The research was conducted in 2 phases: (1) development and internal testing of the app; and (2) feasibility and pilot testing with participants recruited online through earned media (eg, news stories), presentations at a university campus, and social media (eg, online sharing of earned media and presentations). The feasibility study employed a mixed methods, within-subjects, pre-/posttest design. At baseline and 30-day follow-up, we assessed stress-related variables via validated self-report measures and saliva samples for determination of cortisol concentrations.

Results
We met or surpassed all our feasibility benchmarks for recruitment (101 participants recruited), retention (91% [90/99] of 30-day assessment completed), and data collection (99 participants completed all baseline data; 85% [84/99] of salivary cortisol samples returned). Participants adhered to the intervention. On average, participants listened to 48.2 audio files over 30 days or approximately 1.6 audio files per day. Participants were satisfied with the app, with 87% (78/90) rating the app as helpful in dealing with stress and anxiety. The app showed the potential to reduce stress, anxiety, loneliness, and worry. We did not find significant differences (P=.41) in cortisol levels over time. Our findings suggest that future research is warranted to test the efficacy of the See Me Serene app with a representative, diverse sample.

Conclusions
There is a need for evidence-based and easily disseminable stress-reduction interventions. See Me Serene is a feasible intervention and has the potential to reduce stress related to COVID-19 and other forms of social isolation. More research on See Me Serene is warranted.
Values of Importance to Patients With Cardiovascular Disease as a Foundation for eHealth Design and Evaluation: Mixed Methods Study
Authors: Bente Britt E , Wentzel Jobke , Groeneveld Rik GH , IJzerman Renée VH , de Buisonjé David R , Breeman Linda D , Janssen Veronica R , Kraaijenhagen Roderik , Pieterse Marcel E , Evers Andrea WM , van Gemert-Pijnen Julia EWC 
Published: September 2021

Background
eHealth interventions are developed to support and facilitate patients with lifestyle changes and self-care tasks after being diagnosed with a cardiovascular disease (CVD). Creating long-lasting effects on lifestyle change and health outcomes with eHealth interventions is challenging and requires good understanding of patient values.

Objective
The aim of the study was to identify values of importance to patients with CVD to aid in designing a technological lifestyle platform.

Methods
A mixed method design was applied, combining data from usability testing with an additional online survey study, to validate the outcomes of the usability tests.

Results
A total of 11 relevant patient values were identified, including the need for security, support, not wanting to feel anxious, tailoring of treatment, and personalized, accessible care. The validation survey shows that all values but one (value 9: To have extrinsic motivation to accomplish goals or activities [related to health/lifestyle]) were regarded as important/very important. A rating of very unimportant or unimportant was given by less than 2% of the respondents (value 1: 4/641, 0.6%; value 2: 10/641, 1.6%; value 3: 9/641, 1.4%; value 4: 5/641, 0.8%; value 5: 10/641, 1.6%; value 6: 4/641, 0.6%; value 7: 10/639, 1.6%; value 8: 4/639, 0.6%; value 10: 3/636, 0.5%; value 11: 4/636, 0.6%) to all values except but one (value 9: 56/636, 8.8%).

Conclusions
There is a high consensus among patients regarding the identified values reflecting goals and themes central to their lives, while living with or managing their CVD. The identified values can serve as a foundation for future research to translate and integrate these values into the design of the eHealth technology. This may call for prioritization of values, as not all values can be met equally.
ISOLATION OF PHYTOCONSTITUENT, IN VITRO ANTICANCER STUDY IN HELA AND MCF-7 CELL LINES AND MOLECULAR DOCKING STUDIES OF POTHOS SCANDENS LINN
Authors: NAIR SEEMA S. , VARKEY JOYAMMA 
Published: September 2021

Objective: This study aims to isolate an active phytoconstituent from ethanolic leaf extract of Pothos scandens Linn., to evaluate in vitro anticancer activity, and to carry out molecular docking studies of the isolated phytoconstituent. Methods: The bioactive constituent 1,1’-(4,5-dihydroxy benzene-1,2-diyl) bisoct-7-en-1-one, a phenolic compound, was isolated by using chromatographic methods and the structure was elucidated by various spectroscopic techniques. In vitro anticancer activity was evaluated against HeLa and MCF 7 cell lines. The viability of cells was evaluated by direct observation of cells by an Inverted phase-contrast microscope and by the MTT assay method. IC50 was calculated using the linear regression model. Results: The results of anticancer studies revealed that different concentrations of the ethanolic extract of leaves of Pothos scandens Linn. exhibited cytotoxic activity against HeLa and MCF 7 cell lines with IC50 of 22.9 and 18.32 μg/ml, respectively. The anticancer potential of the plant was revalidated by in silico molecular docking study with Vascular Endothelial Growth Factor Receptor 2 (VEGFR2, PDB ID: 4AG8) using Discovery studio 2018. Results of the docking study showed that the ligand exhibited strong interaction with the VEGFR2 kinase with significant binding energy. Conclusion: Pothos scandens linn. can be used for the isolation of potent anticancer agents.