bluuebunny/crossref_metadata_2025_split huggingface dataset:
Awareness of and Attitudes Toward User Involvement in Research on Aging and Health: Protocol for a Quantitative Large-Scale Panel Study
Authors: Kylén Maya , Schmidt Steven M , Jonsson Oskar , Slaug Björn , Iwarsson Susanne 
Published: June 2020

Background
User involvement is a requirement of most research funders. There is a growing body of literature exploring the benefits and challenges of user involvement in research, but such studies are scarce in the field of aging and health. Moreover, the majority of such research is qualitative, which limits the generalizability of results. The UserAge panel study will be instrumental in expanding knowledge that will benefit the quality and impact of user involvement in future research.

Objective
The aim of this study is to determine the awareness and understanding of and attitudes toward user involvement in research among different categories of knowledge users and researchers over time.

Methods
A panel study will be implemented with 3 different categories of knowledge users (people aged 60 years and older, informal carers, and professionals in health care and architecture) and researchers in aging and health. A professional survey company will collect data from all samples in parallel. Potential participants will be asked to complete the survey via telephone or online, or participants can request a paper survey to be sent to them in the post. A draft set of questions on attitudes and behavioral patterns related to research utilization and user involvement in research was compiled based on existing literature and input from the research team. Using a participatory approach, we engaged a user forum, where 8 older people and 3 researchers jointly refined the survey for time/length to complete, terminology, readability, and context. Data collected via the internet or telephone will be automatically processed, and data collected on paper forms will be entered in machine-readable forms. The survey company will store all data and deliver the quality-controlled database to the university for further storage. Analyses of frequencies and measures of central tendency will be used for descriptive purposes. To compare groups, state-of-the art statistical analyses will be used.

Results
Data collection for the first study wave started in September 2019 and will be completed in spring 2020. Data will be ready for analysis following cleaning and quality control, which started during summer 2020 and will be completed autumn 2020. We anticipate the data collection for the second study wave to start in September 2021.

Conclusions
This is the first quantitative large-scale panel study focusing on trends in attitudes toward, awareness of, and knowledge about user involvement in research on aging and health in Sweden. The results will generate new and important knowledge to advance the understanding of user needs and preferences as well as the relevance of user involvement in research on aging and health.

International Registered Report Identifier (IRRID)
DERR1-10.2196/17759
Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review
Authors: Abd-Alrazaq Alaa A , Alajlani Mohannad , Ali Nashva , Denecke Kerstin , Bewick Bridgette M , Househ Mowafa 
Published: June 2020

Background
Chatbots have been used in the last decade to improve access to mental health care services. Perceptions and opinions of patients influence the adoption of chatbots for health care. Many studies have been conducted to assess the perceptions and opinions of patients about mental health chatbots. To the best of our knowledge, there has been no review of the evidence surrounding perceptions and opinions of patients about mental health chatbots.

Objective
This study aims to conduct a scoping review of the perceptions and opinions of patients about chatbots for mental health.

Methods
The scoping review was carried out in line with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews guidelines. Studies were identified by searching 8 electronic databases (eg, MEDLINE and Embase) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. In total, 2 reviewers independently selected studies and extracted data from the included studies. Data were synthesized using thematic analysis.

Results
Of 1072 citations retrieved, 37 unique studies were included in the review. The thematic analysis generated 10 themes from the findings of the studies: usefulness, ease of use, responsiveness, understandability, acceptability, attractiveness, trustworthiness, enjoyability, content, and comparisons.

Conclusions
The results demonstrated overall positive perceptions and opinions of patients about chatbots for mental health. Important issues to be addressed in the future are the linguistic capabilities of the chatbots: they have to be able to deal adequately with unexpected user input, provide high-quality responses, and have to show high variability in responses. To be useful for clinical practice, we have to find ways to harmonize chatbot content with individual treatment recommendations, that is, a personalization of chatbot conversations is required.
Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers
Authors: Jang Ryoungwoo , Kim Namkug , Jang Miso , Lee Kyung Hwa , Lee Sang Min , Lee Kyung Hee , Noh Han Na , Seo Joon Beom 
Published: June 2020

BackgroundComputer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors.ObjectiveThis study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling.MethodsWe trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist.ResultsIn comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%.ConclusionsThe deep learning–based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning–based computer-aided diagnosis.
Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review
Authors: Xie Bo , Tao Cui , Li Juan , Hilsabeck Robin C , Aguirre Alyssa 
Published: June 2020

Background
Artificial intelligence (AI) has great potential for improving the care of persons with Alzheimer’s disease and related dementias (ADRD) and the quality of life of their family caregivers. To date, however, systematic review of the literature on the impact of AI on ADRD management has been lacking.

Objective
This paper aims to (1) identify and examine literature on AI that provides information to facilitate ADRD management by caregivers of individuals diagnosed with ADRD and (2) identify gaps in the literature that suggest future directions for research.

Methods
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for conducting systematic literature reviews, during August and September 2019, we performed 3 rounds of selection. First, we searched predetermined keywords in PubMed, Cumulative Index to Nursing and Allied Health Literature Plus with Full Text, PsycINFO, IEEE Xplore Digital Library, and the ACM Digital Library. This step generated 113 nonduplicate results. Next, we screened the titles and abstracts of the 113 papers according to inclusion and exclusion criteria, after which 52 papers were excluded and 61 remained. Finally, we screened the full text of the remaining papers to ensure that they met the inclusion or exclusion criteria; 31 papers were excluded, leaving a final sample of 30 papers for analysis.

Results
Of the 30 papers, 20 reported studies that focused on using AI to assist in activities of daily living. A limited number of specific daily activities were targeted. The studies’ aims suggested three major purposes: (1) to test the feasibility, usability, or perceptions of prototype AI technology; (2) to generate preliminary data on the technology’s performance (primarily accuracy in detecting target events, such as falls); and (3) to understand user needs and preferences for the design and functionality of to-be-developed technology. The majority of the studies were qualitative, with interviews, focus groups, and observation being their most common methods. Cross-sectional surveys were also common, but with small convenience samples. Sample sizes ranged from 6 to 106, with the vast majority on the low end. The majority of the studies were descriptive, exploratory, and lacking theoretical guidance. Many studies reported positive outcomes in favor of their AI technology’s feasibility and satisfaction; some studies reported mixed results on these measures. Performance of the technology varied widely across tasks.

Conclusions
These findings call for more systematic designs and evaluations of the feasibility and efficacy of AI-based interventions for caregivers of people with ADRD. These gaps in the research would be best addressed through interdisciplinary collaboration, incorporating complementary expertise from the health sciences and computer science/engineering–related fields.
Patients’ Convergence of Mass and Interpersonal Communication on an Online Forum: Hybrid Methods Analysis
Authors: Sanders Remco , Araujo Theo B , Vliegenthart Rens , van Eenbergen Mies C , van Weert Julia C M , Linn Annemiek J 
Published: June 2020

Background
Patients are increasingly taking an active role in their health. In doing so, they combine both mass and interpersonal media to gratify their cognitive and affective needs (ie, convergence). Owing to methodological challenges when studying convergence, a detailed view of how patients are using different types of media for needs fulfillment is lacking.

Objective
The aim of this study was to obtain insight into the frequency of reported convergence, how convergence affects what posters write online, motives for posting, and the needs posters are trying to fulfill.

Methods
Using a hybrid method of content analysis and supervised machine learning, this study used naturally available data to fill this research gap. We analyzed opening posts (N=1708) of an online forum targeting cancer patients and their relatives (Kanker.nl).

Results
Nearly one-third of the forum opening posts contained signs of convergence in mass or interpersonal media. Posts containing mass media references disclosed less personal information and were more geared toward community enhancement and sharing experiences compared to posts without convergence. Furthermore, compared to posts without signs of convergence, posts that included interpersonal media references disclosed more personal information, and posters were more likely to ask for the experiences of fellow users to fulfill their needs. Within posts containing signs of convergence, posts including interpersonal media references reported fewer shortages of information, disclosed more information about the disease, and were more active in seeking other posters’ experiences compared to posts containing mass media references.

Conclusions
The current study highlights the intertwining of media platforms for patients. The insights of this study can be used to adapt the health care system toward a new type of health information–seeking behavior in which one medium is not trusted to fulfill all needs. Instead, providers should incorporate the intertwinement of sources by providing patients with reliable websites and forums through which they can fulfill their needs.
Evaluation of the Sexual Health Behaviors of Black Male Adolescents and Young Adults Through Social Media Platforms: Web-Based Survey Study
Authors: Burns Jade , Johnstone Keith , Chavanduka Tanaka , Jamison Cornelius , Pena Valery , Stephenson Rob , Darbes Lynae 
Published: June 2020

Background
Social media platforms such as Facebook, Instagram, and Twitter, which have millions of users who interact and communicate every day, have been effective in promoting sexual health interventions and in disseminating reproductive health education. They have also been shown to be useful in health promotion and have been used to track several key metrics (eg, comments, posts) among users of all demographics. However, there is a lack of research on the impact and reach of these social media platforms as a community-based tool for disseminating sexual health information and for increasing engagement among Black adolescents and young adults, which is a targeted high-risk population.

Objective
The purpose of this study was to determine the social media platforms and banner advertisements that affected engagement among Black male adolescents and young adults in participating in web-based health surveys.

Methods
A web-based survey was conducted from March 2019 to July 2019 to assess sexual health and health behaviors in a convenience sample of Black male adolescents and young adults in the age range of 18-24 years (N=170). Social media metrics from Facebook, Instagram, and Twitter were monitored. This cross-sectional survey comprised several categories, including basic personal information, drug-related risk behaviors, health care, sexual reproductive health questions, attitudes, norms, and perceived control, mental health, violence-related risk behaviors, and social media preferences.

Results
Social media advertisements on the Black Male Opinion survey reached approximately 146,412 individuals. Our primary finding of the web-based survey engagement was that referral (eg, group chat, indirect social media sharing) led to as the greatest proportion of recruitment, with Twitter and YouTube as the preferred sites to receive sexual health information.

Conclusions
Recognizing the variety of technologies being used among Black male young adults and adolescents can help the community, researchers, and health care providers understand the web-based engagement of this high-risk population. This information may also promote culturally sensitive, customized marketing on sexual health information for this population.
Rapid Utilization of Telehealth in a Comprehensive Cancer Center as a Response to COVID-19: Cross-Sectional Analysis
Authors: Lonergan Peter E , Washington III Samuel L , Branagan Linda , Gleason Nathaniel , Pruthi Raj S , Carroll Peter R , Odisho Anobel Y 
Published: June 2020

Background
The emergence of the coronavirus disease (COVID-19) pandemic in March 2020 created unprecedented challenges in the provision of scheduled ambulatory cancer care. As a result, there has been a renewed focus on video-based telehealth consultations as a means to continue ambulatory care.

Objective
The aim of this study is to analyze the change in video visit volume at the University of California, San Francisco (UCSF) Comprehensive Cancer Center in response to COVID-19 and compare patient demographics and appointment data from January 1, 2020, and in the 11 weeks after the transition to video visits.

Methods
Patient demographics and appointment data (dates, visit types, and departments) were extracted from the electronic health record reporting database. Video visits were performed using a HIPAA (Health Insurance Portability and Accountability Act)-compliant video conferencing platform with a pre-existing workflow.

Results
In 17 departments and divisions at the UCSF Cancer Center, 2284 video visits were performed in the 11 weeks before COVID-19 changes were implemented (mean 208, SD 75 per week) and 12,946 video visits were performed in the 11-week post–COVID-19 period (mean 1177, SD 120 per week). The proportion of video visits increased from 7%-18% to 54%-72%, between the pre– and post–COVID-19 periods without any disparity based on race/ethnicity, primary language, or payor.

Conclusions
In a remarkably brief period of time, we rapidly scaled the utilization of telehealth in response to COVID-19 and maintained access to complex oncologic care at a time of social distancing.
Application and Preliminary Outcomes of Remote Diagnosis and Treatment During the COVID-19 Outbreak: Retrospective Cohort Study
Authors: Liu Luwen , Gu Jianqin , Shao Fengmin , Liang Xinliang , Yue Lixia , Cheng Qiaomei , Zhang Lianzhong 
Published: June 2020

Background
The coronavirus disease (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in the self-quarantine of countless people due to possible infection. This situation makes telemedicine necessary as it can overcome geographical barriers, increase the number of people served, and provide online clinical support for patients. However, the outcomes of telemedicine have not yet been evaluated.

Objective
The aim of our study is to describe the epidemiological features and clinical symptoms of patients receiving remote diagnosis and treatment at the online outpatient clinic of our hospital, as well as to analyze the outcomes and advantages of telemedicine, during the COVID-19 pandemic.

Methods
Data from patients receiving remote diagnosis and treatment via consultation services for COVID-19 concerns at the online outpatient clinic of Henan Provincial People's Hospital from January 24 to February 17, 2020, were collected. A retrospective analysis was performed on epidemiological features, clinical symptoms, and preliminary outcomes.

Results
Online inquiry, consultation, and suggestions were provided for patient concerns related to COVID-19. Our hospital also offered offline noncontact drug delivery services following online ordering and payment. A total of 4589 patients receiving remote diagnosis and treatment were recruited. The daily number of online outpatient visits initially increased and then decreased, reaching its peak on January 28 when the daily number of online outpatient visits totaled 612. Of 4589 patients, 1940 (42.3%) were males and 2649 (57.7%) were females (age range: 78 days to 85 years). Most patients were aged 20-39 years (n=3714, 80.9%) and came from Henan Province (n=3898, 84.9%). The number of patients from other provinces was 691 (15.1%). During the online consultations, patients discussed the following symptoms: fever (n=2383), cough (n=1740), nasal obstruction (n=794), fatigue (n=503), and diarrhea (n=276). A total of 873 orders of noncontact drug delivery following online payment was completed. The daily number of such orders gradually stabilized after the initial, steady increase. For offline drug delivery orders, the median (IQR) was 36 (58). An online satisfaction survey was filled out postconsultation by patients; of the 985 responses received, 98.1% (n=966) of respondents were satisfied with the service they received.

Conclusions
Remote diagnosis and treatment offered via online outpatient consultations effectively reduced the burden on hospitals, prevented overcrowding, reduced the risk of cross-infection, and relieved patients' anxiety during the COVID-19 outbreak. This plays an essential role in pandemic management.
Implementing mHealth Interventions in a Resource-Constrained Setting: Case Study From Uganda
Authors: Meyer Amanda J , Armstrong-Hough Mari , Babirye Diana , Mark David , Turimumahoro Patricia , Ayakaka Irene , Haberer Jessica E , Katamba Achilles , Davis J Lucian 
Published: June 2020

Background
Mobile health (mHealth) interventions are becoming more common in low-income countries. Existing research often overlooks implementation challenges associated with the design and technology requirements of mHealth interventions.

Objective
We aimed to characterize the challenges that we encountered in the implementation of a complex mHealth intervention in Uganda.

Methods
We customized a commercial mobile survey app to facilitate a two-arm household-randomized, controlled trial of home-based tuberculosis (TB) contact investigation. We incorporated digital fingerprinting for patient identification in both study arms and automated SMS messages in the intervention arm only. A local research team systematically documented challenges to implementation in biweekly site visit reports, project management reports, and minutes from biweekly conference calls. We then classified these challenges using the Consolidated Framework for Implementation Research (CFIR).

Results
We identified challenges in three principal CFIR domains: (1) intervention characteristics, (2) inner setting, and (3) characteristics of implementers. The adaptability of the app to the local setting was limited by software and hardware requirements. The complexity and logistics of implementing the intervention further hindered its adaptability. Study staff reported that community health workers (CHWs) were enthusiastic regarding the use of technology to enhance TB contact investigation during training and the initial phase of implementation. After experiencing technological failures, their trust in the technology declined along with their use of it. Finally, complex data structures impeded the development and execution of a data management plan that would allow for articulation of goals and provide timely feedback to study staff, CHWs, and participants.

Conclusions
mHealth technologies have the potential to make delivery of public health interventions more direct and efficient, but we found that a lack of adaptability, excessive complexity, loss of trust among end users, and a lack of effective feedback systems can undermine implementation, especially in low-resource settings where digital services have not yet proliferated. Implementers should anticipate and strive to avoid these barriers by investing in and adapting to local human and material resources, prioritizing feedback from end users, and optimizing data management and quality assurance procedures.

Trial Registration
Pan-African Clinical Trials Registration PACTR201509000877140; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=877