bluuebunny/crossref_metadata_2025_split huggingface dataset:
Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines
Authors: O'Connor Karen , Sarker Abeed , Perrone Jeanmarie , Gonzalez Hernandez Graciela 
Published: December 2019

Background
Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter.

Objective
This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described.

Methods
We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data.

Results
Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271).

Conclusions
Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.
A Sexual Health Promotion App for Transgender Women (Trans Women Connected): Development and Usability Study
Authors: Sun Christina J , Anderson Kirsten M , Kuhn Tamara , Mayer Liat , Klein Charles H 
Published: December 2019

Background
HIV severely impacts the transgender communities in the United States, and transgender women have the highest HIV incidence rates among any identified risk group. Guided by formative research with transgender women and by an expert advisory panel of transgender women, we designed a prototype mobile app to promote HIV prevention among transgender women.

Objective
This study aimed to develop and test the usability and acceptability of the prototype Trans Women Connected mobile app.

Methods
We engaged in a 3-phase prototype development process. After conducting formative research about the health needs of this population, we outlined a theory-based app framework and developed three prototype activities (ie, a vision board, a pre-exposure prophylaxis [PrEP] education activity, and an interactive map). We then tested the usability and acceptability of the mobile app and activities with 16 transgender women using pre- and posttests, think-aloud protocols, and open-ended questions.

Results
Participants reported high acceptability for the mobile app; the mean rating across all usability and likability questions was 5.9 out of 7. Service utilization intention, goal setting, and social support increased at posttest compared with pretest. Increases in self-efficacy in finding lesbian, gay, bisexual, transgender, and queer–friendly services; intention to seek online social support; and PrEP knowledge were statistically significant. Participants described the app as attractive and useful and perceived all three activities positively.

Conclusions
This study describes the development and usability and acceptability evaluation of a prototype mobile app designed for and with transgender women for HIV prevention. The usability testing findings provided important insights toward refining and the further development of the Trans Women Connected mobile app. The results suggest that a mobile health intervention can support positive changes. The remaining development and efficacy randomized trial of the Trans Women Connected mobile app is currently underway.
Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development
Authors: Chiang Ting-Wei , Chen Si-Yu , Pan Yuan-Chien , Lin Yu-Hsuan 
Published: December 2019

Background
There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties.

Objective
The specific aims of this study were to (1) identify the Staff Hours app users’ GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants’ self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations.

Methods
We developed an app, Staff Hours, to automatically calculate a user’s work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non–health care professionals (non-HCPs; n=21).

Results
The total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004).

Conclusions
Staff Hours is the first automatic GPS location–based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations.
Lock to Live—An Interactive Web-Based Lethal Means Safety Decision Aid for Suicidal Adults: Pilot Randomized Controlled Trial
Authors: Betz Marian E , Knoepke Christopher E , Simpson Scott , Siry Bonnie J , Clement Ashley , Saunders Tamara , Johnson Rachel , Azrael Deborah , Boudreaux Edwin D , Omeragic Faris , Adams Leah M , Almond Sydney , Juarez-Colunga Elizabeth , Matlock Daniel D 
Published: December 2019

Background
Counseling to reduce access to lethal means such as firearms and medications is recommended for suicidal adults but does not routinely occur. We developed the Web-based Lock to Live (L2L) decision aid to help suicidal adults and their families choose options for safer home storage.

Objective
This study aimed to test the feasibility and acceptability of L2L among suicidal adults in emergency departments (EDs).

Methods
At 4 EDs, we enrolled participants (English-speaking, community-dwelling, suicidal adults) in a pilot randomized controlled trial. Participants were randomized in a 13:7 ratio to L2L or control (website with general suicide prevention information) groups and received a 1-week follow-up telephone call.

Results
Baseline characteristics were similar between the intervention (n=33) and control (n=16) groups. At baseline, many participants reported having access to firearms (33/49, 67%), medications (46/49, 94%), or both (29/49, 59%). Participants viewed L2L for a median of 6 min (IQR 4-10 min). L2L also had very high acceptability; almost all participants reported that they would recommend it to someone in the same situation, that the options felt realistic, and that L2L was respectful of values about firearms. In an exploratory analysis of this pilot trial, more participants in the L2L group reported reduced firearm access at follow-up, although the differences were not statistically significant.

Conclusions
The L2L decision aid appears feasible and acceptable for use among adults with suicide risk and may be a useful adjunct to lethal means counseling and other suicide prevention interventions. Future large-scale studies are needed to determine the effect on home access to lethal means.

Trial Registration
ClinicalTrials.gov NCT03478501; https://clinicaltrials.gov/ct2/show/NCT03478501
Delivery of Compassionate Mental Health Care in a Digital Technology–Driven Age: Scoping Review
Authors: Kemp Jessica , Zhang Timothy , Inglis Fiona , Wiljer David , Sockalingam Sanjeev , Crawford Allison , Lo Brian , Charow Rebecca , Munnery Mikayla , Singh Takhar Shuranjeet , Strudwick Gillian 
Published: December 2019

Background
Compassion is a vital component to the achievement of positive health outcomes, particularly in mental health care. The rise of digital technologies may influence the delivery of compassionate care, and thus this relationship between compassion and digital health care needs to be better understood.

Objective
This scoping review aimed to identify existing digital technologies being used by patients and health professionals in the delivery of mental health care, understand how digital technologies are being used in the delivery of compassionate mental health care, and determine the facilitators of and barriers to digital technology use among patients and health professionals in the delivery of compassionate mental health care.

Methods
We conducted this scoping review through a search of Cumulative Index to Nursing and Allied Health Literature, Medical Literature Analysis and Retrieval System Online (MEDLINE), MEDLINE In-Process and EPub Ahead of Print, PsycINFO, and Web of Science for articles published from 1990 to 2019.

Results
Of the 4472 articles screened, 37 articles were included for data extraction. Telemedicine was the most widely used technology by mental health professionals. Digital technologies were described as facilitating compassionate care and were classified using a conceptual model to identify each digital intersection with compassionate care. Facilitators of and barriers to providing compassionate care through digital technology were identified, including increased safety for providers, health care professional perceptions and abilities, and the use of picture-in-picture feedback to evaluate social cues.

Conclusions
Implementing digital technology into mental health care can improve the current delivery of compassionate care and create novel ways to provide compassion. However, as this is a new area of study, mental health professionals and organizations alike should be mindful that compassionate human-centered care is maintained in the delivery of digital health care. Future research could develop tools to facilitate and evaluate the enactment of compassion within digital health care.
Feasibility and Accuracy of a Computer-Assisted Self-Interviewing Instrument to Ascertain Prior Immunization With Human Papillomavirus Vaccine by Self-Report: Cross-Sectional Analysis
Authors: Oliveira Carlos R , Avni-Singer Lital , Badaro Geovanna , Sullivan Erin L , Sheth Sangini S , Shapiro Eugene D , Niccolai Linda M 
Published: December 2019

Background
Ascertaining history of prior immunization with human papillomavirus (HPV) vaccine can be challenging and resource-intensive. Computer-assisted self-interviewing instruments have the potential to address some of the challenges of self-reporting, and may also reduce the time, costs, and efforts associated with ascertaining immunization status.

Objective
This study assesses both the feasibility and the accuracy of a computer-assisted self-interviewing instrument to ascertain a patient’s history of immunization with the HPV vaccine.

Methods
We developed both a survey and a Web-based data collection system using computer-assisted self-interviewing to ascertain self-reported HPV vaccine immunization history. We implemented the instrument in a sample of adult women enrolled in an ongoing study of the HPV vaccine. Vaccine records from prior sources of care were reviewed to verify reported immunization history.

Results
Among the 312 participants who provided HPV vaccine immunization history by self-report, almost all (99%) were able to do so using the computer-assisted self-interviewing instrument. The median survey completion time was 10 minutes (IQR 7-17). The accuracy of self-report was 84%, sensitivity was 89%, specificity was 80%, and the negative predictive value was 92%.

Conclusions
We found that it is feasible to collect a history of immunization with the HPV vaccine using a computer-assisted self-interviewing instrument. This approach is likely to be acceptable to adult women and is reasonably accurate in a clinical research setting.
Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon Format
Authors: Schoeb Dominik , Suarez-Ibarrola Rodrigo , Hein Simon , Dressler Franz Friedrich , Adams Fabian , Schlager Daniel , Miernik Arkadiusz 
Published: December 2019

Background
Mapping out the research landscape around a project is often time consuming and difficult.

Objective
This study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic.

Methods
To evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system.

Results
The final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (“spot on”) were reported by each group.

Conclusions
AI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results.
Nurse Coaching and Mobile Health Compared With Usual Care to Improve Diabetes Self-Efficacy for Persons With Type 2 Diabetes: Randomized Controlled Trial
Authors: Young Heather M , Miyamoto Sheridan , Dharmar Madan , Tang-Feldman Yajarayma 
Published: December 2019

Background
Type 2 diabetes is a growing public health problem amenable to prevention and health promotion. As healthy behaviors have an impact on disease outcomes, approaches to support and sustain diabetes self-management are vital.

Objective
This study aimed to evaluate the effectiveness of a nurse coaching program using motivational interviewing paired with mobile health (mHealth) technology on diabetes self-efficacy and self-management for persons with type 2 diabetes.

Methods
This randomized controlled trial compared usual care with an intervention that entailed nurse health coaching and mHealth technology to track patient-generated health data and integrate these data into an electronic health record. The inclusion criteria were as follows: (1) enrolled at 1 of 3 primary care clinics, (2) aged 18 years or above, (3) living with type 2 diabetes, and (4) English-speaking. We collected outcome measures at baseline, 3 months, and 9 months. The primary outcome was diabetes self-efficacy; secondary outcomes were depressive symptoms, perceived stress, physical functioning, and emotional distress and anxiety. Linear regression mixed modeling estimated the population trends and individual differences in change.

Results
We enrolled 319 participants; 287 participants completed the study (155 control and 132 intervention). The participants in the intervention group had significant improvements in diabetes self-efficacy (Diabetes Empowerment Scale, 0.34; 95% CI –0.15,0.53; P<.01) and a decrease in depressive symptoms compared with usual care at 3 months (Patient Health Questionnaire-9; 0.89; 95% CI 0.01-1.77; P=.05), with no differences in the other outcomes. The differences in self-efficacy and depression scores between the 2 arms at 9 months were not sustained. The participants in the intervention group demonstrated a significant increase in physical activity (from 23,770 steps per week to 39,167 steps per week at 3 months and 32,601 per week at 9 months).

Conclusions
We demonstrated the short-term effectiveness of this intervention; however, by 9 months, although physical activity remained above the baseline, the improvements in self-efficacy were not sustained. Further research should evaluate the minimum dose of coaching required to continue progress after active intervention and the potential of technology to provide effective ongoing automated reinforcement for behavior change.

Trial Registration
ClinicalTrials.gov NCT02672176; https://clinicaltrials.gov/ct2/show/NCT02672176
Measuring Interests Not Minutes: Development and Validation of the Adolescents’ Digital Technology Interactions and Importance Scale (ADTI)
Authors: Moreno Megan A , Binger Kole , Zhao Qianqian , Eickhoff Jens 
Published: December 2019

BackgroundInteractive digital technology use is integral to adolescents’ lives and has been associated with both health benefits and risks. Previous studies have largely focused on measuring the quantity of technology use or understanding the use of specific platforms. To better understand adolescents’ interactive digital technology use, we need new approaches that consider technology interactions and their importance.ObjectiveThis study aimed to develop an assessment tool to evaluate adolescents’ digital technology interactions and their perceived importance.MethodsWe used a validated scale development approach comprising 2 initial steps to create an item pool: item pool development and item pool refinement. These steps relied upon empirical literature review and an expert convening. We then evaluated the item pool using a Web-based survey. Data were collected via Qualtrics panel recruitment from a national sample of 12- to 18-year-olds. Participant data were randomly split into a development subsample for exploratory factor analysis (EFA) and a test subsample for confirmatory factor analysis (CFA). We assessed Cronbach alpha as well as model fit characteristics including root mean square error of approximation (RMSEA) and comparative fit index (CFI).ResultsOur initial item pool had 71 items and the refined item pool contained 40. A total of 761 adolescents assessed the item pool via Web-based survey. Participants had a mean age of 14.8 (SD 1.7) years and were 52.8% (402/761) female and 77.5% (590/761) white. The EFA analysis included 500 participants and an 18-item draft scale was created. The CFA included 261 participants to test the draft scale. Adequate model fit for the scale was indicated by an RMSEA of 0.063 and a CFI of 0.95. The final scale included 18 items in a 3-factor model, with Cronbach alpha for the 3 factors of .87 (factor 1), .90 (factor 2) and .82 (factor 3). The 3 factors were named (1) technology to bridge online and offline experiences, (2) technology to go outside one’s identity or offline environment, and (3) technology for social connection.ConclusionsThe resulting Adolescents’ Digital Technology Interactions and Importance (ADTI) scale is a promising and psychometrically validated tool for identifying the importance of distinct technology interactions. The scale is informed by relevant theory and expert input. The 3 subscales have utility for future studies to understand whether certain subscale score ranges are associated with health or well-being outcomes.