bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating the Food and Drug Administration Biotherapeutics Review and Approval Process: Narrative Review
Authors: Bonet Olivencia Samuel , Sasangohar Farzan 
Published: January 2021

Background
The development, review, and approval process of therapeutic biological products in the United States presents two primary challenges: time and cost. Advancing a biotherapeutic from concept to market may take an average of 12 years, with costs exceeding US $1 billion, and the product may still fail the US Food and Drug Administration (FDA) approval process. Despite the FDA’s practices to expedite the approval of new therapies, seeking FDA approval remains a long, costly, and risky process.

Objective
The objective of this paper is to explore the factors and gaps related to the FDA review and approval process that contribute to process inefficiencies and complexities as well as proposed methods and solutions to address such gaps. This paper also aims to investigate the available modeling efforts for the FDA approval process of therapeutic biological products.

Methods
A narrative review of literature was conducted to understand the scope of published knowledge about challenges, opportunities, and specific methods to address the factors and gaps related to the review and approval of new drugs, including therapeutic biological products. Relevant peer-reviewed journal articles, conference proceedings, book chapters, official reports from public policy professional centers, and official reports and guidelines from the FDA were reviewed.

Results
Of the 23 articles identified in this narrative literature review, none modeled the current FDA review and approval process structure to address issues related to the robustness, reliability, and efficiency of its operations from an external point of view. Although several studies summarize the FDA approval process with clarity, in addition to bringing to light the problems and challenges faced by the regulatory agency, only a few attempts have been made to provide solutions for the problems and challenges identified. In addition, although several reform models have been discussed, these models lack the application of scientific methodologies and modeling techniques in understanding FDA as a complex sociotechnical system. Furthermore, tools and methods to assess the efficacy of the models before implementation are largely absent.

Conclusions
The findings suggest the efficacy of model-based systems engineering approaches for identifying opportunities for significant improvements to the FDA review and approval process. Using this holistic approach will serve several investigative purposes: identify influential sources of variability that cause major delays, including individual, team, and organizational decision making; identify the human-system bottlenecks; identify areas of opportunity for design-driven improvements; study the effect of induced changes in the system; and assess the robustness of the structure of the FDA approval process in terms of enforcement and information symmetry.
A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development
Authors: Heidary Zahra , Cochran Jeffrey M , Peters-Strickland Timothy , Knights Jonathan 
Published: January 2021

Background
Adherence to medication regimens and patient rest are two important factors in the well-being of patients with serious mental illness. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations.

Objective
A digital medicine system that provides objective time-stamped medication ingestion records was used by patients with serious mental illness. Accelerometer data from the digital medicine system was used to assess rest quality and thus allow for investigation into correlations between rest and medication ingestion.

Methods
Longest daily rest periods were identified and then evaluated using a k-means clustering algorithm and distance metric to quantify the relative quality of patient rest during these periods. This accelerometer-derived quality-of-rest metric, along with other accepted metrics of rest quality, such as duration and start time of the longest rest periods, was compared to the objective medication ingestion records. Overall medication adherence classification based on rest features was not performed due to a lack of patients with poor adherence in the sample population.

Results
Explorations of the relationship between these rest metrics and ingestion did seem to indicate that patients with poor adherence experienced relatively low quality of rest; however, patients with better adherence did not necessarily exhibit consistent rest quality. This sample did not contain sufficient patients with poor adherence to draw more robust correlations between rest quality and ingestion behavior. The correlation of temporal outliers in these rest metrics with daily outliers in ingestion time was also explored.

Conclusions
This result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the participant population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data.
Automated Identification of Common Disease-Specific Outcomes for Comparative Effectiveness Research Using ClinicalTrials.gov: Algorithm Development and Validation Study
Authors: Elghafari Anas , Finkelstein Joseph 
Published: January 2021

Background
Common disease-specific outcomes are vital for ensuring comparability of clinical trial data and enabling meta analyses and interstudy comparisons. Traditionally, the process of deciding which outcomes should be recommended as common for a particular disease relied on assembling and surveying panels of subject-matter experts. This is usually a time-consuming and laborious process.

Objective
The objectives of this work were to develop and evaluate a generalized pipeline that can automatically identify common outcomes specific to any given disease by finding, downloading, and analyzing data of previous clinical trials relevant to that disease.

Methods
An automated pipeline to interface with ClinicalTrials.gov’s application programming interface and download the relevant trials for the input condition was designed. The primary and secondary outcomes of those trials were parsed and grouped based on text similarity and ranked based on frequency. The quality and usefulness of the pipeline’s output were assessed by comparing the top outcomes identified by it for chronic obstructive pulmonary disease (COPD) to a list of 80 outcomes manually abstracted from the most frequently cited and comprehensive reviews delineating clinical outcomes for COPD.

Results
The common disease-specific outcome pipeline successfully downloaded and processed 3876 studies related to COPD. Manual verification indicated that the pipeline was downloading and processing the same number of trials as were obtained from the self-service ClinicalTrials.gov portal. Evaluating the automatically identified outcomes against the manually abstracted ones showed that the pipeline achieved a recall of 92% and precision of 79%. The precision number indicated that the pipeline was identifying many outcomes that were not covered in the literature reviews. Assessment of those outcomes indicated that they are relevant to COPD and could be considered in future research.

Conclusions
An automated evidence-based pipeline can identify common clinical trial outcomes of comparable breadth and quality as the outcomes identified in comprehensive literature reviews. Moreover, such an approach can highlight relevant outcomes for further consideration.
Rural Residents’ Perspectives on an mHealth or Personalized Health Coaching Intervention: Qualitative Study With Focus Groups and Key Informant Interviews
Authors: Schoenberg Nancy , Dunfee Madeline , Yeager Hannah , Rutledge Matthew , Pfammatter Angela , Spring Bonnie 
Published: January 2021

BackgroundCompared with national averages, rural Appalachians experience extremely elevated rates of premature morbidity and mortality. New opportunities, including approaches incorporating personal technology, may help improve lifestyles and overcome health inequities.ObjectiveThis study aims to gather perspectives on whether a healthy lifestyle intervention, specifically an app originally designed for urban users, may be feasible and acceptable to rural residents. In addition to a smartphone app, this program—Make Better Choices 2—consists of personalized health coaching, accelerometer use, and financial incentives.MethodsWe convened 4 focus groups and 16 key informant interviews with diverse community stakeholders to assess perspectives on this novel, evidence-based diet and physical activity intervention. Participants were shown a slide presentation and asked open-ended follow-up questions. The focus group and key informant interview sessions were audiotaped, transcribed, and subjected to thematic analysis.ResultsWe identified 3 main themes regarding Appalachian residents’ perspectives on this mobile health (mHealth) intervention: personal technology is feasible and desirable; challenges persist in implementing mHealth lifestyle interventions in Appalachian communities; and successful mHealth interventions should include personal connections, local coaches, and educational opportunities. Although viewed as feasible and acceptable overall, lack of healthy lifestyle awareness, habitual behavior, and financial constraints may challenge the success of mHealth lifestyle interventions in Appalachia. Finally, participants described several minor elements that require modification, including expanding the upper age inclusion, providing extra coaching on technology use, emphasizing personal and supportive connections, employing local coaches, and ensuring adequate educational content for the program.ConclusionsBlending new technologies, health coaching, and other features is not only acceptable but may be essential to reach vulnerable rural residents.
Facebook Ads Manager as a Recruitment Tool for a Health and Safety Survey of Farm Mothers: Pilot Study
Authors: Burke Richard R , Weichelt Bryan P , Namkoong Kang 
Published: January 2021

Background
Social media platforms have experienced unprecedented levels of growth and usage over the past decade, with Facebook hosting 2.7 billion active users worldwide, including over 200 million users in the United States. Facebook users have been underutilized and understudied by the academic community as a resource for participant recruitment.

Objective
We performed a pilot study to explore the efficacy and cost-effectiveness of Facebook advertisements for the recruitment of an online agricultural health and safety survey.

Methods
We undertook a 1-week advertising campaign utilizing the integrated, targeted advertising platform of Facebook Ads Manager with a target-spending limit of US $294. We created and posted three advertisements depicting varying levels of agricultural safety adoption leading to a brief survey on farm demographics and safety attitudes. We targeted our advertisements toward farm mothers aged 21-50 years in the United States and determined cost-effectiveness and potential biases. No participant incentive was offered.

Results
We reached 40,024 users and gathered 318 advertisement clicks. Twenty-nine participants consented to the survey with 24 completions. Including personnel costs, the cost per completed survey was US $17.42. Compared to the distribution of female producers in the United States, our advertisements were unexpectedly overrepresented in the eastern United States and were underrepresented in the western United States.

Conclusions
Facebook Ads Manager represents a potentially cost-effective and timely method to recruit participants for online health and safety research when targeting a specific population. However, social media recruitment mirrors traditional recruitment methods in its limitations, exhibiting geographic, response, and self-selection biases that need to be addressed.
Measuring Public Reaction to Violence Against Doctors in China: Interrupted Time Series Analysis of Media Reports
Authors: Yang Qian , Tai-Seale Ming , Liu Stephanie , Shen Yi , Zhang Xiaobin , Xiao Xiaohua , Zhang Kejun 
Published: January 2021

BackgroundViolence against doctors in China is a serious problem that has attracted attention from both domestic and international media.ObjectiveThis study investigates readers’ responses to media reports on violence against doctors to identify attitudes toward perpetrators and physicians and examine if such trends are influenced by national policies.MethodsWe searched 17 Chinese violence against doctors reports in international media sources from 2011 to 2020. We then tracked back the original reports and web crawled the 19,220 comments in China. To ascertain the possible turning point of public opinion, we searched violence against doctors–related policies from Tsinghua University ipolicy database from 2011 to 2020, and found 19 policies enacted by the Chinese central government aimed at alleviating the intense patient–physician relationship. We then conducted a series of interrupted time series analyses to examine the influence of these policies on public sentiment toward violence against doctors over time.ResultsThe interrupted time series analysis (ITSA) showed that the change in public sentiment toward violence against doctors reports was temporally associated with government interventions. The declarations of 10 of the public policies were followed by increases in the proportion of online public opinion in support of doctors (average slope changes of 0.010, P<.05). A decline in the proportion of online public opinion that blamed doctors (average level change of –0.784, P<.05) followed the declaration of 3 policies.ConclusionsThe government’s administrative interventions effectively shaped public opinion but only temporarily. Continued public policy interventions are needed to sustain the reduction of hostility toward medical doctors.
Combining Web-Based Gamification and Physical Nudges With an App (MoveMore) to Promote Walking Breaks and Reduce Sedentary Behavior of Office Workers: Field Study
Authors: Mamede André , Noordzij Gera , Jongerling Joran , Snijders Merlijn , Schop-Etman Astrid , Denktas Semiha 
Published: January 2021

Background
Sedentary behavior (SB) and lack of physical activity (PA) have been associated with poorer health outcomes and are increasingly prevalent in individuals working in sedentary occupations such as office jobs. Gamification and nudges have attracted attention as promising strategies to promote changes in health behavior. However, most effectiveness studies thus far lacked active controls, and few studies have tested interventions combining these strategies.

Objective
This study investigates the effectiveness of combining a gamified digital app with physical nudges to increase PA and reduce SB in Dutch office workers.

Methods
Employees in the municipality of Rotterdam (N=298) from two office locations were randomized at the location level to either a 10-week intervention, combining a 5-week gamification phase encompassing a gamified digital app with social support features and a 5-week physical nudges phase, or to an active control (ie, basic digital app with self-monitoring and goal setting). The primary outcome was the daily step count, objectively measured via accelerometers. Secondary outcomes were self-reported PA and SB measured at baseline and at 5, 10, and 14 weeks. Mixed effects models were used to analyze the effects of the intervention on the outcome measures.

Results
A total of 78.5% (234/298) of participants completed the study and provided accelerometer data, whereas 36.9% (110/298) participants completed the self-report measures at 14 weeks. In the gamification phase, step count data were missing for 13.5% (473/3492) of observations in the control and 11.4% (445/3888) in the intervention condition; however, these percentages increased to 39.6% (1154/2910) and 59.6% (1932/3492) at follow-up, respectively. During the gamification phase, intervention participants increased their number of daily steps by 634 (95% CI 154.2-1113.8; P=.01) more than participants in the control group, after controlling for relevant factors. Improvements were not sustained during the physical nudges phase (P=.76) or follow-up (P=.88).

Conclusions
A digital intervention with gamification and social support features significantly increased the step count of office workers compared with an active control. Physical nudges in the workplace were insufficient to promote the maintenance of behavioral changes achieved in the gamification phase. Future research should explore the long-term effectiveness of similar gamified digital interventions.

Trial Registration
International Standard Randomized Controlled Trial Number (ISRCTN) 49129401; https://www.isrctn.com/ISRCTN14881571
Evaluating a Hybrid Web-Based Training Program for Panic Disorder and Agoraphobia: Randomized Controlled Trial
Authors: Ebenfeld Lara , Lehr Dirk , Ebert David Daniel , Kleine Stegemann Stefan , Riper Heleen , Funk Burkhardt , Berking Matthias 
Published: January 2021

Background
Previous studies provide evidence for the effectiveness of web-based interventions for panic disorder with and without agoraphobia. Smartphone-based technologies hold significant potential for further enhancing the accessibility and efficacy of such interventions.

Objective
This randomized controlled trial aims to evaluate the efficacy of a guided, hybrid web-based training program based on cognitive behavioral therapy for adults with symptoms of panic disorder.

Methods
Participants (N=92) with total scores in the Panic and Agoraphobia Scale ranging from 9 to 28 were recruited from the general population and allocated either to a hybrid intervention (GET.ON Panic) or to a wait-list control group. The primary outcome was the reduction in panic symptoms, as self-assessed using a web-based version of the Panic and Agoraphobia Scale.

Results
Analysis of covariance-based intention-to-treat analyses revealed a significantly stronger decrease in panic symptoms posttreatment (F=9.77; P=.002; Cohen d=0.66; 95% CI 0.24-1.08) in the intervention group than in the wait-list control group. Comparisons between groups of the follow-up measures at 3 and 6 months yielded even stronger effects (3-month follow-up: F=17.40, P<.001, Cohen d=0.89, 95% CI 0.46-1.31; 6-month follow-up: F=14.63, P<.001, Cohen d=0.81, 95% CI 0.38-1.24).

Conclusions
Hybrid web-based training programs may help reduce the symptoms of panic disorder and hence play an important role in improving health care for patients with this debilitating disorder.

Trial Registration
German Clinical Trial Register DRKS00005223; https://tinyurl.com/f4zt5ran

International Registered Report Identifier (IRRID)
RR2-10.1186/1745-6215-15-427
COVID-19–Related Knowledge and Practices Among Health Care Workers in Saudi Arabia: Cross-sectional Questionnaire Study
Authors: Shaikhain Talal Ahmad , Al-Husayni Faisal Abdulmohsen , Alhejaili Essam Awadh , Al-Harbi Maha Nawaf , Bogari Anas Abdullah , Baghlaf Bayan Abdulaziz , Alzahrani Mohammed Saeed 
Published: January 2021

Background
Health care workers are at the front line against COVID-19. The risk of transmission decreases with adequate knowledge of infection prevention methods. However, health care workers reportedly lack a proper attitude and knowledge of different viral outbreaks.

Objective
This study aimed to assess the knowledge and attitude of health care workers in Saudi Arabia toward COVID-19. Assessment of these parameters may help researchers focus on areas that require improvement.

Methods
A cross-sectional questionnaire study was conducted among 563 participants recruited from multiple cities in Saudi Arabia. An online questionnaire was shared via social media applications, which contained questions to health care workers about general information regarding COVID-19 and standard practices.

Results
The mean age of the study population was 30.7 (SD 8) years. Approximately 8.3% (47/563) of the health care workers were isolated as suspected cases of COVID-19, and 0.9% (n=5) were found positive. The majority agreed that social distancing, face masks, and hand washing are effective methods for preventing disease transmission. However, only 63.7% (n=359) knew the correct duration of hand washing. Almost 70% (n=394) strictly adhered to hand hygiene practices, but less than half complied with the practice of wearing a face mask. Significant differences in health care workers' attitudes were observed on the basis of their city of residence, their adherence to COVID-19 practices, and their compliance with the use of a face mask. Among the health care workers, 27.2% (n=153) declared that they will isolate themselves at home and take influenza medication if they experience COVID-19 symptoms.

Conclusions
The majority of health care workers in Saudi Arabia presented acceptable levels of general knowledge on COVID-19, but they lack awareness in some crucial details that may prevent disease spread. Intense courses and competency assessments are highly recommended. Prevention of disease progression is the only option for the time being.
Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
Authors: Tena Alberto , Claria Francec , Solsona Francesc , Meister Einar , Povedano Monica 
Published: January 2021

BackgroundBulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement.ObjectiveThe main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis.MethodsThe study focused on the extraction of features from the phonatory subsystem—jitter, shimmer, harmonics-to-noise ratio, and pitch—from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained.ResultsTo date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement.ConclusionsThe results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.