Enabling distributed electronic research data collection for a rural Appalachian tobacco cessation study

Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics"

In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity

Objectives

To share our approach for designing, developing, and deploying the Research Electronic Data Capture (REDCap) Mobile Application, details about its dissemination and support through the REDCap Consortium, and a set of lessons learned and guidance recommendations for others developing mobile platforms to support research in regions or situations with internet scarcity.

Materials and Methods

We defined minimum viable product requirements centered around Android and iOS platform availability, data capture specifications and project initiation workflow, study data synchronization, and data security. After launch, we added features based on feedback from end-users and REDCap administrators. We prioritized new features based on expected impact, difficulty, and anticipated long-term cost for sustainability.

Results

We chose Apache Cordova, a combined iOS and Android development framework, based on targeted end-user technology expectations, available programmer resources, and the need to provide solutions for resource-limited settings. The REDCap Mobile Application was launched in 2015, has been enabled at over 800 REDCap Consortium partner organizations, and has supported diverse scientific studies around the world.

Discussion

Apache Cordova enabled early software releases for both iOS and Android, but required ongoing optimization efforts to improve software responsiveness. Developing a robust and efficient mobile device synchronization architecture was difficult without direct access to global network infrastructures for testing. Research teams in sub-Saharan Africa helped our development team understand and simulate real-world scenarios of intermittent internet connectivity.

Conclusion

Guidance recommendations based on designing, developing, deploying, and disseminating the REDCap Mobile Application may help other teams looking to develop clinical research informatics applications.

Mobile electronic versus paper case report forms in clinical trials: a randomized controlled trial

BACKGROUND

Regulations, study design complexity and amounts of collected and shared data in clinical trials render efficient data handling procedures inevitable. Recent research suggests that electronic data capture can be key in this context but evidence is insufficient. This randomized controlled parallel group study tested the hypothesis that time efficiency is superior when electronic (eCRF) instead of paper case report forms (pCRF) are used for data collection. We additionally investigated predictors of time saving effects and data integrity.

METHODS

This study was conducted on top of a clinical weight loss trial performed at a clinical research facility over six months. All study nurses and patients participating in the clinical trial were eligible to participate and randomly allocated to enter cross-sectional data obtained during routine visits either through pCRF or eCRF. A balanced randomization list was generated before enrolment commenced. 90 and 30 records were gathered for the time that 27 patients and 2 study nurses required to report 2025 and 2037 field values, respectively. The primary hypothesis, that eCRF use is faster than pCRF use, was tested by a two-tailed t-test. Analysis of variance and covariance were used to evaluate predictors of entry performance. Data integrity was evaluated by descriptive statistics.

RESULTS

All randomized patients were included in the study (eCRF group n = 13, pCRF group n = 14). eCRF, as compared to pCRF, data collection was associated with significant time savings  across all conditions (8.29 ± 5.15 min vs. 10.54 ± 6.98 min, p = .047). This effect was not defined by participant type, i.e. patients or study nurses (F_(1,112) = .15, p = .699), CRF length (F_(2,112) = .49, p = .609) or patient age (Beta = .09, p = .534). Additional 5.16 ± 2.83 min per CRF were saved with eCRFs due to data transcription redundancy when patients answered questionnaires directly in eCRFs. Data integrity was superior in the eCRF condition (0 versus 3 data entry errors).

CONCLUSIONS

This is the first study to prove in direct comparison that using eCRFs instead of pCRFs increases time efficiency of data collection in clinical trials, irrespective of item quantity or patient age, and improves data quality.

TRIAL REGISTRATION

Clinical Trials NCT02649907 .

Risk Factors for Smoking in Rural Women: The Role of Gender-Based Sexual and Intimate Partner Violence

BACKGROUND

Women living in Ohio Appalachia experience cervical cancer at disproportionately high rates. Intimate partner and sexual gender-based violence (GBV) and smoking are independent risk factors for cervical cancer and interact to heighten risk. Appalachian women smoke at higher rates than other Ohio women, but little is known about GBV exposure in the region. The purpose of this study was to establish prevalence of women's exposure to GBV in Ohio Appalachia and examine the association between GBV and smoking among women in the region.

METHODS

A two-phase address-based random sampling approach was used in three purposefully selected Ohio Appalachian counties to identify women to complete an interviewer administered cross-sectional survey (n = 398). The primary exposure variable was GBV Index Score, a 4 level indices representing increasing exposure to eight abuse types. Correlation analysis and logistic regression were used to examine smoking correlations and risk.

RESULTS

Almost 57% of women in the three selected Ohio Appalachian counties experienced GBV, with rate increasing to 77.5% among current smokers. The distribution of the GBV Exposure Index Score was significantly different across smoking status (p < = 0.0001), with exposure of GBV increasing when moving from never, to former, to current smokers. When controlling for depression, age, and adult socioeconomic position, GBV Exposure Index was significantly associated with current smoking behavior (OR:1.62, 95% CI [1.21-2.17]).

DISCUSSION

Professionals working to reduce disparate disease burden among women in Ohio Appalachia should consider the role GBV plays in health behavior and behavioral change interventions, including smoking and smoking cessation.

Software and hardware infrastructure for research in electrophysiology

As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

Clinical research informatics: a conceptual perspective

Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.

The clinical assessment and remote administration tablet

Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject’s response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use.