One Mind

Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.

BackgroundThe COVID-19 pandemic has ignited wider clinical adoption of digital health tools, including mobile health apps (mHealth apps), to address mental and behavioral health concerns at a distance. While mHealth apps offer many compelling benefits, identifying effective apps in the crowded and largely unregulated marketplace is laborious. Consumer demand and industry productivity are increasing, although research is slower, making it challenging for providers to determine the most credible and safe apps for patients in need.Objectives/MethodsThis commentary offers a practical, empirically guided framework and associated resources for selecting appropriate mHealth apps for pediatric populations during the pandemic and beyond.ResultsIn the first stage, Narrow the target problem, end user, and contender apps. Beginning the search with continuously updated websites that contain expert app ratings can help expedite this process (e.g., Psyberguide). Second, Explore each contender app’s: (a) scientific and theoretical support (e.g., are app components consistent with health behavior change theories?), (b) privacy policies, and (c) user experience (e.g., through crowdsourcing feedback about app usability and appeal via social media). Third, use clinical expertise and stakeholder feedback to Contextualize whether the selected app is a good fit for a particular patient and/or caregiver (e.g., by considering age, race/ethnicity, ability, gender, sexual orientation, technology access), including conducting a brief self-pilot of the app.ConclusionYouth are increasingly turning to technology for support, especially during the pandemic, and pediatric psychologists must be primed to recommend the most credible tools. We offer additional recommendations for rapidly disseminating evidence-based apps to the public.

BackgroundIdentifying and engaging youth with early stage psychotic disorders in order to facilitate timely treatment initiation remains a major public health challenge. While advertisers routinely use the internet to directly target consumers, limited efforts have focused on applying available technology to proactively encourage help seeking in the mental health community. This study explores how one might take advantage of Google Adwords in order to reach prospective patients with early psychosis.MethodsA landing page was developed with the primary goal of encouraging help seeking individuals in New York City to contact their local early psychosis intervention clinic. In order to provide the best opportunity to reach the intended audience, Google AdWords was utilized linking over 2,000 manually selected search terms to strategically placed landing page advertisements. The campaign ran for 14 weeks between April 11th and July 18th 2016 with a total budget of $1427.ResultsThe ads appeared 191,313 times and were clicked on 4,350 times at a per-click cost of $.33. Many users took additional help seeking steps including obtaining psychosis specific information/education (n=1,918 / 44%), completing a psychosis self-screener (n=671 / 15%) and contacting the Early Treatment Program (n=57 / 1%).DiscussionDigital ads appear to be a reasonable and cost effective method to reach individuals who are searching for behavioral health information online. More research is needed to better understand the many complex steps between online search inquiries and making first clinical contact.

Advances in science frequently precede changes in clinical care by several years or even decades. To better understand the path to translation, we invited experts to share their perspectives at the 5th Annual One Mind Summit: “Science Informing Brain Health Policies and Practice,” which was held on May 24–25, 2016, in Crystal City, VA. While the translation of brain research throughout the pipeline—from basic science research to patient care—was discussed, the focus was on the implementation of “best evidence” into patient care. The Summit identified key steps, including the need for professional endorsement and clinical guidelines or policies, acceptance by regulators and payers, dissemination and training for clinicians, patient advocacy, and learning healthcare models. The path to implementation was discussed broadly, as well as in the context of a specific project to implement concussion screening in emergency and urgent care centers throughout the United States.

Traumatic brain injury (TBI) is a major public health issue exacting a substantial personal and economic burden globally. With the advent of "big data" approaches to understanding complex systems, there is the potential to greatly accelerate knowledge about mechanisms of injury and how to detect and modify them to improve patient outcomes. High quality, well-defined data are critical to the success of bioinformatics platforms, and a data dictionary of "common data elements" (CDEs), as well as "unique data elements" has been created for clinical TBI research. There is no data dictionary, however, for preclinical TBI research despite similar opportunities to accelerate knowledge. To address this gap, a committee of experts was tasked with creating a defined set of data elements to further collaboration across laboratories and enable the merging of data for meta-analysis. The CDEs were subdivided into a Core module for data elements relevant to most, if not all, studies, and Injury-Model-Specific modules for non-generalizable data elements. The purpose of this article is to provide both an overview of TBI models and the CDEs pertinent to these models to facilitate a common language for preclinical TBI research.