Choosing an appropriate electronic data capture system (EDC) is a critical decision for all randomized controlled trials (RCT). In this paper, we document our process for developing and implementing an EDC for a multisite RCT evaluating the efficacy and implementation of an enhanced primary care model for individuals with opioid use disorder who are returning to the community from incarceration.

Informed by the Knowledge-to-Action conceptual framework and user-centered design principles, we used Claris Filemaker software to design and implement CRICIT, a novel EDC that could meet the varied needs of the many stakeholders involved in our study.

CRICIT was deployed in May 2021 and has been continuously iterated and adapted since. CRICIT’s features include extensive participant tracking capabilities, site-specific adaptability, integrated randomization protocols, and the ability to generate both site-specific and study-wide summary reports.

CRICIT is highly customizable, adaptable, and secure. Its implementation has enhanced the quality of the study’s data, increased fidelity to a complicated research protocol, and reduced research staff’s administrative burden. CRICIT and similar systems have the potential to streamline research activities and contribute to the efficient collection and utilization of clinical research data.

Recent revisions to the US Federal Common Rule governing human studies funded or conducted by the federal government require the provision of a “concise and focused” key information (KI) section in informed consent forms (ICFs). We performed a systematic study to characterize KI sections of ICFs for federally funded trials available on ClinicalTrials.gov.

We downloaded ICFs posted on ClinicalTrials.gov for treatment trials initiated on or after the revised Common Rule effective date. Trial records (n = 102) were assessed by intervention type, study phase, recruitment status, and enrollment size. The ICFs and their KI sections, if present, were characterized by page length, word count, readability, topic, and formatting elements.

Of the 102 trial records, 76 had identifiable KI sections that were, on average, 10% of the total length of full ICF documents. KI readability grade level was not notably different from other sections of ICFs. Most KI sections were distinguished by section headers and included lists but contained few other formatting elements. Most KI sections included a subset of topics consistent with the basic elements of informed consent specified in the Common Rule.

Many of the KI sections in the study sample aligned with practices suggested in the preamble to the revised Common Rule. Further, our results suggest that some KI sections were tailored in study-specific ways. Nevertheless, guidelines on how to write concise and comprehensible KI sections would improve the utility and readability of KI sections.

We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes.

We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression.

In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both p < 0.0001). Code-based rates were higher in men compared to women (p = 0.03), but NLP-based rates were similar by sex (p = 0.89). Code-based rates were similar by race and ethnicity, but NLP-based rates were higher in the White population compared to the Black and Asian populations (p = 0.001). Both types of rates increased significantly over time (both p values < 0.001).

The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.

Socially assistive robots (SARs) are a promising tool to manage children’s pain and distress related to medical procedures, but current options lack autonomous adaptability. The aim of this study was to understand children’s and caregivers' perceptions surrounding the use of an artificial intelligence (AI)-enhanced SAR to provide personalized procedural support to children during intravenous insertion (IVI) to inform the design of such a system following a user-centric approach.

This study presents a descriptive qualitative needs assessment of children and caregivers. Data were collected via semi-structured individual interviews and focus groups. Participants were recruited from two Canadian pediatric emergency departments (EDs) between April 2021 and January 2022.

Eleven caregivers and 19 children completed 27 individual interviews and one focus group. Three main themes were identified: A. Experience in the clinical setting, B. Acceptance of and concerns surrounding SARs, and C. Features that support child engagement with SARs. Most participants expressed comfort with robot technology, however, concerns were raised about sharing personal information, photographing/videotaping, and the possibility of technical failure. Suggestions for feature enhancements included increasing movement to engage a child’s attention and tailoring language to developmental age. To enhance the overall ED experience, participants also identified a role for the SAR in the waiting room.

Artificial intelligence-enhanced SARs were perceived by children and caregivers as a promising tool for distraction during IVIs and to enhance the overall ED experience. Insights collected will be used to inform the design of an AI-enhanced SAR.

Insufficient recruitment of groups underrepresented in medical research threatens the generalizability of research findings and compounds inequity in research and medicine. In the present study, we examined barriers and facilitators to recruitment of underrepresented research participants from the perspective of clinical research coordinators (CRCs).

CRCs from one adult and one pediatric academic medical centers completed an online survey in April-May 2022. Survey topics included: participant language and translations, cultural competency training, incentives for research participation, study location, and participant research literacy. CRCs also reported their success in recruiting individuals from various backgrounds and completed an implicit bias measure.

Surveys were completed by 220 CRCs. CRCs indicated that recruitment is improved by having translated study materials, providing incentives to compensate participants, and reducing the number of in-person study visits. Most CRCs had completed some form of cultural competency training, but most also felt that the training either had no effect or made them feel less confident in approaching prospective participants from backgrounds different than their own. In general, CRCs reported having greater success in recruiting prospective participants from groups that are not underrepresented in research. Results of the implicit bias measure did not indicate that bias was associated with intentions to approach a prospective participant.

CRCs identified several strategies to improve recruitment of underrepresented research participants, and CRC insights aligned with insights from research participants in previous work. Further research is needed to understand the impact of cultural competency training on recruitment of underrepresented research participants.

Greater than 40% of women are obese, a key risk factor for cardiometabolic, neurocognitive disease, mood disorders, and certain cancers. Obesity and unfavorable body composition can compromise physical and psychological health and well-being. Preliminary evidence demonstrates Meditative Movement (i.e., Tai Chi Easy) improves health outcomes and body composition among midlife/older women. This single-group pilot study explored relationships between well-being predictors related to body composition and associated behavioral risk factors in midlife/older women pre-to-post Tai Chi Easy intervention.

Eligible women 45–75 years old, participated in once-weekly 30-minute Tai Chi Easy classes over 8-weeks. Pre/post-intervention data included self-report surveys and on-site body composition. Multivariate linear regression models were fitted with putative predictor variables having correlations p-values of 0.20 or less with sleep quality and eating behaviors.

Participants (N = 36) (M age = 53.7) were White (80.4%) and attended ≥ 4 years of college (70.6%). Analyses resulted in one independent variable per model as a predictor of the dependent variables of sleep quality and emotional eating. Results indicated: (1) stress explained 13.4% sleep quality variance (F (2, 20) = 2.71, p = 0.09) and (2) self-compassion explained 42.1% emotional eating variance (F (2, 31) = 12.54, p < .01).

Findings suggest stress and self-compassion partially explain variance in the dependent variables of sleep quality and emotional eating, both associated behavioral risk factors of body composition. Additional research may guide interventions to test efficacy and examine mediators to improve well-being predictors, body composition, and associated behavioral risk factors among midlife/older women.

Community health workers and promotoras (CHW/Ps) have a fundamental role in facilitating research with communities. However, no national standard training exists as part of the CHW/P job role. We developed and evaluated a culturally- and linguistically tailored online research best practices course for CHW/Ps to meet this gap.

After the research best practices course was developed, we advertised the opportunity to CHW/Ps nationwide to complete the training online in English or Spanish. Following course completion, CHW/Ps received an online survey to rate their skills in community-engaged research and their perceptions of the course using Likert scales of agreement. A qualitative content analysis was conducted on open-ended response data.

104 CHW/Ps completed the English or Spanish course (n = 52 for each language; mean age 42 years SD ± 12); 88% of individuals identified as female and 56% identified as Hispanic, Latino, or Spaniard. 96%–100% of respondents reported improvement in various skills. Nearly all CHW/Ps (97%) agreed the course was relevant to their work, and 96% felt the training was useful. Qualitative themes related to working more effectively as a result of training included enhanced skills, increased resources, and building bridges between communities and researchers.

The CHW/P research best practices course was rated as useful and relevant by CHW/Ps, particularly for communicating about research with community members. This course can be a professional development resource for CHW/Ps and could serve as the foundation for a national standardized training on their role related to research best practices.

Housing instability is a social determinant of health associated with multiple negative health outcomes including substance use disorders (SUDs). Real-world evidence of housing instability is needed to improve translational research on populations with SUDs.

We identified evidence of housing instability by leveraging structured diagnosis codes and unstructured clinical data from electronic health records of 20,556 patients from 2017 to 2021. We applied natural language processing with named-entity recognition and pattern matching to unstructured clinical notes with free-text documentation. Additionally, we analyzed semi-structured addresses containing explicit or implicit housing-related labels. We assessed agreement on identification methods by having three experts review of 300 records.

Diagnostic codes only identified 58.5% of the population identifiable as having housing instability, whereas 41.5% are identifiable from addresses only (7.1%), clinical notes only (30.4%), or both (4.0%). Reviewers unanimously agreed on 79.7% of cases reviewed; a Fleiss’ Kappa score of 0.35 suggested fair agreement yet emphasized the difficulty of analyzing patients having ambiguous housing situations. Among those with poisoning episodes related to stimulants or opioids, diagnosis codes were only able to identify 63.9% of those with housing instability.

All three data sources yield valid evidence of housing instability; each has their own inherent practical use and limitations. Translational researchers requiring comprehensive real-world evidence of housing instability should optimize and implement use of structured and unstructured data. Understanding the role of housing instability and temporary housing facilities is salient in populations with SUDs.

Large, transdisciplinary research consortia have increasingly been called upon to address complex and challenging health problems. The National Institutes of Health’s (NIH) Environmental influences on Child Health Outcomes (ECHO) Program developed multisite collaboration strategies to promote impactful collaborative observational research on child health. Team science and implementation science offer theoretical and methodological structure to answer questions about the strategies that facilitate successful consortia. We sought to characterize the elements and conditions that influence the implementation of a complex, interdisciplinary longitudinal research program, ECHO.

Informed by the Practical, Robust, Implementation and Sustainability Model, our ethnographic research included semi-structured interviews with internal stakeholders and program evaluation metrics. We conducted template and matrix analysis and triangulated the qualitative and quantitative data to understand the implementation of ECHO.

Between February and May 2022, we conducted 24 virtual interviews with representatives from ECHO components. The main cross-cutting topics that emerged from thematic analysis were collaboration and team science; communication and decision-making; data processes and harmonization; and diversity, equity, and inclusion. Both the qualitative and secondary quantitative evaluation data provided insights into the reach, adoption, implementation, and effectiveness of the program.

A large, multidisciplinary research consortium such as ECHO has produced conceptual, instrumental, capacity building, and connectivity impact for internal and external stakeholders. Facilitators included infrastructure that supported collaboration and learning, alignment of data processes, and harmonization. Opportunities for enhanced impact include multidisciplinary, multimethod communication strategies, and alignment of research priorities.

Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.

Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.

CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features.

This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.

Older adults have a high disease burden but are often underrepresented in research studies due to recruitment and retention obstacles, among others. Geriatric research specialists have identified solutions to these challenges and designed frameworks to help other researchers. Our team utilized three frameworks to create an interactive webinar series aimed to educate research team members on Age-Friendly practices.

We recruited 40 non-aging-trained research team members to participate in a six-session, real-time webinar series from October to November 2022. Sessions were comprised of 20–30 minute didactics and 30–40 minute group discussions. Participants completed pre- and post-program surveys, commitment to change forms, and post-webinar session surveys. Responses were examined for strengths and areas for improvement. Wilcoxon signed-rank tests assessed differences in confidence scores.

Self-reported confidence scores improved after the webinar series. Most participants provided positive feedback and high likeliness to use what they learned and recommend the webinar to others. The strengths were practical tips, applicable tools, and real-world examples. The major area for improvement was information on industry-sponsored trials. The commitment to change responses varied from pledging to use more inclusive language to adapting materials to improve the consent process.

This interactive Age-Friendly Research webinar series was feasible and well received by participants. We created an Age-Friendly Research community fostering commitment to change clinical and translational research to be more inclusive of older adults. Future work will include more information on industry-sponsored trials and expand to other research centers.