Identifying confidence in recovering balance following destabilizing events is essential for helping older adults prevent falls. However, existing measures primarily assess balance confidence. The Balance Recovery Confidence (BRC) scale was developed to address this gap, but its length may limit clinical feasibility. This study aimed to develop and validate a Short-Form Balance Recovery Confidence (SF-BRC) scale that retains the psychometric integrity of the original version while improving practicality.

A cross-sectional dataset comprising 309 older adults (≥ 65 years) was analyzed. Exploratory factor analysis using unweighted least squares extraction on polychoric correlations was conducted to determine the underlying factor structure. Confirmatory factor analysis using the weighted least squares mean and variance adjusted (WLSMV) estimator evaluated model fit. Rasch modeling assessed item fit, item difficulty, and person separation reliability. Criterion and convergent validity were examined through correlations with the original BRC, Activities-specific Balance Confidence (ABC), and Falls Efficacy Scale–International (FES-I) scales.

The final 7-item SF-BRC demonstrated a unidimensional structure with excellent model fit (CFI = 0.99, TLI = 0.99, RMSEA = 0.06, SRMR = 0.02). Internal consistency was high (α = 0.94), and Rasch person separation reliability was 0.93. Criterion validity with the full BRC was strong (r = 0.99), while convergent validity with ABC and FES-I scales was moderate, supporting theoretical distinctiveness of the construct.

The SF-BRC is a psychometrically robust instrument for assessing BRC. Its strong reliability and practical utility support its use in rehabilitation, fall prevention, and community-based settings.

Asthma is a prevalent chronic pediatric condition associated with significant health disparities. The Better Asthma Control for Kids (BACK) program aims to reduce asthma disparities and improve asthma control for children in high-need schools in four regions of Colorado.

We conducted in-depth, semi-structured interviews (Dec 2023–June 2024) with key roles involved in BACK including school nurses, asthma navigators, and caregivers of participating students with asthma. Interviews and rapid qualitative analysis were informed by the Practical Robust, Implementation, and Sustainability Model (PRISM). We gathered perspectives, feedback, and recommendations about BACK to inform adaptation of the intervention and implementation strategy packages.

Participants (n = 39) included 6 asthma navigators, 17 school nurses, and 16 caregivers. Four overarching themes emerged: 1) perceived benefits of the BACK program, 2) challenges with school nurse engagement, communication, and perceptions of BACK, 3) difficulty with identification, documentation, and enrollment of students with asthma at the beginning of the school year, and 4) mismatches in program scope and alignment with school and family contexts.

Identifying key challenges and participant recommendations supported the research team’s “adapt and tailor to context” strategy in annual rapid evaluation and planning cycles. Obtaining feedback from program adopters, implementers, and recipients led to complementary recommendations to improve program delivery and user experiences. This approach may be transferable to other implementation-effectiveness trials, particularly those in schools or with other natural pauses that facilitate annual iterations in program delivery.

Clinicaltrials.gov identifier NCT06003569, registered on August 22, 2023, https://clinicaltrials.gov/study/NCT06003569

Reproducibility, consistency, and transparency are essential to responsible and ethical scientific inquiry, though practices supporting these qualities are often neglected. However, in many cases data are confidential or otherwise unable to be shared publicly. This tutorial describes a method utilizing generative adversarial networks (GANs) to create synthetic data that are sufficiently similar to the original dataset in such cases where the source data cannot be shared or where the source data are too sparse as to internally validate results.

Utilizing an exemplar study that aimed to create a clinical prediction model employing a novel echocardiographic measurement to differentiate between acute coronary syndrome and Takotsubo syndrome, we demonstrate the procedure of fitting a GAN and evaluating the resulting synthetic dataset against the results from the source dataset using conventional analytic methodologies. Further, we include relevant R code and output from this process to aid in implementation.

The procedure we detail yielded a synthetic dataset that was largely similar to the source data used in univariate descriptive statistics, significance testing comparing variables across datasets, data visualizations, and yielded largely comparable secondary model fit and accuracy metrics.

We demonstrated that through the implementation of a well-tuned GAN, synthetic data can be generated as a sufficiently faithful simulacrum of the source data for the purposes of internal validation, transparency of method, and reproducibility of analytic results.

It remains unclear whether the US clinical trial ecosystem is optimized to evaluate medical interventions efficiently. This study characterizes interventional clinical trials in the USA and examines trial progress over five years.

Using the Aggregate Analysis of ClinicalTrials.gov (AACT) database, we conducted a cross-sectional study of interventional trials initiated in 2023 and a follow-up cohort tracking five-year completion for trials started in 2018 and registered in ClinicalTrials.gov as of 05/01/2024 by sponsor and therapeutic area. Trials with at least one US site were included. Primary outcomes were enrollment, completion status, intervention type, study arm design, and plan to share individual participant data.

Over 7,500 trials met inclusion criteria for each cohort. Most trials started in 2018 (68.4%) and 2023 (62.5%) enrolled fewer than 100 participants. Median enrollment in 2023 was higher for NIH (90) than industry-sponsored (76) trials. Within five years, 60.5% of the 2018 trials were completed, with higher completion among industry (60.9%) than NIH-sponsored (54.3%) trials. In 2023, industry predominantly funded cancer studies and trials testing drug interventions, whereas the NIH prioritized mental health studies and behavioral interventions. More than one-quarter of trials used a single-group design, 72.0% did not plan to share participant-level data, and 72.6% of drug trials were early phase.

Many clinical trials are small, lack a control group, and are incomplete within five years, with differences by sponsor. Given the urgent need to improve health through rigorous evidence generation, there are likely opportunities to improve the US clinical trial ecosystem.

Clinical researchers at U.S. academic health centers are becoming more attuned to the perspectives and values of research participants, seeking to partner with them to enhance their satisfaction and improve recruitment strategies.

We surveyed current or recent participants on their perspectives about the return of study results. Through a multi-site consortium of academic medical centers assessing the experiences of research participants using an online satisfaction survey, we added three questions to our institution’s version of the survey to assess the value placed on return of research results (RoR) to current or recent adult participants. Survey participants were offered anonymous participation using four different recruitment mechanisms (“sites”) hosted by our institution. Most recruitment was disease-agnostic.

A total of 506 heterogeneous respondents completed the survey. Although differences were found across recruitment sites, 73% of all participants desired and 49% expected to receive their own RoR, while 61% expected to receive the study’s aggregate results. The importance of receiving their own results was especially salient for respondents from historically underserved communities, identifying as non-white, Latino/Hispanic, primarily Spanish-speaking, older or less educated. Respondents’ sex was not a significant factor in preferences for return of results.

Our results indicated our research participants’ expectations and perceived value of receiving the results from studies in which they participated, especially their individual results. This study provides direct evidence of the desires of our research participant community and suggests that institutional support for the return of study results would better serve participants’ interests and expectations in future research.

Team science, defined as scientific collaboration across disciplines and knowledge domains, has become essential for translational research, yet teams face recurrent challenges that can impede progress and are often difficult to overcome without additional support. We describe the implementation and initial outcomes of our Team Advice and Consultation Service (TACS), a program designed to address team science challenges at a research-intensive academic medical center engaged in translational science across a broad range of disciplines.

Grounded in Knowledge-to-Action framework and Transdisciplinary Innovation theory, TACS provides tailored, case-specific support across the team lifecycle.

Through thematic analysis of consultations with seven teams (2023–2024), we identified five recurrent challenge domains: organization/structural complexity, team leadership and management, team dynamics and communication, authorship and credit allocation, and conflict resolution.

Findings underscore the value of structured support for team science and provide insights and potential strategies for institutions seeking to implement similar services.

Funders increasingly emphasize the ethical imperative to return research results, yet researchers often lack training and clear strategies for effectively sharing findings with lay audiences. While publishing in academic journals is standard practice for dissemination, little guidance exists on translating findings for communities, particularly in rural areas. This qualitative community-guided pilot project aimed to explore and strengthen strategies for sharing study results in accessible ways.

The Penn State Clinical and Translational Science Institute conducted six semi-structured focus groups in Fall 2023 with geographically dispersed Pennsylvanians. Focus groups introduced participants with and without prior research experience to evidence-based and novel dissemination methods – such as lay summaries and data walks – to gather feedback on preferences and experiences. Data were coded and analyzed using MAXQDA, achieving strong interrater reliability (kappa > 0.70). Themes were developed inductively.

Focus group participants (N = 45) were predominantly women (N = 39, mean age = 56); 10% identified as Black/African American. Geographically, 49% were rural, 44% suburban, and 7% urban. Major themes included lack of effective communication in the research process, poor representation, and limited access to results. Most participants had never received study findings. Participants preferred receiving easy-to-understand summaries shared by individuals with established community relationships. They also found data walks, where researchers bring key findings printed on posters to community events, to be especially engaging and valuable.

Community-informed dissemination approaches can increase research transparency, engagement, and results translation in communities, particularly in rural areas where accessibility is limited.

This paper presents the development and psychometric analyses of the Mentee Competency Self-Assessment (MCSA), a tool designed to evaluate mentee skills in research mentoring relationships and assess the Mentoring Up curriculum. By assessing mentee competencies across diverse settings, the study aims to enhance understanding of mentoring dynamics and highlight the importance of considering both mentor and mentee perspectives in assessing mentorship effectiveness.

The 26-item MCSA instrument was developed based on the validated Mentoring Competency Assessment (MCA) to evaluate the Mentoring Up curriculum. Data was obtained from 401 mentees who attended Mentoring Up workshops between 2015 and 2022. Principal component analysis (PCA) was performed with varimax rotation to examine the internal structure of the MCSA. A team of mentoring experts independently reviewed and reached consensus on component alignment. Confirmatory factor analysis (CFA) and internal consistency were performed to assess construct validity and reliability.

Factor and reliability analyses support an eight-component structure of a 22-item MCSA. All parameter estimates were significant, and the components demonstrated acceptable to high internal consistency (Cronbach’s alpha = 0.58–0.90).

The final 22-item scale (MCSA-22) aligns with eight competencies and is now suited for measuring mentee mentorship skills. Given the modest sample size and other study limitations, replication of this proposed modification of the MCSA is an important next step. Additional recommendations for future scale development are offered.

The National Dental PBRN Central Institutional Review Board (CIRB) at the University of Alabama-Birmingham (UAB) was established in 2014 to reduce administrative burden while allowing Local Context Review (LCR) by local institutions (LIs). The National Institutes of Health implemented a single IRB policy in 2018; CIRB meets its requirements.

The CIRB reviews study protocols, documents, consent forms, and HIPAA forms. Once CIRB approval is obtained, the study packet is sent to LIs for LCR. LIs may revise specific sections agreed on in reliance agreements but must make determinations regarding the HIPAA Privacy Rule. Once a LI completes a reliance agreement and the LCR, the CIRB becomes responsible for subsequent reviews.

The CIRB has reviewed 27 studies, 8 of which were declared exempt; LIs are responsible for exempt studies. Nineteen studies were declared expedited or full review. For all expedited/full review studies combined, the mean (SD) calendar days from initial submission to approval was 55.2 (35.2) days; for studies with available tracking, 35% of this time was due to waiting on study investigator responses. Post-approval submissions for 19 studies, such as revision amendments, continuing reviews, site amendments, prompt reports, and personnel amendments, totaled to 374.

The CIRB did not ensure a shorter approval time because LCR approvals must wait for initial CIRB approval, but LIs did benefit by starting with an already-approved packet. However, substantial benefits were realized later because subsequent amendments and annual reviews only required CIRB review, saving LIs from involvement in these reviews.

Emerging evidence describes the experiences of individuals participating in health research, but insights into the barriers and motivations around research participation in rural communities are limited. We developed and administered a human-centered, evidence-informed survey to assess motivators and barriers to research participation among adults in Pennsylvania.

The online survey captured differences between individuals with and without prior research participation and living in rural and urban settings. We hypothesized that individuals with prior research experience would report different motivators and barriers than those who had never participated in research. We also anticipated that rural and urban respondents would differ in their reported motivators and barriers to participation.

Participants (n = 284, 75% female, 63% urban, 73% with prior research) completed the survey in spring of 2025. Overall top motivators to research participation included a willingness to “contribute to knowledge and medicine,” to “help others,” to “make a difference,” “because the research was personally important,” and “financial compensation.” Top barriers included an “inconvenient research site,” “limited transportation access,” and “time/work constraints.” A variety of motivators and barriers differed by prior research experience. There were no significant differences between the proportion of rural and urban prior research participants who endorsed any of the motivators or barriers. Rural, non-research participants drew greater motivation from “family influence” and “volunteering commitment.”

The results of this work can inform the development of targeted strategies to improve research engagement, particularly among rural populations.

Researchers have traditionally engaged Community Advisory Boards (CABs) to meaningfully involve community members in the research process. However, CABs are often formed only after a grant is funded, leading to criticisms of functioning merely as “ethical approvers.” For substance use researchers, the inclusion of people with living experience (PWLE; current experience with drug use) across all stages of research is especially important as they have historically been the experts in the needs of their community.

In response to this need for community engagement in research, the University of Kentucky, in partnership with a local recovery community center (RCC; Voices of Hope), established the Survivors Union of the Bluegrass (SUB). The SUB is a stand-alone CAB (i.e., not tied to any particular research study) comprised of PWLE. Biannually, the SUB leadership team conducts quality improvement process evaluation focus groups with SUB members.

The SUB has held 33 monthly meetings with 52 university substance use research teams, all unanimously approved by members to attend. Members identified benefits to participating in the SUB, felt researchers treated them as experts, and supported other institutions establishing similar boards. Members also described feelings of belonging and empowerment, and valued being viewed as experts.

To our knowledge, the SUB is one of the first CABs comprised of PWLE housed at a university that is not tied to a specific study or project. The SUB’s high retention rate and member feedback demonstrate the benefit of meaningfully involving PWLE throughout the research process.

Persistent disparities in child health highlight the need for clinical and public health research approaches to identify and address risks with greater spatial precision. This study linked residence-and neighborhood-specific socio-environmental data to population-wide healthcare data to characterize pediatric hospitalization risk for every residential address in Cincinnati, Ohio.

We linked hospitalization data (07/01/2016–06/30/2022) to parcel-level housing data from the Hamilton County Auditor and Cincinnati Department of Buildings & Inspections and street-range crime data from the Cincinnati Police Department. Addresses were localized to 2010 census tracts to join variables from the US Census American Community Survey and Eviction Lab. Generalized random forest models estimated address-level hospitalization risk and birth-adjusted hospitalization risk, accounting for child residency using vital birth records. Model performance was assessed based on varying diagnostic thresholds; fairness was evaluated by census block-level racial demographics.

We matched 81.5% of hospitalizations to residential addresses. Among 77,077 addresses, 7.4% had ≥1 hospitalization. Our model performed well (ROC-AUC: 0.98–0.99; PR-AUC: 0.65–0.72) in characterizing high-risk addresses, with housing violations, violent crime, and market total value among top features. The birth-adjusted model also showed high performance (ROC-AUC: 0.92–0.93; PR-AUC: 0.65–0.78) and moderate agreement with the hospitalization risk model (κ = 0.43).

Our results highlight the potential of address-level modeling and multiscale data integration to build on traditional area-level analyses and advance precision population health. Future directions include geographic expansion, stakeholder engagement, and patient-level validation. This work offers a scalable approach to precisely identifying pediatric health risks, supporting targeted clinical and policy interventions.

Manual submission of clinical trial data to the ClinicalTrials.gov registry is labor-intensive and error-prone, contributing to variability in the completeness and consistency of registry entries. To explore whether recent advances in large language models could support this process, we developed ChatCT, a pilot retrieval-augmented system that drafts ClinicalTrials.gov registry elements.

We evaluated ChatCT-generated registry elements across three dimensions: 1. semantic similarity to the public ClinicalTrials.gov record, 2. formatting compliance with ClinicalTrials.gov requirements, and 3. coverage of key trial biomedical concepts.

ChatCT-generated registry elements were highly semantically similar to human-authored ClinicalTrials.gov records (median BERTScore F1 ≈ 0.82). Formatting compliance was high for structured elements, including Study Design (91% of required fields present; mean completeness 0.897) and Arms/Interventions (75%; 0.772), while narrative sections showed greater variability, including Outcome Measures (79%; 0.929) and Study Description (57%; 0.784). Ontology-based concept extraction and matching demonstrated consistently high precision, with scores ranging from 90% to 100%.

A retrieval-augmented large language model can generate ClinicalTrials.gov registry drafts that preserve essential protocol details and adhere to most formatting requirements. However, light post-processing (e.g., automated schema validation) remains necessary for full submission readiness. This proof-of-concept evaluation suggests that ChatCT-assisted drafting could support registry reporting by improving consistency between protocol documents and publicly reported trial information.

Evidence suggests that partnerships between researchers and patient communities result in clinical trials that better reflect the patient experience, but there are few documented and successful models of such partnerships. Within the clinical research landscape, the Duchenne Muscular Dystrophy (DMD) community has emerged as a positive outlier in this regard, having engaged in a research partnership over multiple years and in the approval of several disease-modifying therapies where there were previously none.

The successful partnership factors observed in the DMD community were mapped onto the three levels of the Behavior Change Wheel framework. This mapping created an academic model of patient community readiness for research partnerships. The academic model was further translated into a user-friendly, pragmatic model and blueprint.

The FOCUS Model outlines the development of a research partnership from the ground up, where each theme represents an essential component of the partnership structure. Accompanying this model is a discussion guide that communities can use in meetings or workshops to assess and enhance their readiness for research partnerships.

The FOCUS model and its blueprint offer generalizable approaches that other rare disease patient communities can leverage to foster effective and lasting partnerships with scientific, medical, and regulatory stakeholders. The approach proposed in this study has the potential to help both the clinical research community and rare disease patient communities overcome typical barriers to rare disease clinical development.