Journal of Clinical and Translational Science最新文献

Advancing community engagement and participatory research approaches necessitates shifting cultural norms. The paper describes a program designed to explicitly embed and reinforce a culture of engagement through resource allocation, modeling, and recognition that was initiated by a Clinical and Translational Science Institute Community Engagement Program CE Program. Resources were allocated to the relationship development process between researchers and community partners. Funded partnerships were provided with guidance to support the equitable distribution of resources. Partnerships received additional reinforcement through participation in a learning collaborative, intended to support community partnership development, model best practices in community engagement and to build a network of community engaged, and participatory researchers at the institution. Investigators reported the learning collaborative "gave them permission" to focus on the process. Overall, lessons learned indicate embedding and reinforcing practices that center relationship and reward time spent building partnerships is a promising strategy to buffer against cultural norms that favor outcomes and over process.

Introduction: Mentoring is an important developmental tool for academic scientists. The match between mentor and mentee is a critical factor than can influence outcomes for mentees. However, we know little about what factors should be considered when matching mentors and mentees. We draw on person-environment fit theory to examine how different factors related to the fit between mentors and mentees may influence outcomes for mentees in health-related scientific disciplines.

Methods: Data were collected from 76 mentor-mentee pairs who participated in a nine-month mentoring program for scientists interested in clinical and translational research. An index of supplementary fit was calculated to reflect mentor-mentee similarity in terms of race/ethnicity, gender, academic discipline, professional track, percent time allocated to research, and type of research. Complementary fit reflected the proportion of skills mentees identified as needs that their mentor felt comfortable providing. Mentee outcomes assessed included satisfaction with one's mentor, learning and development experiences, and short-term research output.

Results: As predicted, we found that supplementary fit was positively related to mentee satisfaction with the mentor. We found no support for the expected relationship between complementary fit and mentee learning development experiences or short-term research output. Supplementary analyses explored other non-hypothesized relationships among study variables.

Conclusions: This research underscores the need to consider different types of fit when matching academic mentors and mentees in clinical and translational science-related disciplines. Our results can be leveraged during the matching process in academic mentoring programs to maximize the success of mentoring relationships for scientists in health-related fields.

Evaluation teams have been critical to the success of Clinical and Translational Science Award (CTSA) programs funded by the National Center for Advancing Translational Sciences (NCATS). Given the limited resources often available to evaluation teams and the growing emphasis on impact evaluation and continuous quality improvement (CQI), CTSA programs may need to develop innovative strategies to build capacity for effectively implementing CQI and impact evaluation, while still tracking commonly reported metrics. To address this challenge, the Boston University (BU) Clinical and Translational Science Institute (CTSI) partnered with the BU Hariri's Software and Application Innovation Lab (SAIL) to develop a web-based digital tool, known as TrackImpact, that streamlines data collection, saving significant time and resources, and increasing evaluation team capacity for other activities. Time and cost saving analyses are used to demonstrate how we increased evaluation team capacity by using this innovative digital tool.

Numerous symposia and conferences have been held to discuss the promise of Artificial Intelligence (AI). Many center on its potential to transform fields like health and medicine, law, education, business, and more. Further, while many AI-focused events include those data scientists involved in developing foundational models, to our knowledge, there has been little attention on AI's role for data science and the data scientist. In a new symposium series with its inaugural debut in December 2024 titled AI for Data Science, thought leaders convened to discuss both the promises and challenges of integrating AI into the workflows of data scientists. A keynote address by Michael Pencina from Duke University together with contributions from three panels covered a wide range of topics including rigor, reproducibility, the training of current and future data scientists, and the potential of AI's integration in public health.

Introduction: Decentralized clinical trials (DCTs) are often hindered by challenges in remotely capturing biomarkers. To address this gap, we developed MyTrials, a mobile application integrated with REDCap, designed to facilitate the remote capture of biomarkers via Bluetooth-enabled remote patient monitoring (RPM) devices. The purpose of the present study was to evaluate the feasibility and acceptability of MyTrials among participants within a DCT design.

Methods: In this four-arm randomized trial, 47 participants were allocated to receive zero, one, two, or three RPM devices. Participants were asked to use their devices once per week for a total of four weeks to remotely provide biomarkers via MyTrials. Feasibility was assessed using objective metrics of successful biomarker submission (i.e., valid device data accompanied by a video confirming participant identity) alongside the participant-reported Feasibility of Intervention Measure (FIM). Acceptability was evaluated via the Acceptability of Intervention Measure (AIM) and the System Usability Scale (SUS).

Results: Among participants assigned at least one device, the successful biomarker submission rate was 74% across all study weeks. FIM and AIM scores exceeded prespecified feasibility benchmarks across all conditions except the zero-device condition. SUS scores consistently indicated high usability across all conditions (range: 77.29-94.29).

Conclusions: The MyTrials platform is a feasible and acceptable solution for remote biomarker capture in DCTs. These findings support the potential of MyTrials to advance remote data collection in clinical research.

Introduction: Traumatic brain injury (TBI) is a leading cause of disability and death. Both repetitive transcranial magnetic stimulation (rTMS) and Cerebrolysin (CRB) are promising therapies regulating neural plasticity. This study aimed to assess the changes in resting-state brain activity following CRB, rTMS, or combined CRB-rTMS therapy.

Methods: This secondary analysis of the CAPTAIN-rTMS trial analyzed eyes-closed segments from EEG recordings at 30 days (baseline) and 180 days (after treatment) respectively. We computed relative power spectral densities for delta, theta, alpha and beta frequency bands, for the entire scalp and different regions. We conducted neuropsychological assessments and evaluated the correlations between resting-state relative power spectral density values and neuropsychological assessment performance.

Results: We analyzed a total of 50 patients. For the entire scalp, we found statistically significant decreases in relative alpha power (p = 0.02) and significant increases in relative delta power (p = 0.02), further subgroup analysis showing differences between visits in the CRB + sham group (paired Cliff's δ = 0.6, p = 0.012 for Delta band, δ = 0.6, p = 0.064 for Alpha band). The differences were higher in the central (alpha p = 0.004, delta p = 0.002) and parietal (alpha p = 0.012, delta p = 0.03), and lower in the frontal (alpha p = 0.05, delta p = 0.026), temporal (alpha p = 0.065, delta p = 0.077), and occipital (alpha p = 0.064, delta p = 0.084) regions. Neuropsychological tests performance was negatively correlated with resting-state relative delta power, and positively correlated with alpha power.

Conclusion: We found overall slowing of brain electrical activity during recovery after TBI, which was further influenced by rTMS and CRB treatment. Resting-state relative power spectral densities correlate with neuropsychological measurements.

Background: IMARA-South Africa (SA) is an HIV/STI prevention program for adolescent girls and young women (AGYW) and their female caregivers (FC). We examined six implementation outcomes of IMARA-SA (acceptability, appropriateness, feasibility, reach, adoption, and sustainability) from the perspectives of study staff, investigators, and collaborators.

Methods: We used a sequential explanatory mixed-methods design. We administered surveys, hosted three focus group discussions with study staff/facilitators (n = 5), clinic staff (n = 3), and community advisory board members (n = 5), and conducted seven key informant interviews with investigators and study staff. We used descriptive statistics and rapid qualitative analyses, merging quantitative and qualitative data by implementation outcome to achieve triangulation.

Results: On 27 surveys analyzed, mean scores were highest for acceptability (2.8/3, SD = 0.6), appropriateness (2.7/3, SD = 0.5), and reach (2.7/3, SD = 0.5), followed by feasibility (2.1/3, SD = 0.5), adoption (3.8/5, SD = 0.3), and sustainability (5.9/7, SD = 0.8). All perceived the AGYW and FC to love the program, which fit well with South African culture and addressed AGYW's needs. The delivery site was deemed highly appropriate for reaching vulnerable populations. The lowest scoring items concerned time constraints (2.2/3, SD = 0.9), safety concerns (1.4/3, SD = 0.7), complexity (2.9/5, SD = 1.3), and cost (2.8/5, SD = 0.9). Qualitative participants attributed complexity and cost challenges to the research procedures, not the intervention. Participants proposed potential avenues for future implementation (e.g., schools, clinics) and interest in engaging males.

Conclusion: IMARA-SA is implementable. Findings reveal challenges with navigating trade-offs between implementation outcomes and surveys distinguishing between intervention and research activities. Findings can inform future delivery of IMARA-SA and similar programs regionally.

Introduction: The conduct of Clinical and Translational Research (CTR) requires the engagement of highly effective collaborative teams. Clinical and Translational Science Award hubs have employed team-building strategies to improve team processes and interpersonal relationships in CTR teams. As previously reported, the University of Wisconsin Institute for Clinical and Translational Research (UW-ICTR) team science core operationalized and implemented one such strategy: Collaboration Planning. Here, we report on optimization of that intervention and assessment of three outcomes: (1) Changes in clarity and confidence around team processes; (2) Value and usefulness; and (3) Plans for future behavior change.

Materials and methods: Collaboration Planning 2.0 improves upon our initial implementation by (1) optimizing the worksheet for flow, accessibility, and deeper discussion; (2) expanding the evaluation process; and (3) creating a facilitator training to support broad dissemination. We tested this iteration in 11 UW-ICTR pilot teams using pre- and post-session self-assessment surveys.

Results: Data indicated an increase in participants' clarity and confidence around all measured team processes except authorship. Ninety-one percent of participants found the intervention both valuable and useful. Participants indicated plans for future behavior change, including increased attention to team processes. To date, more than 400 individuals have completed the Collaboration Planning Facilitator Training, indicating a deep need in the community for tools for effective team-focused interventions.

Conclusion: These results provide evidence that Collaboration Planning is an effective, accessible, low-barrier intervention for improving team processes and interpersonal relationships in CTR teams. Future work includes expanded evaluation, greater personalization of the intervention, and self-administered facilitation.

Assessing the long-term impact of community-engaged research (CEnR) programs remains a significant challenge in translational science, such as those conducted by Clinical and Translational Science Awards (CTSAs). The Translational Science Benefits Model (TSBM) is a framework designed to evaluate impact across four health and social domains (clinical/medical, community, economic, and political/legislative). TSBM offers a comprehensive framework for evaluating CEnR projects, as it extends beyond short-term outcomes to highlight distal impacts and sustainable benefits. Progress reports from three Cincinnati CTSA CEnR programs (Community Leaders Institute [CLI; n = 170], Community Health Grant [CHG; n = 82], and Partnership Development Grant [PDG; n = 21]) completed between 2010 and 2023 were coded by three reviewers using the TSBM. As expected, CEnR programs primarily demonstrated community & public health benefits. Economic, policy, and clinical benefits were also identified, further amplifying the impact of this work. The adoption of frameworks like the TSBM could lead to a more standardized approach for evaluating the impact of CEnR programs and facilitate comparisons across CTSAs. Future studies that track the impact of CEnR programs on health and social systems could provide valuable insights into the long-term benefits of these initiatives.