Chronic diseases are ubiquitous and costly in American populations. Interventions targeting health behavior change to manage chronic diseases are needed, but previous efforts have fallen short of producing meaningful change on average. Adaptive stepped-care interventions, that tailor treatment based on the needs of the individual over time, are a promising new area in health behavior change. We therefore conducted a systematic review of tests of adaptive stepped-care interventions targeting health behavior changes for adults with chronic diseases. We identified 9 completed studies and 13 research protocols testing adaptive stepped-care interventions for health behavior change. The most common health behaviors targeted were substance use, weight management, and smoking cessation. All identified studies test intermediary tailoring for treatment non-responders via sequential multiple assignment randomized trials (SMARTs) or singly randomized trials (SRTs); none test baseline tailoring. From completed studies, there were few differences between embedded adaptive interventions and minimal differences between those classified as treatment responders and non-responders. In conclusion, updates to this work will be needed as protocols identified here publish results. Future research could explore baseline tailoring variables, apply methods to additional health behaviors and target populations, test tapering interventions for treatment responders, and consider adults’ context when adapting interventions.

Community-engaged research (CEnR) is now an established research approach. The current research seeks to pilot the systematic and automated identification and categorization of CEnR to facilitate longitudinal tracking using administrative data. We inductively analyzed and manually coded a sample of Institutional Review Board (IRB) protocols. Comparing the variety of partnered relationships in practice with established conceptual classification systems, we developed five categories of partnership: Non-CEnR, Instrumental, Academic-led, Cooperative, and Reciprocal. The coded protocols were used to train a deep-learning algorithm using natural language processing to categorize research. We compared the results to data from three questions added to the IRB application to identify whether studies had a community partner and the type of engagement planned. The preliminary results show that the algorithm is potentially more likely to categorize studies as CEnR compared to investigator-recorded data and to categorize studies at a higher level of engagement. With this approach, universities could use administrative data to inform strategic planning, address progress in meeting community needs, and coordinate efforts across programs and departments. As scholars and technical experts improve the algorithm’s accuracy, universities and research institutions could implement standardized reporting features to track broader trends and accomplishments.

Rapid development and deployment of diagnostic testing for COVID-19 have been a key component of the public health response to the pandemic. Out of necessity, academic and other clinical laboratories developed laboratory testing innovations for COVID-19 to meet clinical testing demands. In addition to constraints on local testing supplies and equipment, a rapidly changing regulatory framework created challenges for translational scientists. Illustrative examples of approaches used to develop laboratory tests during the early stages of the COVID-19 pandemic demonstrate effective team science approaches to this challenging clinical care and public health emergency. These experiences and the associated lessons learned are relevant to the development of public health response plans for future pandemics.

OBJECTIVES/SPECIFIC AIMS: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects one in 68 children. Children with ASD have 2 core areas of difficulty: social communication skills and restricted and repetitive interests and patterns of behavior. Children with social skills deficits are at higher risk of developing mental health problems, and underdeveloped social skills predict poorer quality of life in adulthood. Therapies have been developed to help people with ASD improve social abilities in childhood, often involving a clinician directly teaching social skills lessons, either one-on-one or in a group setting. However, children with ASD can become anxious when interacting with other people and have an intrinsic motivation to interact with technology. To capitalize on this interest, this research team developed a robot, the socially animated machine (SAM) to teach social skills to children with ASD. Previous research found that this intervention was feasible and enjoyable for children with ASD and average cognitive ability, and participants improved in complex emotion recognition following intervention. The purpose of this study was to determine whether participants of all IQ levels were motivated by the SAM intervention, and whether they improved on emotion identification, facial recognition, social skills, and adaptive behavior. METHODS/STUDY POPULATION: This study recruited 20 children with ASD ages 5–14. Children completed tasks measuring ASD symptoms, IQ, receptive language, facial recognition, and emotion identification and were assigned to the control group (nonemotion dance games with SAM robot) or the intervention group (emotion games with SAM robot). Parents and teachers completed questionnaires about the child’s social skills. Following the robot intervention, facial recognition, emotion identification, and social skills were measured again, and parents and children rated participant enjoyment during the robot interaction. RESULTS/ANTICIPATED RESULTS: Overall, parents and children in both groups rated the robot interaction as highly enjoyable and motivating (parent ratings: M=26.4 out of 30, child ratings: M=17.5 out of 20). There were no differences between groups on post-test measures when controlling for pre-test scores (all p>0.05). Both groups improved over time on emotion identification accuracy (intervention: M=13.0% improvement, t=2.57, p<0.05; control: M=10.2% improvement, t=2.38, p<0.05) and parent-rated social skills (intervention: pre-test M=113.8, post-test M=100.6, t=−3.37, p=0.01; Control: pre-test M=107.9, post-test M=89.0, t=−2.83, p<0.05; decrease in scores indicates improvement). Teachers saw a decrease in problem behaviors for the intervention group (pre-test M=127.4, post-test M=119.6, t=−3.79, p<0.01, decrease in scores indicates improvement). DISCUSSION/SIGNIFICANCE OF IMPACT: This study shows that children with ASD and all levels of cognitive ability enjoyed and were motivated by the SAM robot intervention. This is particularly important for children with ASD who often have difficulty with attention and motivation. Children who are intrinsically motivated by the learning process will be more likely to benefit from it; therefore, continuing to pursue the methodology of robot-based interventions with this population is a worthwhile endeavor.

The COVID-19 pandemic had an immediate impact on the lives and work of early-career researchers. We leveraged a cluster-randomized trial and compared survey data collected over two timepoints to explore whether these impacts persisted. Although more than a year had passed, 74% of participants reported that their research was affected in multiple ways in both 2020 and 2021. These data suggest that the effects of the pandemic on early-career researchers may be prolonged. Our findings additionally serve as an impetus to identify and implement solutions to early-career challenges that undoubtedly existed before the pandemic, but which COVID-19 brought into the spotlight.

Community research advisory councils (C-RAC) bring together community members with interest in research to support design, evaluation, and dissemination of research in the communities they represent. There are few ways for early career researchers, such as TL1 trainees, to develop skills in community-engaged research, and there are limited opportunities for C-RAC members to influence early career researchers. In our novel training collaboration, TL1 trainees presented their research projects to C-RAC members who provided feedback. We present on initial evidence of student learning and summarize lessons learned that TL1 programs and C-RACs can incorporate into future collaborations.

OBJECTIVES/SPECIFIC AIMS: Objective: Identify barriers and facilitators of positive patient-provider interactions for AAN patients. METHODS/STUDY POPULATION: Methods: Using a mixed-methods, longitudinal, patient-interview design, N = 29 (to date) women with a history of AAN have been enrolled. Each patient completed a diagnostic interview and standardized surveys to establish ED diagnosis, severity, and associated psychopathology. Transcribed, semi-structured qualitative interviews are assessed for common themes using content analysis methods. RESULTS/ANTICIPATED RESULTS: Anticipated Results: Patients aged 18 to 74 (M = 36.3, SD = 12.0), with mean BMI = 39.8 (22.7-61.1; SD = 11.3), and mean weight suppression (lbs) during their illness = 119.41 (SD = 69.3). Women reported a mean=12.75 years (0-37 years, SD = 10.5) treatment delay. Qualitative analysis revealed the following barriers in healthcare provider interactions: 1) experiences of provider weight-bias, 2) low specificity in ED screening questions, 3) lack of indicated screening procedures/diagnostic tests (orthostatic screening, EKG, food log, labs), and 4) provider praise of ED behaviors. Facilitators: 1) lower BMI at presentation, 2) provider education in EDs, 3) community-provider collaboration, and 4) patient self- and family- advocacy. DISCUSSION/SIGNIFICANCE OF IMPACT: Discussion: Unfortunately, while early intervention best predicts positive outcomes, higher-weight patients in this study experienced significant treatment delay. Translationally, it is taking too long for AAN patients to receive the right treatment at the right time. However, findings indicate that interventions at the provider level (increasing ED education, building community partnerships, increased adherence to screening protocols) could improve screening, diagnostic, and referral practices—and ultimately long-term outcomes for this unique patient population.

In recent years, there have been concerted efforts to better recruit, support, and retain diverse faculty, staff, and trainees in academic medicine. However, many institutions lack comprehensive and strategic plans to provide support to retain and recruit individuals from historically underrepresented groups. In this article, we itemize specific mechanisms through which institutions can support diverse individuals with the goal of improving inclusion and belonging in the workforce to better reflect the diversity of the intended patient and research participant population.

Racially and ethnically minoritized populations have been historically excluded and underrepresented in research. This paper will describe best practices in multicultural and multilingual awareness-raising strategies used by the Recruitment Innovation Center to increase minoritized enrollment into clinical trials. The Passive Immunity Trial for Our Nation will be used as a primary example to highlight real-world application of these methods to raise awareness, engage community partners, and recruit diverse study participants.

Dental service providers have limited capacity to identify strategies to implement evidence-based practices (EBPs). We developed a rigorous yet parsimonious scoping review approach to identify, select, and rate implementation strategies based on an oral health system context. From 153 strategies identified, we selected the top 11 strategies, which had a moderate level of support of evidence and where managers were the main actors. The main actions were to educate, remind, structure, and influence. Targets included dentists, dental hygienists, and assistants and managers from a large prepaid dental care delivery system. This approach responds to calls for rapid and innovative methods to implement EBPs in oral health.

This special communication provides an approach for applying implementation science frameworks to a Clinical and Translational Science Institutes (CTSIs) community engagement (CE) program that measures the use of implementation strategies and outcomes that promote the uptake of CE in research. Using an iterative multi-disciplinary group process, we executed a four-phased approach to developing an evaluation plan: 1) creating an evaluation model adapted from Proctor’s conceptual model of implementation research; 2) mapping implementation strategies to CTSI CE program interventions that support change in research practice; 3) identifying and operationalizing measures for each strategy; and 4) conducting an evaluation. Phase 2 employed 73 implementation strategies across 9 domains generated by the Expert Recommendations for Implementing Change project. The nine domains were used to classify each CE program implementation strategy. In Phase 3, the group used the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework to define measures for each individual strategy. Phase 4 demonstrates the application of this framework and measures Year 1 outcomes for the strategy providing interactive assistance, which we implemented using a centralized consultation model. This approach can support the CTSA program in operationalizing CE program measurement to demonstrate which activities and strategies may lead to benefits derived by the program, institution, and community.

Within Wisconsin, our residents experience some of the worst health disparities in the nation. Public reporting on disparities in the quality of care is important to achieving accountability for reducing disparities over time and has been associated with improvements in care. Disparities reporting using statewide electronic health records (EHR) data would allow efficient and regular reporting, but there are significant challenges with missing data and data harmonization. We report our experience in creating a statewide, centralized EHR data repository to support health systems in reducing health disparities through public reporting. We partnered with the Wisconsin Collaborative for Healthcare Quality (the “Collaborative”), which houses patient-level EHR data from 25 health systems including validated metrics of healthcare quality. We undertook a detailed assessment of potential disparity indicators (race and ethnicity, insurance status and type, and geographic disparity). Challenges for each indicator are described, with solutions encompassing internal (health system) harmonization, central (Collaborative) harmonization, and centralized data processing. Key lessons include engaging health systems in identifying disparity indicators, aligning with system priorities, measuring indicators already collected in the EHR to minimize burden, and facilitating workgroups with health systems to build relationships, improve data collection, and develop initiatives to address disparities in healthcare.