Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed.

In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017−2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs.

We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender.

Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.

Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial.

We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders.

Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index (p < 0.0001) controlling for age and race/ethnicity.

Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.

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.

The time required to obtain Institutional Review Board (IRB) approval is a frequent subject of efforts to reduce unnecessary delays in initiating clinical trials. This study was conducted by and for IRB directors to better understand factors affecting approval times as a first step in developing a quality improvement framework.

807 IRB-approved clinical trials from 5 University of California campuses were analyzed to identify operational and clinical trial characteristics influencing IRB approval times.

High workloads, low staff ratios, limited training, and the number and types of ancillary reviews resulted in longer approval times. Biosafety reviews and the need for billing coverage analysis were ancillary reviews that contributed to the longest delays. Federally funded and multisite clinical trials had shorter approval times. Variability in between individual committees at each institution reviewing phase 3 multisite clinical trials also contributed to delays for some protocols. Accreditation was not associated with shorter approval times.

Reducing unnecessary delays in obtaining IRB approval will require a quality improvement framework that considers operational and study characteristics as well as the larger institutional regulatory environment.

Understanding how different symptoms co-occur and are correlated may provide insights into the pathophysiology of disease. The lack of group-to-individual generalizability of co-occurrence of symptoms was recently demonstrated by comparing intra-individual and inter-individual correlations in several psychological studies. Here, we investigate this phenomenon for lower urinary tract symptoms (LUTS).

We analyzed data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network Recall Study. Participants responded to questions about their urinary symptoms for 25 consecutive days. These questions queried urologic symptoms including storage (urinary urgency, frequency, nocturia, and urinary incontinence), voiding (slow/weak stream), and post-micturition (incomplete emptying and post-micturition dribble) symptoms. We calculated Pearson correlation coefficients and cosine similarity measures and compared distributions of intra-individual and inter-individual (cohort) metrics.

Among 234 participants, distributions of intra-individual measures were 10-fold wider than those of inter-individual correlations. There are pairs of questions with distributions of correlations and cosine similarities containing individuals with extreme positive (>0.8) and extreme negative values (<–0.8). There are groups of participants with strong positive and negative correlations of urinary frequency and nocturia, urinary incontinence and weak flow, as well as strong negative and positive correlations of urinary frequency and dribbling. Information on these extreme groups is averaged out and lost in the inter-individual correlations.

Lack of group-to-individual generalizability previously shown for psychological symptoms is confirmed for LUTS. Wealth of information on the co-occurrence and co-evolution of LUTS in the intra-individual correlations and cosine similarities corroborates heterogeneity of LUTS and can be useful for deep phenotyping and for identifying personalized treatments of LUTS.

Ensuring equitable access to health care is a widely agreed-upon goal in medicine, yet access to care is a multidimensional concept that is difficult to measure. Although frameworks exist to evaluate access to care generally, the concept of “access to genomic medicine” is largely unexplored and a clear framework for studying and addressing major dimensions is lacking.

Comprised of seven clinical genomic research projects, the Clinical Sequencing Evidence-Generating Research consortium (CSER) presented opportunities to examine access to genomic medicine across diverse contexts. CSER emphasized engaging historically underrepresented and/or underserved populations. We used descriptive analysis of CSER participant survey data and qualitative case studies to explore anticipated and encountered access barriers and interventions to address them.

CSER’s enrolled population was largely lower income and racially and ethnically diverse, with many Spanish-preferring individuals. In surveys, less than a fifth (18.7%) of participants reported experiencing barriers to care. However, CSER project case studies revealed a more nuanced picture that highlighted the blurred boundary between access to genomic research and clinical care. Drawing on insights from CSER, we build on an existing framework to characterize the concept and dimensions of access to genomic medicine along with associated measures and improvement strategies.

Our findings support adopting a broad conceptualization of access to care encompassing multiple dimensions, using mixed methods to study access issues, and investing in innovative improvement strategies. This conceptualization may inform clinical translation of other cutting-edge technologies and contribute to the promotion of equitable, effective, and efficient access to genomic medicine.

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.

The National Institutes of Health (NIH) Institutional Development Award (IDeA) program was created to build capacity and enhance research in states with historically low levels of NIH funding. IDeA Clinical and Translational Research (CTR) networks are focused on building statewide and regional capacity to conduct biomedical research. The tracking and evaluation component of each CTR is tasked with collecting data to facilitate continuous improvement and measure impact.

This paper presents findings from a survey conducted with IDeA-CTR evaluators examining the following questions: 1) To what extent do evaluators use meta-evaluative practices and how does meta-evaluation inform their evaluation? and 2) What challenges evaluators face in their evaluation planning and implementation?

Findings show that 50% of CTRs conducted some form of meta-evaluation. Further, quantitative and qualitative responses tell a compelling story of the challenges in translational research evaluation. The most prominent were the development of feasible and useful data management systems, the selection and endorsement of program-wide impact metrics, and the promulgation of realistic expectations regarding feasibility and utility for recipients of the evaluation, including expectations for project impacts that lead to systemic change.

Findings suggest the importance of internally adopting a participatory, collaborative approach to evaluation and externally sharing insights with and adopting strategies from fellow evaluators within a learning community. This study promotes the value of conducting meta-evaluation in CTR settings, demonstrates means for and results from doing so, and shares best practices for addressing challenges encountered by many CTR evaluators.

Interdisciplinary collaboration is essential for addressing scientific challenges, particularly in integrated fields like mobile health (mHealth), which combines computer communication and medicine to deliver healthcare services. The formation of collaborative relationships in such field is an emerging topic, with conversations among interdisciplinary scholars serving as a critical indicator of relationship development. This study aims to examine the specific effects of different conversation types (research or social oriented) on interdisciplinary collaboration and explore the impact of communication mode.

We tracked conversations among interdisciplinary scholars participating in a 15-day hybrid mHealth training program, which uniquely captures both scholars’ conversation networks and the conversation quality. Three types of conversation networks were recorded (topics about current research, future research, or small talk). Using longitudinal network models, we compared the effect of different types of conversation quality on network formation and evaluated the interaction between conversation quality and communication mode (in-person or online).

We found that the quality of social conversations on non-research-related topics had robust effects in promoting the formation of interdisciplinary communications. In-person communication is more conducive for current and future research conversations, while online communication is valued for small talk.

This study highlights the power of perception of personal conversation in interdisciplinary collaboration formation. The diverse effects of communication mode on different conversation networks are revealed. Our findings offer valuable insights for the event designs of interdisciplinary training program.

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.