Introduction: Barriers to research participation by racial and ethnic minority group members are multi-factorial, stem from historical social injustices and occur at participant, research team, and research process levels. The informed consent procedure is a key component of the research process and represents an opportunity to address these barriers. This manuscript describes the development of the Strengthening Translational Research in Diverse Enrollment (STRIDE) intervention, which aims to improve research participation by individuals from underrepresented groups. Methods: We used a community-engaged approach to develop an integrated, culturally, and literacy-sensitive, multi-component intervention that addresses barriers to research participation during the informed consent process. This approach involved having Community Investigators participate in intervention development activities and using community engagement studios and other methods to get feedback from community members on intervention components. Results: The STRIDE intervention has three components: a simulation-based training program directed toward clinical study research assistants that emphasizes cultural competency and communication skills for assisting in the informed consent process, an electronic consent (eConsent) framework designed to improve health-related research material comprehension and relevance, and a "storytelling" intervention in which prior research participants from diverse backgrounds share their experiences delivered via video vignettes during the consent process. Conclusions: The community engaged development approach resulted in a multi-component intervention that addresses known barriers to research participation and can be integrated into the consent process of research studies. Results of an ongoing study will determine its effectiveness at increasing diversity among research participants.

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.

OBJECTIVE: To discuss the study design and data analysis for three-phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies. METHODS: We consider the design and analysis of three-phase ITS studies using a study funded by National Institutes of Health as an exemplar. The design and analysis of both one-arm and two-arm three-phase ITS studies are introduced. RESULTS: A simulation-based approach, with ready-to-use computer programs, was developed to determine the power for two types of three-phase ITS studies. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 with various effect sizes. The power increased as the sample size or the effect size increased. The power to detect the same effect sizes varied largely, depending on testing level change, trend changes, or both. CONCLUSION: This article provides a convenient tool for investigators to generate sample sizes to ensure sufficient statistical power when three-phase ITS study design is implemented.

Osteoporosis treatment rates are declining, even among those with past fractures. Novel, low-cost approaches are needed to improve osteoporosis care. We conducted a parallel group, controlled, randomized clinical trial evaluating a behavioral intervention for improving osteoporosis medication use. A total of 2684 women with self-reported fracture history after age 45 years not using osteoporosis therapy from US Global Longitudinal Study of Osteoporosis in Women (GLOW) sites were randomized 1:1 to receive a multimodal, tailored, direct-to-patient, video intervention versus usual care. The primary study outcome was self-report of osteoporosis medication use at 6 months. Other outcomes included calcium and vitamin D supplementation, bone mineral density (BMD) testing, readiness for behavioral change, and barriers to treatment. In intent-to-treat analyses, there were no significant differences between groups (intervention versus control) in osteoporosis medication use (11.7% versus 11.4%, p = 0.8), calcium supplementation (31.8% versus 32.6%, p = 0.7), vitamin D intake (41.3% versus 41.9%, p = 0.8), or BMD testing (61.8% versus 57.1%, p = 0.2). In the intervention group, fewer women were in the precontemplative stage of behavior change, more women reported seeing their primary care provider, had concerns regarding osteonecrosis of the jaw, and difficulty in taking/remembering to take osteoporosis medications. We found differences in BMD testing among the subgroup of women with no prior osteoporosis treatment, those who provided contact information, and those with no past BMD testing. In per protocol analyses, women with appreciable exposure to the online intervention (n = 257) were more likely to start nonbisphosphonates (odds ratio [OR] = 2.70; 95% confidence interval [CI] 1.26-5.79) compared with the usual care group. Although our intervention did not increase the use of osteoporosis therapy at 6 months, it increased nonbisphosphonate medication use and BMD testing in select subgroups, shifted participants' readiness for behavior change, and altered perceptions of barriers to osteoporosis treatment. Achieving changes in osteoporosis care using patient activation approaches alone is challenging. (c) 2018 American Society for Bone and Mineral Research.

OBJECTIVE: To develop an innovative and effective educational intervention to inform patients about the need for osteoporosis treatment and to determine factors associated with its online uptake. METHODS: Postmenopausal women with a prior fracture and not currently using osteoporosis therapy were eligible to be included in the Activating Patients at Risk for OsteoPOroSis (APROPOS). Four nominal groups with a total of 18 racially/ethnically diverse women identified osteoporosis treatment barriers. We used the Information, Motivation, Behavior Skills conceptual model to develop a direct-to-patient intervention to mitigate potentially modifiable barriers to osteoporosis therapy. The intervention included videos tailored by participants' race/ethnicity and their survey responses: ranked barriers to osteoporosis treatment, deduced barriers to treatment, readiness to behavior change, and osteoporosis treatment history. Videos consisted of "storytelling" narratives, based on osteoporosis patient experiences and portrayed by actresses of patient-identified race/ethnicity. We also delivered personalized brief phone calls followed by an interactive voice-response phone messages aimed to promote uptake of the videos. RESULTS: To address the factors associated with online intervention uptake, we focused on participants assigned to the intervention arm (n = 1342). These participants were 92.9% Caucasian, with a mean (SD) age 74.9 (8.0) years and the majority (77.7%) had some college education. Preference for natural treatments was the barrier ranked #1 by most (n = 130; 27%), while concern about osteonecrosis of the jaw was the most frequently reported barrier (at any level; n = 322; 67%). Overall, 28.1% (n = 377) of participants in the intervention group accessed the videos online. After adjusting for relevant covariates, the participants who provided an email address had 6.07 (95% CI 4.53-8.14) higher adjusted odds of accessing their online videos compared to those who did not. CONCLUSION: We developed and implemented a novel tailored multi-modal intervention to improve initiation of osteoporosis therapy. An email address provided on the survey was the most important factor independently associated with accessing the intervention online. The design and uptake of this intervention may have implications for future studies in osteoporosis or other chronic diseases.

BACKGROUND: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. RESULTS: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. CONCLUSION: The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.