Given the recent trend of the athleisure lifestyle in the U.S., this study sought to examine female millennial consumers’ value perceptions for purchasing recycled polyester-made athleisure apparel. Using an inductive approach with grounded theory, the perceived green value (PGV) framework was applied to guide data collection through the in-depth interviews and data analysis. The findings of the study provide theoretical insights and sustainable solutions for the industry and academia. Five main values identified include functional value, social value, emotional value, conditional value, and epistemic value. Within these perceived values, 13 sub-themes emerged. Female millennials consider price and performance key deciding factors for purchasing recycled polyester-made athleisure apparel. However, this is contingent upon products being fashionable, comfortable, and versatile for numerous occasions. The perceived social value reaffirms that peers and family can strongly influence purchasing decisions. Female millennials are more likely to purchase from well-known sustainable companies whose products are aligned with their beliefs and are exempted from false advertising. Companies need to empower consumers with the necessary product information and environmental knowledge. Epistemic value relies on consumers being educated and aware of sustainable products and their benefits.

6506 Background: R policies for NGS testing vary widely among private and public insurers. While drug costs are the greatest challenge in personalized or precision medicine, cost and R are substantial barriers to genomic profiling with NGS. We examined variation in coverage and R for a cohort of cancer patients (pts) treated at a tertiary oncology center. Methods: An Institutional Review Board approved prospective registration protocol was activated with the objective of establishing a centralized longitudinal clinical, molecular phenotypic, and research data repository for pts diagnosed with cancer. Based on provider assessment of medical necessity, mutations in 68 cancer associated genes were analyzed. Evaluation of R for NGS was performed from Sept, 2014 through Jan, 2017, with use of CPT code 81455. R was analyzed based on: payer type; pt age; localized vs. metastatic disease; and actionability of data. Results: 588 pts with evaluable analytic cases, and NGS testing, with R results shown in the table below. For groups with >= 10 pts: R frequency was highest in managed care programs, either private or Medicare, and least frequent in non-HMO Medicare (p<.001). In pts receiving R, payments by private HMOs were highest (p<.02). NGS results with labelled drug indications were associated with less frequent R (26% vs. 35%; p<.05), and lower payments (mean of $358 vs. $567; p<.02) compared to other NGS results. Younger age was associated with more frequent R (38% in pts <60 years, 24% in pts >= 60 years; p<.005). Neither cancer diagnosis nor stage were significantly associated with R. Conclusions: One third of pts received some R for NGS testing. R was more frequent and higher in managed care programs, both private and Medicare. R was more likely for younger age pts, while actionable NGS results were associated with lower R. These data demonstrate the need for rational, transparent, and consistent R policies, along with a value-based R model for NGS across all payer groups. [Table: see text]

Her research interests in engineering education focus on the role of self-efficacy, belonging, and other noncognitive aspects of the student experience on engagement, success, and persistence and on effective methods for teaching global issues such as those pertaining to sustainability.

Background: In the era of personalized medicine, a major challenge is harnessing longitudinal data across the cancer care continuum, which includes multimodal data sets of biologic, molecular, and clinical information about patients (pts) and their tumors. There is a growing need for new computing analytics, such as machine learning–an important tool in healthcare bio-informatics. We report our approach to building cancer disease models in an unbiased manner through utilization of a causal machine learning and simulation platform. Methods: The Swedish Cancer Institute (SCI) Personalized Medicine Research Program (PMRP) is a prospective registration protocol with the objective of establishing a centralized longitudinal, molecular, and phenotypic data repository. Since 2014, over 1,030 pts have been enrolled, having undergone next-generation sequencing (NGS) profiling of their tumors. Of these pts, we identified 100 breast cancer pts who also have detailed longitudinal clinical annotation within our SCI Breast Cancer Registry. All de-identified data, variables, and data points in the multimodal data types are integrated into normalized data frames to include demographics, cancer risks, tumor specifications, tumor sequencing, initial and subsequent cancer treatments, and outcomes data. A reverse engineering approach, via the Reverse Engineering and Forward Simulation (REFS) platform, is being utilized, focusing on discovering the complex causal mechanisms that determine which therapies will produce the best outcomes for an individual pt. This method goes beyond traditional approaches that rely on data correlations to match treatments to pts. The breast cancer causal model uncovers many of the possible combinations of causal relationships that drive outcomes and enables “what if?” simulations of a variety of interventions, across pts, to determine optimal therapies. Performance metrics and model robustness will be explored using a stratified, n-fold (e.g., 10-fold) cross-validation procedure, which is designed to provide an unbiased estimate of model generalization to new observations. Results: The causal model and simulations can elevate the providers' abilities to better understand treatment responses based on pts' unique clinical data and mutational statuses; study different treatment options to optimize management; and understand the complex interactions among variables that lead to a range of treatment outcomes. Conclusions: Knowledge generated from the simulations of the disease model can potentially streamline and support the clinical decision-making process, to include molecular tumor board deliberations, and ultimately assist providers in arriving at optimal treatment recommendations for pts. Citation Format: Henry Kaplan, Anna Berry, Kristine Rinn, Erin Ellis, George Birchfield, Tanya Wahl, Xiaoyu Liu, Mariko Tameishi, J D. Beatty, Patricia Dawson, Vivek Mehta, Anna Holman, Mary Atwood, Shlece Alexander, Candy Bonham, Lauren Summers, Iya Khalil, Boris Hayete, Diane Wuest, Wei Zheng, Yuhang Liu, Xulong Wang, Thomas David Brown. Machine learning approach to personalized medicine in breast cancer patients: Development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5299.

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