Research Project
Using the multiphase optimization strategy for the development of a culturally tailored digital resilience-building intervention…
The specific aims are to (1) conduct a qualitative analysis of semi-structured interviews with religious leaders to identify barriers and facilitators to discussing ACP and death-related topics among Asian Americans with cancer; (2) conduct a usability test of a culturally tailored digital resilience-building intervention prototype to collect feedback on the intervention’s content using think-aloud interviews with Asian American patients with cancer and family caregivers; and (3) conduct a process evaluation to determine the feasibility, acceptability, and appropriateness of the intervention prototype. The expected outcomes are identification and development of the essential components of the intervention and data that contribute to a revised version of the intervention to be optimized in future research that follows the second phase of MOST.
- Principal Investigator
- Longcoy, Li-Ting H.
- Start Date
- 2023-07-01
- End Date
- 2025-06-30
Abstract
Psychoneurological (PN) symptoms such as pain, fatigue, depression, anxiety, and sleep disturbance are the most distressing symptoms among cancer survivors. These symptoms have a detrimental impact on cancer survivors’ functional status and quality of life. Inter-individual variability of the experience of PN symptoms has been reported in cancer survivors. It is critical to identify individualized biomarkers to better understand the inter-individual variability of PN symptoms. Genetics and metabolomics are promising omics approaches for understanding symptom phenotypes. Recently, our research group identified three genes regulating the hypothalamic-pituitary-adrenal axis associated with PN symptoms among women with breast cancer. We also identified 10 functional genetic polymorphisms to serve as stable common data elements for PN symptoms. In addition, metabolomics provides a more accurate representation of phenotypes and reflects metabolic changes resulting from genetic variation and environmental stimulation. The untargeted metabolomics profiling can provide a holistic understanding of mechanisms underlying PN symptoms. To our knowledge, no previous studies have integrated genomic and metabolomic data to understand PN symptoms among cancer survivors. Consequently, the purpose of the proposed study is to combine genetic and metabolomics data and apply a machine learning technique to better understand inter-individual variability in PN symptoms among cancer survivors. This study will leverage symptom data (n=82) currently being collected among cancer survivors under a K23 study and will include collection of new biospecimen samples. Results from this study will better explain individual differences and mechanisms underlying PN symptoms and will serve as a foundation for future interventions studies to manage symptoms.