Access and Outcomes - Active Research
Exploring Allostasis, Cellular Aging, and Cancer Outcomes (Allegiant) Study
PI: Margaret Rosenzweig
Funding source: Genentech
Dates: 2020 - 2026
Description: In this mixed methods approach, Dr. Margaret Rosenzweig and her team quantify how social, cultural and physical factors (including race, racism, poverty, trauma and neighborhood deprivation) comprising allostatic load influences subsequent disease outcomes directly, or are moderated through patient physical, emotional and financial distress. This examination provides risk metrics and patient voice for the creation and testing of a personalized and proactive assessment and supportive intervention protocol. A previous project specifically focused on non-small-cell lung carcinoma (NSCLC) patients. The project was recently renewed and expanded to include all patients in the region with cancers in which survival disparities are present.
Related Studies: The Influence of Life Stress on Advanced Lung Cancer Outcomes (L-SOALCO)
Ethically focused multimodal AI models for precision treatments of breast cancer
Co-I: Margaret Rosenzweig; PI: Shandong Wu
Funding source: NIH/Office of Data Science
Dates: 2025-2026
Description: The Advancing Health Research through Multimodal AI Initiative aims to develop data-driven multimodal AI approaches to more closely model, interpret, and predict complex biological, behavioral, and health systems and enhance our understanding of health.
Related Studies: N/A
The Cancer Advocacy, Resources, and Equity (CARE) Navigation Partnership
PI: Margaret Rosenzweig
Funding source: Pitt Un-Challenge
Dates: 2026
Description: This study aims to use a variety of methods including a community social needs assessment survey and qualitative data collection methods to identify resources being used by cancer patients in ten neighborhoods in the east end of Pittsburgh and the eastern suburbs.
Related Studies: N/A
Preserving Geriatric Muscle with an Osteoporosis Medication
PI: Christine Kistler
Funding source: NIH/NIA - 5R01AG081359-04
Dates: 2023-2028
Description: The goal of this study is to demonstrate efficacy of the novel agent DEMAB to improve or preserve muscle health, strength, mobility and function in frail older adults. If successful, this would lay the groundwork for a larger multicenter trial to examine the dual-action for fall and fracture prevention. To bridge this knowledge gap the study team will conduct a 1-year, randomized, double-blind, active-controlled trial to test the efficacy of DEMAB versus zoledronic acid in 248 underserved, LTC, frail institutionalized men and women (age≥65) with osteoporosis. The study will evaluate efficacy of DEMAB to preserve/improve muscle strength, power, mass and structure; examine the mechanistic biochemical components of the muscle-bone connection; and explore if the DEMAB effect extends to distal functional outcomes.
Related Studies: N/A
Telemedicine Officer of the Day Program Evaluation
PI: Christine Kistler
Funding source: Veterans' Affairs Virtual Care Core
Dates: 2023-2026
Description: Dr. Kistler will study the Tele-MOD program, an after-hours telemedicine service, focusing on transfers and video visits, including utilization and cost savings, at two Community Living Centers (VA nursing homes), to examine the effect of the program on hospitalizations and emergency department use.
Related Studies: N/A
Publications: Clinical Performance of the Remote After-Hours Telemedicine Medical Officer of the Day (Tele-MOD) Program: A Cohort Study
Extension: A Mobile Sensing System to Monitor Symptoms During Chemotherapy
PI: Carissa Low
Funding source: NIH/NCI - 5R37CA242545-07
Dates: 2024-2026
Description: This extension, building on Dr. Carissa Low's ongoing study developing machine learning models to predict real-time symptoms during chemotherapy using mobile sensor data as input, will enable us to design and develop a web application that shares real-time symptom predictions with patients and uses them to trigger personalized symptom management instructions. The team will develop prediction models that can predict specific, clinically significant and actionable symptoms rather than overall symptom burden, and determine the best way to share machine learning-based predictions and recommendations in a way that fosters trust and that supports patient autonomy as well as patient-provider communication. This work represents continued refinement of both the clinical utility of our machine learning models as well as our integration of these models into supportive cancer care within an academic oncology clinic.
Related Studies: A Mobile Sensing System to Monitor Symptoms During Chemotherapy
TeleHealth Resistance exercise Intervention to preserve dose intensity and Vitality in Elder breast cancer patients (THRIVE)
PI: Kathryn H. Schmitz
Funding source: NIH/NCI U01-CA271277
Dates: 2022 - 2027
Description: Older women have worse outcomes from breast cancer treatment, in part due to under treatment and toxicities. Exercise and protein intake support, in combination, are a promising intervention to improve these outcomes. This randomized controlled trial assesses the effects of a multicomponent, hybrid telehealth (TH) exercise and protein intake support (the THRIVE Intervention) on received dose intensity, incidence and severity of chemotoxicities, functional status, muscle mass, and patient reported outcomes in 270 breast cancer patients age ≥ 65 receiving neo/adjuvant chemotherapy. This study is part of the ENICTO Research Consortium.
Related Studies: N/A
Publications:
Exercise and Nutrition to Improve Cancer Treatment-Related Outcomes (ENICTO)
TeleHealth Resistance exercise Intervention to preserve dose intensity and Vitality in Elder Breast Cancer Patients (THRIVE 65)
Nurse AMIE: Addressing Metastatic Individuals Everyday in Rural PA and WV
PI: Kathryn H. Schmitz
Funding source: NIH/NCI R01-CA254659
Description: Rural patients with metastatic cancer are less likely to have access to high quality supportive care. This may have a negative effect on survival and well-being. This follow-up randomized controlled trial will assess the survival benefits of a high quality technology based supportive care platform among 344 underserved, low income patients with metastatic cancer living in rural Pennsylvania or West Virginia. This study builds on a previous pilot study to develop the Nurse AMIE intervention, which uses digital supportive care to suggest evidence-based self-care interventions to address self-reported symptoms with a goal of improving overall health and well-being by reducing fatigue, distress, and pain and improving sleep.
Related Studies: N/A
Publications: Nurse AMIE randomized controlled trial to address symptom management among rural advanced Cancer patients: Addressing malignancies in everyday life
Worse Function and Symptoms Among the Most Rural Patients With Advanced Cancer: A Cross‐Sectional Analysis
https://pmc.ncbi.nlm.nih.gov/articles/PMC12785009/
Refining and Pilot Testing a Meaning-Centered and Cognitive-Behavioral Intervention to Improve the Emotional Well-Being of Family Caregivers of Children with Medical Complexity
PI: Justin Yu
Funding source: NIH/NICHD - 1K23HD115947
Dates: 2025-2028
Description: The overall objective of this application is to refine and pilot trial a psychological intervention aiming to improve CMC (Children with Medical Complexity) caregivers’ emotional well-being by enhancing their sense of meaning and coping abilities. Dr. Yu will first conduct preliminary testing of a psychological intervention combining components of meaning-centered psychotherapy (MCP) and cognitive-behavioral therapy (CBT) among CMC family caregivers. Using human-centered design methods, Dr. Yu will collaborate with CMC caregivers and clinicians to iteratively refine the MCP-CBT intervention prototype using feedback form preliminary testing. Dr. Yu will then conduct a feasibility and acceptability pilot randomized controlled trial of the refined MCP-CBT intervention with 60 CMC caregivers.
Related Studies: Developing a Behavioral Intervention to Improve Coping Among Family Caregivers of Children with Medical Complexity (CMC)