AbstractData comparing outcomes in prostate cancer and factors affecting treatment choice are sparse. To inform the design of a comparative effectiveness clinical trial, we engaged patients in developing a 28‐question survey about decision making on treatment and research participation and dispersed it among men greater than or equal to 50 years of age. The 1046 respondents ranked long‐term clinical outcomes as most important in making treatment decisions, specific functional outcomes as slightly less important, and duration, location, and cost of treatment as least important. Treatment choice was strongly impacted by side effect profile. Responses to whether the subject would agree to participation in a randomized trial between two types of radiation with minimal differences in outcomes were “yes” in 15%, “no” in 39%, and “undecided” in 46%. Responses to whether the subject would agree to participation in a randomized trial between two treatment durations with similar outcomes were yes in 36%, no in 24%, and undecided in 40%. Findings suggest many potential patients have strong treatment preferences and are averse to randomization, particularly when outcomes of importance may be affected. Patient engagement in study design and novel nonrandomized trial designs may offer a path to increase clinical trial success.
Improving Spiritual Well-Being in Patients with Lung Cancers
Journal of Pastoral Care & Counseling: Advancing theory and professional practice through scholarly and reflective publicati | October 13, 2015 | Volume: 69 | Issue: 3 | Pages: 156-162
Patients with lung cancer report more disease burden and lower spiritual well-being (SWB) compared with other cancer patients. Understanding variables that lessen disease burden and improve SWB is essential. The aim of this study was to explore the relationship between motivational level for physical activity and SWB in patients with lung cancer. Linear regression showed increased SWB as stage of change for physical activity increased (p < 0.0001), even after adjusting for multiple demographic variables.