Engaging Men of Diverse Racial and Ethnic Groups With Advanced Prostate Cancer in the Design of an mHealth Diet and Exercise Intervention: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Healthy diet and exercise can improve quality of life and prognosis among men with prostate cancer. Understanding the perceived barriers to lifestyle change and patient preferences in a diverse cohort of men with prostate cancer is necessary to inform mobile health (mHealth) lifestyle interventions and increase health equity. OBJECTIVE: We conducted a multisite study to understand the preferences, attitudes, and health behaviors related to diet and lifestyle in this patient population. This report focuses on the qualitative findings from 4 web-based focus groups comprising a racially and ethnically diverse group of patients with advanced prostate cancer who are on androgen deprivation therapy. METHODS: We used grounded theory analyses including open, axial, and selective coding to generate codes. Qualitative data were analyzed as a whole rather than by focus group to optimize data saturation and the transferability of results. We present codes and themes that emerged for lifestyle intervention design and provide recommendations and considerations for future mHealth intervention studies. RESULTS: Overall, 14 men participated in 4 racially and ethnically concordant focus groups (African American or Black: 3/14, 21%; Asian American: 3/14, 21%; Hispanic or Latino: 3/14, 21%; and White: 5/14, 36%). Analyses converged on 7 interwoven categories: context (home environment, access, competing priorities, and lifestyle programs), motivation (accountability, discordance, feeling supported, fear, and temptation), preparedness (health literacy, technological literacy, technological preferences, trust, readiness to change, identity, adaptability, and clinical characteristics), data-driven design (education, psychosocial factors, and quality of life), program mechanics (communication, materials, customization, and being holistic), habits (eg, dietary habits), and intervention impressions. These results suggest actionable pathways to increase program intuitiveness. Recommendations for future mHealth intervention design and implementation include but are not limited to assessment at the individual, household, and neighborhood levels to support a tailored intervention; prioritization of information to disseminate based on individuals' major concerns and the delivery of information based on health and technological literacy and communication preferences; prescribing a personalized intervention based on individuals' baseline responses, home and neighborhood environment, and support network; and incorporating strategies to foster engagement (eg, responsive and relevant feedback systems) to aid participant decision-making and behavior change. CONCLUSIONS: Assessing a patient's social context, motivation, and preparedness is necessary when tailoring a program to each patient's needs in all racial and ethnic groups. Addressing the patients' contexts and motivation and preparedness related to diet and exercise including the household, access (to food and exercise), competing priorities, health and technological literacy, readiness to change, and clinical characteristics will help to customize the intervention to the participant. These data support a tailored approach leveraging the identified components and their interrelationships to ensure that mHealth lifestyle interventions will engage and be effective in racially and ethnically diverse patients with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT05324098; https://clinicaltrials.gov/ct2/show/NCT05324098.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".