A Culturally Tailored Mobile Health Intervention to Improve Quality of Life in Black Survivors With Prostate Cancer: Protocol for a Stratified Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (CaP) disproportionately affects Black men in the United States, leading to significant disparities in incidence, survival, and quality of life (QoL). Treatment-related side effects, including urinary dysfunction, pain, fatigue, and psychological distress, contribute to poor long-term outcomes. There is an urgent need for culturally-tailored, technology-based interventions to support symptom self-management and survivorship care. OBJECTIVE: This study aims to develop, refine, and evaluate the Survivorship App for Ethnically Diverse Black Prostate Cancer Survivors (SAFE-CaPS), a tailored mobile health intervention designed to enhance QoL, improve symptom self-management, and provide psychosocial support. METHODS: This 12-month stratified randomized clinical trial will enroll 248 Black survivors with CaP (including native born, African-born, and Caribbean-born men) into the SAFE-CaPS intervention arm and standard care (SC) control group (1:1 allocation). Eligible participants are self-identified Black men (including native born, African-born, and Caribbean-born) aged 40 to 80 years with a confirmed CaP diagnosis within the past 5 years who own or are willing to use a smartphone. SC will consist solely of routine oncology or primary care follow-up, including clinic visits, referrals, and supportive services as needed, with no digital components. The SAFE-CaPS intervention, developed using the Insight platform, includes daily and weekly ecological momentary assessments of pain, urinary and bowel symptoms, sleep, fatigue, sexual function, mood, physical activity, and diet; adaptive educational modules addressing treatment-related side effects and culturally grounded survivorship concerns; automated symptom alerts prompting nurse follow-up and provider notification; and engagement features such as reminders, goal-setting, and literacy-appropriate content. Intervention content incorporates culturally tailored messaging, patient narratives, and feedback from prior qualitative work with ethnically diverse Black CaP. Stratified randomization will ensure balanced representation of ethnic subgroups. Primary outcomes include overall QoL. Secondary outcomes include specific symptom domains, mental health (depression and anxiety), health care engagement, and app acceptability. Assessments will occur at baseline, 3, 6, 9, and 12 months, and linear mixed models with intent-to-treat principles will evaluate intervention effects over time. RESULTS: The study was funded in 2024, institutional review board approval was received in mid-2024, and the study began recruitment on August 24, 2025. Overall, 6 participants have been enrolled, with 2 in the SAFE-CaPS intervention and 4 in SC. Recruitment is expected to conclude in July 2027. Data analysis will occur in late 2027, with article preparation and publication planned for mid-2028 and early 2029. We hypothesize that SAFE CaPS participants will experience greater improvements in QoL, reduced symptom burden, and higher engagement in care compared to SC. CONCLUSIONS: This study will provide critical evidence on the feasibility, acceptability, and preliminary effectiveness of a culturally tailored mobile health intervention designed to enhance survivorship outcomes among Black survivors with CaP, informing future large-scale trials and real-world implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT06651359; https://clinicaltrials.gov/study/NCT06651359. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/81503.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".