Mobile App Promoting Resilience in Stress Management for Adolescents and Young Adults With Cancer: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYAs) with cancer are at risk of poor psychosocial outcomes. AYAs grew up with the internet and digital technology, and mobile Health (mHealth) psychosocial interventions have the potential to overcome care access barriers. OBJECTIVE: This pilot randomized controlled trial (RCT) aimed to establish the feasibility, acceptability, and preliminary efficacy of a fully automated mobile app version of the Promoting Resilience in Stress Management intervention (mPRISM). Promoting Resilience in Stress Management is an evidence-based intervention developed in collaboration with AYAs, based on stress and coping theory, resilience theory, and evidence-based coping strategies. We hypothesized that mPRISM would be feasible, acceptable, and appropriate. METHODS: This is a parallel, 2-arm, single-site pilot RCT with a waitlist control design. The study will recruit 80 AYAs with cancer from a clinic. Eligible AYAs are aged 12 to 25 years, within 12 months of a new cancer diagnosis, receiving chemotherapy or radiation therapy, speak, read, or write in English, and are cognitively able to participate in study procedures. Recruitment by clinical research coordinators will occur remotely by phone, video, or text. Participants will be randomized to psychosocial usual care (UC) alone or UC plus mPRISM for an 8-week intervention period, and will remain unblinded to study condition. Enrolled participants will complete surveys at baseline before randomization, 8 weeks, and 3-month follow-up. Using a waitlist design, the UC arm will receive mPRISM upon completion of 3-month follow-up surveys. Those in the UC arm will complete 2 additional measurement points at immediate posttreatment and 3 months later. The primary outcomes of interest are feasibility, defined as ≥60% enrollment and ≥70% retention (ie, percentage of participants who completed the study), and "feasibility, acceptability, and appropriateness" as defined by cut-off scores ≥4/5 on 3 brief validated implementation outcome measures (feasibility of implementation measure, acceptability of intervention measure [AIM], intervention appropriateness measure [IAM]). We will apply top-box scoring for the implementation measures. Exploratory outcomes of interest include patient-reported health-related quality of life, resilience, distress, anxiety, depression, pain, and sleep. We will conduct an intention-to-treat analysis to compare the outcomes of the mPRISM arm versus the control arm with covariate-adjusted regression models. We will summarize individual digital usage metrics using descriptive statistics. RESULTS: Since September 2023, we have enrolled 20 participants and recruitment is ongoing. CONCLUSIONS: Although our previous work suggests AYAs with cancer are interested in mHealth psychosocial interventions, such interventions have not yet been sufficiently evaluated or implemented among AYA oncology patients. mPRISM may serve as a potential mHealth intervention to fill this gap. In this study, we will test the feasibility, acceptability, and preliminary efficacy of mPRISM. This work will inform future larger-scale RCTs powered for efficacy outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05842902; https://clinicaltrials.gov/study/NCT05842902. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57950.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.098 | 0.013 |
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".