RecoverEsupport: A Pilot Randomised Controlled Trial of a Digital Health Intervention for Recovery After Breast Cancer Surgery (Feasibility and Acceptability outcomes). (Preprint)
Bibliographic record
Abstract
Background: Optimizing recovery following breast cancer surgery is critical for restoring usual function, minimizing complications, and enabling timely initiation of adjuvant therapies. Enhanced Recovery After Surgery protocols are internationally endorsed recommendations and include patient-led behaviors such as early mobilization, early oral intake of fluids and food, postoperative rehabilitation exercises, and multimodal pain management. However, adherence to these behaviors is often suboptimal, and strategies to support patients are limited. Digital health interventions (DHIs) may offer scalable solutions. Objective: The aim of the study is to assess the acceptability of the RecoverEsupport Breast DHI, designed to increase adherence to patient-led Enhanced Recovery After Surgery recommendations across the perioperative period for breast cancer surgery, and to assess the feasibility of conducting a randomized controlled trial to evaluate it. Methods: In this single-site, 2-arm randomized pilot trial, conducted at a major cancer hospital in New South Wales, Australia, between July 2024 and October 2025, participants were consecutively recruited from the surgical list at the study site, supplemented by referrals from surgeons' private rooms, and included individuals having a mastectomy with or without implant-based reconstruction. Participants were allocated to usual care (control) or usual care plus the RecoverEsupport DHI (intervention). Trial feasibility outcomes included participant recruitment, retention, data completeness, and postoperative safety (adverse events). Intervention acceptability was assessed via the System Usability Scale, participant engagement rates, and willingness to recommend the intervention to others undergoing surgery. Descriptive analyses were conducted, and outcomes were compared to prespecified targets and progression criteria. Results: In total, 23 participants were recruited (control: n=12, intervention: n=11), which was below the target of 70, while participant retention and data completeness were 100% (23/23), both exceeding the targets. No grade 3+ adverse events occurred; minor grade 2 events occurred in both groups. Acceptability outcomes exceeded targets: usability was high (mean System Usability Scale score 83.2, SD 17.7; target >68), 100% (11/11; target >75%) of participants logged in to the DHI at least once, and 88% (10/11; target >75%) would recommend the program to others undergoing surgery. According to prespecified progression criteria, 3 of 4 feasibility targets were met, indicating that a revised recruitment strategy would be required before proceeding. The restrictive eligibility criteria may have contributed to the lower than expected recruitment rate. All 3 acceptability targets were met. Conclusions: The RecoverEsupport intervention was acceptable and safe and had high participant engagement. The trial processes were feasible; however, recruitment barriers, including restrictive eligibility criteria, highlight the need for more robust and integrated recruitment strategies to enable progression to a fully powered randomized controlled trial.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".