The Effectiveness of the Be Prepared mHealth App on Recovery of Physical Functioning After Major Elective Surgery: Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
Background: Patients undergoing major surgery are at risk of complications and delayed recovery. Prehabilitation has shown promise in improving postoperative outcomes. Offering prehabilitation by means of mHealth can help overcome barriers to participating in prehabilitation and empower patients prior to major surgery. We developed the Be Prepared mHealth app, which has shown potential in an earlier pilot study. Objective: This study aims to evaluate the effectiveness of the Be Prepared app on postoperative recovery of physical functioning (PF) in patients undergoing major elective surgery. Methods: This study was a multicenter randomized controlled trial with 2 arms. Adults scheduled for major elective surgery were randomly assigned to the control (usual care) or intervention group (Be Prepared app in addition to usual care). The Be Prepared app is a smartphone app with pre- and postoperative information and instructions on changing risk behavior for patients undergoing major elective surgery. The primary outcome was recovery of postoperative PF up to 12 weeks after hospital discharge measured with the Computer Adaptive Test Patient-Reported Outcomes Measurement Information System-PF. Secondary outcomes included social participation, self-reported recovery, health-related quality of life, postoperative outcomes, and patient satisfaction. Measurements were performed at 5 time points: before random assignment and 1, 3, 6, and 12 weeks after hospital discharge. Results: A total of 369 patients were analyzed, 181 in the control group and 188 in the intervention group. The result of the linear mixed effects model showed a mean slope difference in recovery of PF over 12 weeks of 2.97 (95% CI 0.90-5.02) in favor of the intervention group. However, this effect was not clinically relevant and was negated by the significantly lower PF score 1 week after hospital discharge in the intervention group (mean difference -1.72, 95% CI -3.38 to -0.07). Most secondary outcome measures did not show significantly greater improvements in the intervention group compared to the control group. Patient satisfaction with overall perioperative care was significantly higher in the intervention group compared to the control group and satisfaction with the Be Prepared app was high. Conclusions: The use of the Be Prepared app as a stand-alone intervention does not seem beneficial for improving postoperative recovery in patients undergoing major surgery. However, satisfaction with perioperative care was higher in patients using the app. Given the advantages of digital technology in health care, it can be considered a basis for prehabilitation care pathways, complemented by guidance from health care professionals as needed.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".