A Web- and Mobile-Based Intervention for Women Treated for Breast Cancer to Manage Chronic Pain and Symptoms Related to Lymphedema: Results of a Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: The-Optimal-Lymph-Flow (TOLF) is a patient-centered, web- and mobile-based mHealth system that delivers safe, easy, and feasible digital therapy of lymphatic exercises and limb mobility exercises. OBJECTIVE: The purpose of this randomized clinical trial (RCT) was to evaluate the effectiveness of the web- and mobile-based TOLF system for managing chronic pain and symptoms related to lymphedema. The primary outcome includes pain reduction, and the secondary outcomes focus on symptom relief, limb volume difference measured by infrared perometer, BMI, and quality of life (QOL) related to pain. We hypothesized that participants in the intervention group would have improved pain and symptom experiences, limb volume difference, BMI, and QOL. METHODS: A parallel RCT with a control-experimental, pre- and posttest, and repeated-measures design were used. A total of 120 patients were recruited face-to-face at the point of care during clinical visits. Patients were randomized according to pain in a 1:1 ratio into either the arm precaution (AP) control group to improve limb mobility and arm protection or The-Optimal-Lymph flow (TOLF) intervention group to promote lymph flow and limb mobility. Trial outcomes were evaluated at baseline and at week 12 after the intervention. Descriptive statistics, Fisher exact tests, Wilcoxon rank-sum tests, t test, and generalized linear mixed effects models were performed for data analysis. RESULTS: At the study endpoint of 12 weeks, significantly fewer patients in the TOLF intervention group compared with the AP control group reported chronic pain (45% [27/60] vs 70% [42/60]; odds ratio [OR] 0.39, 95% CI 0.17-0.90; P=.02). Patients who received the TOLF intervention were significantly more likely to achieve a complete reduction in pain (50% [23/46] vs 22% [11/51]; OR 3.56, 95% CI 1.39-9.76; P=.005) and soreness (43% [21/49] vs 22% [11/51]; OR 2.60, 95% CI 1.03-6.81; P=.03). Significantly lower median severity scores were found in the TOLF group for chronic pain (MedTOLF=0, IQR 0-1 vs MedAP=1, IQR 0-2; P=.02) and general bodily pain (MedTOLF=1, IQR=0-1.5 vs MedAP=1, IQR 1-3; P=.04). Compared with the AP control group, significantly fewer patients in the TOLF group reported arm/hand swelling (P=.04), heaviness (P=.03), redness (P=.03), and limited movement in shoulder (P=.02) and arm (P=.03). No significant differences between the TOLF and AP groups were found in complete reduction of aching (P=.12) and tenderness (P=.65), mean numbers of lymphedema symptom reported (P=.11), ≥5% limb volume differences (P=.48), and BMI (P=.12). CONCLUSIONS: The TOLF intervention had significant benefits for breast cancer survivors to manage chronic pain, soreness, general bodily pain, arm/hand swelling, heaviness, and impaired limb mobility. The intervention resulted in a 13% reduction (from 40% [24/60] to 27% [16/60]) in proportions of patients who took pain medications compared with the AP control group, which had a 5% increase (from 40% [24/60] to 45% [27/60]). A 12% reduction (from 27% [16/60] to 15% [9/60]) in proportions of patients with ≥5% limb volume differences was found in the TOLF intervention, while a 5% increase in the AP control group (from 40% [24/60] to 45% [27/60]) was found. In conclusion, the TOLF intervention can be a better choice for breast cancer survivors to reduce chronic pain and limb volume. TRIAL REGISTRATION: Clinicaltrials.gov NCT02462226; https://clinicaltrials.gov/ct2/show/NCT02462226. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5104.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".