A Digital Program for Daily Life Management With Endometriosis: Pilot Cohort Study on Symptoms and Quality of Life Among Participants
Bibliographic record
Abstract
BACKGROUND: After experiencing symptoms for an average of 7 years before diagnosis, patients with endometriosis are usually left with more questions than answers about managing their symptoms in the absence of a cure. To help women with endometriosis after their diagnosis, we developed a digital program combining user research, evidence-based medicine, and clinical expertise. Structured around cognitive behavioral therapy and the quality of life metrics from the Endometriosis Health Profile score, the program was designed to guide participants for 3 months. OBJECTIVE: This cohort study was designed to measure the impact of a digital health program on the symptoms and quality of life levels of women with endometriosis. METHODS: In total, 63% (92/146) of the participants were included in the pilot study, recruited either free of charge through employer health insurance or via individual direct access. A control group of 404 women with endometriosis who did not follow the program, recruited through social media and mailing campaigns, was sampled (n=149, 36.9%) according to initial pain levels to ensure a similar pain profile to participants. Questionnaires assessing quality of life and symptom levels were emailed to both groups at baseline and 3 months. Descriptive statistics and statistical tests were used to analyze intragroup and intergroup differences, with Cohen d measuring effect sizes for significant results. RESULTS: Over 3 months, participants showed substantial improvements in global symptom burden, general pain level, anxiety, depression, dysmenorrhea, dysuria, chronic fatigue, neuropathic pain, and endo belly. These improvements were significantly different from the control group for global symptom burden (participants: mean -0.7, SD 1.6; controls: mean -0.3, SD 1.3; P=.048; small effect size), anxiety (participants: mean -1.1, SD 2.8; controls: mean 0.2, SD 2.5; P<.001; medium effect size), depression (participants: mean -0.9, SD 2.5; controls: mean 0.0, SD 3.1; P=.04; small effect size), neuropathic pain (participants: mean -1.0, SD 2.7; controls: mean -0.1, SD 2.6; P=.004; small effect size), and endo belly (participants: mean -0.9, SD 2.5; controls: mean -0.3, SD 2.4; P=.03; small effect size). Participants' quality of life improved between baseline and 3 months and significantly differed from that of the control group for the core part of the Endometriosis Health Profile-5 (participants: mean -5.9, SD 21.0; controls: mean 1.0, SD 14.8; P=.03; small effect size) and the EQ-5D (participants: mean 0.1, SD 0.1; controls: mean -0.0, SD 0.1; P=.001; medium effect size). Perceived knowledge of endometriosis was significantly greater at 3 months among participants compared to the control group (P<.001). CONCLUSIONS: This study's results suggest that a digital health program providing medical and scientific information about endometriosis and multidisciplinary self-management tools may be useful to reduce global symptom burden, anxiety, depression, neuropathic pain, and endo belly while improving knowledge on endometriosis and quality of life among participants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".