Real-World Implementation of EndoConnect in Brazilian Primary Care: Formative Study of Usability, Engagement, and Equity in Digital Endometriosis Care
This study found that the EndoConnect digital health platform showed high usability, acceptability, and user engagement in Brazilian primary care, with exploratory signals of improved symptoms and knowledge, especially among rural and less educated participants.
One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works
This formative applied methodological study developed and deployed EndoConnect Alpha, an offline-capable progressive web app for digital endometriosis care in Brazil’s SUS primary care units in Ceará, enrolling 45 women aged 18–45 with suspected/confirmed endometriosis and 15 primary care professionals across 10 units (with 60% rural participation). Over 8 weeks (Jan 2024–Nov 2025), the authors assessed usability (System Usability Scale), acceptability (Technology Acceptance Model), and engagement (Firebase Analytics), along with pre/post pelvic pain VAS, knowledge (EKES-15), anxiety (GAD-7), adherence, and referral rates, and reported mean usability as excellent (SUS 88.9 ± 9.8) with 79% trail completion. They found significant improvements including reduced pelvic pain (−23%, P=.02), increased adherence (+17%, P=.01) and knowledge (+21%, P<.001), reduced anxiety (−14%, P=.04), and increased referral rate (+15%, P=.04), with the largest benefits in rural and underserved subgroup categories; a stated limitation is that the work is formative with no clinical trial framework. The paper is centrally about endometriosis — it evaluates real-world feasibility, usability, engagement, equity impacts, and an ethical AI governance framework for a digital endometriosis care tool in SUS primary care.
Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works
Abstract
Full text
3,441 characters
· extracted from
oa-html
· 5 sections
· click to expand
Abstract
Objective
Methods
Results
Conclusions
Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.
My notes (saved in your browser only)
Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works
Outcome instruments
Condition tags
MeSH descriptors
Citation neighborhood (no data yet)
We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.
Source provenance
- europepmc
- last seen: 2026-08-03T06:10:56.557307+00:00
- pubmed
- last seen: 2026-08-03T06:06:01.247347+00:00
- unpaywall
- last seen: 2026-05-11T08:34:28.763810+00:00
Courtesy of the U.S. National Library of Medicine