Exploring Self-Reported Symptoms for Developing and Evaluating Digital Symptom Checkers for Polycystic Ovarian Syndrome, Endometriosis, and Uterine Fibroids: Exploratory Survey Study (Preprint)

Aidan P Wickham, Yella Hewings-Martin, Frederick Gb Goddard, Allison K Rodgers, Adam C Cunningham, Carley Prentice, Octavia Wilks, Yusuf C Kaplan, Andrei Marhol, András Meczner, Heorhi Stsefanovich, Anna Klepchukova, Liudmila Zhaunova
2024 · doi:10.2196/preprints.65469 · W4405299956
preprint OA: gold CC0
🔓 Open OA copy Full text JSON View on OpenAlex View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study identified key predictive symptoms for PCOS, endometriosis, and uterine fibroids and evaluated the accuracy of three Flo symptom checkers in diagnosing these conditions.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-06, 2026-06-22 · read from full text ⓘ

This exploratory survey study used separate web-based versions of three Flo Health UK symptom checkers to characterize self-reported symptom prevalence and identify predictive symptoms for PCOS, endometriosis, and uterine fibroids. US participants aged 18+ (female) were either condition-positive with confirmed diagnoses and reported retrospective symptoms at diagnosis or condition-negative and reported current symptoms at the time of the survey; the study used LASSO regression to select key predictive symptoms and compared symptom-checker outputs to participants’ condition designation to assess accuracy. It included 1317 participants and found condition-specific prevalent and predictive symptom patterns, with symptom checker accuracy of 78% for PCOS, 73% for endometriosis, and 75% for uterine fibroids, while the paper’s main caveat is its reliance on self-reported, retrospective/at-survey symptoms rather than clinical verification. Relevance to endometriosis: the paper evaluates a dedicated endometriosis symptom checker, reports prevalence/predictive symptoms for endometriosis, and quantifies endometriosis symptom-checker accuracy (73%), though it also covers PCOS and uterine fibroids.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

BACKGROUND Reproductive health conditions such as polycystic ovary syndrome (PCOS), endometriosis, and uterine fibroids pose a significant burden to people who menstruate, health care systems, and economies. Despite clinical guidelines for each condition, prolonged delays in diagnosis are commonplace, resulting in an increase to health care costs and risk of health complications. Symptom checker apps have the potential to significantly reduce time to diagnosis by providing users with health information and tools to better understand their symptoms. OBJECTIVE This study aims to study the prevalence and predictive importance of self-reported symptoms of PCOS, endometriosis, and uterine fibroids, and to explore the efficacy of 3 symptom checkers (developed by Flo Health UK Limited) that use self-reported symptoms when screening for each condition. METHODS Flo’s symptom checkers were transcribed into separate web-based surveys for PCOS, endometriosis, and uterine fibroids, asking respondents their diagnostic history for each condition. Participants were aged 18 years or older, female, and living in the United States. Participants either had a confirmed diagnosis (condition-positive) and reported symptoms retrospectively as experienced at the time of diagnosis, or they had not been examined for the condition (condition-negative) and reported their current symptoms as experienced at the time of surveying. Symptom prevalence was calculated for each condition based on the surveys. Least absolute shrinkage and selection operator regression was used to identify key symptoms for predicting each condition. Participants’ symptoms were processed by Flo’s 3 single-condition symptom checkers, and accuracy was assessed by comparing the symptom checker output with the participant’s condition designation. RESULTS A total of 1317 participants were included with 418, 476, and 423 in the PCOS, endometriosis, and uterine fibroids groups, respectively. The most prevalent symptoms for PCOS were fatigue (92%), feeling anxious (87%), BMI over 25 (84%); for endometriosis: very regular lower abdominal pain (89%), fatigue (85%), and referred lower back pain (80%); for uterine fibroids: fatigue (76%), bloating (69%), and changing sanitary protection often (68%). Symptoms of anovulation and amenorrhea (long periods, irregular cycles, and absent periods), and hyperandrogenism (excess hair on chin and abdomen, scalp hair loss, and BMI over 25) were identified as the most predictive symptoms for PCOS, while symptoms related to abdominal pain and the effect pain has on life, bleeding, and fertility complications were among the most predictive symptoms for both endometriosis and uterine fibroids. Symptom checker accuracy was 78%, 73%, and 75% for PCOS, endometriosis, and uterine fibroids, respectively. CONCLUSIONS This exploratory study characterizes self-reported symptomatology and identifies the key predictive symptoms for 3 reproductive conditions. The Flo symptom checkers were evaluated using real, self-reported symptoms and demonstrated high levels of accuracy.
Full text 4,076 characters · extracted from oa-html · 6 sections · click to expand

Abstract

Background:

Background

Reproductive health conditions such as polycystic ovarian syndrome (PCOS), endometriosis, and uterine fibroids pose a significant burden to people who menstruate, healthcare systems, and economies. Despite clinical guidelines for each condition, prolonged delays in diagnosis are commonplace, resulting in an increase to healthcare costs and risk of health complications. Symptom checker apps have the potential to significantly reduce time to diagnosis by providing users with health information and tools to better understand their symptoms.

Objective

To study the prevalence and predictive importance of self-reported symptoms of PCOS, endometriosis, and uterine fibroids, and to investigate the efficacy of three symptom checkers (developed by Flo Health UK Limited) that use self-reported symptoms when screening for each condition.

Methods

Flo’s symptom checkers were transcribed into separate web-based surveys for PCOS, endometriosis, and uterine fibroids, asking respondents their diagnostic history for each condition. Participants were aged 18 years or older, female, and living in the United States. Participants either had a confirmed diagnosis (condition-positive) and reported symptoms retrospectively as experienced at the time of diagnosis, or they had not been examined for the condition (condition-negative) and reported their current symptoms as experienced at the time of surveying. Symptom prevalence was calculated for each condition based on the surveys. Least absolute shrinkage and selection operator (LASSO regression) was used to identify key symptoms for predicting each condition. Participants’s symptoms were processed by Flo’s three single-condition symptom checkers, and accuracy was assessed by comparing the symptom checker output to the participant’s condition designation.

Results

1317 participants were included with 418, 476, and 423 in the PCOS, endometriosis, and uterine fibroids groups, respectively. The most prevalent symptoms for PCOS were: fatigue (92%), anxious (87%), BMI over 25 (84%); for endometriosis: very regular lower abdominal pain (89%), fatigue (85%) and referred lower back pain (80%); for uterine fibroids: fatigue (76%), bloating (69%), and changing sanitary protection often (68%). Symptoms of anovulation and amenorrhea (long periods, irregular cycles, absent periods), and hyperandrogenism (excess hair on chin and abdomen, scalp hair loss, BMI over 25) were identified as the most predictive symptoms for PCOS, while symptoms related to abdominal pain and the effect pain has on life, bleeding, and fertility complications were among the most predictive symptoms for both endometriosis and uterine fibroids. Symptom checker accuracy was 78%, 73%, and 75% for PCOS, endometriosis, and uterine fibroids, respectively.

Conclusions

This study characterizes self-reported symptomatology and identifies the key predictive symptoms for three reproductive conditions. The Flo symptom checkers were evaluated using real, self-reported symptoms and demonstrated high levels of accuracy. Citation Request queued. Please wait while the file is being generated. It may take some time. Copyright © The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-html ⓘ

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

Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (56)

Source provenance

openalex
last seen: 2026-06-10T17:14:06.276822+00:00
License: CC0 · commercial use OK