Exploring Symptoms for Digital Health Tools in Women's Reproductive Health
Author Information
Author(s): Mavragani Amaryllis, Doust Jenny, Marshall Robert, Wickham Aidan P, Hewings-Martin Yella, Goddard Frederick GB, Rodgers Allison K, Cunningham Adam C, Prentice Carley, Wilks Octavia, Kaplan Yusuf C, Marhol Andrei, Meczner András, Stsefanovich Heorhi, Klepchukova Anna, Zhaunova Liudmila
Primary Institution: Flo Health UK Limited
Hypothesis
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 symptom checkers developed by Flo Health UK Limited.
Conclusion
The Flo symptom checkers demonstrated high levels of accuracy in identifying self-reported symptoms for reproductive conditions.
Supporting Evidence
- Symptom checker accuracy was 78%, 73%, and 75% for PCOS, endometriosis, and uterine fibroids, respectively.
- The most prevalent symptoms for PCOS included fatigue (92%) and feeling anxious (87%).
- Participants reported significant delays in diagnosis, ranging from 2 to 12 years.
- Self-reported symptoms correlated with diagnosis, indicating their importance in identifying health conditions.
- Participants were compensated for their time, ensuring ethical considerations were met.
Takeaway
This study looked at how women describe their symptoms for conditions like PCOS and endometriosis, and found that a special app can help figure out if they might have these conditions.
Methodology
Participants completed web-based surveys about their symptoms and diagnostic history for PCOS, endometriosis, and uterine fibroids.
Potential Biases
Recall bias may affect self-reported symptoms, especially for those diagnosed long ago.
Limitations
Self-reported symptoms may contain bias or inaccuracies, particularly in participants diagnosed many years ago.
Participant Demographics
Participants were aged 18 years or older, female, and living in the United States.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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