Dataset Information

Characterization of polycystic ovary syndrome among Flo app users around the world.

ABSTRACT:

Background

Polycystic ovary syndrome (PCOS) is a complex and multi-faceted endocrine disorder that affects 5-20% of women. Literature is limited regarding potentially differing PCOS phenotypes among women around the world.

Objective

To use Flo app technology to understand the multifaceted characteristics of PCOS across several countries and identify contributing risk factors to the development of this condition.

Study design

Flo is a widely used female health and wellbeing app with period tracking functionality that provides a globally representative and medically unbiased perspective on PCOS symptomatology. A chatbot dialog on PCOS was subsequently administered on the Flo application (app) to users from 142 countries (with at least 100 respondents) who have the app running in English during September-October 2019.

Results

For analyses, we selected the five countries with the greatest number of respondents: US (n?=?243,238), UK (n?=?68,325), India (n?=?40,092), Philippines (n?=?35,131), and Australia (n?=?29,926). Bloating was the most frequently reported symptom among PCOS-positive women and appeared to be the main predictor of PCOS in our model (odds ratio 3·76 [95% CI 3·60-3·94]; p?ConclusionOur findings are based on the largest known PCOS dataset and indicate that symptoms are more complex than previously understood. The most frequently reported symptoms (bloating, facial hirsutism, irregular cycles, hyperpigmentation, and baldness) are broader than those included in the Rotterdam criteria. Future work should reevaluate and refine the criteria utilized in PCOS diagnosis.

SUBMITTER: Jain T

PROVIDER: S-EPMC7927251 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Characterization of polycystic ovary syndrome among Flo app users around the world.

Jain Tarun T Negris Olivia O Brown Dannielle D Galic Isabel I Salimgaraev Rodion R Zhaunova Liudmila L

Reproductive biology and endocrinology : RB&E 20210303 1

<h4>Background</h4>Polycystic ovary syndrome (PCOS) is a complex and multi-faceted endocrine disorder that affects 5-20% of women. Literature is limited regarding potentially differing PCOS phenotypes among women around the world.<h4>Objective</h4>To use Flo app technology to understand the multifaceted characteristics of PCOS across several countries and identify contributing risk factors to the development of this condition.<h4>Study design</h4>Flo is a widely used female health and wellbeing ...[more]

PMID: 33658043

Similar Datasets

Project description:BACKGROUND:Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. OBJECTIVE:The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature's risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. METHODS:A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature's questions. Upon completion of the questionnaire, a Result Screen and Doctor's Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature's probability capabilities. RESULTS:The irregular cycle feature's first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects' risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; P=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (P=.03). CONCLUSIONS:The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.

Dataset Information

Characterization of polycystic ovary syndrome among Flo app users around the world.

Background

Objective

Study design

Results

Publications

Characterization of polycystic ovary syndrome among Flo app users around the world.

Similar Datasets

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