Who would benefit from the early diagnosis of the disease?

In patients with negative imaging, the definitive diagnosis of endometriosis is still laparoscopic intervention and histological analysis, which requires invasive evaluation and direct visualization of lesions, followed by histological analysis [6]. To initiate treatment, including surgery, we need a clinical hypothesis based on signs and symptoms of endometriosis, which can lead to a late diagnosis of the disease. Due to the lack of non-invasive diagnostic tools, the average delay in diagnosing endometriosis from the onset of the symptoms is 11.7 years in the United States, 8 years in the United Kingdom, and 3.9 years in Hungary [14, 15]. A non-invasive diagnostic tool would be crucial in early endometriosis diagnosis, especially the minimal-mild (#Enzian P, ASRM Stage I-II) endometriosis [16, 17].

Primary objective

To establish a comprehensive real-world big data repository using the Lucy app. This initiative aims to identify unique clinical cohorts by leveraging various factors such as digital footprints, symptoms, patient experience, comorbidities, clinical severity, and lifestyle patterns. Using ML for big data analytics, patient profiles and structured clinical data that facilitate the earlier detection of endometriosis can be generated. Ultimately, this approach has the potential to reduce the current diagnostic delay associated with endometriosis significantly.

Secondary objectives

• To explore whether there is an association between removing or restricting dietary components and quality of life, pain, and central sensitization.
• To investigate the connections between different types of exercise, relaxation techniques, and the symptoms or diagnosis of individuals.
• To measure the financial implications of specific diets and different dietary supplements.

Ethics statement

This non-interventional study protocol was approved by the ethics committee of the Medical Research Council of Hungary and authorized by the National Institute of Pharmacy and Nutrition of Hungary (OGYÉI/31355/2021). It is conducted following the tenets of the Declaration of Helsinki. Before completing the questionnaires for the first time, participants will receive an informational briefing detailing the nature of the research. The application necessitates accepting the privacy policy and informational disclosure, explicitly stating that their data may be used for research purposes. The data controller only stores aggregated, anonymous data from the data provided in the Lucy app and the use of the application. See https://hellolucy.app/hu/protection and https://hellolucy.app/hu/agreement for detailed information.

Study design

This study is conducted as a multicenter, parallel-group trial. Study participants are being recruited through the Lucy app. Data are collected in the following countries: Hungary, Denmark, Sweden, Germany, Austria, Italy, Romania, and Poland. Participant recruitment began on 08 February 2022 and is expected to continue until 31 December 2024.

Eligibility criteria

5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group aged 18–45 years will be enrolled and followed up for one year.

Exclusion criteria

The following exclusion criteria have been defined:

• Ongoing pregnancy.
• Lactation.
• Concern for pre-malignancy or presence/history of malignancy.

Questionnaire

Participants will be asked to complete a baseline questionnaire once and a longitudinal questionnaire monthly for one year. The questionnaire includes questions about endometriosis symptoms, socio-demographic information, mental and physical health, known medical conditions, medication use, dietary habits, economic burden, and lifestyle factors. These include free-text responses, single or multiple-choice questions, and numerical rating scales. See Supplementary Data for the questionnaires and https://hellolucy.app/en/ for the Lucy app.

Self-reported data of the participants will be measured as follows:

• Evaluating the quality of life using the 5-level EQ-5D (EQ-5D-5L) and Endometriosis Health Profile 5 (EHP-5) [18].
• Assessing pain scores using the Visual Analogue Scale (VAS) [19].
• Examining somatic and emotional symptoms associated with central pain sensitization using the short version of the Central Sensitization Inventory (CSI-9) [20].

If using the same user account, Lucy app users can pause and resume completing the questionnaire, even on different devices. Once all questions have been answered, users can review and modify their answers on a summary screen. When they submit the questionnaire, their responses are stored on a server. At this point, the questionnaire’s content is fixed and cannot be changed.

In longitudinal studies, minimizing the drop-out rate and encouraging maximum user participation in completing the desired quantity of questionnaires is crucial. Lucy app users are anonymous; therefore, personal email contact is impossible. To address this challenge and reduce dropouts, the application uses several methods to notify users when the questionnaire becomes available for completion again. The questionnaire menu is prominently displayed on the application’s main page, and a pop-up window alerts users that the questionnaire is complete. Users can track the number of times and when they completed the questionnaire, but detailed completion data is not visible. The app displays a pop-up window with motivational quotes and positive messages to encourage and support users. In addition, users who complete the questionnaire are entered into a drawing as an additional incentive to participate. These measures are designed to minimize dropout and maximize user engagement in completing the questionnaires for the study.

Data quality assessment and preprocessing

Given that real-world data inevitably contain inaccuracies, inconsistencies, text inputs, and non-qualitative information, the initial phase of our analysis is dedicated to data quality assessment and filtering. Various data quality metrics will be used, such as confirming the expected data types for each column. We will exclude records with invalid activity dates, missing activity information, users with implausible ages, and a low frequency of activity records. Statistical evaluations will be performed to assess the general data quality. For instance, the distribution of records across different days of the month should be consistent, without significant drop-offs on specific days such as the 31st. The data quality assessment metrics will understand the validity, integrity, precision, reliability, and timeliness of the data to ensure that the primary and secondary objectives can be met with the collected data. After initial filtering, we’ll dive deeper into removing non-qualitative or irrelevant data. Each symptom will be analyzed in terms of its absolute frequency and temporal distribution. Symptoms with sparse records will be discarded to prevent statistical anomalies. Any symptoms showing unrealistic temporal distributions will be subject to medical review to ensure the data’s authenticity.

Statistical and machine learning analyses

Our dataset comprises both diagnosed and undiagnosed patients, enabling us to evaluate the quality of the data. The goal is to have a case-control ratio of 1:1 in the dataset. Specifically, records from patients diagnosed with endometriosis but not yet received treatment should highly correlate with well-known endometriosis-associated symptoms, such as pelvic pain.

The normal distribution of the data will be tested with histograms and QQ-plots. Continuous variables will be presented as averages (mean ± SD), nominal variables as numbers (n) and percentages (%). A level of statistical significance of P < 0.05 will be used. The VAS scores of the endometriosis group and the reference group will be compared with independent sample t-tests. Also, independent samples t-tests will be conducted to see whether EQ-5D-5L, CSI-9 and EHP-5 scores were different between diet-users and non-diet users. Subsequently, a one-way analysis of variance of no diet use and the various diets will be carried out for the EQ-5D-5L, CSI-9, and EHP-5 scores. A post-hoc multiple comparisons analysis (Tukey honestly significant difference) will be used to identify where the differences between these diet groups were located. Preliminary analyses will involve various correlation techniques and visual representations, including heat maps contrasting diseases against symptoms.

To explore associations between diseases, symptoms, severity, dietary habits, and lifestyle activities, we will employ Cramér’s V statistical method. Additional validation of patient segmentation will be conducted to confirm that emerging patient cohorts exhibit anticipated symptoms. Machine learning algorithms will be deployed to uncover hidden relationships or patterns. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Uniform Manifold Approximation and Projection (UMAP) will be employed to visualize high-dimensional symptom and activity spaces in 2D or 3D representations for dimensionality reduction and clustering. Risk assessments for undiagnosed patients will be computed using Random Forest and Long Short-Term Memory (LSTM) models. The LSTM models will fully leverage the time-series data, offering predictive insights into disease outcomes based on the temporal progression of symptoms.

All the methodologies above will be tested under various scenarios, such as:

• Including all available symptoms and lifestyle/dietary activities.
• Excluding variables considered irrelevant based on existing medical literature.
• Segmenting the analysis for diagnosed versus undiagnosed patients.

These tests will help validate whether the methodologies yield consistent and reliable results. Given the substantial volume of our dataset—exceeding a million patient records—the data should be sufficient to use novel Transformer network models for pattern recognition and predictive analytics for at least low-complexity cases.

Data safety and management

The Lucy app prioritizes the data security and anonymity of its users. It employs robust encryption measures to safeguard all data. Without the user’s password, it is technically impossible to decrypt any stored information. Furthermore, even in the unlikely event of decryption, it is essential to note that our servers do not store personal identifier information. This ensures an additional layer of protection for user privacy and data security.

The app does not ask for personal data (e.g., name, date of birth, e-mail address, etc.) from the user, and registration is not needed to use it; moreover, the IP address is not displayed and stored. The questionnaire by itself does not permit the direct identification of an individual and does not contain other information that, when combined, allows a particular individual to be indirectly identified. Accordingly, indirect identifiability is possible when data is combined with additional information that distinguishes and allows for the identification of an individual.

The free text fields within the application are securely encrypted on the servers, ensuring they remain protected. These fields are only decrypted and displayed in an unencrypted format on the user’s device. This approach allows the data to be utilized for research purposes while maintaining the utmost privacy and preventing any possibility of personal identification.