Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects

Edgley, Katherine; Horne, Andrew W.; Saunders, Philippa T.K.; Tsanas, Athanasios

doi:10.1016/j.xcrm.2023.101192

Cited by 5 publications

(4 citation statements)

References 73 publications

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“…Collectively, these advances lead to unprecedented opportunities for disruptive healthcare, and there is a clear vision to transform the landscape for monitoring chronic diseases, with recent indicative works including studies on diabetes [2], Parkinson's [8], endometriosis [9], and rheumatoid arthritis [10]. Along with these advances, there are new practical and social challenges, including data privacy [11], empowering individuals make decisions about their health trajectories, considerations regarding widening the gap with health inequalities, implementations at scale, and liability for the application [12], [13].…”

Section: Guest Editorialmentioning

confidence: 99%

Guest Editorial Pervasive Computing in Healthcare

Tsanas,

Triantafyllidis,

Tsiknakis

2024

IEEE J. Biomed. Health Inform.

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Section: Guest Editorialmentioning

confidence: 99%

Guest Editorial Pervasive Computing in Healthcare

Tsanas,

Triantafyllidis,

Tsiknakis

2024

IEEE J. Biomed. Health Inform.

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“…A baseline questionnaire and a longitudinal questionnaire were developed and implemented as an additional module in the Lucy app. It collects self-reported data on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutrition, and other lifestyle factors [ 10 ]. By linking the collected information with existing registry-based information on diagnoses of endometriosis, healthcare use, and the big data approach, we can develop a phenotype description of individuals with endometriosis and structured clinical data [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial

Balogh,

Hudelist,

Bļizņuks

et al. 2024

PLoS ONE

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Introduction Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. Aims This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. Methods A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. Conclusions We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

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“…Common disease symptoms, including severe chronic pelvic pain, secondary dysmenorrhea and infertility, substantially alter the patient's work productivity, social life and psychological well-being. This condition affects 6%-10% of reproductive-age women worldwide and represents a considerable burden on society [2,3] . The core goals of endometriosis treatment proclaimed by the UK Endometriosis Association are to minimize diagnosis time and ensure patients have access to comprehensive treatment and support [4] .…”

Section: Introductionmentioning

confidence: 99%

Dietary factors and risk for endometriosis: a Mendelian randomization analysis

Zhang,

Zheng,

Chen

2024

Preprint

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Aims Observational studies have reported an association between dietary factors and endometriosis, but the causality remains unknown. The study aimed to evaluate the potential causal effect of dietary factors on endometriosis. Methods We performed a two-sample Mendelian randomization (MR) analysis to investigate the effects of 18 diet-related exposure factors (alcoholic drinks per week, alcohol intake frequency, processed meat intake, poultry intake, beef intake, non-oily fish intake, oily fish intake, pork intake, lamb/mutton intake, bread intake, cheese intake, cooked vegetable intake, tea intake, fresh fruit intake, cereal intake, salad/raw vegetable intake, coffee intake, dried fruit intake) on the risk of endometriosis using summary statistics from the genome-wide association study (GWAS). The inverse variance weighted (IVW) method was used to deduce the causal association between dietary factors and endometriosis, and sensitivity analyses were further performed. Results Processed meat intake (OR=0.550; 95%CI:0.314-0.965; p=0.037) and salad / raw vegetable intake (OR=0.346; 95%CI:0.127-0.943; p=0.038) were discovered as protective factors for endometriosis. Heterogeneity test revealed no significant heterogeneity (processed meat intake: pIVW=0.607, pMR-Egger=0.548; salad / raw vegetable intake: pIVW=0.678, pMR-Egger=0.620). MR-Egger regression test didn’t support any evidence for horizontal pleiotropy (processed meat intake: p for intercept=0.865; salad / raw vegetable intake: p for intercept=0.725). No causal relationship was found between other dietary intakes and endometriosis. Conclusion These findings suggest that processed meat intake and salad/raw vegetable intake are associated with a decreased risk of endometriosis, but further investigation is required.

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Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects

Cited by 5 publications

References 73 publications

Guest Editorial Pervasive Computing in Healthcare

Guest Editorial Pervasive Computing in Healthcare

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial

Dietary factors and risk for endometriosis: a Mendelian randomization analysis

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