Observational studyApplied clinical informatics2020
Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis.
Observational study in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Is endometriosis associated with poor sleep quality? A meta-analysis.Women's health (London, England)Pooled it
- Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence.Extended abstracts on Human factors in computing systems. CHI Conference · 2026Article
- Epistemic agency in self-tracking: a conceptual literature review of quantified self as citizen science for health.Frontiers in public health · 2026Review
- Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models.BMC women's health · 2025Article
- Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025Article
- Good-Quality mHealth Apps for Endometriosis Care: Systematic Search.Journal of medical Internet research · 2025Article
- Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders.Journal of pain research · 2025Article
- Day-to-day associations of intersectional minority stressors with sleep health in sexual and gender minority people of color.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2024Article
- The double-edged sword of self-tracking: investigating factors of technostress in performance-oriented cycling and triathlon.Frontiers in sports and active living · 2024Article
- Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects.Cell reports. Medicine · 2023Review
- Leveraging electronic health record data for endometriosis research.Frontiers in digital health · 2023Review
- Observational
- Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.Sleep medicine · 2021Article
- Computational Models for Diagnosing and Treating Endometriosis.Frontiers in reproductive health · 2021Review
- User types, psycho-social effects and societal trends related to the use of consumer health technologies.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSelf-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.
objectivesThis study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.
methodsThis was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.
resultsPelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).
conclusionFor enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.