Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

Abstract

Background Self-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. Objectives This 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. Methods This 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. Results Pelvic 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%). Conclusion For 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.

Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation

“…We computed a heuristic, composite day-level pain score to capture participants’ conceptualisation of their pain experience by summing the severity scores reported for each body area (eg, moderate pain in abdomen, mild pains in chest and leg would yield 2+1+1=4 as the total score) 44. This allowed consideration of the multidimensional pain experience in a single outcome.…”

Evaluation of a disease-specific mHealth-based exercise self-tracking measure

“…These findings are in line with previous studies that assess endometriosis-related impairment in daily functioning using mHealth-based self-tracking data. [5] Moreover, those in the 30+ BMI category (currently considered as “obese”) were statistically significantly less likely to report any exercise, based on the sample median habitual exercise frequency of zero. However, there were no significant differences between the other BMI categories.…”

“…Details of recruitment, enrollment, informed consent are described elsewhere. [5, 38] Briefly, participants consisted of a subset of Phendo App users who had longitudinal self-tracked exercise data for derivation of a habitual exercise measure and self-reported a surgery-, clinician-, or suspected (i.e., self-) diagnosis of endometriosis within their App profiles. This was an a priori decision based on the focus of the study (i.e., exercise behavior) and our previous findings indicating no substantial differences in exercise patterns between those with self-diagnosed vs formally-diagnosed endometriosis.…”

“…The emergence of mobile and wearable health (mHealth) technologies is rapidly expanding their use in research and clinical settings, [1][2][3] and engaging patients in selfmanagement and monitoring of their conditions. [4,5] mHealth-based digital measures that allow daily self-tracking and event-based reporting constitute a promising method for administering frequent, low-burden assessments in natural settings to gain important information about patients' health status and make clinical decisions. Moreover, such measures are often shorter (e.g., single-item) compared to traditional self-report measures and user-customizable, i.e., the same construct could be measured through a different set of items by the participants.…”

“…[15,16] It has high societal burden due to loss of work productivity and impact on quality of life (QoL). [17][18][19][20] There are substantial betweenpatient heterogeneity and day-to-day fluctuations in its symptomology, [5,21] and existing medical therapies have limited efficacy, often confounded by side effects. [22] Moreover, evidence suggests that individuals with endometriosis are interested in daily self-monitoring of symptoms and various health behaviors daily for better disease selfmanagement.…”