Dataset Information

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

ABSTRACT: The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.

SUBMITTER: Li K

PROVIDER: S-EPMC7250828 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

Li Kathy K Urteaga Iñigo I Wiggins Chris H CH Druet Anna A Shea Amanda A Vitzthum Virginia J VJ Elhadad Noémie N

NPJ digital medicine 20200526

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that sel ...[more]

PMID: 32509976

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Project description:Study questionWhat variations underlie the menstrual cycle length and ovulation day of women trying to conceive?Summary answerBig data from a connected ovulation test revealed the extent of variation in menstrual cycle length and ovulation day in women trying to conceive.What is known alreadyTiming intercourse to coincide with the fertile period of a woman maximises the chances of conception. The day of ovulation varies on an inter- and intra-individual level.Study design size durationA total of 32?595 women who had purchased a connected ovulation test system contributed 75?981 cycles for analysis. Day of ovulation was determined from the fertility test results. The connected home ovulation test system enables users to identify their fertile phase. The app benefits users by enabling them to understand their personal fertility information. During each menstrual cycle, users input their perceived cycle length into an accessory application, and data on hormone levels from the tests are uploaded to the application and stored in an anonymised cloud database. This study compared users' perceived cycle characteristics with actual cycle characteristics. The perceived and actual cycle length information was analysed to provide population ranges.Participants/materials setting methodsThis study analysed data from the at-home use of a commercially available connected home ovulation test by women across the USA and UK.Main results and the role of chanceOverall, 25.3% of users selected a 28-day cycle as their perceived cycle length; however, only 12.4% of users actually had a 28-day cycle. Most women (87%) had actual menstrual cycle lengths between 23 and 35 days, with a normal distribution centred on day 28, and over half of the users (52%) had cycles that varied by 5 days or more. There was a 10-day spread of observed ovulation days for a 28-day cycle, with the most common day of ovulation being Day 15. Similar variation was observed for all cycle lengths examined. For users who conducted a test on every day requested by the app, a luteinising hormone (LH) surge was detected in 97.9% of cycles.Limitations reasons for cautionData were from a self-selected population of women who were prepared to purchase a commercially available product to aid conception and so may not fully represent the wider population. No corresponding demographic data were collected with the cycle information.Wider implications of the findingsUsing big data has provided more personalised insights into women's fertility; this could enable women trying to conceive to better time intercourse, increasing the likelihood of conception.Study funding/competing interestsThe study was funded by SPD Development Company Ltd (Bedford, UK), a fully owned subsidiary of SPD Swiss Precision Diagnostics GmbH (Geneva, Switzerland). I.S., B.G. and S.J. are employees of the SPD Development Company Ltd.

Project description:Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

Project description:IntroductionAlzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.MethodsParticipants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes.ResultsWe found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8).DiscussionOur findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

Project description:The molecular and cellular processes required for development, function, and regression of the primate corpus luteum (CL) are poorly defined. We hypothesized that there are dynamic changes in gene expression occurring during the CL lifespan, which represent proteins and pathways critical to its regulation. Therefore, a genomic approach was utilized to systematically identify differentially expressed genes in the rhesus macaque CL during the luteal phase of natural menstrual cycles. CL were collected between days 3-5 (early stage), 7-8 (mid), 10-12 (mid-late), 14-16 (late), or 18-19 (very-late) after the midcycle LH surge. From the early through very-late stages, 3234 transcripts were differentially expressed, with 879 occurring from the early through late stages that encompass the processes of luteinization, maintenance, and functional regression. To characterize gene changes most relevant to these processes, ontology analysis was performed using the list of 879 differentially expressed transcripts. Four main groups of related genes were identified with relevance to luteal physiology including: 1) immune function; 2) hormone and growth factor signaling; 3) steroidogenesis; and 4) prostaglandin biosynthesis, metabolism, and signaling. A subset of genes representing each of the four major categories was selected for validation of microarray results by quantitative real-time PCR. Results in mRNA levels were similar between the two methodologies for 17 of 18 genes. Additionally, protein levels for 3 genes were determined by Western blot analysis to parallel mRNA levels. This database will facilitate the identification of many novel or previously underappreciated pathways that regulate the structure and function of the primate CL. Keywords: time course The sole experimental factor is the stage of the luteal phase when the CL was collected. There were five stages: 1) between days 3-5 post LH-surge (early stage), 2) 7-8 (mid stage), 3) 10-12 (mid-late stage), 4) 14-16 (late stage), and 5) 18-19 (very-late stage). The early CL are undergoing luteinization, the mid are fully functional CL that are at their peak progesterone producing capacity, the mid-late stage is a transitionary period where CL are still producing significant quantities of progesterone but are nearing the time when luteolysis initiates in non-conception cycles, the late stage corresponds to CL undergoing functional regression (cessation of progesterone secretion), and the very-late stage (menses) is when the structural remodeling and apoptosis associated with luteolysis is occurring. Measuring mRNA levels in CL collected during these periods provides a comprehensive analysis of the primate transcriptome throughout CL lifespan. There were 20 biological replicates: n = 4 per stage, 5 stages in total. The early CL were used as the baseline reference for gene expression.

Dataset Information

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

Publications

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

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