Maternal health Aggregated Trends can be Misleading: The power of N-of-1 Level Wearable Data Analysis for Personalized Pregnancy Monitoring

2025 · doi:10.1101/2025.10.20.25338363
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This paper compared aggregate versus individual-level (N-of-1) analyses of personal digital health technology data for detecting pregnancy-related condition signals, using the BUMP study cohort of 256 individuals who wore Oura rings and completed self-reported surveys from Jan 2021 to May 2022. It analyzed physiological and behavioral metrics including heart rate variability (HRV), sleep, and fatigue, finding substantial intra- and inter-individual variability where aggregate week-level inflection patterns generalized poorly (e.g., only 4.76% of individuals matched the aggregate HRV inflection around week 33, despite a 14.24% coefficient of variation). The authors also reported no significant demographic or pregnancy-complication-based subgroup differences in driving variability, while case studies highlighted adverse events and severe symptoms as important modifiers, and they developed statistical approaches to assess effects of such events. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Background Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including HRV, supporting early detection of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability. Objective To compare aggregate and individual-level analyses of digital health technology (DHT) data for early detection of pregnancy-related conditions, using the comprehensive BUMP dataset to highlight the importance of individual variability and data heterogeneity. Methods This BUMP study (Jan 2021 – May 2022) analyzed physiological and behavioral metrics, such as heart rate variability (HRV), sleep, and fatigue, in 256 individuals using Oura rings and self-reported surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch’s t-tests, with inter-individual variability flagged based on high-variability thresholds. Results Results: revealed significant variability in HRV, sleep, and fatigue throughout pregnancy. For instance, only 4.76% of individuals had HRV inflection points at the aggregate week 33 inflection, with a 14.24% coefficient of variation. Our analysis found no significant p-values for demographic or pregnancy complication-based subgrouping, suggesting these factors alone do not drive the observed variability. Case studies further highlighted both intra- and inter-individual differences, emphasizing the importance of considering external factors like adverse events and severe symptoms. Conclusions Our findings show that aggregate wearable data often fails to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women’s experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.
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Abstract

Background Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including HRV, supporting early detection of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability.

Objective

To compare aggregate and individual-level analyses of digital health technology (DHT) data for early detection of pregnancy-related conditions, using the comprehensive BUMP dataset to highlight the importance of individual variability and data heterogeneity.

Methods

This BUMP study (Jan 2021 – May 2022) analyzed physiological and behavioral metrics, such as heart rate variability (HRV), sleep, and fatigue, in 256 individuals using Oura rings and self-reported surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch’s t-tests, with inter-individual variability flagged based on high-variability thresholds.

Results

Results revealed significant variability in HRV, sleep, and fatigue throughout pregnancy. For instance, only 4.76% of individuals had HRV inflection points at the aggregate week 33 inflection, with a 14.24% coefficient of variation. Our analysis found no significant p-values for demographic or pregnancy complication-based subgrouping, suggesting these factors alone do not drive the observed variability. Case studies further highlighted both intra- and inter-individual differences, emphasizing the importance of considering external factors like adverse events and severe symptoms.

Conclusions

Our findings show that aggregate wearable data often fails to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women’s experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care. Competing Interest Statement The authors have declared no competing interest. Funding Statement The study was funded by 4YouandMe(nonprofit), Seattle, WA, USA. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The BUMP study was ethically approved by the Institutional Review Board/IRB, Advarra (Pro00047893). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes List of Abbreviations - DHT - Digital Health Technologies - HRV - Heart Rate Variability - AE - Adverse Events - GDM - Gestational Diabetes Mellitus - ICC - Intraclass Correlation Coefficient - BUMP study - Better Understanding the Metamorphosis of Pregnancy study - RMSSD - Root Mean Square of Successive Differences Between Normal Heartbeats - BMI - Body Mass Index - REM - Rapid Eye Movement

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