Radically different
Background: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis 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: This study aims to compare aggregate and individual-level analyses of DHT data for pregnancy-related conditions, using the comprehensive BUMP (Better Understanding the Metamorphosis of Pregnancy) dataset to highlight the importance of individual variability and data heterogeneity. Methods: We analyzed wearable and self-reported data from 256 participants enrolled in the BUMP study (January 2021 to May 2022), including HRV, sleep, and fatigue measured via Oura Rings and smartphone 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 tests, with interindividual variability flagged based on high-variability thresholds. Results: Substantial interindividual variability was observed across all features. Only 4.76% (12/256) of participants exhibited an HRV inflection at the aggregate week-33 inflection point, with a coefficient of variation of 14.24%. The median value of the gestational week in individual fatigue troughs was 23 (IQR 8; range 8-38) weeks, differing from aggregate estimates. Distributional comparisons showed no statistically significant differences in individual-level model fit (²) by pregnancy complications or age ( values ranging from .06 to .99 across all model fit comparisons). Case studies further highlighted both intraindividual and interindividual differences, emphasizing the importance of considering external factors, such as adverse events and severe symptoms. Conclusions: Our findings show that aggregate wearable data often fail 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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Background: Digital behaviors such as sleep, social interactions, and productivity reflect how individuals structure their daily lives. Among university students, online activity patterns mirror academic schedules, social rhythms, and lifestyle habits, with disruptions linked to sleep, stress, and well-being. Existing approaches—including wearables, apps, and surveys—depend on self-report or active participation, limiting long-term adherence. Passive sensing of network traffic offers a scalable alternative for the unobtrusive capture of smartphone usage patterns that preserves privacy. Objective: This study evaluated the degree to which encrypted smartphone network traffic, collected via a standard virtual private network (VPN), can capture patterns of digital behavior. We assessed feasibility (sustained data capture) and acceptability (usability, burden, and privacy perceptions) and examined how traffic-derived features reveal aspects of digital behavior—including timing, intensity, and regularity—relevant to health and daily functioning. Methods: We conducted a 2-week prospective observational study at New York University. Participants installed the WireGuard VPN client on personal smartphones, enabling passive capture of encrypted network traffic. Feasibility was assessed using a mixed methods approach combining quantitative measures of user retention and data coverage with qualitative analysis of semistructured exit interviews. Acceptability was evaluated using the System Usability Scale, NASA Task Load Index, and qualitative interview analysis. Exploratory analyses visualized traffic-derived features in relation to digital activity patterns. Results: Thirty-eight students consented, of whom 29 (76.3%) contributed valid network traffic data and formed the analytic cohort. Within this cohort, 93% of participants (27/29; Wilson 95% CI 78%‐98%) contributed at least 5 days of monitoring, corresponding to 71% retention relative to all consented participants (27/38; Wilson 95% CI 55%‐83%). The mean data coverage within the analytic cohort (n=24) was 74.1% (SD 19.3%; median 77.1%, IQR 63.6%-90.0%; bootstrap 95% CI 66.3%‐81.4%). These participants contributed an average of 311.6 (∼13 d, SD 3.5) hours of monitored traffic, ranging from 121 to 496 hours. Acceptability outcomes were evaluated among participants completing the exit survey and interview. Usability ratings were high (System Usability Scale score: mean 78, SD 14.96), and perceived workload was low (NASA Task Load Index scores were minimal). Participants described the system as easy to install, unobtrusive, and generally trustworthy, although some reported temporarily disabling the VPN during activities they considered private. No inferential statistical tests were conducted; analyses were descriptive. Exploratory analyses indicated that traffic-derived features reflected daily digital activity rhythms and revealed distinctive lifestyle patterns, including gaming and irregular late-night food delivery use. Conclusions: VPN-based monitoring of encrypted smartphone traffic was feasible and acceptable, enabling sustained passive data collection with minimal burden. This approach shows promise as a scalable, device-agnostic method for digital phenotyping that captures fine-grained behavioral rhythms while preserving privacy. With broader validation, this technique could expand the toolkit for studying health and well-being in everyday life.
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Nature Medicine, Published online: 27 April 2026; doi:10.1038/s41591-026-04352-3
The authors present the All of Us Research Program dataset, containing Fitbit data from 59,000 participants spanning 14 years, 39 million steps and 31 million sleep observations.
Data from a new study in Nature Neuroscience shows that the brain may be more mechanically connected to the body than previously appreciated. Using mice and computational simulations of fluid motion, the team identified a possible biological mechanism that helps explain why exercise benefits brain health. Specifically, they found that abdominal contractions compress blood vessels that are connected to the spinal cord and brain, which helps the organ move gently within the skull. This movement facilitates the flow of cerebrospinal fluid over the brain, potentially washing away neural waste and preventing the development of neurodegenerative disorders.
The work, which is described in a paper titled “Brain motion is driven by mechanical coupling with the abdomen,” builds on past studies exploring how sleep and neuron loss influence how and when cerebrospinal fluid flushes the brain, according to Patrick Drew, PhD, a professor of engineering science and mechanics, neurosurgery, biology, and biomedical engineering at Penn State University. Drew is the corresponding author on the study.
“Our research explains how just moving around might serve as an important physiological mechanism promoting brain health,” said Drew. The contraction of abdominal muscles to push blood from the abdomen into the spinal cord acts “just like in a hydraulic system” that puts pressure on the vertebral venous plexus, a network of veins that connect the abdominal cavity to the spinal cavity which causes the brain to move. Computational simulations show “that this gentle brain movement will drive fluid flow in and around the brain” removing harmful waste.
To view this mechanism in moving mice, the scientists used two-photon microscopy, which allows for high-definition imaging of living tissue, and microcomputed tomography, which supports high-resolution three-dimensional examination of whole organs. They observed the brains shifting in the moments before the mouse moved and right after their abdominal muscles tightened, anticipating further movement.
To ensure that the abdominal contractions were the reason for the observed shift rather than other movements, the scientists applied gentle and controlled pressure to the abdomens of anesthetized mice. They observed that the mice’s brains moved in response. “Importantly, the brain began moving back to its baseline position immediately upon relief of the abdominal pressure,” Drew said, suggesting “that abdominal pressure can rapidly and significantly alter the position of the brain within the skull.”
The next step was digging deeper into the fluid’s movement in the brain as well as assessing if the brain’s movement could induce fluid flow. For this task, members of the team developed various techniques to capture this information including conducting imaging experiments of living mice and generating computational simulations of fluid motion.
“Modeling fluid flow in and around the brain offers unique challenges because there are simultaneous, independent movements, as well as time-dependent, coupled movements,” explained Francesco Costanzo, PhD, a professor of engineering science and mechanics, biomedical engineering, mechanical engineering, and mathematics, who led the computational modeling aspects of the project. “Accounting for all of them requires accounting for the special physics that happens every time a fluid particle crosses one of the many membranes in the brain. So, we simplified it” using the analogy of a sponge for the brain. By simplifying it in this way, Costanzo explained, the team could model how fluid flows through a structure with varied spaces.
Sticking with the analogy, “we also thought of it as a dirty sponge—how do you clean a dirty sponge?” Costanzo continued. “You run it under a tap and squeeze it out. In our simulations, we were able to get a sense of how the brain moving from an abdominal contraction can help induce fluid flow over the brain to help clear waste products.”
Further studies are necessary to understand how this mechanism works in human bodies particularly how it cycle cerebrospinal fluid around the brain, and helps to protect against neurodegenerative disease. “This kind of motion is so small. It’s what’s generated when you walk or just contract your abdominal muscles, which you do when you engage in any physical behavior. It could make such a difference for your brain health,” Drew said. Overall, “our research shows that a little bit of motion is good, and it could be another reason why exercise is good for our brain health.”
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