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DTSTAMP:20251016T135134Z
LOCATION:Riverside East
DTSTART;TZID=America/Chicago:20251014T173000
DTEND;TZID=America/Chicago:20251014T183000
UID:HFESAM_ASPIRE 2025_sess131_POST161@linklings.com
SUMMARY:Continuous Stress-Monitoring and Personalized Interventions for Ex
 pecting Mothers: Wireframe
DESCRIPTION:Sarah Naidu (University of Virginia), Kim Smith (Health Evolve
 ), and Qian Zhang (College of Charleston)\n\nContinuous stress during preg
 nancy can negatively affect both maternal and fetal health, increasing ris
 ks such as postpartum depression, maternal mortality, and developmental de
 lays. To address this, we designed a mobile application prototype that int
 egrates wearable sensors, machine learning, and personalized interventions
  to help expecting mothers monitor and manage their stress in real time. T
 he app wireframe captures physiological signals—like heart rate variabilit
 y—and applies predictive algorithms to classify current stress levels and 
 forecast next-day stress. When elevated stress is detected or predicted, t
 he app offers tailored coping strategies, including guided mindfulness exe
 rcises and brief cognitive-behavioral techniques. Developed through iterat
 ive user-centered design and informed by existing research on bio-signal–b
 ased stress prediction, the prototype emphasizes ease of use and sustained
  engagement. By delivering continuous insights and timely recommendations,
  our approach empowers both users and healthcare providers to intervene pr
 oactively, potentially improving pregnancy outcomes and long-term well-bei
 ng. Preliminary usability testing suggests the wireframe is intuitive and 
 accessible, laying a foundation for future trials to evaluate effectivenes
 s across diverse populations. This work highlights the promise of combinin
 g wearable technology and predictive analytics to support personalized mat
 ernal health.\n\n
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