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<p>Psychological stress is a key driver of short-term blood pressure (BP) elevations and cardiovascular risk, yet its moment-to-moment impact in daily life remains difficult to predict. In this longitudinal observational study, we collected multimodal data from 20 adults with self-reported hypertension, including continuous wearable-derived heart rate and activity, ecological momentary assessment (EMA) stress ratings, and ambulatory BP measurements in free-living conditions. The dataset comprised 3694 EMA responses and 3812 BP measurements collected over approximately four weeks per participant (mean 24.1 ± 8.5 days). We evaluated whether participant-specific (“personalized”) models outperform a single pooled population model. Two prediction tasks were examined: (i) prediction of near-term BP elevations from wearable signals and stress EMA responses and (ii) prediction of self-reported stress from wearable signals and BP. Across both tasks, personalized models consistently improved predictive performance. For BP prediction, personalized models achieved a mean AUROC of 0.803, exceeding the population model by 0.235, while for stress prediction they achieved a mean AUROC of 0.849, exceeding the population model by 0.208. These findings suggest that personalized wearable-based models can capture individual patterns of stress and BP dynamics, with direct implications for precision mental health assessment and just-in-time adaptive intervention design in future work.</p>

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Personalized modeling of stress and blood pressure reactivity using mobile health data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

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  • Ali Kargarandehkordi,
  • Aditi Jaiswal,
  • Agnik Banerjee,
  • Yang Qian,
  • Christopher R. Slade,
  • Yinan Sun,
  • Tanvir Islam,
  • Hiwot Belay Tadesse,
  • Alexander Kostrinsky-Thomas,
  • Chanhyun Park,
  • Urmimala Sarkar,
  • Elaine C. Khoong,
  • Nhung Nguyen,
  • Xuhai “Orson” Xu,
  • Kristina T. Phillips,
  • Roberto M. Benzo,
  • Adrian Aguilera,
  • Haopeng Zhang,
  • Finale Doshi-Velez,
  • Peter Washington

摘要

Psychological stress is a key driver of short-term blood pressure (BP) elevations and cardiovascular risk, yet its moment-to-moment impact in daily life remains difficult to predict. In this longitudinal observational study, we collected multimodal data from 20 adults with self-reported hypertension, including continuous wearable-derived heart rate and activity, ecological momentary assessment (EMA) stress ratings, and ambulatory BP measurements in free-living conditions. The dataset comprised 3694 EMA responses and 3812 BP measurements collected over approximately four weeks per participant (mean 24.1 ± 8.5 days). We evaluated whether participant-specific (“personalized”) models outperform a single pooled population model. Two prediction tasks were examined: (i) prediction of near-term BP elevations from wearable signals and stress EMA responses and (ii) prediction of self-reported stress from wearable signals and BP. Across both tasks, personalized models consistently improved predictive performance. For BP prediction, personalized models achieved a mean AUROC of 0.803, exceeding the population model by 0.235, while for stress prediction they achieved a mean AUROC of 0.849, exceeding the population model by 0.208. These findings suggest that personalized wearable-based models can capture individual patterns of stress and BP dynamics, with direct implications for precision mental health assessment and just-in-time adaptive intervention design in future work.