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<p>Chronic pain (CP) is a debilitating condition that extends beyond persistent pain, influenced by physiological and psychological factors. However, clinical trials often evaluate outcomes solely on self-reported pain amplitude. To address this, we aimed to derive a single metric from multidimensional digital data to comprehensively represent wellness in lower back and leg pain. Daily-reported data were collected for five years (&gt;190 K samples, <i>n</i> = 498, from NCT01719055/NCT03240588), comprised of clinical assessments, digitally-reported symptoms, text responses, and smartwatch-based actigraphy. Clustering analysis of the digital data identified five novel symptom clusters. They were validated by comparing centroid distances to standard assessments, revealing five ordinal best-to-worst states (r = 0.34 to r = −0.51, ps &lt; 0.001), even when pain magnitude was similar. Further, patients’ text messages about their status associated better with the clusters than pain reports alone. This solution extends beyond a recapitulation of pain level, yielding non-obvious, meaningful states that serve as an actionable metric in CP care.</p>

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Defining and validating a multidimensional digital metric of health states in chronic back and leg pain

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Jenna M. Reinen,
  • Carla Agurto,
  • Guillermo Cecchi,
  • Richard Rauck,
  • Eric Loudermilk,
  • Julio Paez,
  • Louis Bojrab,
  • John Noles,
  • Todd Turley,
  • Mohab Ibrahim,
  • Amol Patwardhan,
  • James Scowcroft,
  • Rene Przkora,
  • Nathan Miller,
  • Gassan Chaiban,
  • Dat Huynh,
  • Kristen Lechleiter,
  • Brad Hershey,
  • Rex Woon,
  • Matt McDonald,
  • Jeffrey L. Rogers

摘要

Chronic pain (CP) is a debilitating condition that extends beyond persistent pain, influenced by physiological and psychological factors. However, clinical trials often evaluate outcomes solely on self-reported pain amplitude. To address this, we aimed to derive a single metric from multidimensional digital data to comprehensively represent wellness in lower back and leg pain. Daily-reported data were collected for five years (>190 K samples, n = 498, from NCT01719055/NCT03240588), comprised of clinical assessments, digitally-reported symptoms, text responses, and smartwatch-based actigraphy. Clustering analysis of the digital data identified five novel symptom clusters. They were validated by comparing centroid distances to standard assessments, revealing five ordinal best-to-worst states (r = 0.34 to r = −0.51, ps < 0.001), even when pain magnitude was similar. Further, patients’ text messages about their status associated better with the clusters than pain reports alone. This solution extends beyond a recapitulation of pain level, yielding non-obvious, meaningful states that serve as an actionable metric in CP care.