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Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
Published on: April 5, 2019
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Latent variables may be useful in pain's assessment
Donald R Royall1, Ricardo Salazar, Raymond F Palmer
1Department of Psychiatry, The University of Texas Health Science Center At San Antonio, 7703 Floyd Curl Drive MC 7792, San Antonio, TX 78229, USA. royall@uthscsa.edu.
Health and Quality of Life Outcomes
|February 1, 2014
Summary
Latent variables effectively reduce measurement error in pain assessment by creating distinct constructs for depressive symptom-related pain and somatic pain. These unbiased measures offer unique predictive insights for chronic pain conditions.
Area of Science:
- Gerontology
- Epidemiology
- Psychometrics
Background:
- Unobserved latent variables can minimize measurement error in clinical assessments.
- Latent variable constructs offer a novel approach to understanding complex health phenomena like pain.
Purpose of the Study:
- To demonstrate the utility of latent variable constructs in assessing pain.
- To differentiate between depressive symptom-related pain (Pd) and somatic pain (Ps).
Main Methods:
- Developed two latent variables, Pd and Ps, from survey data in the Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE) cohort.
- Utilized data from 1,078 community-dwelling elderly Mexican-Americans, including pain reports, depression scores, and functional assessments.
Main Results:
- The latent variable model demonstrated excellent fit.
- Depressive symptom-related pain (Pd) was strongly linked to sleep disturbances, while somatic pain (Ps) was associated with physician visits.
- Both constructs independently predicted chronic pain, with Pd uniquely associated with self-rated health and life satisfaction, and Ps with NSAID use.
Conclusions:
- Latent variables provide unbiased constructs with unique predictive profiles, reducing measurement error in pain assessment.
- These validated phenotypes can serve as outcome measures in clinical trials and be linked to biomarkers.
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