Related Experiment Video
Updated: Jun 6, 2026
Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams
Published on: July 7, 2023
Development of a composite pain measure for persons with advanced dementia: exploratory analyses in self-reporting
Mary Ersek1, Nayak Polissar, Moni Blazej Neradilek
1University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania 19104-6096, USA. ersekm@nursing.upenn.edu
Context:
Experts agree that pain assessment in noncommunicative persons requires data from sources that do not rely on self-report, including proxy reports, health history, and observation of pain behaviors. However, there is little empirical evidence to guide clinicians in weighting or combining these sources to best approximate the person's experience.
Objectives:
The aim of this exploratory study was to identify a combination of observer-dependent pain indicators that would be significantly more predictive of self-reported pain intensity than any single indicator. Because self-reported pain is usually viewed as the criterion measure for pain, self-reported usual and worst pains were the dependent variables.
Methods:
The sample consisted of 326 residents (mean age: 83.2 years; 69% female) living in one of 24 nursing homes. Independent variables did not rely on self-report: surrogate reports from certified nursing assistants (CNAs) using the Iowa Pain Thermometer (IPT), Checklist of Nonverbal Pain Indicators (CNPI), Cornell Scale for Depression in Dementia (CSDD), Pittsburgh Agitation Scale (PAS), number of painful diagnoses, and Minimum Data Set (MDS) pain variables.
Results:
In univariate analyses, the CNA IPT scores were correlated most highly with self-reported pain. The final multivariate model for self-reported usual pain included CNA IPT, CSDD, PAS, and education; this model accounted for only 14% of the variance. The more extensive of the two final models for worst pain included MDS pain frequency, CSDD, CNA IPT, CNPI, and age (R(2)=0.14).
Conclusion:
Additional research is needed to develop a predictive pain model for nonverbal persons.
