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Updated: Sep 2, 2025

Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing
Published on: February 16, 2017
Optimizing chronic pain management through patient engagement with quality of life measures: a randomized controlled
John C Licciardone1, Hanna McDonald2, McKenna Yablon2
1Osteopathic Research Center and the Department of Family Medicine, University of North Texas Health Science Center-Texas College of Osteopathic Medicine, Fort Worth, TX, USA.
Context:
Health-related quality of life (HRQOL) represents a new approach for guiding chronic pain management because it is patient-centered and more likely to be understood and accepted by patients.
Objectives:
To assess the value and utility of an eHealth intervention for patients with chronic low back pain (CLBP) that was primarily based on HRQOL measures and to measure the clinical outcomes associated with its use.
Methods:
A randomized controlled trial was conducted within the Pain Registry for Epidemiological, Clinical, and Interventional Studies and Innovation (PRECISION Pain Research Registry) using participants screened from November 2019 through February 2021. A total of 331 registry participants within the 48 contiguous states and the District of Columbia met the eligibility criteria, which included having CLBP and HRQOL deficits. Almost three-fourths of the participants were enrolled after onset of the COVID-19 pandemic. The participants were randomized to an eHealth intervention for HRQOL or wait list control. The primary outcome measures involved HRQOL based on the Patient-Reported Outcomes Measurement Information System (PROMIS), including the SPADE cluster (Sleep disturbance, Pain interference with activities, Anxiety, Depression, and low Energy/fatigue) and each of its five component scales. Secondary outcome measures involved low back pain intensity and back-related functioning. Changes over time for each outcome measure reported by participants in each treatment group were compared utilizing the student's t-test for statistical significance and Cohen's d statistic for clinical importance. Outcomes were reported as between-group differences in change scores and the d statistic, with positive values favoring the experimental treatment group.
Results:
There were no significant differences between the experimental and control treatment groups for changes over time in any primary outcome measure. The d statistic (95% confidence interval) for the difference between the experimental and control treatment groups on the SPADE cluster was 0.04 (-0.18-0.25). The corresponding d statistics for the SPADE scales ranged from -0.06 (-0.27 to 0.16) for anxiety to 0.11 (-0.10 to 0.33) for sleep disturbance. There were also no significant or clinically important differences between the experimental and control treatment groups on the secondary outcome measures. Additionally, in subgroup analyses involving participants treated by osteopathic vs allopathic physicians, no significant interaction effects were observed.
Conclusions:
The eHealth intervention studied herein did not achieve statistically significant or clinically important improvements in any of the primary or secondary outcome measures. However, the validity and generalizability of the findings may have been limited by the unforeseen onset and impact of the COVID-19 pandemic shortly after beginning the trial.
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