Objectives: Acute postoperative pain (APP) is the main cause of postoperative dissatisfaction; however, traditional methods of pain assessment provide limited insights into the dynamics and development of APP. This study used the experience sampling method to understand the dynamics of APP over time in relation to various patient factors.
Materials and methods: Forty patients scheduled to undergo total knee replacement surgery were recruited in this study. Following an initial assessment, a short report questionnaire was sent to the patients through 10 digital alerts per day to assess the pain levels during 2 preoperative and the first 6 postoperative days. The data were analyzed using multilevel regression, including random intercept and slope.
Results: Thirty-two patients submitted the prespecified minimum of 30% of their short reports, yielding 1217 records. The analysis revealed significant ( P <0.001) linear and quadratic decreases in APP and a quadratic time effect. The lowest between-day and within-day pain levels were observed on postoperative day 4.8 and during the time slot 3.8 or ~19:15, respectively. Significant random intercepts and slopes were noted, indicating variations in the mean pain level between patients and a decrease in pain. None of the 10 patient factors had any confounding effect.
Discussion: Using the experience sampling method data combined with multilevel analysis, we were able to evaluate the postoperative pain course while considering inter-individual differences in the baseline pain level and nonlinear pain course over time. The findings of this study could aid clinicians in personalizing the treatment for APP.
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