The Effectiveness of Aromatherapy in Reducing Pain: A Systematic Review and Meta-Analysis

Abstract

Background. Aromatherapy refers to the medicinal or therapeutic use of essential oils absorbed through the skin or olfactory system. Recent literature has examined the effectiveness of aromatherapy in treating pain. Methods. 12 studies examining the use of aromatherapy for pain management were identified through an electronic database search. A meta-analysis was performed to determine the effects of aromatherapy on pain. Results. There is a significant positive effect of aromatherapy (compared to placebo or treatments as usual controls) in reducing pain reported on a visual analog scale (SMD = -1.18, 95% CI: -1.33, -1.03; p < 0.0001). Secondary analyses found that aromatherapy is more consistent for treating nociceptive (SMD = -1.57, 95% CI: -1.76, -1.39, p < 0.0001) and acute pain (SMD = -1.58, 95% CI: -1.75, -1.40, p < 0.0001) than inflammatory (SMD = -0.53, 95% CI: -0.77, -0.29, p < 0.0001) and chronic pain (SMD = -0.22, 95% CI: -0.49, 0.05, p = 0.001), respectively. Based on the available research, aromatherapy is most effective in treating postoperative pain (SMD = -1.79, 95% CI: -2.08, -1.51, p < 0.0001) and obstetrical and gynecological pain (SMD = -1.14, 95% CI: -2.10, -0.19, p < 0.0001). Conclusion. The findings of this study indicate that aromatherapy can successfully treat pain when combined with conventional treatments.

Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1

Flowchart of studies that met inclusion/exclusion criteria for qualitative and quantitative analyses.

Figure 2

Publication bias funnel plot. A funnel plot was used to assess risk of publication bias. A symmetrical funnel plot is an indicator for lack of bias in a meta-analysis. A funnel plot loses its utility with a cut-off of 10 studies and this analysis included only 12. The funnel plot for this final analysis was not fully symmetrical, but publication bias cannot be concluded based on the small sample size and heterogeneity of studies. The diagonal lines represent the limits of 95% confidence. Because strict 95% limits are not reported, they are referred to as “pseudo 95% confidence limits.”

Figure 3

Forest plot: results of all included studies. This forest plot summarizes the results of all included studies. The numbers on the x-axis measure treatment effect. The gray squares represent the weight of each study. The larger the sample size, the larger the weight and the size of gray box. The small black boxes with the gray squares represent the point estimate of the effect size and sample size. The black lines on either side of the box represent a 95% confidence interval.

Figure 4

Forest plot: nociceptive versus inflammatory pain. This forest plot summarizes the results of nociceptive pain studies and inflammatory pain studies. The numbers on the x-axis measure treatment effect. The gray squares represent the weight of each study. The larger the sample size, the larger the weight and the size of gray box. The small black boxes with the gray squares represent the point estimate of the effect size and sample size. The black lines on either side of the box represent a 95% confidence interval.

Figure 5

Forest plot: acute versus chronic pain. This forest plot summarizes the results of acute pain studies and chronic pain studies. The numbers on the x-axis measure treatment effect. The gray squares represent the weight of each study. The larger the sample size, the larger the weight and the size of gray box. The small black boxes with the gray squares represent the point estimate of the effect size and sample size. The black lines on either side of the box represent a 95% confidence interval.

Figure 6

Forest plot: postoperative pain. This forest plot summarizes the results of postoperative pain studies. The numbers on the x-axis measure treatment effect. The gray squares represent the weight of each study. The larger the sample size, the larger the weight and the size of gray box. The small black boxes with the gray squares represent the point estimate of the effect size and sample size. The black lines on either side of the box represent a 95% confidence interval.

Figure 7

Forest plot: obstetrical and gynecological pain. This forest plot summarizes the results of obstetrical and gynecological pain studies. The numbers on the x-axis measure treatment effect. The gray squares represent the weight of each study. The larger the sample size, the larger the weight and the size of gray box. The small black boxes with the gray squares represent the point estimate of the effect size and sample size. The black lines on either side of the box represent a 95% confidence interval.