Editorial: Demystifying the Placebo Effect

Vividness of visual imagery is associated with the effect of relaxation response meditation training in elderly people with nonorganic insomnia: A randomized, double-blind, multi-center clinical trial

BACKGROUND

This study aims to explore the efficacy of Relaxation Response Meditation Training (RRMT) on elderly individuals with different levels of vividness of visual imagery.

METHODS

In this randomized controlled, double-blind, multi-center clinical trial, we recruited a total of 136 elderly individuals who were over 60 years with nonorganic sleep disorders to participate in a 4-week RRMT intervention from October 2020 to October 2022. The intervention occurred twice a week, totaling eight times. These individuals were divided into high and low groups based on the vividness of visual imagery, and then randomly assigned to either the control or intervention groups, as follows: low-visualizers intervention group (LI group); low-visualizers control group (LC group); high-visualizers intervention group (HI group); high-visualizers control group (HC group). Their social and psychological parameters were assessed before and after the intervention by the Pittsburgh Sleep Quality Index (PSQI), the Revised Piper's fatigue scale (RPFS), General well-being scale (GWB), and Satisfaction rating. The alpha waves of patients were also collected through electroencephalogram to assess their level of relaxation.

RESULTS

Compared to the LI group, the HI group had a greater reduction rate in the PSQI score [25.2 % (18.8 % to 31.7 %), P < 0∙001], shorter sleep latency (P = 0.001), lower frequency of sleep medication (P < 0.001), lower PSQI scores (P < 0.001), and higher GWB scores (P < 0.001). There were significant differences in all indicators in the HI group vs. HC group and in the LI group vs. LC group. In the first five relaxation training sessions, there was no statistically significant difference in the proportion of α waves between the LI group and the LC group; however, from the sixth session onward, we observed a statistically significant difference (t = 2.86, P = 0.019)，while The HI group and HC group showing significant differences in the first relaxation training session (t = 4.464, P < 0.001). There was a statistically significant difference in subjective satisfaction between the intervention group and the control group (x2 = 49.605, P < 0.001).

CONCLUSION

In this study, we found that most elderly people benefitted from RRMT regardless of their vividness of visual imagery. However, low-visualizers experienced slower and less effective results, so these patients may benefit more from alternative approaches.

Distinguishing Immunologic and Behavioral Effects of Vaccination

The interpretation of vaccine efficacy estimands is subtle, even in randomized trials designed to quantify the immunologic effects of vaccination. In this article, we introduce terminology to distinguish between different vaccine efficacy estimands and clarify their interpretations. This allows us to explicitly consider the immunologic and behavioral effects of vaccination, and establish that policy-relevant estimands can differ substantially from those commonly reported in vaccine trials. We further show that a conventional vaccine trial allows the identification and estimation of different vaccine estimands under plausible conditions if one additional post-treatment variable is measured. Specifically, we utilize a "belief variable" that indicates the treatment an individual believed they had received. The belief variable is similar to "blinding assessment" variables that are occasionally collected in placebo-controlled trials in other fields. We illustrate the relations between the different estimands, and their practical relevance, in numerical examples based on an influenza vaccine trial.

Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues

Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. However, policy impact evaluations require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate strength of evidence in COVID-19 health policy papers. We (1) introduce the basic suite of policy impact evaluation designs for observational data, including cross-sectional analyses, pre/post, interrupted time-series, and difference-in-differences analysis, (2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19, and (3) provide decision-makers and reviewers a conceptual and graphical guide to identifying these key violations. The overall goal of this paper is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.