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Purpose: Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet some people may react to overdiagnosis with doubt and skepticism. The present research assessed 4 related reactions to overdiagnosis: reactance, self-exemption, disbelief, and source derogation (REDS). The degree to which the concept of overdiagnosis conflicts with participants' prior beliefs and health messages (information conflict) was also assessed as a potential antecedent of REDS. We developed a scale to assess these reactions, evaluated how those reactions are related, and identified their potential implications for screening decision making., Methods: Female participants aged 39 to 49 years read information about overdiagnosis in mammography screening and completed survey questions assessing their reactions to that information. We used a multidimensional theoretical framework to assess dimensionality and overall domain-specific internal consistency of the REDS and Information Conflict questions. Exploratory and confirmatory factor analyses were performed using data randomly split into a training set and test set. Correlations between REDS, screening intentions, and other outcomes were evaluated., Results: Five-hundred twenty-five participants completed an online survey. Exploratory and confirmatory factor analyses identified that Reactance, Self Exemption, Disbelief, Source Derogation, and Information Conflict represent unique constructs. A reduced 20-item scale was created by selecting 4 items per construct, which showed good model fit. Reactance, Disbelief, and Source Derogation were associated with lower intent to use information about overdiagnosis in decision making and the belief that informing people about overdiagnosis is unimportant., Conclusions: REDS and Information Conflict are distinct but correlated constructs that are common reactions to overdiagnosis. Some of these reactions may have negative implications for making informed screening decisions., Highlights: Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet when provided information about overdiagnosis, some people are skeptical.This research developed a measure that assessed different ways in which people might express skepticism about overdiagnosis (reactance, self-exemption, disbelief, source derogation) and also the perception that overdiagnosis conflicts with prior knowledge and health messages (information conflict).These different reactions are distinct but correlated and are common reactions when people learn about overdiagnosis.Reactance, disbelief, and source derogation are associated with lower intent to use information about overdiagnosis in decision making as well as the belief that informing people about overdiagnosis is unimportant., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by a grant from the National Cancer Institute, R37CA254926. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Background: Despite widespread use, there are few studies evaluating the consumer Choosing Wisely questions., Methods: We evaluated the impact of the Choosing Wisely questions on consumers' decision-making outcomes. Adults living in Australia were presented with a hypothetical low-value care scenario. Using a 2×2×2 between-subjects factorial design, they were randomized to either the Choosing Wisely questions ("Questions"), a shared decision-making (SDM) preparation video ("Video"), both interventions, or control (no intervention). Primary outcomes were 1) self-efficacy to ask questions and be involved in decision-making and 2) intention to engage in SDM., Results: A total of 1,439 participants (45.6% with "inadequate" health literacy) were eligible and included in the analysis. Intention to engage in SDM was higher in people randomized to the Video (mean difference [MD] = 0.24 [scale 0-6], 95% confidence interval [CI]: 0.14, 0.35), Questions (MD = 0.12, 95% CI: 0.01, 0.22), and both interventions (MD = 0.33, 95% CI: 0.23-0.44, P  < 0.001, d  = 0.28) compared with control. Combining interventions had a greater impact than presenting the Questions alone (MD = 0.22, 95% CI: 0.11, 0.32; P  < 0.001). Those who received the Video or both interventions reported lower intention to follow the low-value treatment plan without further questioning (all P  < 0.05) and more positive attitudes toward SDM (all P  < 0.05) compared with control. Intervention acceptability was high in all study arms (>80%), but proactive access was low (1.7%-20.8%). Compared with control, participants who received one or both interventions asked more questions that mapped to the Choosing Wisely questions (all P  < .001). There were no main effects of either intervention on self-efficacy or knowledge., Conclusions: The Choosing Wisely questions and a video to promote SDM may improve intention to engage in SDM and support patients in identifying questions that align with the Choosing Wisely campaign (with some additional benefits of the video intervention)., Trial Registration: ANZCTR376477., Highlights: We conducted a randomized controlled trial online with adults living in Australia to test the effectiveness of the consumer Choosing Wisely questions and a shared decision-making (SDM) preparation video.Both interventions improved intention to engage in SDM and supported participants to identify questions that align with the Choosing Wisely campaign.There were some additional benefits of the Video intervention in reducing willingness to accept low-value treatment for low-back pain without asking questions; however, neither intervention changed participants' self-efficacy to ask questions and be involved in decision-making nor affected perceptions of preparedness to engage in SDM or knowledge of rights to be involved in health care decision-making.The simple, low-cost nature of the interventions may make them appropriate for implementation within a suite of approaches to address low-value care at a population level.

Background: A 2-stage randomized trial design, incorporating participant choice, provides unbiased estimates of the effects of the treatment or intervention (treatment effect), the difference between outcomes for participants who prefer one treatment compared with another (selection effect), and the interaction between participants' preferences for treatment and the treatment actually received (preference effect). It is important to ensure that such trials are adequately powered to estimate these effects., Sample Size Formulas: This paper presents methods for determining the required sample sizes for estimating treatment, selection, and preference effects. We demonstrate the changes in sample size as various key parameters are changed. In general, approximately twice as many participants (in total) are needed to have equivalent power for detecting both treatment and selection/preference effects compared with a trial of the treatment effect alone., Primary Screening Example: We illustrate their application for the design of a primary screening trial comparing human papillomavirus DNA testing versus cervical screening (by Pap smear). Our example would require 520 participants to have 80% power to detect moderate-sized preference and selection effects and a small to moderate treatment effect., Conclusions: With the growing interest in understanding treatment choices and with the use of decision aids, well-designed and adequately powered 2-stage randomized trial designs offer the opportunity to determine the effects of participants' preferences. Our sample size formulas will help future studies ensure that they have adequate power to detect selection and preference effects., (© The Author(s) 2014.)

Background: Making informed decisions about cancer screening involves understanding the benefits and harms in conjunction with personal values. There is little research examining factors associated with informed decision making or participation in screening in the context of a decision aid trial., Objectives: To identify factors associated with informed choice and participation in fecal occult blood testing (FOBT) among lower education populations., Design: Randomized controlled trial of an FOBT decision aid conducted between July and November 2008., Setting: Socioeconomically disadvantaged areas in New South Wales, Australia., Participants: Included 572 adults aged 55 to 64 years with lower education., Measurements: Sociodemographic variables, perceived health literacy, and involvement preferences in decision making were examined to identify predictors of informed choice (knowledge, attitudes, and behavior)., Results: Multivariate analysis identified independent predictors of making an informed choice as having higher education (relative risk [RR], 1.49; 95% confidence interval [CI], 1.13-1.95; P = 0.001), receiving the decision aid (RR, 2.88; 95% CI, 1.87-4.44; P < 0.001), and being male (RR, 1.48; 95% CI, 1.11-1.97; P = 0.009). Participants with no confidence in completing forms and poorer self-reported health were less likely to make an informed choice (RR, 0.74; 95% CI, 0.53-1.03; P = 0.05 and RR, 0.57; 95% CI, 0.36-0.89; P = 0.007, respectively). Independent predictors of completing the FOBT were positive screening attitudes, receiving the standard information, preference for making the decision alone, and knowing that screening may lead to false-positive/negative results., Limitations: We did not objectively measure health literacy., Conclusions: Participants with the lowest levels of education had greater difficulties making an informed choice about participation in bowel screening. Alternative methods are needed to support informed decision making among lower education populations., (© The Author(s) 2014.)

Objective: To test optimal graphic risk communication formats for presenting small probabilities using graphics with a denominator of 1000 to adults with lower education and literacy., Methods: A randomized experimental study, which took place in adult basic education classes in Sydney, Australia. The participants were 120 adults with lower education and literacy. An experimental computer-based manipulation compared 1) pictographs in 2 forms, shaded "blocks" and unshaded "dots"; and 2) bar charts across different orientations (horizontal/vertical) and numerator size (small <100, medium 100-499, large 500-999). Accuracy (size of error) and ease of processing (reaction time) were assessed on a gist task (estimating the larger chance of survival) and a verbatim task (estimating the size of difference). Preferences for different graph types were also assessed., Results: Accuracy on the gist task was very high across all conditions (>95%) and not tested further. For the verbatim task, optimal graph type depended on the numerator size. For small numerators, pictographs resulted in fewer errors than bar charts (blocks: odds ratio [OR] = 0.047, 95% confidence interval [CI] = 0.023-0.098; dots: OR = 0.049, 95% CI = 0.024-0.099). For medium and large numerators, bar charts were more accurate (e.g., medium dots: OR = 4.29, 95% CI = 2.9-6.35). Pictographs were generally processed faster for small numerators (e.g., blocks: 14.9 seconds v. bars: 16.2 seconds) and bar charts for medium or large numerators (e.g., large blocks: 41.6 seconds v. 26.7 seconds). Vertical formats were processed slightly faster than horizontal graphs with no difference in accuracy. Most participants preferred bar charts (64%); however, there was no relationship with performance., Conclusions: For adults with low education and literacy, pictographs are likely to be the best format to use when displaying small numerators (<100/1000) and bar charts for larger numerators (>100/1000).

Background: The Rucker 2-stage randomized trial (RCT) design and method allows treatment, preference, and selection effects to be estimated separately in clinical trials., Objective: To understand the effect of patient choice on patient outcomes, the authors applied the Rucker design and analysis method., Design: They used data from a trial of management strategies for women with atypical cells of undetermined significance (ASCUS) detected at routine cervical screening, in which informed choice using a decision aid was compared to no choice., Setting: Women's health clinics across Australia., Patients: Women aged 18 to 70 years (n = 314) with ASCUS., Intervention: Women were randomized to either an informed choice of human papillomavirus (HPV) triage testing or repeat Pap testing or to no choice with random allocation to management by either option., Measurements: Health-related quality of life (SF36) and satisfaction were measured over the course of management and up to 1 year after triage., Results: Using the Rucker analysis, patients who received their choice had higher quality of life scores than those who did not choose (SF36 MCS, 6% higher, 6.0; 95% confidence interval: -0.6 to 12.9; P = 0.07; effect size 0.61 [moderate]). In contrast, the traditional RCT analysis suggested there was little difference in quality of life between the choice and no-choice trial arms., Limitations: The Rucker method assumes that the declared preferences for treatment in the choice arm are representative of the preferences that would have been observed in the no-choice arms if choice was available., Conclusions: The Rucker method should be used to estimate treatment, preference, and selection effects in randomized trials, as it adds to our understanding of the effect of choice on patient outcomes.

There have been major advances in techniques to increase patient involvement in health decisions with the benefits of greater involvement and shared decision making now widely recognized. However, there has been little attention in the development of tools and strategies to support patient participation among adults with lower literacy, a group with poor health knowledge, limited involvement in health decisions, and poor health outcomes. The authors put forward a framework to consider the different stages of shared health decision making and the tasks and skills required to achieve each stage. They consider where current research exists in the decision making literature and where more is needed if adults with limited literacy are to be better engaged in shared decision making in health care.