A Randomized Trial of a Computer-Tailored Decision Aid to Improve Prostate Cancer Screening Decisions: Results from the Take the Wheel Trial

Prostate cancer (CaP) is the most commonly diagnosed cancer among men in the United States. Established risk factors (age, family history, black race) are not modifiable (1). Therefore, cancer control efforts have focused on early detection with the prostate-specific antigen (PSA) test for men ages 50 years and over. However, PSA screening remains controversial, and data from two long-awaited trials have only intensified debate about the role of routine screening (2, 3).

Until recently, routine screening for CaP was recommended only by a few organizations, including the American Cancer Society (4) and the American Urological Association (5). Other medical organizations advised men to learn about the potential benefits, limitations, and harms of the test, through a process termed “informed decision making” (IDM; refs. 6-9). In 2010, the American Cancer Society revised their recommendations, emphasizing the importance of informed decision making rather than mass screening (4). According to the U.S. Preventive Services Task Force (USPSTF), an informed decision is one in which an individual: (a) understands the nature of the disease being addressed as well as the risks, limitations, and benefits of clinical services (that is, adequate knowledge); (b) is confident in his ability to participate in decision making at a personally desired level (decision self-efficacy); and (c) has considered his personal preferences and makes a decision consistent with his values (decisional consistency; ref. 10).

Recently, there has been a proliferation of interventions to promote IDM, particularly in the form of decision aids (DA). DAs are tools designed to help individuals make choices by providing pertinent information, presenting data about the likelihood of potential outcomes, and elucidating personal values associated with each option, with a goal of promoting decisional consistency (11, 12). There are few published trials of computer-based DAs for CaP screening (13-16) and, with one exception (15), have all been conducted in clinical settings. The present study was conducted in work sites, a setting that affords access to a large segment of men who are age appropriate for IDM about CaP screening, has existing communication channels that can facilitate promotion efforts, and offers an infrastructure through which intervention activities may be institutionalized and sustained. By targeting particular industries, it is also possible to gain access to workers in particular occupations. We recruited manufacturing industries with a goal of including high proportions of workers, who, by nature of their income and educational level, may have diminished access to information about CaP screening.

The purpose of this randomized trial was to evaluate the efficacy of a computer-tailored DA designed to promote IDM for CaP screening among employed men. Primary outcomes were decisional status (decided/undecided) and individual components of IDM as defined by the U.S. Preventive Services Task Force: knowledge, decision self-efficacy, and decisional consistency (6). Secondary outcomes included desire for involvement in decision making (control preferences) and decisional conflict. We hypothesized that at the conclusion of the study, relative to men in comparison work sites, men in intervention work sites would (a) be more likely to have made a screening decision and (b) show significantly higher levels of knowledge, decision self-efficacy, and decisional consistency. Further, we anticipated that men in the intervention group would desire greater involvement in the decision-making process and would report lower decisional conflict.

In this trial, work sites were the unit of randomization and intervention, whereas individual employees constitute the unit of measurement. Twelve work sites were randomly assigned to a 3-month intervention or to a nonintervention comparison group. Using an intention-to-treat analysis, the efficacy of the DA was evaluated by comparing mean changes in decisional status (decided/undecided) and IDM variables between baseline and follow-up in intervention and comparison sites. A process-tracking system documented intervention delivery and characteristics of DA users.

The Dun & Bradstreet database was used to identify work sites with Standard Industrial Classification codes 20 to 39 that represent manufacturing industries. Additional criteria for site selection included the following: (a) ≥100 men in the targeted age range (45-70 y), (b) <20% employee turnover in the year before study initiation, and (c) location within 90 minutes of the study center (Boston, MA). A total of 161 companies were contacted to determine eligibility, which was assessed through a five-page mailed survey documenting organizational and workforce characteristics. Seventy-one companies (44%) did not return the survey; 74 companies (46%) completed the survey but were ineligible (n = 45 due to size). Four sites (n = 3%) declined participation, citing layoffs, acquisitions, or restructuring as reasons for nonparticipation (see Fig. 1). The 12 participating work sites ranged in size from 100 to 1,000 employees (mean = 650). Sites were blocked on size (total employees <500; ≥500) and percent of male employees (<50%; ≥50%), and randomly assigned by computer-generated random numbers to condition within blocks.

The DA development process followed a strategy described by Bartholomew et al. (17) for the development of theory-based interventions. The first step was articulation of a conceptual framework. We employed the Ottawa Decision Support Framework, which identifies factors that influence decision making and are amenable to modification through decision support (12). The Decision Support Framework also suggests a step-by-step approach for decision making: identification of available options, acquisition of information necessary for decision making, clarification of values relevant to the decision, and development of a plan for action.

Content of the DA was based on expert opinion about information necessary for IDM (10, 18), as well as guidelines from the International Patient Decision Aid Standards. The International Patient Decision Aid Standards is an international group of experts in the field of decision making that has developed guidelines for the creation of high-quality DA tools (19). Criteria include the provision of (a) balanced information about various options based on evidence, (b) information about the probabilities of potential outcomes associated with each option, (c) exercises to elucidate values relevant to available options, and (d) guidance about the development of a plan that will facilitate progress toward the chosen option. Factual content considered “required” for IDM included the following: prevalence of CaP; CaP risk factors; methods used for early detection and their operating characteristics (that is, sensitivity, specificity); potential advantages and disadvantages of screening, and men's evaluation of the relative importance of these factors; the recommendation of major medical organizations that men make individualized decisions; and meaning of an elevated PSA test and methods for diagnosis. After viewing the aforementioned required IDM content, men were able to select from a menu of additional topics (e.g., treatment options) according to their interests.

During the development process, focus groups (k = 4; n = 29) were conducted to gauge men's responses to educational messages and to assess their reactions to various communication strategies. All participants were recruited from work sites not participating in the trial. During these discussions, several themes became evident, including the importance of taking charge of one's health, making independent decisions, and masculinity. In addition, some men used a “road map” analogy to communicate about the various decision points in screening. Therefore, we conceived of the study name “Take the Wheel,” to reflect a masculine theme in which men were taking an active role in decisions about their own health care and “steering their own course” (that is, making decisions). Based on focus group feedback, a functional version of the computer DA was developed and tested among men (n = 10) recruited from nonparticipating work sites to assess acceptability of the graphics, actors, narrator, and script. Following revisions based on audience feedback, the prototype was further refined. Usability testing was conducted with an additional 15 men recruited from nonparticipating sites to assess navigability, efficiency of use, error frequency and severity, and satisfaction among end users. The final DA consisted of interactive video and audio components, with a computer touch screen and minimal on-screen text.

Based on our conceptual model and prior work, the DA was tailored on three characteristics: personal risk for CaP, individual ratings of the pros and cons of screening, and decisional consistency. Personalized risk was calculated using the YourDiseaseRisk algorithm (20). Men input data in response to questions about risk factors and were provided with an on-screen graphic of their risk relative to other men their age (that is, greater than average, average, less than average). In addition, men were asked to weigh the pros and cons of screening. This information was then pictorially presented as a balance scale, with pros on one side and cons on the other. Men were then asked about their screening preference (“decided to be screened,” “undecided,” or “decided not to be screened”). If their rating of the pros/cons was not compatible with their screening decision, the user was informed that these positions were “inconsistent.” In this case, the individual was encouraged to return to the menu of topics to learn more, to take more time to think things over, or to discuss their decision with their health care provider or significant other. To promote self-efficacy with respect to decision making, the DA presented several scenarios with different men going through the decision-making process to role model a variety of decision-making approaches. Men were subsequently coached through the steps of decision making, as outlined by the Decision Support Framework. Additional information about the DA is available elsewhere (21).

Between November 2006 and June 2007, the DA was delivered in intervention sites through tablet computers made available in common gathering areas (e.g., break rooms, cafeterias). We elected to make computers available in public spaces, with the assumption that visibility would generate interest and, thus, promote participation. Each location afforded sufficient privacy so that DA users could sit individually and view the computer screen without their responses being seen by others. Headphones were provided. The DA was designed to be independently administered even for those with minimal or no computer skills. A health educator was available to provide assistance with computers if needed, although no individual required assistance, other than initial start-up of the program.

We used multiple strategies to publicize and promote the intervention, including posters placed in high-visibility areas, distribution of fliers, announcements made at regularly scheduled meetings, and provision of small incentives (e.g., key ring flashlights). Computers were made available in work sites at prespecified days, based on agreements between management and study staff. The computers were available during the day, generally in 6-hour periods, based on managements' request. Each site had at least three computers available on site for a minimum of 15 days over the 3-month intervention period (roughly once per week). Men were allowed release-time from work to use the DA. Information was saved at each time of use; men could either complete the DA session at one time or return at multiple time points to complete it (mean time spent, 28 min). At the conclusion of the session, men were provided with a printed tailored report summarizing their estimated risk for CaP, assessment of pros/cons, decisional status, and pages visited during DA use. This report was designed to facilitate communication about screening with primary care providers.

Baseline data were collected through self-administered pencil-and-paper surveys between September 2006 and March 2007; follow-up assessments were conducted between March 2007 and July 2007. Those eligible were men age ≥45 years who were permanent employees working ≥20 hours per week. Forty-five years was selected as a minimum because some medical organizations advise that men be offered screening at <50 years if they are at higher-than-average risk for CaP (22). Men employed temporarily, or those who worked less than half-time, were excluded from data collection because these factors would diminish access to the intervention. Employee rosters were used to identify eligible men. In sites that had >100 eligible men, a random sample was selected. In sites with 100 men, a census was surveyed.

Eligible men were sent a letter inviting them to participate and that provided informed consent information. Two weeks later, surveys were distributed and collected through company mail or by hand by study staff. The survey cover sheet reiterated informed consent information; completion of surveys was taken as consent to participate. All written communication was at the sixth-grade level. Men were allowed to complete the survey on site during work hours and were provided a financial incentive ($25) after each time point. Nonrespondents were contacted up to three times by electronic mail, telephone, or in person. Across the 12 sites, the mean baseline response rate was 72% (n = 812; range = 59-86%). At follow-up, the mean response rate among those who completed the baseline survey was 79% (n = 639; range = 61-85%), resulting in a final cohort of 625 men. All procedures were approved by Institutional Review Board at the Dana-Farber Cancer Institute.

The validated Stage of Decision Making Scale (23) asked respondents to rate their readiness to make a decision, with five response options ranging from “I haven't thought about it before” to “I have made a decision and I am not likely to change my mind.” Men were classified as having “decided” if they stated either that they had made a decision, but were willing to reconsider, or if they responded that they had made a decision, but were unlikely to change their mind. Those “undecided” reported that they had not thought about the decision, or were uncertain.

Men's recognition of the PSA test was assessed using a standard single item: “The prostate specific antigen test (PSA) is a blood test that is used to find prostate cancer. Before now, had you ever heard of the PSA test?” (yes/no). Fourteen validated questions assessed men's knowledge of CaP prevalence, risk factors, screening modalities, diagnostic procedures, and treatment-related complications (24). The proportion of accurate responses was transformed to a percentage scale ranging from 0% (no correct responses) to 100% (all correct responses). The internal reliability in our sample was adequate (Cronbach's α = 0.69).

The validated 11-item Decision Self-Efficacy Scale with three response categories was used to assess confidence in one's ability to participate in decision making to the extent desired (25). Respondents were asked to reflect on their confidence level about various aspects of the decision-making process, with response options of “very confident” (score = 4) to “not at all confident” (score = 0). Scores were summed, divided by 11, and multiplied by 25, to arrive at a range of scores from 0 (no self-efficacy) to 100 (higher self-efficacy). In this sample, the internal consistency coefficient was high (α = 0.91).

We assessed the congruence between screening preference and personal values relevant to the screening decision, an approach similar to that used by Sepucha et al. (26) in a recent study of breast cancer treatment decisions. First, to assess screening preference, men were asked “If you had to decide now, what would you choose?” Options included “to get a PSA test,” “not to get a PSA test,” and “I could not decide.” In the literature, assessment of values has primarily been measured with probability-based risk-benefit trade-offs (27). We pretested these items in focus groups (k = 1; n = 15) and found them unacceptable to a majority of men. Therefore, we developed items to assess the personal importance or relative worth of the advantages and limitations of screening, based on focus groups themes and published literature (e.g., importance of information, accuracy of test, potential side effects of treatment). Further information about scale development is available elsewhere (28). Individual items are presented in Appendix 1. High positive scores reflect values strongly in favor of screening, and high negative scores reflect low importance of screening (range, +16 to −16). Principle components analysis identified a single factor, providing support for combining all items in one scale. The internal reliability of the values questions was good (α= 0.81). In test-retest assessments 6 months apart, men (n = 812) rated their values consistently (concordance coefficient = 0.682; ref. 28).

Based on a receiver operating characteristic curve of the sensitivity and specificity of responses to questions related to values, we set the cutoff for favorable versus unfavorable value ratings of screening at “zero.” An individual's screening decision was considered “consistent” with their values if they reported an intention to be screened and their values score was greater than zero. Having a negative value score and lack of intention to be screened was considered consistent. Those considered “inconsistent” had value ratings that did not align with screening preference.

Preference for Control in Decision Making was assessed through the Control Preference Scale (29) with a single item. Individuals were asked “Who should make medical decisions?” Response options included the following: (a) “I make the final decision about screening on my own”; (b) “I make the decision after seriously considering my doctor's opinion”; (c) “My doctor and I share responsibility for the decision”; (d) “I prefer that the doctor make the decision after seriously considering my opinion”; and (e) “I prefer that the doctor make the decision.” In analyses, responses were collapsed to reflect active decision-making styles (options a and b), collaborative styles (option c), and passive styles (options d and e; ref. 29).

Decisional Conflict was measured through the validated Decisional Conflict Scale (12). Respondents were asked to rate statements such as: “The decision I made was the best decision possible for me personally.” Reponses were gauged on a five-point scale ranging from “strongly agree” to “strongly disagree.” Scales were standardized from 0 (no conflict) to 100 (extreme conflict). In this sample, internal consistency was good (α = 0.84).

Sociodemographic characteristics, health behaviors, screening history, and access to health care were also assessed, using standard items from the CDC's Behavioral Risk Factor Surveillance Surveys.

The work site was the unit of recruitment and intervention. All analyses incorporated the clustering of respondents within work sites (intraclass correlation coefficient = 0.0068) using generalized estimating equations to fit generalized linear models. An intention-to-treat approach was used in all analyses, using all available data. P values were two sided, and a P value of <0.05 was termed significant. With this sample size, assuming a type one error of 0.05, the design had 79% power when the coefficient of variation (ratio of SD to mean) was 1.2 (30). All analyses were carried out using SAS statistical software, version 9.1 (SAS Institute).

Discrete variables, such as age group, race/ethnicity, and marital status, were compared using χ² tests. The Wilks-Shapiro test was used to assess the normality of continuous variables. Linear regression was used to examine mean change between baseline and follow-up for the outcomes of knowledge, decision self-efficacy, and decisional conflict across intervention and comparison groups. Logistic regression was used to examine follow-up values of the outcomes of decisional status, decisional consistency, and control preferences, controlling for baseline values across intervention and comparison groups.

Control variables included age, race/ethnicity, income, education, marital status, family history of CaP, and previous PSA test. We included some of these variables although they were not significantly associated with outcomes at the P > 0.05 level in bivariate analyses, with a goal of controlling for potential confounding (that is, change in coefficients of ≥10%). However, some outcomes had limited variability (that is, decisional consistency and control preferences). In these cases, we were unable to fit adjusted models with all of the aforementioned control variables. Adjusted analyses for decisional status include only age and education. Adjusted models were not calculable for control preferences or decisional consistency, due to limited variability in responses.

Table 1 summarizes characteristics of the samples at baseline and follow-up, by intervention group. Overall, the cohort was characterized by a slightly larger percentage of non-Hispanic white men, compared with the total baseline sample (92% versus 89%, P < 0.01). Other sociodemographic and health characteristics were not significantly different between baseline and follow-up.

Table 2 presents bivariate relationships among sociodemographic and health characteristics across the range of primary and secondary outcomes. Older men were more likely to show decisional conflict. White race was associated with decreased decisional self-efficacy. Those who had a prior PSA were more likely to be decided, to show decisional consistency, and in addition, had higher levels of decisional conflict.

Table 3 presents changes in primary and secondary outcomes between baseline and follow-up across intervention and comparison sites, in bivariate and adjusted analyses. In these analyses, relative to the comparison group, there was a greater percentage of men in the intervention group who were undecided about screening at baseline and had made a screening decision at follow-up (21% versus 13%, respectively; P = 0.01). Although not an outcome of the study, 77% to 79% of men in both groups preferred CaP screening at both time points. Only 23% of men changed in their screening preferences between baseline and follow-up, and this did not vary by intervention arm. Approximately equal percentages of men in both groups changed from preferring to be screened, to preferring not to be screened (56 men changed from undecided/against screening to want screening, and 66 men changed the preferences in the reverse).

Men in the intervention group experienced greater improvements in knowledge scores than men in the comparison group; more than half (54%) of men in intervention sites had improved knowledge scores versus 39% of men in comparison sites. The average increase was 10 percentage points in the intervention group versus 4 percentage points in the comparison group (P = 0.03). There were no discernable changes in mean decision self-efficacy or decisional consistency scores in either group across time points.

Overall, at both time points across intervention and comparison sites, the majority of men (91-95%) wanted an active or collaborative role in decision making. Preferred role in decision making did not change significantly between baseline and follow-up in either intervention or comparison sites. Mean scores in decisional conflict were low in intervention and comparison groups at baseline (mean scores, 25 versus 18, respectively). In multivariate analyses, men in the intervention group had marginally reduced decisional conflict scores (P = 0.09).

Across the six intervention sites, 30% of the cohort reported using the DA (86 of 291). However, because we used an “intention to treat” model, use of the DA was not required for cohort membership. According to our process-tracking system, overall use of the tool among all age-eligible men in intervention work sites ranged from 23% to 59% (total n = 335 of a possible 1,118 eligible men across sites). DA users did not differ significantly by sociodemographic or health characteristics compared with nonusers (data not shown).

Men who used the DA were significantly more likely to have made a decision about screening at the follow-up survey, compared with nonusers (35% versus 14%, P < 0.01; Table 4). In addition, 79% of users improved their knowledge scores, whereas only 43% of nonusers had improved knowledge at the time of the follow-up survey (+12% points versus +1% points; P < 0.01). DA users also had a greater sense of self-efficacy in decision making; 41% of men in the intervention group versus 38% in the comparison group improved in their decision self-efficacy score (P < 0.01 in bivariate analyses). Nonetheless, decisional consistency was not different between the two groups. Although there were no significant differences between users and nonusers in preferences for control in decision making, DA users experienced a significant decrease in decisional conflict, compared with non-DA users (P = 0.03).

When asked about their satisfaction with the DA, 67% reported that the tool was “very helpful” in making screening decisions, and 70% of users were “very” or “mostly” satisfied with the DA. Importantly, 71% reported that the main message was that “men need to make an individual decision about CaP screening with their medical providers.” However, 26% thought the main message was that “men should get an annual PSA test.”

Access to a computer-tailored DA over a 3-month period at the workplace resulted in a significant increase in the proportion of men who made a decision about CaP screening. In addition, the intervention produced a significant improvement in CaP knowledge, yet mean knowledge scores remained fairly low in both intervention and control groups. Neither decision self-efficacy nor decisional consistency was measurably improved. Yet, men in the intervention group reported marginally reduced decisional conflict. Unlike some prior studies, we used an “intention-to-treat” model in the primary analyses. In subgroup analyses among DA users, men were much more likely to have made a screening decision, have striking improvements in their levels of knowledge, and have increased decision self-efficacy.

Our findings align with some previous IDM trials. The previously cited review found that of 18 trials, 14 reported improvements in knowledge (11). The effect of DAs on decision self-efficacy has received far less attention; only two prior studies assessed this construct, each using one item for assessment. Gattellari et al. (31) found that patients who received DAs had greater confidence in their decision compared with patients who received a leaflet describing CaP screening risks and benefits. In contrast, Frosch et al. (32) reported reduced confidence in screening decisions among DA users compared with individuals in the control condition. Reductions in decisional conflict following DA use have been shown in several studies (31, 33-35). Six (13, 35-39) of nine CaP DA studies (31, 34, 37-43) have also produced a decreased preference for screening, although this was not the case among men in our study. We have been unable to locate any DA trials that evaluated consistency of decision making with one's values; our study found no measurable change in decisional consistency. Only four prior studies have evaluated computer-based DAs (13-16). Of these, two showed improved knowledge (14, 15) and one reported increased desire for decisional control (14). In terms of screening preferences, results have been mixed, with some finding a decreased desire to be screened and others finding an increased desire to be screened (13, 14, 16). Of note, only one of these interventions was tailored to individual user characteristics (although only on the basis of family history; ref. 15).

Before a discussion of implications, limitations of our study must be acknowledged. First, overall use of the DA among men in the intervention group was lower than we had hoped. Prior research has documented low rates of health program participation among males and “blue collar workers” (44). National data show that only 9% of eligible workers participate in available work site wellness programs (44). Thus, the participation rate among eligible men in this study should not have been entirely unexpected, particularly given that CaP is a highly personal topic. Moreover, given production demands in manufacturing work sites, it may have been difficult for men to take time away from their jobs, despite management agreement to let them do so. Still, improving rates of participation is an important goal for future efforts. Second, findings from this sample of predominantly white, employed men may not be generalizable to men of varied racial/ethnic backgrounds or men who are not employed. In particular, African-American men, who are at high risk for CaP, may have unique information needs and concerns (45) that were not addressed here. Given the disproportionate burden of CaP borne by African-American men, development and testing of CaP screening interventions specifically for this audience should be a priority. Third, we used a previously unvalidated measure of decisional consistency. Existing strategies to assess values are based on probability assessments and require a high level of numeracy (27). Although we implemented several strategies (e.g., cognitive testing) to enhance the face and content validity of the measure we developed, additional work to fully evaluate its psychometric properties is needed. In addition, given the high scores on this scale across the sample, a more sensitive measure of values may be needed. Fourth, we used self-reported PSA, which can underreport rates of screening (46). We did find a substantial amount of missing data on PSA history. However, our primary aims were to examine IDM outcomes, not screening participation. Finally, we examined only short-term changes in IDM after a 3-month period. Because PSA screening may be offered annually, it is important to examine how various aspects of decision making change over time.

Nevertheless, this study makes important contributions to the existing literature on interventions to promote IDM for CaP. First, we examined multiple aspects of IDM, as defined by the U.S. Preventive Services Task Force. Prior studies have tended to focus on knowledge, and there has been insufficient study of other features of IDM (47). It is important that interventions assess all aspects of IDM because knowledge is not the sole criterion for high-quality decision making. Second, we report results from a computer-tailored DA. To date, the vast majority of DAs have been nontailored and have taken the form of videos or written materials. As noted, there are numerous advantages to using computer technology to deliver tailored IDM messages. Finally, we present data from an intervention offered in the work setting. To our knowledge, this is the first trial of a workplace intervention directed at IDM for CaP. Most interventions have been conducted in clinical settings despite the call for providing interventions in community settings (10).

The results of this study point to several important issues for consideration in future interventions. First, use of the DA resulted in an increase in decision making, improved knowledge, and enhanced self-efficacy. However, decisional consistency was consistently high and unaffected. This may have been due to “ceiling effects” in the measurement of this variable. Alternatively, it may be that many men overestimate the efficacy of screening and their providers' endorsement of PSA, leading them to disregard the potential disadvantages and harms (33, 48). We found that men are confident in their decision-making capabilities and are making decisions consistent with their values, even in the absence of what most would consider adequate knowledge. Are these truly informed decisions? Future research should consider whether it would be reasonable to identify a required level of knowledge about the benefits, risks, and limitations of CaP screening as a prerequisite for IDM, such is the case for informed consent (18).

Second, more research is needed on how to create “balanced” messages in the context of IDM interventions. Communicating the uncertain balance between potential benefits (e.g., early detection) and harms (e.g., risk of overtreatment) of CaP screening presents a major challenge. In our study, nearly a quarter of DA users thought the main message was that “men should be screened annually.” Other IDM studies have similarly reported that men interpreted educational messages as promoting screening (49). There is ample evidence that message framing and varied formats for presenting risk information can affect decisions (50). Experimental manipulation of these factors may be informative for development of future DAs.

Based on these findings, we conclude that a computer-tailored DA offered at the workplace can be effective in promoting IDM, but participation among male, blue collar workers in this study was suboptimal. A recent review of health promotion programs offered in the work site found that less than 10% of employees accessed program components required for successful interventions (44). It is clear that better strategies for engaging men, particularly those of diverse racial/ethnic backgrounds, are needed. Work sites are a logical venue for reaching men, and many already offer health promotion programs (44). Integrating health topics, such as IDM for CaP screening, into existing programs that are more highly used (e.g., blood pressure screening) could potentially enhance programmatic dose and reach. Framing interventions in terms of “men's health” rather than “cancer screening” may also draw a larger audience. Efforts might include engaging men in more socially relevant or trusted environments (e.g., barbershops, sporting events) where they may be more likely to discuss health issues with one another. Given the effect of the DA among those who used it, we are optimistic that using interactive state-of-the-art technology will help to better engage men and will facilitate the ultimate goal of preparing them to participate in complex decisions about their medical care.

No potential conflicts of interest were disclosed.

We thank the following individuals for their contributions: Christian Brown, Emily Chasson, Stephen Flaherty, Josh Gagne, Elizabeth Harden, Kerry Kokkinogenis, Ruth Lederman, Susan McCabe, Jodi Saia-Witte, Rachel Shelton, Larry Shiman, Jamielle Walker, and David Wilson, and the men who took part in this study and to the participating work sites.

Grant Support: Centers for Disease Control and Prevention (Grant 3U48DP000064-01S1, SIP 21-04 Community Intervention to Increase IDM for Prostate Cancer).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.