The role of perceived benefits and barriers in colorectal cancer screening in intervention trials among African Americans

The role of perceived benefits and barriers in colorectal cancer screening in intervention trials... Abstract The Health Belief Model (HBM) is widely used in health behavior interventions. The lack of diverse samples in the development of this theory warrants additional study on how it performs among minorities. While studies have utilized HBM to address colorectal cancer (CRC) screening, limited information exists confirming how these constructs influence screening. Data from three CRC screening trials were used to examine how perceived benefits/barriers perform among African Americans (AA) and whether they serve as mechanisms of the intervention effects on screening. The data were collected in AA churches (Study 1: N = 103; Study 2: N = 285; Study 3: N = 374) where lay members conducted CRC education to increase screening. Participants perceived benefits from colonoscopy (M = 2.4/3, SD = 0.87) and perceived few barriers (M = 0.63/8, SD = 1.1). Benefits were perceived for the fecal occult blood test (M = 11.4/15, SD = 2.1), and few barriers were reported (M = 11.7/30, SD = 3.4). Benefits more consistently predicted pre-intervention screening relative to barriers. For Study 3, individuals with fewer barriers reported a greater increase in colonoscopy screening at 12-months versus those with higher barriers (OR = 0.595, 95% CI = 0.368–0.964), P = 0.035). Benefits/barriers did not mediate the relationship. Potential measurement limitations, particularly for barriers, were uncovered and further research on how to assess factors preventing AA from screening is needed. Introduction Health behavior theory involves a set of interrelated variables that predict behavior change and theory-based constructs often serve as the basis for health behavior intervention development [1–3]. Beyond being used for design, theory can be used to determine intervention mechanism(s). Specifically, theoretical construct(s) may be manipulated to assess its impact on the outcome of interest. Theory-based constructs have also been analyzed to determine differential intervention effects on the outcome(s). Studies have evaluated theoretical mediators and moderators of intervention outcomes for behaviors including weight loss, physical activity and mammography screening, and some have uncovered full or partial mediation [4–19] or moderation [20, 21]. Use of theory in colorectal cancer (CRC) screening interventions Despite substantial evidence that suggests CRC screening is effective in reducing disease related mortality [22], it is underutilized even with continued research towards improving screening rates [23]. Previous theoretically driven interventions have been implemented aiming to increase CRC screening [24–26]. While studies have evaluated the predictive weights of these theoretical constructs [27, 28], limited information exists regarding the pathways through which these constructs influence CRC screening in intervention trials [29]. Additional study is needed to uncover the theoretical mechanisms of intervention effects on CRC screening. Utility of the health belief model in predicting CRC screening behavior The Health Belief Model (HBM) is a widely used theory for the planning and implementation of interventions aimed at health behavior change [1–3, 30] and is often applied to understanding why individuals engage or do not engage in preventive services, including CRC screening. However, in a review of the literature on individual-level behavioral constructs and CRC screening behavior, Kiviniemi et al. [28] found that while many studies suggest a relationship between theoretical constructs on cancer screening behavior, approximately one-third of these studies reported no relationship between the construct and CRC screening behavior. Given these findings, it is important to continue to research the relationship between theory and CRC screening behaviors. Further, many studies have not gone beyond analyzing associations between HBM constructs and screening behavior. We were unable to find CRC intervention studies that conducted mediation or moderation analysis to examine whether the HBM constructs served as mechanisms of intervention effects on CRC screening or had a differential effect on the intervention outcomes. Applicability of the HBM among African Americans’ CRC screening behaviors Despite the wide use of the HBM, its utility and applicability in diverse populations has not been well studied [31–33]. African Americans are disproportionately impacted by cancer and while the HBM constructs have been used to develop interventions targeted at improving screening rates in this population, most individual-level behavior change theories, including the HBM, were not developed for minorities and the reliability and validity of the measures are not well known for minority populations [34–37]. Analyzing how these items perform across large samples, including diverse populations, will help determine how these measures should be refined. It is important for both researchers to understand if individual-level constructs are important in explaining CRC screening behaviors among culturally diverse populations which may be used for improved development of interventions tailored to minority groups, such as African Americans. The present study The current study analyzes selected HBM theoretical constructs, specifically perceived benefits of CRC screening and perceived barriers to CRC screening, among African American men and women living in mid-Atlantic and Southern regions of the United States, using data from three comparable intervention studies that aimed to increase CRC screening. In the present analysis, we addressed three research questions: (i) How do perceived benefits of screening and perceived barriers to screening indices perform (i.e. variability, floor or ceiling effects) among African American men and women from the two study regions?; (ii) How useful are benefits/barriers in predicting CRC screening adherence before intervention?; and (iii) Do benefits/barriers serve as mechanisms by which the study interventions impact CRC screening behavior? We hypothesized that perceived benefits and barriers would predict pre-intervention screening behavior such that perceived barriers would be inversely related to screening and perceived benefits would be positively associated with screening. Additionally, we hypothesized that targeting perceived benefits and barriers in the interventions would explain changes in screening behavior at 12-month follow-up. Specifically, individuals who were exposed to the study interventions would exhibit higher perceived benefits and decreased barriers and that targeting these constructs in the interventions would result in increased CRC screening rates at follow-up. Materials and methods Intervention study methods This section provides an overview of the three intervention studies led by the PI (CH) that provided the data for the current analyses. Additional detail for each is provided in the original papers and we describe them briefly in the section that follows [38, 39, 40, Holt, under review]. The intervention materials and measurement included in these studies was based on the HBM as the theoretical framework. The material content specifically aimed to outline benefits of CRC screening and to clarify and reduce perceptions about barriers to screening. Each of these studies aimed to increase knowledge and awareness of CRC and early detection and to ultimately increase screening. The current analyses focuses on fecal occult blood test (FOBT) and colonoscopy because of the availability of HBM-specific measures for these two screening methods [41], and because colonoscopy and FOBT are two of the screening modalities recommended by multiple major organizations [22, 42]. All of the studies were implemented in African American churches where we trained lay peer community health advisors to conduct educational activities on CRC with the intent of increasing screening. In all of the studies, the community health advisors distributed culturally targeted print materials on CRC developed by the study team in partnership with local community stakeholders. The studies used harmonized measures including common data elements, [43] which facilitated the current analyses. Data from multiple studies can be harmonized to the extent that the studies are reasonably comparable in elements such as objectives, settings, conceptual framework, design, implementation, participant eligibility, procedures and outcomes [44]. Two of the three studies used a randomized trial design with an intervention and comparison group (e.g. spiritually based compared to non-spiritual intervention materials of the same core CRC content). In the current analyses, we do not combine or pool data from multiple studies as we were interested in potential regional differences but rather we compare findings across the three trials. ‘Your body is the temple’ Hereafter referred to as the ‘Temple’ study [38], this was the first study in the series, named after a scriptural passage involving health. The Temple study was conducted in two Alabama churches. This was the only intervention of the three that did not use formal educational workshops but rather the community health advisors used one-on-one, small group discussions and educational print materials to implement the intervention among their church members. Due to its more modest scope, this is also the only trial of the three that used a pre-post study design, rather than a cluster randomized design. Data collection was conducted at baseline, 6- and 12-month follow-up. One hundred three individuals participated in this study. ‘Take charge of your health’ Hereafter referred to as the ‘Take Charge’ study [39], this was the second trial in the series, also named through a community-engaged process. Take Charge was conducted in 16 Alabama churches. The trial used a randomized design comparing a spiritually based version of the intervention with a non-spiritual comparison of the same core CRC content. The intervention consisted of a series of two group educational workshops on CRC screening led by trained community health advisors. Data collection was conducted at baseline, 1- and 12-month follow-ups. Of the 316 enrolled, 285 participants completed the baseline and follow-up assessments. ‘Project HEAL’ Project HEAL was the third study [Holt, under review], also named through a community-engaged process. Project HEAL took place in 14 Maryland churches, and consisted of a series of three group educational workshops led by trained community health advisors, however CRC was only discussed in the third workshop. This trial also used a cluster-randomized design where churches were assigned to one of two strategies for training the community health advisors. Data collection was conducted at baseline, workshop 3, 12- and 24-month follow-ups. Three hundred seventy-five church members enrolled in Project HEAL with 309 participants completing the 24-month follow-up. These studies were approved by their respective Institutional Review Boards and informed consent was obtained from all individual participants included in these studies. Data harmonization The three interventions used common data elements that enabled direct comparisons across studies. We examined the instruments to verify common data elements (items) and items were eliminated that were not present in all three studies, this resulted in comparable data. While not pooled, the data were standardized for comparison of analyses across the three studies (see Table I). The HBM constructs were assessed using previously validated instruments. Not all the HBM constructs were included in the current analysis because not all were assessed in each of the intervention studies in part due to lack of available validated measures at the time the research was conducted. In some cases, items were eliminated from scale score compositions to ensure exact correspondence between the three data sets (e.g. one study used a 6-item version of a scale where another used a 7-item version, therefore, the current analysis used the six common items). Table I. Participant demographics for the temple, take charge & HEAL trialsa,b Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 a Values are percentages unless otherwise indicated. b ** indicates category not assessed. Table I. Participant demographics for the temple, take charge & HEAL trialsa,b Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 a Values are percentages unless otherwise indicated. b ** indicates category not assessed. Perceived benefits of and perceived barriers to fecal occult blood test (FOBT) Perceived benefits of and barriers to the FOBT were measured using items (three for benefits; six for barriers; e.g. ‘An FOBT will help find CRC early’; ‘An FOBT is embarrassing’.) validated in previous research [41]. Items used a strongly agree, agree, neutral, disagree, or strongly disagree format and were summed to form benefits and barriers indices with higher scores indicating greater benefits/barriers. Internal consistency reliability was computed across the three studies (Cronbach’s alphas ranged from 0.49 to 0.78 for benefits and 0.53 to 0.89 for barriers). Perceived benefits of and perceived barriers to colonoscopy Similarly, perceived benefits/barriers specific to colonoscopy (e.g. ‘…help you not worry as much about CRC’. ‘Having to follow a special diet and take a laxative or enema would keep me from having a colonoscopy’) were assessed using items (three for benefits; eight for barriers) validated in previous research [41] and assessed in agree/disagree/not sure format. Items were summed to form benefits and barriers indices with higher scores indicating greater benefits/barriers (Cronbach’s alpha’s ranged from 0.27 to 0.79 for benefits and 0.58 to 0.60 for barriers). The lower alpha score for the perceived benefits is most likely because of the number of items within the index which directly impacts reliability. CRC screening Self-report CRC screening behavior (FOBT; colonoscopy) was assessed using items from the Behavioral Risk Factor and Surveillance System [45]. Definitions were provided about the screening methods and response categories were based on screening recommendations. Screening adherence was operationalized as within the past 2 years for FOBT and within the past 10 years for colonoscopy. Individuals screened outside of these ranges were considered non-adherent. Participant demographics Baseline participant characteristics included gender, age, education, income, employment status, marital status, health insurance coverage and whether or not the participants had ever been screened for CRC. Statistical analysis Descriptive analysis Baseline frequencies and descriptive statistics were conducted to evaluate the distribution of all variables and measures of central tendency. The means, standard deviations and minimum and maximum values for the perceived benefits/barriers items were analyzed. Perceived benefits/barriers association with pre-intervention screening behavior Logistic regression analyses were conducted to evaluate the relationship between baseline perceived benefits of and perceived barriers to screening scores and participant adherence to CRC screening recommendations at baseline. These models controlled for age. Moderation and mediation analysis Moderation analyses were conducted to determine if perceived benefits/barriers altered the direction and/or strength of screening behavior at intervention follow-up using binary logistic regressions. Utilizing Baron and Kenny procedures [46], mediation analyses were conducted to assess the indirect effects of the intervention on CRC screening behavior via perceived benefits of and perceived barriers to screening. If mediation was detected, the Sobel test would be conducted to assess the statistical significance of the indirect effects. For Temple, exposure to the intervention was operationalized by participants’ exposure to the CRC educational materials (the main intervention component) at the 6-month follow up (‘How much of the booklet did you read?’; Response Options: All, Most, Some, None, Did Not Receive). For the Take Charge study, exposure to the intervention was defined as participants’ reports of their attendance at the intervention workshops at the initial follow-up survey. For Project HEAL exposure to the intervention was also operationalized by the total number of workshops attended by church members (1–3). Attendance data were collected through workshop attendance logs and this information was entered in to the study’s tracking database. All analyses were conducted in SAS Version 9.4 and SPSS Version 23.0. Results Participant demographic characteristics The samples of participants in each of the three intervention studies are described in Table I. Participants were African American, majority middle aged, mostly female, with a median education of some college. Health insurance coverage among participants varied between the three intervention studies. The majority of participants had been screened for CRC at baseline at some point previously. Regional descriptions of CRC perceived benefits and barriers among African Americans Table II describes the perceived benefits of and perceived barriers to CRC screening distributions across each of the timepoints for the three studies. Overall, participants perceived benefits to the screening behavior (e.g. ‘A colonoscopy will help you not worry as much about colorectal cancer’) at baseline and these findings were comparable across studies for both FOBT (M = 11.7; 11.3; 11.3/15) and colonoscopy (M = 2.6; 2.3; 2.3/3). Few screening barriers (e.g. ‘I am afraid to have a colonoscopy because I might find out something is wrong’) were reported across the three studies for both FOBT (M = 12.6; 11.5; 11.1/30) and colonoscopy (M = 0.7; 0.6; 0.6/8), the latter suggestive of a floor effect. Table II. Scale descriptives for perceived benefits of and perceived barriers to colorectal cancer screeninga,b G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 a BL = Baseline Assessment. b ** indicates category not assessed. Table II. Scale descriptives for perceived benefits of and perceived barriers to colorectal cancer screeninga,b G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 a BL = Baseline Assessment. b ** indicates category not assessed. Role of perceived benefits and barriers in pre-intervention screening levels Table III presents the logistic regression models for perceived benefits/barriers as predictors of pre-intervention CRC screening. In Temple, FOBT benefits/barriers were not significantly related to screening. Colonoscopy benefits were associated with greater odds of being adherent to CRC screening at baseline for Temple (P = 0.030), while colonoscopy barriers were not significant. For Take Charge, FOBT benefits/barriers were also not significant. In this study, individuals who identified colonoscopy benefits were more likely to be adherent (P = 0.005), whereas individuals with perceived barriers were less likely to be adherent to colonoscopy screening guidelines at baseline (P = 0.035). In Project HEAL, FOBT benefits positively predicted FOBT screening behavior and barriers negatively influenced behavior (P’s = 0.009 and 0.019, respectively). Colonoscopy benefits were marginally significant (P = 0.062) such that individuals with higher benefits were more likely to report screening, whereas perceived barriers were not related to screening in the Project HEAL sample. Table III. Colorectal cancer screening barriers/benefits as predictors of pre-intervention screening levels for FOBT and colonoscopya Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 a Adjusted for age. Table III. Colorectal cancer screening barriers/benefits as predictors of pre-intervention screening levels for FOBT and colonoscopya Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 a Adjusted for age. Moderation analyses Temple There was no significant moderation for the Temple study. Take charge For Take Charge, while we found no intervention exposure by perceived benefits/barriers interaction, a main effect was detected such that participants with low perceived barriers to colonoscopy were more likely to report colonoscopy screening at the 1-month follow-up (OR = 5.550, 95% CI = 1.346–22.890, P = 0.018). Project HEAL For Project HEAL, participants who had lower perceived benefits of colonoscopy at 12-months had a greater increase in colonoscopy adherence at 12-months in relation to those with higher perceived benefits about colonoscopy (P = 0.045; Fig. 1). Individuals with fewer perceived barriers at 12-months reported a greater increase in colonoscopy screening at 12-months in comparison to those with higher perceived barriers (P = 0.035; Fig. 2). Fig. 1. View largeDownload slide Perceived benefits × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 1. View largeDownload slide Perceived benefits × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 2. View largeDownload slide Perceived barriers × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 2. View largeDownload slide Perceived barriers × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Mediation analyses Tables IV and V report the odds ratios and 95% confidence intervals for each step in the Baron and Kenny method which illustrates mediation was not detected across Take Charge or Project HEAL. Given the small sample sizes for Temple, the mediation analysis data are not shown. Table IV. Mediator and study intervention effects at initial follow-upa Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — a – indicates analyses not conducted because assumption not met. Table IV. Mediator and study intervention effects at initial follow-upa Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — a – indicates analyses not conducted because assumption not met. Table V. Mediator and study intervention effects at final follow-upa,b Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — a – indicates analyses not conducted because assumption not met. b ** indicates category not assessed. Table V. Mediator and study intervention effects at final follow-upa,b Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — a – indicates analyses not conducted because assumption not met. b ** indicates category not assessed. Discussion Leveraging common data elements across three interventional studies, we sought to evaluate the performance of critical components of the HBM and to determine whether these constructs are associated with pre-intervention CRC screening, and serve as mechanisms of intervention effects aimed at increasing CRC screening among African Americans. We were also interested in the performance of the perceived benefits/barriers measures across three studies in two distinct geographical areas. While we observed demographic differences, overall, perceived benefits of CRC screening and perceived barriers to CRC screening were comparable across studies and there were no notable geographical differences. The results confirmed that across the Alabama and Maryland study samples, participants perceived many benefits to screening at baseline for both FOBT and colonoscopy screening tests. However, few screening barriers were reported across the three studies suggesting floor effects. Despite a range of 46–61% of participants having been screened at baseline, the perceived barriers to screening reported are still very low. While we know barriers to screening exist and studies have supported this notion, a number of quantitative studies have detected no relationship between barriers and CRC screening behavior [28]. Among the qualitative studies conducted, they have uncovered other obstacles to screening not currently captured in the measurement tools [47, 48, 49]. A mixed-method study was conducted to understand patient-reported barriers to CRC and through focus groups other, less reported challenges emerged including low self-worth, homosexual sensitivities, fatalism, negative past experiences with testing and concerns with the financial motivation behind screening [49]. Brouse et al. [50] conducted a qualitative study to understand barriers to FOBT in a minority, urban population and several barriers were cited including issues with obtaining FOBT kits from physicians, which is a barrier less understood in the current literature [50]. This phenomenon has also been noted in qualitative studies conducted among minority populations for other cancer screening behaviors [31–33]. We speculate that the quantitative measurement of this theoretical construct may not be adequately capturing the complex factors standing in the way of individuals going through with CRC screening. The current validated scales operationalize barriers as practical constraints, being asymptomatic and being uncomfortable with the nature of the tests [41]. Qualitative examination of this construct has reported additional considerations that may impact a person’s willingness to take the action of screening. Measurement of barriers may need to be refined to incorporate other items to be more responsive to patients’ concerns with screening. Refining these measures would allow for future interventions to target these currently uncaptured barriers to screening. The qualitative data suggest that perceived barriers are relevant to patient’s CRC screening decision-making, but the low reported barriers across the three distinct samples in this study, in particular the barriers to colonoscopy, suggest there may be limitations in how this concept is being measured. The internal consistency of the benefits and barriers indices were evaluated in the present analysis. The majority of the Cronbach’s alphas reported across the studies did not meet the ‘acceptable’ reliability coefficient of .70. In particular, both the benefits and barriers indices for the Temple study and the barriers indices across all three sample were lower than anticipated which may be due to a small number of items as well as the heterogeneity of the items. Adding more items to assess these constructs could improve the reliability and allow for subscales that would delineate discreet factors that contribute to perceived benefits and barriers of CRC screening. We hypothesized that perceived benefits would significantly predict pre-intervention screening behavior and this hypothesis was partially substantiated with individuals reporting higher colonoscopy benefits being more likely to be adherent in all three studies, but this finding was not consistent across the studies for FOBT benefits. Specifically, the Alabama samples (Temple and Take Charge) did not show a significant relationship between FOBT benefits and screening, however, these variables were significantly related in the Maryland study. While FOBT benefits were comparable across these three samples, there may have been other factors that contributed to the likelihood of getting screened for the Temple and Take Charge participants. Health insurance coverage was highest among the Maryland sample and so it is possible that health insurance may have played a role in screening adherence for FOBT for the Alabama study participants. While perceived barriers were significantly related to lower screening for colonoscopy in Take Charge and FOBT in Project HEAL, the perceived barriers constructs did not significantly predict screening behavior to the degree the benefits items related to screening levels. These results are also consistent with the literature suggesting there is variability in the predictive nature of these constructs on screening behavior [28]. This lack of significant findings across all constructs, particularly the fewer significant findings for the barriers items, further suggests the way in which these concepts are presently measured may not be fully covering the complexities of the pros and cons of screening. To evaluate the mechanisms and differential impact of the interventions effects, mediation and moderation analyses were conducted across the three studies. For Project HEAL, moderation was detected for colonoscopy benefits and barriers at the 12-month follow-up. The results suggest that individuals with lower perceived benefits may have more opportunity to move in changing their screening behaviors. Whereas, individuals with more perceived barriers may need further attention in order to impact their screening behavior. Consistent with our findings, this result further underscores the need to better understand those, perhaps uncovered barriers most relevant to changing behavior. Future studies could target individuals with lower benefits and more perceived barriers and the focus of these interventions could aim to address these factors in order to promote screening. Overall, the constructs did not serve as indirect effects of the intervention on screening behavior. In light of our findings, the aforementioned measurement challenges may be playing a role in our inability to detect indirect effects. Additionally, if our current understanding of the most salient barriers is not comprehensive, our interventions may not be fully addressing the issues most pertinent to individuals non-adherent for screening. Strengths and limitations The strengths of this study lie in the use of harmonized data across three church-based intervention trials, the evaluation of two key constructs of the HBM from multiple samples of African Americans, and the mediation and moderation analyses of perceived benefits/barriers of CRC screening on the impact of interventions. There are limitations to be considered in the interpretation of the findings. First, we did not include all of the HBM constructs in the present analyses and it is possible perceived benefits and barriers do not fully explain the relationships between the interventions and the outcomes. In some cases, validated instruments to assess all the constructs did not exist and thus were not measured in the studies. Perceived benefits and barriers are well established as critical constructs within the theory and thus this study sought to gain a better understanding of these items within a priority population. Second, while, a goal of this study was not to pool the data across the Temple, Take Charge and HEAL studies, we observed differences across income, marital status, employment, health insurance coverage and colonoscopy screening history. It is possible some of these differences may relate to the variables of interest, but these were not explored statistically. These samples consisted of African American adults who were enrolled through churches where they were members and were not excluded from participating based upon their cancer screening history. The study results may not be generalizable to African Americans who do not attend church. Although the importance of religion is particularly salient within the African American community with 87% of African Americans reporting being affiliated with an organized religion and 53% attending church routinely [51]. The findings from this study may also not be generalizable to individuals who have never been screened. Future studies could extend this work by interviewing individuals who have not been screened to understand their perceived barriers to getting tested. This study was also at risk of selection bias. This was a group of motivated individuals who enrolled in a 3-workshop series intervention. There may be something inherently different about these individuals compared to those who did not participate. Finally, the data collected in these studies were via self-report. Self-report is subject to bias and is reliant upon participants providing accurate, honest responses. Conclusion Despite the emphasis to use theory in interventions aimed at changing health behavior, limitations exist across our understanding of how theoretical constructs are conceptualized, how they are operationalized, and how they are ultimately analyzed in the evaluation of interventions. The findings from the present study suggest there may be some measurement limitations, especially among African American samples. Among our sample, the results illustrate measurement limitations including floor and ceiling effects and less than optimal reliability. Additionally, these variables did not consistently and significantly predict baseline screening behavior. The constructs also were unable to explain the intervention effects. Based upon these findings, we suggest further research among diverse groups to determine if there are other factors that are possibly not being measured or emphasized in our interventions that play a role in people’s CRC screening decision-making and ultimately their behaviors. Future qualitative work is needed to understand the multi-layered, dynamic nature of barriers that exist among this population. Through this additional study, tools to measure barriers to CRC screening may require further refinement and validation. This may lead to future interventions with new, promising targets aimed at increasing CRC cancer screening among African Americans. Acknowledgements This work was supported by grants from the National Cancer Institute and the Centers for Disease Control and Prevention. We are grateful for the men and women who gave their time to participate in the studies. Funding This work was supported by the National Cancer Institute, National Institutes of Health [#R01CA147313], Centers for Disease Control and Prevention [#C50113185] and Centers for Disease Control and Prevention [#5U48DP00046-03]. This research includes data from three studies. None of the authors have any commercial interests. Conflict of interest statement None declared. References 1 Rosenstock IM , Strecher VJ , Becker MH. Social learning theory and the health belief model . Health Educ Behav 1988 ; 15 : 175 – 83 . 2 Glanz K , Rimer BK. Theory at a glance: a guide for health promotion practice. US Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute; 1997 . 3 Glanz K , Rimer BK , Viswanath K (eds). Health Behavior and Health Education: Theory, Research, and Practice . San Francisco, CA : John Wiley and Sons , 2008 . 4 Crane MM , Ward DS , Lutes LD et al. Theoretical and behavioral mediators of a weight loss intervention for men . Ann Behav Med 2016 ; 50 : 460 – 70 . Google Scholar CrossRef Search ADS PubMed 5 Montanaro EA , Bryan AD. Comparing theory-based condom interventions: health belief model versus theory of planned behavior . Health Psychol 2014 ; 33 : 1251. Google Scholar CrossRef Search ADS PubMed 6 Calfas KJ , Sallis JF , Oldenburg B , Ffrench M. Mediators of change in physical activity following an intervention in primary care: PACE . Prev Med 1997 ; 26 : 297 – 304 . Google Scholar CrossRef Search ADS PubMed 7 Dishman RK , Motl RW , Saunders R et al. Enjoyment mediates effects of a school-based physical-activity intervention . Med Sci Sports Exerc 2005 ; 37 : 478 – 87 . Google Scholar CrossRef Search ADS PubMed 8 Dishman RK , Motl RW , Saunders R et al. Self-efficacy partially mediates the effect of a school-based physical-activity intervention among adolescent girls . Prev Med 2004 ; 38 : 628 – 36 . Google Scholar CrossRef Search ADS PubMed 9 Farhadifar F , Molina Y , Taymoori P , Akhavan S. Mediators of repeat mammography in two tailored interventions for Iranian women . BMC Public Health 2016 ; 16 : 149. Google Scholar CrossRef Search ADS PubMed 10 Haerens L , Cerin E , Maes L et al. Explaining the effect of a 1-year intervention promoting physical activity in middle schools: a mediation analysis . Public Health Nutr 2008 ; 11 : 501 – 12 . Google Scholar CrossRef Search ADS PubMed 11 Lewis BA , Forsyth LH , Pinto BM et al. Psychosocial mediators of physical activity in a randomized controlled intervention trial . J Sport Exerc Psychol 2006 ; 28 : 193 – 204 . Google Scholar CrossRef Search ADS 12 MacKinnon DP , Goldberg L , Clarke GN et al. Mediating mechanisms in a program to reduce intentions to use anabolic steroids and improve exercise self-efficacy and dietary behavior . Prev Sci 2001 ; 2 : 15 – 28 . Google Scholar CrossRef Search ADS PubMed 13 Miller YD , Trost SG , Brown WJ. Mediators of physical activity behavior change among women with young children . Am J Prev Med 2002 ; 23 : 98 – 103 . Google Scholar CrossRef Search ADS PubMed 14 Napolitano MA , Papandonatos GD , Lewis BA et al. Mediators of physical activity behavior change: a multivariate approach . Health Psychol 2008 ; 27 : 409. Google Scholar CrossRef Search ADS PubMed 15 Pinto BM , Lynn H , Marcus BH et al. Physician-based activity counseling: intervention effects on mediators of motivational readiness for physical activity . An Behav Med 2001 ; 23 : 2 – 10 . Google Scholar CrossRef Search ADS 16 Pitpitan EV , Kalichman SC , Garcia RL et al. Mediators of behavior change resulting from a sexual risk reduction intervention for STI patients, Cape Town, South Africa . J Behav Med 2015 ; 38 : 194 – 203 . Google Scholar CrossRef Search ADS PubMed 17 Reynolds KD , Yaroch AL , Franklin FA , Maloy J. Testing mediating variables in a school-based nutrition intervention program . Health Psychol 2002 ; 21 : 51. Google Scholar CrossRef Search ADS PubMed 18 Baruth M , Wilcox S. Psychosocial mediators of physical activity and fruit and vegetable consumption in the faith, activity, and nutrition programme . Public Health Nutr 2015 ; 18 : 2242 – 50 . Google Scholar CrossRef Search ADS PubMed 19 Mosher CE , Fuemmeler BF , Sloane R et al. Change in self‐efficacy partially mediates the effects of the FRESH START intervention on cancer survivors' dietary outcomes . Psycho‐Oncology 2008 ; 17 : 1014 – 23 . Google Scholar CrossRef Search ADS PubMed 20 Conner M , McEachan R , Lawton R , Gardner P. Basis of intentions as a moderator of the intention–health behavior relationship . Health Psychol . 2016 ; 35 : 219. Google Scholar CrossRef Search ADS PubMed 21 Kraemer HC. Messages for clinicians: moderators and mediators of treatment outcome in randomized clinical trials . Am J Psychiatry 2016 ; 173 : 672 – 9 . Google Scholar CrossRef Search ADS PubMed 22 US Preventive Services Task Force . Draft Recommendation Statement: Colorectal Cancer: Screening. Available at http://www.uspreventiveservicestaskforce.org/Page/Document/draft-recommendation-statement38/colorectal-cancer-screening2#Pod3. Accessed: 2 May 2018. 23 Centers for Disease Control and Prevention . Colorectal cancer screening rates remain low. Available at https://www.cdc.gov/media/releases/2013/p1105-colorectal-cancer-screening.html. Accessed: 2 May 2018. 24 James AS , Campbell MK , Hudson MA. Perceived barriers and benefits to colon cancer screening among African Americans in North Carolina . Cancer Epidemiol Prev Biomarkers 2002 ; 11 : 529 – 34 . 25 Tabbarah M , Nowalk MP , Raymund M et al. Barriers and facilitators of colon cancer screening among patients at faith-based neighborhood health centers . J Community Health 2005 ; 30 : 55 – 74 . Google Scholar CrossRef Search ADS PubMed 26 Frank D , Swedmark J , Grubbs L. Colon cancer screening in African American women . ABNF J 2004 ; 15 : 67 . Google Scholar PubMed 27 Sohler NL , Jerant A , Franks P. Socio-psychological factors in the expanded health belief model and subsequent colorectal cancer screening . Patient Educ Couns 2015 ; 98 : 901 – 7 . Google Scholar CrossRef Search ADS PubMed 28 Kiviniemi MT , Bennett A , Zaiter M , Marshall JR. Individual‐level factors in colorectal cancer screening: a review of the literature on the relation of individual‐level health behavior constructs and screening behavior . Psycho‐Oncology 2011 ; 20 : 1023 – 33 . Google Scholar CrossRef Search ADS PubMed 29 Maxwell AE , Bastani R , Crespi CM et al. Behavioral mediators of colorectal cancer screening in a randomized controlled intervention trial . Prev Med 2011 ; 52 : 167 – 73 . Google Scholar CrossRef Search ADS PubMed 30 Becker MH. The Health Belief Model and Personal Health Behavior . San Francisco : Society for Public Health Education ; 1974 . 31 Pasick RJ , Burke NJ , Joseph G. Behavioral theory and culture special issue: authors’ response to commentaries . Health Educ Behav 2009 ; 36 : 167S – 71S . Google Scholar CrossRef Search ADS 32 Pasick RJ , Burke NJ , Barker JC et al. Behavioral theory in a diverse society: like a compass on Mars . Health Educ Behav 2009 ; 36 : 11S – 35S . Google Scholar CrossRef Search ADS PubMed 33 Pasick RJ , Barker JC , Otero-Sabogal R et al. Intention, subjective norms, and cancer screening in the context of relational culture . Health Educ Behav 2009 ; 36 : 91S – 110S . Google Scholar CrossRef Search ADS PubMed 34 Ashing-Giwa K. Health behavior change models and their socio-cultural relevance for breast cancer screening in African American women . Women Health 1999 ; 28 : 53 – 71 . Google Scholar CrossRef Search ADS PubMed 35 Champion VL , Monahan PO , Springston JK et al. Measuring mammography and breast cancer beliefs in African American women . J Health Psychol 2008 ; 13 : 827 – 37 . Google Scholar CrossRef Search ADS PubMed 36 Champion VL , Scott CR. Reliability and validity of breast cancer screening belief scales in African American women . Nurs Res 1997 ; 46 : 331 – 7 . Google Scholar CrossRef Search ADS PubMed 37 Pasick RJ , Burke NJ. A critical review of theory in breast cancer screening promotion across cultures . Annu Rev Public Health 2008 ; 29 : 351 – 68 . Google Scholar CrossRef Search ADS PubMed 38 Holt CL , Shipp M , Eloubeidi M et al. Your body is the temple: impact of a spiritually based colorectal cancer educational intervention delivered through community health advisors . Health Promot Pract 2011 ; 12 : 577 – 88 . Google Scholar CrossRef Search ADS PubMed 39 Holt CL , Litaker MS , Scarinci IC et al. Spiritually based intervention to increase colorectal cancer screening among African Americans: screening and theory-based outcomes from a randomized trial . Health Educ Behav 2013 ; 40 : 458 – 68 . Google Scholar CrossRef Search ADS PubMed 40 Holt CL , Tagai EK , Scheirer MA et al. Translating evidence-based interventions for implementation: experiences from Project HEAL in African American churches . Implement Sci 2014 ; 9 : 66. Google Scholar CrossRef Search ADS PubMed 41 Rawl S , Champion V , Menon U et al. Validation of scales to measure benefits of and barriers to colorectal cancer screening . J Psychosoc Oncol 2001 ; 19 : 47 – 63 . Google Scholar CrossRef Search ADS 42 American Cancer Society . American Cancer Society Recommendations for Colorectal Cancer Early Detection. Available at https://www.cancer.org/cancer/colon-rectal-cancer/detection-diagnosis-staging/acs-recommendations.html. Accessed: 2 May 2018. 43 Sheehan J , Hirschfeld S , Foster E et al. Improving the value of clinical research through the use of Common Data Elements . Clin Trials 2016 ; 13 : 671 – 6 . Google Scholar CrossRef Search ADS PubMed 44 Bangdiwala SI , Bhargava A , O’Connor DP et al. Statistical methodologies to pool across multiple intervention studies . Transl Behav Med 2016 ; 6 : 228 – 35 . Google Scholar CrossRef Search ADS PubMed 45 Centers for Disease Control and Prevention . Behavioral risk factor surveillance survey system online information. Available at http://cdc.gov/brfss/. Accessed 5 May 2017. 46 MacKinnon DP. Introduction to Statistical Mediation Analysis . New York : Lawrence Erlbaum Associates ; 2008 . 47 Jones RM , Devers KJ , Kuzel AJ , Woolf SH. Patient-reported barriers to colorectal cancer screening: a mixed-methods analysis . Am J Prev Med 2010 ; 38 : 508 – 16 . Google Scholar CrossRef Search ADS PubMed 48 Greiner KA , Born W , Nollen N , Ahluwalia JS. Knowledge and perceptions of colorectal cancer screening among urban African Americans . J Gen Intern Med 2005 ; 20 : 977 – 83 . Google Scholar CrossRef Search ADS PubMed 49 Beeker C , Kraft JM , Southwell BG , Jorgensen CM. Colorectal cancer screening in older men and women: qualitative research findings and implications for intervention . J Community Health 2000 ; 25 : 263 – 78 . Google Scholar CrossRef Search ADS PubMed 50 Brouse CH , Basch CE , Wolf RL et al. Barriers to colorectal cancer screening with fecal occult blood testing in a predominantly minority urban population: a qualitative study . Am J Public Health 2003 ; 93 : 1268 – 71 . Google Scholar CrossRef Search ADS PubMed 51 Pew Research Center . A Religious Portrait of African-Americans. Available at http://www.pewforum.org/2009/01/30/a-religious-portrait-of-african-americans/. Accessed: 26 January 2018. 52 Lasser KE , Ayanian JZ , Fletcher RH , Good MJD. Barriers to colorectal cancer screening in community health centers: a qualitative study . BMC Family Practice 2008 ; 9 : 15 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Health Education Research Oxford University Press

The role of perceived benefits and barriers in colorectal cancer screening in intervention trials among African Americans

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Abstract

Abstract The Health Belief Model (HBM) is widely used in health behavior interventions. The lack of diverse samples in the development of this theory warrants additional study on how it performs among minorities. While studies have utilized HBM to address colorectal cancer (CRC) screening, limited information exists confirming how these constructs influence screening. Data from three CRC screening trials were used to examine how perceived benefits/barriers perform among African Americans (AA) and whether they serve as mechanisms of the intervention effects on screening. The data were collected in AA churches (Study 1: N = 103; Study 2: N = 285; Study 3: N = 374) where lay members conducted CRC education to increase screening. Participants perceived benefits from colonoscopy (M = 2.4/3, SD = 0.87) and perceived few barriers (M = 0.63/8, SD = 1.1). Benefits were perceived for the fecal occult blood test (M = 11.4/15, SD = 2.1), and few barriers were reported (M = 11.7/30, SD = 3.4). Benefits more consistently predicted pre-intervention screening relative to barriers. For Study 3, individuals with fewer barriers reported a greater increase in colonoscopy screening at 12-months versus those with higher barriers (OR = 0.595, 95% CI = 0.368–0.964), P = 0.035). Benefits/barriers did not mediate the relationship. Potential measurement limitations, particularly for barriers, were uncovered and further research on how to assess factors preventing AA from screening is needed. Introduction Health behavior theory involves a set of interrelated variables that predict behavior change and theory-based constructs often serve as the basis for health behavior intervention development [1–3]. Beyond being used for design, theory can be used to determine intervention mechanism(s). Specifically, theoretical construct(s) may be manipulated to assess its impact on the outcome of interest. Theory-based constructs have also been analyzed to determine differential intervention effects on the outcome(s). Studies have evaluated theoretical mediators and moderators of intervention outcomes for behaviors including weight loss, physical activity and mammography screening, and some have uncovered full or partial mediation [4–19] or moderation [20, 21]. Use of theory in colorectal cancer (CRC) screening interventions Despite substantial evidence that suggests CRC screening is effective in reducing disease related mortality [22], it is underutilized even with continued research towards improving screening rates [23]. Previous theoretically driven interventions have been implemented aiming to increase CRC screening [24–26]. While studies have evaluated the predictive weights of these theoretical constructs [27, 28], limited information exists regarding the pathways through which these constructs influence CRC screening in intervention trials [29]. Additional study is needed to uncover the theoretical mechanisms of intervention effects on CRC screening. Utility of the health belief model in predicting CRC screening behavior The Health Belief Model (HBM) is a widely used theory for the planning and implementation of interventions aimed at health behavior change [1–3, 30] and is often applied to understanding why individuals engage or do not engage in preventive services, including CRC screening. However, in a review of the literature on individual-level behavioral constructs and CRC screening behavior, Kiviniemi et al. [28] found that while many studies suggest a relationship between theoretical constructs on cancer screening behavior, approximately one-third of these studies reported no relationship between the construct and CRC screening behavior. Given these findings, it is important to continue to research the relationship between theory and CRC screening behaviors. Further, many studies have not gone beyond analyzing associations between HBM constructs and screening behavior. We were unable to find CRC intervention studies that conducted mediation or moderation analysis to examine whether the HBM constructs served as mechanisms of intervention effects on CRC screening or had a differential effect on the intervention outcomes. Applicability of the HBM among African Americans’ CRC screening behaviors Despite the wide use of the HBM, its utility and applicability in diverse populations has not been well studied [31–33]. African Americans are disproportionately impacted by cancer and while the HBM constructs have been used to develop interventions targeted at improving screening rates in this population, most individual-level behavior change theories, including the HBM, were not developed for minorities and the reliability and validity of the measures are not well known for minority populations [34–37]. Analyzing how these items perform across large samples, including diverse populations, will help determine how these measures should be refined. It is important for both researchers to understand if individual-level constructs are important in explaining CRC screening behaviors among culturally diverse populations which may be used for improved development of interventions tailored to minority groups, such as African Americans. The present study The current study analyzes selected HBM theoretical constructs, specifically perceived benefits of CRC screening and perceived barriers to CRC screening, among African American men and women living in mid-Atlantic and Southern regions of the United States, using data from three comparable intervention studies that aimed to increase CRC screening. In the present analysis, we addressed three research questions: (i) How do perceived benefits of screening and perceived barriers to screening indices perform (i.e. variability, floor or ceiling effects) among African American men and women from the two study regions?; (ii) How useful are benefits/barriers in predicting CRC screening adherence before intervention?; and (iii) Do benefits/barriers serve as mechanisms by which the study interventions impact CRC screening behavior? We hypothesized that perceived benefits and barriers would predict pre-intervention screening behavior such that perceived barriers would be inversely related to screening and perceived benefits would be positively associated with screening. Additionally, we hypothesized that targeting perceived benefits and barriers in the interventions would explain changes in screening behavior at 12-month follow-up. Specifically, individuals who were exposed to the study interventions would exhibit higher perceived benefits and decreased barriers and that targeting these constructs in the interventions would result in increased CRC screening rates at follow-up. Materials and methods Intervention study methods This section provides an overview of the three intervention studies led by the PI (CH) that provided the data for the current analyses. Additional detail for each is provided in the original papers and we describe them briefly in the section that follows [38, 39, 40, Holt, under review]. The intervention materials and measurement included in these studies was based on the HBM as the theoretical framework. The material content specifically aimed to outline benefits of CRC screening and to clarify and reduce perceptions about barriers to screening. Each of these studies aimed to increase knowledge and awareness of CRC and early detection and to ultimately increase screening. The current analyses focuses on fecal occult blood test (FOBT) and colonoscopy because of the availability of HBM-specific measures for these two screening methods [41], and because colonoscopy and FOBT are two of the screening modalities recommended by multiple major organizations [22, 42]. All of the studies were implemented in African American churches where we trained lay peer community health advisors to conduct educational activities on CRC with the intent of increasing screening. In all of the studies, the community health advisors distributed culturally targeted print materials on CRC developed by the study team in partnership with local community stakeholders. The studies used harmonized measures including common data elements, [43] which facilitated the current analyses. Data from multiple studies can be harmonized to the extent that the studies are reasonably comparable in elements such as objectives, settings, conceptual framework, design, implementation, participant eligibility, procedures and outcomes [44]. Two of the three studies used a randomized trial design with an intervention and comparison group (e.g. spiritually based compared to non-spiritual intervention materials of the same core CRC content). In the current analyses, we do not combine or pool data from multiple studies as we were interested in potential regional differences but rather we compare findings across the three trials. ‘Your body is the temple’ Hereafter referred to as the ‘Temple’ study [38], this was the first study in the series, named after a scriptural passage involving health. The Temple study was conducted in two Alabama churches. This was the only intervention of the three that did not use formal educational workshops but rather the community health advisors used one-on-one, small group discussions and educational print materials to implement the intervention among their church members. Due to its more modest scope, this is also the only trial of the three that used a pre-post study design, rather than a cluster randomized design. Data collection was conducted at baseline, 6- and 12-month follow-up. One hundred three individuals participated in this study. ‘Take charge of your health’ Hereafter referred to as the ‘Take Charge’ study [39], this was the second trial in the series, also named through a community-engaged process. Take Charge was conducted in 16 Alabama churches. The trial used a randomized design comparing a spiritually based version of the intervention with a non-spiritual comparison of the same core CRC content. The intervention consisted of a series of two group educational workshops on CRC screening led by trained community health advisors. Data collection was conducted at baseline, 1- and 12-month follow-ups. Of the 316 enrolled, 285 participants completed the baseline and follow-up assessments. ‘Project HEAL’ Project HEAL was the third study [Holt, under review], also named through a community-engaged process. Project HEAL took place in 14 Maryland churches, and consisted of a series of three group educational workshops led by trained community health advisors, however CRC was only discussed in the third workshop. This trial also used a cluster-randomized design where churches were assigned to one of two strategies for training the community health advisors. Data collection was conducted at baseline, workshop 3, 12- and 24-month follow-ups. Three hundred seventy-five church members enrolled in Project HEAL with 309 participants completing the 24-month follow-up. These studies were approved by their respective Institutional Review Boards and informed consent was obtained from all individual participants included in these studies. Data harmonization The three interventions used common data elements that enabled direct comparisons across studies. We examined the instruments to verify common data elements (items) and items were eliminated that were not present in all three studies, this resulted in comparable data. While not pooled, the data were standardized for comparison of analyses across the three studies (see Table I). The HBM constructs were assessed using previously validated instruments. Not all the HBM constructs were included in the current analysis because not all were assessed in each of the intervention studies in part due to lack of available validated measures at the time the research was conducted. In some cases, items were eliminated from scale score compositions to ensure exact correspondence between the three data sets (e.g. one study used a 6-item version of a scale where another used a 7-item version, therefore, the current analysis used the six common items). Table I. Participant demographics for the temple, take charge & HEAL trialsa,b Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 a Values are percentages unless otherwise indicated. b ** indicates category not assessed. Table I. Participant demographics for the temple, take charge & HEAL trialsa,b Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 Variable Temple (N = 103) Alabama Take Charge (N = 285) Alabama HEAL (N = 374) Maryland Gender     Male 36.0 30.0 32.0     Female 64.0 70.0 68.0 Age (M, SD) 55.86 (6.55) 59.78(7.15) 55.28 (9.28) Education (median) Some College Some College Some College Income (median) $60k–70k $30k–40k $50k–60k Marital status     Single 8.74 15.41 28.34     Living w/partner 1.94 0.75 1.09     Married 67.96 49.62 47.68     Separated/Divorced 17.48 19.55 15.51     Widowed 3.88 14.66 7.36 Work status     Retired 7.77 25.68 7.34     Disabled ** 11.26 19.84     Not currently working 12.62 9.46 11.96     Part-time 6.80 8.56 7.34     Full-time 72.80 45.05 53.53 Health insurance coverage 65.04 86.32 93.07 Ever screened     Colorectal 75.41 82.39 78.92     Fecal Occult Blood Test 64.29 47.52 54.02     Colonoscopy 45.65 67.39 60.74 a Values are percentages unless otherwise indicated. b ** indicates category not assessed. Perceived benefits of and perceived barriers to fecal occult blood test (FOBT) Perceived benefits of and barriers to the FOBT were measured using items (three for benefits; six for barriers; e.g. ‘An FOBT will help find CRC early’; ‘An FOBT is embarrassing’.) validated in previous research [41]. Items used a strongly agree, agree, neutral, disagree, or strongly disagree format and were summed to form benefits and barriers indices with higher scores indicating greater benefits/barriers. Internal consistency reliability was computed across the three studies (Cronbach’s alphas ranged from 0.49 to 0.78 for benefits and 0.53 to 0.89 for barriers). Perceived benefits of and perceived barriers to colonoscopy Similarly, perceived benefits/barriers specific to colonoscopy (e.g. ‘…help you not worry as much about CRC’. ‘Having to follow a special diet and take a laxative or enema would keep me from having a colonoscopy’) were assessed using items (three for benefits; eight for barriers) validated in previous research [41] and assessed in agree/disagree/not sure format. Items were summed to form benefits and barriers indices with higher scores indicating greater benefits/barriers (Cronbach’s alpha’s ranged from 0.27 to 0.79 for benefits and 0.58 to 0.60 for barriers). The lower alpha score for the perceived benefits is most likely because of the number of items within the index which directly impacts reliability. CRC screening Self-report CRC screening behavior (FOBT; colonoscopy) was assessed using items from the Behavioral Risk Factor and Surveillance System [45]. Definitions were provided about the screening methods and response categories were based on screening recommendations. Screening adherence was operationalized as within the past 2 years for FOBT and within the past 10 years for colonoscopy. Individuals screened outside of these ranges were considered non-adherent. Participant demographics Baseline participant characteristics included gender, age, education, income, employment status, marital status, health insurance coverage and whether or not the participants had ever been screened for CRC. Statistical analysis Descriptive analysis Baseline frequencies and descriptive statistics were conducted to evaluate the distribution of all variables and measures of central tendency. The means, standard deviations and minimum and maximum values for the perceived benefits/barriers items were analyzed. Perceived benefits/barriers association with pre-intervention screening behavior Logistic regression analyses were conducted to evaluate the relationship between baseline perceived benefits of and perceived barriers to screening scores and participant adherence to CRC screening recommendations at baseline. These models controlled for age. Moderation and mediation analysis Moderation analyses were conducted to determine if perceived benefits/barriers altered the direction and/or strength of screening behavior at intervention follow-up using binary logistic regressions. Utilizing Baron and Kenny procedures [46], mediation analyses were conducted to assess the indirect effects of the intervention on CRC screening behavior via perceived benefits of and perceived barriers to screening. If mediation was detected, the Sobel test would be conducted to assess the statistical significance of the indirect effects. For Temple, exposure to the intervention was operationalized by participants’ exposure to the CRC educational materials (the main intervention component) at the 6-month follow up (‘How much of the booklet did you read?’; Response Options: All, Most, Some, None, Did Not Receive). For the Take Charge study, exposure to the intervention was defined as participants’ reports of their attendance at the intervention workshops at the initial follow-up survey. For Project HEAL exposure to the intervention was also operationalized by the total number of workshops attended by church members (1–3). Attendance data were collected through workshop attendance logs and this information was entered in to the study’s tracking database. All analyses were conducted in SAS Version 9.4 and SPSS Version 23.0. Results Participant demographic characteristics The samples of participants in each of the three intervention studies are described in Table I. Participants were African American, majority middle aged, mostly female, with a median education of some college. Health insurance coverage among participants varied between the three intervention studies. The majority of participants had been screened for CRC at baseline at some point previously. Regional descriptions of CRC perceived benefits and barriers among African Americans Table II describes the perceived benefits of and perceived barriers to CRC screening distributions across each of the timepoints for the three studies. Overall, participants perceived benefits to the screening behavior (e.g. ‘A colonoscopy will help you not worry as much about colorectal cancer’) at baseline and these findings were comparable across studies for both FOBT (M = 11.7; 11.3; 11.3/15) and colonoscopy (M = 2.6; 2.3; 2.3/3). Few screening barriers (e.g. ‘I am afraid to have a colonoscopy because I might find out something is wrong’) were reported across the three studies for both FOBT (M = 12.6; 11.5; 11.1/30) and colonoscopy (M = 0.7; 0.6; 0.6/8), the latter suggestive of a floor effect. Table II. Scale descriptives for perceived benefits of and perceived barriers to colorectal cancer screeninga,b G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 a BL = Baseline Assessment. b ** indicates category not assessed. Table II. Scale descriptives for perceived benefits of and perceived barriers to colorectal cancer screeninga,b G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 G/Temple (N = 103) N/Take Charge (N = 285) ZB/HEAL (N = 374) BL 6-mo 12-mo BL Follow-up 1 Follow-up 12 BL Workshop 3 12-mo 24-mo FOBT benefits Mean 11.7 11.5 12.6 (2.1) 11.3 11.1 11.3 11.3 ** 11.2 ** (SD) (1.6) (1.1) 7 (2.2) (2.2) 1.9 (2.4) — (1.9) Min (3) 8 6 15 3 3 3 3 — 3 Max (15) 15 12 15 15 15 15 — 15 FOBT barriers Mean 12.6 12.6 11.3 11.5 10.0 10.7 11.1 ** 11.2 ** (SD) (1.5) (1.3) (3.0) (4.3) (3.8) (3.9) (4.4) — (4.0) — Min (6) 8 10 6 6 6 6 6 — 6 — Max (30) 19 16 17 27 24 26 30 — 30 — Colonoscopy benefits Mean 2.6 1.7 2.6 2.3 1.9 2.6 2.3 ** 2.5 2.5 (SD) (0.6) (0.6) (0.8) (1.1) (1.3) (0.78) (0.91) — (0.75) (0.81) Min (0) 1 0 0 0 0 0 0 — 0 0 Max (3) 3 2 3 3 3 3 3 — 3 3 Colonoscopy barriers Mean 0.7 0.5 0.4 0.6 0.3 0.4 0.6 ** 0.4 0.4 (SD) (1.2) (0.9) (1.0) (1.1) (.62) (1.0) (1.1) — (0.89) (1.0) Min (0) 0 0 0 0 0 0 0 — 0 0 Max (8) 4 3 3 7 4 8 8 — 8 8 a BL = Baseline Assessment. b ** indicates category not assessed. Role of perceived benefits and barriers in pre-intervention screening levels Table III presents the logistic regression models for perceived benefits/barriers as predictors of pre-intervention CRC screening. In Temple, FOBT benefits/barriers were not significantly related to screening. Colonoscopy benefits were associated with greater odds of being adherent to CRC screening at baseline for Temple (P = 0.030), while colonoscopy barriers were not significant. For Take Charge, FOBT benefits/barriers were also not significant. In this study, individuals who identified colonoscopy benefits were more likely to be adherent (P = 0.005), whereas individuals with perceived barriers were less likely to be adherent to colonoscopy screening guidelines at baseline (P = 0.035). In Project HEAL, FOBT benefits positively predicted FOBT screening behavior and barriers negatively influenced behavior (P’s = 0.009 and 0.019, respectively). Colonoscopy benefits were marginally significant (P = 0.062) such that individuals with higher benefits were more likely to report screening, whereas perceived barriers were not related to screening in the Project HEAL sample. Table III. Colorectal cancer screening barriers/benefits as predictors of pre-intervention screening levels for FOBT and colonoscopya Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 a Adjusted for age. Table III. Colorectal cancer screening barriers/benefits as predictors of pre-intervention screening levels for FOBT and colonoscopya Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 Odds ratio and 95% confidence interval Temple (N = 103) FOBT Benefits 0.997 (0.655–1.519), P = 0.990 FOBT barriers 0.874 (0.554–1.378), P = 0.561 Colonoscopy benefits 4.073 (1.149–14.439) P = 0.030 Colonoscopy barriers 1.161 (0.351–3.841) P = 0.807 Take charge (N = 285) FOBT benefits 1.121 (0.937–1.342), P = 0.212 FOBT barriers 0.949 (0.864–1.043), P = 0.277 Colonoscopy benefits 2.078 (1.242–3.477) P = 0.005 Colonoscopy barriers 0.616 (0.393–0.966) P = 0.035 HEAL (N = 374) FOBT benefits 1.197 (1.045–1.370), P = 0.009 FOBT barriers 0.929 (0.874–.0988), P = 0.019 Colonoscopy benefits 1.346 (0.985–1.838), P = 0.062 Colonoscopy barriers 0.885 (0.657–1.113), P = 0.245 a Adjusted for age. Moderation analyses Temple There was no significant moderation for the Temple study. Take charge For Take Charge, while we found no intervention exposure by perceived benefits/barriers interaction, a main effect was detected such that participants with low perceived barriers to colonoscopy were more likely to report colonoscopy screening at the 1-month follow-up (OR = 5.550, 95% CI = 1.346–22.890, P = 0.018). Project HEAL For Project HEAL, participants who had lower perceived benefits of colonoscopy at 12-months had a greater increase in colonoscopy adherence at 12-months in relation to those with higher perceived benefits about colonoscopy (P = 0.045; Fig. 1). Individuals with fewer perceived barriers at 12-months reported a greater increase in colonoscopy screening at 12-months in comparison to those with higher perceived barriers (P = 0.035; Fig. 2). Fig. 1. View largeDownload slide Perceived benefits × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 1. View largeDownload slide Perceived benefits × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 2. View largeDownload slide Perceived barriers × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Fig. 2. View largeDownload slide Perceived barriers × intervention exposure on colonoscopy screening levels at 12-months (Project HEAL). Mediation analyses Tables IV and V report the odds ratios and 95% confidence intervals for each step in the Baron and Kenny method which illustrates mediation was not detected across Take Charge or Project HEAL. Given the small sample sizes for Temple, the mediation analysis data are not shown. Table IV. Mediator and study intervention effects at initial follow-upa Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — a – indicates analyses not conducted because assumption not met. Table IV. Mediator and study intervention effects at initial follow-upa Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 145 1.059 (0.347–3.287) — — FOBT barriers 145 1.059 (0.347–3.287) — — Colonoscopy benefits 141 3.025 (0.565–16.204) — — Colonoscopy barriers 141 3.025 (0.565–16.204) — — Project HEAL FOBT benefits 227 1.337 (0.949–1.885) P = 0.097 1.260 (0.791–2.006) — FOBT barriers 227 1.337 (0.949–1.885) P = 0.097 1.017 (0.678–1.525) — Colonoscopy benefits 272 1.668 (1.224–2.275) P = 0.001 0.895 (0.660–1.213) — Colonoscopy barriers 272 1.668 (1.224–2.275) P = 0.001 0.849 (0.571–1.262) — a – indicates analyses not conducted because assumption not met. Table V. Mediator and study intervention effects at final follow-upa,b Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — a – indicates analyses not conducted because assumption not met. b ** indicates category not assessed. Table V. Mediator and study intervention effects at final follow-upa,b Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — Odds ratio and 95% confidence interval Mediator N Intervention effect on screening Intervention effect on mediator Intervention effect on screening (with mediator) Take charge FOBT benefits 220 0.768 (0.424–1.394) — — FOBT barriers 220 0.768 (0.424–1.394) — — Colonoscopy benefits 225 0.862 (0.340–2.180) — — Colonoscopy barriers 225 0.862 (0.340–2.180) — — Project HEAL FOBT benefits ** ** ** ** FOBT barriers ** ** ** ** Colonoscopy benefits 277 1.453 (1.061–1.989) P = 0.020 0.829 (0.609–1.129) — Colonoscopy barriers 277 1.453 (1.061–1.989) P = 0.020 0.774 (0.548–1.091) — a – indicates analyses not conducted because assumption not met. b ** indicates category not assessed. Discussion Leveraging common data elements across three interventional studies, we sought to evaluate the performance of critical components of the HBM and to determine whether these constructs are associated with pre-intervention CRC screening, and serve as mechanisms of intervention effects aimed at increasing CRC screening among African Americans. We were also interested in the performance of the perceived benefits/barriers measures across three studies in two distinct geographical areas. While we observed demographic differences, overall, perceived benefits of CRC screening and perceived barriers to CRC screening were comparable across studies and there were no notable geographical differences. The results confirmed that across the Alabama and Maryland study samples, participants perceived many benefits to screening at baseline for both FOBT and colonoscopy screening tests. However, few screening barriers were reported across the three studies suggesting floor effects. Despite a range of 46–61% of participants having been screened at baseline, the perceived barriers to screening reported are still very low. While we know barriers to screening exist and studies have supported this notion, a number of quantitative studies have detected no relationship between barriers and CRC screening behavior [28]. Among the qualitative studies conducted, they have uncovered other obstacles to screening not currently captured in the measurement tools [47, 48, 49]. A mixed-method study was conducted to understand patient-reported barriers to CRC and through focus groups other, less reported challenges emerged including low self-worth, homosexual sensitivities, fatalism, negative past experiences with testing and concerns with the financial motivation behind screening [49]. Brouse et al. [50] conducted a qualitative study to understand barriers to FOBT in a minority, urban population and several barriers were cited including issues with obtaining FOBT kits from physicians, which is a barrier less understood in the current literature [50]. This phenomenon has also been noted in qualitative studies conducted among minority populations for other cancer screening behaviors [31–33]. We speculate that the quantitative measurement of this theoretical construct may not be adequately capturing the complex factors standing in the way of individuals going through with CRC screening. The current validated scales operationalize barriers as practical constraints, being asymptomatic and being uncomfortable with the nature of the tests [41]. Qualitative examination of this construct has reported additional considerations that may impact a person’s willingness to take the action of screening. Measurement of barriers may need to be refined to incorporate other items to be more responsive to patients’ concerns with screening. Refining these measures would allow for future interventions to target these currently uncaptured barriers to screening. The qualitative data suggest that perceived barriers are relevant to patient’s CRC screening decision-making, but the low reported barriers across the three distinct samples in this study, in particular the barriers to colonoscopy, suggest there may be limitations in how this concept is being measured. The internal consistency of the benefits and barriers indices were evaluated in the present analysis. The majority of the Cronbach’s alphas reported across the studies did not meet the ‘acceptable’ reliability coefficient of .70. In particular, both the benefits and barriers indices for the Temple study and the barriers indices across all three sample were lower than anticipated which may be due to a small number of items as well as the heterogeneity of the items. Adding more items to assess these constructs could improve the reliability and allow for subscales that would delineate discreet factors that contribute to perceived benefits and barriers of CRC screening. We hypothesized that perceived benefits would significantly predict pre-intervention screening behavior and this hypothesis was partially substantiated with individuals reporting higher colonoscopy benefits being more likely to be adherent in all three studies, but this finding was not consistent across the studies for FOBT benefits. Specifically, the Alabama samples (Temple and Take Charge) did not show a significant relationship between FOBT benefits and screening, however, these variables were significantly related in the Maryland study. While FOBT benefits were comparable across these three samples, there may have been other factors that contributed to the likelihood of getting screened for the Temple and Take Charge participants. Health insurance coverage was highest among the Maryland sample and so it is possible that health insurance may have played a role in screening adherence for FOBT for the Alabama study participants. While perceived barriers were significantly related to lower screening for colonoscopy in Take Charge and FOBT in Project HEAL, the perceived barriers constructs did not significantly predict screening behavior to the degree the benefits items related to screening levels. These results are also consistent with the literature suggesting there is variability in the predictive nature of these constructs on screening behavior [28]. This lack of significant findings across all constructs, particularly the fewer significant findings for the barriers items, further suggests the way in which these concepts are presently measured may not be fully covering the complexities of the pros and cons of screening. To evaluate the mechanisms and differential impact of the interventions effects, mediation and moderation analyses were conducted across the three studies. For Project HEAL, moderation was detected for colonoscopy benefits and barriers at the 12-month follow-up. The results suggest that individuals with lower perceived benefits may have more opportunity to move in changing their screening behaviors. Whereas, individuals with more perceived barriers may need further attention in order to impact their screening behavior. Consistent with our findings, this result further underscores the need to better understand those, perhaps uncovered barriers most relevant to changing behavior. Future studies could target individuals with lower benefits and more perceived barriers and the focus of these interventions could aim to address these factors in order to promote screening. Overall, the constructs did not serve as indirect effects of the intervention on screening behavior. In light of our findings, the aforementioned measurement challenges may be playing a role in our inability to detect indirect effects. Additionally, if our current understanding of the most salient barriers is not comprehensive, our interventions may not be fully addressing the issues most pertinent to individuals non-adherent for screening. Strengths and limitations The strengths of this study lie in the use of harmonized data across three church-based intervention trials, the evaluation of two key constructs of the HBM from multiple samples of African Americans, and the mediation and moderation analyses of perceived benefits/barriers of CRC screening on the impact of interventions. There are limitations to be considered in the interpretation of the findings. First, we did not include all of the HBM constructs in the present analyses and it is possible perceived benefits and barriers do not fully explain the relationships between the interventions and the outcomes. In some cases, validated instruments to assess all the constructs did not exist and thus were not measured in the studies. Perceived benefits and barriers are well established as critical constructs within the theory and thus this study sought to gain a better understanding of these items within a priority population. Second, while, a goal of this study was not to pool the data across the Temple, Take Charge and HEAL studies, we observed differences across income, marital status, employment, health insurance coverage and colonoscopy screening history. It is possible some of these differences may relate to the variables of interest, but these were not explored statistically. These samples consisted of African American adults who were enrolled through churches where they were members and were not excluded from participating based upon their cancer screening history. The study results may not be generalizable to African Americans who do not attend church. Although the importance of religion is particularly salient within the African American community with 87% of African Americans reporting being affiliated with an organized religion and 53% attending church routinely [51]. The findings from this study may also not be generalizable to individuals who have never been screened. Future studies could extend this work by interviewing individuals who have not been screened to understand their perceived barriers to getting tested. This study was also at risk of selection bias. This was a group of motivated individuals who enrolled in a 3-workshop series intervention. There may be something inherently different about these individuals compared to those who did not participate. Finally, the data collected in these studies were via self-report. Self-report is subject to bias and is reliant upon participants providing accurate, honest responses. Conclusion Despite the emphasis to use theory in interventions aimed at changing health behavior, limitations exist across our understanding of how theoretical constructs are conceptualized, how they are operationalized, and how they are ultimately analyzed in the evaluation of interventions. The findings from the present study suggest there may be some measurement limitations, especially among African American samples. Among our sample, the results illustrate measurement limitations including floor and ceiling effects and less than optimal reliability. Additionally, these variables did not consistently and significantly predict baseline screening behavior. The constructs also were unable to explain the intervention effects. Based upon these findings, we suggest further research among diverse groups to determine if there are other factors that are possibly not being measured or emphasized in our interventions that play a role in people’s CRC screening decision-making and ultimately their behaviors. Future qualitative work is needed to understand the multi-layered, dynamic nature of barriers that exist among this population. Through this additional study, tools to measure barriers to CRC screening may require further refinement and validation. This may lead to future interventions with new, promising targets aimed at increasing CRC cancer screening among African Americans. Acknowledgements This work was supported by grants from the National Cancer Institute and the Centers for Disease Control and Prevention. We are grateful for the men and women who gave their time to participate in the studies. Funding This work was supported by the National Cancer Institute, National Institutes of Health [#R01CA147313], Centers for Disease Control and Prevention [#C50113185] and Centers for Disease Control and Prevention [#5U48DP00046-03]. This research includes data from three studies. None of the authors have any commercial interests. Conflict of interest statement None declared. References 1 Rosenstock IM , Strecher VJ , Becker MH. Social learning theory and the health belief model . Health Educ Behav 1988 ; 15 : 175 – 83 . 2 Glanz K , Rimer BK. Theory at a glance: a guide for health promotion practice. US Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute; 1997 . 3 Glanz K , Rimer BK , Viswanath K (eds). Health Behavior and Health Education: Theory, Research, and Practice . San Francisco, CA : John Wiley and Sons , 2008 . 4 Crane MM , Ward DS , Lutes LD et al. Theoretical and behavioral mediators of a weight loss intervention for men . Ann Behav Med 2016 ; 50 : 460 – 70 . Google Scholar CrossRef Search ADS PubMed 5 Montanaro EA , Bryan AD. Comparing theory-based condom interventions: health belief model versus theory of planned behavior . Health Psychol 2014 ; 33 : 1251. Google Scholar CrossRef Search ADS PubMed 6 Calfas KJ , Sallis JF , Oldenburg B , Ffrench M. Mediators of change in physical activity following an intervention in primary care: PACE . Prev Med 1997 ; 26 : 297 – 304 . Google Scholar CrossRef Search ADS PubMed 7 Dishman RK , Motl RW , Saunders R et al. Enjoyment mediates effects of a school-based physical-activity intervention . Med Sci Sports Exerc 2005 ; 37 : 478 – 87 . Google Scholar CrossRef Search ADS PubMed 8 Dishman RK , Motl RW , Saunders R et al. Self-efficacy partially mediates the effect of a school-based physical-activity intervention among adolescent girls . Prev Med 2004 ; 38 : 628 – 36 . Google Scholar CrossRef Search ADS PubMed 9 Farhadifar F , Molina Y , Taymoori P , Akhavan S. Mediators of repeat mammography in two tailored interventions for Iranian women . BMC Public Health 2016 ; 16 : 149. Google Scholar CrossRef Search ADS PubMed 10 Haerens L , Cerin E , Maes L et al. Explaining the effect of a 1-year intervention promoting physical activity in middle schools: a mediation analysis . Public Health Nutr 2008 ; 11 : 501 – 12 . Google Scholar CrossRef Search ADS PubMed 11 Lewis BA , Forsyth LH , Pinto BM et al. Psychosocial mediators of physical activity in a randomized controlled intervention trial . J Sport Exerc Psychol 2006 ; 28 : 193 – 204 . Google Scholar CrossRef Search ADS 12 MacKinnon DP , Goldberg L , Clarke GN et al. Mediating mechanisms in a program to reduce intentions to use anabolic steroids and improve exercise self-efficacy and dietary behavior . Prev Sci 2001 ; 2 : 15 – 28 . Google Scholar CrossRef Search ADS PubMed 13 Miller YD , Trost SG , Brown WJ. Mediators of physical activity behavior change among women with young children . Am J Prev Med 2002 ; 23 : 98 – 103 . Google Scholar CrossRef Search ADS PubMed 14 Napolitano MA , Papandonatos GD , Lewis BA et al. Mediators of physical activity behavior change: a multivariate approach . Health Psychol 2008 ; 27 : 409. Google Scholar CrossRef Search ADS PubMed 15 Pinto BM , Lynn H , Marcus BH et al. Physician-based activity counseling: intervention effects on mediators of motivational readiness for physical activity . An Behav Med 2001 ; 23 : 2 – 10 . Google Scholar CrossRef Search ADS 16 Pitpitan EV , Kalichman SC , Garcia RL et al. Mediators of behavior change resulting from a sexual risk reduction intervention for STI patients, Cape Town, South Africa . J Behav Med 2015 ; 38 : 194 – 203 . Google Scholar CrossRef Search ADS PubMed 17 Reynolds KD , Yaroch AL , Franklin FA , Maloy J. Testing mediating variables in a school-based nutrition intervention program . Health Psychol 2002 ; 21 : 51. Google Scholar CrossRef Search ADS PubMed 18 Baruth M , Wilcox S. Psychosocial mediators of physical activity and fruit and vegetable consumption in the faith, activity, and nutrition programme . Public Health Nutr 2015 ; 18 : 2242 – 50 . Google Scholar CrossRef Search ADS PubMed 19 Mosher CE , Fuemmeler BF , Sloane R et al. Change in self‐efficacy partially mediates the effects of the FRESH START intervention on cancer survivors' dietary outcomes . Psycho‐Oncology 2008 ; 17 : 1014 – 23 . Google Scholar CrossRef Search ADS PubMed 20 Conner M , McEachan R , Lawton R , Gardner P. Basis of intentions as a moderator of the intention–health behavior relationship . Health Psychol . 2016 ; 35 : 219. Google Scholar CrossRef Search ADS PubMed 21 Kraemer HC. Messages for clinicians: moderators and mediators of treatment outcome in randomized clinical trials . Am J Psychiatry 2016 ; 173 : 672 – 9 . Google Scholar CrossRef Search ADS PubMed 22 US Preventive Services Task Force . Draft Recommendation Statement: Colorectal Cancer: Screening. Available at http://www.uspreventiveservicestaskforce.org/Page/Document/draft-recommendation-statement38/colorectal-cancer-screening2#Pod3. Accessed: 2 May 2018. 23 Centers for Disease Control and Prevention . Colorectal cancer screening rates remain low. Available at https://www.cdc.gov/media/releases/2013/p1105-colorectal-cancer-screening.html. Accessed: 2 May 2018. 24 James AS , Campbell MK , Hudson MA. Perceived barriers and benefits to colon cancer screening among African Americans in North Carolina . Cancer Epidemiol Prev Biomarkers 2002 ; 11 : 529 – 34 . 25 Tabbarah M , Nowalk MP , Raymund M et al. Barriers and facilitators of colon cancer screening among patients at faith-based neighborhood health centers . J Community Health 2005 ; 30 : 55 – 74 . Google Scholar CrossRef Search ADS PubMed 26 Frank D , Swedmark J , Grubbs L. Colon cancer screening in African American women . ABNF J 2004 ; 15 : 67 . Google Scholar PubMed 27 Sohler NL , Jerant A , Franks P. Socio-psychological factors in the expanded health belief model and subsequent colorectal cancer screening . Patient Educ Couns 2015 ; 98 : 901 – 7 . Google Scholar CrossRef Search ADS PubMed 28 Kiviniemi MT , Bennett A , Zaiter M , Marshall JR. Individual‐level factors in colorectal cancer screening: a review of the literature on the relation of individual‐level health behavior constructs and screening behavior . Psycho‐Oncology 2011 ; 20 : 1023 – 33 . Google Scholar CrossRef Search ADS PubMed 29 Maxwell AE , Bastani R , Crespi CM et al. Behavioral mediators of colorectal cancer screening in a randomized controlled intervention trial . Prev Med 2011 ; 52 : 167 – 73 . Google Scholar CrossRef Search ADS PubMed 30 Becker MH. The Health Belief Model and Personal Health Behavior . San Francisco : Society for Public Health Education ; 1974 . 31 Pasick RJ , Burke NJ , Joseph G. Behavioral theory and culture special issue: authors’ response to commentaries . Health Educ Behav 2009 ; 36 : 167S – 71S . Google Scholar CrossRef Search ADS 32 Pasick RJ , Burke NJ , Barker JC et al. Behavioral theory in a diverse society: like a compass on Mars . Health Educ Behav 2009 ; 36 : 11S – 35S . Google Scholar CrossRef Search ADS PubMed 33 Pasick RJ , Barker JC , Otero-Sabogal R et al. Intention, subjective norms, and cancer screening in the context of relational culture . Health Educ Behav 2009 ; 36 : 91S – 110S . Google Scholar CrossRef Search ADS PubMed 34 Ashing-Giwa K. Health behavior change models and their socio-cultural relevance for breast cancer screening in African American women . Women Health 1999 ; 28 : 53 – 71 . Google Scholar CrossRef Search ADS PubMed 35 Champion VL , Monahan PO , Springston JK et al. Measuring mammography and breast cancer beliefs in African American women . J Health Psychol 2008 ; 13 : 827 – 37 . Google Scholar CrossRef Search ADS PubMed 36 Champion VL , Scott CR. Reliability and validity of breast cancer screening belief scales in African American women . Nurs Res 1997 ; 46 : 331 – 7 . Google Scholar CrossRef Search ADS PubMed 37 Pasick RJ , Burke NJ. A critical review of theory in breast cancer screening promotion across cultures . Annu Rev Public Health 2008 ; 29 : 351 – 68 . Google Scholar CrossRef Search ADS PubMed 38 Holt CL , Shipp M , Eloubeidi M et al. Your body is the temple: impact of a spiritually based colorectal cancer educational intervention delivered through community health advisors . Health Promot Pract 2011 ; 12 : 577 – 88 . Google Scholar CrossRef Search ADS PubMed 39 Holt CL , Litaker MS , Scarinci IC et al. Spiritually based intervention to increase colorectal cancer screening among African Americans: screening and theory-based outcomes from a randomized trial . Health Educ Behav 2013 ; 40 : 458 – 68 . Google Scholar CrossRef Search ADS PubMed 40 Holt CL , Tagai EK , Scheirer MA et al. Translating evidence-based interventions for implementation: experiences from Project HEAL in African American churches . Implement Sci 2014 ; 9 : 66. Google Scholar CrossRef Search ADS PubMed 41 Rawl S , Champion V , Menon U et al. Validation of scales to measure benefits of and barriers to colorectal cancer screening . J Psychosoc Oncol 2001 ; 19 : 47 – 63 . Google Scholar CrossRef Search ADS 42 American Cancer Society . American Cancer Society Recommendations for Colorectal Cancer Early Detection. Available at https://www.cancer.org/cancer/colon-rectal-cancer/detection-diagnosis-staging/acs-recommendations.html. Accessed: 2 May 2018. 43 Sheehan J , Hirschfeld S , Foster E et al. Improving the value of clinical research through the use of Common Data Elements . Clin Trials 2016 ; 13 : 671 – 6 . Google Scholar CrossRef Search ADS PubMed 44 Bangdiwala SI , Bhargava A , O’Connor DP et al. Statistical methodologies to pool across multiple intervention studies . Transl Behav Med 2016 ; 6 : 228 – 35 . Google Scholar CrossRef Search ADS PubMed 45 Centers for Disease Control and Prevention . Behavioral risk factor surveillance survey system online information. Available at http://cdc.gov/brfss/. Accessed 5 May 2017. 46 MacKinnon DP. Introduction to Statistical Mediation Analysis . New York : Lawrence Erlbaum Associates ; 2008 . 47 Jones RM , Devers KJ , Kuzel AJ , Woolf SH. Patient-reported barriers to colorectal cancer screening: a mixed-methods analysis . Am J Prev Med 2010 ; 38 : 508 – 16 . Google Scholar CrossRef Search ADS PubMed 48 Greiner KA , Born W , Nollen N , Ahluwalia JS. Knowledge and perceptions of colorectal cancer screening among urban African Americans . J Gen Intern Med 2005 ; 20 : 977 – 83 . Google Scholar CrossRef Search ADS PubMed 49 Beeker C , Kraft JM , Southwell BG , Jorgensen CM. Colorectal cancer screening in older men and women: qualitative research findings and implications for intervention . J Community Health 2000 ; 25 : 263 – 78 . Google Scholar CrossRef Search ADS PubMed 50 Brouse CH , Basch CE , Wolf RL et al. Barriers to colorectal cancer screening with fecal occult blood testing in a predominantly minority urban population: a qualitative study . Am J Public Health 2003 ; 93 : 1268 – 71 . Google Scholar CrossRef Search ADS PubMed 51 Pew Research Center . A Religious Portrait of African-Americans. Available at http://www.pewforum.org/2009/01/30/a-religious-portrait-of-african-americans/. Accessed: 26 January 2018. 52 Lasser KE , Ayanian JZ , Fletcher RH , Good MJD. Barriers to colorectal cancer screening in community health centers: a qualitative study . BMC Family Practice 2008 ; 9 : 15 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Health Education ResearchOxford University Press

Published: May 10, 2018

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