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Abstract

There are significant gender disparities in health outcomes and health care utilization in the United States, with men experiencing more of these disparities. It is critical to ascertain the interplay between societal conditions, health behaviors, and access to services and the impact of these factors on health outcomes and utilization of health care. The present study is part of a larger initiative titled, The Men’s Health Study: Addressing Healthy Lifestyle Behaviors, which has two purposes—to annually assess the motivators of and barriers to health-promoting behaviors among culturally diverse men attending the Men’s Health Forum (MHF) and to use this information to develop an intervention program that facilitates healthy lifestyle behaviors among men. The MHF is a community-driven initiative for medically underserved men in Tampa, Florida that offers free health screenings and wellness exhibitors in order to empower men to lead a healthy lifestyle. The purpose of this article is to identify barriers to engaging in health-smart behaviors (e.g., cancer screenings, physical activity) among culturally diverse men who participated in the MHF and to detect any demographic differences among these barriers. A total of 254 men participated in the study. Findings identify that age was the only demographic variable that had a statistically significant association with any of the cancer-screening barriers. Some cancer-screening barriers appear to exist among all demographic groups since no statistical demographic differences were discovered. Income and education were significantly associated with barriers to engaging in health-smart behaviors. This may give researchers, health educators, and providers information needed to customize interventions to promote health and preventive health care among culturally diverse men.

Keywords

cancer prevention health promotion men

Introduction

Since the release of the Institute of Medicine’s landmark report in 2003, Unequal Treatment (Smedley, Stith, & Nelson, 2003), there has been an increased focus on better understanding the predictors of health and health care disparities. Although progress has been achieved scientifically and clinically in understanding the etiology, progression, treatment, and management of many chronic diseases, not all populations in the United States have benefited equitably from this progress (Agency for Healthcare Research and Quality [AHRQ], 2014; Smedley et al., 2003). For example, recent statistics indicate there are significant gender disparities in health outcomes and health care utilization in the United States, with utilization rates generally being lower for men than women (James, Salganicoff, Ranji, Goodwin, & Duckett, 2012). In comparison with men, women are more likely to have visited a doctor within the past year and are less likely to have neglected their cholesterol tests (AHRQ, 2012).

Furthermore, men are increasingly more likely than women to be hospitalized for chronic conditions, such as congestive heart failure, and to experience long-term complications from diabetes and pneumonia (AHRQ, 2012). These gender differences are further compounded by the unique experiences of racial and ethnic minority men in the United States with regard to health outcomes and seeking health care. Men of racial and ethnic minority populations continue to experience higher preventable mortality and morbidity than White men, causing an economic burden on national health expenses (Thorpe, Richard, Bowie, LaVeist, & Gaskin, 2013). Minority men also have higher rates of chronic illnesses, shorter life spans, and higher levels of disability than White men (National Center for Health Statistics [NCHS], 2013). For the past three decades, African American men have experienced lower life expectancy (71.6 years) compared with White men (76.4 years), African American women (77.8 years), Hispanic men (78.9 years), White women (81.1 years), and Hispanic women (83.7 years; NCHS, 2013).

Chronic conditions, such as diabetes, cardiovascular disease, and cancer may be prevented or delayed through the utilization of preventive health services (e.g., screening examinations) and lifestyle modifications (e.g., dietary changes and increased physical activity). Preventive health screenings have been proven to decrease morbidity and mortality, while substantially increasing quality of life (NCHS, 2013). Some of the most common screenings include body mass index, cholesterol level, blood pressure, cardiovascular disease, cancer, depression, and diabetes. However, a recent report indicates that minority men encounter multilevel barriers and challenges with regard to getting health screenings and obtaining health information (James et al., 2012). These barriers and challenges often lead to lower utilization of recommended preventive health services.

Recent studies have identified a host of factors that may be attributable to lower utilization of preventive health services among minority men. For African American men, these include fatalism, socioeconomic barriers, limited health knowledge or awareness, medical mistrust, and feelings of masculinity (Andersen, 1995; Bradley et al., 2002; Cheatham, Barksdale, & Rodgers, 2008; Hammond, Matthews, & Corbie-Smith, 2010; Ravenell, Whitaker, & Johnson, 2008; Wade, 2008; Whitley, Samuels, Wright, & Everhart, 2005). Similarly, Hispanic men have identified lack of trust in medical professionals, fear of being a “guinea pig,” and fear of being embarrassed as barriers to preventive health services (Davis, Bynum, Katz, Buchanan, & Green, 2012).

There is increasing evidence that a number of factors, beyond the individual, affect healthy lifestyles and obtaining sufficient health care. These factors are commonly referred to as social determinants of health, and broadly include both societal conditions (shaped mostly by federal and state policies) and psychosocial factors—such as income, education, occupation, neighborhoods, and housing (Brennan Ramirez, Baker, & Metzler, 2008; Commission on Social Determinants of Health [CSDH], 2008). Emerging findings indicate these social determinants exert a strong influence on individual health behaviors, access to health care, and health outcomes.

Various models suggest that social determinants of health directly affect individual and community health and/or indirectly affect health by influencing health-promoting behaviors (Brennan Ramirez et al., 2008; CSDH, 2008; Gornick, 2002). Further, access to salient health education and health care services is often predicated by not only an individual’s income status or educational attainment but also his or her early life experiences, work environment, housing, and neighborhood characteristics (CSDH, 2008).

Although evidence is still inconclusive, it has been reported that neighborhood characteristics such as the availability of healthy foods, access to parks and other athletic facilities, and the convenience of salient preventive health education and services affect health and health-related outcomes (CSDH, 2008). It is critical to ascertain the unique interplay between societal conditions, health behaviors, and access to health care services in terms of their impact on health outcomes and utilization of health care as this information will inform efforts to promote health and preventive health care and thus promote efforts to eliminate health disparities.

The purpose of this article is to explore self-reported barriers to preventive health behaviors and services among a group of culturally diverse men attending the community-based Men’s Health Forum (MHF). The MHF is a community-driven initiative for medically underserved men (i.e., those who are uninsured, underinsured, or do not have a regular health care provider) in Tampa, Florida (Grant et al., 2012). The MHF is designed to reduce health disparities among the men who attend by helping them to (a) better understand their health status as it relates to the prevention and early detection of infectious and chronic diseases and (b) receive preventive care resources and skills for remaining healthy or addressing health concerns. The MHF is conducted as a collaborative effort by academically based researchers, clinicians (physicians, nurses, pharmacists, health educators, etc.), community and faith-based organizations, and various aspects of local government. The free health screenings, services, and exhibits that are aspects of the MHF are designed to empower medically underserved men to lead a healthy lifestyle.

The findings presented in this article are part of a larger initiative titled, The Men’s Health Study: Addressing Healthy Lifestyle Behaviors. The Men’s Health Study is an ongoing, collaborative study between researchers at Moffitt Cancer Center and the University of Florida. The purposes of the Men’s Health Study are (a) to annually assess the motivators of and barriers to health-promoting behaviors among medically underserved men attending the MHF and (b) to use the assessed motivators of and barriers to develop and evaluate intervention components of the MHF that will further promote healthy lifestyle behaviors among culturally diverse men. The cross-sectional data presented in this article were collected from a convenience sample of adult men who attended the 2013 MHF. The present study sought to (a) identify barriers to engaging in health-smart (health-promoting) behaviors (e.g., cancer screenings, physical activity) among culturally diverse, medically underserved men who participated in the MHF and (b) detect any demographic differences among these barriers. Assessing these barriers will give researchers, health educators, and health care providers information needed to help customize interventions to promote health and preventive health care among culturally diverse men.

Method

Participants

The Men’s Health Study was approved by the Scientific Review Committee at Moffitt Cancer Center and by Institutional Review Boards at the University of South Florida and the University of Florida. Participation eligibility criteria required being (a) male, (b) 18 years of age or older, and (c) able to speak English or Spanish. A total of 254 men participated in the study (539 men attended the 2013 MHF). The demographic characteristics of the sample are listed in Table 1. There was a higher percentage of African Americans/Blacks (44.5%), followed by Hispanics (27.6%), non-Hispanic Whites (15.7%), and Other (6.7%). The ethnic breakdown among African Americans/Blacks and Hispanics are also depicted in Table 1. Because of low sample sizes, those who self-reported as Asian, American Indian, or Other were combined to comprise the Other category. The overall demographic profile of all MHF attendees was slightly different: 41% Hispanics, 37% African Americans/Blacks, 15% non-Hispanic Whites, and 5% Other. Half of the study participants were older than 50 years. Nearly the same percentage of participants earned either a college degree (43.3%) or a high school diploma (42.5%). Almost 70% indicated having an income of less than $50,000 and almost 60% reported not having health insurance.

Table 1.

Demographic Characteristics of the Men’s Health Study Sample (N = 254).

	n (%)
Age (in years)
18-34	24 (9.5)
35-49	88 (34.6)
50-64	109 (42.9)
≥65	17 (6.7)
Missing	16 (6.3)
Race/ethnicity
Hispanic	70 (27.6)
Puerto Rican	13 (18.6)
Dominican	5 (7.1)
Mexican	7 (10.0)
Cuban	7 (10.0)
Colombian	22 (31.4)
Other	16 (22.9)
Non-Hispanic African American/Black	113 (44.5)
African American of American origin (born and grew up in America)	86 (76.1)
African American of African origin (born in Africa but now an American citizen)	7 (6.2)
African American of Caribbean origin (born in one of the Caribbean Islands but now an American citizen)	12 (10.6)
African	4 (3.5)
Caribbean	2 (1.8)
Another African American or Black origin	2 (1.8)
Non-Hispanic White	40 (15.7)
Other	17 (6.7)
Missing	14 (5.5)
Income ($)
<25,000	110 (43.3)
25,000-49,999	67 (26.4)
≥50,000	53 (20.9)
Missing	24 (9.4)
Education
Less than high school	19 (7.5)
High school graduate	108 (42.5)
College graduate or more	110 (43.3)
Missing	17 (6.7)
Marital status
Married	115 (45.3)
Not married	121 (47.6)
Missing	18 (7.1)
Health insurance
Yes	92 (36.2)
No	144 (56.7)
Missing	18 (7.1)

Instruments

The assessment battery used to obtain data from study participants included the Motivators of and Barriers to Health-Smart Behaviors Inventory (MB-HSBI), selected items from the Cancer Screening Questionnaire, and a Demographic Data Questionnaire. Both English and Spanish versions of the assessment battery were available to participants so they could complete the survey in their language of preference. Following are descriptions of these instruments.

The Motivators of and Barriers to Health-Smart Behaviors Inventory

The MB-HSBI (Tucker et al., 2011) assesses the extent to which certain variables are motivators of or barriers to engaging in health-promoting behaviors, called health-smart behaviors, using a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree). The health-smart behaviors constitute four domains of the MB-HSBI, which are as follows: (a) eating a healthy breakfast, (b) eating healthy foods and snacks, (c) drinking healthy drinks, and (d) engaging in physical activity. For each of these four health-smart domains, there is a motivator scale, which assesses level of agreement that the listed variables/items serve as motivators to engage in that behavior, and a barriers scale, which assesses level of agreement that the listed variables/items serve as barriers to engaging in that behavior. Within each motivator and barrier scale, subscales were determined through exploratory factor analysis. The Healthy Breakfast motivator subscales are desire to be healthy, availability, and family influences; and the barrier subscales are cultural/family influences and low priority. The Healthy Drinks motivator subscales are preference, medical issues, and awareness; and the barrier subscales are social influences, knowledge, and availability. The Healthy Foods and Snacks motivator subscales are routine, availability, health benefits, medical issues, and convenience; and the barrier subscales are negative attitudes, availability, and self-control. The Physical Activity motivator subscales are commitment, priority, goals/benefits, personal preference, and medical issues; and the barrier subscale are preferred alternatives, medical issues, environmental support, and self-consciousness.

The published article on the development and validation of the MB-HSBI reports that scores derived from the eight scales demonstrated adequate internal consistencies (i.e., Cronbach’s coefficient alphas ranged from .78 to .92) and adequate concurrent validity (Tucker et al., 2011).

Cancer Screening Questionnaire

Selected questions were taken from the Cancer Screening Questionnaire (CSQ; Katz et al., 2008), which was developed by a multidisciplinary, multi-university research team within the New York University Oral Cancer Research on Adolescent and Adult Health Promotion Center, a National Institute of Dental and Craniofacial Research/National Institutes of Health Oral Health Disparities Center. The CSQ was adapted from the Tuskegee Legacy Project Questionnaire, which was previously validated based on item internal consistency (cutoff of 0.40 or more), item discriminant validity (a 95% confidence interval cutoff point), and internal consistency reliability (Cronbach’s α ≥ .70; Katz et al., 2006). The questions selected for the Men’s Health Study were chosen based on the relevance to the study objectives and therefore assess cancer-screening motivators (e.g., encouragement or support by a close friend or relative), barriers (e.g., fear of being a “guinea pig”), and willingness (e.g., likeliness to have a cancer-screening exam at the current time) to participate. The barrier-related items, which are the focus on this present analysis, were fear of getting AIDS; fear that it might be painful; fear of being a guinea pig; fear results not kept private/confidential; fear disease would upset family; fear knowing you might have cancer; feeling sure you won’t get cancer; lack of trust in medical personnel; and fear of embarrassment during exam. They were recorded using a 5-point Likert-type response scale with the following response options: not interfere at all, interfere a little, interfere some, interfere a great deal, and interfere completely. Since the factors that interfere with cancer-screening participation were of interest rather than the exact degree of interference, the five response categories were collapsed into two: no to little interference and some to complete interference.

Demographic Data Questionnaire

The Demographic Data Questionnaire (DDQ) was constructed by the authors and was used to obtain information on participants’ race/ethnicity, age, education, income, marital status, and health insurance status.

Procedure

Study participants were recruited by research staff using informational flyers in the registration area at the MHF. A room at the MHF was designated as the study location where the men interested in participating gave written consent to volunteer then completed the survey. Men were able to come at their convenience throughout the day of the MHF to participate in the study. If a participant required assistance with the survey, members of the research team were available to assist and respond to any questions. Each participant was instructed not to place his name on the completed survey. After returning a completed survey, individuals received a $10 gift card as compensation.

Statistical Analysis

Frequency distributions of demographic variables were computed to illustrate the characteristic distribution of the sample. Chi-square tests were used to address whether there are significant demographic differences for each cancer-screening barrier. The means and standard deviations of the barrier subscales for each domain of the MB-HSBI were calculated. To address the research question regarding whether there are significant demographic differences in the level of agreement for each investigated barrier to health-smart behaviors, a Kruskal–Wallis test was performed. For any significant demographic differences identified, follow-up multiple pairwise comparisons using a Mann–Whitney test were computed with a Bonferroni p value adjustment (Elliott & Woodward, 2007). Statistical significance was determined at a p value of < .05. SAS Version 9.3 (SAS Institute, Inc., Cary, NC) was used for all data analyses.

Results

Cancer-Screening Barriers

Age group was the only demographic variable that resulted in statistically significant associations with any of the cancer-screening barriers (Table 2). Specifically, “Fear of getting AIDS” and “Fear that it might be painful” were significantly associated with age group (p = .04 and .02, respectively). Men aged 35 to 49 years (34%) and 50 to 64 years (29%) reported that “Fear of getting AIDS” would cause Some to Complete Interference with participating in a cancer screening. A higher proportion (40%) of men aged 50 to 64 years reported that having a “Fear that it might be painful” would cause Some to Complete Interference with participating in a cancer screening.

Table 2.

Barriers to Cancer Screenings by Age Group.

		Ages, n (%)
Barrier	Response	18-34 years	35-49 years	50-64 years	65+ years	p value
Fear of getting AIDS	No or little interference	21 (87.5)	58 (65.9)	75 (70.7)	15 (93.7)	.04^a
Fear of getting AIDS	Some to complete interference	3 (12.5)	30 (34.1)	31 (29.3)	1 (6.3)	.04^a
Fear that it might be painful	No or little interference	19 (79.2)	61 (70.1)	64 (59.8)	15 (93.8)	.02^a
Fear that it might be painful	Some to complete interference	5 (20.8)	26 (29.9)	43 (40.2)	1 (6.2)	.02^a
Fear of being a guinea pig	No or little interference	16 (66.7)	57 (66.3)	65 (61.3)	15 (93.7)	.08
Fear of being a guinea pig	Some to complete interference	8 (33.3)	29 (33.7)	41 (38.7)	1 (6.3)	.08
Fear results not kept private/confidential	No or little interference	17 (70.8)	55 (62.5)	57 (54.8)	13 (81.2)	.14
Fear results not kept private/confidential	Some to complete interference	7 (29.2)	33 (37.5)	47 (45.2)	3 (18.8)	.14
Fear disease would upset family	No or little interference	19 (79.2)	59 (67.0)	68 (64.2)	14 (87.5)	.18
Fear disease would upset family	Some to complete interference	5 (20.8)	29 (33.0)	38 (35.8)	2 (12.5)	.18
Fear knowing you might have cancer	No or little interference	15 (62.5)	57 (65.5)	65 (61.3)	14 (87.5)	.23
Fear knowing you might have cancer	Some to complete interference	9 (37.5)	30 (34.5)	41 (38.7)	2 (12.5)	.23
Feeling sure you won’t get cancer	No or little interference	12 (57.1)	50 (65.8)	60 (61.9)	10 (71.4)	.79
Feeling sure you won’t get cancer	Some to complete interference	9 (42.9)	26 (34.2)	37 (38.1)	4 (28.6)	.79
Lack of trust in medical personnel	No or little interference	19 (79.2)	59 (69.4)	65 (61.9)	14 (93.3)	.05
Lack of trust in medical personnel	Some to complete interference	5 (20.8)	26 (30.6)	40 (38.1)	1 (6.7)	.05
Fear of embarrassment during exam	No or little interference	20 (83.3)	71 (80.7)	81 (75.7)	14 (87.5)	.61
Fear of embarrassment during exam	Some to complete interference	4 (16.7)	17 (19.3)	26 (24.3)	2 (12.5)	.61

Statistically significant results at p value of < .05 using the chi-square test.

Health-Smart Behaviors Barriers

The results from examining the associations between the demographic variables and level of agreement for each barrier to health-smart behaviors revealed that only income and education were significantly associated with any of these barriers. These results are presented in Tables 3 and 4, respectively. Men with lower income or lower education levels had higher barrier subscale scores (i.e., reported higher levels of agreement regarding barriers) within each of the four health-smart behavior domains on the MB-HSBI. Specifically, men with lower income levels had significantly higher levels of agreement with regard to the following barriers: cultural/familial influences on eating a healthy breakfast; lack of knowledge of healthy drinks; negative attitudes toward and unavailability of healthy foods and snacks; and medical/health issues and lack of environmental support that affect physical activity. Men with lower education levels had significantly higher levels of agreement with regard to the following barriers: lack of knowledge of healthy drinks; negative attitudes toward healthy foods and snacks; and medical/health issues, lack of environmental support, and feelings of embarrassment that affect physical activity.

Table 3.

Mean Levels of Agreement on Barriers to Four Health-Smart Behaviors by Income Level.

	<$25,000	$25,000-$49,999	$50,000+	p value	Post hoc comparisons^a
Healthy breakfast
Cultural/familial influences subscale	2.25 ± 0.73	2.01 ± 0.68	1.95 ± 0.63	.02^b	<$25,000 and $50,000+
Low priority subscale	2.12 ± 0.80	1.94 ± 0.86	2.07 ± 0.88	.29
Total domain barriers	2.21 ± 0.69	2.00 ± 0.67	1.99 ± 0.59	.05
Healthy drinks
Social influences subscale	2.32 ± 0.87	2.14 ± 0.81	2.00 ± 0.76	.08
Knowledge subscale	1.98 ± 0.80	1.71 ± 0.73	1.54 ± 0.59	.001^b	<$25,000 and $50,000+
Availability subscale	2.56 ± 0.80	2.53 ± 0.68	2.33 ± 0.62	.06
Total domain barriers	2.32 ± 0.62	2.14 ± 0.56	1.96 ± 0.48	.0003^b	<$25,000 and $50,000+
Healthy foods and snacks
Negative attitudes subscale	2.17 ± 0.80	1.80 ± 0.68	1.85 ± 0.61	.004^b	<$25,000 and $25,000-$49,999
Availability subscale	2.53 ± 0.82	2.31 ± 0.83	2.21 ± 0.66	.02^b	<$25,000 and $50,000+
Self-control subscale	2.59 ± 0.80	2.49 ± 0.73	2.55 ± 0.75	.60
Total domain barriers	2.36 ± 0.67	2.09 ± 0.61	2.11 ± 0.55	.01^b	<$25,000 and $25,000-$49,999;
Total domain barriers	2.36 ± 0.67	2.09 ± 0.61	2.11 ± 0.55	.01^b	<$25,000 and $50,000+
Physical activity
Preferred alternatives subscale	1.84 ± 0.89	1.71 ± 0.81	1.78 ± 0.74	.31
Medical/health issues subscale	1.81 ± 0.81	1.61 ± 0.77	1.44 ± 0.65	.01^b	<$25,000 and $50,000+
Environmental support subscale	2.16 ± 0.85	1.89 ± 0.65	1.64 ± 0.83	.0003^b	<$25,000 and $50,000+
Self-consciousness subscale	1.84 ± 0.76	1.69 ± 0.81	1.66 ± 0.79	.13
Total domain barriers	1.92 ± 0.66	1.69 ± 0.56	1.63 ± 0.59	.01^b	<$25,000 and $50,000+

Comparisons performed using the Mann–Whitney test. ^bStatistically significant results at p value of <.05 using Kruskal–Wallis Test.

Table 4.

Mean Levels of Agreement on Barriers to Four Health-Smart Behaviors by Education Level.

	Less than high school	High school graduate	College graduate+	p value	Post hoc comparisons^a
Healthy breakfast
Cultural/familial influences subscale	2.41 ± 0.85	2.16 ± 0.65	2.00 ± 0.68	.08
Low priority subscale	2.39 ± 0.85	1.99 ± 0.79	2.02 ± 0.85	.16
Total domain barriers	2.42 ± 0.81	2.10 ± 0.61	2.01 ± 0.65	.10
Healthy drinks
Social influences subscale	2.63 ± 1.11	2.27 ± 0.81	2.07 ± 0.78	.04
Knowledge subscale	2.39 ± 1.01	1.81 ± 0.71	1.68 ± 0.70	.01^b	<High school and college graduate+
Availability subscale	2.74 ± 0.71	2.47 ± 0.74	2.47 ± 0.70	.32
Total domain barriers	2.64 ± 0.74	2.21 ± 0.55	2.08 ± 0.56	.004^b	<High school and high school graduate;
Total domain barriers	2.64 ± 0.74	2.21 ± 0.55	2.08 ± 0.56	.004^b	<high school and college graduate+
Healthy foods and snacks
Negative attitudes subscale	2.45 ± 0.78	2.00 ± 0.76	1.89 ± 0.68	.02^b	<High school and college graduate+
Availability subscale	2.53 ± 0.89	2.50 ± 0.73	2.25 ± 0.82	.04^b
Self-control subscale	2.61 ± 0.83	2.58 ± 0.73	2.51 ± 0.80	.78
Total domain barriers	2.52 ± 0.67	2.26 ± 0.63	2.13 ± 0.62	.03^b	<High school and college graduate+
Physical activity
Preferred alternatives subscale	1.76 ± 1.28	1.74 ± 0.82	1.79 ± 0.78	.85
Medical/health issues subscale	2.03 ± 0.84	1.77 ± 0.79	1.49 ± 0.71	.002^b	<High school and college graduate+; high school graduate and college graduate+
Environmental support subscale	2.46 ± 1.02	1.97 ± 0.78	1.85 ± 0.78	.04
Self-consciousness subscale	2.42 ± 1.11	1.66 ± 0.66	1.72 ± 0.79	.03^b	<High school and high school graduate
Total domain barriers	2.19 ± 0.80	1.78 ± 0.58	1.72 ± 0.62	.05

Comparisons performed using the Mann–Whitney test. ^bStatistically significant results at p value of <.05 using Kruskal-Wallis Test.

Discussion

This study sought to (a) identify barriers to engaging in health-smart (health-promoting) behaviors among culturally diverse men who participated in the MHF and (b) detect any demographic differences among these barriers.

Among the demographic variables assessed, age group was the only variable that was significantly associated with any of the cancer-screening barriers analyzed. Men aged 35 to 49 and 50 to 64 years, respectively, reported that a “Fear of getting AIDS” would cause Some to Complete Interference with participating in a cancer screening. Men aged 50 to 64 years reported that having a “Fear that it might be painful” would cause Some to Complete Interference. These interferences or perceived barriers, a construct of the Health Belief Model (Glanz, Rimer, & Lewis, 2002), offers insight into the barriers to engaging in recommended screening behaviors that exist among these age groups for men. Interventions to promote cancer screening among these men should likely include a focus on reassurance that one will not get AIDS because of participating in a cancer screening and correction of misinformation related to the contraction of AIDS. Such interventions should also ideally address the fear some older men have that screening may be painful. The provision of tailored “how-to” information and the perceived benefits of screening should help reduce these interferences or barriers.

Even though the remaining demographic variables resulted in no statistically significant differences with the cancer-screening barriers, findings reveal some barriers that appear to exist among all participants across all the investigated demographic variables. One of these commonly identified cancer-screening barriers was, “Feeling very unlikely to get cancer.” This barrier may be explained by the concept of masculinity and its effect on men’s health. Masculinity has long been identified as a contributor to men’s health outcomes (Campbell, Keefe, McKee, Waters, & Moul, 2012; Courtenay, 2003). Men seem to avoid health-related procedures and treatments that may challenge their masculinity or traditional masculine gender socialization and social norms (e.g., feeling self-reliant, strong, and tough; not expressing emotions or pain; Courtenay, 2000). Furthermore, they often avoid seeking care because they do not want to deal with the next steps of treatment (Evans et al., 2005).

Additionally, findings from a study by Mahalik, Burns, and Syzdek (2007) suggested that masculinity and the perceived normalness of other men’s health behaviors significantly predicted participants’ own health behaviors beyond that accounted for by sociodemographic variables. A similar finding revealed in a study assessing African American men’s preventive behaviors was that participants described the ‘Superman syndrome’ as a barrier to receiving care (Allen, Kennedy, Wilson-Glover, & Gilligan, 2007). In this instance, the “Superman syndrome” was defined as being reluctant to seek care because of feeling invincible to sickness or harm (Shuman, 2011). Commensurate with previous findings, men tend to believe that the assistance of doctors is unnecessary and that going to a doctor is a sign of weakness (American Academy of Family Physicians, 2007). Masculinity and the social norms of men often have a negative effect on their health beliefs and behaviors.

Another commonly identified barrier among the men in the present study was having a fear of getting cancer, which has been frequently identified as a barrier for cancer screenings (Allen et al., 2007; Jones, Devers, Kuzel, & Woolf, 2010; Wang, Moehring, Stuhr, & Krug, 2013). Perceived susceptibility has been reported to be correlated with fear and screening behavior (Kleier, 2010). Perceived susceptibility, a construct of the Health Belief Model, refers to one’s subjective perception of the risk of contracting a health condition (Glanz et al., 2002). Media messages consisting of frightening statistics, contradictions/confusions, or an inevitability in relation to a cancer diagnosis further promote this fear (Clarke & Everest, 2006). Fear and knowledge of cancer influence one another, and both affect health behaviors (Berman & Wandersman, 1990). Therefore, increasing knowledge about cancer and the importance of early detection has potential for diminishing fear of cancer. Discussion of this fear and the sources of its origin can also be helpful in health promotion interventions aimed at promoting cancer screening among target men.

Men with lower income/education levels reported higher levels of agreement for the following barriers to engaging in health-smart behaviors: lack of knowledge of healthy drinks; cultural/familial influences on eating a healthy breakfast; negative attitudes toward and unavailability of healthy foods and snacks; medical/health issues, lack of environmental support, and feelings of embarrassment that affect physical activity. These results highlight specific areas where health promotion and education services could intervene and increase health promotion knowledge among culturally diverse men with lower income/education levels. For example, information on healthy/unhealthy drinks and breakfast choices should likely be included, if it is not already, in nutrition interventions. Health educators can incorporate materials on the quantity of sugar in drinks and food and provide alternative healthier food and drink options (e.g., egg whites, water).

Interventions to promote health among culturally diverse men with lower income/education levels should also likely include a focus on medical/health issues that affect participating in physical activity. Specifically, intervention leaders can provide information on common chronic medical issues men experience and teach participating men ways of overcoming the barriers associated with these medical issues.

In order to have a significant impact on men’s health, innovative psychosocial and psychoeducational interventions are needed that address the assessed barriers to health promotion and preventive health care among men. Such interventions could be incorporated in the MHF or take place in community-based settings, such as barbershops and churches. These interventions could be delivered by male community health workers who can serve as health-oriented role models for the men who participate in the MHF or frequent barbershops or churches. Male community health workers could help link men to cancer screenings and health promotion services in their community, provide culturally appropriate health education for men, and advocate for preventive care services, such as the MHF, in their community (Lehmann & Sanders, 2007). The mentioned interventions could also be led by male leaders within church families, particularly given that men tend to be more likely to share information even on sensitive health topics if they discuss them with close family or among a tight-knit cultural or religious group (Rovito & Leone, 2012).

Limitations

This study does contain limitations associated with the chosen study design and instruments. First, the self-reported data obtained may be influenced by recall bias and reporting error. Yet obtaining self-reported data is common practice in health promotion research. Second, the portions of MB-HSBI and CSQ used may be limited given their focus on only certain potential barriers and behaviors. Though these potential barriers and behaviors are important, there are likely other barriers and behaviors that exist that are not assessed by the MB-HSBI and the CSQ. Last, the results of this study may not be generalizable to all men across the United States. The MHF is geared toward low income, medically underserved men; therefore, the sample is composed of these individuals.

Conclusion

The identified barriers of this study will offer researchers, health professionals, and health care providers useful information for designing health promotion interventions that empower men with strategies for overcoming barriers. Such interventions have the potential for increasing men’s participation in cancer screenings, increasing their level of engagement in health-smart behaviors, and ultimately, increasing their life expectancy. Additionally, these interventions may also help eliminate the health disparities that exist among culturally diverse men. The MB-HSBI is an innovative and useful tool for assessing the barriers to health-promoting behaviors. The MHF offers an example of an innovative, community involved, and trusted venue for engaging culturally diverse men in identifying their barriers to participating in cancer screenings and health-promoting lifestyles.

As Leone and Rovito (2013) noted,

society must continue to realize that the strength of a nation is not necessarily the individual health of one citizen, but rather, the collective health of its citizenry. Male health is not simply an individual issue, but one that warrants social action. (p. 248)

Men’s health affects the health of women, children, and ultimately the community as a whole (Bonhomme, 2007). By identifying barriers to health among men, effective, customizable interventions can be designed and implemented to improve the health of men and their communities.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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A Community-Level Assessment of Barriers to Preventive Health Behaviors Among Culturally Diverse Men

Abstract

Keywords

Introduction

Method

Participants

Instruments

The Motivators of and Barriers to Health-Smart Behaviors Inventory

Cancer Screening Questionnaire

Demographic Data Questionnaire

Procedure

Statistical Analysis

Results

Cancer-Screening Barriers

Health-Smart Behaviors Barriers

Discussion

Limitations

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

References