Health-related quality of life and health-promoting behaviors in black men.
This study examined the health-related quality of life and
health-promoting behaviors in a convenience sample of low-income black
men. Almost three-fourths reported their overall health as good or
better. However, the mean number of recent (that is, past 30 days)
mentally unhealthy days was 13.12, and more than half reported frequent
([greater than or equal to] 14) mentally unhealthy days. There were
strong correlations between mentally unhealthy days and various
health-promoting behaviors. The contrast between participants'
overall health and mental health is disconcerting. Implications for
social work practice with low-income black men to improve health
promotion are discussed.
KEY WORDS: black men; health-promoting behaviors; health-related quality of life; mentally unhealthy days
African Americans (Behavior)
Health promotion (Research)
Mental health (Research)
Quality of life (Health aspects)
Calvert, Wilma J.
Isaac, E. Paulette
|Publication:||Name: Health and Social Work Publisher: Oxford University Press Audience: Academic; Professional Format: Magazine/Journal Subject: Health; Sociology and social work Copyright: COPYRIGHT 2012 Oxford University Press ISSN: 0360-7283|
|Issue:||Date: Feb, 2012 Source Volume: 37 Source Issue: 1|
|Topic:||Event Code: 310 Science & research|
|Geographic:||Geographic Scope: United States Geographic Code: 1USA United States|
Engagement in health-promoting behaviors has been a topic of
concern to social workers since Jane Addams's involvement with the
settlement house movement (Marshall & Altpeter, 2005). According to
NASW (2005b), the profession should educate clients about disease
prevention. The profession's involvement on interdisciplinary
public health teams aimed at ameliorating the negative effects of
conditions such as teenage pregnancy, cancer, and HIV/ AIDS is well
documented (Marshall & Altpeter, 2005). Although prior research
within the field of social work has identified that health-promotion
behaviors can help decrease overall health care expenditures and are
needed for vulnerable populations (Icard, Bourjolly, & Siddiqui,
2003; NASW, 2005b), the focus on the health-promotion needs of
low-income black men has received minimal attention.
Research consistently demonstrates the health disparities experienced by black men in the United States (Centers for Disease Control and Prevention [CDC], 2010; Office of Minority Health, 2006; U.S. Cancer Statistics Working Group, 2009). This group has the lowest life expectancy of all racial or ethnic groups in the United States (National Center for Health Statistics [NCHS], 2010), with a life expectancy of 69.7 years, compared with 75.7 for white men (Heron et al., 2009). Although statistics provide the quantitative data to support disparate health conditions among black men, incidence and prevalence data fail to describe an individual's functional status and individual health perceptions. To date, topics of health disparity and health promotion focused on black men have not garnered significant research, despite the glaring disparities experienced by black men (Gadson, 2006).
There is indication that black men are eager to learn of health-promotion practices (Heeren & Jemmott, 2011), but barriers to health promotion for this population exist. Researchers have found a regular source of medical care is predictive of receipt of preventive services, including secondary prevention interventions, such as screenings for hypertension, diabetes, and prostate cancer, three conditions from which black people, especially black men, suffer disproportionately compared with their white counterparts (Merzel & Moon-Howard, 2002). In addition to having a regular source of care, increases in health promotion activities, such as participating in routine health exams, are related to exposure to other men who support health promotion (Hammond, Matthews, & Corbie-Smith, 2010).
HEALTH-RELATED QUALITY OF LIFE
Although traditional measures of mortality and morbidity (that is, incidence and prevalence data) provide valid and reliable health indicators, they are not reflective of a person's ability to participate in usual activities and do not adequately assess a person's overall sense of well-being (Chowdhury, Balluz, & Strine, 2008). This sense of well-being is referred to as health-related quality of life (HRQOL) and is defined as the perception of one's physical and mental health over time (U.S. Department of Health and Human Services [HSS], 2000). Compared with the objective information obtained from lab tests and even a physical examination, HRQOL provides a more thorough assessment of a person's health because it focuses on the person's perceptions of his or her health. Engaging in health-promoting behaviors can improve one's overall HRQOL.
Although social workers frequently assess HRQOL when determining the effects of an illness on all facets of a person's life, Dempster and Donnelly (2000) recommend social workers assess clients' HRQOL using a valid and reliable instrument, particularly when working in the community. Since 1993, the CDC has included HRQOL measures in the annual Behavioral Risk Factor Surveillance System (BRFSS) (CDC, 1994). Underscoring the importance of HRQOL is the inclusion of HRQOL as one of the new topic areas in Healthy People 2020 (HealthyPeople.gov, 2010). Although the national agenda has always focused on improving health, Healthy People 2020 includes HRQOL as a discrete topic (Healthy People.gov, 2010). Tracking HRQOL in various populations can identify subgroups at increased risk of poor physical or mental health and can assist with identification of the health-promotion and disease-prevention needs of those groups. For instance, using data from a nationally representative sample, Kobau, Safran, Zack, Moriarty, and Chapman (2004) found the mean number of sad, blue, or depressed days (SBDDs) was 3.8 among black people, compared with 2.8 among white people. Among black men specifically, the mean was 2.9, compared with 2.3 for white men (Kobau et al., 2004). Subsyndromal levels of depression, measured through the prevalence of SBDDs, can assist practitioners in identifying populations at risk for major depressive episodes. SBDDs have been found to be associated with less participation in health-promoting behaviors (Kobau et al., 2004), further evidence of the need to assess SBDDs, especially among at-risk populations.
The literature examining how healthy lifestyles may enhance HRQOL is growing (Mokdad, Marks, Stroup, & Gerberding, 2004). Marriage has been associated with numerous physical and mental benefits, including increased prevalence of HRQOL (Williams & Umberson, 2004) and improved mental health (Marks & Lambert, 1998). There has, however, been little research on HRQOL in community-based samples of racial and ethnic minorities. One of the few studies reported that black people were more likely than white people and Asian people to report their health as fair or poor. The odds of reporting frequent mental distress (that is, [greater than or equal to] 14 days during the preceding month in which one's mental health was not good) were higher among black people than white people; black people were also more likely than white people to report that their health prevented them from engaging in their usual activities (Chowdhury et al., 2008).
Given the health disparities faced by black men, especially low-income black men, and the dearth of literature addressing the health-promoting behaviors of this group, the purpose of this research was to identify health-promoting behaviors engaged in by black men and to examine the relationship between their HRQOL and health-promoting behaviors. For this research, health-promoting behaviors are those activities in which individuals engage to maintain or improve their well-being, such as obtainment of adequate nutrition and adequate rest and relaxation. Our aim is to add to the existing body of knowledge in various disciplines, including social work.
For this descriptive research, we gathered data from a community-based sample of black men enrolled in a local organization nationally recognized for its experience working with fathers to help them become more responsible parents, ultimately helping to strengthen families and communities. Fifty-five participants completed the surveys but, given the focus of the research, we only used data provided by black participants (n = 54). We gathered sociodemographic data, including age and highest education level (less than high school diploma, high school diploma/GED, and other). Although we did not gather employment or personal income data on the participants, the agency traditionally serves men who are unemployed at the time of their enrollment in the program. In addition, level of education is frequently used as a proxy measure for income data.
HRQOL and Health-Promoting Behaviors
We used the Healthy Days Measures from the CDC, currently used in the BRFSS. The HRQOL-4 forms the Healthy Days Core Module and queries participants on their self-perceived general health (five-point Likert-type scale; "General health"), recent (that is, past 30 days) physical and mental health symptoms ("Physically unhealthy days" and "Mentally unhealthy days," respectively), and recent (that is, past 30 days) activity limitation ("Limited activity") due to physical or mental health (HHS, 2009). These questions from the BRFSS, designed to track Healthy People 2010 objectives, demonstrate validity and reliability with a variety of populations (Andersen, Catlin, Wyrwich, & Jackson-Thompson, 2003; Nelson, Holtzman, Bole, Stanwyck, & Mack, 2001). Cronbach's alpha for the HRQOL-4 was .540 in this study. "Limited activity days" is a composite score reflecting how often in the past 30 days participants believed poor physical or mental health prevented them from engaging in usual activities.
Using the summary indices suggested by the developers of the HRQOL-4, we dichotomized "General health" into "Good/fair health," reflecting good (excellent, very good, or good health) and fair (fair or poor health). In addition, we created "Sum of unhealthy days," reflecting the number of days during the previous 30 days participants felt their physical or mental health was not good.
The number of days without enough sleep, "Sleep insufficiency days," was determined from the BRFSS question "During the past 30 days, for about how many days have you felt you did not get enough rest or sleep?" (HHS, 2009). We dichotomized physical and mental health, limited activity, and sleep to reflect frequent ([greater than or equal to] 14 days) versus infrequent (<14 days) to be consistent with other researchers (Chowdhury et al., 2008). Clinicians and researchers frequently use this amount of time as an indicator for depression (CDC, 1998).
Health-promoting behaviors include having a personal health care provider and an annual physical examination. Using a BRFSS question, we queried participants to determine if finances prohibited them from seeing a physician in the previous year (yes or no) and the last time they had a complete physical examination. In addition, we asked participants about their health coverage. Lack of health insurance can be a deterrent to seeking health care, especially an annual physical examination, which is often the first step to diagnosing chronic and preventable conditions in the early stages.
Additional health-promoting behaviors were assessed via the Health-Promoting Lifestyle Profile II (HPLP II). The HPLP II consists of 52 items that assess the frequency of engagement in health-promoting behaviors (Walker, Sechrist, & Pender, 1995). It contains six subscales--Health Responsibility, Physical Activity, Nutrition, Spiritual Growth, Interpersonal Relations, Stress Management--and a total score and uses a four-point Likert-type scale ranging from 1 = never to 4 = routinely. Scores on the HPLP II and the subscales are calculated as means, with higher scores indicating increased participation in the activity. The HPLP II has established validity and reliability with black populations (Johnson, 2005; Nies, Buffington, Cowan, & Hepworth, 1998). We used mean imputation for missing data. The Cronbach's alpha for the HPLP II reported by Walker et al. (1995) was .943 for the total scale, and Cronbach's alphas ranged from .793 to .872 for the subscales. For this research, Cronbach's alpha was .932 for the total scale, and Cronbach's alphas ranged from .696 to .860 for the subscales.
Descriptive statistics were used to summarize the demographic data. Evaluation of the linear relationships between the HRQOL measures and the HPLP II scores were measured using Pearson correlations. Data for the HPLP II scores based on the HRQOL measures were compared using Mann-Whitney U tests. All analyses were conducted using Predictive Analytics SoftWare Statistics, formerly the Statistical Package for the Social Sciences.
Participants ranged in age from 16 to 64 years (M = 32.64, SD = 8.88). The majority (68.5 percent) had never been married. Almost two-thirds had a high school diploma or GED (n = 23), whereas 37 percent (n = 19) had dropped out of school or been expelled. Their weight ranged from 127 pounds to 300 pounds, with a mean of 189 pounds (SD = 39.72). Using the standard BMI formula ([kg/m.sup.2]) with a BMI of [greater than or equal to] 25 indicating an overweight status, two-thirds were overweight or obese. The results of the Healthy Days and health-related measures are presented in Table 1.
The means and the results of 285 the correlations between the HRQOL measures and the HPLP II subscales are displayed in Table 2. Analyses using Pearson's correlation coefficients indicated that there are significant linear relationships between several of the HRQOL measures and participation in health-promoting behaviors. Especially striking were the strong and significant correlations between mentally unhealthy days and the overall HPLP score, nutrition score, spiritual growth score, and stress management score.
The results of the 295 Mann-Whitney U test for good/fair health, frequent mental distress, and frequent limited activity are summarized in Table 3. The median scores on all HRQOL indicators for those reporting fair or poor health were significantly lower than scores for those who reported their health was good or better, indicating less participation in that health-promoting behavior. The same is true for participants experiencing frequent mental distress. Except for a correlation between frequent physical distress and the spiritual growth subscale of the HPLP II, results for frequent physical distress and frequent sleep insufficiency were not statistically significant; data are available upon request from the corresponding author.
This research provides a description of some of the health-promoting behaviors engaged in by a group of black men and provides information on their HRQOL. Most of the participants did not have health insurance, and more than half did not have a health care provider. As noted, although we did not collect employment or personal income data from the participants, the organization at which we collected data traditionally serves men who are unemployed. Given the relationship between employment and insurance (approximately 60 percent of people are covered by employment-based insurance [DeNavas-Walt, Proctor, & Smith, 2010]), the percentage of our sample reporting finances prevented them from seeing a physician is not unusual.
This group is at risk for undetected, undiagnosed, and untreated health problems based on the percentage without health insurance (79 percent), without a health care provider (68 percent), and with finances prohibiting a nonemergency physician visit (45 percent). The percentage of participants without health insurance is higher than that of a similar group of low-income men in the Fragile Families and Child Wellbeing Study (Corman, Noonan, Carroll, & Reichman, 2009) and more than double the percentage reported by the Henry J. Kaiser Family Foundation (2006). Without a regular source of medical care, this population may not receive the benefits of early detection or education on how to prevent these conditions. In addition, because 73 percent of the men in this sample believe that their health is good or better, they may not be inclined to seek out a health care provider for routine health care, especially without the benefit of health insurance.
A larger percentage (27 percent) of the participants in our research rated their health status as fair or poor than those in two nationally representative samples of black men: 17 percent in a sample by the Henry J. Kaiser Family Foundation (2006) and 12 percent in a sample by Adams, Martinez, and Vickerie (2010). Using a more restrictive definition for health status (that is, very good or better versus less than very good), Corman and colleagues (2009) reported that 35 percent of the racially diverse, low-income men in their sample reported their health as less than very good.
The mean number of physically unhealthy days (3.43) among the participants in our research was similar with the mean number (3.0) in a nationally representative sample (Zahran et al., 2005). However, the prevalence of mentally unhealthy days reported in the current study (13.12) was approximately four times higher than that reported by Zahran et al. (2005) (3). Although the BRFSS is a nationally representative sample, surveying techniques may omit the difficult-to-access population of unemployed, low-income black men (Corman et al., 2009). Hence, the prevalence in our participants may be even more striking had the BRFSS focused on a similar high-risk, vulnerable population.
Sleep insufficiency is gaining increased recognition as a significant public health problem in the United States and is related to various physical and mental health problems and an increase in premature mortality (Institute of Medicine, 2006). One group of researchers found 26 percent of men and 30 percent of black men reported sleep insufficiency (CDC, 2009), with another group reporting 31 percent of black men get fewer than seven hours of sleep per night, compared with 24 percent of white men (Hale & Do, 2007). Psychosocial stress, including depression, may have contributed to the prevalence of participants with sleep insufficiency in the current study (mean of 13 mentally unhealthy days).
The results from the present study indicate the important relationship between HRQOL and health-promoting behaviors. As reported, there were correlations between mentally unhealthy days and several variables, including nutrition, lack of sleep, and physical activity. Researchers have demonstrated the importance of nutrition and HRQOL (Gaudreau et al., 2007; Wunderlich, McKinnon, Piemonte, & Ahmad, 2009). Interestingly, although the participants reported very few limited activity days and 73 percent reported their overall health as good or better, they still had a high incidence (that is, [greater than or equal to] 14) of mentally unhealthy days. This seems contradictory. One might assume that a person whose general health is good would not experience so many mentally unhealthy days. In fact, an individual's self-reported health status is predictive of one's future health (Boyington, Daniel, & Holmes, 2008). It may be this group does not equate mental stress with poor health. Their definition of health may not include the mental or psychological facets of health.
Ross and Mirowsky (2001) discovered that adults may have poor health (well-being) for other reasons than limited activity. It is not uncommon for a patient's and doctor's perception of health to differ. This study indicates that the quality of life experienced by a group of low-income black men, especially when focusing on their mental health, is compromised. This group of men reported that for almost half of the previous month, they felt their mental health was not good.
Another plausible explanation for the seeming contradiction between participants' general health and their reporting of frequent mentally unhealthy days and days with frequent sleep insufficiency is the unmeasured independent effects of factors such as preexisting mental illness. One cannot assume that an individual's general health or physical health preclude the existence of mental stress. In addition, a question focusing on the past 30 days may reflect temporary stressors, such as stressors associated with unemployment, with other aspects of their lives, or with participation in a highly structured program such as the one from which we selected our sample. Future research should assess participants' perceptions of the source of their mental stress and sleeplessness.
An examination of health-promoting behaviors in the participants indicates their participation is similar to that reported by other researchers. In one of the few published studies using black men, Johnson (2005) found the overall HPLP II mean was 2.60 for the included black men; the HPLP II mean was 2.48 in the men in this current effort. Subscale scores were also similar in the two groups; there was a range of 2.23 to 3.14 in Johnson's sample and a range of 2.19 to 3.14 in this research. Identical scores were found on the spiritual growth subscale. The two groups were similar on age distribution. They differed, however, on highest education level completed and marital status, two factors that have the potential to affect health and participation in health-promoting behaviors. These men, most never married, with limited formal education, and reporting their overall health as good or better, may be at increased risk for future health risks because of the lack of these two social supports.
Although the mean scores on the HPLP II are similar to those reported by Johnson (2005), differences exist when examining the scores in relation to HRQOL measures. Median scores were significantly lower for those who reported fair or poor health than scores for those who reported good or better health (see Table 3). The same is true for those who reported frequent mental distress, compared with those reporting infrequent mental distress and those reporting frequent limited activity. Those men experiencing fair or poor health, infrequent mental distress, and frequent limited activity appear to have difficulty participating in health-promoting behaviors, which might ultimately further compromise their health.
The findings of this research have several implications for social work practice, policy, and research. Research findings underscore the significance of social workers and other professionals working with this vulnerable population group to assess not only their general health, but also indicators of their mental well-being. The high prevalence of mentally unhealthy days in this sample should be a concern for social workers, especially licensed clinical social workers (LCSWs). LCSWs, found in a variety of settings, often provide the initial diagnosis and treatment for people experiencing mental disturbances (NASW, 2005a). With a focus on the mental, emotional, and behavioral health of their clients, LCSWs could use the HRQOL-4 to screen their clients for subsyndromal levels of depression, identify those clients experiencing frequent mental distress (an indicator for depression sometimes used by clinicians) (CDC, 1998), and then begin treatment, as needed. In addition, the mental health needs of this population should be addressed by policy makers (Janzen, Green, & Muhajarine, 2006). Low-income black men should be included in research because often their voice is not heard. The associations found between measures of the HRQOL-4 and the HPLP II suggest these two measures should be assessed together because those experiencing distress, based on the HRQOL-4, were less likely to participate in health-promoting behaviors.
Social workers, like many other professionals within the helping field, are often perplexed about how to address disparate physical and mental health conditions among high-risk populations such as black men when there are apparent internal and external mechanisms that influence health-promoting behaviors. According to Ayner (2010), health-promoting behaviors such as help-seeking are hindered by the population's perception of such help as a sign of weakness that threatens their sense of maleness. This perception, in some instances, may overshadow the populations' perception of their HRQOL. The challenge for social workers is not only to provide quality care, but also to help the population understand the importance of health-promoting behaviors. Health care social workers can begin this challenging task by providing education to the population on the value of health-promoting behaviors in a way that directly confronts the stigma that the population has ascribed to health-promoting behaviors. Recognizing that this population is likely unmarried, social workers should seek to develop alliances that include extended family members and the community and can serve as social support for health-promoting behaviors.
External barriers of affordability, availability, and accessibility also present formidable obstacles to health service utilization. Of particular concern is the fact that a significant proportion of the population has substantial financial limitations that can influence their decisions to seek care. Social workers should be equipped to provide the population with education on how to access and utilize appropriate services for effective health promotion. Many low-income men reside in neighborhoods where health care facilities are scarce, and social workers must recognize that the population will need assistance to locate and access facilities outside of their immediate neighborhoods. In addition, social workers have a responsibility to advocate for services that are accessible to the population. Fortunately social workers are not alone in their efforts to address the public health needs of the population. According to Spencer, Gunter, and Palmisano (2010), social workers should rely on the assistance of community health workers who have similar roles for promotion of practices that are culturally appropriate.
Limitations and Strengths
These findings provide a first step in describing the HRQOL and health-promoting behaviors in an extremely vulnerable population. The cross-sectional nature of the design precludes inferences related to causality. Additional research is needed to identify participants' perceptions of the source or sources of their mentally unhealthy days. Because this research used a convenience sample, results may not be generalizable to other black men or men in general. The small sample size also affects the generalizability of the findings. In addition, because the men in our sample were enrolled in a structured program, they may possess the motivation to engage in healthy behaviors. As with all self-reported data, what they reported may not be an accurate depiction of their behaviors.
Another limitation is with the low (.540) reliability of the HRQOL-4. Although the instrument has established reliability, the low alpha for this population is a concern, especially because HRQOL is one of the major variables in the research. Additional research is needed with a larger sample to validate its use for similar populations.
Despite the limitations, a strength of the research is its focus on an underrepresented group in the literature. Future research will focus on validating these findings with a larger sample. Results can be used to identify the specific health concerns of low-income black men and then tailor programs designed to enhance their HRQOL.
Original manuscript received April 18, 2011
Final revision received July 30, 2011
Accepted August 12, 2011
Advance Access Publication July 4, 2012
Adams, P. F., Martinez, M. E., & Vickerie, J. L. (2010). Summary health statistics for the U.S. population: National Health Interview Survey, 2009. Vital and Health Statistics (Series 10, 248). National Center for Health Statistics. Retrieved from http://www.cdc.gov/ nchs/data/series/sr_10/srl0_248.pdf
Andersen, E. M., Catlin, T. K., Wyrwich, K. W., & Jackson-Thompson, J. (2003). Retest reliability of surveillance questions on health related quality of life. Journal of Epidemiology & Community Health, 57, 339-343.
Ayner, S. R. (2010). Clinical practice with African American men: What to consider and what to do. Smith College Studies in Social Work, 80, 20-34.
Boyington, J.E.A., Daniel, L. H., & Holmes, D. N. (2008). Self-rated health, activities of daily living, and mobility limitations among black and white stroke survivors. Journal of Aging and Health, 20, 920-936.
Centers for Disease Control and Prevention. (1994). Quality of life as a new public health measure-Behavioral Risk Factor Surveillance System. Morbidity and Mortality Weekly Report, 43, 375-380. Retrieved from http://www.cdc.gov/ mmwr/preview/mmwrhtml/00031143.htm
Centers for Disease Control and Prevention. (1998). Self-reported frequent mental distress among adults-United States, 1993-1996. Morbidity and Mortality Weekly Report, 47, 325-331. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/00052469.htm
Centers for Disease Control and Prevention. (2009). Perceived insufficient rest or sleep among adults-United States, 2008. Morbidity and Mortality Weekly Report, 58, 1175-1179.
Centers for Disease Control and Prevention. (2010). Prostate cancer rates by race and ethnicity. Retrieved from http://www.cdc.gov/cancer/prostate/statistics/race.htm
Chowdhury, P. P., Balluz, L., & Strine, T. W. (2008). Health-related quality of life among minority populations in the United States, BRFSS 2001-2002. Ethnicity & Disease, 18, 483-487.
Corman, H., Noonan, K., Carroll, A., & Reichman, N. E. (2009). Low-income fathers' access to health insurance. Journal of Health Care for the Poor and Underserved, 20, 152-164.
Dempster, M., & Donnelly, M. (2000). Selecting a measure of health related quality of life. Social Work in Health Care, 32, 45-55.
DeNavas-Walt, C., Proctor, B. D., & Smith, J. C. (2010). Income poverty, and health insurance coverage in the United States: 2009. Washington, DC: U. S. Government Printing Office.
Gadson, S. L. (2006). The third world health status of black American males. Journal of the National Medication Association, 98, 488-491.
Gaudreau, P., Morais, J. A., Shatenstein, B., Gray-Donald, K., Khalik, A., Dionne, I., et al. (2007). Nutrition as a determinant of successful aging: Description of the Quebec longitudinal study Nuage and results from cross-sectional pilot studies. Rejuvenation Research, 10, 377-386.
Hale, L., & Do, D. P. (2007). Racial differences in self-reports of sleep duration in a population-based study. SLEEP, 30, 1096-1103.
Hammond, W. P., Matthews, D., & Corbie-Smith, G. (2010). Psychosocial factors associated with routine health examination scheduling, and receipt among African American men. Journal of the National Medical Association, 102, 276-289.
HealthyPeople.gov. (2010). Healthy People 2020. Retrieved from http://healthypeople.gov/2020/default.aspx
Henry, J. Kaiser Family Foundation. (2006). Race, ethnicity & health care. Washington, DC: Author.
Hereen, G.A., &Jemmott, J. B. (2011). Health promotion: Results of focus groups with African American men. Journal of Men's Health, 8, 50-55.
Heron, M. P., Hoyert, D. L., Murphy, S. L., Xu, J. Q., Kochanek, K. D., & Tejada-Vera, B. (2009). Deaths: Final data for 2006. National vital statistics reports. Hyattsville, MD: National Center for Health Statistics. Retrieved from http://www.cdc.gov/nchs/data/nvsr/nvsr57/nvsr57_14.pdf
Icard, L. D., Bourjolly, J. N., & Siddiqui, N. (2003). Designing social marketing strategies to increase African American's access to health promotion programs. Health & Social Work, 28, 214-223.
Institute of Medicine. (2006). Sleep disorders and sleep deprivation: An unmet public health problem. Washington, DC: National Academies Press.
Janzen, B. L., Green, K., & Muhajarine, N. (2006). The health of single fathers. Demographic, economic and social correlates. Canadian Journal of Public Health, 97, 440-444.
Johnson, R. L. (2005). Gender differences in health-promoting lifestyles of African Americans. Public Health Nursing, 22, 130-137.
Kobau, R., Safran, M. A., Zack, M. M., Moriarty, D. G., & Chapman, D. (2004). Sad, blue, or depressed days, health behaviors and health-related quality of life, Behavioral Risk Factor Surveillance System, 1995-2000. Health and Quality of Life Outcomes, 2. Retrieved from http://www.hqlo.com/content/pdf/1477-7525-2-40.pdf
Marks, N. F., & Lambert, J. D. (1998). Marital status continuity and change among young and midlife adults: Longitudinal effects and psychological well-being. Journal of Family Issues, 19, 652-686.
Marshall, V. W., & Altpeter, M. (2005). Cultivating social work leadership in health promotion and aging: Strategies for active aging interventions. Health & Social Work, 30, 135-144.
Merzel, C., & Moon-Howard, J. (2002). Access to health services in an urban community: Does source of care make a difference?Journal of Urban Health, 79, 186-199.
Mokdad, A. H., Marks, J. S., Stroup, D. F., & Gerberding, J. U (2004). Actual causes of death in the United States, 2000.JAMA, 291, 1238-1245.
National Association of Social Workers. (2005a). NASW standards for clinical social work. Washington, DC: Author.
National Association of Social Workers. (2005b). NASW standards for social work practice in health care settings. Washington, DC: Author.
National Center for Health Statistics. (2010). Health, United States, 2009: With special feature on medical technology. Retrieved from http://www.cdc.gov/nchs/data/hus/hus09.pdf#listtables
Nelson, D. E., Holtzman, D., Bolen, J., Stanwyck, C. A., & Mack, K. A. (2001). Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soz Praventivmed, 46(Suppl. 1), $3-$42.
Nies, M., Buffington, C., Cowan, G., & Hepworth, J. (1998). Comparison of lifestyles among obese and nonobese African American and European American women in the community. Nursing Research, 47, 251-257.
Office of Minority Health. (2006). Eliminating racial & ethnic disparities. Retrieved from http://www.cdc.gov/omhd/About/disparities.htm
Ross, C. E., & Mirowsky, J. (2001). Neighborhood disadvantage, disorder, and health. Journal of Health and Social Behavior, 42, 377-386.
Spencer, M. S., Gunter, K. E., & Pahnisano, G. (2010). Community health workers and their value to social work. Social Work, 55, 169-180.
U.S. Cancer Statistics Working Group. (2009). United States cancer statistics: 1999-2005 incidence and mortality web-based report. Atlanta: Centers for Disease Control and Prevention. Retrieved from www.cdc.gov/uscs
U.S. Department of Health and Human Services. (2000). Measuring healthy days. Population assessment of health-related quality of life. Atlanta: Centers for Disease Control and Prevention.
U.S. Department of Health and Human Services. (2009). Behavioral Risk Factor Surveillance System Survey questionnaire. Atlanta: Centers for Disease Control and Prevention.
Walker, S. N., Sechrist, K. R., & Pender, N.J. (1995). Health Promoting Lifestyle II. Omaha: University of Nebraska Medical Center, College of Nursing.
Williams, K., & Umberson, D. (2004). Marital status, marital transitions, and health: A gendered life course perspective. Journal of Health and Social Behavior, 45, 81-98.
Wunderlich, S., McKinnon, C., Piemonte, J., & Ahmad, Z. N. (2009). Measuring the impact of nutrition education and physical activity on older adults participating in government sponsored programs. Journal of Nutrition for the Elderly, 28, 255-271.
Zahran, H. S., Kobau, R., Moriarty, D. G., Zack, M. M., Holt, J., & Donehoo, R. (2005). Health-related quality of life surveillance-United States, 1993-2002. Morbidity and Mortality Weekly Report, 54, 1-35.
Wilma J. Calvert, PhD, MPE, MS, RN, is assistant professor, University of Missouri-St. Louis College of Nursing, 1 University Boulevard, St. Louis, MO 63121; e-mail: email@example.com.
E. Paulette Isaac, EdD, is assistant professor, University of Missouri-St. Louis College of Education.
Sharon Johnson, PhD, is associate professor, University of Missouri-St. Louis School of Social Work.
Table 1: Results of the Healthy Days and Health-related Measures from the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (N = 54) Measure n (%) NF (SD) HRQQL-4 General health (n = 53) Excellent 2 (4) Very good 8 (15) Good 25 (47) Fair 12 (26) Poor 1 (2) Don't know/not sure 5 (9) PUDs (n = 53) 3.43 (7.65) MUDs (n= 51) 13.12 (11.29) SUDS (n=51) 15.08 (11.41) Good/fair health (a) (n = 48) 35 (73) Ltd act (n = 50) 4.06 (6.65) Does not have health insurance (n = 52) 41 (78.8) Does not have personal health-care provider (n = 53) 36 (67-9) Finances prohibited seeing physician (for nonemergency health-care needs) (n = 53) 24 (45-3) SLIDs 12.06 (10.44) Participants reporting frequent (b) PUDs 5 (9) MUDs 27 (53) Ltd act 7 (14) SLIDs 21 (40) Note: HRQOL-4=Health-related Quality of Life-4; PUDs=physically unhealthy days in the last 30; MUDs=mentally unhealthy days in the last 30; SUDS=sum of unhealthy days in the last 30; Ltd act=limited activity days in the last 30; SLIDs=sleep insufficiency days in the last 30. (a) Goodtfair health =good or better versus fair or poor. (b) Frequent means [greater than or equal to] 14 days per month. Table 2: Correlations between Health-related Quality of Life and Health-Promoting Lifestyle Profile II Subscales (N=54) Measure M 1 2 3 4 1. Genhith 3.32 -- 2. PUDs 3.43 -.25 -- 3. MUDs 13.12 -.49 ** .16 -- 4. Ltd act 4.06 .25 .23 .47 ** -- 5. SLIDs 12.06 .15 .17 .47 ** .43 ** 6. SUDs 15.08 .46 ** .40 ** .93 ** .51 ** 7. HPLP 2.48 .53 ** -.27 * -.68 ** -.39 ** 8. HR 2.19 .33 ** -.15 -.66 -.20 9. PA 2.19 .45 ** -.15 -.44 ** -.30 * 10. Nutrition 2.15 .39 ** -.15 -.53 ** -.29 * 11. SG 3.14 -.36 ** -.34 * -.55 ** -.46 ** 12. IPR 2.73 -.52 ** -.30 * -.42 ** -.22 13. SM 2.43 -.41 ** -.21 -.64 ** -.34 * Measure 5 6 7 8 9 1. Genhith 2. PUDs 3. MUDs 4. Ltd act 5. SLIDs -- 6. SUDs .42 ** -- 7. HPLP -.39 * -.64 ** -- 8. HR -.20 -.58 ** .80 ** -- 9. PA -.25 -.37 ** .78 ** .58 ** -- 10. Nutrition -.25 -.48 ** .83 ** .73 ** .70 ** 11. SG -.24 -.57 ** .75 ** .46 ** .37 ** 12. IPR -.24 -.43 .72 ** .36 ** .40 ** 13. SM -.37 ** -.61 ** .80 ** .55 .54 ** Measure 10 11 12 13 1. Genhith 2. PUDs 3. MUDs 4. Ltd act 5. SLIDs 6. SUDs 7. HPLP 8. HR 9. PA 10. Nutrition -- 11. SG .43 ** -- 12. IPR .43 ** .67 ** -- 13. SM .55 ** .64 ** .53 ** -- Note: Genhlth=general health; PUDs=physically unhealthy days; MUDs=mentally unhealthy days; Ltd act= limited activity days; SLIDs=sleep insufficiency days; SUDS=sum of unhealthy days; HPLP=Health Promotion Lifestyle Profile II mean; HR=health respon- sibility; PA=physical activity; SG =spiritual growth; IPR=interpersonal relations; SM=stress management. * p.05, two-tailed. ** p.01, two-tailed. Table 3: Effects of Health-related Quality of Life on Participation in Health-Seeking Behaviors Fair or Poor Health Variable Mdn (Range) Health Promotion 2.17 (1.54-2.90) Lifestyle Profile II Health responsibility 2.00 (1.22-2.44) Physical activity 2.13 (1.00-2.75) Nutrition 2.00 (1.44-2.33) Spiritual growth 2.89 (2.11-3.78) Interpersonal relations 2.44 (1.78-2.89) Stress management 2.13 (1.38-3.50) Frequent Mental Distress Mdn (Range) Health Promotion 2.31 (1.54-3.02) Lifestyle Profile 11 Health responsibility 1.89 (1.11-2.56) Physical activity 1.88 (1.00-4.00) Nutrition 2.00 (1.11-2.67) Spiritual growth 2.89 (2.11-4.00) Interpersonal relations 2.44 (1.67-3.67) Stress management 2.13 (1.00-3.25) Frequent Limited Activity Mdn (Range) Health Promotion 2.29 (1.62-2.37) Lifestyle Profile 11 Health responsibility 2.22 (1.11-2.44) Physical activity 1.88 (1.00-2.13) Nutrition 1.67 (1.33-2.44) Spiritual growth 2.44 (2.22-3.22) Interpersonal relations 2.44 (2.00-2.78) Stress management 2.00 (1.88-2.63) Good or Better Health Variable Mdn (Range) U Test (a) Health Promotion 2.69 (1.85-3.33) 75.00 Lifestyle Profile II Health responsibility 2.33 (1.11-3.44) 119.00 Physical activity 2.25 (1.38-4.00) 142.50 Nutrition 2.22 (1.44-3.56) 124.00 Spiritual growth 3.33 (2.22-4.00) 89.00 Interpersonal relations 3.00 (1.67-3.67) 76.00 Stress management 2.63 (1.75-3.25) 135.00 Infrequent Mental Distress Mdn (Range) U Test Health Promotion 2.76 (2.15-3.33) 94.00 Lifestyle Profile 11 Health responsibility 2.61 (1.78-3.44) 84.50 Physical activity 2.56 (1.00-3.75) 153.50 Nutrition 2.39 (1.44-3:56) 148.00 Spiritual growth 3.33 (2.89-3.89) 162.00 Interpersonal relations 3.00 (2.33-3.67) 174.00 Stress management 2.63 (2.00-3.50) 133.50 Infrequent Limited Activity Mdn (Range) U Test Health Promotion 2.63 (1.54-3.33) 50.00 Lifestyle Profile 11 Health responsibility 2.22 (1.11-3.44) 109.50 Physical activity 2.25 (1.00-4.00) 75.00 Nutrition 2.22 (1.11-3.56) 78.00 Spiritual growth 3.22 (2.1114.00) 39.50 Interpersonal relations 2.78 (1.67-3.67) 58.50 Stress management 2.50 (1.00-3.50) 70.00 Variable -- Health Promotion 0.000 Lifestyle Profile II Health responsibility 0.011 Physical activity 0.048 Nutrition 0.016 Spiritual growth 0.001 Interpersonal relations 0.000 Stress management 0.031 p Health Promotion 0.000 Lifestyle Profile 11 Health responsibility 0.000 Physical activity 0.001 Nutrition 0.001 Spiritual growth 0.002 Interpersonal relations 0.005 Stress management 0.000 p Health Promotion 0.005 Lifestyle Profile 11 Health responsibility 0.250 Physical activity 0.034 Nutrition 0.042 Spiritual growth 0.002 Interpersonal relations 0.010 Stress management 0.024 (a) Mann-Whitney U test.
|Gale Copyright:||Copyright 2012 Gale, Cengage Learning. All rights reserved.|