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. Author manuscript; available in PMC: 2023 Feb 1.
Published in final edited form as: J Prev (2022). 2021 Nov 13;43(1):95–110. doi: 10.1007/s10935-021-00652-1

The role of modifiable, self-empowerment-oriented variables to promote health-related quality of life among inadequately insured Americans

Guillermo M Wippold 1, Sarah Grace Frary 2
PMCID: PMC8924987  NIHMSID: NIHMS1770013  PMID: 34773547

Abstract

Inadequately insured Americans experience a disproportionately low health-related quality of life (HRQoL) – a multidimensional and subjective indicator of health associated with premature mortality. Although the inadequately insured are a growing and at-risk group of individuals, little research has examined factors associated with HRQoL within this population. Health Self-Empowerment Theory (HSET) and precision prevention influenced the conceptualization of this study. HSET is a health empowerment theory that recognizes the effects of certain cognitive-behavioral variables on health promotion within at-risk groups. Precision prevention advocates for individual or precise group-specific tailored and optimized health promotion approaches based on key sociodemographic variables. We investigated the impact of HSET variables on mental and physical HRQoL among 279 inadequately insured women and men who completed a questionnaire assessing HRQoL, health self-efficacy, health motivation, and active coping. Among the women in our sample, results indicate that exercise and psychological well-being self-efficacy were significantly and positively associated with mental and physical HRQoL. Among men, psychological well-being and responsible health practices self-efficacy, in addition to active coping, were significantly and positively associated with mental HRQoL. Psychological well-being self-efficacy and active coping were significantly and positively associated with physical HRQoL among the men. The findings of our study suggest that HSET variables play an important role in the development of tailored HRQoL-promotion interventions for inadequately insured Americans, and that the roles of those variables may differ based on gender.

Keywords: Uninsured, Underinsured, Health-related quality of life, Precision medicine, Health self-empowerment theory, Health disparity

Introduction

Uninsured/Underinsured Individuals

Recent reports indicate that in the United States (U.S.), over 43% of non-elderly adults (or roughly 90 million adults) are inadequately insured (Collins, Gunja, & Aboulafia, 2020). This estimate includes the number of uninsured adults (12.5%), insured adults who had a lapse in coverage during the past year (9.5%), and underinsured adults (21.3%). Uninsured adults are those who lack any health insurance coverage whatsoever, whereas underinsured adults are those who have health insurance coverage but are at significant financial risk due to medical expenditures. Unless widespread and systemic changes are made, these high rates of inadequately insured individuals in the U.S. will persist. Meanwhile, immediate efforts to promote the health of the inadequately insured are needed, given that these individuals are at risk for poor indicators of health and higher rates of mortality. Influenced by the Institute of Medicine’s report that health insurance saves lives, Woolhandler and Himmelstein (2017) conducted an extensive review of the literature focusing on insurance and mortality and found that the odds of mortality among the insured compared to the uninsured is between 0.71 and 0.97.

Health-Related Quality of Life

It not uncommon for individuals with similar clinical indicators to report drastically different experiences and health-related outcomes (e.g., odds of mortality). These differences can be explained by the subjective mental and physical burden associated with chronic health conditions (Brown, Jia, Zack, Thompson, Haddix, & Kaplan, 2013). Organizations such as the Centers for Disease Control and Prevention and the National Institutes of Health have highlighted the importance of multidimensional subjective indicators of health as indicators of public health (Forrest, Blackwell, & Camargo, 2018; Slabaugh et al., 2017). These indicators are strong predictors of mortality and morbidity (DeSalvo, Bloser, Reynolds, He, & Muntner, 2006; Dominick, Ahern, Gold, & Heller, 2002), even while controlling for unidimensional indicators of health (e.g., obesity, smoking status; Brown, Thompson, Zack, Arnold, & Barile, 2013). Health-related quality of life (HRQoL) constitutes one such multidimensional indicator of health, and consists of an individual’s subjective appraisal of their physical and psychological functioning. As part of the Healthy People 2020 campaign, the U.S. government established increasing HRQoL as a national health objective (Barile et al., 2013). Research examining the HRQoL among specific populations followed such as those experiencing obesity (Pristed et al., 2012), senior low-income African Americans (Wippold, Tucker, Roncoroni, & Henry, 2020) and urban adults (Wippold, Tucker, Kroska & Hanvey, 2020). Despite this much-needed research, few studies (Wippold, Nmezi, Williams, Butler, & Hodge, 2020; Wippold, & Roncoroni, 2020) have examined factors that are associated with HRQoL and strategies to improve HRQoL among inadequately insured Americans, who have high rates of low HRQoL (i.e., a negative appraisal of physical and psychological functioning; Bharmal, & Thomas, 2005). There is an urgent need to examine factors among inadequately insured individuals that are associated with HRQoL and to develop theory-based, tailored interventions, which have been identified as needed by health care providers and clinic staff working closely with these individuals (Wippold, Nmezi et al., 2020).

Health Self-Empowerment Theory (HSET)

Health Self-Empowerment Theory (HSET) is a population-sensitive, self-empowerment-oriented theory of health promotion (Tucker, Butler, Loyuk, Desmond, & Surrency, 2009). HSET recognizes the role of personal (e.g., health self-efficacy, health motivation, active coping) and social and environmental (e.g., health insurance status) variables on health promotion. HSET draws attention to the reality that many social/environmental variables are intractable (Tucker et al., 2009). Although few would argue that health promotion efforts should ignore the impact of social and environmental variables, attention to the health of the inadequately insured cannot wait. The urgent state of health of these individuals is underscored by a report released by the Agency for Healthcare Research and Quality (AHRQ, 2017) that indicated that health disparities in the U.S. are widening. Given this urgency, HSET highlights the importance of personal cognitive-behavioral variables (e.g., health motivation, health self-efficacy, active coping) in promoting the health of vulnerable populations. These variables are readily modifiable, can be changed in a cost-effective manner, and can produce lasting change. Understanding how these variables influence health outcomes (e.g., HRQoL) among inadequately insured individuals can inform tailored health promotion and prevention efforts among these individuals. Tailored health promotion interventions based in HSET have been successful in promoting health among vulnerable populations (Tucker et al. 2016; Tucker, Wippold, Williams, Arthur, Desmond, & Robinson, 2016).

Precision Health and Precision Prevention

Evidence suggests that non-tailored, “one-sized-fits-all” health promotion and prevention interventions produce limited results (Minivielle, 2018). These interventions often do not take into account the unique needs of the target population. The National Research Council (NRC, 2012) has recently drawn attention to the importance of precision medicine, which advocates for tailoring health promotion strategies to the specific characteristics of the individual. While this approach is novel in that it advocates for individualized health promotion strategies, it has been criticized for being overly focused on biomedical and genomic factors and not focused enough on social and behavioral factors (Hekler, Tiro, Hunter, & Nebeker, 2020). The criticisms levied against it spurred the development of precision public health and prevention (Khoury, Iademarco, & Riley, 2016). These movements pay close attention to the social and behavioral determinants of health as experienced by an individual or precise group of individuals. A precision prevention approach can be used to adapt health promotion efforts to the needs of high risk individuals or groups. In order to do so, health promotion developers need to understand strategies to promote HRQoL that match individuals to their contexts (e.g., insurance status, gender, and country of residence) – an approach that could optimize HRQoL promotion interventions.

Research Questions

Our study draws from precision prevention by seeking to understand strategies to promote HRQoL that match individuals to their contexts. We do so by examining potential gender differences in the association between HSET-informed variables and mental and physical HRQoL among inadequately insured adults living in the U.S. We analyzed data separately for men and women in order to inform gender-specific, tailored approaches. In order to isolate gender-specific HSET-informed variables associated with HRQoL, our analyses controlled for sociodemographic factors that are associated with HRQoL and may extraneously affect the relationships under investigation. We hypothesized that among inadequately insured women and men:

  1. health self-efficacy, health motivation, and active coping are associated with mental HRQoL, while controlling for age, occupational status, highest level of education attained, and income; and that

  2. health self-efficacy, health motivation, and active coping are associated with physical HRQoL, while controlling for age, occupational status, highest level of education attained, and income.

Methods

Participants

Our study’s participants consisted of 279 adults who identified as being either uninsured or underinsured. Only participants who identified as female (n = 123; 44.1%) and male (n = 156; 55.9%) were included. Two participants were removed because they did not self-identify as female or male (one identified as non-binary and the other as transgender female-to-male). The mean age of all participants was 35.7 years (SD = 9.4). The mean of age of participants who identified as female was 36.2 years (SD = 9.3), and for those identifying as male the mean age was 35.2 years (SD = 9.5). See Table 1 for additional demographic information related to our sample.

Table 1.

Additional Demographic Information.

n 1 %
Occupational Status
 Employed 226 81.0
 Unemployed 52 18.6
 Missing 1 0.4
Highest Level of Education
 Less than high school 2 0.7
 High school or GED 33 11.8
 Some college 62 22.2
 Trade/technical school 10 3.6
 2-year college 36 12.9
 4-year college 111 39.8
 Professional/graduate school 25 9.0
Annual income
 Less than $10,000 35 12.5
 $10,000 - $14,999 24 8.6
 $15,000 - $19,999 17 6.1
 $20,000 - $24,999 29 10.4
 $25,000 - $34,999 50 17.9
 $35,000 - $49,999 53 19.0
 $50,000 - $74,999 56 20.1
 $75,000 and above 15 5.4
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1:

N = 279.

The inclusion criteria for our study were: (a) being uninsured or underinsured (defined as: having out-of-pocket costs that are equal to or more than 10% of your household income or 5% of your household income if your income is 200% below the federal poverty level); (b) residing in the U.S.; (c) being 18 years of age or older; (d) being able to read English; (e) having access to the internet; and (f) self-identifying as female or male. Participants were recruited from Amazon Mechanical Turk (MTurk) - an internet-based resource for convenience samples commonly used in social science research (Paolacci & Chandler, 2014). Prior to participating in a task (e.g., completing a survey such as ours), MTurk users must register an account through the MTurk platform which includes a prompt requesting payment routing information and demographic information. Participants can then choose to participate in a task for which they are eligible. Eligibility is determined by their responses when registering for MTurk and by reading the inclusion criteria prior to beginning the task. The task for our study was administered through Qualtrics, a secure survey platform. After completing a task, participants are paid through the MTurk system.

Procedure

Our study received Institutional Review Board approval from The University of South Carolina (No. Pro00084677), and was performed in accordance with the principles of the 1964 Helsinki Declaration. Upon selecting the survey on MTurk, participants were presented with an informed consent form, which specified the expected time required to complete the survey (approximately 6 min.) and the compensation for completion of the task (i.e., $0.75), a rate commensurate with the national minimum wage of $7.25 an hour (U.S. Department of Labor, 2021).

Measures

Demographic Data Questionnaire (DDQ).

The DDQ was a researcher-generated questionnaire that assessed demographic information, and included participants’ (a) health insurance status, (b) gender, (c) occupational status, (d) highest level of education completed, (e) annual income, and (f) race/ethnicity.

RAND-36 (Hays, Sherbourne, & Mazel, 1993).

The RAND-36 is a 36-item self-report measure that assesses health-related quality of life. It is composed of eight subscales: (a) physical functioning, (b) role limitations caused by physical health problems, (c) role limitations caused by emotional problems, (d) social functioning, (e) emotional well-being, (f) energy or fatigue, (g) pain, and (h) general health. Response options for the items constituting this scale vary, with some being Yes/No, though most are either 3- or 5- point Likert-type and Likert scales. A sample item is, “During the past 4 weeks, how much did pain interfere with your normal work (including both work outside the home and housework).” This item is answered using a 5-point Likert scale ranging from “Not at all” to “Extremely.” For the purposes of our study, discrete mental and physical HRQoL component scores were created using an oblique promax rotation as suggested by Ware, Kosinski, and Keller (1994). Higher scores indicate better mental and physical functioning. This is a common scoring method for this measure (Hays & Morales, 2001; Laucis, Hays, & Bhattacharyya, 2014; Ware et al., 1994). The Cronbach’s alphas for women and men were both .96.

Brief Experiential Avoidance Questionnaire (BEAQ; Gámez, Chmielewski, Kotov, Ruggero, Suzuki, & Watson, 2014).

The BEAQ is a 15-item self-report measure that assesses experiential avoidance. Response options for this measure consisted of a 6-level Likert-type scale ranging from “strongly disagree” to “strongly agree.” A sample item is, “The key to a good life is never feeling any pain.” Higher scores indicate more avoidant coping, which is the inverse of active coping. This measure was used to assess the HSET concept of active coping, which is the use of coping efforts or resources to deal with a stressor or other problem. Active coping is associated with positive adaptive coping with stressful events. The Cronbach’s alphas for women and men were .88 and 89, respectively.

Health Motivation Scale (Moorman & Matulich, 1993).

The Health Motivation Scale is an 8-item self-report measure that assesses motivation to receive health-related information. Response options for this measure consisted of an 8-level Likert-type scale ranging from “strongly disagree” to “strongly agree.” A sample item is, “I try to prevent health problems before I feel any symptoms.” Higher scores indicate greater health motivation. This measure was used to assess the HSET concept of health motivation. The Cronbach’s alpha independently for women was .86, and for men was .83.

Self-Rated Abilities for Health Practices Scale (SRAHP; Becker, 1993).

The SRAHP is a 28-item self-report measure that assesses perceived self-efficacy to engage in health-promoting behaviors. It is composed of four subscales: Exercise, Nutrition, Responsible Health Practice, and Psychological Well-Being. Response options for this measure consisted of a 5-point Likert scale ranging from “Not at all” to “Completely.” An example item is, “I am able to watch for negative changes in my body’s condition (pressure sores, breathing problems).” Higher scores indicate more health self-efficacy. This measure was used to assess the HSET concept of health self-efficacy. The Cronbach’s alpha for each subscale of the SRAHP are as follows for women: .84 for nutrition, .90 for psychological well-being, .89 for exercise, and .84 for responsible health practices; and for men: .86 for nutrition, .92 for psychological well-being, .90 for exercise, and .89 for responsible health practices.

Analysis

We performed four hierarchical regressions to test our hypotheses. In all four hierarchical regressions, we entered the covariates (i.e., age, occupational status, highest education level, and annual income) in the first model and the independent variables (i.e., nutrition self-efficacy, psychological well-being self-efficacy, exercise self-efficacy, responsible health practices self-efficacy, health motivation, and active coping [measured using an experiential avoidance questionnaire]) in the second model. We conducted two hierarchical regressions (the dependent variable [DV] for one was mental HRQoL, and the DV for the other was physical HRQoL) among the participants who identified as female and two hierarchical regressions (the DV for one was mental HRQoL and the DV for the other was physical HRQoL) among the participants who identified as male.

Results

Hypothesis #1, women: Among inadequately insured women, health self-efficacy, health motivation, and active coping are associated with mental health-related quality of life, while controlling for age, occupational status, highest level of education attained, and income.

The first model, which included only the covariates, was statistically non-significant, F(4, 100) = 1.10, p = 0.36. The second model, containing the covariates and the independent variables, was statistically significant, F(10, 94) = 6.87, p < 0.001. In this second model, the covariates remained statistically non-significant predictors of mental HRQoL, whereas psychological well-being self-efficacy and exercise self-efficacy were statistically significant predictors of mental HRQoL. In this latter model, nutrition self-efficacy, responsible health practices self-efficacy, health motivation, and active coping were non-significant predictors of mental HRQoL.

Hypothesis #2, women: Among inadequately insured women, health self-efficacy, health motivation, and active coping are associated with physical health-related quality of life, while controlling for age, occupational status, highest level of education attained, and income.

The first model, containing only the covariates, was statistically non-significant, F(4, 100) = 0.66, p = 0.617. The second model, containing the covariates and the independent variables, was statistically significant, F(10, 94) = 4.86, p < 0.001. In this second model, the covariates remained statistically non-significant predictors of physical HRQoL, whereas psychological well-being self-efficacy and exercise self-efficacy were statistically significant predictors of physical HRQoL. In this latter model, nutrition self-efficacy, responsible health practices self-efficacy, health motivation, and active coping were non-significant predictors of physical HRQoL.

Hypothesis #1, men: Among inadequately insured men, health self-efficacy, health motivation, and active coping are associated with mental health-related quality of life, while controlling for age, occupational status, highest level of education attained, and income.

The first model, containing only the covariates, was statistically non-significant, F(4, 127) = 0.88, p = 0.475. The second model, containing the covariates and the independent variables, was statistically significant, F(10, 121) = 15.53, p > 0.001. In this second model, the covariates remained statistically non-significant predictors of mental HRQoL (with the exception of education), whereas psychological well-being self-efficacy, responsible health practices self-efficacy, and active coping were all statistically significant predictors of mental HRQoL. In this latter model, nutrition self-efficacy, exercise self-efficacy, and health motivation were non-significant predictors of mental HRQoL.

Hypothesis #2, men: Among inadequately insured men, health self-efficacy, health motivation, and active coping are associated with physical health-related quality of life, while controlling for age, occupational status, highest level of education attained, and income.

The first model, containing only the covariates, was statistically non-significant, F(4, 127) = 0.379, p = 0.823. The second model, containing the covariates and the independent variables, was statistically significant, F(10, 131) = 12.137, p < 0.001. In this second model, the covariates remained statistically non-significant predictors of physical HRQoL, whereas psychological well-being self-efficacy and active coping were statistically significant predictors of physical HRQoL. In this latter model, nutrition self-efficacy, exercise self-efficacy, responsible health practices self-efficacy, and health motivation were non-significant predictors of physical HRQoL.

Discussion

Our study tested the hypothesis that components of HSET (i.e., health self-efficacy, health motivation, and active coping) would be associated with levels of mental and physical HRQoL within a sample of female and male inadequately insured adults, while controlling for key demographic variables. The study, and thus the analyses, was guided by precision prevention, which is the notion that preventive health promotion interventions can be more effective if they are customized based on key sociodemographic variables (e.g., insurance status and gender). The findings of our study suggest that HSET may be useful in understanding the mental and physical HRQoL among inadequately insured women and men, although the influence of the HSET variables on mental and physical HRQoL differ by gender. The results can be used to inform customized HRQoL-promoting interventions among female and male inadequately insured Americans, who constitute a growing and at-risk group.

Our study’s results indicate that among the female-identifying sample of inadequately insured individuals, exercise self-efficacy and psychological well-being self-efficacy were significantly and positively associated with both mental and physical HRQoL. These results lend partial support to our first and second hypotheses for women. The results also indicate that among the males in our sample of inadequately insured individuals, psychological well-being self-efficacy and responsible health practices self-efficacy were both significantly and positively associated with mental HRQoL, whereas experiential avoidance (the inverse of active coping) was a significantly and negatively associated with mental HRQoL. Additionally, among the males in our sample, psychological well-being self-efficacy was a significantly and positively associated with physical HRQoL, whereas experiential avoidance was significantly and negatively associated with physical HRQoL. These results lend partial support to our first and second hypotheses for men.

These results suggest that interventions seeking to improve the mental and physical HRQoL of inadequately insured women and men may benefit from addressing participants’ beliefs in their abilities to master exercise (for women), psychological well-being management (for both women and men), and responsible health practices behaviors (for men). In addition, interventions might address these participants’ beliefs that engaging in these behaviors can produce desirable health outcomes (e.g., increases in their subjective appraisals of mental and physical health). These results also suggest that interventions seeking to improve mental and physical HRQoL among inadequately insured men may benefit from addressing active coping, which we assessed using an experiential avoidance questionnaire. Experiential avoidance is often defined as one’s unwillingness to attend to distressing emotions, thoughts, and physical sensations, even when doing so is harmful (Gámez et al., 2014; Hayes, Wilson, Gifford, Follette, & Strosahl, 1996).

The health promotion literature is rich with strategies to improve self-efficacy beliefs and reduce experiential avoidance. In line with precision prevention, it is important that these strategies be adapted to address the unique contextual needs of inadequately insured Americans. Bandura (1982) outlined four contributors to self-efficacy: prior experience, vicarious learning, verbal persuasion, and physiological response. Health promotion specialists seeking to target exercise, psychological well-being, and responsible health practices self-efficacy among inadequately insured women and men should be mindful of addressing these contributors. These specialists can design interventions that give inadequately insured individuals the opportunity to practice and master these behaviors, observe similar individuals mastering these behaviors, receive positive messages about their abilities to master these behaviors, and avoid debilitating physiological arousal while attempting to carry out these behaviors (Becker, 1993). Similar to self-efficacy, there are explicit and research-supported contributors to minimizing experiential avoidance. Experiential avoidance is the target of Acceptance and Commitment Therapy (ACT; Hayes & Wilson, 1994), a cognitive-behavioral approach, the efficacy of which is amply supported in the literature (Manchón, Quiles, León, & López-Roig, 2020; Roche, Kroska, & Denburg, 2019). ACT seeks to minimize experiential avoidance, and thus promote psychological flexibility (i.e., active coping), by addressing six key processes: acceptance, cognitive defusion, self as context, committed action, values, and contact with the present moment (Hayes et al., 2012). There are clear instructions elsewhere concerning effective implementation of ACT (Hayes, Strosahl, & Wilson, 2012).

Our study had several noteworthy strengths. First, we used a precision prevention approach. Much of the burden associated with chronic health conditions can be prevented by improving HRQoL (Brown et al., 2013). Precision prevention posits that tailoring behavioral interventions to the unique psychosocial barriers and motivators experienced by the individual or a precise group of individuals will be more effective than approaches that are not tailored to the individual or group of individuals. The social and behavioral sciences have much to contribute to precision prevention (Hekler et al., 2020), insofar as health behaviors and environmental factors predict 70% of an individual’s variability in health, with the remaining 30% explained by biological factors (Gakidou et al., 2017; Hekler et al., 2020; Herbert et al., 2006; McGinnis, Williams-Russo & Knickman, 2002). Second, our study sought to understand the impact of modifiable, cognitive-behavioral variables on HRQoL. As opposed to environmental and genetic variables, cognitive-behavioral variables can be readily changed in a cost-effective manner. In light of widening health disparities in the United States (AHRQ, 2017), the health of the uninsured and underinsured cannot be ignored. Finally, our study sought to understand health, and strategies to promote health, among inadequately insured adults, an under-represented population in research.

Our findings should also be viewed in light of our study’s limitations. First, the data for our study were derived from MTurk. The accuracy of data derived from this source has been called into question in the past (Rouse, 2015), although research indicates that data derived from MTurk are no less accurate than other commonly used convenience samples and that MTurk users are more attentive to instructions than other samples (Ramsey, Thompson, McKenzie, & Rosenbaum, 2016). As is the case in all survey-based research, efforts can be made to prevent or mitigate the impact of inaccurate data on the results. We included three randomly placed attention checks within the assessment battery and only included data from participants who correctly answered all three checks. An additional limitation of MTurk samples is the lack of racial/ethnic diversity of the participants. Future research may build on our study by investigating gender and racial/ethnic factors associated with HRQoL. A further limitation of our study was its focus on personal-level factors of HRQoL, as opposed to its related systemic social and environmental-level factors. Future studies should address this limitation. A final limitation of our study is the cross-sectional nature of the data. These data preclude the ability to make causal inferences.

Our study’s findings have important implications for improving HRQoL among inadequately insured Americans. Despite a surge of research examining HRQoL among at-risk populations, little has been done to understand this indicator among uninsured and underinsured Americans. Inadequately insured Americans experience disproportionately low HRQoL (Bharmal & Thomas, 2005) and earlier mortality than their insured counterparts (Woolhandler & Himmelstein, 2017). Health promotion specialists can leverage these findings to design gender-specific, customized HRQoL-enhancing interventions among inadequately insured Americans. Additionally, health promotion specialists can build on our research to understand additional personal-level and social and environmental-level factors associated with HRQoL among inadequately insured women and men. Such research can eventually lead to optimized and cost-effective HRQoL-enhancing behavioral and policy efforts among an at-risk and growing group of Americans.

Table 2.

Regression analyses among women assessing the impact of HSET on mental HRQoL.

Model B SE p R 2 Sig. △R2
1 0.04
Age 0.02 0.10 0.830
Occupational Status −1.73 2.14 0.420
Level of Education 0.72 0.43 0.097
Income 0.08 0.42 0.853
2 0.42 > 0.001
Age −0.09 0.08 0.275
Occupational Status −2.20 1.73 0.206
Level of Education 0.58 0.35 0.102
Income −0.37 0.36 0.311
Nutrition S.E. 0.03 0.21 0.899
Psychological Well-Being S.E. 0.55 0.18 0.004
Exercise S.E. 0.40 0.16 0.015
Responsible Health Practices S.E. −0.003 0.22 0.991
Health Motivation 0.97 0.75 0.197
Experiential Avoidance −0.02 0.06 0.700
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Note. S.E. = Self Efficacy.

Table 3.

Regression analyses among women assessing the impact of HSET on physical HRQoL.

Model B SE p R 2 Sig. △R2
1 0.03
Age 0.01 0.04 0.913
Occupational Status −0.44 0.97 0.648
Level of Education 0.29 0.19 0.142
Income −0.03 0.19 0.866
2 0.27 > 0.001
Age −0.03 0.039 0.422
Occupational Status −0.59 0.83 0.480
Level of Education 0.23 0.17 0.165
Income −0.21 0.17 0.222
Nutrition S.E. −0.01 0.10 0.923
Psychological Well-Being S.E. 0.21 0.09 0.020
Exercise S.E. 0.24 0.08 0.002
Responsible Health Practices S.E. −0.06 0.10 0.571
Health Motivation 0.38 0.36 0.294
Experiential Avoidance 0.001 0.03 0.984
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Note: S.E. = Self Efficacy.

Table 4.

Regression analyses among men assessing the impact of HSET on mental HRQoL.

Model B SE p R 2 Sig. △R2
1 0.03 -
Age −0.04 0.08 0.635
Occupational Status −2.32 2.62 0.377
Level of Education 0.28 0.37 0.458
Income 0.26 0.43 0.541
2 0.56 > 0.001
Age −0.04 0.06 0.430
Occupational Status 0.35 1.86 0.852
Level of Education 0.62 0.26 0.020
Income −0.17 0.31 0.580
Nutrition S.E. 0.28 0.19 0.152
Psychological Well-Being S.E. 0.75 0.15 0.000
Exercise S.E. 0.11 0.15 0.476
Responsible Health Practices S.E. −0.38 0.19 0.048
Health Motivation −0.15 0.51 0.769
Experiential Avoidance −0.19 0.04 0.000
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Note. S.E. = Self Efficacy.

Table 5.

Regression analyses among men assessing the impact of HSET on physical HRQoL.

Model B SE p R 2 Sig. △R2
1 0.01 -
Age 0.00 0.04 0.897
Occupational Status −0.51 1.20 0.670
Level of Education −0.09 0.17 0.591
Income 0.15 0.20 0.445
2 0.50 > 0.001
Age 0.00 0.03 0.854
Occupational Status 0.93 0.90 0.305
Level of Education 0.03 0.13 0.816
Income 0.02 0.15 0.914
Nutrition S.E. 0.11 0.09 0.228
Psychological Well-Being S.E. 0.19 0.07 0.009
Exercise S.E. 0.11 0.07 0.149
Responsible Health Practices S.E. −0.09 0.09 0.324
Health Motivation 0.14 0.25 0.581
Experiential Avoidance −0.09 0.02 0.000
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Note. S.E. = Self Efficacy.

Funding Information:

Funding for the present study was provided by the University of South Carolina. Dr. Wippold was also funded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under award number K23MD016123. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest

The authors declare they have no conflicts of interest.

Ethics Approval: The present study received institutional review board approval from the University of South Carolina (Pro00084677).

The procedures used in this study adhere to the tenets of the 1964 Declaration of Helsinki.

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Contributor Information

Guillermo M. Wippold, Department of Psychology University of South Carolina.

Sarah Grace Frary, Department of Psychology University of South Carolina.

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