Positivity and Indicators of Health among African Americans with Diabetes Mary A. Steinhardt, EdD, LPC; Susan K. Dubois, MD; Sharon A. Brown, PhD, RN, FAAN; Louis Harrison, Jr., PhD; Kathryn E. Dolphin, PhD; Wonil Park, MS; H. Matthew Lehrer, BS Objectives: To examine the utility of the positivity ratio to distinguish differences in psychological and physiological health in African Americans with type 2 diabetes. Methods: Study participants (N = 93) were recruited through radio and church announcements and grouped by their positivity ratio. Results: Multivariate analyses showed flourishing individuals had the highest resilience and lowest depressive symptoms and HbA1c (A1C), whereas depressed individuals recorded the lowest
T
ype 2 diabetes mellitus (T2DM), the 6th leading cause of death in the United States (US), is a psychologically and behaviorally demanding chronic disease affecting nearly 26 million people. Individuals with the disease typically experience greater stress than non-diseased individuals and have higher levels of depressive symptoms.1-3 In fact, T2DM doubles the odds of depression.4,5 Further, depression plays a critical role in disarming individuals from making healthy lifestyle choices, leaving them particularly vulnerable to complications from the disease.6 Moreover, this unremitting accumulation of damage is largely preventable. Most research, however, has focused on risk factors and adherence to diabetes management guidelines, rather than enhancing psychosocial qualities that build resilient and thriving responses, enhancing the patient’s ability to make healthy lifestyle choices and control the disease. Minorities are disproportionately affected, with African Americans twice as likely to have T2DM as Mary A. Steinhardt, Professor of Health Behavior and Health Education, The University of Texas, Austin, TX. Susan K. Dubois, Medical Director, The University of Texas School of Nursing Family Wellness Center, Austin, TX. Sharon A. Brown, Joseph H. Blades Centennial Memorial Professor of Nursing, The University of Texas, Austin, TX. Louis Harrison, Jr., Professor of Curriculum and Instruction, The University of Texas, Austin, TX. Kathryn E. Dolphin, Assistant Professor of Exercise Science, Pacific University, Forest Grove, OR. Wonil Park, PhD Student and H. Matthew Lehrer, Masters Student, The University of Texas, Austin, TX. Correspondence Dr Steinhardt; [email protected]
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resilience and highest depressive symptoms and A1C. Small to large effect sizes were reported. Conclusions: Further support for the utility and generalizability of the positivity ratio was provided. Cultivating positive emotions may improve the health of individuals with type 2 diabetes. Key words: type 2 diabetes; African American; positivity; resilience; depressive symptoms; A1C Am J Health Behav. 2015;39(1):43-50 DOI: http://dx.doi.org/10.5993/AJHB.39.1.5
Caucasians.7 Approximately one in 5 African Americans aged 20 years or older has T2DM8 with all the accompanying stressors. African Americans of low socioeconomic status incur additional stressors (eg, financial insecurity, lack of health care) that affect these patients both psychologically and physiologically and limit their proclivity for making healthy lifestyle choices and successfully controlling the disease. Further, African Americans report higher levels of life dissatisfaction and depressive symptoms than Caucasians,9 making them more prone to engage in poor diabetes self-management behaviors and medication adherence.10 An emerging line of research suggests one’s emotional state may influence both predisposition to and the clinical course of chronic diseases. Positive emotions, such as hope, are associated with a decreased prevalence of diabetes.11 In fact, after controlling for negative emotions and health status, positive emotions were uniquely associated with a lower mortality risk in diabetic individuals.12 Pressman and Cohen13 suggest that positive affect directly influences health via better behaviors and health practices; thus, positive emotions may play an important role in the course of illnesses influenced by behavioral factors, such as AIDS mortality14 and survival following open heart surgery.15 Similarly, the daily behavioral demands and serious consequences of uncontrolled glucose levels render T2DM a chronic disease whose illness trajectory is largely influenced by behavioral factors. From a theoretical standpoint, Fredrickson’s broaden-and-build theory of positive emotions
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Positivity and Indicators of Health among African Americans with Diabetes provides another mechanism by which positive emotions may result in improved outcomes for those diagnosed with T2DM. According to the theory, positive and negative emotions have distinct, yet complementary functions.16-18 Negative emotions have been believed to narrow the scope of an individual’s attention and cognition to react quickly to an adverse situation in a specific manner.19 A corollary hypothesis holds that positive emotions broaden one’s thought-action repertoire, allowing individuals to choose from a wider range of actions, ideas, and perceptions.20 For example, positive emotions broaden the field of visual attention,21 creativity,22 and openness to new experiences23 and information.24 Over time, this broadened mindset contributes to important long-term life outcomes, including improved decision-making and adaptation to adversity,16-18 greater resilience,25 reduced depressive symptoms,26 and lower risk of mortality in individuals with T2DM.12 Fredrickson and Losada27 identified a positivity ratio– the frequency of experienced positive emotions to negative emotions–that distinguishes individuals who lead a flourishing life, full of meaning, possibility, and growth from languishing individuals who desire more significance and purpose in life. According to the model, a positivity ratio of 2.9 serves as a cutoff score or tipping point between flourishing and languishing individuals. Those who report positivity ratios above 2.9 are satisfied with life, have a sense of fulfillment, are more resilient, and feel as though things in life are going as well as, or better than expected. Fredrickson describes these individuals as experiencing the “good life.”18 Conversely, a positivity ratio below 2.9 is indicative of languishing, or individuals who are “stuck in a rut” and “yearning for more.”28 These individuals describe their lives as being unfulfilled or stagnant, and are burdened by feelings that “the grass is greener on the other side.” They report only moderate levels of mental health and experience frequencies of illness similar to those who are depressed.29 Individuals who report positivity ratios less than 1.0, in which negative emotions are experienced more frequently than positive emotions, feel overwhelmed by difficult life conditions and describe their life as a struggle. Positivity ratios less than 1.0 are reported frequently by the clinically depressed, as this ratio is suggestive of a pathological level of functioning.18,30 Although the precise “tipping points” underlying the positivity ratio have been questioned,31 a large body of empirical evidence continues to support the utility of the positivity ratio as a guideline for distinguishing psychological health.32 Given that positive emotions have the potential to help individuals with T2DM deal more effectively with the chronic stress of the disease, the purpose of this study was to examine whether groups of flourishing, languishing, and depressed individuals with T2DM significantly differ from each other in scores of psychological (viz, resilience and de-
44
pressive symptoms) and physiological (viz, A1C) health. It was hypothesized that: (1) flourishing individuals would report the highest levels of resilience and lowest levels of depressive symptoms and A1C; (2) depressed individuals would report the lowest levels of resilience and highest levels of depressive symptoms and A1C; and (3) languishing individuals would report scores in between the flourishing and depressed groups. METHODS Participants and Procedures Participants for this study were a convenience sample of 93 African Americans (62 women, 31 men) living in a large city in the Southwest United Sates and ranging in age from 31 to 85 years. They had been diagnosed with T2DM an average of 9.5 ± 8.0 years. Overall, 23% of the participants were single, 42% were married, 19% were separated or divorced, and 16% were widowed. With regard to education, 8% had attended high school, 21% had a high school diploma/GED, 40% had attended some college or technical school experience, 19% had a bachelor’s degree, and 12% had a graduate degree. In terms of total family income, 38% earned $19,000 or less, 25% earned $20,000 to $39,000, 23% earned $40,000 to $59,000, 7% earned $60,000 to $79,000, 2% earned $80,000 to $100,000, and 5% earned $100,000 or more. Recruitment for the study sample was through radio and church announcements. Participants were recruited who were diagnosed with T2DM and not currently participating in a diabetes selfmanagement program. Potential participants were excluded if they were pregnant, lactating, or had medical conditions severe enough to preclude participation in a subsequent program intervention (eg, kidney failure requiring dialysis or peripheral vascular disease severe enough to preclude walking). All subjects were asked to complete a survey and have their glycated hemoglobin (A1C) concentration measured, followed by a light healthy breakfast. Subjects with high A1C values were encouraged to see their healthcare provider (HCP). If they did not have a HCP, they were referred to the wellness clinic on the university campus. We did not experience difficulty in enrolling individuals; 85% of those who called and expressed a desire to participate showed up and participated. All participants received $20 for their participation, as well as the opportunity to participate in a diabetes selfmanagement education program as part of a larger intervention study. Measures Data collected included measures of positivity (viz, flourishing, languishing, depressed), and psychological (viz, resilience and depressive symptoms) and physiological (viz, A1C) health. Below we describe each of these measures. Positivity ratio. Positive and negative emotions were measured using the 20-item Positive and
Steinhardt et al Negative Affect Schedule (PANAS)33 with positivity representing the ratio of positive emotions (eg, joyful, determined, inspired) to negative (eg, afraid, distressed, irritable) emotions.27 The PANAS, described in the literature as a well-validated, widelyused34 and popular measure,35 asks participants to respond on a Likert scale from 1 (not at all) to 5 (very much so). The number of positive emotions experienced at least moderately (> 3) and the number of negative emotions experienced at least a little (> 2) were tallied, with the different thresholds in place to account for negativity bias and positivity offset. Negativity bias reflects the phenomenon that individuals give more weight to negative rather than positive emotions,36 whereas positivity offset reflects the phenomenon that people tend to feel at least mild positive emotions most of the time.37 In other words, although people tend to experience more positive than negative emotions, the impact of negative emotions is stronger. A positivity ratio was then calculated by dividing the frequency of the positive emotion items by the frequency of the negative emotion items. Reliability was strong for both the positive emotion items (a = .87) and negative emotion items (a = .86). Based on the cutoff scores identified by Fredrickson and Losada,27 positivity ratios were trichotomized, and participants were classified as flourishing (positivity ratio ≥ 2.9), languishing (positivity ratio = 1.0 to < 2.9), or depressed (positivity ratio < 1.0). Given that the precise “tipping points” underlying the positivity ratio have been questioned,31 we ran an additional analysis using cut scores based on the distribution of our sample. Resilience. Resilience was measured using the 25-item Connor-Davidson Resilience Scale (CDRISC).38 The CD-RISC identifies characteristics that enable individuals to adapt to stress, such as through faith, goal setting, patience, and tolerance of negative affect, as well as the tendency to perceive change as a challenge, maintain a commitment to the people and activities in which one is involved, and have a sense of personal control in handling life events. Participants responded on a 5-point Likert scale ranging from 0 (not true at all) to 4 (always true). A resilience score was calculated as the sum of all items, with higher scores indicating greater resilience. Sample items included: “I believe I can achieve my goals, even if there are obstacles” and “I tend to bounce back after illness, injury, or other hardships.” Reliability of the CDRISC was strong (a = .90). Depressive symptoms. The 20-item Center for Epidemiological Studies Depression Scale (CES-D) was used to measure depressive symptoms experienced, such as depressed mood, feelings of guilt, worthlessness, helplessness, and restless sleep.39 Sample items included: “I was bothered by things that usually don’t bother me,” “I felt everything I did was an effort,” and “I had crying spells.” Responses ranged from 0 (rarely or none of the time; less than one day) to 3 (most or all of the time, 5-7
days), and were summed for a total score. A CESD score of 16 or greater is considered a moderately severe level of depressive symptoms.39 Reliability of the CES-D was strong (a = .82). A1C. An aliquot of whole blood from a blood sample was used to determine glycated hemoglobin A1C concentration measured on a DCA VantageTM (Siemens Medical Solutions Diagnostics, Tarrytown, NY). This method provides results in 6 minutes based on a latex agglutination inhibition methodology. The DCA VantageTM meets the National Glycohemoglobin Standardization Program certification criteria of having a total coefficient of variation < 3% in the clinically relevant range.40
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Statistical Analyses Descriptive statistics and correlations. Means, standard deviations, and bivariate correlations of all study variables were calculated using descriptive statistics. Pearson correlations were used for continuous variables, point-biserial correlations for continuous and dichotomous variables, and chi-square tests for pairs of dichotomous variables. Multivariate analysis of variance. A one-way multivariate analysis of variance (MANOVA) was conducted on the 3 dependent variables (viz, resilience, depressive symptoms, and A1C), with level of positivity (viz, flourishing, languishing, and depressed) as the independent variable and controlling for the variable years diagnosed with diabetes. Post hoc analyses were performed using Tukey’s Honestly Significant Difference (HSD) to control for potential Type 1 error rate inflation. Cohen’s d effect sizes were computed to characterize the size of differences between group means. All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 21. RESULTS Prior to the correlation analysis, multiple-category demographic variables were collapsed into binary variables to produce appropriately sized groups: sex (1 = female, 0 = male), marital status (1 = married, 0 = other), education (1 = some college, 0 = no college), and family income (1 = $40,000 or more, 0 = less than $40,000). Age and number of years diagnosed with T2DM were retained as continuous variables. Descriptive Analysis Table 1 displays the means, standard deviations, and correlations for all study variables. The analyses indicated that higher scores on positivity were related to higher scores on resilience (r = .37, p < .01) and lower scores on depressive symptoms (r = -.48, p < .01). Higher scores on resilience were also related to lower scores on depressive symptoms (r = -.40, p < .01). Among the demographic control variables, results showed that, on average, older individuals (r = -.23, p < .05) and those who were married (r = -.22, p < .05) had lower A1C. As ex-
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Positivity and Indicators of Health among African Americans with Diabetes
Table 1 Means, Standard Deviations (SD), and Bivariate Correlations for All Variables (N = 93) Variable Positivity Ratio (PR)
Mean
SD
PR
Res
DS
A1C
Age
YD
F
M
E
FI
2.73
2.61
--
.37**
-.48**
-.12
.09
.04
-.02
.10
-.02
.15
--
-.40**
-.15
-.02
-.03
.04
.08
.00
-.01
.14
-.01
-.01
.03
-.20
-.06
-.15
--
-.23*
.18
.20
-.22*
-.15
-.11
.31**
.06
-.04
.01
-.07
--
.11
-.23*
-.14
-.09
Resilience 80.91 (Res)
10.95
Depressive 12.86 Symptoms (DS)
7.67
--
A1C Age
1.80
Years Diabetes (YD)
7.22 59.19
10.48
9.50
--
8.0
Female (F) -- .23* .02 -.34** Married (M) -- .02 .24* Education (E) -- .40** Family Income (FI) --
* p < .05, ** p < .01 Note. Sex (Female = 1, Male = 0); Marital Status (Married = 1, Single, Separated/Divorced, Widowed = 0); Education (Some College = 1, No College = 0); Family Income ($40,000 or more = 1, Less Than $40,000 = 0).
pected, age and years diagnosed with diabetes were positively correlated (r = .31, p < .01), and family income was positively correlated with being married (r = .24; p < .05) and having more education (r = 40; p < .01). Inverse correlations between being female and family income (r = -.34; p < .01), and between being married and years diagnosed with diabetes (r = -.23, p < .05) were perhaps attributed to the fact that 16% of the sample was widowed. Multivariate Analysis of Variance Table 2 shows the mean scores for resilience, depressive symptoms, and A1C for flourishing, languishing, and depressed participants with T2DM, controlling for the variable years diagnosed with diabetes. The test of the multivariate null indicated statistically significant group differences on the set of dependent variables [FWilks’ λ (2, 90) = 9.42, p < .001]. The accompanying multivariate partial eta square (hp2 = .25) indicated a moderate effect associated with level of positivity. Examinations of the univariate test for each of the dependent variables indicated that significant group differences were present for both depressive symptoms [F(2, 90) =
46
17.82, p < .001] and resilience [F(2, 90) = 18.85, p < .001], and a non-significant trend for A1C [F(2, 90) = 1.49, p = .232]. All pairwise comparisons between the categories of positivity (viz, flourishing vs languishing, flourishing vs depressed, and languishing vs depressed) yielded statistically significant differences in the mean scores of depressive symptoms (Table 3). Further, flourishing and languishing individuals reported higher levels of resilience than depressed individuals; however, languishing individuals reported similar levels of resilience to flourishing individuals. Effect sizes ranged from small for resilience (d = .21) to very large for depressive symptoms (d = - 1.73). Given that the precise “tipping points” underlying the positivity ratio have been questioned,31 we ran an additional analysis using a different cut score based on the distribution of our sample (ie, ≥ 5.0 for flourishing), and the multivariate results for A1C, depressive symptoms, and resilience remained the same. Thus, whether we used a cut score of 3 or 5 for flourishing, the multivariate results did not change.
Steinhardt et al
Table 2 Mean, Standard Error, and Range for Dependent Variables as Reported by Flourishing, Languishing, and Depressed Participants (N = 93) Dependent Variable
Flourishing (N = 25)
Languishing (N = 45)
Depressed (N = 23)
F (2, 90)
Resilience (range 46-100)
85.58 ± 1.87
83.60 ± 1.40
70.59 ± 1.96
18.85***
Depressive Symptoms (range 0-35)
7.48 ± 1.32
12.77 ± .95
18.88 ± 1.38
17.82***
A1C (range 4.5-14)
6.79 ± .35
7.23 ± .26
7.68 ± .37
1.49
*** p < .001 Note. Flourishing = positivity ratio ≥ 2.9; Languishing = positivity ratio 1.0 to < 2.9; Depressed = positivity ratio < 1.0
DISCUSSION This study examined whether the health of African Americans with T2DM differed by positivity level as theorized by the broaden-and-build theory of positive emotions. As hypothesized, flourishing individuals reported greater health, as evident by higher resilience and lower depressive symptoms and A1C. Conversely, depressed individuals reported the poorest health, with lower resilience and higher depressive symptoms and A1C. Results for psychological health were statistically significant, whereas results for A1C were non-significant, albeit potentially clinically meaningful. In the United Kingdom Prospective Diabetes Study, a decline in A1C of 0.9% in the intensive arm compared with conventional treatment reduced microvascular complications by 25%.41 Furthermore, 10-year follow up of intensive glucose control revealed not only persistent reductions in microvascular disease, but also risk reductions for myocardial infarction and all-cause mortality.42 Thus, the difference in A1C between the depressed group (7.68) and flourishing group (6.79) can be viewed as an indicator of better glucose control, and is congruent with the American Diabetes Association A1C goal of < 7% for individuals with diabetes.43 With respect to psychological health, these results provided further support for the role of positivity in human flourishing.32 Flourishing African Americans with T2DM reported high levels of resilience and minimal depressive symptoms, which supports previous research in other populations.25,26 Conversely, those categorized as experiencing a depressed level of positivity (ie, experiencing more negative emotions than positive emotions) reported depressive symptoms that far exceeded the cutoff for a moderately severe level of depressive symptoms39 and significantly lower levels of resilience. These results support previous
research that human flourishing is characterized not merely by the absence of mental illness29 but by the presence of emotional vitality and growth in the face of adversity.32,44 Surprisingly, flourishing and languishing individuals reported similarly high resilience. One possible explanation for this non-significant pairwise comparison relates to the operational definition of resilience–positive adaptation in the context of significant change or adversity38,45– and potentially added opportunities for building resilience among African Americans. Given that race is a risk factor for negative outcomes such as poor physical health, unemployment, and discrimination,46 it is reasonable to assume African Americans experience more risk and adversity than Caucasians, and thus, more opportunities for resilience. In fact, despite the negative impacts of discrimination and social injustice, African Americans consistently have better mental health than Caucasians,47,48 suggesting a greater ability to bounce back from adversity. Resilience also emerged as a common theme among African American respondents in the face of unemployment, incarceration, and discrimination.49 Older African Americans’ experiences of racism, in which survival and endurance are paramount, are central to their responses to life threatening illnesses.50 Experiences of racism are embedded in their daily lives and become a part of their intentional attitude of overcoming adversity. Strength and perseverance acquired in response to racism may explain the higher level of resilience we found in our study for both languishing and flourishing participants. From an intervention standpoint, programs have been successful in increasing positivity and associated resilience resources.51,52 Positive emotions have been shown to improve adaptation to and management of a number of chronic diseases,14 in-
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Positivity and Indicators of Health among African Americans with Diabetes
Table 3 Pairwise Comparisons for the Dependent Variables Dependent Variable
Comparison
Mean Difference (SE)
Resilience
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
14.99*** (2.70) 1.98 (2.33) 13.01*** (2.41)
1.61 .21 1.39
Depressive Symptoms
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
- 11.39*** (1.91) - 5.29** (1.64) - 6.11** (1.70)
-1.73 - .80 -.93
A1C
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
- .88 (.51) -.44 (.44) -.45 (.46)
Cohen’s d
-.50 -.25 -.26
** p < .01, *** p < .001
cluding type 1 diabetes.53 Positive emotions help restore adaptive coping resources, broaden one’s scope of possibility, and enhance one’s attention.54 Further, positive emotions work like a reset button, putting the brakes on negativity.17,18 The findings of the present study support this, as the flourishing and languishing groups reported higher resilience than the depressed group. Thus, helping African Americans with T2DM enhance their positivity may enable them to improve their long-term adherence to healthy lifestyle choices and delay the progression of T2DM. Celano et al55 concluded that although positive psychological characteristics are increasingly associated with enhanced outcomes in medically ill patients, such interventions remain untested in diabetes. There is a need for interventions that provide diabetes self-management skills integrated with positive psychological characteristics (eg, positivity, resilience strategies) so that patients are motivated to manage their stress more effectively, and thereby, reduce the physiological and psychological burdens associated with this disease. Given the relationships we found, future studies should examine if interventions designed to enhance positivity and build resilience enable African Americans with T2DM to improve their adherence to healthy lifestyle choices, thereby decreasing obesity and delaying the progression of T2DM. Our results also have clinical implications for diabetes care interventions in underserved and socioeconomically challenged African-American populations. Currently, efforts to improve diabetes care in populations are focused on measuring provider adherence to testing guidelines,56,57 as well as outcome measurements such as A1C results and attainment of blood pressure goals.58,59 Although such efforts are important, they may introduce negative emotions that undermine adherence to management goals. For example, increased provider stress may focus negative emotions on patients who are not successful. Patients who fail
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to present for testing or who have poor control of their disease are labeled “non-compliant,” increasing the negativity expressed by the caregiver and felt by the patient. If the healthcare team is going to help such persons make meaningful lifestyle changes, they must be aware of how their behavior and attitudes influence positive emotions in their patients. Limitations Our findings should be considered in light of several limitations. First, this study was cross-sectional in nature, and therefore, causal inference cannot be determined. Second, there are inherent limitations associated with the use of single self-report survey data, including common method variance and potentially untruthful or inaccurate responses due to a lack of self-awareness; future research should incorporate objective measures, especially with regard to positivity and depressive symptoms. Third, although the PANAS is a well-validated widely used instrument, Cohen and Fredrickson35 have suggested that the items underrepresent lowarousal positive emotions such as contentment. Given this, researchers should consider additional scales that balance emotional breadth with quick assessment such as Fredrickson’s Modified Differential Emotions Scale (mDES)18,35 or The Positivity Scale,60 which measures a general tendency to view life and one’s experiences with a positive outlook. Fourth, this study involved a convenience sample of African Americans with T2DM; therefore, the results may not generalize to other ethnicities or health statuses. Nonetheless, examining these constructs in the African-American population is valuable, as this is an underserved group, yet one that bears a disproportionate T2DM burden. Conclusions Our study provides support for the broaden-andbuild theory of positive emotions and the utility of positivity cutoffs using a sample of African Ameri-
Steinhardt et al cans with T2DM. Our results suggest that African Americans with T2DM may benefit from increased positivity, and our findings are consistent with those that suggest positivity is associated with enhanced psychological health.25,61
1. Fisher L, Skaff MM, Mullan JT, et al. Clinical depression versus distress among patients with type 2 diabetes. Diabetes Care. 2007;30(3):542-548. 2. Knol MJ, Twisk JW, Beekman AT, et al. Depression as a risk factor for the onset of type 2 diabetes mellitus: a meta-analysis. Diabetologia. 2006;49(5):837-845. 3. Lloyd C, Smith J, Weinger K. Stress and diabetes: a review of the links. Diabetes Spectrum. 2005;18(2):121127. 4. Ali S, Stone MA, Peters JL, et al. The prevalence of co-morbid depression in adults with type 2 diabetes: a systematic review and meta-analysis. Diabet Med. 2006;23(11):1165-1173. 5. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24(6):10691078. 6. Bonnet F, Irving K, Terra JL, et al. Anxiety and depression are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis. 2005;178(2):339-344. 7. Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and high risk for diabetes using A1c criteria in the U.S. population in 1988-2006. Diabetes Care. 2010;33(3):562-568. 8. Centers for Disease Control and Prevention. National Diabetes Statistics Reports: Estimates of Diabetes and Its Burden in the United States, 2014. Washington, DC: US Department of Health and Human Services; 2014. 9. Fiscella K, Franks P. Does psychological distress contribute to racial and socioeconomic disparities in mortality? Soc Sci Med. 1997;45(12):1805-1809. 10. Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med. 2000;160(21):32783285. 11. Richman LS, Kubzansky L, Maselko J, et al. Positive emotion and health: going beyond the negative. Health Psychol. 2005;24(4):422-429. 12. Moskowitz JT, Epel ES, Acree M. Positive affect uniquely predicts lower risk of mortality in people with diabetes. Health Psychol. 2008;27(1):S73-S82.
13. Pressman SD, Cohen S. Does positive affect influence health? Psychol Bull. 2005;131(6):925-971. 14. Moskowitz JT. Positive affect predicts lower risk of AIDS mortality. Psychosom Med. 2003;65(4):620-626. 15. Chocron S, Etievent JP, Viel JF, et al. Preoperative quality of life as a predictive factor of 3-year survival after open heart operations. Ann Thorac Surg. 2000;69(3):722727. 16. Fredrickson BL. What good are positive emotions? Rev Gen Psychol. 1998;2(3):300-319. 17. Fredrickson BL. The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. Am Psychol. 2001;56(3):218-226. 18. Fredrickson BL. Positivity. New York: Crown Publishers; 2009. 19. Schmitz TW, De Rosa E, Anderson AK. Opposing influences of affective state valence on visual cortical encoding. J Neurosci. 2009;29(22):7199-7207. 20. Garland EL, Fredrickson B, Kring AM, et al. Upward spirals of positive emotions counter downward spirals of negativity: insights from the broaden-and-build theory and affective neuroscience on the treatment of emotion dysfunctions and deficits in psychopathology. Clin Psychol Rev. 2010;30(7):849-864. 21. Fredrickson BL, Branigan C. Positive emotions broaden the scope of attention and thought-action repertoires. Cogn Emot. 2005;19(3):313-332. 22. Rowe G, Hirsh JB, Anderson AK. Positive affect increases the breadth of attentional selection. Proc Natl Acad Sci U S A. 2007;104(1):383-388. 23. Kahn BE, Isen AM. The influence of positive affect on variety seeking among safe, enjoyable products. J Consum Res. 1993;20(2):257-270. 24. Raghunathan R, Trope Y. Walking the tightrope between feeling good and being accurate: mood as a resource in processing persuasive messages. J Pers Soc Psychol. 2002;83(3):510-525. 25. Cohn MA, Fredrickson BL, Brown SL, et al. Happiness unpacked: positive emotions increase life satisfaction by building resilience. Emotion. 2009;9(3):361-368. 26. Fredrickson BL, Cohn MA, Coffey KA, et al. Open hearts build lives: positive emotions, induced through lovingkindness meditation, build consequential personal resources. J Pers Soc Psychol. 2008;95(5):1045-1062. 27. Fredrickson BL, Losada MF. Positive affect and the complex dynamics of human flourishing. Am Psychol. 2005;60(7):678-686. 28. Fredrickson BL. Promoting positive affect. In Eid M, Larsen RJ, (Eds). The Science of Subjective Well-Being. New York: Guilford Press; 2008:449-468. 29. Keyes CLM, Lopez SJ. Toward a science of mental health: positive directions in diagnosis and interventions. In Snyder CR, Lopez SJ, (Eds). Handbook of Positive Psychology. New York: Oxford University Press; 2002:45-59. 30. Schwartz RM, Reynolds CF, Thase ME, et al. Optimal and normal affect balance in psychotherapy of major depression: evaluation of the balanced states of mind model. Behav Cogn Psychother. 2002;30(4):439-450. 31. Brown NJL, Sokal AD, Friedman HL. The complex dynamics of wishful thinking: the critical positivity ratio. Am Psychol. 2013;68(9):801-813. 32. Fredrickson BL. Updated thinking on positivity ratios. Am Psychol. 2013;68(9):814-822. 33. Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988;54(6):10631070. 34. Moskowitz JT. Coping interventions and the regulation of positive affect. In Folkman S, (Ed). The Oxford Handbook of Stress, Health, and Coping. Oxford: Oxford University Press; 2010:407-428. 35. Cohn MA, Fredrickson BL. Positive emotions. In Sny-
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Human Subjects Statement All procedures were approved by The University of Texas at Austin Institutional Review Board. Informed consent was obtained from participants prior to data collection. Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgments The project described was supported by Award Number R34DK085218 from the National Institute of Diabetes and Digestive and Kidney Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Diabetes and Digestive and Kidney Diseases or the National Institutes of Health. References
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Positivity and Indicators of Health among African Americans with Diabetes der CR, Lopez SJ, (Eds). Oxford Handbook on Positive Psychology, 2nd ed. Oxford: Oxford University Press; 2009:13-24. 36. Baumeister RF, Bratslavsky E, Finkenauer C, Vohs KD. Bad is stronger than good. Rev Gen Psychol. 2001;5(4):323-370. 37. Cacioppo JT, Gardner WL, Berntson GG. The affect system has parallel and integrative processing components: form follows function. J Pers Soc Psychol. 1999;76(5):839-855. 38. Connor KM, Davidson JRT. Development of a new resilience scale: the Connor-Davidson Resilience Scale (CDRISC). Depress Anxiety. 2003;18(2):76-82. 39. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385-401. 40. Lenters-Westra, E, Slingerland RJ. Six of eight hemoglobin A1c point-of-care instruments do not meet the general accepted analytical performance criteria. Clin Chem. 2010;56(1):44-52. 41. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352(9131):837-853. 42. Holman RR, Paul SK, Bethel MA, et al. 10-Year follow up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577-1589. 43. American Diabetes Association. Executive summary: standards of medical care in diabetes – 2014. Diabetes Care. 2014;37(Suppl 1):S14-S80. 44. Fredrickson BL, Tugade MM, Waugh CE, Larkin GR. What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. J Pers Soc Psychol. 2003;84(2):365-376. 45. Masten AS, Coatsworth JD. The development of competence in favorable and unfavorable environments: lessons from research on successful children. Am Psychol. 1998;53(2):205-220. 46. Schnittker J, McLeod JD. The social psychology of health disparities. Annu Rev Sociol. 2005;31(1):75-103. 47. Keyes CLM. The Black–White paradox in health: flourishing in the face of social inequality and discrimination. J Pers. 2009;77(6):1677-1706. 48. Williams DR, Gonzalez HM, Neighbors H, et al. Prevalence and distribution of major depressive disorder in African Americans, Caribbean Blacks, and Non-Hispanic Whites: results from the national survey of American life.
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Arch Gen Psychiatry. 2007;64(3):305-315. 49. Teti M, Martin AE, Ranade R, et al. “I’m a keep rising. I’m a keep going forward, regardless”: exploring black men’s resilience amid sociostructural challenges and stressors. Qual Health Res. 2012;22(4):524-533. 50. Becker G, Newsom E. Resilience in the face of serious illness among chronically ill African Americans in later life. J Gerontol B Psychol Sci Soc Sci. 2005;60(4):S214-S223. 51. Emmons RA, McCullough ME. Counting blessings versus burdens: an experimental investigation of gratitude and subjective well-being in daily life. J Pers Soc Psychol. 2003;84(2):377-389. 52. Seligman MEP, Steen TA, Park N, Peterson C. Positive psychology progress: empirical validation of interventions. Am Psychol. 2005;60(5):410-421. 53. Fournier M, de Ridder D, Bensing J. Optimism and adaptation to chronic disease: the role of optimism in relation to self-care options of type 1 diabetes mellitus, rheumatoid arthritis, and multiple sclerosis. Br J Health Psychol. 2002;7(4):409-432. 54. Fredrickson BL, Joiner T. Positive emotions trigger upward spirals toward emotional well-being. Psychol Sci. 2002;13(2):172-175. 55. Celano CM, Beale EE, Moore SV, et al. Positive psychological characteristics in diabetes: a review. Curr Diab Rep. 2013;13(6):917-929. 56. Phillips LS, Ziemer DC, Doyle JP, et al. An endocrinologist-supported intervention aimed at providers improves diabetes management in a primary care site: improving primary care of African Americans with diabetes (IPCAAD) 7. Diabetes Care. 2005;28(10):2352-2360. 57. Renders CM, Valk GD, Griffin SJ, et al. Interventions to improve the management of diabetes in primary care, outpatient, and community settings. Diabetes Care. 2001;24(10):1821-1833. 58. Beulens JWJ, Patel A, Vingerling JR, et al. Effects of blood pressure lowering and intensive glucose control on the incidence and progression of retinopathy in patients with type 2 diabetes mellitus: a randomised controlled trial. Diabetologia. 2009;52(10):2027-2036. 59. Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008;358(6):580-591. 60. Caprara GV, Alessandri G, Eisenberg N, et al. The positivity scale. Psychol Assess. 2012;24(3):701-712. 61. Stein N, Folkman S, Trabasso T, Richards TA. Appraisal and goal processes as predictors of psychological well-being in bereaved caregivers. J Pers Soc Psychol. 1997;72(4):872-884.
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T
ype 2 diabetes mellitus (T2DM), the 6th leading cause of death in the United States (US), is a psychologically and behaviorally demanding chronic disease affecting nearly 26 million people. Individuals with the disease typically experience greater stress than non-diseased individuals and have higher levels of depressive symptoms.1-3 In fact, T2DM doubles the odds of depression.4,5 Further, depression plays a critical role in disarming individuals from making healthy lifestyle choices, leaving them particularly vulnerable to complications from the disease.6 Moreover, this unremitting accumulation of damage is largely preventable. Most research, however, has focused on risk factors and adherence to diabetes management guidelines, rather than enhancing psychosocial qualities that build resilient and thriving responses, enhancing the patient’s ability to make healthy lifestyle choices and control the disease. Minorities are disproportionately affected, with African Americans twice as likely to have T2DM as Mary A. Steinhardt, Professor of Health Behavior and Health Education, The University of Texas, Austin, TX. Susan K. Dubois, Medical Director, The University of Texas School of Nursing Family Wellness Center, Austin, TX. Sharon A. Brown, Joseph H. Blades Centennial Memorial Professor of Nursing, The University of Texas, Austin, TX. Louis Harrison, Jr., Professor of Curriculum and Instruction, The University of Texas, Austin, TX. Kathryn E. Dolphin, Assistant Professor of Exercise Science, Pacific University, Forest Grove, OR. Wonil Park, PhD Student and H. Matthew Lehrer, Masters Student, The University of Texas, Austin, TX. Correspondence Dr Steinhardt; [email protected]
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resilience and highest depressive symptoms and A1C. Small to large effect sizes were reported. Conclusions: Further support for the utility and generalizability of the positivity ratio was provided. Cultivating positive emotions may improve the health of individuals with type 2 diabetes. Key words: type 2 diabetes; African American; positivity; resilience; depressive symptoms; A1C Am J Health Behav. 2015;39(1):43-50 DOI: http://dx.doi.org/10.5993/AJHB.39.1.5
Caucasians.7 Approximately one in 5 African Americans aged 20 years or older has T2DM8 with all the accompanying stressors. African Americans of low socioeconomic status incur additional stressors (eg, financial insecurity, lack of health care) that affect these patients both psychologically and physiologically and limit their proclivity for making healthy lifestyle choices and successfully controlling the disease. Further, African Americans report higher levels of life dissatisfaction and depressive symptoms than Caucasians,9 making them more prone to engage in poor diabetes self-management behaviors and medication adherence.10 An emerging line of research suggests one’s emotional state may influence both predisposition to and the clinical course of chronic diseases. Positive emotions, such as hope, are associated with a decreased prevalence of diabetes.11 In fact, after controlling for negative emotions and health status, positive emotions were uniquely associated with a lower mortality risk in diabetic individuals.12 Pressman and Cohen13 suggest that positive affect directly influences health via better behaviors and health practices; thus, positive emotions may play an important role in the course of illnesses influenced by behavioral factors, such as AIDS mortality14 and survival following open heart surgery.15 Similarly, the daily behavioral demands and serious consequences of uncontrolled glucose levels render T2DM a chronic disease whose illness trajectory is largely influenced by behavioral factors. From a theoretical standpoint, Fredrickson’s broaden-and-build theory of positive emotions
43
Positivity and Indicators of Health among African Americans with Diabetes provides another mechanism by which positive emotions may result in improved outcomes for those diagnosed with T2DM. According to the theory, positive and negative emotions have distinct, yet complementary functions.16-18 Negative emotions have been believed to narrow the scope of an individual’s attention and cognition to react quickly to an adverse situation in a specific manner.19 A corollary hypothesis holds that positive emotions broaden one’s thought-action repertoire, allowing individuals to choose from a wider range of actions, ideas, and perceptions.20 For example, positive emotions broaden the field of visual attention,21 creativity,22 and openness to new experiences23 and information.24 Over time, this broadened mindset contributes to important long-term life outcomes, including improved decision-making and adaptation to adversity,16-18 greater resilience,25 reduced depressive symptoms,26 and lower risk of mortality in individuals with T2DM.12 Fredrickson and Losada27 identified a positivity ratio– the frequency of experienced positive emotions to negative emotions–that distinguishes individuals who lead a flourishing life, full of meaning, possibility, and growth from languishing individuals who desire more significance and purpose in life. According to the model, a positivity ratio of 2.9 serves as a cutoff score or tipping point between flourishing and languishing individuals. Those who report positivity ratios above 2.9 are satisfied with life, have a sense of fulfillment, are more resilient, and feel as though things in life are going as well as, or better than expected. Fredrickson describes these individuals as experiencing the “good life.”18 Conversely, a positivity ratio below 2.9 is indicative of languishing, or individuals who are “stuck in a rut” and “yearning for more.”28 These individuals describe their lives as being unfulfilled or stagnant, and are burdened by feelings that “the grass is greener on the other side.” They report only moderate levels of mental health and experience frequencies of illness similar to those who are depressed.29 Individuals who report positivity ratios less than 1.0, in which negative emotions are experienced more frequently than positive emotions, feel overwhelmed by difficult life conditions and describe their life as a struggle. Positivity ratios less than 1.0 are reported frequently by the clinically depressed, as this ratio is suggestive of a pathological level of functioning.18,30 Although the precise “tipping points” underlying the positivity ratio have been questioned,31 a large body of empirical evidence continues to support the utility of the positivity ratio as a guideline for distinguishing psychological health.32 Given that positive emotions have the potential to help individuals with T2DM deal more effectively with the chronic stress of the disease, the purpose of this study was to examine whether groups of flourishing, languishing, and depressed individuals with T2DM significantly differ from each other in scores of psychological (viz, resilience and de-
44
pressive symptoms) and physiological (viz, A1C) health. It was hypothesized that: (1) flourishing individuals would report the highest levels of resilience and lowest levels of depressive symptoms and A1C; (2) depressed individuals would report the lowest levels of resilience and highest levels of depressive symptoms and A1C; and (3) languishing individuals would report scores in between the flourishing and depressed groups. METHODS Participants and Procedures Participants for this study were a convenience sample of 93 African Americans (62 women, 31 men) living in a large city in the Southwest United Sates and ranging in age from 31 to 85 years. They had been diagnosed with T2DM an average of 9.5 ± 8.0 years. Overall, 23% of the participants were single, 42% were married, 19% were separated or divorced, and 16% were widowed. With regard to education, 8% had attended high school, 21% had a high school diploma/GED, 40% had attended some college or technical school experience, 19% had a bachelor’s degree, and 12% had a graduate degree. In terms of total family income, 38% earned $19,000 or less, 25% earned $20,000 to $39,000, 23% earned $40,000 to $59,000, 7% earned $60,000 to $79,000, 2% earned $80,000 to $100,000, and 5% earned $100,000 or more. Recruitment for the study sample was through radio and church announcements. Participants were recruited who were diagnosed with T2DM and not currently participating in a diabetes selfmanagement program. Potential participants were excluded if they were pregnant, lactating, or had medical conditions severe enough to preclude participation in a subsequent program intervention (eg, kidney failure requiring dialysis or peripheral vascular disease severe enough to preclude walking). All subjects were asked to complete a survey and have their glycated hemoglobin (A1C) concentration measured, followed by a light healthy breakfast. Subjects with high A1C values were encouraged to see their healthcare provider (HCP). If they did not have a HCP, they were referred to the wellness clinic on the university campus. We did not experience difficulty in enrolling individuals; 85% of those who called and expressed a desire to participate showed up and participated. All participants received $20 for their participation, as well as the opportunity to participate in a diabetes selfmanagement education program as part of a larger intervention study. Measures Data collected included measures of positivity (viz, flourishing, languishing, depressed), and psychological (viz, resilience and depressive symptoms) and physiological (viz, A1C) health. Below we describe each of these measures. Positivity ratio. Positive and negative emotions were measured using the 20-item Positive and
Steinhardt et al Negative Affect Schedule (PANAS)33 with positivity representing the ratio of positive emotions (eg, joyful, determined, inspired) to negative (eg, afraid, distressed, irritable) emotions.27 The PANAS, described in the literature as a well-validated, widelyused34 and popular measure,35 asks participants to respond on a Likert scale from 1 (not at all) to 5 (very much so). The number of positive emotions experienced at least moderately (> 3) and the number of negative emotions experienced at least a little (> 2) were tallied, with the different thresholds in place to account for negativity bias and positivity offset. Negativity bias reflects the phenomenon that individuals give more weight to negative rather than positive emotions,36 whereas positivity offset reflects the phenomenon that people tend to feel at least mild positive emotions most of the time.37 In other words, although people tend to experience more positive than negative emotions, the impact of negative emotions is stronger. A positivity ratio was then calculated by dividing the frequency of the positive emotion items by the frequency of the negative emotion items. Reliability was strong for both the positive emotion items (a = .87) and negative emotion items (a = .86). Based on the cutoff scores identified by Fredrickson and Losada,27 positivity ratios were trichotomized, and participants were classified as flourishing (positivity ratio ≥ 2.9), languishing (positivity ratio = 1.0 to < 2.9), or depressed (positivity ratio < 1.0). Given that the precise “tipping points” underlying the positivity ratio have been questioned,31 we ran an additional analysis using cut scores based on the distribution of our sample. Resilience. Resilience was measured using the 25-item Connor-Davidson Resilience Scale (CDRISC).38 The CD-RISC identifies characteristics that enable individuals to adapt to stress, such as through faith, goal setting, patience, and tolerance of negative affect, as well as the tendency to perceive change as a challenge, maintain a commitment to the people and activities in which one is involved, and have a sense of personal control in handling life events. Participants responded on a 5-point Likert scale ranging from 0 (not true at all) to 4 (always true). A resilience score was calculated as the sum of all items, with higher scores indicating greater resilience. Sample items included: “I believe I can achieve my goals, even if there are obstacles” and “I tend to bounce back after illness, injury, or other hardships.” Reliability of the CDRISC was strong (a = .90). Depressive symptoms. The 20-item Center for Epidemiological Studies Depression Scale (CES-D) was used to measure depressive symptoms experienced, such as depressed mood, feelings of guilt, worthlessness, helplessness, and restless sleep.39 Sample items included: “I was bothered by things that usually don’t bother me,” “I felt everything I did was an effort,” and “I had crying spells.” Responses ranged from 0 (rarely or none of the time; less than one day) to 3 (most or all of the time, 5-7
days), and were summed for a total score. A CESD score of 16 or greater is considered a moderately severe level of depressive symptoms.39 Reliability of the CES-D was strong (a = .82). A1C. An aliquot of whole blood from a blood sample was used to determine glycated hemoglobin A1C concentration measured on a DCA VantageTM (Siemens Medical Solutions Diagnostics, Tarrytown, NY). This method provides results in 6 minutes based on a latex agglutination inhibition methodology. The DCA VantageTM meets the National Glycohemoglobin Standardization Program certification criteria of having a total coefficient of variation < 3% in the clinically relevant range.40
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Statistical Analyses Descriptive statistics and correlations. Means, standard deviations, and bivariate correlations of all study variables were calculated using descriptive statistics. Pearson correlations were used for continuous variables, point-biserial correlations for continuous and dichotomous variables, and chi-square tests for pairs of dichotomous variables. Multivariate analysis of variance. A one-way multivariate analysis of variance (MANOVA) was conducted on the 3 dependent variables (viz, resilience, depressive symptoms, and A1C), with level of positivity (viz, flourishing, languishing, and depressed) as the independent variable and controlling for the variable years diagnosed with diabetes. Post hoc analyses were performed using Tukey’s Honestly Significant Difference (HSD) to control for potential Type 1 error rate inflation. Cohen’s d effect sizes were computed to characterize the size of differences between group means. All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 21. RESULTS Prior to the correlation analysis, multiple-category demographic variables were collapsed into binary variables to produce appropriately sized groups: sex (1 = female, 0 = male), marital status (1 = married, 0 = other), education (1 = some college, 0 = no college), and family income (1 = $40,000 or more, 0 = less than $40,000). Age and number of years diagnosed with T2DM were retained as continuous variables. Descriptive Analysis Table 1 displays the means, standard deviations, and correlations for all study variables. The analyses indicated that higher scores on positivity were related to higher scores on resilience (r = .37, p < .01) and lower scores on depressive symptoms (r = -.48, p < .01). Higher scores on resilience were also related to lower scores on depressive symptoms (r = -.40, p < .01). Among the demographic control variables, results showed that, on average, older individuals (r = -.23, p < .05) and those who were married (r = -.22, p < .05) had lower A1C. As ex-
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Positivity and Indicators of Health among African Americans with Diabetes
Table 1 Means, Standard Deviations (SD), and Bivariate Correlations for All Variables (N = 93) Variable Positivity Ratio (PR)
Mean
SD
PR
Res
DS
A1C
Age
YD
F
M
E
FI
2.73
2.61
--
.37**
-.48**
-.12
.09
.04
-.02
.10
-.02
.15
--
-.40**
-.15
-.02
-.03
.04
.08
.00
-.01
.14
-.01
-.01
.03
-.20
-.06
-.15
--
-.23*
.18
.20
-.22*
-.15
-.11
.31**
.06
-.04
.01
-.07
--
.11
-.23*
-.14
-.09
Resilience 80.91 (Res)
10.95
Depressive 12.86 Symptoms (DS)
7.67
--
A1C Age
1.80
Years Diabetes (YD)
7.22 59.19
10.48
9.50
--
8.0
Female (F) -- .23* .02 -.34** Married (M) -- .02 .24* Education (E) -- .40** Family Income (FI) --
* p < .05, ** p < .01 Note. Sex (Female = 1, Male = 0); Marital Status (Married = 1, Single, Separated/Divorced, Widowed = 0); Education (Some College = 1, No College = 0); Family Income ($40,000 or more = 1, Less Than $40,000 = 0).
pected, age and years diagnosed with diabetes were positively correlated (r = .31, p < .01), and family income was positively correlated with being married (r = .24; p < .05) and having more education (r = 40; p < .01). Inverse correlations between being female and family income (r = -.34; p < .01), and between being married and years diagnosed with diabetes (r = -.23, p < .05) were perhaps attributed to the fact that 16% of the sample was widowed. Multivariate Analysis of Variance Table 2 shows the mean scores for resilience, depressive symptoms, and A1C for flourishing, languishing, and depressed participants with T2DM, controlling for the variable years diagnosed with diabetes. The test of the multivariate null indicated statistically significant group differences on the set of dependent variables [FWilks’ λ (2, 90) = 9.42, p < .001]. The accompanying multivariate partial eta square (hp2 = .25) indicated a moderate effect associated with level of positivity. Examinations of the univariate test for each of the dependent variables indicated that significant group differences were present for both depressive symptoms [F(2, 90) =
46
17.82, p < .001] and resilience [F(2, 90) = 18.85, p < .001], and a non-significant trend for A1C [F(2, 90) = 1.49, p = .232]. All pairwise comparisons between the categories of positivity (viz, flourishing vs languishing, flourishing vs depressed, and languishing vs depressed) yielded statistically significant differences in the mean scores of depressive symptoms (Table 3). Further, flourishing and languishing individuals reported higher levels of resilience than depressed individuals; however, languishing individuals reported similar levels of resilience to flourishing individuals. Effect sizes ranged from small for resilience (d = .21) to very large for depressive symptoms (d = - 1.73). Given that the precise “tipping points” underlying the positivity ratio have been questioned,31 we ran an additional analysis using a different cut score based on the distribution of our sample (ie, ≥ 5.0 for flourishing), and the multivariate results for A1C, depressive symptoms, and resilience remained the same. Thus, whether we used a cut score of 3 or 5 for flourishing, the multivariate results did not change.
Steinhardt et al
Table 2 Mean, Standard Error, and Range for Dependent Variables as Reported by Flourishing, Languishing, and Depressed Participants (N = 93) Dependent Variable
Flourishing (N = 25)
Languishing (N = 45)
Depressed (N = 23)
F (2, 90)
Resilience (range 46-100)
85.58 ± 1.87
83.60 ± 1.40
70.59 ± 1.96
18.85***
Depressive Symptoms (range 0-35)
7.48 ± 1.32
12.77 ± .95
18.88 ± 1.38
17.82***
A1C (range 4.5-14)
6.79 ± .35
7.23 ± .26
7.68 ± .37
1.49
*** p < .001 Note. Flourishing = positivity ratio ≥ 2.9; Languishing = positivity ratio 1.0 to < 2.9; Depressed = positivity ratio < 1.0
DISCUSSION This study examined whether the health of African Americans with T2DM differed by positivity level as theorized by the broaden-and-build theory of positive emotions. As hypothesized, flourishing individuals reported greater health, as evident by higher resilience and lower depressive symptoms and A1C. Conversely, depressed individuals reported the poorest health, with lower resilience and higher depressive symptoms and A1C. Results for psychological health were statistically significant, whereas results for A1C were non-significant, albeit potentially clinically meaningful. In the United Kingdom Prospective Diabetes Study, a decline in A1C of 0.9% in the intensive arm compared with conventional treatment reduced microvascular complications by 25%.41 Furthermore, 10-year follow up of intensive glucose control revealed not only persistent reductions in microvascular disease, but also risk reductions for myocardial infarction and all-cause mortality.42 Thus, the difference in A1C between the depressed group (7.68) and flourishing group (6.79) can be viewed as an indicator of better glucose control, and is congruent with the American Diabetes Association A1C goal of < 7% for individuals with diabetes.43 With respect to psychological health, these results provided further support for the role of positivity in human flourishing.32 Flourishing African Americans with T2DM reported high levels of resilience and minimal depressive symptoms, which supports previous research in other populations.25,26 Conversely, those categorized as experiencing a depressed level of positivity (ie, experiencing more negative emotions than positive emotions) reported depressive symptoms that far exceeded the cutoff for a moderately severe level of depressive symptoms39 and significantly lower levels of resilience. These results support previous
research that human flourishing is characterized not merely by the absence of mental illness29 but by the presence of emotional vitality and growth in the face of adversity.32,44 Surprisingly, flourishing and languishing individuals reported similarly high resilience. One possible explanation for this non-significant pairwise comparison relates to the operational definition of resilience–positive adaptation in the context of significant change or adversity38,45– and potentially added opportunities for building resilience among African Americans. Given that race is a risk factor for negative outcomes such as poor physical health, unemployment, and discrimination,46 it is reasonable to assume African Americans experience more risk and adversity than Caucasians, and thus, more opportunities for resilience. In fact, despite the negative impacts of discrimination and social injustice, African Americans consistently have better mental health than Caucasians,47,48 suggesting a greater ability to bounce back from adversity. Resilience also emerged as a common theme among African American respondents in the face of unemployment, incarceration, and discrimination.49 Older African Americans’ experiences of racism, in which survival and endurance are paramount, are central to their responses to life threatening illnesses.50 Experiences of racism are embedded in their daily lives and become a part of their intentional attitude of overcoming adversity. Strength and perseverance acquired in response to racism may explain the higher level of resilience we found in our study for both languishing and flourishing participants. From an intervention standpoint, programs have been successful in increasing positivity and associated resilience resources.51,52 Positive emotions have been shown to improve adaptation to and management of a number of chronic diseases,14 in-
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Positivity and Indicators of Health among African Americans with Diabetes
Table 3 Pairwise Comparisons for the Dependent Variables Dependent Variable
Comparison
Mean Difference (SE)
Resilience
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
14.99*** (2.70) 1.98 (2.33) 13.01*** (2.41)
1.61 .21 1.39
Depressive Symptoms
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
- 11.39*** (1.91) - 5.29** (1.64) - 6.11** (1.70)
-1.73 - .80 -.93
A1C
Flourishing vs Depressed Flourishing vs Languishing Languishing vs Depressed
- .88 (.51) -.44 (.44) -.45 (.46)
Cohen’s d
-.50 -.25 -.26
** p < .01, *** p < .001
cluding type 1 diabetes.53 Positive emotions help restore adaptive coping resources, broaden one’s scope of possibility, and enhance one’s attention.54 Further, positive emotions work like a reset button, putting the brakes on negativity.17,18 The findings of the present study support this, as the flourishing and languishing groups reported higher resilience than the depressed group. Thus, helping African Americans with T2DM enhance their positivity may enable them to improve their long-term adherence to healthy lifestyle choices and delay the progression of T2DM. Celano et al55 concluded that although positive psychological characteristics are increasingly associated with enhanced outcomes in medically ill patients, such interventions remain untested in diabetes. There is a need for interventions that provide diabetes self-management skills integrated with positive psychological characteristics (eg, positivity, resilience strategies) so that patients are motivated to manage their stress more effectively, and thereby, reduce the physiological and psychological burdens associated with this disease. Given the relationships we found, future studies should examine if interventions designed to enhance positivity and build resilience enable African Americans with T2DM to improve their adherence to healthy lifestyle choices, thereby decreasing obesity and delaying the progression of T2DM. Our results also have clinical implications for diabetes care interventions in underserved and socioeconomically challenged African-American populations. Currently, efforts to improve diabetes care in populations are focused on measuring provider adherence to testing guidelines,56,57 as well as outcome measurements such as A1C results and attainment of blood pressure goals.58,59 Although such efforts are important, they may introduce negative emotions that undermine adherence to management goals. For example, increased provider stress may focus negative emotions on patients who are not successful. Patients who fail
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to present for testing or who have poor control of their disease are labeled “non-compliant,” increasing the negativity expressed by the caregiver and felt by the patient. If the healthcare team is going to help such persons make meaningful lifestyle changes, they must be aware of how their behavior and attitudes influence positive emotions in their patients. Limitations Our findings should be considered in light of several limitations. First, this study was cross-sectional in nature, and therefore, causal inference cannot be determined. Second, there are inherent limitations associated with the use of single self-report survey data, including common method variance and potentially untruthful or inaccurate responses due to a lack of self-awareness; future research should incorporate objective measures, especially with regard to positivity and depressive symptoms. Third, although the PANAS is a well-validated widely used instrument, Cohen and Fredrickson35 have suggested that the items underrepresent lowarousal positive emotions such as contentment. Given this, researchers should consider additional scales that balance emotional breadth with quick assessment such as Fredrickson’s Modified Differential Emotions Scale (mDES)18,35 or The Positivity Scale,60 which measures a general tendency to view life and one’s experiences with a positive outlook. Fourth, this study involved a convenience sample of African Americans with T2DM; therefore, the results may not generalize to other ethnicities or health statuses. Nonetheless, examining these constructs in the African-American population is valuable, as this is an underserved group, yet one that bears a disproportionate T2DM burden. Conclusions Our study provides support for the broaden-andbuild theory of positive emotions and the utility of positivity cutoffs using a sample of African Ameri-
Steinhardt et al cans with T2DM. Our results suggest that African Americans with T2DM may benefit from increased positivity, and our findings are consistent with those that suggest positivity is associated with enhanced psychological health.25,61
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Am J Health Behav.™ 2015;39(1):43-50
DOI:
Human Subjects Statement All procedures were approved by The University of Texas at Austin Institutional Review Board. Informed consent was obtained from participants prior to data collection. Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgments The project described was supported by Award Number R34DK085218 from the National Institute of Diabetes and Digestive and Kidney Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Diabetes and Digestive and Kidney Diseases or the National Institutes of Health. References
http://dx.doi.org/10.5993/AJHB.39.1.5
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