The Science of Diabetes Self-Management and Care 2025, Vol. 51(5) 487 –496 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/26350106251371084 journals.sagepub.com/home/tde
Abstract
Purpose: The purpose of the study was to examine the relationship between diabetes distress and various factors with patient activation among adults with type 2 diabetes.
Methods: Linear and nonlinear relationships were evaluated using hierarchical polynomial regression models. Diabetes distress was measured using the Diabetes Distress Scale (DDS-17), with its 4 subscales, including emotional burden and regimen-, physician-, and interpersonal-related distress. Patient activation was measured using the Patient Activation Measure (PAM-13).
Results: There was a weak linear association between total DDS-17 and PAM-13 scores (R2 = 2.8%, P = .003). Across all subscales, results revealed robust quadratic trends that accounted for large increases in variance above and beyond linear trends (R2 s = 8.6%-23.0%, Ps < .001).
Conclusions: Results indicate a robust U-shaped relationship between diabetes distress and patient activation. These findings have the potential to inform treatment approaches for individuals with moderate to high diabetes distress and/or low patient activation.
Approximately 1 in 10 US adults has diabetes.1 Adults with diabetes often experience significant distress resulting from complex diabetes self-management regimens, interpersonal conflicts stemming from diabetes self-management, emotional burden associated with having diabetes (eg, functional impairment and fear of complications), and relationships with health care providers.2 Diabetes distress is associated with less adaptive self-management behavior (eg, poor diet, limited exercise, poor medication adherence, poor health literacy, lower glycemic control, higher levels of non-high-density lipoprotein cholesterol, and greater obesity, ie, higher body mass index).3-11 Clearly, diabetes distress presents a major barrier to self-management of diabetes.
Extant literature indicates that adults with type 2 diabetes have particularly high levels of diabetes-related distress.10 A longitudinal study of adults with type 2 diabetes showed that more than half of the individuals experienced diabetes distress that persisted and increased over 18 months.9 Fisher and colleagues12 examined a national sample of adults with type 2 diabetes who showed elevated (61.8%) and persistent diabetes distress (69.9%) over 6 months, with a majority (77.1%) experiencing diabetes distress from multiple sources (ie, management demands, health care providers, interpersonal issues, health care access, etc).
Patient activation is the knowledge, skills, and confidence an individual has in managing their own health care.13 Growing evidence has linked greater patient activation with better diabetes self-management. A cross-sectional study found that patients with type 2 diabetes who had higher patient activation had better health status, more social support, greater control over their chronic illness, and greater self-reported understanding of their illness.14 Participatory decision-making for patients with type 2 diabetes during primary care encounters led to improvements in medication adherence, glycemic control, and low-density lipoprotein cholesterol values via greater patient activation.15 Conversely, longitudinal studies in adults with type 2 diabetes indicate that lower patient activation predicts unhealthy cholesterol and blood pressure control, poor medication adherence, lower diabetes selfmanagement knowledge, and greater emergency department visits and hospitalizations.16,17 These findings underscore the importance of patient activation for self-management, clinical outcomes, and health behaviors among adults with type 2 diabetes.13,18-20
Previous work has identified linear and nonlinear associations between diabetes distress and diabetes-related factors. Fisher and colleagues21 discovered significant linear and quadratic relationships between diabetes distress and diabetes-related outcomes, including poorer glycemic control, diabetes self-efficacy, diet, and physical exercise, in 2 samples of adults with type 2 diabetes. Importantly, quadratic trends showed the greatest increase in poor diabetes outcomes at low levels of diabetes distress. For example, these authors21 found that A1C levels increased rapidly across low levels of diabetes distress and leveled off at high levels of diabetes distress. Quadratic trends reported in this study suggest that risk of poor disease self-management and negative health outcomes do not increase uniformly across increasing levels of diabetes distress.21
To date, only 1 prior study has examined the relationship between diabetes distress and patient activation. Specifically, van Vugt and colleagues14 found a weak, negative linear relationship between overall diabetes distress and patient activation among patients with type 2 diabetes. No studies have examined the linear and nonlinear relationship between specific facets of diabetes distress and patient activation. The nature of the relationship between these important clinical variables is critical to identify levels of diabetes distress associated with low patient activation.
The purpose of the study was to examine the relationship between diabetes distress and various factors with patient activation among adults with type 2 diabetes. Specifically, the aim of this study is to examine and compare the linear and curvilinear relationships between specific sources of diabetes distress and patient activation among adults with type 2 diabetes. Consistent with prior literature, it was hypothesized that there would be a negative linear trend between diabetes distress and patient activation. It was specifically hypothesized that greater levels of each type of diabetes distress (ie, emotional burden and physician-, regimen-, and interpersonal-related distress) would demonstrate a negative linear association with lower patient activation. On an exploratory basis, both the quadratic and cubic relationships between total and subdomains of diabetes distress and patient activation were also examined.
This is an online cross-sectional study that employed a battery of self-reported measures related to health, diabetes symptoms and self-care behaviors, and psychosocial factors. Data were analyzed to examine the relationship between diabetes distress and its facets with patient activation among the sample of adults with type 2 diabetes.
Participants were recruited using Prime Panels (cloudresearch.com), a web-based survey recruitment platform that uses multiple research market panels to provide targeted sampling of groups with specific characteristics.22 For this study, we recruited participants from a panel of adults who had previously endorsed a diagnosis of type 2 diabetes. Participants were provided a link to the study survey, sent via email, and were prompted to complete the survey in 1 sitting. All study procedures were administered via Qualtrics, a web-based survey program. To be eligible, participants had to be 18 years of age or older, living in the United States, and endorse a prior diagnosis of type diabetes. Participants’ diagnosis of type 2 diabetes was reconfirmed by asking them to indicate (yes, no, or unsure) whether they had previously been diagnosed by health care providers with several chronic health conditions, including obesity, hypertension, type 1 diabetes, type 2 diabetes, or heart disease. Respondents who did not endorse having a diagnosis of type 2 diabetes were excluded from further participation. This measure was taken to ensure that all participants had a diagnosis of type 2 diabetes. Furthermore, participants who endorsed a diagnosis of type 1 diabetes were excluded from participation. This step was taken to ensure that the study sample included only adults with type 2 diabetes.
There were 420 participants that were eligible based on these criteria and provided their consent to participating in this study. Two items assessed participant attention to study measures and to ensure fidelity of responses (eg, “Please select ‘Orange’ from the list of colors below”). Forty-seven participants did not provide accurate responses and were excluded. Three participants were excluded due to missing data for control variables (sex, education level, or diabetes duration). Two participants did not have Patient Activation Measure (PAM-13) scores and were excluded from analyses using listwise deletion. Thus, the final sample consisted of 368 adults with type 2 diabetes.
Diabetes Distress Scale-17. The Diabetes Distress Scale-17 (DDS-17) is a 17-item self-report measure of distress related to diabetes. The DDS-17 has 4 subscales that measure distress resulting from emotional burden, regimenrelated distress, physician-related distress, and interpersonal distress.2 Participants rate each item on a scale from 1 (not a problem) to 6 (a very significant problem). The total DDS-17 score is calculated by taking the average of all 17 items. Subscale scores of the DDS-17 are calculated by taking the average of the items that correspond to each respective subscale. Higher total and subscale scores indicate greater distress. The DDS-17 is a validated measure of diabetes distress and has demonstrated a high level of reliability among adults with type 2 diabetes in prior work (α = 0.93, α= 0.88)9,11 and in the present sample (α = 0.97).
PAM-13. The PAM-13 is a 13-item self-report measure of patient activation—which includes patients’ knowledge, skill, and confidence for disease self-management.23,24 Participants rate each item on a scale from 1 (strongly disagree) to 4 (strongly agree). Raw PAM-13 scores are generated by calculating the mean of responses across all items. Scores are then transformed to scaled scores using calibration tables.23 Total scaled scores ranging from 0 to 100 were used for analysis, with higher scores indicating higher patient activation. The PAM-13 has demonstrated excellent reliability in previous studies and in the present sample (α = 0.90).
Control variables. Participants reported their sex (male or female) and education level (less or more than high school). Diabetes duration was calculated by subtracting their reported current age at data collection from their age at diabetes diagnosis.
Descriptive statistics were used to examine the sample characteristics. Polynomial regression models examined the linear, quadratic, and cubic relationships of DDS-17 total and subscale scores and scaled PAM-13 scores. Polynomial regression models are used to examine nonlinear relationships between variables. Quadratic trends indicate a U- or inverted U-shaped relationship with a single point of inflection where the slope of the relationship between the predictor (ie, DDS-17) and outcome (ie, PAM-13) changes across levels of the predictor variable. Cubic trends indicate an S-shaped relationship with 2 points of inflection. Each term is entered into the model hierarchically. Thus, each trend is examined while controlling for the lower order trend(s). Assumptions of ordinary least squares regression models (ie, independence of observations, normal distribution of residuals, homoskedasticity, and no multicollinearity) were met for each model. Step 1 included the control variables (ie, sex, education level, and diabetes duration). Step 2 added the linear DDS-17 term. Step 3 and 4 added the quadratic (DDS-172 ) and cubic (DDS-173 ) terms, respectively. This sequence of steps was repeated for models predicting the total DDS-17 scores and the emotional burden, regimen distress, interpersonal distress, and physician-related distress subscales. All tests were 2-tailed with α = .05. All analyses were conducted using IBM SPSS Statistics (version 29).
Table 1 provides the descriptive statistics for the final sample (N = 368). The sample was predominantly White and female and on average 55 years old. A majority were more than high school educated, and the average duration of having diabetes was 12.6 years. The average PAM-13 scaled score for the sample was 68.7, which is within level 3 (ie, “taking action”).25
To examine the nonlinear relationship between diabetes distress (DDS-17) and its subscales with patient activation (PAM-13), a series of hierarchical polynomial regressions were conducted. Control variables of sex, education level, and duration of diabetes were included in Step 1 of the models, as indicated in Tables 2 and 3.
Table 2 depicts the results of the 4-step hierarchical polynomial regression model examining the relationship of DDS-17 total scores with PAM-13 scores. None of the control variables were significantly associated with PAM-13 scores. Collectively, these variables did not explain a significant amount of variance in PAM-13 (Ps > .05, R2 = .004). The linear term for DDS-17 total score, entered in Step 2, explained a small but significant amount of variance in PAM-13 scores (ΔR2 = .02, P = .003) after controlling for the variables entered in Step 1.
Specifically, there was a significant negative linear relationship between DDS-17 and PAM-13 scores (B = −1.96, SE = 0.65, P = .003). The addition of the quadratic term in Step 2 accounted for an additional 16.5% of variance in PAM-13 scores beyond the linear trend (P < .001). The quadratic relationship of DDS-17 total score and PAM-13 is depicted in Figure 1a. The cubic trend, added in Step 4, was not significant (B = −0.27, SE = 0.31, P = .388). These findings support our hypothesis that total diabetes distress would demonstrate a significant negative linear relationship to patient activation. However, examination of polynomial trends suggests that the relationship between total diabetes distress and patient activation is best characterized by a U-shaped quadratic relationship.
Table 3 depicts the results of the 4-step hierarchical polynomial regression models examining the linear, quadratic, and cubic relationship of DDS-17 subscales with PAM-13 scores. The linear term for the DDS-17 emotional burden subscale, entered in Step 2, explained a significant amount of variance in PAM-13 scores above and beyond the control variables entered in Step 1 (ΔR2 = .03, P = .002). Specifically, the emotional burden subscale showed a significant negative linear relationship with PAM-13 scores (B = −1.82, SE = 0.60, P = .002). The addition of the quadratic term in Step 3 was associated with 10.8% increase explained variance (P < .001). The quadratic relationship of emotional burden and PAM-13 is depicted in Figure 1b. The addition of the cubic term in Step 4 did not account for a significant amount of variance in PAM-13 scores (ΔR2 = .001, P = .864). These findings support our hypothesis that emotional burden would demonstrate a negative linear relationship with patient activation. However, examination of polynomial tends indicated that the relationship between emotional distress and patient activation is best characterized by a U-shaped quadratic relationship.
The linear relationship between the physician distress subscale of the DDS-17 and PAM-13 scores was nonsignificant (B = –0.66, SE = 0.62, P = .292). However, there was a significant quadratic relationship between the physician distress subscale and PAM-13 scores (B = 2.69, SE = 0.46, P < .001). The addition of the quadratic term in Step 3 accounted for 8.6% of the variance in PAM-13 scores beyond the control variables and linear terms (P < .001). Figure 1c displays the quadratic relationship between physician distress and PAM-13. The addition of the cubic term in Step 4 did not account for a significant amount of variance in PAM-13 scores (ΔR2 = .002, P = .364). These findings did not support our hypothesis that physician distress would have a negative linear relationship with patient activation. However, examination of polynomial trends revealed a significant U-shaped quadratic trend between physician-related distress and patient activation.
The linear term for DDS-17 regimen distress, entered in Step 2, explained a significant amount of variance in PAM-13 scores above and beyond the control variables entered in Step 1 (ΔR2 = .06, P < .001). Specifically, the regimen distress subscale showed a significant linear relationship with PAM-13 scores (B = –2.76, SE = 0.59, P < .001). The addition of the quadratic term in Step 3 was associated with a 17.0% increase in explained variance (P < .001). The quadratic relationship of regimen distress and PAM-13 is depicted in Figure 1d. The addition of the cubic term in Step 4 did not account for a significant amount of variance in PAM-13 scores (ΔR2 = .001, P = .901). These findings support our hypothesis that regimen distress would demonstrate a negative linear relationship with patient activation. However, examination of polynomial trends indicated that the relationship between regimen distress and patient activation is best characterized by a U-shaped quadratic relationship.
The interpersonal distress subscale showed no significant linear relationship with PAM-13 scores (B = –0.84, SE = 0.59, P = .155). However, there was a significant quadratic relationship between the interpersonal distress subscale and PAM-13 scores (B = 2.20, SE = 0.40, P < .001). The addition of the quadratic term in Step 3 explained an additional 7.7% of the variance in PAM-13 scores (P < .001). Figure 1e displays the quadratic relationship between interpersonal distress and PAM-13. The addition of the cubic term in Step 4 did not account for a significant amount of variance in PAM-13 scores (ΔR2 = .001, P = .551). These findings did not support our hypothesis that interpersonal distress would have a negative linear relationship with patient activation. However, examination of polynomial trends revealed a significant U-shaped quadratic trend between interpersonal distress and patient activation.
The present study examined the linear and, uniquely, the nonlinear relationships between diabetes distress and patient activation in adults with type 2 diabetes. This study is the first to explore such relationships between specific facets of diabetes distress with patient activation. Although weak negative linear trends were found, the present study provides evidence for a robust curvilinear, U-shaped relationship between diabetes distress and patient activation. Generally, these curvilinear relationships suggest that at both low and very high levels of diabetes distress, patients show high levels of patient activation. Individuals who show moderate levels of diabetes distress show lower levels of patient activation. This pattern of findings suggests that patients with moderate levels of diabetes distress may benefit from interventions to reduce distress and promote patient activation. These novel findings highlight the nonlinear nature of the relationship between diabetes distress and patient activation and suggest a need for diversified treatment approaches.
The current study provides evidence of a modest linear relationship between distress resulting from diabetes and patient activation. Our findings show that increases in total diabetes distress are associated with small but significant decreases in patient activation. This is consistent with findings from the only other study to date that demonstrated a similar weak, negative linear relationship between these variables among adults with type 2 diabetes.14 These findings suggest overall diabetes distress has a relatively weak linear relationship with patient activation, which is of questionable clinical value.
Diabetes distress is a multidimensional construct comprised of specific types of distress resulting from the experience of living with diabetes.2 The present study builds on previous findings by examining the relationship of specific types of diabetes distress with patient activation.14 Weak negative associations were found with patient activation for emotional burden and regimen-related distress but not physician or interpersonal distress. This combination of weak and inconsistent findings further underscores the potentially tenuous nature of the linear relationship of diabetes distress to patient activation.
Importantly, this study provides evidence for a strong curvilinear relationship between diabetes distress and patient activation that supersedes the previously discussed linear relationships. Our findings show a robust U-shaped relationship between overall diabetes distress and patient activation such that individuals with very low and very high levels of diabetes distress have the highest levels of patient activation and adults with moderate diabetes distress have the lowest levels of patient activation. A remarkably similar pattern of curvilinear relationships emerged for each type of diabetes distress, with the quadratic trends accounting for between approximately 8% and 17% of the variance in patient activation—even after controlling for sex, education level, and duration of diabetes. These results indicate that patient activation does not decrease monotonically as diabetes distress increases. Rather, these findings suggest that patient activation varies substantially across the full range of diabetes distress.
Fisher and colleagues21 identified clinically meaningful cut points of diabetes distress using the DDS-17 (low distress: <2.0; moderate distress: 2.0 to 2.9; high distress ≥ 3.0). Adults with low, moderate, and high levels of diabetes distress may represent distinct subgroups with different levels of self-management and patient activation. In the present study, there was variation in levels of patient activation across the low range of diabetes distress (DDS-17 < 2.0). Specifically, high patient activation was observed in individuals who were in the very low distress range, whereas those with scores at the higher end of the low distress range had poorer patient activation. It is possible that adults with very low diabetes distress may be successfully managing their diabetes, which may be due in part to their relatively high level of patient activation. Interestingly, individuals with moderate levels of diabetes distress (DDS-17: 2.0 to 2.9) had comparatively lower patient activation. This group may include adults with type 2 diabetes who experience disease-related distress that is significant yet not sufficient to motivate greater patient activation.
Finally, our results showed considerable variation in levels of patient activation across the range of high diabetes distress (DDS-17 > 3.0) identified by Fisher and colleagues.21 For example, individuals at the low end of the high range had the lowest levels of patient activation observed in this study. In contrast, those with the highest levels of diabetes distress had among the highest levels of patient activation. This finding suggests that “high” level of diabetes distress described by Fisher and colleagues21 (ie, DDS-17 > 3.0) may include distinct subgroups of patients (eg, high vs very high diabetes distress) with differing levels of patient activation. High levels of diabetes distress may undermine patients’ engagement in their health care and diminish patient activation. In contrast, patients with very high diabetes distress may have experienced greater burden of diabetes and/or a recent history of comparatively poor self-management and thus may have elevated patient activation as they attempt to gain greater control of their diabetes. There was no evidence for any cubic trends between any measures of diabetes distress and patient activation. These findings suggest that the curvilinear relationship between these variables is best characterized by a U-shape trend with a single point of inflexion near the midpoint of the 6-point DDS-17.
This study has several noteworthy limitations. First, this observational study used a cross-sectional design, which precludes causal interpretation. For example, diabetes distress could impact patient activation, or patient activation could influence individuals’ experience of diabetes distress. Regardless of the directional relationship, results from this study suggest that individuals with type 2 diabetes with moderate to high levels of diabetes distress may be at greater risk of having low patient activation. Second, the study sample was predominantly White, non-Hispanic/Latino, and educated (more than high school level education). Prior work has demonstrated higher diabetes distress in individuals who are non-White, Hispanic/Latino, and have fewer years of formal education.7,26-28 It is not known whether the present findings generalize to groups with different sociodemographic characteristics.
Our findings provide evidence for a robust, curvilinear relationship between diabetes distress and patient activation. These results better elucidate the varying relationship between diabetes distress and its dimensions with patient activation. Among adults with type 2 diabetes, patient activation is predictive of critically important patient outcomes, such as self-management behaviors, medication adherence, exercise, healthy diet, and glycemic control, and should be an important consideration in treatment planning.4,6,8,29,30 However, researchers and health care providers should not assume that higher levels of diabetes distress will be uniformly associated with poorer diabetes outcomes. Patients with moderate levels of diabetes distress may benefit from assessment of patient activation and if necessary, efforts to remediate low levels of patient activation. Future studies in this area should examine both linear and nonlinear relationships of diabetes distress and important clinical outcomes.
We thank the participants, graduate students, and undergraduate lab members that contributed to this study.
Both coauthors contributed to the conceptualization, data analysis, and writing of this manuscript. The corresponding author (Dr Aaron Lee) contributed to collecting the data.
Funding Funding for this study was provided to corresponding author (Dr Aaron Lee) by the University of Mississippi.
This study was approved by the University of Mississippi Institutional Review Board, and all participants provided written consent to participate.
Rhea S. Mundle https://orcid.org/0000-0001-7430-1998
Aaron A. Lee https://orcid.org/0000-0003-0493-8509
Data may be made available upon request to the authors.
Centers for Disease Control and Prevention. Type 2 diabetes. April 18, 2023. Accessed October 9, 2023. https://www.cdc.gov/diabetes/basics/type2.html
Polonsky WH, Fisher L, Earles J, et al. Assessing psychosocial distress in diabetes. Diabetes Care. 2005;28(3):626-631. doi:10.2337/diacare.28.3.626
Young CF, Mullin R, Moverley JA, Shubrook JH. Associations between diabetes-related distress and predicted cardiovascular complication risks in patients with type 2 diabetes. J Osteopath Med. 2022;122(6):319-326. doi:10.1515/jom-2021-0204
Gonzalez JS, Kane NS, Binko DH, Shapira A, Hoogendoorn CJ. Tangled up in blue: unraveling the links between emotional distress and treatment adherence in type 2 diabetes. Diabetes Care. 2016;39(12):2182-2189. doi:10.2337/dc16-1657
Jannoo Z, Wah YB, Lazim AM, Hassali MA. Examining diabetes distress, medication adherence, diabetes self-care activities, diabetes-specific quality of life and health-related quality of life among type 2 diabetes mellitus patients. J Clin Transl Endocrinol. 2017;9:48-54. doi:10.1016/j.jcte.2017.07.003
Wardian J, Sun F. Factors associated with diabetes-related distress: implications for diabetes self-management. Soc Work Health Care. 2014;53(4):364-381. doi:10.1080/00981389.2014.884038
Chen P, Callisaya M, Wills K, Greenaway T, Winzenberg T. Cognition, educational attainment and diabetes distress predict poor health literacy in diabetes: a cross-sectional analysis of the SHELLED study. PLOS ONE. 2022;17(4):e0267265. doi:10.1371/journal.pone.0267265
Aikens JE. Prospective associations between emotional distress and poor outcomes in type 2 diabetes. Diabetes Care. 2012;35(12):2472-2478. doi:10.2337/dc12-0181
Fisher L, Skaff MM, Mullan JT, Arean P, Glasgow R, Masharani U. A longitudinal study of affective and anxiety disorders, depressive affect and diabetes distress in adults with type 2 diabetes. Diabet Med J Br Diabet Assoc. 2008;25(9):1096-1101. doi:10.1111/j.1464-5491.2008.02533.x
Schmitt A, Reimer A, Kulzer B, Haak T, Gahr A, Hermanns N. Negative association between depression and diabetes control only when accompanied by diabetes-specific distress. J Behav Med. 2015;38(3):556-564. doi:10.1007/s10865-014-9604-3
Aljuaid MO, Almutairi AM, Assiri MA, Almalki DM, Alswat K. Diabetes-related distress assessment among type 2 diabetes patients. J Diabetes Res. 2018;2018:7328128. doi:10.1155/2018/7328128
Fisher L, Polonsky WH, Perez-Nieves M, Desai U, Strycker L, Hessler D. A new perspective on diabetes distress using the type 2 diabetes distress assessment system (T2-DDAS): prevalence and change over time. J Diabetes Complications. 2022;36(8):108256. doi:10.1016/j.jdiacomp.2022.108256
Hibbard JH, Gilburt H. Supporting People to Manage Their Health: An Introduction to Patient Activation. King’s Fund; 2014.
van Vugt HA, Boels AM, de Weerdt I, de Koning EJ, Rutten GE. Patient activation in individuals with type 2 diabetes mellitus: associated factors and the role of insulin. Patient Prefer Adherence. 2019;13:73-81. doi:10.2147/PPA.S188391
Parchman ML, Zeber JE, Palmer RF. Participatory decision making, patient activation, medication adherence, and intermediate clinical outcomes in type 2 diabetes: a STARNet Study. Ann Fam Med. 2010;8(5):410-417. doi:10.1370/afm.1161
Hibbard JH, Greene J, Shi Y, Mittler J, Scanlon D. Taking the long view: how well do patient activation scores predict outcomes four years later? Med Care Res Rev. 2015;72(3):324-337. doi:10.1177/1077558715573871
Sacks RM, Greene J, Hibbard J, Overton V, Parrotta CD. Does patient activation predict the course of type 2 diabetes? A longitudinal study. Patient Educ Couns. 2017;100(7):1268-1275. doi:10.1016/j.pec.2017.01.014
Ho TG, Hosseinzadeh H, Rahman B, Sheikh M. Health literacy and health-promoting behaviours among Australian-Singaporean communities living in Sydney metropolitan area. Proc Singap Healthc. 2018;27(2):125-131. doi:10.1177/2010105817741906
Hosseinzadeh H, Verma I, Gopaldasani V. Patient activation and type 2 diabetes mellitus self-management: a systematic review and meta-analysis. Aust J Prim Health. 2020;26(6):431. doi:10.1071/PY19204
Regeer H, Van Empelen P, Bilo HJG, De Koning EJP, Huisman SD. Change is possible: how increased patient activation is associated with favorable changes in well-being, self-management and health outcomes among people with type 2 diabetes mellitus: a prospective longitudinal study. Patient Educ Couns. 2022;105(4):821-827. doi:10.1016/j.pec.2021.07.014
Fisher L, Hessler DM, Polonsky WH, Mullan J. When is diabetes distress clinically meaningful? Diabetes Care. 2012;35(2):259-264. doi:10.2337/dc11-1572
Chandler J, Rosenzweig C, Moss AJ, Robinson J, Litman L. Online panels in social science research: expanding sampling methods beyond Mechanical Turk. Behav Res Methods. 2019;51(5):2022-2038. doi:10.3758/s13428-019-01273-7
Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005;40(6, Pt 1):1918-1930. doi:10.1111/j.1475-6773.2005.00438.x
Moljord IEO, Lara-Cabrera ML, Perestelo-Pérez L, Rivero-Santana A, Eriksen L, Linaker OM. Psychometric properties of the Patient Activation Measure-13 among out-patients waiting for mental health treatment: a validation study in Norway. Patient Educ Couns. 2015;98(11):1410-1417. doi:10.1016/j.pec.2015.06.009
Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the patient activation measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, Pt 1):1005-1026. doi:10.1111/j.1475-6773.2004.00269.x
German J, Kobe EA, Lewinski AA, et al. Factors associated with diabetes distress among patients with poorly controlled type 2 diabetes. J Endocr Soc. 2023;7(5):bvad031. doi:10.1210/jendso/bvad031
Spencer MS, Kieffer EC, Sinco BR, et al. Diabetes-specific emotional distress among African Americans and Hispanics with type 2 diabetes. J Health Care Poor Underserved. 2006;17(2):88-105. doi:10.1353/hpu.2006.0095
Walker RJ, Strom Williams J, Egede LE. Influence of race, ethnicity and social determinants of health on diabetes outcomes. Am J Med Sci. 2016;351(4):366-373. doi:10.1016/j.amjms.2016.01.008
Fisher L, Mullan JT, Arean P, Glasgow RE, Hessler D, Masharani U. Diabetes distress but not clinical depression or depressive symptoms is associated with glycemic control in both cross-sectional and longitudinal analyses. Diabetes Care. 2010;33(1):23-28. doi:10.2337/dc09-1238
Gao Y, Xiao J, Han Y, et al. Self-efficacy mediates the associations of diabetes distress and depressive symptoms with type 2 diabetes management and glycemic control. Gen Hosp Psychiatry. 2022;78:87-95. doi:10.1016/j.genhosppsych.2022.06.003
From Department of Psychology, University of Mississippi, Oxford, Mississippi (Ms Mundle, Dr Lee).
Corresponding Author: Aaron A. Lee, Department of Psychology, University of Mississippi, 310C Peabody Hall, University, MS 38677, USA. Email: aalee2@olemiss.edu
