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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: Public Health Nurs. 2019 Dec 13;37(2):178–187. doi: 10.1111/phn.12693

Impact of a Community-Based Approach to Patient Engagement in Rural, Low-Income Adults with Type 2 Diabetes

Lynn E Glenn 1, Michelle Nichols 2, Maithe Enriquez 3, Carolyn Jenkins 4
PMCID: PMC7067669  NIHMSID: NIHMS1061699  PMID: 31833102

Abstract

Objective:

This secondary analysis examined the relationships between Patient Activation Measure (PAM) scores, use of health services, and HgA1C.

Design:

A feasibility study was conducted for a community-based intervention for high-risk adults with uncontrolled diabetes. Data were collected at baseline and monthly, including PAM and modified Diabetes Self-Management Assessment Report Tool.

Intervention:

Participants (n=48) were randomized to a 3-month nurse (RN) telephone management or community health worker (CHW) in-home intervention, focusing on medication adherence, timely follow-up, diabetes self-management coaching, and linkage to resources.

Results:

Sample was mostly female (73%), African-American (90%), low-income (75%), high school education or less (80%) and mean age 59 years. A positive association between PAM score and self-reported diabetes care recommendations was found (r=.356, p=.014) and significant correlation between baseline PAM score and HgA1C levels (r= − .306, p=.029). A paired samples t-test showed statistically significant increases in PAM scores in the CHW intervention group [mean increase +8.5, CI (+2.49 –+14.65)]; baseline (M=60.31, SD=13.3) to end of study [(M=68.89,SD=16.39), t(22)=2.924, p=.008 (two-tailed)].

Conclusion:

A community-based approach to diabetes management demonstrated a positive effect on patient activation. Although disparities in healthcare access among rural, low-income populations exist, community-based interventions show potential for improving patient engagement in diabetes management and recommended health services.

Keywords: Patient engagement, Patient activation, Community Health Worker, Diabetes Mellitus-Type 2, Poverty, Rural population, Health Promotion, Self-Management, Patient Activation Measure

Background

The prevalence of diabetes is substantially higher in rural populations compared to urban populations (Towne et al., 2017). Diabetes is also more common among African-Americans compared to non-Hispanic whites (Centers for Disease Control & Prevention [CDC], 2019). Furthermore, diabetes-related mortality rates are much higher in rural, non-metropolitan areas compared to urban, metropolitan areas and persist among rural African-Americans compared to rural whites at estimated rates of 42.8 per 100,000 versus 33.2 per 100,000, respectively (Callaghan, Towne, Bolin, & Ferdinand, 2017, 2019). Glycemic control is typically worse in younger adults, non-Hispanic blacks, Hispanics, non-married individuals, and uninsured populations than their respective counterparts (Ali, McKeever Bullard, Imperatore, Barker, & Gregg, 2012). For example, approximately 17% non-Hispanic Blacks have higher rates of uncontrolled diabetes, or HgA1C levels greater than 9.0%, compared to 9% in Whites. Diabetes-related complications, such as stroke and kidney disease, disproportionately affect rural and minority populations as well (CDC, 2017; Garcia et al., 2017). Such disparities must be urgently addressed to improve health outcomes in these communities.

Health disparities among rural and minority populations have been well established. These disparities can be partially explained by a lack of access and participation in routine preventive care services (Spleen, Lengerich, Camacho, & Vanderpool, 2014). Rural and minority populations continually face challenges related to diabetes care. African-Americans have a 1.5 higher risk of forgoing medical care due to cost compared to whites, despite recent Medicaid expansion and health insurance reform efforts (Towne et al., 2017). Rural residents are also more likely than urban residents to delay seeking health care and to receive diabetes preventive health services [i.e. diagnostic tests (glucose, urinalysis, A1C, and blood pressure) or patient education (diet/nutrition, exercise, and stress management)], largely due to cost (Hale, Bennett, & Probst, 2010; Towne et al., 2017). Lack of engagement in diabetes preventive services may result in poor health outcomes (Hibbard & Greene, 2013).

Other barriers, such as transportation and competing family priorities, further constrain access to diabetes-related preventive health services. Limited access to specialists, such as eye care professionals, can reduce rural residents’ likelihood of obtaining diabetes-related preventive services (Chou et al., 2012, 2016; Lee et al., 2014). Younger adults, especially those between 18 and 39 years old, are lagging behind their older counterparts in diabetes preventive services, such as dilated eye exams, cholesterol checks, blood pressure screenings, and urine albumin testing (CDC, 2017; Villaroel, Vahratian, & Ward, 2015; Ziller, Lenardson, Paluso, & Janis, 2019). Higher uninsured rates, lower educational attainment, reduced access and lower perceived need for services among younger, rural and minority residents may partially explain lower receipt of preventive services (Ziller et al., 2019).

Conversely, participation in health services may lead to better diabetes health outcomes (Greene & Hibbard, 2012). Individuals who are actively involved in their health are more likely to have lower overall health care costs, obtain recommended diabetes preventive services, and exhibit better health outcomes (Aung, Coll, Williams, & Doi, 2016; Rask et al., 2009; Rogvi, Tapager, Almdal, Schiotz, & Willaing, 2012; Sacks, Greene, Hibbard, Overton, & Parrotta, 2017). Engagement, defined as the active partnership between patients and health care professionals, is necessary for improved health outcomes (Carman et al., 2013). However, scant research has validated the relationship between the level of engagement in diabetes care and health outcomes, especially among rural and minority populations (Schoenberg, Ciciurkaite, & Greenwood, 2017).

Community-based interventions have been known to improve diabetes outcomes, with most evidence for minority, under-resourced, and urban populations; however, increasing evidence is available regarding rural communities (Lepard et al., 2015; Palmas et al., 2015). A recent study of rural-dwelling, African-American women found no significant improvements in glycemic control or blood pressure in the community health worker (CHW) intervention group compared to the control group, but significant weight loss (−1.35 +/− 6.22 kg) was reported (Lutes, Cummings, Littlewood, Dinatale, & Hambridge, 2017). Greater reductions in HgA1C have resulted from exposure to more contact hours from CHW’s or peer advisors, otherwise known as the “dosage effect” (Lepard et al., 2015; Palmas, 2014; Samuel-Hodge et al., 2009; Tang et al., 2014).

The purpose of this secondary data analysis was to examine the association between the level of engagement and utilization of diabetes preventive health services in a rural, Southeastern U.S. community of predominantly African-American adults with type 2 diabetes. The analysis also illustrates the construct of engagement, which Hibbard, Stockard, Mahoney, and Tusler (2004) operationalized as patient activation. Findings from this study can help confirm the potential of community-based interventions to enhance patient engagement and, more importantly, to lessen health disparities among rural, minority adults with diabetes.

In the context of this study, the chosen community is classified as ‘rural’ based on the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties and the estimated county population being 14,275 (U.S. Census Bureau, 2018). According to NCHS, a rural population is generally defined as one that meets the criteria for a non-metropolitan statistical area (non-MSA), or core urban area less than 50,000 population.

Conceptual Framework

The concept of engagement has evolved with patient-centered health care delivery models, specifically the Chronic Care Model (CCM) (Wagner, Austin, & Von Korff, 1996). This model transformed the conventional health care system, allowing for a more collaborative, patient-focused approach (Emanuel & Emanuel, 1992; Wagner et al., 1996). The CCM emphasizes patients as active participants and informed consumers of health care while gaining skills to promote disease self-management (Wagner et al., 1996). The concept of engagement has also been framed as patient activation, namely “[one’s] knowledge, skill and confidence for managing his/her own health and health care” (Hibbard, Mahoney, Stockard, & Tusler, 2005, p. 1919). In this case, patients learn how to participate as effective members of the health care team.

Patient activation comprises a broad definition and continuum of health behaviors, as opposed to the similar construct of self-efficacy that relates to a specific skill or health behavior (Hibbard et al., 2004). Patient activation is a developmental, cognitive process that ultimately promotes adoption of health behaviors (see Figure 1). As patients gain knowledge about their health condition(s) and develop confidence to take corresponding action, patient activation develops. This process is comprised of four levels and starts with acknowledging the importance of patients’ roles in their health care. The next level encompasses various patient-based health behaviors, such as making lifestyle changes, talking to health care providers about their health care, and knowing when to seek help (Harvey, Fowles, Xi, & Terry, 2012). The third level of patient activation consists of gaining more independence in self-management of health care, following providers’ recommendations, and continuing to make necessary lifestyle changes to prevent potential chronic health complications. The final level involves maintaining lifestyle changes and necessary medical treatments, even during times of stress (Graffigna & Barello, 2018). Throughout this continuum, patients maintain active roles in their health care and have the opportunity to become increasingly more confident in self-managing health problems with minimal interference in everyday life activities (Hibbard et al., 2004; Rask et al., 2009; Rijken, Heijmans, Jansen, & Rademakers, 2014).

Figure 1:

Figure 1:

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Conceptual Model - Levels of Patient Activation (PAM). Copyright 2018 by Insignia. Reprinted with permission.

The Patient Activation Measure (PAM) has been applied to predict health outcomes in numerous populations (Hibbard & Greene, 2013). Higher patient activation has been shown to be positively related to a variety of diabetes outcomes (e.g., control of blood glucose, cholesterol, and blood pressure) (Rogvi et al., 2012). Moreover, higher patient activation levels are associated with a greater likelihood of reported medication adherence, regular physical activity, and diabetes self-management behaviors (Frosch, Rincon, Ochoa, & Mangion, 2010; Mosen et al., 2007; Parchman, Zeber, & Palmer, 2010; Wolever & Dreusicke, 2016). Thus, the results of this secondary data analysis will validate the relationship between level of engagement, as measured by Patient Activation Measure scores, and preventive health behaviors.

The results will also add to the gap in literature concerning the relationship between patient engagement and enhanced health outcomes among rural, minority populations. The potential to enhance patient engagement and improve diabetes outcomes has been demonstrated in several interventional studies with various populations. Examples include African-Americans and Hispanics as well as patients with higher baseline HgA1C levels, low levels of medication adherence, and poor diabetes self-management (Bolen et al., 2014; D’Eramo Melkus et al., 2010; Lorig, Ritter, Ory, & Whitelaw, 2013; Moskowitz, Thom, Hessler, Ghorob, & Bodenheimer, 2013; Philis-Tsimikas, Fortmann, Lleva-Ocana, Walker, & Gallo, 2011; Wolever et al., 2010).

Methods

Design and sample.

Data for this secondary analysis was obtained from a pilot study (Authors, 2016) which examined the feasibility of primary care medical homes using community health workers as care extenders among high-risk persons with type 2 diabetes. High-risk persons targeted were those with uncontrolled diabetes or HgA1C greater than 8%, uncontrolled hypertension, recent hospitalization or urgent care/emergency room visit for diabetes-related diagnosis, reported problems with obtaining medications, or reported need for diabetes management from the primary care provider. The pilot study recruited persons with diabetes from primary care offices (60%), urgent care clinics (16%), hospital inpatient admissions (14%), and emergency departments (10%) within the identified small, rural county. The county’s population characteristics where the study was conducted were comparable to the demographics of the study sample: African-American (60%), high school education or less (79%), and median 2017 income $32,330 (U.S. Census Bureau, 2018). The mean age of the sample was 59 ± 11.6 years and participants were predominantly female (73%) and African-American (90%). More than 75% (n = 33) were low-income (earning less than $25,000 annually) and had a high school education or less (n = 39). The sample included larger proportions of unemployed (34%) or retired (36%) adults compared with working adults (26%).

In the pilot study, participants (n = 58) were randomly assigned to one of three groups: a 3-month intervention group, receiving either nurse (RN) management via telephone (n = 27); a face-to-face management from a community health worker (CHW; n = 26); and a usual care/control group (n = 5). Interventions in the RN telephone management and CHW groups each focused on medication adherence, timely follow-up, diabetes self-management coaching, and referrals to community resources. Baseline and monthly data were collected using eight measures, including PAM scores. Patient activation levels assessed the readiness to change in increasing diabetes self-management behaviors. Several other health behaviors were assessed, such as patients’ eating patterns, tobacco and alcohol use, medication adherence, and diabetes self-efficacy.

The secondary data analysis focused on two variables of interest: PAM scores and the number of diabetes recommendations met. The purpose of this analysis was to examine the relationship between patients’ level of engagement, as quantified by PAM scores, and use of diabetes preventive health services in a rural community of predominantly African-American adults with diabetes. The proposed secondary data analysis was reviewed and approved by the Institutional Review Board of the University of Missouri‐Columbia.

Research Questions

  1. Is there a relationship between PAM score and number of diabetes recommendations met?

  2. Is there a relationship between PAM score and HgA1C level?

  3. Is there a significant change in PAM score following a 3-month RN telephone intervention or CHW intervention?

Measures

Patient Activation Measure.

PAM was developed as a measure of patient engagement (Hibbard et al., 2004). The instrument is well-validated and has demonstrated good reliability (Rasch: 0.81); possible scores range from 0 to 100, corresponding to patient activation levels 1, 2, 3, and 4 (Hibbard et al., 2005). The measure has demonstrated strong associations with constructs such as preventative, self-management, and consumeristic behaviors (Hibbard et al., 2005). The short-form PAM-10, containing 10 items, was used for analysis (Hibbard et al., 2005).

Diabetes Educators’ Self-Management Assessment Reporting Tool (D-SMART).

In the pilot study, the number of self-reported diabetes recommendations met (# DM REC met) were extracted from the Diabetes Educators’ Self-Management Assessment Reporting Tool (D-SMART) used by the RN and CHW interventionists. The American Diabetes Association of Diabetes Educators’ (AADE) identification of seven diabetes self-management behaviors, otherwise known as AADE-7, was based on preliminary D-SMART development (Peyrot et al., 2007). Evaluation of behavioral outcomes includes seven areas of diabetes self-management: being active, healthy eating, taking medication, monitoring, problem-solving, reducing risks, and healthy coping (Peyrot et al., 2007).

Thirteen possible diabetes recommendations were coded as “met” if patients reported the following health care behaviors: HgA1C tested within the past 6 months; a doctor visit, foot exam from a health professional, blood pressure check, cholesterol check, or urine check for protein within the past 12 months; dilated eye exam, dental visit, or flu shot within the past 2 years; and any history of seeing a dietician, attending a diabetes education class, or receiving a pneumonia or hepatitis B vaccine.

Analytic Strategy

PAM scores and D-SMART data from the pilot study (Authors, 2016) were exported into SPSS data files for secondary analysis. Self-reported adverse events were extracted from the D-SMART at baseline and at 3 months. Adverse events included the total number of emergency department visits for high or low blood sugar within the past 3 months, number of hospital admissions within the past 3 months, and number of days missed from school or work. The total number of self-reported adverse events were collapsed into a binary variable, coded as “0” for no adverse events and “1” for one or more adverse events.

Other analyzed covariates included HgA1C level (extracted from each patient’s electronic medical record) and demographic information collected at baseline, including gender, marital status, education, employment status, years of diagnosed diabetes, health insurance status, and literacy level.

The data analysis was based on 48 participants who completed the pilot study, drawn from the RN telephone intervention group (n = 20) and CHW intervention group (n = 24). The usual-care or control group (n = 5) was excluded from analysis due to an insufficient sample size. PAM scores and # DM REC met were analyzed at baseline and 3 months. Data collection for PAM occurred at baseline and at 1-, 2-, and 3-month intervals; however, only baseline and 3-month scores were incorporated for analysis. PAM scores were analyzed for descriptive statistics, independent samples t-test (RN vs. CHW), and a paired samples t-test (mean change baseline to 3 months). SPSS v.24 software was used for analysis.

Results

The independent samples t-test revealed no significant differences in PAM score means between the intervention groups. Comparisons of median PAM scores using the non-parametric Mann-Whitney test of independent samples also demonstrated non-significant differences between the RN telephone intervention and CHW intervention groups (baseline median PAM score: 56.0 [RN], 57.65 [CHW]; 3-month median PAM score: 59.3 [RN], 62.6 [CHW]). At baseline, mean PAM scores were 61.98 (SD = 15.55) for the RN telephone intervention group and 60.48 (SD = 12.88) for the CHW intervention group. At 3 months, the mean PAM scores were 63.85 (SD = 17.85) for the RN telephone intervention group and 68.82 (SD = 16.08) for the CHW intervention group (see Table 1).

Table 1.

Statistical Results for CHW and RN Telephone Management Intervention Groups

CHW Intervention RN Telephone Management
Baseline PAM score (mean) 60.48 (SD = 12.9) 61.98 (SD = 15.5)
3-month PAM score (mean) 68.82 (SD = 16.1) 63.85 (SD = 17.8)
Mean change + 8.57 ** + 1.32 (ns)
Baseline PAM score (median) 57.65 56.0
3-month PAM score (median) 62.6 59.3
Mann-Whitney test ns ns
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(** = p < .01; ns = not significant)

A paired samples t-test was conducted to evaluate the impact of the intervention on PAM scores. For the RN telephone management group, the mean increase in PAM scores from baseline to month 3 was +1.32, but the difference was not statistically significant (p = .73). A statistically significant increase in PAM scores was observed from baseline to month 3 in the CHW intervention group (M = 60.31, SD = 13.3 [baseline]; M = 68.89, SD = 16.39 [month 3]), t(22) = −2.924, p = .008. The mean increase in PAM score was +8.57 at a 95% confidence interval [2.49, 14.65]. The CHW intervention exhibited an estimated medium effect size (d = 0.58).

Pearson correlations were performed with PAM scores, # DM REC met, and HgA1C levels at baseline and 3 months. At baseline, the mean # DM REC met was 8.9 (SD = 1.89); at 3 months, the mean rose to 9.5 (SD = 1.73) of 13 possible recommendations. A positive association emerged between PAM scores and self-reported #DM REC met (r = .356, p = .014) at the 3-month point but was not significant at baseline. A significant correlation was observed between PAM scores and HgA1C levels (r = −.306, p = .029) at baseline only. The mean HgA1C at baseline was 9.6 (SD = 2.20) compared to 8.59 (SD = 1.71) at 3 months.

Pearson correlations with PAM scores were tested using the following covariates: randomization group, gender, employment status, years of diagnosed diabetes, health insurance status, and literacy level. No significant correlations were found among PAM scores and covariates. Next, a simple linear regression was performed for PAM scores and # DM REC met; PAM scores increased by 3.6 points on average for every additional diabetes recommendation met. Pearson correlations were also determined between PAM scores and the number of adverse events. No significant associations emerged between PAM scores and total adverse events or between PAM scores and individual adverse events.

Poisson regression models were assessed using baseline and 3-month PAM scores as the predictor variable and # DM REC met as the dependent count variable. The model did not result in a significant difference in log expected counts, nor did PAM scores predict # DM REC met.

Discussion

This secondary data analysis focused on a group of rural-dwelling adults living with type 2 diabetes. Results revealed a positive association between patient activation and reported use of diabetes preventive health services. Consistent with prior research, this study found PAM scores to be strong predictors of preventive health behaviors, such as following American Diabetes Association (ADA) recommendations for diabetes preventive services (Frosch et al., 2010; Hibbard & Greene, 2013). On average, PAM scores increased by 3.6 points for every additional diabetes recommendation met. These findings indicated that higher patient activation levels were associated with engagement in preventive health behaviors and medical care.

The preliminary findings of the pilot study, especially the face-to-face CHW intervention, demonstrated potential for a positive impact on patient activation, which could also facilitate patient engagement in recommended diabetes preventive health services. Diabetes self-management can be onerous; the ADA recommends more than 13 preventive activities (American Diabetes Association, 2018). Individuals with diabetes are initially responsible for seeking relevant preventive services; however, collaboration between the health care system and individuals with type 2 diabetes is crucial to promoting engagement in such services (Smith, Berman, Hiratsuka, & Frazier, 2015). Health care providers must consistently monitor patients for diabetes-related complications in addition to patients actively seeking preventive services. Thus, the burden of responsibility in adhering to ADA recommendations falls on both parties.

In the general population, the rate of participation in diabetes preventive health services is higher among women, adults older than 65, and those with higher education levels (CDC, 2018; Hale et al., 2010; Villaroel et al., 2015). Our study sample, which was predominantly older, female, and African-American, was consistent with similar populations in being relatively adherent to recommended diabetes preventive services. On average, our sample of rural-dwelling adults self-reported meeting approximately 9 of 13 diabetes recommendations (i.e., complying with 70% of recommendations). Although the sample had low educational attainment, participants’ level of engagement in preventive services was fairly adequate.

Participants tended to have lower educational status, and approximately 50% reported having attended at least one diabetes class. According to the CDC (2018), attendance at diabetes education remains relatively low (54% on average) for persons with diagnosed diabetes. Rural populations are also less likely to participate in diabetes education, partly due to lack of access to diabetes self-management education (DSME) providers or programs. More than 60% of rural counties in the U.S. lack a DSME program (Rutledge, Masalovich, Blacher & Saunders, 2017). Several community-based DSME programs have been developed and show promise for improving diabetes self-management behavioral outcomes in various populations, including rural communities and minority groups (Lorig et al., 2010; Philis-Tsimikas et al., 2012; Samuel-Hodge et al., 2009; Towne, Smith, Ahn, & Ory, 2014). DSME programs have also demonstrated positive effects on patient activation (Flode, Iversen, Aarflot, & Haltbakk, 2017; Frosch et al., 2010; Ledford, Ledford, & Childress, 2013; Lorig, Ritter, Villa, & Armas, 2009, 2010). However, little is known about how DSME can facilitate patient activation and engagement in diabetes preventive health services. Some studies using CHWs and peer coaches to deliver DSME interventions have found improvements in patient activation levels but did not identify clinically significant improvements in HgA1C levels (Lawson et al., 2013; Lorig et al., 2009; Safford et al., 2015). Although DSME can promote positive health outcomes, education alone may not be sufficient for sustained behavior change (Lepard, Joseph, Agne, & Cherrington, 2015; Norris, Lau, Smith, Schmid, & Engelgau, 2002).

The results of this secondary data analysis support prior evidence that the level of patient activation is associated with use of diabetes preventive health services as well as glycemic control. A retrospective study demonstrated that a 1-point increase in PAM score was associated with a 1.8% increased likelihood of reducing HgA1C less than 8% (Remmers et al., 2009). The high rate of poorly controlled diabetes in the present study’s predominantly low socioeconomic, African-American sample of rural-dwelling adults reflects disparities in U.S adults with diagnosed diabetes. The baseline average HgA1C in this sample was 9.6%, exceeding the ADA recommendation of 7% or less (ADA, 2018). Moreover, the study population had other risk factors for uncontrolled diabetes, including a high poverty rate and median income of $32,300. Living in poverty or in disadvantaged neighborhoods is another risk factor for uncontrolled diabetes (Tabaei et al., 2017).

Although improvements in HgA1C were not investigated in this analysis, a positive relationship between higher patient activation levels and glycemic control was found. Based on previous research, higher baseline HgA1C levels have been associated with larger improvements in glycemic control in patient activation interventions for adults with type 2 diabetes (Bolen et al., 2014). Consequently, individuals with poorly controlled diabetes or with lower patient activation might benefit from CHW or RN telephone-assisted diabetes self-management interventions. The impact of diabetes interventions on long-term diabetes outcomes remains unknown, but improvements in glycemic control could help to prevent diabetes-related complications; a reduction of 1 absolute percentage point in HgA1C among adults with type 2 diabetes has been associated with a 21% reduction in mortality (Stratton et al., 2000).

The CHW intervention group in this study displayed significant improvements in diabetes outcomes (e.g., patient activation) compared to the RN telephone management group. A face-to-face intervention with CHW’s could offer advantages for rural populations compared to telephone support modalities. In several studies, diabetes self-management interventions used a combination of in-person and telephone visits in accordance with patient preferences (Allen et al., 2011; Kangovi et al., 2017; Palmas et al., 2015; Tang et al., 2014). The convenience of either in-home or telephone visits also improved retention compared to travel required for DSME interventions (Lepard et al., 2015). Overall, regardless of the intervention modality, positive clinical outcomes have followed from CHW- and RN-assisted interventions (Rosal et al., 2014; Rothschild et al., 2014; Safford et al., 2015; Wolever et al., 2010). The effect of CHW interventions has demonstrated a more significant reduction of HgA1C levels in older adults, namely over 55 years, than younger counterparts (Campos et al., 2018). However, there remains insufficient evidence to determine whether in-person visits are superior to telephone visits (Ciemins, Coon, Peck, Holloway, & Min, 2011; Lepard et al., 2015; Rosal et al., 2014). Overall, adding collaborative goal setting and motivational support to CHW or peer support appears to offer an advantage over DSME alone (Lepard et al., 2015).

The relationship between PAM and health utilization, namely adverse events, remains unclear. Additionally, the effects of diabetes self-management interventions on lowering hospital admissions and emergency department visits have been inconsistent (Begum, Ozolins, & Dower, 2011; Lorig, 2012; Sacks et al., 2017). A recent randomized controlled trial using a CHW intervention demonstrated significant decreases in hospital admissions but non-significant improvements in PAM scores (Kangovi et al., 2017).

Limitations of our study include higher mean baseline PAM scores compared to similar intervention studies in rural areas (Safford et al., 2015; Schoenberg et al., 2017). Furthermore, more than 75% of the sample was at PAM Level 3 or higher at baseline. The sensitivity of PAM is lower for higher activation levels (Level 4), and ceiling effects have been identified (Hung et al., 2013). Overall, participants with higher baseline HgA1C levels experience greater improvements in clinical outcomes (Bolen et al., 2014). More research is therefore needed to clarify whether participants with lower baseline PAM scores would experience greater improvements in clinical outcomes compared to those with higher baseline scores.

To date, no systematic review or meta-analysis has evaluated the effects of diabetes-focused interventions on patient activation. Bolen et al. (2014) synthesized 138 randomized controlled trials of patient activation interventions for adults with type 2 diabetes; however, PAM levels were not assessed as a diabetes outcome. Patient activation can be a useful measure of diabetes self-management behaviors but has not been widely studied as a primary diabetes outcome (Brewster, Tarrant, & Armstrong, 2015). A comparative effectiveness trial using PAM as a primary outcome was recently designed to evaluate the impacts of diabetes self-management and social support (Page-Reeves et al., 2017). Patient activation may function as a mediator; however, the mechanism of the relationship between patient activation and diabetes outcomes requires further substantiation (Parchman et al., 2010; Williams et al., 2005). More randomized controlled trials, especially among under-resourced, rural, and minority populations, are needed to fully evaluate the usefulness of PAM in health research.

Interventions focused on facilitating engagement in diabetes preventive health services could exert strong influences on diabetes-related long-term morbidity and mortality. The past decade has witnessed an increase in the use of diabetes preventive services along with a reduction in the rate of diabetes-related complications (CDC, 2018; Gregg et al., 2014). A recent 3-year longitudinal study showed that higher patient activation levels resulted in better diabetes outcomes and lower odds of developing diabetes in persons with pre-diabetes (Sacks et al., 2017). More longitudinal studies are needed to evaluate the effect of patient engagement in preventive health behaviors on long-term diabetes outcomes, but current evidence appears promising (Hibbard, Greene, Shi, Mittler, & Scanlon, 2015). A recent 3-year longitudinal study showed that higher patient activation levels resulted in better diabetes outcomes and lower odds of developing diabetes in persons with pre-diabetes (Sacks et al., 2017).

Conclusion

Results of secondary data analysis highlight the need to promote engagement in diabetes preventive services and minimize the risk of long-term complications associated with diabetes. More research is needed to determine whether facilitating engagement in diabetes preventive services could lead to a decrease in health disparities among rural, minority populations. Furthermore, lower rates of engagement in diabetes preventive services found in younger adult populations, specifically those 18 to 39 years, warrant attention. Subsequent studies are needed to determine whether community-based interventions could benefit younger adult populations with diabetes, particularly in enhancing their engagement in relevant preventive services.

Funding:

The primary feasibility study was supported by the NIH National Center for Advancing Translational Sciences (NCATS) through Grant Number UL1 TR001450

Contributor Information

Lynn E. Glenn, University of Missouri Sinclair School of Nursing, Columbia, MO.

Michelle Nichols, Medical University of South Carolina, Charleston, SC.

Maithe Enriquez, University of Missouri Sinclair School of Nursing, Columbia, MO.

Carolyn Jenkins, Medical University of South Carolina, Charleston, SC.

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