Introduction

The number of American Indians and Alaska Natives (AI/ANs) who have diabetes is growing rapidly, especially among young people. At more than 16%, AI/ANs have the highest age-adjusted prevalence of diabetes among all U.S. racial and ethnic groups [1]. One such population is the Zuni Pueblo home to a small, geographically isolated tribe located in a rural portion of New Mexico, USA. It is home to ~11,000 Zuni Indians and over 90% of all Zunis live in the Pueblo. This socioeconomically disadvantaged population faces a major public health challenge from growing health disparities. Changing lifestyles have led to decreased physical activity and increased caloric intake with high consumption of fast food, soda pop and alcohol. Therefore, the Zuni are experiencing interrelated epidemics of obesity, diabetes, hypertension, kidney disease and intermediate phenotypes.

In the late 1980’s, the prevalence of diabetes in persons aged 35 years and older among Zuni Indians was 28 percent [2], and 32% of individuals aged 20 years and older who were receiving care at the Zuni Indian Health Service (IHS) clinic had diabetes. Diabetes control is currently suboptimal in underserved populations despite a plethora of new therapies. Patient empowerment is a key determinant of diabetes control, but such empowerment can be difficult to achieve due to resource limitation and cultural, language and health literacy barriers. This bourgeoning diabetes crisis requires an aggressive multifactorial approach to treatment and prevention. Patient self-care is a necessary component of glycemic control. Patients need to be involved in all aspects of their care, including decision-making around medical management, healthy eating, weight loss, physical activity, and glucose self-monitoring.

The Chronic Care Model re-orients healthcare practice to promote a systematic, planned approach by emphasizing productive interactions between informed, activated patients, their families, and proactive healthcare teams [3, 4]. A central tenet of this concept is that healthcare workers cannot achieve optimum outcomes when working alone. Rather, desired outcomes are achieved when communities and healthcare organizations work together toward shared goals. The Patient Activation Measure (PAM) is a validated tool that assesses a patient’s ability to effectively participate in their care. It specifically evaluate an individual’s knowledge, skill, and confidence for managing their health and healthcare. Individuals that measure high on this instrument typically understand the importance of taking a proactive role in managing their health and have the skills and confidence to do so. The PAM survey measures patients on a 0–100 scale and can differentiate patients into one of four “activation levels” along an empirically derived continuum. Each activation level reveals insight into an array of health-related characteristics, including attitudes, motivators, behaviors, and outcomes.

Previously, as part of the Zuni Health Initiative (ZHI), we surveyed participants regarding barriers to healthcare [5], with particular attention to diabetes care. In ZHI we also collected and stored clinical phenotype information and anthropological measurements from all participants. Participants identified the following barriers: access to care, language barriers, limited patient education, and anxiety around diagnosis, fear of chronic disease, reluctance to participate in self-care, resistance to dietary change, and reluctance to engage in regular exercise. We have previously documented suboptimal glycemic control with a high burden of kidney disease among the Zuni [6, 7]. The burden presented by these barriers ultimately translates into a lack of patient activation and engagement in their healthcare, effectively hindering adoption of healthy behaviors. Focus groups subsequently identified common solutions to overcome some of these barriers, including home-based care, point of care testing, individualized exercise and nutrition prescriptions, and care providers with knowledge of the Zuni language, community and culture.

The information gathered during these focus groups [8] was used to design and implement a Zuni culture specific educational intervention in diabetes. We devised an innovative educational intervention based on the coordination of four key elements: (a) delivering healthcare that incorporated collaborative communication within the healthcare team and emphasized greater autonomy in care, adherence to the medical regimen, and patient-centered goal setting, all while retaining the ability to address the needs of patients, family members, the healthcare team, and/or the healthcare system; (b) providing innovative educational and organizational approaches, as well as behavior change strategies, that enhanced adherence; (c) addressing health beliefs that reduced adherence by over- or under-predicting maladaptive thoughts (e.g., catastrophizing, minimizing, cognitive dissonance, invincibility, or fatalism) or that interfered with weight control; and (d) using technology to address barriers to achieving desired health outcomes. We report here the results from an intervention conducted by Community Health Representative (CHRs) using group classes and home-based teaching, point of care testing, and individualized exercise and nutritional programs.

Methods

The protocol for this trial and supporting TREND checklist are available as supporting information; see S1 TREND Checklist and S1 Protocol. This study was approved by the University of New Mexico Health Sciences Center Human Research Review Committee and the Indian Health Service Institutional Review Board, and all participants rendered written informed consent. Study subjects received $25 each time they participated. This observational clinical study was conducted from June 2012 to Dec 2012 and was retrospectively registered at ClinicalTrials.gov (NCT02339311). The study was not initially registered due to the fact that it is an observational design with education as the intervention that did not clearly meet the criteria for inclusion on ClinicalTrials.gov, and registration was not required by our Institutional Review Board. We confirm that all ongoing and related trials for this observational educational intervention are now registered.

Study Participants

Inclusion criteria incorporated adults aged 18 years or older with a diagnosis of diabetes mellitus. Potential study participants were recruited by Zuni CHRs using the ZHI project database, which contains diabetes status as established by the American Diabetes Association (ADA) within the past 12 months(i.e. fasting plasma glucose >125 mg/dl, A1C > 6.5%, and/or random blood glucose >200 mg/dl with symptoms [9]. CHRs then contacted eligible participants based on A1C > 6.5% randomly by phone or home visit. Additional subjects were recruited using fliers around town and newspaper announcements. CHRs did home visits to complete baseline study measurements, including physical exams, point of care testing and survey completion. All 60 participants with diabetes recruited at baseline remained throughout the six months of pragmatic trial of educational intervention (Fig 1) and there was no dropout from meetings or classes, and there were no missing observations due to the fact that the intervention was home based outreach as an important component of our study design.

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Fig 1. Pragmatic trial of educational intervention—CONSORT Participant Flow Chart – Study Design.

https://doi.org/10.1371/journal.pone.0125820.g001

Results

Sixty diabetic participants completed the study. Study demographics are shown in Table 1. The average age of participants at baseline was 49.4±12.0 years and it did not change significantly at six month follow-up (50.1±12.0, P>0.07). The majority of participants were women (68% females, 32% males). As shown in Table 1, pre- and post-intervention values for BMI, A1C, TC, and TG, all showed statistically significant improvements (P<0.05).

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Table 1. Demographic and clinical characteristics of fasting study participants at baseline and at 6 month of follow-up.

https://doi.org/10.1371/journal.pone.0125820.t001

The agreement plots shown in Fig 2 depict the reductions in BMI, A1C, TC, and TG at base-line (time1) vs. 6 months of educational intervention (time 2).

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Fig 2. Agreement plots (n = 60).

For each clinical parameter, the patient’s value at baseline screening (time 1) is on the horizontal axis and the 6-month follow-up value (time 2) is on the vertical axis. The plotted line is the line of perfect agreement between times 1 and 2. Points below the line represent a decrease from time 1 to time 2, while points above the line represent an increase from time 1 to time 2.

https://doi.org/10.1371/journal.pone.0125820.g002

We observed the overall PAM distribution to be significantly changed after six months of educational intervention. At baseline, we found 9 patients (15%) at level 1; 11 patients (18%) at level 2; 19 patients (32%) at level 3, and 21 patients (35%) at level 4 of activation. The distribution of PAM after the six month intervention was 3 patients (5%) in level 1; 0 patients (0%) in level 2; 7 patients (12%) in level 3 and 50 patients (83%) in level 4. Thirty-five participants increased their PAM level by one or more levels, 24 participants remained at the same PAM level, and one patient’s PAM level declined. These findings were statistically significant (P<0.0001).

Correlation between PAM levels at baseline screening and changes in clinical parameters from baseline to 6-months follow-up, and correlations between changes in PAM level and changes in clinical parameters from baseline to 6-months follow-up were calculated. Our six month educational intervention demonstrated significant improvement in activation levels (Fig 2), but changes in clinical parameters were not correlated with either PAM level at baseline nor change in PAM level from baseline to 6 months reflecting limitation in our analysis which was not sufficient for a causal inference. PAM level was not a significant predictor of any follow-up clinical value, after adjusting for baseline clinical value (Fig 3), reflecting our distribution of 67% participants in levels 3 and level 4 of activation at the baseline.

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Fig 3. Regression analysis showing changes in clinical parameters vs. changes in PAM levels (n = 60).

For each clinical parameter the change in PAM level from baseline to 6 months is depicted on the horizontal axis and change in the clinical parameter from baseline to 6 months is depicted on the vertical axis. Reported values are Spearman’s rho (nonparametric correlation coefficient), and the corresponding p-value (calculated on original, not jittered values). A more sophisticated regression analysis adjusting for age, gender and baseline values had similar p-values and is not shown.

https://doi.org/10.1371/journal.pone.0125820.g003

Discussion

Multiple studies show that PAM scores are predictive of health behaviors, including prevention behaviors (e.g. obtaining screenings), healthy behaviors (e.g. healthy diet and regular exercise), self-management behaviors (e.g. monitoring and medication management), and health information seeking [22–25, 30]. Individuals with higher levels of activation have better health outcomes and lower rates of healthcare utilization, such as emergency department use and hospitalization [31, 32]. There is further evidence that it is possible to increase activation levels with education and appropriate intervention [33, 34].

Studies have demonstrated that patients at lower activation levels do not take control of their own health and often lack basic knowledge about their condition, whereas patients with high activation scores tend to possess the knowledge, skills, and confidence to self-manage their disease under adverse circumstances. Patients with higher activation scores are also more likely to exercise on a regular basis, eat a low fat diet with more fruits and vegetables, and abstain from smoking, resulting in better self-reported health and fewer ER visits [35]. A retrospective analysis showed that patients with higher PAM scores had lower A1C levels and lower rates of all-cause hospitalization [36].

We have observed that a home-based, CHR-implemented educational intervention using POC testing and individualized therapeutic goals significantly increases PAM and occurs concomitantly with improvements in metabolic parameters relevant to diabetes, including BMI, A1C, and lipid concentrations. While it is possible that these metabolic improvements are attributable to factors other than the intervention, it seems likely that the home based intervention we employed was responsible for these improvements and that the inability to establish a correlation between improvement in PAM and improvement in metabolic parameters is a by-product of our inclusion of several participants who had high levels of activation (and thereby, self-efficacy) at baseline, as discussed below. The PAM instrument has been validated in several populations, including older adults [30, 37–38]. Various interventions have been shown to increase patient activation and possibly improve outcomes, but previous studies have not shown this increase to translate into improved diabetes control. Mayberry et al. demonstrated that high levels of patient activation correlated with self-management behaviors but not with glycemic control and concluded that in order for PAM to affect glycemic control, the highest level of activation may need to be achieved [35].

In our study, PAM levels increased significantly. After six months, 83% of patients were at stage 4 of activation, compared with 35% at baseline. There are likely to be several reasons PAM scores increased in our study. Using CHRs who were members of the participant community likely increased their knowledge of patient culture, language, resources, and barriers, thereby allowing provision of education in a culturally sensitive manner and at an appropriate level of health literacy. Such CHRs, when appropriately trained, can work individually with patients to tailor care programs to available community resources and overcome barriers through local knowledge. Numerous studies have demonstrated beneficial outcomes from having CHRs as part of the diabetes care team [39–41] and patients report feeling more empowered in their care as a result of CHR interventions.

In our previous study detailing diabetes focus groups, patients reported that access to care, including travel to clinic visits, prolonged wait times, and the lack of a relationship with care providers were major impediments to care. Home-based visits are thus an attractive alternative, offering patient convenience, avoidance of waiting rooms and the sterile atmosphere of clinics, and allowing patients to be in a comfortable and familiar environment during the intervention sessions.

Point of Care testing also offers many benefits. Receiving laboratory results at the time of the visit allows the medical team and the patient to make immediate decisions based upon those test results, including recommended changes to self-care behaviors, medication adjustments, and/or referral to specialist. POC testing has been shown to be accurate as long as the equipment is certified and routinely calibrated, and it is cost effective [42]. In gestational diabetes patients receiving a simple exercise program without behavioral recommendations, POC testing was associated with a reduction in maternal postprandial glucose, A1C, CRP, triglycerides and maternal or neonatal complications, but not in fasting glucose values [43].

It is beyond the scope of the study to determine which individual aspect of the intervention (CHR home visits or POC testing) may have contributed to the beneficial changes observed in PAM. Regardless, we have demonstrated that an innovative, six month, patient-centered intervention can empower patients to become active participants in their care, and such activation can result in improved clinical outcomes. Although improvement was observed in many of the biomarkers of diabetes and metabolic control that we collected among the subjects who participated in our educational intervention, we were unable to relate this improvement to an improvement in the PAM score. This likely reflects the relatively large number of participants who had high PAM scores at baseline, suggesting a high degree of self-efficacy at the outset. Additionally, by forcing the raw PAM scores into four categories, we lost important information, and potentially power, in our regression analysis. Future studies will need to be powered to include only those who can realistically improve their PAM score at baseline. One of our challenges and limitations to this study is the lack of a no-intervention control group. We used a longitudinal study design in T2DM patients with baseline data collection before and after the six month of intervention. This design is appropriate for the demonstration of feasibility and for initial proof of concept, and it is cost effective, provides reliable parameter estimates with small samples, and is pragmatic because it allows for “real-world” variability in variables. The design has informed us that the educational intervention we employed is associated with statistically significant changes in clinically relevant outcomes but these are not necessarily causally related. Aside from these advantages, we chose this particular design because the Zuni culture is somewhat resistant to testing interventions with no-intervention control groups. Another limitation of the study is that it was initially powered to observe a change in A1C rather than PAM scores.

Based on our progress to date and knowledge gained, diabetes prevention in real-world settings should continue to be a major focus among health-disparate Native American groups [44]. In conclusion, a home-based, CHR-driven educational intervention led to significant improvements in patient activation and biochemical parameters of diabetes control, as well as risk factors associated with diabetes, in this cohort of Zuni Natives with diabetes.

Supporting Information

Acknowledgments

The authors acknowledge the mentoring support from Dr Sanjeev Arora, MD in training the staff to conduct diabetes education. We also recognize the approval and support of the Zuni Governor and the Tribal Council. We sincerely thank the Zuni people for welcoming us in their homes. Donica Ghahate, Jeanette Bobelu, and Phillip Sandy are Zuni community members and stake holders.