Diabetes is the seventh leading cause of death in the United States, with the number of adults diagnosed more than doubled in the last 20 years.1 Currently, there are more than 122 million Americans living with diabetes, which is about 10% of the US population.1 Diabetes also carries a substantial cost burden. According to the 2018 American Diabetes Association (ADA) report, the estimated cost of managing diabetes is about $327 billion annually, which is 1 in 4 of all health care dollars.2
A review of the literature showed that suboptimal engagement in diabetes selfmanagement interventions was attributed to decreased self-efficacy, leading to increased diabetes-related complications.3 Bandura’s selfefficacy theory assumes that behavior changes are highly associated with individual levels of self-efficacy.4 Although treatments for diabetes are widely studied and efficacious, these treatment regimens can be demanding for patients with diabetes, impacting patients’ self-efficacy to participate in the treatment plan due to the distress associated with managing diabetes.5 When diabetes distress screening was included in the clinical setting, evidence shows that it helped lessen diabetes-related morbidity and mortality; however, diabetes distress screening is not commonly used in inpatient clinical practice.6
The concept of diabetes distress was first introduced in the literature in 1995 to describe the emotional experiences associated with the daily challenges of living with diabetes, which act as barriers to optimal self-care and diabetes self-management.7 Diabetes distress affects glycemic management, which further leads to increased distress and suboptimal health outcomes. Recognizing distress symptoms can help clinicians to collaborate with patients to implement interventions that improve patients’ glycemic targets and overall quality of life.8 In hospitals, it is estimated that about 50% of admitted patients have diabetes as a primary or secondary diagnosis.9 Diabetes distress is, however, underestimated in about 25% of those with diabetes.10 Evidence shows that prevalence of diabetes distress increases with levels of care, with hospitalized patients at 8.9% prevalence compared to 1.2% in primary care settings.10 Routine screening for diabetes distress is therefore important, especially among hospitalized patients.
Current practice at a tertiary, adult, Level I, acute care hospital in the Midwestern US region does not include screening patients with diabetes for distress. A needs assessment identified optimization of patient glycemic targets as a priority. Lack of diabetes distress screening was identified as a practice gap at this hospital and a contributing factor to suboptimal A1C levels due to lack of self-efficacious diabetes selfmanagement behaviors.
Of patients who presented to the hospital in December 2021 with unmanaged diabetes, 43% indicated by A1C levels of 9% or greater, prompting consults with diabetes care and education specialists (DCESs) for diabetes selfmanagement education and support (DSMES). Each patient with unmanaged diabetes received a comprehensive DSMES plan of care from a DCES at the hospital. These current practices do not align with ADA standards that recommend assessing each patient for psychosocial problems such as distress and individualizing their DSMES plan of care to include emotional support interventions with the goal to achieve individualized glycemic targets for each patient with diabetes.2 Bridging this practice gap would positively impact the care provided to patients with diabetes at this hospital.
A pilot project was initiated to evaluate the impact of diabetes distress screening and individualizing DSMES to optimize glycemic targets for patients with diabetes by engaging DCESs in diabetes distress screening and evaluating patient diabetes distress levels.
The revised IOWA EBP model was used, with permission, as the guiding framework for the project, which uses a stepwise, evidence-based practice (EBP) approach to implement research findings in clinical practice.11 The setting for this pilot project was an urban, tertiary, acute care, Level 1 trauma, adult hospital in the midwestern US.
The hospital tracks quality metrics, such as glycemic index (using A1C levels) and diabetes readmissions, using a diabetes scorecard. A1C greater than 9% and diabetes readmissions are priority metrices tracked annually to ensure the hospital meets Centers for Medicare and Medicaid Services benchmarks. In fiscal year 2021, the hospital did not meet the diabetes-related hospital readmissions target of 9.4% and identified lack of diabetes distress screening as a plausible gap in clinical practice. If this practice gap is bridged, it could help achieve the targeted diabetes-related hospital readmissions benchmark of 9.4%. Patients with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus admitted with an A1C level of 9% or greater or with diabetes-related complications were identified as the target group.
An implementation team with expertise in diabetes management was formed to lead this pilot project. The project team was led by an EBP specialist who guided the team through the EBP process. The director for the diabetes department was a key stakeholder in decision-making processes and ensured the pilot project aligned with the organization’s strategic priorities. DCESs served as technical experts and project change champions whose engagement was instrumental to successful implementation of this practice change at this hospital.
The EBP specialist, with assistance from the hospital’s medical librarian, conducted a review of the literature where 4 databases were searched (PubMed, CINAHL, PsycINFO, and Scopus), yielding 50 articles that were screened for relevance to the pilot project’s topic. A total of 6 articles (5 Level II randomized controlled trials [RCTs] and 1 Level VI mixed-methods exploratory study) were selected for critical appraisal and synthesis of the evidence. The 5 selected RCTs were also assessed for quality and risk of bias using Cochrane Collaboration’s Tool.12
Of the RCTs, 2 were of medium quality and evaluated the effects of small change lifestyle interventions (EMPOWER, using a peer advisor, and COMRADE, using cognitive-behavioral therapy). They found that participants in the intervention groups experienced a reduction in diabetes distress (measured using the Diabetes Distress Screening [DDS-17] tool) and significant improvement in A1C levels.13-14 Another 2 RCTs of high quality were identified. One evaluated DMSES programs delivered by community health care workers trained in patient empowerment and motivational interviewing, and the other, PLEASED intervention, provided group DSMES education and ongoing support.15,16 Both RCTs noted a significant reduction in A1C levels that was sustained at 18 months in the peer-led intervention group and significant decrease in diabetes distress up to 6 months using DDS-17 tool.15,16
Another RCT evaluated effects of a cognitive behavioral and social problem-solving skills STEPs program on individuals with T1DM and found a significant decrease in diabetes distress in the treatment group and stabilized glycemic management postintervention.17 Lastly, a Level VI mixed-methods exploratory study of high quality evaluated an mHealth-enhanced DSMES program with peer support from community health workers and found that the intervention group experienced clinically significant reduction in diabetes distress compared to the control group, and both intervention and control groups experienced clinically meaningful reductions in A1C.18
Overall, synthesis of the evidence showed that when patients with diabetes were screened for diabetes distress using the DDS-17 tool and tailored DMSES interventions were implemented, there was an overall significant reduction in diabetes distress and A1C levels. This synthesis provided sufficient evidence to support the proposed clinical practice change at this hospital.
For feasibility purposes, the team piloted the practice change in tiered phases. The first pilot phase, discussed in this article, was completed by focusing on 2 aims: (1) evaluating DCES engagement to administering the DDS-17 tool and (2) evaluating the practicability of using the DDS-17 tool to educate patients with diabetes about diabetes distress. The implementation team created a logic model that was used at each plando-study-act (PDSA) cycle as a guide during the change process.
Prior to implementing the practice change, the implementation team submitted a proposal to the hospital Nursing Evidence-Based Practice Review Committee (NEBPRC) for ethical consideration. The NEBPRC approved the proposal to implement the EBP pilot project. Permission to use the DDS-17 tool was also obtained from the original author, Dr Polonsky, prior to implementation. The original DDS-17 tool and scoring sheet created by Dr Polonsky were the instruments used in this pilot project.
Education on how to administer the DDS-17 tool was also provided by the EBP specialist to all DCESs prior to implementation. A go-live date of May 2, 2022, was set by the implementation team, with weekly meetings thereafter to address any concerns that arose in a timely manner. Patient participants were identified by DCESs through DSMES consults placed by providers for patients with unstable A1C levels or diabetes-related complications. The implementation team had mutually agreed to exclude newly diagnosed patients with diabetes during the pilot phase of the project because their perception of diabetes distress would not have reflected their experience with managing diabetes in the past month.
The project’s implementation timeline was from May 2022 to August 2022. During the implementation period, DCESs reviewed DSMES consults daily and used a diabetes distress screening project checklist, created by the implementation team, to standardize the change process. The checklist provided instructions on how to introduce discussions and patient education about diabetes distress to patients. DCESs then introduced the DDS-17 tool and assisted patients with completing the tool at the bedside, allowing time for the patient to complete the tool and ask questions.
DCESs collected completed DDS-17 tools that also included the date the tool was completed and each patient’s most recent A1C level. Completed DDS-17 tools were hand-delivered to the EBP specialist for evaluation each week. Comprehensive DSMES was also provided to each patient according to the organization’s current practice.
The DDS-17 tool is a reliable and well-validated instrument with a Cronbach’s alpha of .93.7 It is a 17-item scale that categorizes diabetes distress into 4 domains of emotional burden, regimen distress, interpersonal distress, and providerrelated distress experienced by patients over the past month.19 The mean score ranges from 1 (no distress) to 6 (serious distress). A mean score of 2 to 3 indicates moderate distress, and a mean score >3 indicates high distress that requires clinical attention.
Emotional burden is the stress, worry, and overwhelmed feeling patients experience while managing the daily demands of diabetes.19 Regimen distress is the overwhelming burden of receiving, interpreting, and responding to frequent feedback from treatment decisions, diabetes devices, and providers.5 Interpersonal distress refers to patients’ family and friends’ lack of understanding of the patients’ difficulties while living with diabetes, and provider-related distress is the lack of confidence that patients have in their knowledge about diabetes or their plan of care because of unclear instructions, difficulty accessing their provider, or feeling like their provider lacks empathy.
Descriptive statistics were used to analyze data for this pilot EBP project. Data without any protected health information (PHI) were entered in a Microsoft Excel spreadsheet by the EBP specialist, who also completed weekly chart audits of total consults and completed DDS-17 screening tools. The total number of diabetes consults was compared with the completed DDS screening tools and analyzed for staff’s engagement to the new diabetes distress screening protocol. Additionally, each completed DDS-17 tool was scored using the diabetes distress domain’s scoring sheet.
Additional feedback provided by patients about their distressing experiences with daily diabetes management while completing the DDS-17 tool were collected verbatim by DCESs, who then provided patients with emotional support interventions as part of usual care. DCESs then shared patients’ feedback (without PHI) with the implementation team during weekly meetings. The team reviewed patients’ feedback comments for common themes independently and as a group.
A total of 83 patients with an A1C level greater than 9% were consulted for DSMES during the 13-week implementation period that the project was piloted. Of these, 16% (n = 13) were excluded from the pilot project because they were newly diagnosed patients with diabetes and the DDS-17 tool used in this pilot project assessed for diabetes distress experienced by patients over the past month. The remaining 84% (n = 70) of patients were therefore included in the pilot project. Patients’ demographics were not collected during this pilot phase of the project because it would have not added meaningful data to the results and outcomes of the pilot project.
Staff engagement to screening patients for diabetes distress over the 13-week period was 73% (n = 51) of consulted patients. In 6 of the 13 weeks, all patients consulted for DSMES were screened for diabetes distress. However, in 2 of the 13 weeks (weeks 7 and 9, shown in Figure 1), none of the patients consulted for DSMES were screened for diabetes distress. Rapid PSDA cycles were implemented to address barriers to screening patients with solutions that improved adherence to screening, as shown in weeks 12 and 13, depicted in Figure 1.
Figure 2 shows the barriers to diabetes distress screening by DCESs where 100% patient screening was not achieved. Of the 27% (n = 19) of consulted patients who did not get screened for diabetes distress, 47% (n = 9) declined completing the DDS-17 screening tool, citing the length of the tool as a barrier; 26% (n = 5) had cognitive impairment that limited their ability to complete the DDS-17 tool; 16% (n = 3) were related to staffing reasons; and 11% (n = 2) of the patients had language barriers identified by DCESs during the initial introduction and patient assessment step of pilot project.
Figure 3 shows the levels of distress experienced by 73% (n = 51) of patients screened for diabetes distress across all 4 diabetes distress domains. The highest level of distress was experienced in the regimen domain, where 41% (n = 21) of patients experienced high levels of regimen distress and 43% (n = 22) experienced moderate levels of regimen distress. Emotional burden was the next highly distressed domain, where 39% (n = 20) of patients experienced high levels of emotional-burden-related distress and 31% (n = 16) experienced moderate distress levels related to emotional burden. About 63% (n = 32) of patients did not report experiencing interpersonal distress, and 59% (n = 30) did not experience provider-related distress.
Figure 4 shows the correlation between diabetes distress and A1C. The correlation coefficient of (r) .33 showed a positive relationship between A1C and diabetes distress. This positive correlation coefficient supported the synthesis from the evidence that showed when diabetes distress levels increase, A1C levels also increase.
The prevalence of diabetes distress and its impact on patient engagement in DSMES is underreported, which could be attributed to the lack of consistent screening of patients with diabetes for diabetes distress in clinical settings. This pilot project identified that as a gap in clinical practice and introduced an evidence-based diabetes distress screening (DDS-17) tool, which resulted in 73% (n = 51) of patients consulted for DSMES to be screened for diabetes distress.
Regimen distress and emotional burden were the 2 domains that patients experienced the highest distress in their daily self-management of diabetes. This aligned with the evidence that showed regimen distress and emotional burden were the most highly experienced distress domains. Patients who declined completing the diabetes distress screening tool (n = 19) cited the length of the tool (17 questions) and repetitious questions in the tool as reasons. These outcome data and patient preferences were helpful in guiding discussions for the next phase of the pilot project.
This pilot project showed a positive, medium correlation between diabetes distress and A1C levels, which supports the importance of screening patients for diabetes distress and individualizing DSMES to optimize glycemic management. The results of this pilot project align with the evidence in the literature showing a positive correlation between diabetes distress and low glycemic management. This data outcome will also help guide the next phase of this pilot project, where patient perspectives and preferences inform individualization of DSMES through patient-centered care. The next phase of the pilot project will therefore use the abbreviated DDS-2 tool, which screens for regimen distress and emotional burden and has a strong correlation coefficient (r = .89).20
Feedback provided by patients who scored high (mean score 3 or higher) in their diabetes distress screening, related to the challenges they experienced in their daily diabetes selfmanagement, were extracted for common themes. Dietary challenges, inconsistent glucose checks, missed insulin doses, and financial challenges related to lack of or fixed income were the common themes identified. These common themes were related to regimen distress and emotional burden, which supports the evidence in the literature. The next pilot project phase will therefore focus on implementing the abbreviated DDS-2 tool, which focuses on these two highly distressed domains: regimen distress and emotional burden.
The use of PDSA cycles was a project strength that led to high staff engagement to screening patients for diabetes distress. PDSA cycles are used to test proposed changes during the pilot phase of project implementation.21 For example, a PDSA cycle implemented during project implementation identified process barriers to screening that were mitigated through staff reeducation on reframing conversations with patients to help motivate patients who felt overwhelmed by the length and repetitive questions in the DDS-17 tool. Another PDSA cycle included utilization of translated DDS-17 tools when assessing diabetes distress in patients who did not speak or write in English as their primary language. These translated DDS-17 tools were also used with permission from the original author, Dr Polonsky.
Another strength of the project was the use of DCESs as change champions and subject matter experts, who had a significant role in identifying barriers and facilitators to diabetes distress screening. Successful teams are those that use their expertise collaboratively to improve quality of patient care.22
In addition, the inclusion of patient preferences in the early stages of the project phases aligns with the evidence that patient self-efficacy and perceptions toward barriers that impede their engagement in optimal diabetes selfcare behaviors should be considered in clinical practice. The organization’s current practice uses a comprehensive DMSES plan, thus individualizing DSMES to mitigate diabetes distress and increase patient self-efficacy in diabetes self-management behaviors. Next steps will include individualizing patient DSMES plans of care to include evidencebased interventions that mitigate diabetes distress related to regimen distress and emotional burden.
This was a small, localized practice change project at an individual hospital and was not designed to produce generalizable knowledge; therefore, number of participants and setting should be considered alongside the results.
The 2022 Merit-based Incentive Payment System (MIPS) lists diabetes as a top ranked quality and patient safety measure, with the goal to nationally decrease A1C levels to 7%. This project aligned with MIPS initiatives by aiming to individualize DSMES action plans to reduce diabetes distress and improve glycemic management. The evidence shows that decreasing diabetes distress in patients with unstable diabetes (A1C greater than 9%) leads to an increase in self-efficacious diabetes management, which can decrease A1C levels.
ADA highlights the importance of screening patients for distress and individualizing care plans to optimize glycemic management.2 This impacts the Institute for Healthcare Improvement’s (IHI) triple aims of reducing health care costs through optimal glycemic targets, which prevents avoidable hospital readmissions and improves overall population health for patients with diabetes.23 Implementing this pilot project in the hospital’s urban (downtown) location allows for individualized, evidence-based care availability to an underserved patient population, a population health initiative that aligns with ADA and IHI’s mission to decrease disease burden for underserved patients through implementation of EBPs that incorporate patient values.
The next steps to sustain the project will be to improve screening of patients with unstable diabetes or diabetes complications from 73% to 100% by educating bedside nurses and providers to screen patients on admission using the abbreviated DDS-2 tool. A positive diabetes distress screen will prompt a consult to a DCES for further patient assessment. DCESs will then administer the complete DDS-17 tool during their consultation with patients who screen positive for diabetes distress. Implementing the DDS-17 tool at this point will help identify specific indicators and perceptions of patient’s diabetes distress to individualize DSMES interventions and develop specific, measurable, achievable, realistic, and timely (SMART) action plans collaboratively with patients.
To further assist with individualizing DSMES plans of care, the next pilot project steps will also introduce problem-solving therapy (PST): an evidence-based, structured interventional approach that encourages patients with unstable diabetes, through clinician support, to identify and implement SMART goals tailored to distress needs.24 Use of PST in the project’s next steps will be a new process for this organization.
Transition-of-care (TOC) leaders and behavioral health specialists (BHSs) will be included to the implementation team in the next phase of the pilot project to ensure PST is implemented successfully. Patients’ referral to a TOC manager will help identify and alleviate regimen-distressing barriers, such as lack of resources (income, access, etc), that may be impacting patients’ regimen distress. Patients with high levels of emotional burden will be referred to a BHS to develop a plan that positively impacts patients’ self-efficacy to implement DMSES interventions. Evidence shows linking those patients with appropriate care resources that address needs that impact DSMES is a positive precursor to optimal diabetes distress and A1C levels.25
Lastly, the next pilot project phase will include newly diagnosed patients with diabetes who were excluded during the pilot phase. The inclusion of newly diagnosed patients with diabetes aligns with the recommendations from the Diabetes Distress Assessment and Resource Center that added newly diagnosed patients among those who are prone to experience diabetes distress.26
Diabetes distress is a central construct related to self-care identified in the literature as challenging and one that affects patients’ diabetes self-care and glycemic management. Suboptimal self-care in patients with diabetes has been associated with low glycemic management and increased rates of diabetes-related complications. Identifying factors associated with suboptimal diabetes self-care is important in the clinical setting and individualizing DSMES interventions geared to optimizing patients’ glycemic targets.
Screening for diabetes distress using an evidence-based screening tool, such as the DDS-17 tool, is recommended by the ADA to help identify patients at risk for suboptimal diabetes self-care. Through this pilot project, patients consulted for DSMES were screened for diabetes distress with the goal to individualize their DSMES, which is a best practice recommendation.
Next project steps will focus on implementing an abbreviated DDS-2 tool to screen patients for regimen distress and emotional burden. A positive screen will prompt timely consults to DCESs, who will further assess patients’ distressing needs and use PST skills to assist patients with developing SMART goals and action plans collaboratively. Referrals to BHS and TOC managers based on patients’ highest distressing needs will be utilized as needed to support patients in optimizing their glycemic targets.
Judy Caroline Kariuki, DNP, APRN-CNS, AGCNS-BC, CMSRN, EBP-C, is with the OhioHealth Grant Medical Center in Columbus, OH.
Barbara Alenik, MSN, RN, NEA-BC, diabetes department director, OhioHealth Grant Medical Center; Teresa Wood, PhD, RN, NEA-BC, nurse scientist, OhioHealth Grant Medical Center; Eileen Werdman, DNP, APRN-CNS, associate professor, University of Cincinnati.
The author declares having no professional or financial association or interest in an entity, product, or service related to the content or development of this article.
The author declares having received no specific grant from a funding agency in the public, commercial, or not-for-profit sectors related to the content or development of this article.
Judy Caroline Kariuki https://orcid.org/0009-0009-0916-1904
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