Chronic diseases have become a primary concern for healthcare systems worldwide.1 The World Health Organization states that 24 million people per year (about 50% of all deaths worldwide) die due to chronic conditions.2 In 2006 alone, chronic diseases were responsible for 35 million deaths worldwide.3 Projections are that chronic conditions will account for 60% and 70% of the global disease burden by 2020 and 2035, respectively.4 Chronically, ill patients are frequent and long-term users of health services. The evidence suggests that about two-thirds of patient encounters with health professionals are for the management of chronic conditions.5

One of the major chronic diseases causing worldwide public health challenge is diabetes mellitus (DM).6,7 It is the fifth leading cause of death in high-income countries and is rapidly becoming epidemic in many low and middle-income countries.8 Globally, the number of people who have diabetes in 2014 was estimated at 422 million, and this number is expected to rise to 592 million by 2035, while 175 million people remain undiagnosed.8 Diabetes care is expensive,9 and the condition can lead to serious complications such as kidney failure, myocardial infarction, stroke, blindness, and limb amputation.10 It imposes a huge economic burden on national healthcare systems. Depending on the country, it could take up between 5–15% of total health expenditure.11

Three main types of diabetes are known: 1) type 1 diabetes (caused by the body’s failure to produce insulin), 2) type 2 diabetes (resulting from insulin resistance), and 3) gestational diabetes (which occurs in pregnant women without a previous diagnosis of diabetes).12,13 Of the three, type 2 diabetes is the most common, constituting approximately 95% of all DM cases.8,11 The prevalence varies with age, gender, ethnic background, and genetic susceptibility. It is associated with environmental risk factors such as lack of physical activity, nutritional status, and obesity.12

Available evidence indicates that early diagnosis and effective management increases the chances of preventing harmful and costly complications associated with diabetes.14 Evidence has also been established regarding the benefits associated with glycemic control in reducing the risk for and delaying the progression of diabetes complications.8 Achieving effective glycemic control requires a lifelong adherence to complex lifestyle management, involving regular blood glucose monitoring, self-adjustment of medications, and a physically active lifestyle. Self-management education (SME), which is based on patient empowerment,15 is recognized globally as a tool that enables patients to achieve optimal glucose control by increasing knowledge and awareness and learning behavioral strategies to manage diabetes.6,16

Diabetes self-management education (DSME) is defined as the “ongoing process of facilitating the knowledge, skill, and ability necessary for self-care”.17 The process, guided by evidence-based research, incorporates the needs, goals, and life experiences of people with diabetes.8 The overall objectives of DSME are to support self-care behaviors, problem-solving, informed decision-making, and active collaboration with healthcare teams and improve clinical outcomes, health status, and quality of life.17 The intervention involves various educational programs, ranging from brief instructions by lay leaders, physicians, dieticians, or nurses to more formal and comprehensive programs.14 In the 1960s and 1970s, DSME interventions were individually oriented and mostly delivered in hospital settings by either nurses or dietitians.8 From 1980 onwards, more specific programs have been developed for diabetes patients and their families.18 Health professionals with different backgrounds educate patients in their domain of expertise. In addition to individual education, more cost-effective interventions such as group-based education,19 information technology-based education,20 and self-help and support group programs18 have been developed.

The efficacy of DSME is supported by a large number of studies, including randomized controlled trials. DSME is thought to improve glycemic control by 1% in people with type 2 diabetes.21 A meta-analysis of 18 randomized studies published between 1966 and 1999 found a significant decrease in mean glycated hemoglobin (HbA1c) by 0.43%.22 Another review of 11 studies from 1988 to 2002 also reported a significant HbA1c reduction of 1.4% at 4–6 months.23 More recently, a meta-review of systematic reviews found that 34 out of the 35 included reviews reported a statistically significant improvement in HbA1c following a DSME intervention.24

Given this demonstrated efficacy of the existing interventions on glycemic control,25–30 the current policy challenge is not to find new efficacious treatments but to implement the proven programs more cost-effectively.8 Over the last decades, many reports on factors influencing the efficacy and cost-effectiveness of DSEM programs have been published. Some target intervention attributes such as mode of delivery (group sessions vs. individual teaching),31,32 delivery setting (community gathering places vs. medical settings),33 timing and program content,34,35 and trainer’s background (medical, allied health, or peer).34,36,37 Others focus on patients’ personal characteristics, such as health status,38,39 disease duration,40 age, educational level, and partner’s influence,41,42 economic status,43 language and culture,44 and gender difference.40,45,46

While many of these factors affecting the effectiveness of DSEM programs have been studied extensively, the impact of program length on the clinical outcomes of type 2 diabetics has received little research attention. Self-management interventions vary substantially in terms of duration.47 For instance, the majority of the programs modeled after the Stanford Model usually involve six weeks of education. Others with different theoretical underpinnings have varied time lengths, ranging from one day to five years and above. It is thus vital to ascertain the impact of program duration on the outcomes of these DSME interventions. Such information will inform policy decisions on optimizing the design and implementation of more cost-effective self-management programs.

Recently, in a review to assess the effect of DSME interventions on glycemic control, the authors concluded that program duration (i.e., total hours of engagement) could affect the likelihood of achieving significant improvements in clinical outcomes.48 However, because they included trials enrolling participants with type 1 and type 2 DM patients in their analysis, their conclusion appears more general than being specific to patients with type 2 DM.

Therefore, the present study is a systematic review of the existing, published, randomized controlled trials to assess the impact of DSME program length on HbA1c in adults with type 2 DM. We chose HbA1c as the clinical endpoint of this study because glycemic control has been demonstrated to strongly predict both the microvascular (diabetic, nephropathy, neuropathy, and retinopathy) and the macrovascular (coronary artery disease, peripheral arterial disease, etc.) complications associated with diabetes.49,50 For instance, the UK Prospective Diabetes Study has demonstrated that a 1% reduction in HbA1c is associated with a 21% decrease in diabetes-related endpoints, an 18% reduction in combined fatal and non-fatal myocardial infarction, a 12% decrease in stroke, a 37% decrease in microvascular endpoints, a 25% reduction in diabetes-related deaths, and a 7% decrease in all-cause mortality.51 Moreover, HbA1c has been considered the primary outcome measure in most diabetes SME intervention studies.21–24 This study contributes to the current knowledge of the factors affecting the efficacy and cost-effectiveness of DSME interventions.

Methods

We designed and conducted a systematic literature search according to the Population, Intervention, Comparison, Outcome, and Study design (PICOS) framing. The population of interest was adults with type 2 DM. The intervention considered was DSME program. The comparison groups were patients receiving ‘usual care’ or ‘standard care’. The outcome of interest was glycemic control. Study designs considered were randomized or clinical controlled trials. Our research question was: What is the impact of DSME program length on glycemic control in adults with type 2 DM?

We identified relevant literature through electronic searches. A librarian from the School of Medical Sciences, Kwame Nkrumah University of Science and Technology, was consulted in developing our search strategy, which included identifying keywords and medical subject headings (i.e., MeSH terms). Electronic searches were made on Web of Science, PubMed, Scopus, MEDLINE, EMBASE, PsychINFO, and the Cochrane Central Register of Controlled Trials. We restricted the search to only English language medical literature published between January 2000 and April 2019. The medical subject headings searched included: ‘SME program’ combined with: ‘type 2 diabetes’, ‘glycemic control’, and ‘controlled trial’. We identified additional relevant articles by manually searching the bibliographies of the retrieved papers.

After removing duplicate publications, 1081 records were screened for eligibility. We carefully read the titles and abstracts of these 1081 papers focusing on two main criteria: 1) Does the paper discuss diabetic patient/individual? 2) Does it focus on a SME program? A paper had to meet both criteria to be considered in the next stage of our review. Following this procedure, we excluded 804 articles.

The remaining 277 articles were retrieved and read by three reviewers. A study had to fulfill six inclusion criteria to be eligible for inclusion in the final analysis:

1. Must not be a review article or a report.
2. Should evaluate the effectiveness of the DSME program on HbA1c.
3. Should focus on type 2 diabetes patients.
4. Should indicate program length or duration.
5. Must be a randomized controlled trial, comparing an intervention group to a control group.
6. Should not compare two or more educational programs (e.g., group vs. individual education).

Any differences in opinion regarding papers’ eligibility were discussed and resolved by the reviewers.

A further 252 articles were excluded after reading the full-text. The most common reason for exclusion was a lack of reporting on program length/duration (n = 78). Other common exclusions included: not assessing program effect on HbA1c (n = 31), focusing on either type 1 (n = 32) or both type 1 and type 2 (n = 37) diabetes patients, focusing on more than one chronic disease (n = 28), and comparing the effectiveness of two educational programs
(n = 25). The flow diagram in Figure 1 depicts the stages of study identification and reasons for paper exclusion. Twenty-five articles were included in the final analysis.

Internal validity of the individual studies was assessed using the Cochrane Collaboration Criteria for four types of bias: selection, performance, attrition, and detection biases52 [Table 1]. Studies were scored from 0 to 4, with a score of 4 indicating the absence of the four potential sources of bias and a score of 0 signifying the possibility of the presence of these biases.

Two members of the research team did data extraction from the selected publications. A subsample of the articles was also evaluated by a second assessor (a third member of the research team), yielding an assessor agreement of 95%. The assessors then discussed the differences and reached a consensus. A fourth member verified all the extracted information against each of the selected papers to ensure data consistency and accuracy. The information recorded about each study included: authors and year of publication, study sample, study site (country), study aim/objective, length of the program, SME provider (dietitian, nurse, peer educator, physician, etc.), description of the intervention, study outcome (effect on HbA1c), and conclusions drawn from the findings.

We categorized the studies into three program lengths (short, medium, and long-term) and compared interventions’ effects on HbA1c across these categories. For this study, we defined short-term programs as interventions having a duration of three months or less (i.e., ≤ 3 months); medium-term as between three and 12 months duration (> 3 to < 12 months); and long-term as those lasting 12 months or more (≥ 12 months).

We judged the effectiveness of each program based on the level of significance of the HbA1c mean difference between the intervention and the control groups. Although a section of the literature considers an intervention effective once the experimental subjects achieve a greater reduction in HbA1c levels than those in the control group, we believe that a successful intervention should, in addition to this, have a statistically significant difference in HbA1c change between the two groups. Thus, only programs that reported a significant HbA1c mean difference between the intervention and the control groups were considered effective in the current study.

Results

Validity scores of the 25 studies were moderate, with a mean of 2.1±0.8 (range = 0.0–4.0). Scores for each study on the Cochrane Collaboration Criteria for the four types of bias are shown in the Appendix. Generally, studies (96.0%) met the criteria for a possible absence of performance bias by demonstrating no possibility of contamination between the study and the control groups and no treatment differences between the groups, apart from the DSME intervention. A moderate number of studies (52.0%) met the criteria for an absence of selection bias. However, the marked difference in characteristics between the intervention and the control groups at baseline was a major concern for some of the studies (48.0%). The majority of the studies (56.0%) did not meet the attrition bias absence criteria. These studies either did not meet a retention rate of ≥ 80%, as recommended by the Cochrane Criteria52 or failed to compare the baseline characteristics of those who completed the study and those who dropped out. Almost all of the studies (96.0%) did not meet the criteria for a possible absence of detection bias by failing to report on blinding of those who carried out the outcome assessment and statistical analysis.

A detailed description of the selected studies is presented in Table 2. A total of 28 216 type 2 diabetes patients, made up of 2746 intervention subjects and 25 470 controls, were included in the 25 studies. The mean age of the participants reported was approximately 58.0 years, with the mean percentage of female and male patients being 54.2% to 45.8%, respectively. The majority of the studies were conducted in the USA (40.0%), followed by the UK (16.0%). The remaining studies were carried out in Brazil, the Netherlands, Iran, Japan, Greece, China, Norway, Australia, Taiwan, Iraq, and Kenya. The sample size ranged from 25 to 824. The studies were conducted between 2002 and 2018. Patients were recruited from several settings including primary or general medical practice, hospitals, community health centers, churches, outpatient diabetes clinics, university-affiliated clinics, and the general community (via advertisements and rosters of previous research studies). Most educational interventions were led by health professionals such as dietitians, nurses, clinical pharmacists, physicians, physiotherapists, and community health workers. Two publications did not provide information on SME providers, while two mentioned lay leaders as program instructors.

The selected studies and their outcomes are grouped under the short, medium, and long-term classifications in Tables 3, 4, and 5, respectively. In all, 16 (64.0%) studies53,55-58,60–64,67–70,73,74 reported significant differences in HbA1C changes between the intervention and the control groups. Nine (36.0%) studies showed no significant effects. One study reported66 a significant difference in HbA1c change between the groups at the initial stage of the intervention (0.53%, p = 0.008), but this decreased and lost significance during the follow-up assessment (0.25%, p = 0.293). In the remaining eight studies,54,59,65,71,72,75–77 though significant reductions were recorded at the end of the interventions, between-group differences were not significant.

Nine studies were classified as short-term programs; five55,57,58,61,69 reported significant differences in changes in HbA1c between the intervention and the control groups, while four59,71,75,76 showed no significant differences [Table 3]. The overall difference in the mean change in HbA1c between the intervention and the control subjects for the nine studies was 0.4±0.3%
(range = 0.02–1.40). With the medium-term programs, eight studies were identified; four56,62,63,73 reported significant differences in between-group changes, while four65,66,72,77 showed no significant differences [Table 4]. The overall difference in the mean change in HbA1C between the groups was 0.4±0.2% (range = 0.1–0.8). The long-term interventions had eight studies; seven53,60,64,67,68,70,74 recorded significant differences in between-group changes, whereas one54 indicated no significant difference [Table 5]. The studies recorded an overall difference in between-group HbA1C mean change of 0.6±0.3% (range = 0.2–1.2).

The long-term studies were associated with the greatest number of interventions achieving statistically significant differences in changes in HbA1C between the intervention and the control groups (87.5% significant vs. 12.5% non-significant, and accounting for 43.8% of the 16 studies that reported significant changes). This was followed by the short-term interventions (55.6% significant vs. 44.4% non-significant, and representing 31.2% of the 16 significant studies). In comparison, the medium-term programs were associated with the least number (50.0% significant vs. 50.0% non-significant, and accounting for 25.0% of the 16 significant studies) [Figures 2 and 3].

Discussion

Diabetes is a complex, chronic condition that requires both high-quality clinical care and effective self-management. While many factors affecting the effectiveness of SEM programs have been studied extensively, the impact of program length on the clinical outcomes of type 2 diabetics has received little research attention. The current study was, therefore, conducted as an attempt to fill this gap.

Generally, the outcomes reported by the studies showed positive effects. None of the studies that showed no significant changes indicated that patients’ HbA1c levels worsened after participating in the educational programs. Our findings support the literature that diabetes SME programs produce beneficial effects on patients’ glycemic control.78,79

Our analysis revealed that the majority of the long-term interventions were more effective on changes in HbA1c levels. Participants enrolled in the long-term programs also achieved the greatest overall mean reduction in HbA1c than the control subjects. Although not enough published studies on the topic exist for detailed comparisons, a 2009 technical report presented to the Ontario Health Technology Advisory Committee in Canada shares our findings. The report, which was based on a systematic review of type 2 diabetes SEM programs, concluded that interventions with the largest effects on glycemic control lasted at least one year in duration.79

One factor that differentiated the long-term studies from both the short and medium-term interventions was that most of the participants enrolled in the long-term programs received ongoing reinforcement in addition to the educational sessions. This was done through clinic visits,60,67 weekly support group sessions,70 and follow-up telephone calls.53,60,64,68 Reinforcing the themes addressed during the educational sessions might have provoked thoughts and emotional experiences, thus consolidating educational experiences. This may partly explain why most long-term interventions were more effective on changes in levels.

The claim that initial improvements in SME outcomes diminish after six months80,81 is partly supported by our review. The long-term studies had most (87.5%) of the interventions lasting six months. Initially, the interventions recorded significant improvements. However, not all could sustain these gains to the end or immediately after program implementation. It is possible the short-term interventions would have reported similar results if long term follow-up assessments were made. This suggests that one-time education is not sufficient for patients to sustain a lifetime of diabetes care. As theories of science and behavior changes argue: “Changing one’s health behavior is a complex process which does not occur rapidly”.82 Thus, patients will need ongoing self-management and support for a sustained behavior change.

Program length in terms of days, weeks, months, or years may not be the same as program length in terms of total hours of engagement. For instance, we found that two61,71 of the included studies had the same total hours of engagement (six hours). Whereas one61 was spread over three weeks, and the other71 was delivered in either one day or two half-day equivalents. In another study,56 though the total contact time was short (2.5 hours), educational sessions were spread over six months (sessions were given at intervals of three to six weeks and took between 15 and 45 minutes per session). Thus, depending on program intensity, an intervention can last several days, but with few contact hours. Therefore, it is vital for researchers to clarify this difference when analyzing the effect of program length on SEM interventions’ outcomes.

We observed that most of the studies included in this review did not blind those measuring and analyzing the interventions’ main outcomes. This observation is not surprising because research indicates that few randomized controlled studies report blinding data analysts and outcome assessors.83 As argued, outcome assessors who are aware of the actual treatment may unintentionally or consciously alter their assessment, thereby posing the risk of detection bias.84 Blinding data collectors, outcome assessors, and data analysts are crucial to ensure unbiased ascertainment of outcomes.85 Researchers find it difficult to blind outcome assessors because, in most cases, as we observed in our analysis, the principal investigators are themselves the outcome adjudicators. Future research should consider using independent individuals unaware of treatment allocations as outcome assessors to avoid this methodological error.

This study is among the few that have attempted to investigate the impact of DSME program length on glycemic control in adults with type 2 diabetes. It provides scientific evidence that may inform policy decisions about more cost-effective ways of implementing SEM interventions. Conclusions are based on high-quality evidence, as randomized controlled trials are considered the strongest research design for evaluating the effects of health interventions.86 That notwithstanding, the study has some methodological limitations which are worth acknowledging. A meta-analysis would have been the most appropriate method for determining the summary effects of the interventions in each of the three program lengths. However, because of intervention heterogeneity, it was not suitable to conduct a meta-analysis. The interventions differed with respect to a number of factors, including participants’ personal characteristics, mode of program delivery (e.g., group, individual, telephone, mail, online, video, etc.), and SME provider type. These factors could impact the programs’ outcomes hence, the inappropriateness of meta-synthesizing the information. Another limitation pertains to the rigorous inclusion and exclusion criteria we adopted. For instance, restricting the search strategy to only studies published in peer-reviewed English journals from January 2000 to April 2019 may have resulted in excluding useful information that may not have been peer-reviewed, in other languages, or published before 2000. Finally, focusing the analysis on only one clinical endpoint (HbA1C) may not represent a comprehensive evaluation of the success of the interventions. HbA1C was not the only primary outcome measure of the included studies; other physical, behavioral, and psychological endpoints were also considered.

Based on the evidence presented, one-time education may not be an effective intervention for achieving longer-term glycemic control in adults with type 2 diabetes. However, while one-time educational programs are not recommended, the current situation, where most SME interventions are organized separately from healthcare systems (mostly by voluntary organizations), makes it difficult for long-term, ongoing SEM programs to be provided. Integrating SME programs into diabetes care pathways is the surest way to achieve lifelong patient education and support. As full integration has not been attained yet, organizers of SME interventions should consider providing additional sessions periodically to reinforce what patients are taught in the educational programs. This will not only ensure cost-efficiency but will also result in long-lasting benefits to health and psychosocial outcomes.

Conclusion

Our findings suggest that program length may change the effectiveness of educational interventions. Long-term SMEs with reinforcement components appear to have the largest effects on glycemic control. Achieving sustained improvements in patients’ HbA1C levels will require long-term, ongoing SME, and support. However, it is worth noting that none of the included publications in this review directly measured the association between program length and SME outcomes. The effects of the interventions we observed may be due to factors other than program duration. For instance, we observed that the long-term interventions employed a mix of program delivery modes. Also, patients recruited into the various interventions had different baseline HbA1C values. Moreover, disease duration varied among the study participants. Our findings be interpreted as suggestive rather than being conclusive. This points to the need for more methodologically rigorous research to be conducted with diverse subject populations in real-world clinical and community settings to understand the actual impact of SME program length on physiological, behavioral, and patient-reported outcomes. Our study could provide background information for further developments.

Disclosure

The authors declared no conflicts of interest. No funding was received for this study.

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