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Address correspondence to: Mary Ann Sevick, ScD, RN, VA Pittsburgh Healthcare System and the School of Medicine, University of Pittsburgh, Suite 600, 230 McKee Pl, Pittsburgh PA, 15213
Behavioral research to improve lifestyle in broadly defined populations of patients with type 2 diabetes is limited.
Objective
We evaluated a behavioral intervention featuring technology-based self-monitoring on biophysiologic outcomes of glycemic control and markers of cardiovascular risk.
Design
In this single-site, randomized clinical trial, participants were stratified by good and poor glycemic control (glycated hemoglobin <8% or ≥8%) and absence or presence of kidney disease, (estimated glomerular filtration rate ≥60 or <60 mL/min) and randomized within strata. Measurements were obtained at 0, 3, and 6 months.
Participants/setting
Self-referred, community-dwelling adults with type 2 diabetes mellitus.
Intervention
The intervention group received Social Cognitive Theory-based counseling paired with technology-based self-monitoring, and results were compared with an attention control group.
Main outcome measures
Glycated hemoglobin, fasting serum glucose, lipid levels, blood pressure, weight, body mass index, and waist circumference were evaluated.
Statistical analyses performed
Mean differences within and between randomization groups were compared over time. Intervention effects over time were estimated using random intercept models.
Results
Two hundred ninety-six subjects were randomized, 256 (86.5%) completed 3-month and 246 (83.1%) completed 6-month assessments. Glycated hemoglobin was reduced in the intervention group by 0.5% at 3 months and 0.6% at 6 months (P<0.001 for each), and the control group by 0.3% (P<0.001) at 3 months and 0.2% (P<0.05) at 6 months; but between-group differences were not significant. In those with baseline glycated hemoglobin ≥8% and estimated glomerular filtration rate ≥60 mL/min, glycated hemoglobin was reduced in the intervention group by 1.5% at 3 months and 1.8% at 6 months (P<0.001 for each), and the control group by 0.9% (P<0.001) at 3 months and 0.8% (P<0.05) at 6 months; but between-group differences were not significant. In random intercept models, the estimated reduction in glycated hemoglobin of 0.29% was not significant.
Conclusions
Two behavioral approaches to improving general lifestyle management in individuals with type 2 diabetes mellitus were effective in improving glycemic control, but no significant between-group differences were observed.
Centers for Disease Control and Prevention National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States.
The majority of adults with type 2 diabetes are overweight or obese, and have comorbidities that increase the risk of CVD (eg, hyperlipidemia and/or hypertension). Those with chronic kidney disease, a common complication of diabetes,
Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis.
Chronic hyperglycemia is widely known to increase the risk of CVD.
To address CVD risk, the American Diabetes Association recommends the following biophysiologic targets for the majority of patients with type 2 diabetes: glycated hemoglobin (HbA1c) <7%; fasting lipid targets (eg, low-density lipoprotein cholesterol <100 mg/dL (2.6 mmol/L), high-density lipoprotein cholesterol >50 mg/dL (1.3 mmol/L), and triglycerides <150 mg/dL [1.7 mmol/L]); systolic blood pressure ≤130 mm Hg and diastolic blood pressure ≤80 mm Hg; and weight loss for those who are overweight or obese.
Recently reported trials of aggressive medication management of glycemia, blood pressure, and lipidemia in type 2 diabetes have not demonstrated significant differences in CVD outcomes, including the most recently completed Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.
ACCORD evaluated aggressive medication management in adults with established type 2 diabetes who were at especially high risk of CVD. Following the negative results of the ACCORD trial, the American College of Cardiology issued press releases stressing the importance of lifestyle modification to reduce CVD risk in those with type 2 diabetes.
Although behavior is an important target for CVD risk reduction, few published studies have evaluated behavioral approaches for engaging patients with multiple comorbidities and complex self-management regimens in a healthier lifestyle to reduce CVD risk.
The purpose of the Enhancing Adherence in Type 2 Diabetes (ENHANCE) study was to evaluate a behavioral intervention to enhance lifestyle behavior change within the context of a complex diabetes self-management regimen in a broadly defined clinical population with few exclusion criteria. The behavioral intervention was paired with personal digital assistant (PDA)-based self-monitoring of diet and physical activity. In this report the authors focus on biophysiologic outcomes and examine the hypotheses that, compared to those randomized to the attention control group, intervention group participants would demonstrate improved glycemia, serum lipid levels, blood pressure, weight, body mass index (BMI), and waist circumference.
Methods
Design
The ENHANCE study was a single center, randomized controlled trial of adults with type 2 diabetes. Participants were stratified according to the baseline level of kidney function (ie, decreased, defined as estimated glomerular filtration rate (eGFR) <60 mL/min, or normal, defined as eGFR ≥60 mL/min), and good or poor baseline glycemic control (ie, HbA1c of <8% or ≥8%). Participants were randomized within these a priori defined HbA1c/eGFR strata to either the intervention or attention control groups using computer-generated permuted blocks. Because of the nature of behavioral interventions, neither participants nor investigators could be blinded to group assignment. The study was approved by the University of Pittsburgh Institutional Review Board. All participants provided written consent before baseline assessment.
Study Population
Participants were recruited into the study between September 2004 and December 2008. Participants self-referred to the study in response to newspaper advertisements, mass transit (bus) advertisements, exhibits at two local health fairs, posters placed throughout the University of Pittsburgh Medical Center and liberal arts campuses, direct mailings, voicemail (targeting University of Pittsburgh employees), word of mouth, or the ClinicalTrials.gov website.
Adults ages ≥18 years with a self-reported diagnosis of type 2 diabetes were eligible for this study. Exclusion criteria included hypoglycemic coma/seizure within the past 12 months; hypoglycemia requiring third-party assistance within the past 3 months; unwillingness or inability to self-monitor capillary blood glucose (CBG) or to participate in scheduled group sessions; history of type 1 diabetes; current receipt of renal dialysis or expectation of dialysis treatment before the conclusion of the 6-month intervention period; history of dementia, alcohol, substance abuse, or other issues likely to interfere with adherence to the study protocol; intention to move outside of the study region within the study period; lack of support from the participant's primary health care provider; or participation in another clinical study.
Intervention and Attention Control Group Conditions
Participants of both groups received training in use of a study-provided glucose meter and sufficient supplies to perform ≥2 CBG measures per day. All participants also were given a pedometer with instructions for use and a target level of physical activity of 10,000 steps per day.
Intervention group members were exposed to group counseling sessions guided by Social Cognitive Theory, which focused on building self-efficacy or sense of perceived mastery over the diabetes self-management regimen.
Intervention group participants were provided with a PalmOne Tungsten/E2 PDA (PalmOne, Inc) with a dietary self-monitoring program containing 4,300 foods, with nutrient composition derived from the US Department of Agriculture.
The software was programmed to permit entry of three meals and one snack daily. Energy targets were based on each participant's resting metabolic rate, estimated from bioelectrical impedance analysis, (Tanita Body Composition Analyzer TBF-300A, Tanita Corporation of America, Inc) and the expected energy expenditure from usual activities. Unless the participant was underweight, energy targets were set to allow a weight loss of no more than 4.54 kg (∼10 lb) over 6 months, with 55% of energy derived from carbohydrates, 30% from fats (10% from saturated fats), and 15% from protein. Dietary prescriptions were reviewed by a study registered dietitian who was also a certified diabetes educator. Participants were shown how to use the PDA data to stay within their daily energy target, distribute their carbohydrate intake throughout the day, and balance their intake of proteins, fats, and carbohydrates. Participants also were advised to pay attention to the connections between glycemic control and their PDA records of carbohydrate and fat intake, medication management, and physical activity. PDA use was intended to enhance self-management self-efficacy by enabling vigilance to diet and physical activity without unduly burdening the participant, providing immediate meal-by-meal feedback regarding achievement of dietary and energy expenditure goals, permitting straight-forward integration of information regarding energy intake and energy expenditures, and allowing participants to make real-time connections between carbohydrate and saturated fat intake (which increases insulin resistance
Intervention group sessions were held weekly during Months 1 and 2, biweekly during Months 3 and 4, and monthly during Months 5 and 6. The frequency of group sessions was gradually decreased to permit the participant to develop independence in self-management. CBG results and PDA diet and physical activity data were uploaded at each meeting and printed as reports to participants, with written and verbal feedback provided by two clinical diabetes educators (one registered dietitian and one registered nurse). Participants were instructed how to interpret these reports and use them to develop explicit diabetes self-management goals to achieve before the next meeting. As reported elsewhere,
intervention group participants entered an average of 11 meals per week during the first 2 months of the study, seven meals per week during Months 3 and 4, and four meals per week in the final 2 months of the study. By the end of the study, approximately 20% of participants continued to enter more than half of the expected meals consumed/week (ie, 11 meals per week; assuming they consumed at least 21 meals per week).
The attention control group had monthly contact with the study team. During Months 1, 3, and 5 they attended group seminars, including general diabetes education and stress management instruction as well as an executive chef demonstration. During Months 2, 4, and 6 they received a lay diabetes magazine. Additional details regarding the Intervention and Attention Control Group activities are reported elsewhere.
Data were collected at the Clinical and Translational Research Center at the University of Pittsburgh. Measurements were obtained at baseline, 3 months, and 6 months by a trained research assistant. BMI was computed from height (measured at baseline only, with a stadiometer) and weight (measured in light clothing and bare feet, using the Tanita scale). Waist circumference was obtained with a Gulick tape measure (Power Systems, Inc) on bare skin, at the natural waist with the abdomen relaxed. HbA1c and lipids were evaluated from blood, obtained from a venipuncture performed by a phlebotomist, after an 8-hour fast. Blood samples were collected, spun, refrigerated, batched, and sent for processing in the Clinical Laboratory Improvement Amendments-certified University of Pittsburgh Medical Center laboratories by personnel blinded to treatment assignment. Coefficients of variation for HbA1c and lipid levels were all <10%. Upon completion of each measurement visit, participants were compensated for their time with a $20 grocery store gift card.
Statistical Analysis
This study was designed to provide at least 80% power to detect a small to medium effect size (f=0.14) suggested by Cohen,
based on a 2-degree of freedom (df) test of the group by time interaction in a repeated measures analysis of variance with a two-sided 0.01 level test (to allow for multiple comparisons). The required sample size of 240 participants provided at least 80% power to detect medium (Cohen's d=0.40) treatment differences in time-specific means, using two-sided 0.01 level tests. No interim analyses were planned or conducted. The sample size estimation allowed for 17% attrition; the primary analysis included all randomized participants who had at least one follow-up visit. Analyses were conducted using Stata version 11 (Stata Corp).
Baseline characteristics in Table 1 were compared using χ2 statistics. In descriptive analyses, time-specific means, differences within each treatment group over time, and differential change over time between the two treatment groups were compared using t tests with bootstrap standard errors. Glycemia outcomes (ie, HbA1c and fasting serum glucose) within the 3 eGFR/HbA1c strata were also described.
Table 1Baseline characteristics of the Enhancing Adherence in Type 2 Diabetes study participants overall, and by treatment arm
Each outcome over time was modeled separately using a random intercept model that allowed each participant to have his or her own mean value. Fixed effects included time, treatment group, stratum, and interactions of these variables. The primary treatment effect in these models was a two parameter treatment by time interaction; the intervention effects at 3 and 6 months were estimated using linear contrasts. Preliminary analysis indicated that drop-out varied by age and marital status, so these variables were included as fixed effects in random intercept models that were estimated using maximum likelihood; this approach provides unbiased parameter estimates when data are assumed to be missing at random (ie, in this study the missing data are assumed to depend on age and marital status but not on other variables). The statistical significance of main effects and interactions was assessed using Wald statistics.
diagram describes study recruitment, enrollment, and retention (see the Figure). Of the 296 participants randomized to the study, 256 (86.5%) completed the 3-month assessment and 246 (83.1%) completed the 6-month assessment. Participants who were lost to follow-up were younger and less likely to be married than those who completed the 6-month assessment (P<0.01 for each).
FigureSummary of Enhancing Adherence in Type 2 Diabetes study screening, enrollment, randomization, and follow-up (F/U).
Table 1 shows the baseline characteristics of the 263 participants having any follow-up data. The sample tended to be middle-aged; women; and white, insured, employed, and well educated. A majority of individuals (59.7% overall) had good baseline glycemic control (HbA1c<8%) and normal kidney function (eGFR ≥60 mL/min); 28.1% had poor baseline glycemic control and normal eGFR. Only 12.2% of participants had decreased kidney function, so those with eGFR <60 having good and poor glycemic control were combined for subgroup analyses. The intervention and attention control groups did not differ significantly on any of these baseline characteristics.
At baseline, 90.4% of participants were prescribed one or more diabetes medications; 69.2% were taking antihypertensive medications; and 56.4% were taking lipid-lowering medications (Table 1). Baseline medication regimens did not differ significantly by treatment group (P>0.54 for each).
Table 2 summarizes time-specific mean physiologic outcomes by treatment group. No significant differences were observed in any of these measures at baseline, 3, or 6 months (P>0.06 for each).
Table 2Time-specific physiologic outcomes, and within- and between-treatment group mean differences over time (ie, baseline-3 months and baseline-6 months) in Enhancing Adherence in Type 2 Diabetes study participants
Physiologic outcome
Time-Specific Outcomes
Within- and Between-Treatment Group Differences Over Time
Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention group and 6 attention control group participants. Other intermittent missing data are rare, except for 8.4% missing waist circumference in the technology-supported behavioral intervention group.
Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention group and 6 attention control group participants. Other intermittent missing data are rare, except for 8.4% missing waist circumference in the technology-supported behavioral intervention group.
Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention group and 6 attention control group participants. Other intermittent missing data are rare, except for 8.4% missing waist circumference in the technology-supported behavioral intervention group.
Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention group and 6 attention control group participants. Other intermittent missing data are rare, except for 8.4% missing waist circumference in the technology-supported behavioral intervention group.
To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
To convert mg/dL triglyceride to mmol/L, multiply mg/dL by 0.0113. To convert mmol/L triglyceride to mg/dL, multiply mmol/L by 88.6. Triglyceride of 159 mg/dL=1.8 mmol/L.
a Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention group and 6 attention control group participants. Other intermittent missing data are rare, except for 8.4% missing waist circumference in the technology-supported behavioral intervention group.
b Two sample t tests with bootstrap standard errors.
c One sample t tests of difference scores with bootstrap standard errors.
d Two sample t tests of difference scores with bootstrap standard errors.
e To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
f To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
g To convert mg/dL triglyceride to mmol/L, multiply mg/dL by 0.0113. To convert mmol/L triglyceride to mg/dL, multiply mmol/L by 88.6. Triglyceride of 159 mg/dL=1.8 mmol/L.
Table 2 also summarizes mean differences across time within and between treatment groups. Relative to baseline, the intervention group experienced a significant within-group reduction in HbA1c of 0.5% at 3 months and 0.6% at 6 months, and the attention control group experienced a significant within-group reduction in HbA1c of 0.3% at 3 months (P<0.001 for each). Statistically significant within-group reductions in fasting serum glucose, and diastolic blood pressure were observed in the intervention group at 3 months; significant overall within-group reductions in weight and BMI were observed in both the intervention and the control groups at 3 months. There were no statistically significant between-group differences over time for any of the main outcome variables.
Table 3 summarizes the stratum-specific analyses for the glycemia outcomes (HbA1c and fasting serum glucose). The greatest reductions from baseline HbA1c were observed in the stratum with poor baseline glycemic control and normal eGFR, with within-group reductions of 1.5% at 3 months and 1.8% at 6 months in the intervention group participants, and 0.9% at 3 months in the attention control group participants (P<0.001 for each). At 6 months, fasting serum glucose also decreased significantly from baseline (by 44.7 mg/dL [2.5 mmol/L]) among intervention group participants with poor baseline glycemic control and normal eGFR. However, none of the corresponding between-group differences over time approached statistical significance.
Table 3Enhancing Adherence in Type 2 Diabetes study stratum-specific within- and between-group differences in glycemia over time, where strata are defined by baseline glycated hemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR) status
Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention and 6 attention control group participants.
To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
a A total of 157 participants had HbA1c <8% and eGFR ≥60 mL/min, 74 had HbA1c ≥8% and eGFR ≥60 mL/min, and 32 had eGFR <60 mL/min.
b Assessments at 3 mo are missing for 3 technology-supported behavioral intervention group and 4 attention control group participants; assessments at 6 months are missing for 11 technology-supported behavioral intervention and 6 attention control group participants.
c One sample t tests of difference scores with bootstrap standard errors.
d Two sample t tests of difference scores with bootstrap standard errors.
e To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
In the random intercept models, the interaction of stratum and treatment was not statistically significant for any of the physiologic outcomes (Table 4), so this term was dropped from the models. The remaining fixed effects were age group, time, treatment, stratum, stratum by time interaction, and treatment by time interaction. The estimated mean differences between otherwise similar participants in the intervention and attention control groups at baseline, 3 months, and 6 months are summarized in Table 4. No significant differences were observed at baseline. The estimated mean difference in HbA1c of 0.29% at 6 months (ie, HbA1c decreased an average of 0.29% more in intervention participants than in the attention control group) was not significant.
Table 4Estimated mean differences in the Enhancing Adherence in Type 2 Diabetes study physiologic outcomes between technology-supported behavioral intervention (INT) (n=131) and attention control (n=132) participants at baseline, 3 mo, and 6 mo, based on random intercept models adjusted for glycated hemoglobin (HbA1c)/estimated glomerular filtration rate (eGFR) stratum, age group, and marital status
Random intercept model with fixed effects of time, treatment group, stratum, and interactions of fixed effects, adjusting for age group and marital status. Statistical significance of main effects and treatment by time interaction assess using Wald statistic. Mean differences (INT-control) at 3 and 6 mo were estimated using linear contrasts.
To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
To convert mg/dL triglyceride to mmol/L, multiply mg/dL by 0.0113. To convert mmol/L triglyceride to mg/dL, multiply mmol/L by 88.6. Triglyceride of 159 mg/dL=1.8 mmol/L.
)
2.21
−20.73 to 25.15
0.85
−10.23
−33.27 to 12.81
0.38
−2.12
−25.37 to 21.12
0.86
0.38
Systolic blood pressure (mm Hg)
−3.55
−8.19 to 1.10
0.14
−2.88
−7.56 to 1.81
0.23
−1.86
−6.62 to 2.90
0.44
0.79
Diastolic blood pressure (mm Hg)
−0.26
−2.70 to 2.19
0.84
−1.00
−3.47 to 1.46
0.42
−0.23
−2.73 to 2.27
0.86
0.79
Weight (kg)
−3.42
−8.46 to 1.62
0.18
−3.66
−8.70 to 1.38
0.16
−3.26
−8.31 to 1.78
0.21
0.65
Body mass index
−1.26
−2.99 to 0.47
0.16
−1.33
−3.06 to 0.40
0.13
−1.19
−2.92 to 0.54
0.18
0.65
Waist circumference (in)
−0.92
−2.53 to 0.69
0.26
−1.01
−2.62 to 0.60
0.22
−0.80
−2.42 to 0.82
0.33
0.86
a Random intercept model with fixed effects of time, treatment group, stratum, and interactions of fixed effects, adjusting for age group and marital status. Statistical significance of main effects and treatment by time interaction assess using Wald statistic. Mean differences (INT-control) at 3 and 6 mo were estimated using linear contrasts.
b To convert mg/dL glucose to mmol/L, multiply mg/dL by 0.0555. To convert mmol/L glucose to mg/dL, multiply mmol/L by 18.0. Glucose of 108 mg/dL=6.0 mmol/L.
c To convert mg/dL cholesterol to mmol/L, multiply mg/dL by 0.026. To convert mmol/L cholesterol to mg/dL, multiply mmol/L by 38.6. Cholesterol of 193 mg/dL=5.0 mmol/L.
d To convert mg/dL triglyceride to mmol/L, multiply mg/dL by 0.0113. To convert mmol/L triglyceride to mg/dL, multiply mmol/L by 88.6. Triglyceride of 159 mg/dL=1.8 mmol/L.
The data safety and monitoring protocol included prospective assessment of self-reported hypoglycemic episodes requiring third-party assistance. Four (2.7%) intervention group participants had such an event, compared with five (3.4%) attention control group participants (P>0.99). Three (2.5%) intervention group participants and one (0.8%) attention control group participant gained >10 kg (P=0.36).
Discussion
The observed differential reduction of 0.4 in HbA1c at 6 months was not significant at the a prior-defined level of P<0.01. The investigators returned to the literature to explore the extent to which the size of the reduction is consistent with similar studies. In a meta-analysis of 12 randomized clinical trials of psychological interventions in people with type 2 diabetes in which HbA1c was reported as an outcome, Ismail and colleagues
Systematic review and meta-analysis of randomized controlled trials of psychological interventions to improve glycaemic control in patients with type 2 diabetes.
found a pooled mean difference of −0.32% (95% CI −0.57 to −0.07). It is important to note that over the years several methods have been developed for the measurement of HbA1c and a single sample can produce widely varying results among methods and laboratories used.
Although caution must be used in drawing firm conclusions about the comparability of ENHANCE HbA1c reductions to the pooled mean difference found by Ismail and colleagues,
Systematic review and meta-analysis of randomized controlled trials of psychological interventions to improve glycaemic control in patients with type 2 diabetes.
Systematic review and meta-analysis of randomized controlled trials of psychological interventions to improve glycaemic control in patients with type 2 diabetes.
the mean baseline HbA1c exceed 8%, whereas the majority of ENHANCE participants had good baseline glycemic control.
The ENHANCE study's inability to demonstrate a significant improvement appears to be due to improved glycemia in the attention control group. The attention control experience was designed to maintain participant interest in the study rather than promote a healthier lifestyle. Provision of a glucose meter, testing supplies, a pedometer with advice to increase number of steps per day, and three educational seminars are not typical of routine diabetes care. In particular, CBG testing strips are expensive and not universally covered under health insurance plans. Although the importance of CBG checks is somewhat controversial, three recent meta-analyses show such checks to be associated with improved glycemia.
This level of lifestyle intervention may encourage compliance when patients are provided with the tools to achieve self-management goals. In addition, improvements in glycemic control could have resulted from a study reactivity (ie, Hawthorne) effect. Finally, it is possible that any level of intervention in a predominantly white, female, employed, well-educated, and insured sample of individuals with type 2 diabetes who are sufficiently motivated to refer themselves to a lifestyle management study would improve glycemic control.
Stratum-specific analyses showed that both intervention and attention control group participants with suboptimal baseline glycemic control and normal kidney function experienced significant within-group reductions in HbA1c, although these did not differ significantly between treatment groups. Regression-to-the-mean likely accounts for some of the observed within-group reductions. Nevertheless, the size of the within-group HbA1c reductions in this subgroup is somewhat surprising, as they are comparable to those achieved with intensive medication management in the glycemia arm of the ACCORD study, where an absolute HbA1c reduction of 1.4% was observed at 4 months and 1.7% at 1 year.
The ENHANCE study reductions of 1.5% and 1.8% at 3 months and 6 months, respectively, were achieved without the risks for hypoglycemia or weight gain that would be expected with aggressive medication management (which were observed in ACCORD).
There are several limitations to this study. ENHANCE evaluated markers of CVD risk rather than actual CVD outcomes. These self-referred study participants may have been more motivated to manage their disease than the general population of individuals with type 2 diabetes. The study was not designed to estimate the separate effects of PDA self-monitoring-only or group sessions-only on the study outcomes. Future researchers may wish to evaluate the efficacy of an intervention that involves self-monitoring alone. It may be useful to evaluate a similar intervention approach in patients with type 2 diabetes selected on the basis of poor glycemic control, and to compare the intervention group to a control condition that more closely resembles routine care. Finally, PDAs have become an outmoded technology, so future versions of this technology-based behavioral intervention would require adaptation to newer technologies, such as smart phones or tablet personal computers with web-based self-monitoring.
Conclusions
Both behavior-based intervention approaches resulted in within-group improvements in glycemia, weight, and BMI, but the between-group differences were not statistically significant. Incorporating newer technologies and comparing subsequent interventions to a control condition that more closely resembles routine care might demonstrate stronger intervention effects.
Acknowledgements
The authors thank Jolynn Gibson, MSN, RN, and Mehry Safaien, MS, RD, University of Pittsburgh Medical Center, and Rita Marsh, RN, MSN, Deborah Klinvex, Tienna Luster, and Kathleen O'Malley, University of Pittsburgh. The contents of this article do not represent the views of the Department of Veterans Affairs or of the United States Government.
References
Centers for Disease Control and Prevention
National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States.
Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis.
Systematic review and meta-analysis of randomized controlled trials of psychological interventions to improve glycaemic control in patients with type 2 diabetes.
M. A. Sevick is a professor of Medicine, Public Health and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, PA, and a research scientist, Center for Research and Promotion and the Geriatric Research Education and Clinical Center, Department of Veterans Affairs, VA Pittsburgh Health Care System, Pittsburgh PA.
M. Korytkowski is a professor of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
B. Piraino is a professor of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
R. A. Stone is an associate professor of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA.
D. Ren is an assistant professor of Nursing, School of Nursing, University of Pittsburgh, Pittsburgh, PA.
S. Sereika is an associate professor of Nursing, Biostatistics, Epidemiology, and Clinical Translational Research, University of Pittsburgh, Pittsburgh, PA.
L. E. Burke is a professor of Nursing and Public Health, Graduate School of Public Health and the School of Nursing, University of Pittsburgh, Pittsburgh, PA.
Y. Wang is a biostatistician, INC Research, Philadelphia, PA.
A. Steenkiste is a research assistant, Veterans Research Foundation of Pittsburgh, Pittsburgh, PA.
Article info
Publication history
Published online: July 19, 2012
Accepted:
April 24,
2012
Footnotes
STATEMENT OF POTENTIAL CONFLICT OF INTEREST M. Korytkowski serves as a consultant to Eli Lilly and Company, and also serves as a grant reviewer to Pfizer, Inc. M. A. Sevick and L. E. Burke have received grants from the National Institutes of Health to carry out separate protocols involving self-management interventions involving PDA-based self-monitoring. None of the other authors reported any financial conflicts of interest.
FUNDING/SUPPORT This work was supported by the following grants: National Institutes of Health/National Institute of Nursing Research no. NR-R01008792, National Institutes of Health/National Center for Research Resources no. CTSA-UL1-RR024153, and National Institutes of Health/National Center for Research Resources no. GCRC-M01-RR000056. Clinical trials.gov identifier: NCT00222846. The sponsors initially approved the study concept and protocol, required regular updates regarding protocol accrual and data safety and monitoring, and provided funds to the participating organization. The sponsor did not have any role in the analysis or interpretation of data or in the preparation, review, or approval of the manuscript.
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