2013 ADA Posters 386-1338.Indd
Total Page:16
File Type:pdf, Size:1020Kb
DIABETESCATEGORY EDUCATION nephropathy, foot ulcer, retinopathy, CAD, PAD, HDL-C (mg/dl) and LDL-C college (3.4), Hispanic (0.47), and white race (1.72), all p < 0.0001], diabetes (mg/dl). The factors which predict the risk of developing ulcer independent (OR = 0.82) was no longer a predictor of internet usage (p = 0.12). Both of 25(OH) D status were A1c (>6.9%) [OR 4.37; RR 1.77], HDL-C(<40mg/dl) [OR crude and adjusted health-related internet use were comparable between 1.16; RR 1.07], LDL-C (>100mg/dl) [OR 1.07; RR 1.03], triglycerides(>200mg/dl) diabetes and no diabetes for seeking information on medical problems (OR = [OR 1.40; RR 1.19], neuropathy [OR 6.88; RR 3.12], retinopathy [OR 3.34; RR 1.06), treatments (0.88) and health care providers (1.07), all p > 0.4. For all 1.91], hypertension [OR 1.64; RR 1.28], nephropathy [OR 3.12; RR 1.87] & respondents mean ± SD internet/email use from home [rated 1 (never) - 7 smoking [OR 4.53; RR 2.99] using odds and risk ratios. In conclusion, it is not (several times/day)] was 5.4±0.03, but 0.3 units lower in persons with clear whether the suppression of delayed wound healing seen during 25(OH) diabetes (p = 0.007). Internet/email usage from home was more frequent D defi ciency is due to the secondary effect or is a direct action of vitamin D than at work (5.4 vs. 3.8, p < 0.001). Effect modifi cation analysis indicated on certain components of the immune system. Long-term randomized trials that higher socioeconomic and education status increased internet use more are needed to see the impact of vitamin D supplementation on the outcome for those with diabetes. Thus, age, education, income, race and ethnicity of diabetic foot patients. were important mediators of internet use among patients with diabetes. These factors need to be considered when implementing internet-based diabetes management and education programs. DIABETES EDUCATION & 682-P POSTERS Launching a Lifestyle Intervention Program for Diabetes Prevention Guided Audio Tour: Improving Diabetes Prevention and Treatment through at the Worksite Education (Posters: 680-P to 687-P), see page 15. M. KAYE KRAMER, KARL K. VANDERWOOD, VINCENT C. ARENA, RACHEL G. MILLER, GERALD L. SCHAFER, ELIZABETH M. VENDITTI, ANDREA M. KRISKA, Behavioral Medicine, Clinical & 680-P Pittsburgh, PA Nutrition, Education, and Exercise Integration and Utilization of Peer Leaders for Long-Term Self-Man- The Group Lifestyle Balance (GLB) program, adapted from the Diabetes agement Support: Results from Project SEED (Support, Education, Prevention Program’s lifestyle intervention, has been shown to reduce risk and Evaluation in Diabetes) factors for diabetes and cardiovascular disease (CVD) in several settings. The GRETCHEN PIATT, SHARLENE EMERSON, PATRICIA JOHNSON, DEBRA TILVES, current project evaluated the effectiveness of GLB program implementation JANICE C. ZGIBOR, Ann Arbor, MI, Pittsburgh, PA at a large corporate worksite. Study investigators collaborated with worksite Long-term self-management support (SMS) is critical; however, effective, representatives to develop and implement GLB program recruitment and sustainable models for SMS are scarce. We determined whether a peer leader intervention strategies. A progressive screening approach using telephone (PL) SMS model was as effective in achieving and maintaining improvements and in-person methods was employed. Adults with a BMI≥ 24 kg/m2 and in glycemic control following a DSME and SMS intervention compared pre-diabetes and/or the metabolic syndrome were eligible to participate. A to a traditional DSME support model in 6 rural primary care practices in randomized controlled-delay study design was utilized with two-thirds of Pennsylvania. Practices and eligible participants (n=221; mean age: 63.0 subjects randomly assigned to begin the intervention immediately (NOW), years, 63.8% female, 96.8% Caucasian, 28.5% at or below poverty level, and one-third delayed for 6 months (DELAYED). Weight loss and physical 32.5% using insulin, A1c ≥ 7%: 54.2%) were randomized to the intervention activity were assessed at 6 and 12 months from baseline, as were changes (DSME+PL SMS; n=119) or usual care (UC) group (DSME+traditional DSME in risk factors for diabetes and CVD. A total of 89 individuals enrolled in support with no PL; n=102). Each intervention practice had one PL to support the study (N=60 NOW, 29 DELAYED). At 6 months the NOW group (N=55) participants. Data were collected at baseline, 6 weeks, 6, and 12 months. demonstrated signifi cantly greater mean weight loss compared to the Marked decreases in A1c were observed in both groups following DSME DELAYED group (N=29) (-5.3% v. -1%, p<0.001), as well as signifi cantly (intervention: -0.24%, p<0.0001; UC: -0.31%, p=0.01); however, as SMS greater improvements in HbA1c (-0.1% v. -.004%, p=0.01) and other diabetes continued over time, intervention participants’ average A1c levels continued and CVD risk factors. Further, a signifi cantly higher proportion of the NOW to signifi cantly decrease at 6 months (-0.32%, p=0.04) and at 12 months group achieved 5% weight loss than the DELAYED group (46% v. 7%), with (-0.37%, p=0.02) in comparison to UC which had increases in average A1c a signifi cantly higher proportion of the DELAYED group demonstrating during the support period (6 months: +0.08%, p=0.09; 12 months: +0.02%, weight gain than the NOW group (7% v. 45%). Most importantly, regardless p=0.13). In those who achieved A1c <7% following DSME, 94% and 100% of of when they started the intervention, both NOW and DELAYED groups participants sustained glycemic control at 6 and 12 months, respectively, in demonstrated signifi cant weight loss (~5%) and improvements in diabetes the intervention group. Similarly, in those who achieved A1c<8% following and CVD risk factors after 6 and 12 months of intervention, as well as a DSME, 98% and 96% of participants sustained glycemic control at 6 and 12 signifi cant increase in physical activity levels. These results suggest that the months, respectively, in the intervention group. Results were similar for UC. GBL lifestyle intervention can be effectively delivered in a worksite setting These data demonstrate that PL SMS is as effective as traditional DSME and can serve as a potential model for other worksite programs. support in helping participants to maintain glycemic control in the long- Supported by: NIH term. With the growing prevalence of diabetes and shortage of diabetes educators, it is important to integrate and utilize low-cost interventions in & 683-P high-risk communities that build on available resources. Virtual Diabetes Education Improves Resident Physician Knowl- Supported by: IDF edge and Performance: A Cluster-Randomized Trial JOANN SPERL-HILLEN, PATRICK J. O’CONNOR, HEIDI EKSTROM, STEVE ASCHE, DEEPA APPANA, PAUL E. JOHNSON, Minneapolis, MN & 681-P We conducted a cluster-randomized trial to evaluate the impact of Socioeconomic and Demographic Predictors of Internet and Social an online virtual diabetes educational experience on resident physician Media Use in Persons With Diabetes knowledge and ability to care for patients with diabetes mellitus. MARCIA A. TESTA, DONALD C. SIMONSON, Boston, MA Nineteen primary care residency programs with 341 consented residents Internet and social media tools to access and disseminate health were randomly assigned to receive (n=177) or not receive (n=164) the information have increased dramatically; however, there are limited data on intervention. The intervention included 18 virtual learning cases, 3 of which their use in persons with diabetes. To help design internet-based technologies were performed each month for 6 months. Each unique learning case required to monitor diabetes patients and enhance health messaging, we analyzed about 15 minutes to complete using a Web-based interactive electronic health two (2008 & 2010) Pew Research Center cross-sectional telephone surveys. record-like interface that challenged providers to take clinical actions to bring Logistic regression was used to assess the impact of clinical diagnoses, patients to evidence-based care goals within 6 months of simulated time. demographic and socioeconomic factors on internet usage. A total of 5,254 Physiologic modeling simulated realistic outcomes of provider actions, and individuals participated [age 50±19 yrs, 44% male, 50% married, 42% HS providers received learning feedback designed to critique and guide them grad or lower, 73% Caucasian, 41% full-time employed, 25% retired, median between simulated encounters in each learning case. After the intervention income $35,000]. 678 individuals (13%) reported having diabetes with period, all residents were assigned a 10-question knowledge test and 4 comorbidities of hypertension in 67%, heart disease (24%), and pulmonary virtual assessment cases. Generalized linear mixed models were used to disease (20%). Internet usage was reported by 73% of those with no test for study-arm differences in knowledge scores and the proportion of diabetes and 47% with diabetes (p < 0.001). After adjustment [(Odds Ratio) residents bringing each assessment case to composite goals for glucose, age in yrs (OR = 0.94), male gender (0.64), income / $10,000 (1.55), some blood pressure, and lipids. For author disclosure information, see page 829. & Guided Audio Tour poster ADA-Funded Research A172 DIABETES EDUCATION A total of 232 residents (97 intervention, 135 control) completed at least emphasize use of diabetes clinical guidelines by providers and improve 1 case. Residents were 52% female, 52% white, mean age 31, 44% family knowledge of pharmacological therapies are needed. medicine, 53% internal medicine, and in postgraduate years 1 (34%), 2 (35%), and 3 and 4 (28.5%). The proportion of residents bringing patients & 686-P to composite goal using case-specifi c pre-determined appropriate and safe Factors Infl uencing Uptake of Structured Education for Type 1 Dia- treatment criteria was signifi cantly higher in the intervention group than in betes and Long-Term Outcomes in an Urban Clinic Population; Im- controls: case 1: 21.2% vs.1.8%, P=.002; case 2: 15.7% vs.