[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 54.197.171.35. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Download PDF
Figure 1. Screening, randomization, and assessments of study participants. EHR indicates electronic health record.

Figure 1. Screening, randomization, and assessments of study participants. EHR indicates electronic health record.

Figure 2. Estimated mean ± SE weight change over a 15-month period in the intention-to-treat population. A, All participants. B, Women. C, Men.

Figure 2. Estimated mean ± SE weight change over a 15-month period in the intention-to-treat population. A, All participants. B, Women. C, Men.

Figure 3. Categorical weight loss at 6 and 15 months in the intention-to-treat population. Weight loss (A) less than or equal to baseline weight, (B) greater than or equal to 5% of baseline weight, (C) greater than or equal to 7% of baseline weight, and (D) greater than or equal to 10% or baseline weight.

Figure 3. Categorical weight loss at 6 and 15 months in the intention-to-treat population. Weight loss (A) less than or equal to baseline weight, (B) greater than or equal to 5% of baseline weight, (C) greater than or equal to 7% of baseline weight, and (D) greater than or equal to 10% or baseline weight.

Table 1. Features of the Coach-Led Intervention
Table 1. Features of the Coach-Led Intervention
Table 2. Baseline Characteristics of the Study Participants
Table 2. Baseline Characteristics of the Study Participants
Table 3. Estimated Mean Change in BMI, Weight Change, and Percentage of Weight Change Over a 15-Month Period in the Intention-to-Treat Populationa
Table 3. Estimated Mean Change in BMI, Weight Change, and Percentage of Weight Change Over a 15-Month Period in the Intention-to-Treat Populationa
Table 4. Estimated Mean Changes From Baseline to 15 Months in Cardiometabolic Risk Factors in the Intention-to-Treat Populationa
Table 4. Estimated Mean Changes From Baseline to 15 Months in Cardiometabolic Risk Factors in the Intention-to-Treat Populationa
1.
Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.  JAMA. 2012;307(5):491-497PubMedArticle
2.
Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L.NEDCOM, the Netherlands Epidemiology and Demography Compression of Morbidity Research Group.  Obesity in adulthood and its consequences for life expectancy: a life-table analysis.  Ann Intern Med. 2003;138(1):24-32PubMed
3.
Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates.  Health Aff (Millwood). 2009;28(5):w822-w831PubMedArticle
4.
Pan XR, Li GW, Hu YH,  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study.  Diabetes Care. 1997;20(4):537-544PubMedArticle
5.
Tuomilehto J, Lindström J, Eriksson JG,  et al; Finnish Diabetes Prevention Study Group.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.  N Engl J Med. 2001;344(18):1343-1350PubMedArticle
6.
Knowler WC, Barrett-Connor E, Fowler SE,  et al; Diabetes Prevention Program Research Group.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.  N Engl J Med. 2002;346(6):393-403PubMedArticle
7.
Knowler WC, Fowler SE, Hamman RF,  et al; Diabetes Prevention Program Research Group.  Ten-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.  Lancet. 2009;374(9702):1677-1686PubMedArticle
8.
NIH and National Heart Lung Blood Institute.  Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. Rockville, MD: DHHS, Public Health Service; 1998. NIH Publication No. 00-4084
9.
US Preventive Services Task Force.  Screening for obesity in adults: recommendations and rationale.  Ann Intern Med. 2003;139(11):930-932PubMed
10.
Agency for Healthcare Research and Quality.  2010 National Healthcare Quality Report. Rockville, MD: US Dept of Health and Human Services, Agency for Healthcare Research and Quality; 2010. AHRQ Publication No. 11-0004
11.
Ali MK, Echouffo-Tcheugui J, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program?  Health Aff (Millwood). 2012;31(1):67-75PubMedArticle
12.
Appel LJ, Clark JM, Yeh HC,  et al.  Comparative effectiveness of weight-loss interventions in clinical practice.  N Engl J Med. 2011;365(21):1959-1968PubMedArticle
13.
Wadden TA, Volger S, Sarwer DB,  et al.  A two-year randomized trial of obesity treatment in primary care practice.  N Engl J Med. 2011;365(21):1969-1979PubMedArticle
14.
Ma J, King AC, Wilson SR, Xiao L, Stafford RS. Evaluation of lifestyle interventions to treat elevated cardiometabolic risk in primary care (E-LITE): a randomized controlled trial.  BMC Fam Pract. 2009;10:71PubMedArticle
15.
Yank V, Xiao L, Stafford RS, Rosas LG, Wilson SR, Ma J. Translating the Diabetes Prevention Program (DPP) into primary care: a randomized trial.  Diabetes. 2012;(61):(suppl 1)  A155
16.
Grundy SM, Cleeman JI, Daniels SR,  et al; American Heart Association; National Heart, Lung, and Blood Institute.  Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.  Circulation. 2005;112(17):2735-2752PubMedArticle
17.
Efron B. Forcing sequential experiment to be balanced.  Biometrika. 1971;58(3):403-417Article
18.
Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial.  Biometrics. 1975;31(1):103-115PubMedArticle
19.
Pocock SJ. Clinical Trials: A Practical Approach. Chichester, England: John Wiley and Sons; 1991
20.
Kramer MK, Kriska AM, Venditti EM,  et al.  Translating the Diabetes Prevention Program: a comprehensive model for prevention training and program delivery.  Am J Prev Med. 2009;37(6):505-511PubMedArticle
21.
Diabetes Prevention Support Center.  Group Lifestyle Balance materials: a modification of the Diabetes Prevention Program's Lifestyle Change Program. Pittsburgh, PA: University of Pittsburgh. http://www.diabetesprevention.pitt.edu/glbmaterials.aspx. Accessed May 17, 2011
22.
Kramer MK, Kriska AM, Venditti EM,  et al.  A novel approach to diabetes prevention: evaluation of the Group Lifestyle Balance program delivered via DVD.  Diabetes Res Clin Pract. 2010;90(3):e60-e63PubMedArticle
23.
Tang L, Duan N, Klap R, Asarnow JR, Belin TR. Applying permutation tests with adjustment for covariates and attrition weights to randomized trials of health-services interventions.  Stat Med. 2009;28(1):65-74PubMedArticle
24.
DiCiccio TJ, Efron B. Bootstrap confidence intervals.  Statistical Sci. 1996;11(3):189-228Article
25.
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum; 1988
26.
Kraemer HC, Kupfer DJ. Size of treatment effects and their importance to clinical research and practice.  Biol Psychiatry. 2006;59(11):990-996PubMedArticle
27.
Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in randomised trials.  BMJ. 1998;316(7132):690-693PubMedArticle
28.
Kriska AM, Delahanty LM, Pettee KK. Lifestyle intervention for the prevention of type 2 diabetes: translation and future recommendations.  Curr Diab Rep. 2004;4(2):113-118PubMedArticle
29.
Tsai AG, Wadden TA. Treatment of obesity in primary care practice in the United States: a systematic review.  J Gen Intern Med. 2009;24(9):1073-1079PubMedArticle
30.
Li Z, Maglione M, Tu W,  et al.  Meta-analysis: pharmacologic treatment of obesity.  Ann Intern Med. 2005;142(7):532-546PubMed
31.
King AC, Ahn DF, Atienza AA, Kraemer HC. Exploring refinements in targeted behavioral medicine intervention to advance public health.  Ann Behav Med. 2008;35(3):251-260PubMedArticle
32.
 Latinos online, 2006-2008: narrowing the gap. Pew Internet and American Life Project. http://pewhispanic.org/files/reports/119.pdf. Accessed January 5, 2010
33.
Sarasohn-Kahn J. How Smartphones Are Changing Health Care for Consumers and Providers. Oakland: California HealthCare Foundation; 2010
34.
Blumenthal D. Stimulating the adoption of health information technology.  N Engl J Med. 2009;360(15):1477-1479PubMedArticle
35.
Centers for Medicare and Medicaid Services.  Decision Memo for Intensive Behavioral Therapy for Obesity (CAG-00423N). http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?&NcaName=Intensive%20Behavioral%20Therapy%20for%20Obesity&bc=ACAAAAAAIAAA&NCAId=253&. Accessed December 6, 2011
36.
Centers for Disease Control and Prevention (CDC).  Centers for Disease Control and Prevention Diabetes Prevention Recognition Program Standards and Operating Procedures. Atlanta, GA: Centers for Disease Control and Prevention; 2011
Original Investigation
Jan 28, 2013

Translating the Diabetes Prevention Program Lifestyle Intervention for Weight Loss Into Primary CareA Randomized Trial

Author Affiliations

Author Affiliations: Department of Health Services Research, Palo Alto Medical Foundation Research Institute, Palo Alto, California (Drs Ma, Yank, Xiao, and Wilson); and Departments of Medicine (Drs Ma, Yank, Wilson, Rosas, and Stafford) and Health Research and Policy (Dr Lavori), Stanford University School of Medicine, Stanford, California.

JAMA Intern Med. 2013;173(2):113-121. doi:10.1001/2013.jamainternmed.987
Abstract

Background The Diabetes Prevention Program (DPP) lifestyle intervention reduced the incidence of type 2 diabetes mellitus (DM) among high-risk adults by 58%, with weight loss as the dominant predictor. However, it has not been adequately translated into primary care.

Methods We evaluated 2 adapted DPP lifestyle interventions among overweight or obese adults who were recruited from 1 primary care clinic and had pre-DM and/or metabolic syndrome. Participants were randomized to (1) a coach-led group intervention (n = 79), (2) a self-directed DVD intervention (n = 81), or (3) usual care (n = 81). During a 3-month intensive intervention phase, the DPP-based behavioral weight-loss curriculum was delivered by lifestyle coach–led small groups or home-based DVD. During the maintenance phase, participants in both interventions received lifestyle change coaching and support remotely—through secure email within an electronic health record system and the American Heart Association Heart360 website for weight and physical activity goal setting and self-monitoring. The primary outcome was change in body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) from baseline to 15 months.

Results At baseline, participants had a mean (SD) age of 52.9 (10.6) years and a mean BMI of 32.0 (5.4); 47% were female; 78%, non-Hispanic white; and 17%, Asian/Pacific Islander. At month 15, the mean ± SE change in BMI from baseline was −2.2 ± 0.3 in the coach-led group vs −0.9 ± 0.3 in the usual care group (P < .001) and −1.6 ± 0.3 in the self-directed group vs usual care (P = .02). The percentages of participants who achieved the 7% DPP-based weight-loss goal were 37.0% (P = .003) and 35.9% (P = .004) in the coach-led and self-directed groups, respectively, vs 14.4% in the usual care group. Both interventions also achieved greater net improvements in waist circumference and fasting plasma glucose level.

Conclusion Proven effective in a primary care setting, the 2 DPP-based lifestyle interventions are readily scalable and exportable with potential for substantial clinical and public health impact.

Trial Registration clinicaltrials.gov Identifier: NCT00842426

An estimated 69% of US adults are overweight or obese,1 and those with modifiable cardiometabolic risk factors are a critical target group for intervention.2,3 Lifestyle modification focused on modest (5%-10%) weight loss and moderate-intensity physical activity can significantly reduce the incidence of type 2 diabetes mellitus (DM) (as much as 58% as shown in the Diabetes Prevention Program [DPP]) and cardiometabolic risk factors in high-risk individuals,46 with benefits sustained for at least 10 years.7 Evidence-based guidelines therefore recommend effective lifestyle intervention for weight management and disease prevention.8,9

However, national surveys reveal a continuing failure to incorporate weight management into clinical practice.10 Implementation of efficacious lifestyle interventions in the real world will require adaptation to improve generalizability and sustainability while maintaining intervention effectiveness. A meta-analysis of translation studies based on the DPP lifestyle intervention showed promising results, but most studies used a single-group design, few leveraged information technology (IT), and none had follow-up past 12 months.11 Two recent trials provide further evidence on the effectiveness of alternative weight management models in primary care settings.12,13

Evaluation of Lifestyle Interventions to Treat Elevated Cardiometabolic Risk in Primary Care (E-LITE) was a 3-arm, primary care–based randomized trial designed to evaluate the effectiveness of 2 adapted DPP lifestyle interventions among overweight or obese adults with pre-DM, metabolic syndrome, or both: (1) a coach-led, face-to-face group intervention and (2) a self-directed DVD intervention. We hypothesized that, compared with usual care, each intervention would result in greater mean reduction in body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 15 months.

METHODS

The E-LITE protocol was approved by the Palo Alto Medical Foundation's (Palo Alto, California) institutional review board and was published previously.14 Some outcome data have been published in abstract form.15 All participants gave written informed consent.

RECRUITMENT AND PARTICIPANTS

Participants were recruited (July 2009–June 2010) from a single primary care clinic within the Silicon Valley (Los Altos, California) that is part of a large multispecialty group practice in the San Francisco Bay Area. All data collection and intervention visits occurred at the clinic. Inclusion criteria included an age of at least 18 years, a BMI of at least 25, and the presence of pre-DM (defined by impaired fasting plasma glucose level of 100 to 125 mg/dL) or metabolic syndrome (defined by 2005 joint criteria of the American Heart Association [AHA] and National Heart, Lung, and Blood Institute).16 (To convert glucose to millimoles per liter, multiply by 0.0555.) Exclusion criteria included serious medical or psychiatric conditions (eg, stroke, psychotic disorder) or special life circumstances (eg, pregnancy, planned move). Of the 3439 patients approved for study contact by their primary care provider, 1057 were determined to be ineligible, 972 declined participation, 363 were unreachable, 806 were not screened because of recruitment success, and 241 were fully eligible and randomized (Figure 1).

RANDOMIZATION AND ALLOCATION CONCEALMENT

We applied a covariate-adaptive, Efron's biased coin method1719(pp84-86) to assure better than chance group balance across prognostic factors (age, sex, race/ethnicity, BMI, fasting blood glucose level, waist circumference, and existing patient account to access personal electronic health record [EHR] system). Participants were randomized to (1) a coach-led, group-delivered intervention (n = 79), (2) a self-directed DVD intervention (n = 81), or (3) usual care (n = 81). While study group assignment was identifiable to participants and interventionists, blinding was otherwise maintained for data collection, outcome adjudication, and data analysis.

INTERVENTIONS

All participants continued to receive standard medical care. Participants' primary care providers were not involved in the conduct of the study. The study provided no information about weight loss or weight-loss goals to participants in the usual care group. Participants in both intervention groups completed a 3-month intensive intervention phase and a 12-month maintenance phase. During the intensive intervention phase, participants received an adapted, 12-session DPP lifestyle intervention curriculum, Group Lifestyle Balance (GLB), that was developed by DPP investigators at the University of Pittsburgh (Pittsburgh, Pennsylvania) after conclusion of the DPP trial.2022 The curriculum was delivered face-to-face in 12-weekly classes to coach-led intervention participants or via a home-based DVD to self-directed intervention participants. In addition to receiving GLB intervention materials, coach-led intervention participants had food tastings at check-in and 30 to 45 minutes of guided physical activity at the end of each weekly class. The E-LITE lifestyle coach, a registered dietitian certified to deliver the GLB program,20 and a contracted fitness instructor jointly taught all the classes at the participating clinic. We made no modifications to the GLB DVD, although self-directed intervention participants attended a single orientation class. During this class (class 1 in the coach-led intervention), participants were trained to use the AHA free Heart360 web portal (www.heart360.org) for weight and physical activity goal setting and self-monitoring and were given a weight scale and pedometer. Via secure email embedded in the EHR and available to all intervention participants, the lifestyle coach sent standardized biweekly reminder messages about self-monitoring to self-directed intervention participants throughout the intensive and maintenance phases and standardized monthly motivational messages to participants in both interventions during the maintenance phase. Participants in both interventions could submit questions or concerns and received responses within 1 to 2 business days. Only coach-led intervention participants received personalized messages on at least a monthly basis that provided progress feedback and lifestyle coaching based on their Heart360 self-monitoring records during the maintenance phase. Table 1 shows key features of the coach-led intervention. (For more information on the interventions, see the protocol.14)

OUTCOME MEASURES

The primary outcome was change in BMI from baseline to 15 months. Trained research assistants who were unaware of participants' group assignment performed anthropometric and blood pressure measurements using standard protocols14 at baseline and at 3, 6, and 15 months, except for height (measured at baseline only). At all time points except 3 months, blood samples were taken after an overnight fast. Possible adverse events were assessed by questionnaire at each follow-up visit and reviewed by a study physician per protocol.

STATISTICAL ANALYSIS

Between-group differences in primary and secondary outcomes were evaluated by intention-to-treat using tests of group by time interactions in repeated-measures mixed-effects linear (for continuous outcomes) or logistic models (for categorical outcomes). The fixed effects of each model consisted of the baseline value of the outcome of interest, randomization balancing factors, recruitment cohort, group, time point (3, 6, or 15 months), and group-by-time interaction. The random effects accounted for repeated measures with an unstructured covariance matrix and clustering of patients within primary care providers. Least-square means ± SE were obtained from the models. We verified that mixed-model–based results were not sensitive to violations of modeling assumptions with permutation and bootstrap resampling tests.23,24

Of the 241 randomized participants, 205 (85.1%) had study-measured weights at 3 months, 201 (83.4%) at 6 months, and 194 (80.5%) at 15 months (Figure 1). After replacing missing study weights with measurements obtained from the EHR (for 14 participants at 3 months, 18 at 6 months, and 24 at 15 months) and by self-report (for 5 participants at 6 months and 3 at 15 months), 22 participants at 3 months (9.1%), 17 (7.1%) at 6 months, and 20 (8.3%) at 15 months had no weight measurement from any source. Similarly, missing blood pressure and laboratory values were replaced with EHR-recorded values. Primary analyses used all available data, but sensitivity analyses were performed that included only participants with study-measured values. Missing data were handled directly through maximum-likelihood estimation via mixed modeling.

Our primary aim was to compare change in BMI from baseline to 15 months between each intervention and the usual care control group. Our secondary aims were to (1) perform similar comparisons for secondary outcomes, (2) compare primary and secondary outcomes between the 2 interventions, and (3) evaluate sex as a prespecified potential moderator. Clinical interest in weight-loss outcomes by sex led to an analysis of intervention effects separately in women and men despite the absence of significant group-by-sex interaction.

The targeted sample size of 80 participants in each group was designed to provide 80% power to detect a 0.5-SD difference (medium effect by Cohen's standards25) in the primary outcome between each intervention and usual care, using t tests at 5% α (2-sided) and assuming up to a 20% loss to follow-up at 15 months. All analyses were conducted using SAS statistical software (version 9.2; SAS Institute Inc).

RESULTS
STUDY PARTICIPANTS

At baseline, participants had a mean (SD) age of 52.9 (10.6) years and a mean BMI of 32.0 (5.4) (weight, 93.8 [17.7] kg); 47% were female, 78% were non-Hispanic white, 17% were Asian/Pacific Islander, and 4.1% were Hispanic/Latino. Most participants had high educational attainment and family annual income (Table 2). Approximately 54% of participants had pre-DM, 87% had metabolic syndrome, and 41% had both conditions.

WEIGHT LOSS

At month 15, the mean ± SE change in BMI from baseline was −2.2 ± 0.3 in the coach-led intervention (P < .001 vs usual care; P = .03 vs self-directed intervention), −1.6 ± 0.3 in the self-directed intervention (P = .02 vs usual care), and −0.9 ± 0.3 in the usual care group (Table 3). Results remained unchanged in sensitivity analyses using study-measured weights only (eTable 1).

At month 15, the mean ± SE change in weight from baseline was −6.3 ± 0.9 kg in the coach-led intervention, −4.5 ± 0.9 kg in the self-directed intervention, −2.4 ± 0.9 kg in the usual care control group, corresponding to a weight change of −6.6%, −5.0%, and −2.6%, respectively (Table 3 and Figure 2). The percentage of participants who achieved the 7% DPP-based weight-loss goal at 15 months was 37.0% (P = .003) in the coach-led intervention and 35.9% (P = .004) in the self-directed intervention vs 14.4% in the usual care group. Findings were similar for 5% and 10% weight-loss goal cut-points (Figure 3). Complete fitted distributions of percentage of weight changes at 15 months are shown in the eFigure.

During the trial period, 15 of 81 participants in the usual care group reported joining a weight-loss program outside the study (12 used commercial programs, 2 used nutrition classes offered by the care delivery system, and 1 used a personal trainer), compared with 5 of 79 in the coach-led group (4 used personal trainers, and 1 used a commercial program) and 3 of 81 in the self-directed group (2 used personal trainers, and 1 used a commercial program) (P = .003). No participants reported undergoing pharmacological or surgical weight-loss treatment.

For women, weight loss was significantly greater in the coach-led intervention than in the usual care control group (P = .003) and the self-directed intervention (P = .02) at 15 months, whereas the difference between the latter 2 groups was not statistically significant (P = .49) (Figure 2). For men, both the coach-led (P = .002) and self-directed (P = .007) interventions resulted in significantly greater weight loss than did usual care at 15 months, while the 2 interventions did not differ significantly (P = .68). The difference by group between men and women was not statistically significant (P = .31).

Estimates of number needed to treat and area under the receiver operating characteristic curve26,27 for all participants and for women and men separately are shown in eTable 2.

CHANGES IN CARDIOMETABOLIC RISK FACTORS

Compared with usual care controls, improvements reached statistical significance for waist circumference and fasting plasma glucose levels in both interventions and for diastolic blood pressure and triglyceride to high-density lipoprotein cholesterol ratio in the coach-led intervention (Table 4). Total cholesterol levels increased significantly less in the self-directed intervention vs the usual care group.

INTERVENTION PARTICIPATION RATES

Participants in the coach-led intervention attended a mean ± SE of 75.1% ± 25.6% (74.6% ± 26.3% among men, 75.7% ± 25.2% among women) of the 12 weekly group sessions (median number of sessions attended, 10; interquartile range [IQR], 9-11). Only 4 participants (1 man, 3 women) in the self-directed intervention did not attend the single group orientation session. Self-directed intervention participants had a median number of 31 secure email messages (IQR, 30-32) during the 15-month period, and coach-led intervention participants had 19 (IQR, 18-22) during the 12-month period after weekly classes were over.

ADVERSE EVENTS

Five serious adverse events were detected in 4 coach-led intervention participants that may have been related to the intervention: 3 fractures and 1 case of chronic subdural hematoma requiring surgery several months following the participant's syncopal episode during a group intervention session. Six other hospitalizations were reported, which were judged to be unrelated to the study (2 in the usual care group, 1 in the self-directed group, 3 in the coach-led group). One coach-led intervention participant and 1 usual care participant developed type 2 DM during the 15-month period. There were no deaths.

COMMENT

This primary care–based translational intervention trial demonstrated that 2 IT-supported, DPP-based lifestyle interventions both led to clinically significant reductions in body weight (measured as change in BMI), accompanied by improvements in waist circumference and fasting plasma glucose level compared with usual care over a 15-month period.

Successful adaptation of proven lifestyle interventions such as the DPP for multiple channels of delivery, all populations at risk, and primary care settings will be critical to stem the tide of obesity and lessen its disease burden.28 Until recently, rigorous trial evidence on effective, scalable treatment models in primary care practice has been lacking.29 Newly published trials demonstrated the effectiveness of 2 primary care models, 1 involving in-person or remote (primarily by phone) professional weight management support12 and the other combining lifestyle counseling with meal replacement or weight-loss medication.13 The E-LITE trial makes a unique contribution to this growing literature in that its interventions integrate standardized, packaged DPP translational programs (delivered in groups or by DVD) with existing health IT. Although these intervention components and delivery channels are not new, their integration into structured interventions for use in primary care is novel.

The maximum weight loss achieved within the coach-led intervention was substantial (6.3 kg, net loss of 3.9 kg relative to usual care) and similar in magnitude to that achieved by the DPP lifestyle intervention and other behavioral or drug-based weight-loss trials.6,12,13,30 Weight loss in the self-directed intervention was less pronounced (a net loss of 2.1 kg) but noteworthy given its low resource requirements and high potential for dissemination. In this real-world translation study, we did not restrict participants from seeking other weight-loss treatment. Nevertheless, the net intervention effects were significant even though a higher proportion of usual care participants reported attending outside weight-loss programs during the study period. The fact that usual care participants lost some weight emphasizes the robustness of findings regarding the effectiveness of the interventions.

Women seemed to respond more favorably to the coach-led intervention compared with self-directed intervention, whereas men seemed to respond comparably to both. These sex-specific findings need to be confirmed in studies adequately powered to investigate sex differences. Future research also should investigate whether empirically supported, sex-based intervention targeting strategies can improve the effectiveness of the interventions.31

The present trial has several limitations. First, study participants were primarily of high socioeconomic status and from a single primary care clinic located within the Silicon Valley of the San Francisco Bay Area and within a parent health system that was one of the first in the nation to adopt a fully functional EHR system. Therefore, its findings may not be directly generalizable to other populations and settings. Second, replacement of missing weights with clinical values recorded in the EHR or self-reported weights might have introduced bias, but sensitivity analyses using only study-measured weights did not change the results. Finally, the trial lasted only for 15 months and was not designed to evaluate event-based outcomes (eg, type 2 DM incidence) or cost-effectiveness. Thus, the long-term effects and comparative cost-effectiveness of the 2 interventions await further investigation.

Independent efforts to broadly disseminate the DPP-based GLB in-person and DVD programs have been under way.20,22 In E-LITE, these programs were integrated with health IT tools that have low-additive cost but high reach to maximize translation potential and clinical and public health impact. Technology-based interventions need to be first evaluated where the technology is available, with broad dissemination following as adoption of the technology expands. Use of computers and the Internet has increased markedly in all population segments, including in lower socioeconomic groups.32 The AHA's Heart360 self-management web portal is free, trustworthy, and easily accessible for patients and primary care providers. Heart360 automated mobile texting for reminders and data transmission (without requiring Web logon after account set-up) is an enhancement that became available only after initiation of intervention with all participants in our study. It substantially extends the potential reach of the system.33 Moreover, health care reform provisions have accelerated adoption of EHR systems.34 Although the cost of acquiring an EHR system is substantial, use of a system already in place for disease management requires minimal additional investment (eg, low time commitment by the lifestyle coach to communicate with participants via secure e-mail).

The E-LITE interventions respond to the need for innovative, effective methods to manage obesity in primary care settings that do not overly burden practicing primary care providers. These interventions are demonstrably beneficial, with potential for high clinical and public health impact, but they do not meet the current definitions of primary care provider (primary care clinicians only) and delivery channel (face-to-face visits only) required to receive Centers for Medicare and Medicaid Services coverage of intensive behavioral therapy for obesity in primary care.35 The Centers for Disease Control and Prevention also require that lifestyle interventions be delivered in person as one of the standards for recognition in its National Diabetes Prevention Program.36 Consideration of expanded program criteria in these national initiatives might encourage wider adoption of alternative and, potentially, more cost-effective lifestyle interventions such as the ones evaluated in this study, assuming that their effectiveness can be documented in more nationally representative populations.

Back to top
Article Information

Correspondence: Jun Ma, MD, PhD, Department of Health Services Research, Palo Alto Medical Foundation Research Institute, 795 El Camino Real, Ames Building, Palo Alto, CA 94301 (maj@pamfri.org).

Accepted for Publication: June 26, 2012.

Published Online: December 10, 2012. doi:10.1001/2013.jamainternmed.987

Author Contributions:Study concept and design: Ma, Lavori, Wilson, and Stafford. Acquisition of data: Ma, Yank, and Wilson. Analysis and interpretation of data: Ma, Yank, Xiao, Lavori, Wilson, Rosas, and Stafford. Drafting of the manuscript: Ma. Critical revision of the manuscript for important intellectual content: Ma, Yank, Xiao, Lavori, Wilson, Rosas, and Stafford. Statistical analysis: Ma, Xiao, and Lavori. Obtained funding: Ma and Wilson. Administrative, technical, and material support: Ma, Wilson, Rosas, and Stafford. Study supervision: Ma, Wilson, and Stafford. Event adjudication: Yank.

Conflict of Interest Disclosures: Dr Stafford has provided consulting services to Mylan Pharmaceuticals.

Funding/Support: The E-LITE study was supported by grant R34DK080878 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), a Scientist Development Grant award (0830362N) from the AHA, and internal funding from the Palo Alto Medical Foundation Research Institute. Dr Lavori acknowledges support by the Clinical and Translational Science Award 1UL1 RR025744 for the Stanford Center for Clinical and Translational Education and Research (Spectrum) from the National Center for Research Resources.

Role of the Sponsors: No sponsor or funding source had a role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDDK or the AHA.

Additional Contributions: We thank the following individuals for their contributions to the design and/or conduct of the study: Amy L. Muzaffar, MD (study physician); Andrea Blonstein, MBA, RD, and Rachel Press, BA (lifestyle coaches); Veronica Luna, BS (project coordinator); Alicia Geurts, BS, Elizabeth Jameiro, MD, and Debbie Miller, MBA (research assistants). We also thank the E-LITE Data and Safety Monitoring Board members (Kimberly Buss, MD, and Deborah Greenwood, MEd, CNS, BC-ADM, CDE) and extend special thanks to the E-LITE participants and their families who made this study possible. We acknowledge the Diabetes Prevention Support Center of the University of Pittsburgh for training and support in the Group Lifestyle Balance program; the current program is a derivative of this material.

REFERENCES
1.
Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.  JAMA. 2012;307(5):491-497PubMedArticle
2.
Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L.NEDCOM, the Netherlands Epidemiology and Demography Compression of Morbidity Research Group.  Obesity in adulthood and its consequences for life expectancy: a life-table analysis.  Ann Intern Med. 2003;138(1):24-32PubMed
3.
Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates.  Health Aff (Millwood). 2009;28(5):w822-w831PubMedArticle
4.
Pan XR, Li GW, Hu YH,  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study.  Diabetes Care. 1997;20(4):537-544PubMedArticle
5.
Tuomilehto J, Lindström J, Eriksson JG,  et al; Finnish Diabetes Prevention Study Group.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.  N Engl J Med. 2001;344(18):1343-1350PubMedArticle
6.
Knowler WC, Barrett-Connor E, Fowler SE,  et al; Diabetes Prevention Program Research Group.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.  N Engl J Med. 2002;346(6):393-403PubMedArticle
7.
Knowler WC, Fowler SE, Hamman RF,  et al; Diabetes Prevention Program Research Group.  Ten-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.  Lancet. 2009;374(9702):1677-1686PubMedArticle
8.
NIH and National Heart Lung Blood Institute.  Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. Rockville, MD: DHHS, Public Health Service; 1998. NIH Publication No. 00-4084
9.
US Preventive Services Task Force.  Screening for obesity in adults: recommendations and rationale.  Ann Intern Med. 2003;139(11):930-932PubMed
10.
Agency for Healthcare Research and Quality.  2010 National Healthcare Quality Report. Rockville, MD: US Dept of Health and Human Services, Agency for Healthcare Research and Quality; 2010. AHRQ Publication No. 11-0004
11.
Ali MK, Echouffo-Tcheugui J, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program?  Health Aff (Millwood). 2012;31(1):67-75PubMedArticle
12.
Appel LJ, Clark JM, Yeh HC,  et al.  Comparative effectiveness of weight-loss interventions in clinical practice.  N Engl J Med. 2011;365(21):1959-1968PubMedArticle
13.
Wadden TA, Volger S, Sarwer DB,  et al.  A two-year randomized trial of obesity treatment in primary care practice.  N Engl J Med. 2011;365(21):1969-1979PubMedArticle
14.
Ma J, King AC, Wilson SR, Xiao L, Stafford RS. Evaluation of lifestyle interventions to treat elevated cardiometabolic risk in primary care (E-LITE): a randomized controlled trial.  BMC Fam Pract. 2009;10:71PubMedArticle
15.
Yank V, Xiao L, Stafford RS, Rosas LG, Wilson SR, Ma J. Translating the Diabetes Prevention Program (DPP) into primary care: a randomized trial.  Diabetes. 2012;(61):(suppl 1)  A155
16.
Grundy SM, Cleeman JI, Daniels SR,  et al; American Heart Association; National Heart, Lung, and Blood Institute.  Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.  Circulation. 2005;112(17):2735-2752PubMedArticle
17.
Efron B. Forcing sequential experiment to be balanced.  Biometrika. 1971;58(3):403-417Article
18.
Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial.  Biometrics. 1975;31(1):103-115PubMedArticle
19.
Pocock SJ. Clinical Trials: A Practical Approach. Chichester, England: John Wiley and Sons; 1991
20.
Kramer MK, Kriska AM, Venditti EM,  et al.  Translating the Diabetes Prevention Program: a comprehensive model for prevention training and program delivery.  Am J Prev Med. 2009;37(6):505-511PubMedArticle
21.
Diabetes Prevention Support Center.  Group Lifestyle Balance materials: a modification of the Diabetes Prevention Program's Lifestyle Change Program. Pittsburgh, PA: University of Pittsburgh. http://www.diabetesprevention.pitt.edu/glbmaterials.aspx. Accessed May 17, 2011
22.
Kramer MK, Kriska AM, Venditti EM,  et al.  A novel approach to diabetes prevention: evaluation of the Group Lifestyle Balance program delivered via DVD.  Diabetes Res Clin Pract. 2010;90(3):e60-e63PubMedArticle
23.
Tang L, Duan N, Klap R, Asarnow JR, Belin TR. Applying permutation tests with adjustment for covariates and attrition weights to randomized trials of health-services interventions.  Stat Med. 2009;28(1):65-74PubMedArticle
24.
DiCiccio TJ, Efron B. Bootstrap confidence intervals.  Statistical Sci. 1996;11(3):189-228Article
25.
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum; 1988
26.
Kraemer HC, Kupfer DJ. Size of treatment effects and their importance to clinical research and practice.  Biol Psychiatry. 2006;59(11):990-996PubMedArticle
27.
Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in randomised trials.  BMJ. 1998;316(7132):690-693PubMedArticle
28.
Kriska AM, Delahanty LM, Pettee KK. Lifestyle intervention for the prevention of type 2 diabetes: translation and future recommendations.  Curr Diab Rep. 2004;4(2):113-118PubMedArticle
29.
Tsai AG, Wadden TA. Treatment of obesity in primary care practice in the United States: a systematic review.  J Gen Intern Med. 2009;24(9):1073-1079PubMedArticle
30.
Li Z, Maglione M, Tu W,  et al.  Meta-analysis: pharmacologic treatment of obesity.  Ann Intern Med. 2005;142(7):532-546PubMed
31.
King AC, Ahn DF, Atienza AA, Kraemer HC. Exploring refinements in targeted behavioral medicine intervention to advance public health.  Ann Behav Med. 2008;35(3):251-260PubMedArticle
32.
 Latinos online, 2006-2008: narrowing the gap. Pew Internet and American Life Project. http://pewhispanic.org/files/reports/119.pdf. Accessed January 5, 2010
33.
Sarasohn-Kahn J. How Smartphones Are Changing Health Care for Consumers and Providers. Oakland: California HealthCare Foundation; 2010
34.
Blumenthal D. Stimulating the adoption of health information technology.  N Engl J Med. 2009;360(15):1477-1479PubMedArticle
35.
Centers for Medicare and Medicaid Services.  Decision Memo for Intensive Behavioral Therapy for Obesity (CAG-00423N). http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?&NcaName=Intensive%20Behavioral%20Therapy%20for%20Obesity&bc=ACAAAAAAIAAA&NCAId=253&. Accessed December 6, 2011
36.
Centers for Disease Control and Prevention (CDC).  Centers for Disease Control and Prevention Diabetes Prevention Recognition Program Standards and Operating Procedures. Atlanta, GA: Centers for Disease Control and Prevention; 2011
×