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Background: Cardiac Rehabilitation (CR) is an effective but highly underutilized treatment for heart disease. Prior studies have demonstrated that the use of strategies such as systematic referral (as part of an order set that does not depend on physician initiative or memory, usually with an opt-out option), liaison facilitated referral (a specific staff member who discusses the program, encourages attendance, and facilitates referral), an early appointment (< 2 weeks), and telephone appointment reminder calls can all improved enrollment rates. However, it is unclear how frequently these strategies are utilized across the United States, how often CR programs monitor their participation rates, and how often quality improvement (QI) projects designed to improve participation rates are performed. Methods: We surveyed all CR program directors in the American Association of Cardiovascular and Pulmonary Rehabilitation’s (AACVPR) database. We assessed basic program features, use of systematic referral, liaison facilitated referral, early appointments, and telephone reminder calls and QI projects. We also assessed self-reported referral, enrollment, and completion rates. Results: Response rate was 36% (290/812). Non-response bias analysis showed reasonable survey representativeness. Over the past 5 years, 49% of programs measured hospital referral, 21% office/clinic referral, 71% program enrollment, and 74% program completion rates. Self-reported rates (interquartile range) were 68 [32 to 90]% for hospital referral, 35 [15 to 60]% for office/clinic referral, 70[46 to 80]% for program enrollment, and 75[62 to 82]% for program completion. Programs reported utilizing a hospital-based systematic referral, liaison facilitated referral, or inpatient CR program 64%, 68%, and 60% of the time, respectively. Early appointments (< 2 weeks) were utilized by 35% and consistent phone call appointment reminders were utilized by 50%. Over the past 5 years, the percent or programs performing QI projects were 62% for hospital referral, 42% for clinic referral, 60% for program enrollment, and 57% for program completion. Measurement of participation rates was highly correlated with performing QI projects (p < 0.0001.) Conclusions: Although programs are aware of participation rate gaps, the monitoring of participation rates is suboptimal, quality improvement initiatives are infrequent, and proven strategies for increasing patient participation are inconsistently utilized. Awareness in outpatient clinical settings is particularly low. These issues likely contribute to the national CR participation gap and may prove to be useful targets for national QI initiatives.

Background: Regular exercise is considered a key element to maintain weight loss, but its role to induce weight loss has been controversial. Methods: In this retrospective study of 7237 individuals attending a worksite wellness center from September 2008 to October 2011 to assess predictors of weight loss at 12 months after joining. Upon joining the wellness center, participants completed a survey that assessed for the presence of cardiovascular risk factors, height, weight, self-perceived health behaviors and psychosocial factors using validated 11-point scale items. We limited the sample to 1927 overweight (25 < BMI ≤ 30) and obese (BMI > 30) participants who completed the survey in full at baseline and at 12 months (+/- 90 days). The outcome variable was weight change at 12 months after enrollment. Weight loss was defined as a loss of > 1kg. Results: Of 1927 participants, 998 (52%) were either overweight (n=573; 57%) or obese (n=425; 43%), and 60% were women. Mean (SD) age was 41 (11.5) yr, BMI 30.7 (5.5) kg/m 2 and they visited the wellness center 49 (90) times/year. At 12 months, 416 (42%) of the participants had lost >1 kg and 19% had lost > 5kg. The predictors of weight loss were: baseline BMI, [Odds Ratio (OR)= 1.56 (1.15-2.13); p < .0044] and [OR= 1.75 (1.26-2.44); p < .0010] for BMI between 30-35 and BMI ≥35, vs BMI between 25-29, respectively. In addition, visits/year [OR= 1.60 (1.19-2.16); p < .0010] and [OR= 1.85 (1.37-2.61); p < .0001] for those attending 13-52 sessions and > 52 visits/year, vs ≤ 12 visits/year, respectively. The association persisted after controlling for all covariates (ie, cardiovascular risk factors, self-perceived health behavior and psychosocial factors). The Table shows the results of the multivariate analysis assessing predictors for >1kg weight loss. Neither age, gender, perceived stress, self-perception of being overweight, following a healthy diet, nor history of hypertension, high blood cholesterol or diabetes predicted weight loss. Conclusions: A significant percentage of overweight or obese individuals lose weight after enrolling in a worksite wellness center. Frequency of attendance, baseline BMI, and support for maintaining a healthy living are associated with weight loss. These results suggest interventions to promote usage of wellness centers maybe be beneficial for the obese adult.

Background: Physical activity (PA) prevents cardiovascular (CV) disease and maintains health. It is unclear which factors predict the adoption or maintenance of PA at a worksite wellness center. Methods: To assess the predictors of increase in PA we conducted a retrospective study on 7237 adults attending a worksite wellness center between September 2008 and October 2011. Upon joining the wellness center, participants completed an electronic or paper survey that assessed for the presence of CV risk factors, height, weight, self-perceived health behaviors and psychosocial factors using eleven validated 0-10 point scale. We included 1927 participants who completed the standard survey at the time of enrollment and at 12 months (+/- 90 days). For logistic regression analysis PA increase at 12 months was defined as an increase of ≥2 points of self-reported PA, on a 0 to 10 point scale, or maintenance of a high level of PA at follow up (FU). Results: Mean (SD) age was 38 (11) yr., BMI was 26.93 (6.06) kg/m 2 and 32% were men. From the study sample, 717 (37%) participants increased their PA level or maintained a high level of PA at FU. When comparing people who increased or maintained a high level of PA with the group without improvement or a decline in PA, there were differences in age and BMI: age [39 (11) vs. 38 (11) yr.; p=0.04] and BMI [27.37 (5.92) vs. 26.68 (5.92) kg/m 2 ; p=0.024]. The Table reports the results of the multivariate logistic regression model. While CV risk factors (tobacco use, hyperglycemia/diabetes mellitus, and high blood cholesterol), perceived stress level and self-perception of being overweight were not significant predictors of increase PA, low muscular strength and flexibility and interactions with close friends were significantly associated with PA. Conclusion: A significant proportion of people, over one-third, enrolled in a wellness center increase their PA level or maintain high levels of PA at 12 months after enrollment. Increasing age and BMI, and a measure of social support; interaction with close friends, and perceived muscular strength and flexibility, predict increase in PA. Presence of CV risk factors do not predict increase in PA. Tailoring programs to address these domains may help younger, less fit, and those with less social support improve their PA level.

Introduction: Cardiac rehabilitation (CR) is an effective but highly underutilized therapy for heart disease. Efforts are underway to increase CR referral and enrollment, but little is known about the potential capacity for growth in CR utilization in the United States. To address this concern, we estimated the current national capacity of CR programs across the United States and assessed obstacles to potential growth. Methods: We surveyed all CR Program Directors listed in the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) database in November of 2012. Respondents reported current enrollment levels, current program capacity, program capacity given reasonable expansion, and obstacles to growth. Results: Of 812 Program Directors in the AACVPR database, 290 (36%) completed the full survey. Respondents represented somewhat larger programs compared to non-respondents (4.0 vs. 4.6 full time employees, p = 0.01), but were otherwise similar. Current enrollment levels demonstrated a median [IQ range] of 140 [75,232] patients per year and a current estimated capacity of 192 [100,300] patient per year. Respondents estimated that programs could expand services by 25% to 240 [141,380] patients annually, assuming a significant increase in patient demand and a feasible increase in resources. We estimate that if programs filled to their current capacity, national CR utilization rates would increase from 34.7% to 46.4% (95% CI, 43.9 to 48.9) of eligible US patients each year. Given feasible program expansion, utilization could increase to a maximum of 58.4% (95% CI, 55.2 to 61.6) of qualifying patients. Capacity projections based upon absolute patient numbers and national statistics revealed concordant results. The most commonly cited obstacles to increasing patient participation are shown in the figure. As seen, the majority (88%) are controllable system-related factors unrelated to patient behavior. Conclusions: Even with substantial expansion of all existing CR programs, there is currently insufficient capacity in current CR programs to meet national service needs. Solutions to this problem will likely include the creation of new CR programs, and new policies that improve reimbursement for CR and also embrace new models of CR delivery.

Objective We studied the effect of a lifestyle therapy program using the resources already available in a cardiac rehabilitation setting for patients at risk for metabolic syndrome (MetS). Methods We created a six-week cardiometabolic program (CMP) using facilities and resources of a cardiac rehabilitation setting. The CMP included a minimum of 6 once-a-week 60 min sessions consisting of exercise training and nutrition instruction, in addition to 6 once-a-week 60 min group behavioral modification classes following the LEARN program. We also assessed the outcomes in 239 patients (pts) who had at either obesity or hyperglycemia: 58 pts had enrolled and completed the CMP, 58 pts had signed up for the CMP but either never attended or dropped out early (Control 1) and had follow-up data, and 123 pts had not signed up for the CMP but had been seen in the outpatient preventive cardiology practice at the Mayo Clinic (Control 2). We compared changes in components of the MetS from baseline to six weeks (for the CMP group) and at 6 months for all pts. Results Of 187 people who signed up for the CMP, only 62 completed the program and from those, 58 gave authorization to use their clinical data in research. Of the 125 that never attended or dropped out early, 58 had follow-up data. Clinical characteristics of the CMP, Control 1, and Control 2 groups and improvement in each of the outcome measures are shown in the Table. In the CMP group, there was a significant improvement at 6 weeks in waist circumference, weight, diastolic blood pressure and total cholesterol. At 6 months the fasting glucose had also significantly improved. In contrast, improvements in Control 1 and Control 2 were modest at best. See Table. When comparing the 6 month changes in the CMP and Control 1, those in the CMP had more pronounced weight loss (-4.5±5 kg vs -0.14±6 kg, p Conclusion A lifestyle therapy program for patients at risk for MetS can be implemented using existing resources of a cardiac rehabilitation program, although enrollment and completion rates are low. Such a program, however, is effective at improving cardiometabolic outcomes in individuals at risk for MetS.