Search

Background: Few studies have rigorously evaluated whether providing biologically based health-risk feedback is more effective than standard interventions in increasing smokers' motivation to quit and their long-term abstinence.Design: An RCT was conducted from 2005 to 2008. Data were analyzed in 2008.Setting/participants: Smokers (N=536) were recruited from the community, regardless of their interest in quitting smoking.Intervention: Smokers either received brief ( approximately 20 minutes), personally tailored counseling sessions based on their lung functioning, carbon monoxide (CO) exposure, and smoking-related health conditions, or they received generic smoking-risk information and personalized counseling about their diet, BMI, and physical activity. All were advised to quit smoking and were offered access to a free phone-counseling program.Main Outcome Measures: Treatment utilization and abstinence at 6 and 12 months post-intervention.Results: Participants who received the experimental treatment demonstrated no greater motivation to quit, use of treatment services, or abstinence compared to controls at either follow-up assessment. In fact, controls reported greater motivation to quit at 12 months (M 3.42 vs 3.20, p=0.03), greater use of pharmacotherapy at 6 months (37.8% vs 28.0%, p=0.02), and greater 30-day point prevalent abstinence at 6 months, after controlling for relevant covariates (10.8% vs 6.4%, adjusted p=0.04).Conclusions: The present study found no support for adding a personalized health-risk assessment emphasizing lung health and CO exposure to generic cessation advice and counseling for community-based smokers not otherwise seeking treatment.Trial Registration: NCT00169260. [ABSTRACT FROM AUTHOR]

Background: Pharmacist- or nurse-led team care decreases patient blood pressure (BP) and cardiovascular disease (CVD) risk., Purpose: To evaluate whether a Web-based dietitian-led (WD) team care intervention was feasible and resulted in decreased BP, CVD risk, and weight compared to usual care (UC)., Methods: Electronic health record (EHR) data identified patients aged 30-69 years with BMI >26, elevated BP, and 10%-25% 10-year Framingham CVD risk who were registered patient website users. Patients with uncontrolled BP at screening were randomized to UC or WD, which included a home BP monitor, scale, and dietitian team care. WD participants had a single in-person dietitian visit to obtain baseline information and create a plan to reduce CVD risk. Planned follow-up occurred via secure messaging to report BP, weight, and fruit and vegetable intake and receive ongoing feedback. If needed, dietitians encouraged patients and their physicians to intensify antihypertensive and lipid-lowering medications. Primary outcomes were change in systolic BP and weight loss ≥4 kg at 6 months. Feasibility outcomes included intervention utilization and satisfaction., Results: Between 2010 and 2011, a total of 90 of 101 participants completed 6-month follow-ups. The WD group had higher rates of secure messaging utilization and patient satisfaction. The WD group lost significantly more weight than the UC group (adjusted net difference=-3.2 kg, 95% CI=-5.0, -1.5, p<0.001) and was more likely to lose ≥4 kg (adjusted relative risk [RRadj]=2.96, 95% CI=1.16, 7.53). BP control and CVD risk reduction were greater in WD than UC, but differences were not statistically significant., Conclusions: WD intervention was feasible and resulted in decreased weight, BP, and CVD risk. A larger trial is justified., Trial Registration Number: Trial Registration Number: NCT01077388., (Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Multivariable cardiovascular disease (CVD) risk calculators, such as the Framingham risk equations, can be used to identify populations most likely to benefit from treatments to decrease risk., Purpose: To determine the proportion of adults within an electronic health record (EHR) for whom Framingham CVD risk scores could be calculated using cholesterol (lab-based) and/or BMI (BMI-based) formulae., Methods: EHR data were used to identify patients aged 30-74 years with no CVD and at least 2 years continuous enrollment before April 1, 2010, and relevant data from the preceding 5-year time frame. Analyses were conducted between 2010 and 2011 to determine the proportion of patients with a lab- or BMI-based risk score, the data missing, and the concordance between scores., Results: Of 122,270 eligible patients, 59.7% (n=73,023) had sufficient data to calculate the lab-based risk score and 84.1% (102,795) the BMI-based risk score. Risk categories were concordant in 78.2% of patients. When risk categories differed, BMI-based risk was almost always in a higher category, with 20.3% having a higher and 1.4% a lower BMI- than lab-based risk score. Concordance between lab- and BMI-based risk was greatest among those at lower estimated risk, including people who were younger, female, without diabetes, not obese, and those not on blood pressure- or lipid-lowering medications., Conclusions: EHR data can be used to classify CVD risk for most adults aged 30-74 years. In the population for the current study, CVD risk scores based on BMI could be used to identify those at low risk for CVD and potentially reduce unnecessary laboratory cholesterol testing., Trial Registration: This study is registered at clinicaltrials.gov NCT01077388., (Copyright © 2012 American Journal of Preventive Medicine. All rights reserved.)

Background: Smoking remains the primary preventable cause of death and illness in the U.S. Effective, convenient treatment programs are needed to reduce smoking prevalence., Purpose: This study compared the effectiveness of three modalities of a behavioral smoking-cessation program in smokers using varenicline., Methods: Current treatment-seeking smokers (n=1202) were recruited from a large healthcare organization between October 2006 and October 2007. Eligible participants were randomized to one of three smoking-cessation interventions: web-based counseling (n=401); proactive telephone-based counseling (PTC; n=402); or combined PTC and web counseling (n=399). All participants received a standard 12-week FDA-approved course of varenicline. Self-report determined the primary outcomes (7-day point prevalent abstinence at 3- and 6-month follow-ups); the number of days varenicline was taken; and treatment-related symptoms. Behavioral measures determined utilization of both the web- and Phone-based counseling., Results: Intent-to-treat analyses revealed relatively high percentages of abstinence at 3 months (38.9%, 48.5%, 43.4%) and at 6 months (30.7%, 34.3%, 33.8%) for the web, PTC, and PTC-web groups, respectively. The PTC group had a significantly higher percentage of abstinence than the web group at 3 months (OR=1.48, 95% CI=1.12, 1.96), but no between-group differences in abstinence outcomes were seen at 6 months., Conclusions: Phone counseling had greater treatment advantage for early cessation and appeared to increase medication adherence, but the absence of differences at 6 months suggests that any of the interventions hold promise when used in conjunction with varenicline., (2010 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: With the growing use of Internet-based interventions, strategies are needed to encourage broader participation. This study examined the effects of combinations of monetary incentives and mailing characteristics on enrollment, retention, and cost effectiveness for an online health program., Methods: In 2004, a recruitment letter was mailed to randomly selected Midwestern integrated health system members aged 21-65 and stratified by gender and race/ethnicity; recipients were randomly pre-assigned to one of 24 combinations of incentives and various mailing characteristics. Enrollment and 3-month retention rates were measured by completion of online surveys. Analysis, completed in 2005, compared enrollment and retention factors using t tests and chi-square tests. Multivariate logistic regression modeling assessed the probability of enrollment and retention., Results: Of 12,289 subjects, 531 (4.3%) enrolled online, ranging from 1% to 11% by incentive combination. Highest enrollment occurred with unconditional incentives, and responses varied by gender. Retention rates ranged from 0% to 100%, with highest retention linked to higher-value incentives. The combination of a $2 bill prepaid incentive and the promise of $20 for retention (10% enrollment and 71% retention) was optimal, considering per-subject recruitment costs ($32 enrollment, $70 retention) and equivalent enrollment by gender and race/ethnicity., Conclusions: Cash incentives improved enrollment in an online health program. Men and women responded differently to mailing characteristics and incentives. Including a small prepaid monetary incentive ($2 or $5) and revealing the higher promised-retention incentive was cost effective and boosted enrollment.

Background: Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation., Design: Randomized fractional factorial design., Setting: Two HMOs: Group Health in Washington State and Henry Ford Health System in Michigan., Participants: 1866 smokers., Intervention: A web-based smoking-cessation program plus nicotine patch. Five components of the intervention were randomized using a fractional factorial design: high- versus low-depth tailored success story, outcome expectation, and efficacy expectation messages; high- versus low-personalized source; and multiple versus single exposure to the intervention components., Measurements: Primary outcome was 7 day point-prevalence abstinence at the 6-month follow-up., Findings: Abstinence was most influenced by high-depth tailored success stories and a high-personalized message source. The cumulative assignment of the three tailoring depth factors also resulted in increasing the rates of 6-month cessation, demonstrating an effect of tailoring depth., Conclusions: The study identified relevant components of smoking-cessation interventions that should be generalizable to other cessation interventions. The study also demonstrated the importance of higher-depth tailoring in smoking-cessation programs. Finally, the use of a novel fractional factorial design allowed efficient examination of the study aims. The rapidly changing interfaces, software, and capabilities of eHealth are likely to require such dynamic experimental approaches to intervention discovery.

Background: Nearly half of the leading causes of death in our society are attributable to behavioral risk factors. As such, it is critical that we continue to develop and refine effective interventions for health behavior change. Some researchers have suggested that using biomarkers to educate individuals about their health status and disease risk may be an effective strategy to promote behavior change. This tactic is also commonly employed by healthcare providers, but its empirical support is unclear. This article reviews the research literature to determine the effectiveness of using biomarker feedback to motivate and enable health behavior change. Potential limitations of this treatment strategy and issues requiring additional research are also discussed., Methods: Articles were identified through PubMed (MEDLINE), PsychInfo, and the reference lists of pertinent manuscripts and book chapters., Results: Eight published, randomized trials were identified that met criteria for review. The results of this work were mixed, but suggest that biological information conveying harm exposure, disease risk, or impaired physical functioning may increase motivation to change. Subsequent behavior change is also affected by the availability and intensity of concomitant treatment., Conclusions: Preliminary findings suggest that combining biomarkers with appropriate behavioral treatment may enhance health behavior change, but more research in this area is warranted.