Mark R. Cobain

General Cardiovascular Risk Profile for Use in Primary Care The Framingham Heart Study

Background— Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. Methods and Results— We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions (“general CVD” algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. Conclusions— A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.

Estimated Risks for Developing Obesity in the Framingham Heart Study

Context What are the risks for becoming overweight or obese after age 30 years? Contribution This study tracked 4117 normal-weight white adults 30 to 59 years of age who participated in the Framingham Offspring Study during 1971 to 2001. Within 4 years, 14% to 19% of the women and 26% to 30% of the men became overweight and 5% to 9% of both groups became obese. Within 30 years, more than half of both groups became overweight and about one third of the women and one quarter of the men became obese. Implications Long-term risks for white U.S. adults developing obesity are very high. The Editors Obesity, defined as a body mass index (BMI) of 30 kg/m2 or more (1, 2), is a major public health problem that has reached epidemic proportions in the United States (3-7). Excess weight is associated with increased risk for cardiovascular disease, several forms of cancer, and death (8, 9). Combating obesity requires knowledge of the incidence, prevalence, and rates of transition between stages of the condition. Data on the prevalence of obesity (including secular trends) are readily available through serial national cross-sectional surveys (3-7). However, insufficient information is available on the short-term, age-conditional risk and the residual lifetime probability of becoming overweight or obese. The short-term, age-conditional risk estimates present people with risk information tailored to their age (10) and may encourage lifestyle changes in the short run. The residual lifetime risk statistic (the probability of developing a condition over the remainder of an individuals life) is easily understood by the general public and is more meaningful to health planners (11-14). Accordingly, we estimated the short-term (4 years), long-term (10 to 30 years), and lifetime risks for developing overweight or obesity by using longitudinal observations on a community-based sample. Methods The design and selection criteria of the Framingham Offspring Study have been described previously (15). Participants in the study are examined approximately every 4 years. Individuals (men and nonpregnant women) were eligible for our investigation if they attended at least 2 routine examinations between 1971 and 2001 and were not underweight (BMI < 18.5 kg/m2). At each routine Framingham Offspring Study examination, investigators used a standardized protocol to measure height (to the nearest 0.25 inch) and weight (to the nearest pound) (16, 17). Investigators calculated BMI as the weight (kg) divided by height squared (m2). All participants gave written informed consent, and the institutional review board of the Boston Medical Center approved the study protocol. Body Mass Index on Follow-Up: Definitions of Overweight and Obesity We followed all eligible participants from the time of entry into the investigation until the end of the observation period, the development of overweight or obesity (the BMI outcomes of interest, as defined later), death (without obesity or the BMI outcome of interest), or the last follow-up examination. The World Health Organization (WHO) (2) provided a classification system for BMI that the National Institutes of Health (NIH) has adopted (1). A BMI between 18.5 kg/m2 and 25.0 kg/m2 is considered normal (1, 2). We examined the risk for development of the following states (BMI outcomes): overweight (BMI 25 kg/m2 but <30 kg/m2), overweight or more (BMI 25 kg/m2), obesity (BMI 30 kg/m2), stage II obesity (BMI 35 kg/m2 but <40 kg/ m2), and stage III obesity (BMI 40 kg/m2). Statistical Analysis Short-Term (4 Years) Rates of Developing Overweight or Obesity Participants who attended 2 or more consecutive examinations approximately 4 years apart were eligible for these computations. Participants attending the first examination (in 1971) were not eligible for these analyses because the second examination was 8 years later (in 1979). We categorized participants into 10-year age groups, that is, ages 30 to 39 years, 40 to 49 years, and 50 to 59 years (called baseline ages). We constructed sex-specific transition matrices by cross-tabulating each participants BMI category at the beginning of the 4-year observation period against his or her BMI at the end of that period. For each age group, we estimated crude sex-specific incidence rates of overweight and obesity for participants without the condition (overweight or obesity) at baseline. Participants were eligible for inclusion at more than 1 examination cycle if they reached the next examination without overweight or obesity (for analyses of new-onset overweight) or obesity (for analyses of new-onset obesity). Errors in measurement of BMI can influence incidence rates of overweight or obesity because individuals with BMI values close to the chosen thresholds (25 kg/m2 or 30 kg/m2) may be categorized as having become overweight or obese simply because of small variations in BMI that may not represent true change (18). Therefore, we repeated our analyses with the additional requirement that an individual should change BMI by at least 0.5 kg/m2 over 4 years to qualify for a change in BMI category. Short-term rates of development of overweight or obesity also may be sensitive to weight cycling. Accordingly, we tested the sensitivity of our short-term estimates of incidence of overweight or obesity by performing analyses that excluded individuals with potential mild or greater weight cycling (3 episodes of gain or loss of 10 pounds [4.5 kg] of weight before the baseline age [that is, before the observation period] [19]). Smoking status and smoking cessation have been reported to influence weight gain (20, 21). Therefore, we evaluated the sensitivity of our short-term risk estimates to the inclusion of smokers in the sample by repeating our analyses in a subsample of individuals who never smoked (or never smokers) before and at the baseline age and during follow-up. In addition, because obesity rates have increased considerably over the past 3 decades (3-6, 22), we repeated our analyses by deriving our risk estimates from individuals who attended 2 consecutive examinations between 1991 and 2001 (examination cycles 5 [1991 to 1994] through 7 [1998 to 2001]). Since individuals could contribute to more than 1 baseline age group in primary analyses, we repeated analyses with the additional constraint that individuals could contribute only once during the period of observation (to the first eligible age group). Long-Term (10 to 30 Years) Risk and Residual Lifetime Risk for Ever Developing New-Onset Overweight or More and Obesity in Individuals without the Conditions at Baseline We estimated the long-term (10 to 30 years) risks for ever becoming overweight or more or obese (including different stages of obesity) for study participants without the condition of interest who attended at least 2 examinations during the time period (1971 to 2001). Participants who attended the first examination (in 1971) were eligible for these analyses (though not for the analyses of short-term risk estimates) because attendance of consecutive examinations 4 years apart is not required for long-term risk estimation. We categorized participants into 5-year age groups: 30 to 34 years, 35 to 39 years, 40 to 44 years, 45 to 49 years, 50 to 54 years, and 55 to 59 years. We constructed 5-year age groups (as opposed to 10-year age groups for short-term risks) because narrower age groups are optimal for use with the Practical Incidence Estimators macro (11) that we used for risk estimation. We performed sex-specific analyses separately for the baseline age groups and for each BMI outcome. For the sake of simplicity, we present only estimates for baseline ages 30 to 34 years, 40 to 44 years, and 50 to 54 years. Because we have approximately 30 years of follow-up data for the offspring cohort, we estimated the risk for developing BMI outcomes through age 60 to 64 years for participants 30 to 34 years of age, through age 70 to 74 years for participants 40 to 44 years of age, and through age 80 to 84 years for participants 50 to 54 years of age. The 30-year estimate for baseline age of 50 to 54 years approximates the residual lifetime risk (that is, cumulative risk over remaining lifetime) for any BMI outcome. For calculating long-term and lifetime risks for ever developing each BMI outcome, we used a modified technique of survival analysis adjusted for competing risk for death (with the Practical Incidence Estimators macro, detailed elsewhere [11]; see Appendix for details and model assumptions). For example, to estimate the cumulative incidence of obesity by age 60 years for participants entering the study without the condition at 40 years of age, we would add the probabilities of becoming obese between ages 40 and 41 years, 41 and 42 years, and so on up to ages 59 and 60 years. All long-term risk estimates presented in our report are adjusted for mortality. Individuals could contribute to more than 1 baseline age group. We repeated analyses with the additional requirement that individuals could contribute only to 1 age group: the first age group that they were eligible for during the observation period. The aforementioned techniques estimate risk for ever developing obesity but may overestimate risk because obesity may not be a permanent state (23, 24). Therefore, we conducted secondary analysis with sustained obesity as an outcome of interest. Sustained obesity was defined as a BMI of 30 kg/m2 or greater at 2 consecutive Framingham Offspring Study examinations. Only participants with 2 sets of 2 consecutive examinations were eligible for this analysis. We also assessed the long-term and lifetime risk for overweight or more or obesity in never smokers. Long-Term Risk and Residual Lifetime Risk for Ever Developing New-Onset Overweight or More and Obesity in Individuals, Accounting for Prevalence at Baseline Age Long-term risk measures derived by using aforementioned methods work well for estimating public health burden

Trajectories of Entering the Metabolic Syndrome The Framingham Heart Study

Background— We evaluated the progression of the metabolic syndrome (MetS) and its components, the trajectories followed by individuals entering MetS, and the manner in which different trajectories predict cardiovascular disease and mortality. Methods and Results— Using data from 3078 participants from the Framingham Offspring Study (a cohort study) who attended examinations 4 (1987), 5 (1991), and 6 (1995), we evaluated the progression of MetS and its components. MetS was defined according to the Adult Treatment Panel III criteria. Using logistic regression, we evaluated the predictive ability of the presence of each component of the MetS on the subsequent development of MetS. Additionally, we examined the probability of developing cardiovascular disease or mortality (until 2007) by having specific combinations of 3 that diagnose MetS. The prevalence of MetS almost doubled in 10 years of follow-up. Hyperglycemia and central obesity experienced the highest increase. High blood pressure was most frequently present when a diagnosis of MetS occurred (77.3%), and the presence of central obesity conferred the highest risk of developing MetS (odds ratio, 4.75; 95% confidence interval, 3.78 to 5.98). Participants who entered the MetS having a combination of central obesity, high blood pressure, and hyperglycemia had a 2.36-fold (hazard ratio, 2.36; 95% confidence interval, 1.54 to 3.61) increase of incident cardiovascular events and a 3-fold (hazard ratio, 3.09, 95% confidence interval, 1.93 to 4.94) increased risk of mortality. Conclusions— Particular trajectories and combinations of factors on entering the MetS confer higher risks of incident cardiovascular disease and mortality in the general population and among those with MetS. Intense efforts are required to identify populations with these particular combinations and to provide them with adequate treatment at early stages of disease.

Rates and Determinants of Uptake and Use of an Internet Physical Activity and Weight Management Program in Office and Manufacturing Work Sites in England: Cohort Study

Background Internet-based physical activity (PA) and weight management programs have the potential to improve employees’ health in large occupational health settings. To be successful, the program must engage a wide range of employees, especially those at risk of weight gain or ill health. Objective The aim of the study was to assess the use and nonuse (user attrition) of a Web-based and monitoring device–based PA and weight management program in a range of employees and to determine if engagement with the program was related to the employees’ baseline characteristics or measured outcomes. Methods Longitudinal observational study of a cohort of employees having access to the MiLife Web-based automated behavior change system. Employees were recruited from manufacturing and office sites in the North West and the South of England. Baseline health data were collected, and participants were given devices to monitor their weight and PA via data upload to the website. Website use, PA, and weight data were collected throughout the 12-week program. Results Overall, 12% of employees at the four sites (265/2302) agreed to participate in the program, with 130 men (49%) and 135 women (51%), and of these, 233 went on to start the program. During the program, the dropout rate was 5% (11/233). Of the remaining 222 Web program users, 173 (78%) were using the program at the end of the 12 weeks, with 69% (153/222) continuing after this period. Engagement with the program varied by site but was not significantly different between the office and factory sites. During the first 2 weeks, participants used the website, on average, 6 times per week, suggesting an initial learning period after which the frequency of website log-in was typically 2 visits per week and 7 minutes per visit. Employees who uploaded weight data had a significant reduction in weight (−2.6 kg, SD 3.2, P< .001). The reduction in weight was largest for employees using the program’s weight loss mode (−3.4 kg, SD 3.5). Mean PA level recorded throughout the program was 173 minutes (SE 12.8) of moderate/high intensity PA per week. Website interaction time was higher and attrition rates were lower (OR 1.38, P= .03) in those individuals with the greatest weight loss. Conclusions This Web-based PA and weight management program showed high levels of engagement across a wide range of employees, including overweight or obese workers, shift workers, and those who do not work with computers. Weight loss was observed at both office and manufacturing sites. The use of monitoring devices to capture and send data to the automated Web-based coaching program may have influenced the high levels of engagement observed in this study. When combined with objective monitoring devices for PA and weight, both use of the website and outcomes can be tracked, allowing the online coaching program to become more personalized to the individual.

Evaluation of a cardiovascular disease risk assessment tool for the promotion of healthier lifestyles

Background Although percentage risk formats are commonly used to convey cardiovascular disease (CVD) risk, people find it difficult to understand these representations. Aims To compare the impact of providing a CVD risk message in either a traditional format (% risk) or using an analogy of risk (Heart-Age) on participants risk perceptions and intention to make lifestyle changes. Methods Four hundred and thirteen men and women were randomly allocated to one of two conditions; CVD risk as a percentage or as a Heart-Age score (a cardiovascular risk adjusted age). Results There was a graded relationship between perceived and actual CVD risk only in those participants receiving a Heart-Age message (P<0.05). Heart-Age was more emotionally impactful in younger individuals at higher actual CVD risk (P<0.01). Self-reported emotional reactions further mediated the relationship between risk perception and intention to make lifestyle changes. Conclusion This study found that the Heart-Age message significantly differed from percentage CVD risk score in risk perceptions and was more emotionally impactful in those participants at higher actual CVD risk levels.

Dietary Quality Predicts Adult Weight Gain: Findings from the Framingham Offspring Study

Objective: We tested the hypothesis that dietary quality, measured by adherence to the Dietary Guidelines, was related to weight change in adults.

Using hypothetical data to assess the effect of numerical format and context on the perception of coronary heart disease risk.

Purpose. To test the hypothesis that responses to coronary heart disease (CHD) risk estimates are heightened by use of ratio formats, peer group risk information, and long time frames. Design. Cross-sectional, experimental, between-factors design. Setting. Three regions in England. Subjects. A total of 740 men and women ages 30 to 70 years. Measures. Risk perception, “emotional” response, intention to change lifestyle. Analysis. Logistic regression was used to investigate the impact of numerical format (ratio vs. percentage), peer group risk (personal vs. peer group), and time frame (10-year vs. 30-year) on risk perception. Analysis of variance was used to investigate the impact of these factors on emotional response and intention to change lifestyle questions. Results. Higher perceived risk was observed when risk was presented as a ratio (p < .001) and when it was supplemented with peer group risk estimates (p = .006). Emotional responses to risk information were heightened when risk was presented as a ratio (p = .0004) and supplemented with peer group risk estimates (p = .002). Presentation with ratios also increased intention to make lifestyle changes (p = .047). Conclusion. Perception of CHD risk information is affected by the presentation format. Where absolute risks may appear low, use of ratios and supplementation of personal risk estimates with peer group risk may increase risk perception.

Exploring the question-behaviour effect: Randomized controlled trial of motivational and question-behaviour interventions

PURPOSEnMeasuring intentions and other cognitions to perform a behaviour can promote performance of that behaviour (the question-behaviour effect, QBE). It has been suggested that this effect may be amplified for individuals motivated to perform the behaviour. The present research tested the efficacy of combining a motivational intervention (providing personal risk information) with measuring intentions and other cognitions in a fully crossed 2 × 2 design with an objective measure of behaviour in an at-risk population using a randomized controlled trial (RCT).nnnMETHODSnParticipants with elevated serum cholesterol levels were randomized to one of four conditions: a combined group receiving both a motivational intervention (personalized cardiovascular disease risk information) and a QBE manipulation (completing a questionnaire about diet), one group receiving a motivational intervention, one group receiving a QBE intervention, or one group receiving neither. All participants subsequently had the opportunity to obtain a personalized health plan linked to reducing personal risk for coronary heart disease.nnnRESULTSnNeither the motivational nor the QBE manipulations alone significantly increased rates of obtaining the health plan. However, the interaction between conditions was significant. Decomposition of the interaction indicated that the combined condition (motivational plus QBE manipulation) produced significantly higher rates of obtaining the health plan (96.2%) compared to the other three groups combined (80.3%).nnnCONCLUSIONSnThe findings provide insights into the mechanism underlying the QBE and suggest the importance of motivation to perform the behaviour in observing the effect.nnnSTATEMENT OF CONTRIBUTIONnWhat is already known on this subject? Research has indicated that merely asking questions about a behaviour may be sufficient to produce changes in that or related behaviours (referred to as the question-behaviour effect; QBE). Previous studies have suggested that the QBE may be moderated by the individuals motivation to change the behaviour, i.e., the QBE will only produce increases in the behaviour among those with strong motivation to perform the behaviour. However, no study has directly tested this prediction by manipulating motivation and examining impacts on the QBE. What does this study add? The present study tested the individual and combined effects of a motivational and a QBE intervention in a fully crossed design using a randomized controlled trial (RCT) and showed that: a combined intervention significantly increased behaviour. effect partially mediated by cognitions.

Exploratory Study of Web-Based Planning and Mobile Text Reminders in an Overweight Population

Background Forming specific health plans can help translate good intentions into action. Mobile text reminders can further enhance the effects of planning on behavior. Objective Our aim was to explore the combined impact of a Web-based, fully automated planning tool and mobile text reminders on intention to change saturated fat intake, self-reported saturated fat intake, and portion size changes over 4 weeks. Methods Of 1013 men and women recruited online, 858 were randomly allocated to 1 of 3 conditions: a planning tool (PT), combined planning tool and text reminders (PTT), and a control group. All outcome measures were assessed by online self-reports. Analysis of covariance was used to analyze the data. Results Participants allocated to the PT (meansat urated fat 3.6, meancopingplanning 3) and PTT (meansaturatedfat 3.5, meancopingplanning 3.1) reported a lower consumption of high-fat foods (F 2,571 = 4.74, P = .009) and higher levels of coping planning (F 2,571 = 7.22, P < .001) than the control group (meansat urated f at 3.9, meancopingplanning 2.8). Participants in the PTT condition also reported smaller portion sizes of high-fat foods (mean 2.8; F 2, 569 = 4.12, P = .0) than the control group (meanportions 3.1). The reduction in portion size was driven primarily by the male participants in the PTT (P = .003). We found no significant group differences in terms of percentage saturated fat intake, intentions, action planning, self-efficacy, or feedback on the intervention. Conclusions These findings support the use of Web-based tools and mobile technologies to change dietary behavior. The combination of a fully automated Web-based planning tool with mobile text reminders led to lower self-reported consumption of high-fat foods and greater reductions in portion sizes than in a control condition. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 61819220; http://www.controlled-trials.com/ISRCTN61819220 (Archived by WebCite at http://www.webcitation.org/63YiSy6R8)

Designing “lifestyle interventions” with the brain in mind

The central hypothesis examined in this issue is that insulin resistance promotes maladaptive brain function and contributes to reduced neuronal plasticity, potentially accelerating brain aging. Therefore, if we were to prevent or treat insulin resistance, through weight loss and exercise, cognitive function would be improved. In this article, we argue that successful interventions influencing these outcomes depend upon overriding maladaptive neurobiology. This maladaptation may have developed over the course of the lifespan through interaction with modern environments. Furthermore, we emphasize the need to take this emergent neurobiology into account when designing interventions.