Patients with cardiovascular risk factors can reduce their risk of cardiovascular disease by increasing their physical activity and their physical fitness. According to the guidelines for cardiovascular risk management, health professionals should encourage their patients to engage in physical activity. In this paper, we provide insight regarding the systematic development of a Web-based intervention for both health professionals and patients with cardiovascular risk factors using the development method Intervention Mapping. The different steps of Intervention Mapping are described to open up the “black box” of Web-based intervention development and to support future Web-based intervention development.
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Introduction: The optimal pre-participation screening strategy to identify athletes at risk for exercise-induced cardiovascular events is unknown. We therefore aimed to compare the American College of Sports Medicine (ACSM) and European Society of Cardiology (ESC) pre-participation screening strategies against extensive cardiovascular evaluations in identifying high-risk individuals among 35.50- year-old apparently healthy men. Methods: We applied ACSM and ESC pre-participation screenings to 25 men participating in a study on first-time marathon running. We compared screening outcomes against medical history, physical examination, electrocardiography, blood tests, echocardiography, cardiopulmonary exercise testing, and magnetic resonance imaging. Results: ACSM screening classified all participants as "medical clearance not necessary."ESC screening classified two participants as "high-risk."Extensive cardiovascular evaluations revealed ≥1 minor abnormality and/or cardiovascular condition in 17 participants, including three subjects with mitral regurgitation and one with a small atrial septal defect. Eleven participants had dyslipidaemia, six had hypertension, and two had premature atherosclerosis. Ultimately, three (12%) subjects had a serious cardiovascular condition warranting sports restrictions: aortic aneurysm, hypertrophic cardiomyopathy (HCM), and myocardial fibrosis post-myocarditis. Of these three participants, only one had been identified as "high-risk"by the ESC screening (for dyslipidaemia, not HCM) and none by the ACSM screening. Conclusion: Numerous occult cardiovascular conditions are missed when applying current ACSM/ ESC screening strategies to apparently healthy middle-aged men engaging in their first high-intensity endurance sports event.
Background: Cardiovascular risk factors are associated with physical fitness and, to a lesser extent, physical activity. Lifestyle interventions directed at enhancing physical fitness in order to decrease the risk of cardiovascular diseases should be extended. To enable the development of effective lifestyle interventions for people with cardiovascular risk factors, we investigated motivational, social-cognitive determinants derived from the Theory of Planned Behavior (TPB) and other relevant social psychological theories, next to physical activity and physical fitness. Methods: In the cross-sectional Utrecht Police Lifestyle Intervention Fitness and Training (UP-LIFT) study, 1298 employees (aged 18 to 62) were asked to complete online questionnaires regarding social-cognitive variables and physical activity. Cardiovascular risk factors and physical fitness (peak VO2) were measured. Results: For people with one or more cardiovascular risk factors (78.7% of the total population), social-cognitive variables accounted for 39% (p < .001) of the variance in the intention to engage in physical activity for 60 minutes every day. Important correlates of intention to engage in physical activity were attitude (beta = .225, p < .001), self-efficacy (beta = .271, p < .001), descriptive norm (beta = .172, p < .001) and barriers (beta = -.169, p < .01). Social-cognitive variables accounted for 52% (p < .001) of the variance in physical active behaviour (being physical active for 60 minutes every day). The intention to engage in physical activity (beta = .469, p < .001) and self-efficacy (beta = .243, p < .001) were, in turn, important correlates of physical active behavior. In addition to the prediction of intention to engage in physical activity and physical active behavior, we explored the impact of the intensity of physical activity. The intentsity of physical activity was only significantly related to physical active behavior (beta = .253, p < .01, R2 = .06, p < .001). An important goal of our study was to investigate the relationship between physical fitness, the intensity of physical activity and social-cognitive variables. Physical fitness (R2 = .23, p < .001) was positively associated with physical active behavior (beta = .180, p < .01), self-efficacy (beta = .180, p < .01) and the intensity of physical activity (beta = .238, p < .01). For people with one or more cardiovascular risk factors, 39.9% had positive intentions to engage in physical activity and were also physically active, and 10.5% had a low intentions but were physically active. 37.7% had low intentions and were physically inactive, and about 11.9% had high intentions but were physically inactive. Conclusions: This study contributes to our ability to optimize cardiovascular risk profiles by demonstrating an important association between physical fitness and social-cognitive variables. Physical fitness can be predicted by physical active behavior as well as by self-efficacy and the intensity of physical activity, and the latter by physical active behavior. Physical active behavior can be predicted by intention, self-efficacy, descriptive norms and barriers. Intention to engage in physical activity by attitude, self-efficacy, descriptive norms and barriers. An important input for lifestyle
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