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      Overweight and obese adults have low intentions of seeking weight-related care: a cross-sectional survey

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          The prevalence of obesity is growing worldwide. Obesity guidelines recommend increasing the level of weight-related care for persons with elevated levels of weight-related health risk (WRHR). However, there seems to be a discrepancy between need for and use of weight-related care. The primary aim of this study is to examine predisposing factors that may influence readiness to lose weight and intention to use weight-related care in an overweight population.


          A population-based, cross-sectional survey was conducted. Data were collected using an online self-administered questionnaire sent to a population-representative sample of 1,500 Dutch adults on the Health Care Consumer Panel (n = 861 responded). Data were used from individuals (n = 445) with a mildly, moderately or severely elevated level of WRHR. WRHR status was based on self-reported data on Body Mass Index, risk assessment for diabetes mellitus type 2 (DM2) and cardiovascular disease (CVD), or co-morbidities.


          55.1% of persons with increased WRHR were ready to lose weight (n = 245). Depending on level of WRHR; educational level, marital status, individuals with an accurate perception of their weight and better perceptions and expectations of dietitians were significantly related to readiness to lose weight. Most of them preferred individual weight-loss methods (82.0% of n = 245). 11% (n = 26 of n = 245) intended to use weight-related care. Weight-related care seeking was higher for those with moderate or severe WRHR. Expectations and trust in dietitians did not seem to influence care seeking.


          Many Dutch adults who are medically in need of weight-related care are ready to lose weight. Most intend to lose weight individually, and only a few intend to use weight-related care. Therefore, obesity prevention initiatives should focus on monitoring weight change and weight-loss plans, and timely referral to obesity management. However, many people are not ready to lose weight. For this group, strategies for behaviour change may depend on WRHR, perceptions of weight and dietitians, educational level and marital status. Obesity prevention initiatives should focus on increasing the awareness of the seriousness of their condition and offering individually appropriate weight management programmes.

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          Most cited references 19

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          Revisiting the behavioral model and access to medical care: does it matter?

          The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.
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            Purposeful selection of variables in logistic regression

            Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
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              Socioeconomic Disparities in Health Behaviors.

              The inverse relationships between socioeconomic status (SES) and unhealthy behaviors such as tobacco use, physical inactivity, and poor nutrition have been well demonstrated empirically but encompass diverse underlying causal mechanisms. These mechanisms have special theoretical importance because disparities in health behaviors, unlike disparities in many other components of health, involve something more than the ability to use income to purchase good health. Based on a review of broad literatures in sociology, economics, and public health, we classify explanations of higher smoking, lower exercise, poorer diet, and excess weight among low-SES persons into nine broad groups that specify related but conceptually distinct mechanisms. The lack of clear support for any one explanation suggests that the literature on SES disparities in health and health behaviors can do more to design studies that better test for the importance of the varied mechanisms.

                Author and article information

                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central
                11 June 2014
                : 14
                : 582
                [1 ]NIVEL (The Netherlands Institute for Health Services Research), P.O. box 1568, Utrecht 3500 BN, The Netherlands
                [2 ]TRANZO (Tilburg University, Scientific Centre for Transformation in Care and Welfare), P.O. box 90153, Tilburg 5000 LE, The Netherlands
                [3 ]VU University of Amsterdam, De Boelelaan 1085, Amsterdam 1081 HV, The Netherlands
                Copyright © 2014 Tol et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

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