Digital inmigrants and digital natives expectation collection in the Andalusian Public Health System Virtual Library: communication with our users

The Andalusian Public Health System Virtual Library (Biblioteca Virtual del Sistema Sanitario Público de Andalucía, BV-SSPA) provides access to health information resources and services to healthcare professionals through its Website. This virtual environment demands higher users’ knowledge in order to satisfy of the need of information of our users, as digital natives as digital immigrants, improving at the same time the communication with all of them. 1. To collect clients' views and expectations according to their nature of digital natives and immigrants. 2. To know our online reputation. A Collecting User Expectation Questionnaire will be built, taking into account the segmentation of the BV-SSPA users’ professional groups of the Andalusian Public Health System. A pilot test will be run to check the survey dimensions and items about practices, attitudes and knowledge of our users. Two Quality Function Deployment (QFD) matrices will enable the BV-SSPA services to be targeted to our digital natives or digital immigrants, according to their nature, finding the best way to satisfy their information needs. We provide feedback on BV-SSPA: users can have the opportunity to post feedback about the site via the 'Contact us' section and comment about their experience. And Web 2.0 is a shop window, providing the opportunity to show the comments; and through time, our online reputation will be built, but the BV-SSPA must manage its own personal branding. Web 2.0 tools are a driver of improvement, because they provide a key source of insight into people's attitudes. Besides, the BV-SSPA digital identity will be analyzed through indicators like major search engine referrals breakdown, top referring sites (non search engines), or top search engine referral phrases, among others. Definition of digital native and digital immigrant profiles of the BV-SSPA, and their difference, will be explained by their expectations. The design of the two QFD matrices will illustrate in just one graph the requirements of both groups for tackling digital abilities and inequalities. The BV-SSPA could deliver information and services through alternative channels. On the other hand, we are developing a strategy to identify, to measure and to manage a digital identity through communication with the user and to find out our online reputation. With the use of different tools from quantitative and qualitative methodology, and the opportunities offered by Web 2.0 tools, the BV-SSPA will know the expectations of their users as a first step to satisfy their necessities. Personalization is pivotal to the success of the Site, delivering tailored content to individuals based on their recorded preferences. The valuable user research can be used during new product development and redesign. Besides positive interaction let us build trust, show authenticity, and foster loyalty: we improve with effort, communication and show.

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.