No abstract

No abstract

BackgroundWhat is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.ObjectiveThe objective of our study was to uncover barriers and challenges to using recommender systems in health promotion.MethodsWe conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results.ResultsWe describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems.ConclusionsWe promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Though many lesbian veterans have fears of stigma and discrimination in the context of VHA care, few have experienced this. Most lesbian veterans believed the VHA was trying to create a welcoming environment for its LGBT veterans. Future research should focus on expanding this study to include a larger and more diverse sample of lesbian, gay, bisexual, and transgender veterans receiving care at VA facilities across the country.

Background: Obesity is at epidemic proportions. This study examined the extent to which obesity is being diagnosed at a community health center residency-training site. Results were examined by provider type. Characteristics of patients with obesity diagnosed by primary care providers were compared with characteristics of patients determined to be obese by body mass index (BMI) calculation exclusively.Methods: A cross-sectional design was used. Medical records of 465 adult patients were audited. Data collected included diagnosis of obesity, height and weight, demographics, and comorbidity.Results: Of the 465 patients' charts audited, 83 contained a provider diagnosis of obesity, and 74 additional patients were determined to be obese by BMI calculation exclusively. Significant underdiagnosis occurred among all provider types (P ‫؍‬ .036). Patients with a diagnosis of obesity had significantly higher BMI scores (38.4 vs 34.4, P ‫؍‬ .002). Obesity was more likely to be diagnosed in female than in male patients (P ‫؍‬ .001). Differences related to age, insurance coverage, and comorbidity were not significant.Conclusions: Obesity was found to be an underdiagnosed condition among all provider types. As evidenced by significantly higher BMI scores for provider-diagnosed obesity, the data suggest that the obesity diagnosis is made by appearance. The importance of teaching and modeling the use of BMI to diagnose obesity is underscored.