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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.

BackgroundThe quality of transitional care is associated with important health outcomes such as rehospitalization and costs. The widely used Care Transitions Measure (CTM‐15) was developed with a classic test theory approach; its short version (CTM‐3) was included in the CAHPS Hospital Survey. We conducted a psychometric evaluation of both measures and explored whether item response theory (IRT) could produce a more precise measure.Methods and ResultsAs part of the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education, 1545 participants were interviewed during an acute coronary syndrome hospitalization, providing information on general health status (Short Form‐36), CTM‐15, health utilization, and care process questions at 1 month postdischarge. We used classic and IRT analyses and compared the measurement precision of CTM‐15–, CTM‐3–, and CTM‐IRT–based score using relative validity.Participants were 79% non‐Hispanic white and 67% male, with an average age of 62 years. The CTM‐15 had good internal consistency (Cronbach's α=0.95) but demonstrated acquiescence bias (8.7% participants responded “Strongly agree” and 19% responded “Agree” to all items) and limited score variability. These problems were more pronounced for the CTM‐3. The CTM‐15 differentiated between patient groups defined by self‐reported health status, health care utilization, and care transition process indicators. Differences between groups were small (2 to 3 points). There was no gain in measurement precision from IRT scoring. The CTM‐3 was not significantly lower for patients reporting rehospitalization or emergency department visits.ConclusionWe identified psychometric challenges of the CTM, which may limit its value in research and practice. These results are in line with emerging evidence of gaps in the validity of the measure.

This study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.

BackgroundSmoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions.ObjectiveThe objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system.MethodsWe will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population.ResultsThis protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation.ConclusionsShare2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions.