Rebecca L. Kinney

Internet health information seeking is a team sport: Analysis of the Pew Internet Survey

BACKGROUND Previous studies examining characteristics of Internet health information seekers do not distinguish between those who only seek for themselves, and surrogate seekers who look for health information for family or friends. Identifying the unique characteristics of surrogate seekers would help in developing Internet interventions that better support these information seekers. OBJECTIVE To assess differences between self seekers versus those that act also as surrogate seekers. METHODS We analyzed data from the cross-sectional Pew Internet and American Life Project November/December 2008 health survey. Our dependent variable was self-report of type of health information seeking (surrogate versus self seeking). Independent variables included demographics, health status, and caregiving. After bivariate comparisons, we then developed multivariable models using logistic regression to assess characteristics associated with surrogate seeking. RESULTS Out of 1250 respondents who reported seeking health information online, 56% (N=705) reported being surrogate seekers. In multivariable models, compared with those who sought information for themselves only, surrogate seekers were more likely both married and a parent (OR=1.57, CI=1.08, 2.28), having good (OR=2.05, CI=1.34, 3.12) or excellent (OR=2.72, CI=1.70, 4.33) health status, being caregiver of an adult relative (OR=1.76, CI=1.34, 2.30), having someone close with a serious medical condition (OR=1.62, CI=1.21, 2.17) and having someone close to them facing a chronic illness (OR=1.55, CI=1.17, 2.04). CONCLUSIONS Our findings provide evidence that information needs of surrogate seekers are not being met, specifically of caregivers. Additional research is needed to develop new functions that support surrogate seekers.

The association between patient activation and medication adherence, hospitalization, and emergency room utilization in patients with chronic illnesses: A systematic review

OBJECTIVE A systematic review of the published literature on the association between the PAM (Patient Activation Measure) and hospitalization, emergency room use, and medication adherence among chronically ill patient populations. METHODS A literature search of several electronic databases was performed. Studies published between January 1, 2004 and June 30, 2014 that used the PAM measure and examined at least one of the outcomes of interest among a chronically ill study population were identified and systematically assessed. RESULTS Ten studies met the eligibility criteria. Patients who scored in the lower PAM stages (Stages 1 and 2) were more likely to have been hospitalized. Patients who scored in the lowest stage were also more likely to utilize the emergency room. The relationship between PAM stage and medication adherence was inconclusive in this review. CONCLUSION Chronically ill patients reporting low stages of patient activation are at an increased risk for hospitalization and ER utilization. PRACTICAL IMPLICATIONS Future research is needed to further understand the relationship between patient activation and medication adherence, hospitalization and/or ER utilization in specific chronically ill (e.g. diabetic, asthmatic) populations. Research should also consider the role of patient activation in the development of effective interventions which seek to address the outcomes of interest.

An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets.

Background Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers. Objective Our aim was to examine current Twitter activity for smoking cessation. Methods Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational). Results The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the change in followership significantly increased by 43.94 (P=.027); the association was slightly attenuated after adjusting by account type and was not significant (P=.064). Conclusions Smoking cessation activity does exist on Twitter. Preliminary findings suggest that certain content strategies can be used to encourage followership, and this needs to be further investigated.

Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation

Background Smoking 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. Objective The 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. Methods We 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. Results This 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. Conclusions Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Background What 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. Objective The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. Methods We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. Results We 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. Conclusions We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Who Participates in Web-Assisted Tobacco Interventions? The Quit-Primo and National Dental Practice-Based Research Network Hi-Quit Studies

Introduction Smoking is the most preventable cause of death. Although effective, Web-assisted tobacco interventions are underutilized and recruitment is challenging. Understanding who participates in Web-assisted tobacco interventions may help in improving recruitment. Objectives To understand characteristics of smokers participating in a Web-assisted tobacco intervention (Decide2Quit.org). Methods In addition to the typical Google advertisements, we expanded Decide2Quit.org recruitment to include referrals from medical and dental providers. We assessed how the expanded recruitment of smokers changed the users’ characteristics, including comparison with a population-based sample of smokers from the national Behavioral Risk Factors Surveillance Survey (BRFSS). Using a negative binomial regression, we compared demographic and smoking characteristics by recruitment source, in particular readiness to quit and association with subsequent Decide2Quit.org use. Results The Decide2Quit.org cohort included 605 smokers; the 2010 BRFSS dataset included 69,992. Compared to BRFSS smokers, a higher proportion of Decide2Quit.org smokers were female (65.2% vs 45.7%, P=.001), over age 35 (80.8% vs 67.0%, P=.001), and had some college or were college graduates (65.7% vs 45.9%, P=.001). Demographic and smoking characteristics varied by recruitment; for example, a lower proportion of medical- (22.1%) and dental-referred (18.9%) smokers had set a quit date or had already quit than Google smokers (40.1%, P<.001). Medical- and dental-referred smokers were less likely to use Decide2Quit.org functions; in adjusted analysis, Google smokers (predicted count 17.04, 95% CI 14.97-19.11) had higher predicted counts of Web page visits than medical-referred (predicted count 12.73, 95% CI 11.42-14.04) and dental-referred (predicted count 11.97, 95% CI 10.13-13.82) smokers, and were more likely to contact tobacco treatment specialists. Conclusions Recruitment from clinical practices complimented Google recruitment attracting smokers less motivated to quit and less experienced with Web-assisted tobacco interventions. Trial Registration Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/6F3tqz0b3)

Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems

Introduction Although technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook). Methods We recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers. Results Overall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons). Conclusions Peer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research. Implications 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.

PERSPeCT: collaborative filtering for tailored health communications

The goal of computer tailored health communications (CTHC) is to elicit healthy behavior changes by sending motivational messages personalized to individual patients. One prominent weakness of many existing CTHC systems is that they are based on expert-written rules and thus have no ability to learn from their users over time. One solution to this problem is to develop CTHC systems based on the principles of collaborative filtering, but this approach has not been widely studied. In this paper, we present a case study evaluating nine rating prediction methods for use in the Patient Experience Recommender System for Persuasive Communication Tailoring, a system developed for use in a clinical trial of CTHC-based smoking cessation support interventions.

Survivors of an Acute Coronary Syndrome With Lower Patient Activation Are More Likely to Experience Declines in Health-Related Quality of Life

Background:Patient activation comprises the knowledge, skills, and confidence for self-care and may lead to better health outcomes. Objectives:We examined the relationship between patient activation and changes in health-related quality of life (HRQOL) after hospitalization for an acute coronary syndrome (ACS). Methods:We studied patients from 6 medical centers in central Massachusetts and Georgia who had been hospitalized for an ACS between 2011 and 2013. At 1 month after hospital discharge, the patients completed the 6-item Patient Activation Measure and were categorized into 4 levels of activation. Multinomial logistic regression analyses compared activation level with clinically meaningful changes (≥3.0 points, generic; ≥10.0 points, disease-specific) in generic physical (SF-36v2 Physical Component Summary [PCS]), generic mental (SF-36v2 Mental Component Summary [MCS]), and disease-specific (Seattle Angina Questionnaire [SAQ]) HRQOL from 1 to 3 and 1 to 6 months after hospitalization, adjusting for potential sociodemographic and clinical confounders. Results:The patients (N = 1042) were, on average, 62 years old, 34% female, and 87% non-Hispanic white. A total of 10% were in the lowest level of activation. The patients with the lowest activation had 1.95 times (95% confidence interval, 1.05–3.62) and 2.18 times (95% confidence interval, 1.17–4.05) the odds of experiencing clinically significant declines in MCS and SAQ HRQOL, respectively, between 1 and 6 months than the most activated patients. The patient activation level was not associated with meaningful changes in PCS scores. Conclusions:Hospital survivors of an ACS with lower activation may be more likely to experience declines in mental and disease-specific HRQOL than more-activated patients, identifying a group at risk of poor outcomes.

How do features of Electronic Health Records Impact Prescription of Nicotine Replacement Therapy

Nicotine Replacement Therapy (NRT) is an effective medication to help patients quit smoking tobacco. Yet, 18% of adults in the United States still smoke cigarettes. With advancements in health technology and improved features within electronic health record (EHR) systems, it is crucial to understand how differences in EHR features influence the prescribing of NRT. We conducted a cross-sectional study of 174 primary care practices to better understand how EHR features, including drug reference databases in EHRs, were associated with NRT prescribing at a practice level. Regression models were created to understand NRT prescribing patterns among clinics with varying EHR features and found that practices using an EHR with a drug reference database were 2.3 times more likely to view NRT as a high priority for treating smokers. Use of NRT in primary care differs significantly in relation to the capability of a clinic’s technology. Clinics with more EHR features, specifically EHR drug reference databases, favored NRT. Our study suggests that pharmacotherapy could become the preferred activity in smoking cessation treatment, as EHR-integrated drug reference database prevalence increases.