Rajani S. Sadasivam

Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations

Mobile applications (apps) to improve health are proliferating, but before healthcare providers or organizations can recommend an app to the patients they serve, they need to be confident the app will be user-friendly and helpful for the target disease or behavior. This paper summarizes seven strategies for evaluating and selecting health-related apps: (1) Review the scientific literature, (2) Search app clearinghouse websites, (3) Search app stores, (4) Review app descriptions, user ratings, and reviews, (5) Conduct a social media query within professional and, if available, patient networks, (6) Pilot the apps, and (7) Elicit feedback from patients. The paper concludes with an illustrative case example. Because of the enormous range of quality among apps, strategies for evaluating them will be necessary for adoption to occur in a way that aligns with core values in healthcare, such as the Hippocratic principles of nonmaleficence and beneficence.

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.

Development of an Interactive, Web-Delivered System to Increase Provider–Patient Engagement in Smoking Cessation

Background Patient self-management interventions for smoking cessation are effective but underused. Health care providers do not routinely refer smokers to these interventions. Objective The objective of our study was to uncover barriers and facilitators to the use of an e-referral system that will be evaluated in a community-based randomized trial. The e-referral system will allow providers to refer smokers to an online smoking intervention during routine clinical care. Methods We devised a four-step development and pilot testing process: (1) system conceptualization using Delphi to identify key functionalities that would overcome barriers in provider referrals for smoking cessation, (2) Web system programming using agile software development and best programming practices with usability refinement using think-aloud testing, (3) implementation planning using the nominal group technique for the effective integration of the system into the workflow of practices, and (4) pilot testing to identify practice recruitment and system-use barriers in real-world settings. Results Our Delphi process (step 1) conceptualized three key e-referral functions: (1) Refer Your Smokers, allowing providers to e-refer patients at the point of care by entering their emails directly into the system, (2) practice reports, providing feedback regarding referrals and impact of smoking-cessation counseling, and (3) secure messaging, facilitating provider–patient communication. Usability testing (step 2) suggested the system was easy to use, but implementation planning (step 3) suggested several important approaches to encourage use (eg, proactive email cues to encourage practices to participate). Pilot testing (step 4) in 5 practices had limited success, with only 2 patients referred; we uncovered important recruitment and system-use barriers (eg, lack of study champion, training, and motivation, registration difficulties, and forgetting to refer). Conclusions Implementing a system to be used in a clinical setting is complex, as several issues can affect system use. In our ongoing large randomized trial, preliminary analysis with the first 50 practices using the system for 3 months demonstrated that our rigorous preimplementation evaluation helped us successfully identify and overcome these barriers before the main trial. Trial Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/61feCfjCy)

A Meta-Composite Software Development Approach for Translational Research

Translational researchers conduct research in a highly data-intensive and continuously changing environment and need to use multiple, disparate tools to achieve their goals. These researchers would greatly benefit from meta-composite software development or the ability to continuously compose and recompose tools together in response to their ever-changing needs. However, the available tools are largely disconnected, and current software approaches are inefficient and ineffective in their support for meta-composite software development. Building on the composite services development approach, the de facto standard for developing integrated software systems, we propose a concept-map and agent-based meta-composite software development approach. A crucial step in composite services development is the modeling of users’ needs as processes, which can then be specified in an executable format for system composition. We have two key innovations. First, our approach allows researchers (who understand their needs best) instead of technicians to take a leadership role in the development of process models, reducing inefficiencies and errors. A second innovation is that our approach also allows for modeling of complex user interactions as part of the process, overcoming the technical limitations of current tools. We demonstrate the feasibility of our approach using a real-world translational research use case. We also present results of usability studies evaluating our approach for future refinements.

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)

Web-based smoking cessation intervention that transitions from inpatient to outpatient: study protocol for a randomized controlled trial

BackgroundE-health tools are a new mechanism to expand patient care, allowing supplemental resources to usual care, including enhanced patient-provider communication. These applications to smoking cessation have yet to be tested in a hospitalized patient sample. This project aims to evaluate the effectiveness and cost-effectiveness of a tailored web-based and e-message smoking cessation program for current smokers that, upon hospital discharge, transitions the patient to continue a quit attempt when home (Decide2Quit).DesignA randomized two-arm follow-up design will test the effectiveness of an evidence- and theoretically-based smoking cessation program designed for post-hospitalization.MethodsA total of 1,488 patients aged 19 or older, who smoked cigarettes in the previous 30 days, are being recruited from 27 patient care areas of a large urban university hospital. Study-eligible hospitalized patients receiving usual tobacco cessation usual care are offered study referral. Trained hospital staff assist the 744 patients who are being randomized to the intervention arm with registration and orientation to the intervention website. This e-mail and web-based program offers tailored messages as well as education, self-assessment and planning aids, and social support to promote tobacco use cessation. Condition-blind study staff assess participants for tobacco use history and behaviors, tobacco use cost-related information, co-morbidities and psychosocial factors at 0, 3, 6, and 12 months. The primary outcome is self-reported 30-day tobacco abstinence at 6 months follow-up. Secondary outcomes include 7-day point prevalence quit rates at 3-, 6-, and 12-month follow-up, 30-day point prevalence quit rates at 3 and 12 months, biologically confirmed tobacco abstinence at 6-month follow-up, and multiple point-prevalence quit rates based on self-reported tobacco abstinence rates at each follow-up time period. Healthcare utilization and quality of life are assessed at baseline, and 6- and 12-month follow-up to measure program cost-effectiveness from the hospital, healthcare payer, patient, and societal perspectives.DiscussionGiven the impact of tobacco use on medical resources, establishing feasible, cost-effective methods for reducing tobacco use is imperative. Given the minimal hospital staff burden and the automated transition to a post-hospitalization tailored intervention, this program could be an easily disseminated approach.Trial registrationCurrent Intervention Trial NCT01277250

Impact of content-specific email reminders on provider participation in an online intervention: a dental PBRN study

Engaging busy healthcare providers in online continuing education interventions is challenging. In an Internet-delivered intervention for dental providers, we tested a series of email-delivered reminders - cues to action. The intervention included case-based education and downloadable practice tools designed to encourage providers to increase delivery of smoking cessation advice to patients. We compared the impact of email reminders focused on 1) general project announcements, 2) intervention related content (smoking cessation), and 3) unrelated content (oral cancer prevention focused content). We found that email reminders dramatically increased participation. The content of the message had little impact on the participation, but day of the week was important - messages sent at the end of the week had less impact, likely due to absence from clinic on the weekend. Email contact, such as day of week an email is sent and notice of new content post-ing, is critical to longitudinal engagement. Further research is needed to understand which messages and how frequently, will maximize participation.