Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review.

BACKGROUND: The rise in usage of and access to new technologies in recent years has led to a growth in digital health behavior change interventions. As the shift to digital platforms continues to grow, it is increasingly important to consider how the field of information architecture (IA) can inform the development of digital health interventions. IA is the way in which digital content is organized and displayed, which strongly impacts users' ability to find and use content. While many information architecture best practices exist, there is a lack of empirical evidence on the role it plays in influencing behavior change and health outcomes. OBJECTIVE: Our aim was to conduct a systematic review synthesizing the existing literature on website information architecture and its effect on health outcomes, behavioral outcomes, and website engagement. METHODS: To identify all existing information architecture and health behavior literature, we searched articles published in English in the following databases (no date restrictions imposed): ACM Digital Library, CINAHL, Cochrane Library, Google Scholar, Ebsco, and PubMed. The search terms used included information terms (eg, information architecture, interaction design, persuasive design), behavior terms (eg, health behavior, behavioral intervention, ehealth), and health terms (eg, smoking, physical activity, diabetes). The search results were reviewed to determine if they met the inclusion and exclusion criteria created to identify empirical research that studied the effect of IA on health outcomes, behavioral outcomes, or website engagement. Articles that met inclusion criteria were assessed for study quality. Then, data from the articles were extracted using a priori categories established by 3 reviewers. However, the limited health outcome data gathered from the studies precluded a meta-analysis. RESULTS: The initial literature search yielded 685 results, which was narrowed down to three publications that examined the effect of information architecture on health outcomes, behavioral outcomes, or website engagement. One publication studied the isolated impact of information architecture on outcomes of interest (ie, website use and engagement; health-related knowledge, attitudes, and beliefs; and health behaviors), while the other two publications studied the impact of information architecture, website features (eg, interactivity, email prompts, and forums), and tailored content on these outcomes. The paper that investigated IA exclusively found that a tunnel IA improved site engagement and behavior knowledge, but it decreased users' perceived efficiency. The first study that did not isolate IA found that the enhanced site condition improved site usage but not the amount of content viewed. The second study that did not isolate IA found that a tailored site condition improved site usage, behavior knowledge, and some behavior outcomes. CONCLUSIONS: No clear conclusion can be made about the relationship between IA and health outcomes, given limited evidence in the peer-reviewed literature connecting IA to behavioral outcomes and website engagement. Only one study reviewed solely manipulated IA, and we therefore recommend improving the scientific evidence base such that additional empirical studies investigate the impact of IA in isolation. Moreover, information from the gray literature and expert opinion might be identified and added to the evidence base, in order to lay the groundwork for hypothesis generation to improve empirical evidence on information architecture and health and behavior outcomes.

Analysing user-reported data for enhancement of SmokefreeTXT: a national text message smoking cessation intervention.

OBJECTIVE: This observational study highlights key insights related to participant engagement and cessation among adults who voluntarily subscribed to the nationwide US-based SmokefreeTXT program, a 42-day mobile phone text message smoking cessation program. METHODS: Point prevalence abstinence rates were calculated for subscribers who initiated treatment in the program (n=18 080). The primary outcomes for this study were treatment completion and point prevalence abstinence rate at the end of the 42-day treatment. Secondary outcomes were point prevalence abstinence rates at 7 days postquit, 3 months post-treatment and 6 months post-treatment, as well as response rates to point prevalence abstinence assessments. RESULTS: Over half the sample completed the 42-day treatment (n=9686). The end-of-treatment point prevalence abstinence for subscribers who initiated treatment was 7.2%. Among those who completed the entire 42 days of treatment, the end-of-treatment point prevalence abstinence was 12.9%. For subscribers who completed treatment, point prevalence abstinence results varied: 7 days postquit (23.7%), 3 months post-treatment (7.3%) and 6 months post-treatment (3.7%). Response rates for abstinence assessment messages ranged from 4.36% to 34.48%. CONCLUSIONS: Findings from this study illuminate the need to more deeply understand reasons for subscriber non-response and opt out and, in turn, improve program engagement and our ability to increase the likelihood for participants to stop smoking and measure long-term outcomes. Patterns of opt out for the program mirror the relapse curve generally observed for smoking cessation, thus highlighting time points at which to increase efforts to retain participants and provide additional support or incentives.

Social Network Behavior and Engagement Within a Smoking Cessation Facebook Page.

BACKGROUND: Social media platforms are increasingly being used to support individuals in behavior change attempts, including smoking cessation. Examining the interactions of participants in health-related social media groups can help inform our understanding of how these groups can best be leveraged to facilitate behavior change. OBJECTIVE: The aim of this study was to analyze patterns of participation, self-reported smoking cessation length, and interactions within the National Cancer Institutes' Facebook community for smoking cessation support. METHODS: Our sample consisted of approximately 4243 individuals who interacted (eg, posted, commented) on the public Smokefree Women Facebook page during the time of data collection. In Phase 1, social network visualizations and centrality measures were used to evaluate network structure and engagement. In Phase 2, an inductive, thematic qualitative content analysis was conducted with a subsample of 500 individuals, and correlational analysis was used to determine how participant engagement was associated with self-reported session length. RESULTS: Between February 2013 and March 2014, there were 875 posts and 4088 comments from approximately 4243 participants. Social network visualizations revealed the moderator's role in keeping the community together and distributing the most active participants. Correlation analyses suggest that engagement in the network was significantly inversely associated with cessation status (Spearman correlation coefficient = -0.14, P=.03, N=243). The content analysis of 1698 posts from 500 randomly selected participants identified the most frequent interactions in the community as providing support (43%, n=721) and announcing number of days smoke free (41%, n=689). CONCLUSIONS: These findings highlight the importance of the moderator for network engagement and provide helpful insights into the patterns and types of interactions participants are engaging in. This study adds knowledge of how the social network of a smoking cessation community behaves within the confines of a Facebook group.

Household and community-level Adverse Childhood Experiences and adult health outcomes in a diverse urban population.

Adverse Childhood Experiences (ACEs), which include family dysfunction and community-level stressors, negatively impact the health and well being of children throughout the life course. While several studies have examined the impact of these childhood exposures amongst racially and socially diverse populations, the contribution of ACEs in the persistence of socioeconomic disparities in health is poorly understood. To determine the association between ACEs and health outcomes amongst a sample of adults living in Philadelphia and examine the moderating effect of Socioeconomic Status (SES) on this association, we conducted a cross-sectional survey of 1,784 Philadelphia adults, ages 18 and older, using random digit dialing methodology to assess Conventional ACEs (experiences related to family dysfunction), Expanded ACEs (community-level stressors), and health outcomes. Using weighted, multivariable logistic regression analyses along with SES stratified models, we examined the relationship between ACEs and health outcomes as well as the modifying effect of current SES. High Conventional ACE scores were significantly associated with health risk behaviors, physical and mental illness, while elevated Expanded ACE scores were associated only with substance abuse history and sexually transmitted infections. ACEs did have some differential impacts on health outcomes based on SES. Given the robust impact of Conventional ACEs on health, our results support prior research highlighting the primacy of family relationships on a child's life course trajectory and the importance of interventions designed to support families. Our findings related to the modifying effect of SES may provide additional insight into the complex relationship between poverty and childhood adversity.

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

BACKGROUND: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. OBJECTIVE: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. METHODS: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. RESULTS: The analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. CONCLUSIONS: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. OBJECTIVE: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. METHODS: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. RESULTS: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. CONCLUSIONS: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Adverse Childhood Experiences: Expanding the Concept of Adversity.

INTRODUCTION: Current knowledge of Adverse Childhood Experiences (ACEs) relies on data predominantly collected from white, middle- / upper-middle-class participants and focuses on experiences within the home. Using a more socioeconomically and racially diverse urban population, Conventional and Expanded (community-level) ACEs were measured to help understand whether Conventional ACEs alone can sufficiently measure adversity, particularly among various subgroups. METHODS: Participants from a previous large, representative, community-based health survey in Southeast Pennsylvania who were aged ≥18 years were contacted between November 2012 and January 2013 to complete another phone survey measuring ACEs. Ordinal logistic regression models were used to test associations between Conventional and Expanded ACEs scores and demographic characteristics. Analysis was conducted in 2013 and 2014. RESULTS: Of 1,784 respondents, 72.9% had at least one Conventional ACE, 63.4% at least one Expanded ACE, and 49.3% experienced both. A total of 13.9% experienced only Expanded ACEs and would have gone unrecognized if only Conventional ACEs were assessed. Certain demographic characteristics were associated with higher risk for Conventional ACEs but were not predictive of Expanded ACEs, and vice versa. Few adversities were associated with both Conventional and Expanded ACEs. CONCLUSIONS: To more accurately represent the level of adversity experienced across various sociodemographic groups, these data support extending the Conventional ACEs measure.