Heather Cole-Lewis

Like Father, Like Son: The Intergenerational Cycle of Adolescent Fatherhood

OBJECTIVES Strong evidence exists to support an intergenerational cycle of adolescent fatherhood, yet such a cycle has not been studied. We examined whether paternal adolescent fatherhood (i.e., father of study participant was age 19 years or younger when his first child was born) and other factors derived from the ecological systems theory predicted participant adolescent fatherhood. METHODS Data included 1496 young males who were interviewed annually from the National Longitudinal Survey of Youth 1997. Cox regression survival analysis was used to determine the effect of paternal adolescent fatherhood on participant adolescent fatherhood. RESULTS Sons of adolescent fathers were 1.8 times more likely to become adolescent fathers than were sons of older fathers, after other risk factors were accounted for. Additionally, factors from each ecological domain-individual (delinquency), family (maternal education), peer (early adolescent dating), and environment (race/ethnicity, physical risk environment)-were independent predictors of adolescent fatherhood. CONCLUSIONS These findings support the need for pregnancy prevention interventions specifically designed for young males who may be at high risk for continuing this cycle. Interventions that address multiple levels of risk will likely be most successful at reducing pregnancies among partners of young men.

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.

Capitalizing on the Characteristics of mHealth to Evaluate Its Impact

The field of mHealth has made significant advances in a short period of time, demanding a more thorough and scientific approach to understanding and evaluating its progress. A recent review of mHealth literature identified two primary research needs in order for mHealth to strengthen health systems and promote healthy behaviors, namely health outcomes and cost-benefits (Mechael et al., 2010). In direct response to the gaps identified in mHealth research, the aim of this paper is to present the study design and highlight key observations and next steps from an evaluation of the mHealth activities within the electronic health (eHealth) architecture implemented by the Millennium Villages Project (MVP) by leveraging data generated through mobile technology itself alongside complementary qualitative research and costing assessments. The study, funded by the International Development and Research Centre (IDRC) as part of the Open Architecture Standards and Information Systems research project (OASIS II) (Sinha, 2009), is being implemented on data generated by 14 MVP sites in 10 Sub-Saharan African countries including more in-depth research in Ghana, Rwanda, Tanzania, and Uganda. Specific components of the study include rigorous quantitative case-control analyses and other epidemiological approaches (such as survival analysis) supplemented by in-depth qualitative interviews spread out over 18 months, as well as a costing study to assess the impact of mHealth on health outcomes, service delivery, and efficiency.

Pregnancy-specific Stress, Preterm Birth, and Gestational Age Among High-risk Young Women

OBJECTIVE There is evidence that pregnancy-specific stress is associated with preterm birth. The purpose of this study is to examine the association between change in pregnancy-specific stress over the course of pregnancy and birth outcomes (i.e., preterm birth and gestational age) in an understudied but vulnerable group using a theoretically derived model. METHODS Multivariate linear and logistic regression techniques were used to examine the association between pregnancy-specific stress (measured in second and third trimester) and length of gestation (i.e., preterm birth and gestational age) among a sample of 920 Black and/or Latina adolescent and young women. RESULTS Second trimester pregnancy-specific stress was not associated with preterm birth or gestational age. Third trimester pregnancy-specific stress was associated with preterm birth but not with gestational age. Change in pregnancy-specific stress between second and third trimester was significantly associated with increased likelihood of preterm delivery and shortened gestational age, even after controlling for important biological, behavioral, psychological, interpersonal, and sociocultural risk factors. CONCLUSIONS Findings emphasize the importance of measuring pregnancy-specific stress across pregnancy, as the longitudinal change from second to third trimester was significantly associated with length of gestation measured both as a dichotomous variable (preterm birth) and a continuous variable (gestational age). Furthermore, this is the first study to observe the association of pregnancy-specific stress with length of gestation in this understudied population-unique in age, race, and ethnicity.

Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective

OBJECTIVE To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD). METHODS In this qualitative study, researchers conducted semi-structured interviews with individuals from economically disadvantaged communities and ethnic minorities who are participating in a randomized controlled trial of MoDD. The transcripts of the interviews were analyzed using inductive thematic analysis; usage logs were analyzed to determine how actively the study participants used MoDD. RESULTS Fifteen participants in the MoDD randomized controlled trial were recruited for the qualitative interviews. Usage log analysis showed that, on average, during the 4 weeks of the study, the study participants logged into MoDD twice per week, reported 120 blood glucose readings, and set two behavioral goals. The qualitative interviews suggested that individuals used MoDD to follow the steps of the problem-solving process, from identifying problematic blood glucose patterns, to exploring behavioral triggers contributing to these patterns, to selecting alternative behaviors, to implementing these behaviors while monitoring for improvements in glycemic control. DISCUSSION This qualitative study suggested that informatics interventions for reflection and problem solving can provide structured scaffolding for facilitating these processes by guiding users through the different steps of the problem-solving process and by providing them with context-sensitive evidence and practice-based knowledge related to diabetes self-management on each of those steps. CONCLUSION This qualitative study suggested that MoDD was perceived as a useful tool in engaging individuals in self-monitoring, reflection, and problem solving.

Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management

OBJECTIVE To develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. MATERIALS AND METHODS The structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York. RESULTS The knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. DISCUSSION The resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable. CONCLUSION The knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions.

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.

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.