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BackgroundElectronic 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. ObjectiveOur 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. MethodsManual 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. ResultsPredictive 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. ConclusionsSocial 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.

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using 'ontologies.' In information science, an ontology is a systematic method for articulating a 'controlled vocabulary' of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science., Author(s): Kai R. Larsen [sup.1] , Susan Michie [sup.2] , Eric B. Hekler [sup.3] , Bryan Gibson [sup.4] , Donna Spruijt-Metz [sup.5] , David Ahern [sup.6] , Heather Cole-Lewis [sup.7] [...]

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. (Am J Public Health. 2010;100:517-524. doi: 10.2105/AJPH.2009.177600)

Digital health promises to increase intervention reach and effectiveness for a range of behavioral health outcomes. Behavioral scientists have a unique opportunity to infuse their expertise in all phases of a digital health intervention, from design to implementation. The aim of this study was to assess behavioral scientists' interests and needs with respect to digital health endeavors, as well as gather expert insight into the role of behavioral science in the evolution of digital health. The study used a two-phased approach: (a) a survey of behavioral scientists' current needs and interests with respect to digital health endeavors (n = 346); (b) a series of interviews with digital health stakeholders for their expert insight on the evolution of the health field (n = 15). In terms of current needs and interests, the large majority of surveyed behavioral scientists (77%) already participate in digital health projects, and from those who have not done so yet, the majority (65%) reported intending to do so in the future. In terms of the expected evolution of the digital health field, interviewed stakeholders anticipated a number of changes, from overall landscape changes through evolving models of reimbursement to more significant oversight and regulations. These findings provide a timely insight into behavioral scientists' current needs, barriers, and attitudes toward the use of technology in health care and public health. Results might also highlight the areas where behavioral scientists can leverage their expertise to both enhance digital health's potential to improve health, as well as to prevent the potential unintended consequences that can emerge from scaling the use of technology in health care. [ABSTRACT FROM AUTHOR]

The rapid expansion of technology promises to transform the behavior science field by revolutionizing the ways in which individuals can monitor and improve their health behaviors. To fully live into this promise, the behavior science field must address distinct challenges, including: building interventions that are not only scientifically sound but also engaging; using evaluation methods to precisely assess intervention components for intervention optimization; and building personalized interventions that acknowledge and adapt to the dynamic ecosystem of individual and contextual variables that impact behavior change. The purpose of this paper is to provide a framework to address these challenges by leveraging behavior science, human-centered design, and data science expertise throughout the cycle of developing and evaluating digital behavior change interventions (DBCIs). To define this framework, we reviewed current models and practices for intervention development and evaluation, as well as technology industry models for product development. The framework promotes an iterative process, aiming to maximize outcomes by incorporating faster and more frequent testing cycles into the lifecycle of a DBCI. Within the framework provided, we describe each phase, from development to evaluation, to discuss the optimal practices, necessary stakeholders, and proposed evaluation methods. The proposed framework may inform practices in both academia and industry, as well as highlight the need to offer collaborative platforms to ensure successful partnerships that can lead to more effective DBCIs that reach broad and diverse populations. [ABSTRACT FROM AUTHOR]

Mobile text-messaging smoking cessation interventions have demonstrated their efficacy in increasing cessation rates. These interventions tend to be multifaceted and there is a need to specify their building blocks. The purpose of this study was to use the Behavioural Change Techniques Taxonomy V1 (BCTTv1) to systematically analyse the behaviour change techniques present in the SmokefreeTXT (SFTXT) adult programme. The entire SFTXT library was coded using the BCTTv1. Frequencies were calculated to assess the presence of BCT groups and unique BCTs in the entire programme. The mix of BCTs was also examined by programme week and during periods of high user dropout. Of the 16 groups of behavioural techniques, 14 were present in SFTXT. Of the 93 distinct BCTs, 41 were present in the full SFTXT message library. The most prevalent BCT groups were Feedback and Monitoring, Natural Consequences, Social Support, and Shaping Knowledge. There were differences in the mix of BCTs across the duration of the intervention. The results will enable us to test how changes in the use of specific BCTs and their frequency of use over time, impact (1) engagement with the programme (particularly during the days with high dropout rates), and (2) smoking cessation outcomes over time. [ABSTRACT FROM PUBLISHER]