The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.

Background Chronic diseases require chronic disease self-management (CDSM). Existing CDSM interventions, while improving outcomes, often do not lead to long-lasting effects. To render existing and new CDSM interventions more effective, an exploration of the concept of CDSM from both the literature and patient perspectives is needed. Purpose To describe the current conceptualization of CDSM in the literature, identify potential inadequacies in this conceptualization based on a comparison of literature- and patient-based CDSM descriptions, and to offer a more comprehensive definition of CDSM. Method A hybrid concept analysis was completed. Discussion In the literature, CDSM is defined as behaviors influenced by individual characteristics. Patients in the fieldwork phase discussed aspects of CDSM not well-represented in the literature. Conclusions CDSM is a complex process involving behaviors at multiple levels of a person's environment. Pilot work to develop and test CDSM interventions based on both individual and external characteristics is needed.

BackgroundSmokers are a stigmatized population, but an important population to reach for the purpose of research. Therefore, innovative recruitment methods are needed that are both cost-effective and efficacious in recruiting this population.ObjectiveThe aim of the present article was to evaluate the feasibility of Facebook-targeted advertisement to recruit long-term smokers eligible for lung cancer screening for a descriptive, cross-sectional survey.MethodsA social media recruitment campaign was launched using Facebook-targeted advertisement to target age and keywords related to tobacco smoking in the Facebook users profile, interests, and likes. A 3-day newspaper advertisement recruitment campaign was used as a comparison. The study that used both recruitment methods aimed to test the psychometric properties of 4 newly developed lung cancer screening health belief scales. Data were collected via cross-sectional survey methodology using an Web-based survey platform.ResultsThe Facebook-targeted advertisements were viewed 56,621 times over an 18-day campaign in 2015 in the United States. The advertisement campaign yielded 1121 unique clicks to the Web-based survey platform at a cost of $1.51 per completed survey. Of those who clicked through to the study survey platform, 423 (37.7%) consented to participate; 92 (8.2%) dropped out during completion of the survey yielding a final study pool of 331 completed surveys. Recruitment by newspaper advertisement yielded a total of 30 participants in response to a 3-day advertisement campaign; recruitment efficacy resulted in 10 participants/day at $40.80 per completed survey. Participants represented current (n=182; 51%) and former smokers (n=178; 49%) with a mean age of 63.4 years (SD 6.0). Cost of the advertisement campaign was $500 total for the 18-day campaign.ConclusionsRecruitment by Facebook was more efficacious and cost-effective compared with newspaper advertisement. Facebook offers a new venue for recruitment into research studies that offer the potential for wider reach at a lower cost while providing privacy and flexibility for potential study participants. The study’s findings extend recent work of other researchers who have demonstrated Facebook’s utility with younger smokers, and Facebook is an effective tool to recruit older smokers. Furthermore, Facebook is a cost-effective alternative to traditional newspaper advertisement offering a new, affordable venue to recruit large numbers of older smokers efficiently.

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.