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Highlights • We describe communication campaigns in an intervention to reduce opioid deaths. • The community-engaged model includes three phases. • Five campaigns will focus on naloxone, MOUD, and stigma reduction. • Community surveys and fidelity measures gauge process and impact. • Our model could help communities address other public health issues., Background The HEALing Communities Study (HCS) is testing whether the Communities that Heal (CTH) intervention can decrease opioid overdose deaths through the implementation of evidence-based practices (EBPs) in highly impacted communities. One of the CTH intervention components is a series of communications campaigns to promote the implementation of EBPs, increase demand for naloxone and medications for opioid use disorder (MOUD), and decrease stigma toward people with opioid use disorder and the use of EBPs, especially MOUD. This paper describes the approach to developing and executing these campaigns. Methods The HCS communication campaigns are developed and implemented through a collaboration between communication experts, research site staff, and community coalitions using a three-stage process. The Prepare phase identifies priority groups to receive campaign messages, develops content for those messages, and identifies a “call to action” that asks people to engage in a specific behavior. In the Plan phase, campaign resources are produced, and community coalitions develop plans to distribute campaign materials. During the Implement stage, these distribution plans guide delivery of content to priority groups. Fidelity measures assess how community coalitions follow their distribution plan as well as barriers and facilitators to implementation. An evaluation of the communication campaigns is planned. Conclusions If successful, the Prepare-Plan-Implement process, and the campaign materials, could be adapted and used by other communities to address the opioid crisis. The campaign evaluation will extend the evidence base for how communication campaigns can be developed and implemented through a community-engaged process to effectively address public health crises.

Highlights • HEALing Communities Study is a parallel-group cluster randomized controlled trial. • Communities That HEAL intervention’s goal is to reduce opioid overdose deaths. • Structured consensus decision-making strategy guided study measure development. • More than 80 study measure specifications and a common data model were developed. • The study will provide methodology and longitudinal community data for research., Background Opioid overdose deaths remain high in the U.S. Despite having effective interventions to prevent overdose deaths, there are numerous barriers that impede their adoption. The primary aim of the HEALing Communities Study (HCS) is to determine the impact of an intervention consisting of community-engaged, data-driven selection, and implementation of an integrated set of evidence-based practices (EBPs) on reducing opioid overdose deaths. Methods The HCS is a four year multi-site, parallel-group, cluster randomized wait-list controlled trial. Communities (n = 67) in Kentucky, Massachusetts, New York and Ohio are randomized to active intervention (Wave 1), which starts the intervention in Year 1 or the wait-list control (Wave 2), which starts the intervention in Year 3. The HCS will test a conceptually driven framework to assist communities in selecting and adopting EBPs with three components: 1) A community engagement strategy with local coalitions to guide and implement the intervention; 2) A compendium of EBPs coupled with technical assistance; and 3) A series of communication campaigns to increase awareness and demand for EBPs and reduce stigma. An implementation science framework guides the intervention and allows for examination of the multilevel contexts that promote or impede adoption and expansion of EBPs. The primary outcome, number of opioid overdose deaths, will be compared between Wave 1 and Wave 2 communities during Year 2 of the intervention for Wave 1. Numerous secondary outcomes will be examined. Discussion The HCS is the largest community-based implementation study in the field of addiction with an ambitious goal of significantly reducing fatal opioid overdoses.

Contemporary societies around the world face a number of what have been termed ‘wicked problems’ (Churchman, 1967; Rittel & Webber, 1973). These issues include but are not restricted to health ineq...

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

This article documents the emergence of social media, and specifically social network sites (SNS) and their impact on health information–seeking and health-related behaviors. We review surveys of user behavior on SNS to document how health information is being transformed into a social health experience rather than an individual or clinical endeavor. We then turn to the research evidence for how SNS may influence health behaviors. Although there is a substantial literature that provides support for the role of social variables in the genesis and management of health and disease, there is little scientific grounding for how to leverage these variables to improve health in either online or offline milieus. We conclude with recommendations for practice to optimize the use of social media and its contribution to improved health outcomes, and pose a series of questions that may guide the development of a research agenda in this area. The technological innovations of blogs, podcasts, interactive media, and SNS have enabled many people to create, post, and share their own messages and content by using a variety of digital social communication tools and platforms. People and organizations can now quickly create and deliver content through more interactive Web sites and online communities where, for example, people with medical conditions can seek, give, and receive advice from other patients and healthcare providers.1,2 New communication technologies and the emergence of what has been dubbed Web 2.0 are providing the opportunity for health professionals and patients alike to engage with 1 another, their peers, friends, and families in ways unimaginable just a few years ago. The speed and scale of adoption of social media is changing the way we think about and communicate with people formerly known as audiences and the way doctors and patients interact,2,3 bringing about a …

A perennial problem in social marketing and public policy is the plight of at-risk consumers. The authors define at-risk consumers as marketplace participants who, because of historical or personal circumstances or disabilities, may be harmed by marketers’ practices or may be unable or unwilling to take full advantage of marketplace opportunities. This definition refers to either objective reality or perceptions. Early research focused on consumers who were at risk because they were poor, ethnic or racial minorities, immigrants, women, or elderly. Today's researchers also study consumers who are at risk because they are from religious minorities, disabled, illiterate, homeless, indigent, lesbian, gay, bisexual, or transgender. The authors identify four tensions affecting research on and policy and marketing applications for at-risk populations: the value of focusing on (1) vulnerabilities versus strengths, (2) radical versus marginal change, (3) targeting versus nontargeting, and (4) encouraging knowledgeable versus naive consumers. They conclude with a discussion of the significance of including at-risk consumers as full marketplace participants and identify future research directions.

A scale for measuring the engagement properties of eHealth content was adapted from commercial advertising research. We define engagement as the process of involving users in health content in ways that motivate and lead to health behavior change. Complete responses were obtained from 230/260 participants exposed to health content from seven content areas in online remote testing. After viewing each of three randomly assigned health content areas, the participants completed two questionnaires. The first one assessed the appropriateness, applicability, motivation, and intentions to change or engage in health behaviors relevant to the set of content components displayed for that health topic. The second questionnaire was the eHealth Engagement Scale in which participants rated each of 12 descriptors on a 5-point Likert scale. Internal reliability of each of the two multi-item subscales of the Engagement Scale was .878 for Involving and .805 for Credible. A 4-factor solution eliminating three of the original 12 word descriptors was found to be the superior in the subsequent analysis of predictive validity. The eHealth Engagement Scale may prove to be an important mediator of user retention of information, intentions to change, and ultimately efforts to undertake and achieve behavior change.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

Introduction As part of its mission, the US Food and Drug Administration (FDA) communicates with the public regularly about the benefits and risks of prescription and over-the-counter (OTC) drugs. Effectively communicating risk, however, is a significant public health challenge. Objective To better understand how different populations understand information communicated by the FDA about drug safety, we conducted a randomized experiment to examine comprehension and other measures of effectiveness of drug safety messages that occurred in a post-market surveillance phase. Methods We used an Internet panel survey of 1244 consumers, of whom 58 % used prescription drugs in the past year. Half of the sample panel was randomized to read a previous FDA Drug Safety Communication (DSC) with the drug name changed, and the other half was randomized to read a revised version of the same DSC. We examined how making certain modifications to the way drug risk information is communicated has an impact on comprehension and behavioral intentions, including the user’s likelihood of discontinuing the drug. We also studied how comprehension varied by respondent characteristics, health literacy skills, risk perceptions, and trust in the message. Results Based on a five-item comprehension index, the revised version of the message was associated with significantly greater comprehension of the information relative to the standard version (63 vs 52 % correct, p