Best practices and lessons learned from implementing a massive Ebola vaccination program: Summarizing UMURINZI team experience.

Background and Aims: The unified Rwandan initiative for national ZEBOVAC immunization (UMURINZI) program's community engagement component was enacted to mobilize and vaccinate high-risk community members. This article describes best practices and lessons learned from the implementation of UMURINZI, a large-scale Ebola vaccination program. Methods: The population deemed to be at risk for EVD consisted of people who frequently cross Rwanda and the Democratic Republic of Congo (DRC) borders including those coming from Kigali City, potential first responders who have not previously been vaccinated against EVD, as well as people who reside in high-risk border-proximate areas of the Rubavu and Rusizi districts in the Western Province of Rwanda. These districts were selected because of their proximity to high-traffic borders linking Rwanda to DRC's cities near an active Ebola outbreak. Volunteers of this program were adults, adolescents, and children aged 2 years or above who resided in the selected communities. Recruitment at the sites was conducted in close collaboration with each health area's Community Health Workers (CHWs). Volunteers were informed that the program involved being fully vaccinated (two doses of Ebola vaccines) within 2 months apart in the allocated vaccination sites. Results: Lessons learned were categorized into four pillars: infrastructure, leadership, myths, and partnership with respect. The best practices that were used during the implementation of the UMURINZI program were the results of a collaboration among CHWs, the involvement of national and local leaders, the use of a comprehensive engagement plan, and training. The study also had limitations. Conclusion: We described best practices and lessons learned during the implementation of the UMURINZI program in Rwanda. These practices and lessons learned represent promising options that could contribute to better community members' participation in mass vaccination programs. Hence, we demonstrated that rigorously designed community awareness and sensitization programs are effective for the implementation of similar programs in resource-limited settings.

Leapfrogging with technology: introduction of a monitoring platform to support a large-scale Ebola vaccination program in Rwanda.

Continued outbreaks of Ebola virus disease, including recent outbreaks in the Democratic Republic of the Congo (DRC), highlight the need for effective vaccine programs to combat future outbreaks. Given the population flow between DRC and Rwanda, the Rwanda Ministry of Health initiated a preventive vaccination campaign supported by a vaccination monitoring platform (VMP). The campaign aimed to vaccinate approximately 200,000 people from Rwanda's Rubavu and Rusizi districts with the two-dose vaccine regimen Ad26.ZEBOV, MVA-BN-Filo. The VMP encompassed: biometric identification (iris scanning), mobile messaging, and an interactive reporting dashboard. The VMP collected data used to register and identify participants at subsequent visits. Mobile message reminders supported compliance. To 13 November 2020, the campaign was half complete with Ad26.ZEBOV administered to 116,974 participants and MVA-BN-Filo to 76,464. MVA-BN-Filo should be given to participants approximately 8 weeks after the Ad26.ZEBOV with a compliance window of -14 and +28 days. Of the 83,850 participants who were eligible per this dosing window for the subsequent MVA-BN-Filo vaccine, 91.2% (76,453/83,850) received it and 82.9% (69,505/83,850) received it within the compliance window defined for this campaign. Utilization of the VMP was instrumental to the success of the campaign, using biometric technology, dashboard reporting of near real-time data analysis and mobile phone communication technology to support vaccine administration and monitoring. A comprehensive VMP is feasible in large-scale health-care campaigns, beneficial for public health surveillance, and can allow effective response to an infectious disease outbreak.

Factors indicating intention to vaccinate with a COVID-19 vaccine among older U.S. adults.

BACKGROUND: The success of vaccination efforts to curb the COVID-19 pandemic will require broad public uptake of immunization and highlights the importance of understanding factors associated with willingness to receive a vaccine. METHODS: U.S. adults aged 65 and older enrolled in the HeartlineTM clinical study were invited to complete a COVID-19 vaccine assessment through the HeartlineTM mobile application between November 6-20, 2020. Factors associated with willingness to receive a COVID-19 vaccine were evaluated using an ordered logistic regression as well as a Random Forest classification algorithm. RESULTS: Among 9,106 study participants, 81.3% (n = 7402) responded and had available demographic data. The majority (91.3%) reported a willingness to be vaccinated. Factors most strongly associated with vaccine willingness were beliefs about the safety and efficacy of COVID-19 vaccines and vaccines in general. Women and Black or African American respondents reported lower willingness to vaccinate. Among those less willing to get vaccinated, 66.2% said that they would talk with their health provider before making a decision. During the study, positive results from the first COVID-19 vaccine outcome study were released; vaccine willingness increased after this report. CONCLUSIONS: Even among older adults at high-risk for COVID-19 complications who are participating in a longitudinal clinical study, 1 in 11 reported lack of willingness to receive COVID-19 vaccine in November 2020. Variability in vaccine willingness by gender, race, education, and income suggests the potential for uneven vaccine uptake. Education by health providers directed toward assuaging concerns about vaccine safety and efficacy can help improve vaccine acceptance among those less willing. TRIAL REGISTRATION: Clinicaltrials.gov NCT04276441.

An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions.

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.

Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions.

Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term "engagement," thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as "Big E," and DBCI engagement, referred to as "Little e." DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.

Health Worker mHealth Utilization: A Systematic Review.

This systematic review describes mHealth interventions directed at healthcare workers in low-resource settings from the PubMed database from March 2009 to May 2015. Thirty-one articles were selected for final review. Four categories emerged from the reviewed articles: data collection during patient visits, communication between health workers and patients, communication between health workers, and public health surveillance. Most studies used a combination of quantitative and qualitative methods to assess acceptability of use, barriers to use, changes in healthcare delivery, and improved health outcomes. Few papers included theory explicitly to guide development and evaluation of their mHealth programs. Overall, evidence indicated that mobile technology tools, such as smartphones and tablets, substantially benefit healthcare workers, their patients, and healthcare delivery. Limitations to mHealth tools included insufficient program use and sustainability, unreliable Internet and electricity, and security issues. Despite these limitations, this systematic review demonstrates the utility of using mHealth in low-resource settings and the potential for widespread health system improvements using technology.

From Foucault to Freire Through Facebook: Toward an Integrated Theory of mHealth.

OBJECTIVE: To document the integration of social science theory in literature on mHealth (mobile health) and consider opportunities for integration of classic theory, health communication theory, and social networking to generate a relevant theory for mHealth program design. METHOD: A secondary review of research syntheses and meta-analyses published between 2005 and 2014 related to mHealth, using the AMSTAR (A Measurement Tool to Assess Systematic Reviews) methodology for assessment of the quality of each review. High-quality articles from those reviews using a randomized controlled design and integrating social science theory in program design, implementation, or evaluation were reviewed. Results There were 1,749 articles among the 170 reviews with a high AMSTAR score (≥30). Only 13 were published from 2005 to 2014, used a randomized controlled design and made explicit mention of theory in any aspect of their mHealth program. All 13 included theoretical perspectives focused on psychological and/or psychosocial theories and constructs. Conclusions There is a very limited use of social science theory in mHealth despite demonstrated benefits in doing so. We propose an integrated theory of mHealth that incorporates classic theory, health communication theory, and social networking to guide development and evaluation of mHealth programs.

Adolescent Mobile Phone Use and Mobile Phone-Based Health Promotion.

The past 20 years of mobile technology has shifted the ways in which young people communicate and network with one another. Mobile phone use is prevalent among adolescents and therefore is an ideal form of communication for tailored health care. This is especially so given the dramatic uptake in use of mobile technologies in this age group. Technology developments have also altered how physicians and public health interventionists communicate with adolescents for prevention messages. Rapidly changing technology has made the study of how best to communicate with youth a challenge because research can scarcely keep up with advances in the technology. However, best practices from behavior change and communication theories remain a foundation for future intervention directions and how we might best meet the health promotion needs of adolescents.