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 health care 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.

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.

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.