This review articulates the context of such worries, and discusses a range of negative outcomes that may result from such (largely unfounded) beliefs, including the nocebo effect (i.e., unpleasant physiological symptoms without physical ...
Author: Frank Materia
Mobile health (i.e., mHealth) is a field that broadly investigates the use and effectiveness of mobile technologies for enhancing health, including via the remote monitoring of patients, enhancing communication and "reach" of clinical services, and directly delivering interventions. The ubiquity of wireless technologies allows mHealth to supplement (or even replace) face-to-face clinical care via technology, and holds the potential to reduce costs, limit disparities in access, and enhance delivery of effective treatment approaches. An emergent evidence base is beginning to support mHealth's efficacy in these domains, including regarding mHealth interventions. Yet, even if efficacious mHealth interventions exist, they must be implemented successfully to have positive impacts. Systematic research on the actual processes involved for integrating mHealth into clinical and community contexts is relatively sparse, with only a limited literature examining mHealth implementational factors (i.e., process factors rather than examining outcomes). How socio-cultural factors (e.g., beliefs), user characteristics (e.g., agreeability), and larger system-level considerations (e.g., cellular infrastructure) may influence the development and delivery of mHealth interventions to support uptake, adherence, and patient needs is largely unclear. A better understanding of implementational factors will help mHealth technologies and approaches reach their full potential for the widespread delivery of effective mobile interventions, allowing it to reach its potential for improving well-being on a global level. This dissertation presents three papers, each a different contextual application case of mHealth, to investigate and elucidate factors (design, reach, and adoption) related to successful mHealth implementation. The first study, focused on design, used experimental manipulation (i.e., randomization to condition) to investigate how mHealth intervention design factors (i.e., number of required devices and tailoring of content) affect participant acceptability (willingness to engage with mobile interventions). Results demonstrate that participants are sensitive to mHealth design components; in particular, providing tailored content appears to enhance acceptability and requiring participants to carry/use multiple devices increases perceived burden. The second study, focused on reach, reports on the development and implementation of a text messaging system to facilitate the delivery and effectiveness of an adapted Diabetes Prevention Program in a resource-poor area of South Africa. Through iterative design and testing, this study clarified factors (e.g., target population norms, training international staff, technology developer reliability) that should be considered when implementing text-based mHealth systems as part of face-to-face interventions in low- and middle-income international contexts. The third study, focusing on adoption, presents a narrative review regarding a set of potential barriers to mHealth implementation -- that users may have worries about negative impacts of technology on their health. Although most mHealth technologies have convincingly demonstrated safety, some individuals nonetheless continue to worry about negative health impacts. This review articulates the context of such worries, and discusses a range of negative outcomes that may result from such (largely unfounded) beliefs, including the nocebo effect (i.e., unpleasant physiological symptoms without physical cause), technological avoidance behavior, and larger socio-economic implications (e.g., influence on regulatory decisions). With the use of mHealth increasing rapidly, understanding factors and processes related to mHealth implementation is critical to effective scaling. This dissertation identifies and evaluates several mHealth implementation factors related to design, reach, and adoption, across varying contexts and using evidence-driven approaches (e.g., experimental manipulation, iterative design and testing processes, using evidence to guide model development), in three exemplar use-cases. Ultimately such work is intended to help inform best-practices in mHealth implementation, allowing mHealth to fulfill its potential for advancing human health and well-being.