Models for enhancing the development of experiential learning approaches within mobile health technologies.

Abstract

The explosion of technological applications in health care has provided new avenues for clinical change to occur. Mobile health (mHealth) apps, which include clinical tools that run on smartphones, are one such demonstration of these technologies. Benefits of mHealth apps are numerous, but potential limitations include poorer understanding of material due to the lack of real-time support from providers, limited scope and depth of interventions, and absence of theoretical grounding and/or empirical support. A potential reason for these limitations is that treatment experts and app developers lack a common theory-based conceptual model to inform the objectives of mHealth interventions. We discuss how clinicians and app developers can use models of learning processes, such as Experiential Learning Theory (ELT) and Bloom's Taxonomy, to facilitate collaboration among professionals and to increase experiential learning within mHealth interventions. First, we present ELT and Bloom's Taxonomy as frameworks to aid in the development of mHealth apps for behavioral health. Next, we outline some key ingredients of experiential learning to consider during the development stages of mHealth interventions, namely concrete experience, interactivity, experimentation, scaffolding, and integration (i.e., including affective, behavioral, and cognitive elements). Within these key ingredients, we consider how such factors can enhance outcomes for mHealth apps through processes such as engagement, shaping of beliefs, skills development, generalization, and scope of learning. Public Significance StatementThis work provides guidance to mental health professionals and technology experts in the use of models of experiential learning to aid in the development of mobile mental health (mHealth) apps. The intended purpose of using these models is to improve the quality, utility, and efficacy of mHealth interventions.

Apps With Maps—Anxiety and Depression Mobile Apps With Evidence-Based Frameworks: Systematic Search of Major App Stores

“…However, the research found to accompany the apps listed here does appear to vary greatly in methodology; this reaffirms the claims of heterogeneity made in those literature reviews. These reviews all call for more research and ongoing evaluation of research methodology into studying apps, as the current methodologies may not be the most appropriate [ 62 , 67 , 68 ].…”

Improving Adherence to Behavioral Parent Training for ADHD Using Digital Health Tools

“…As depicted in our conceptual model ( Figure 1 ), the tool targets empirically-supported barriers to parent adherence [ 12 , 13 ]: skill competence, EF processes, and motivation/attitudes. We applied frameworks for digital behavior change interventions [ 30 – 32 ], to select the primary features addressing each barrier. For example, features addressing Skill Competence were informed by experiential learning theory [ 32 ] and include interactive practice activities via skill modules which provide opportunities for active learning and engage parents in setting their own personalized short-term goals and action planning [ 20 , 33 ].…”

“…We applied frameworks for digital behavior change interventions [ 30 – 32 ], to select the primary features addressing each barrier. For example, features addressing Skill Competence were informed by experiential learning theory [ 32 ] and include interactive practice activities via skill modules which provide opportunities for active learning and engage parents in setting their own personalized short-term goals and action planning [ 20 , 33 ]. Features for overcoming barriers due to Executive Functioning Processes are informed by self-regulation theories [ 30 ] and include automated reminders, streamlined content, and personalized goal-setting and action planning.…”

Debiasing of gambling beliefs and behaviors using a digital gambling accelerator.

“…Indeed, debiasing techniques using training programs and experiential exercises are somewhat common in education-based interventions and prevention programs for problem gambling (Abel, Cole, & Zia, 2015; Oh, Ong, & Loo, 2017). However, it has been suggested that experiential exercises offered through digital interventions may have advantages over traditional methods (e.g., advanced tracking, interactivity, greater opportunities for experimentation; Broussard & Teng, in press). Debiasing interventions using interactive computer simulations and instructional videos, especially interactive games, have been shown to produce both immediate and persistent improvements in decision making (Morewedge, Yoon, Scopelliti, Symborski, Korris, & Kassam, 2015).…”