No abstract

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, future possibilities and set a grand goal for the emerging field of mHealth research.

Mindfulness-based interventions (MBIs) targeting eating behaviors have gained popularity in recent years. A literature review was conducted to determine the effectiveness of MBIs for treating obesity-related eating behaviors, such as binge eating, emotional eating, and external eating. A search protocol was conducted using the online databases Google Scholar, PubMed, PsycINFO, and Ovid Healthstar. Articles were required to meet the following criteria to be included in this review: (1) describe a MBI or the use of mindfulness exercises as part of an intervention, (2) include at least one obesity-related eating behavior as an outcome, (3) include quantitative outcomes, and (4) be published in English in a peer-reviewed journal. A total of N=21 articles were included in this review. Interventions used a variety of approaches to implement mindfulness training, including combined mindfulness and cognitive behavioral therapies, mindfulness-based stress reduction, acceptance-based therapies, mindful eating programs, and combinations of mindfulness exercises. Targeted eating behavior outcomes included binge eating, emotional eating, external eating, and dietary intake. Eighteen (86%) of the reviewed studies reported improvements in the targeted eating behaviors. Overall, the results of this first review on the topic support the efficacy of mindfulness-based interventions for changing obesity-related eating behaviors, specifically binge eating, emotional eating, and external eating.

Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)--suites of interventions that adapt over time to an individual’s changing status and circumstances with the goal to address the individual’s need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs.