ABSTRACT
Modifying key obesity-related behaviors, as outlined in previous chapters, has been a prime focus of pediatric obesity prevention and intervention efforts. In recent years, the incorporation of mobile or wireless health technologies, also known as mHealth solutions, into pediatric obesity prevention and intervention studies has rapidly accelerated [1]. The rise of mHealth is partially fueled by the rapid growth in wearable device availability and mobile phone ownership across age groups, geographic regions, and socioeconomic strata. To date, there are an estimated 7 billion mobile phone subscriptions worldwide [2]. In the United States, it is estimated that over 90% of adults and 88% of youth aged 13-17 years own a mobile phone [3]. mHealth technologies have the potential to capture and transmit a wide array of participant data in an accurate and timely fashion. These data range from ubiquitously measured (via sensor) behavioral, biological, and other contextual data (e.g., social interaction and physical location) to self-reported survey questionnaires via ecological momentary assessment (EMA). These temporally dense data are also highly contextualized, taken in the course of daily life, where, when, and how it most matters for understanding and intervening in behavior. With technological innovations that advance both the hardware [4] (i.e., lower power requirement, wireless data transmission, smaller portable sensors) and the software aspects [5] (i.e., increasingly sophisticated algorithms) of mobile devices, mHealth solutions offer clinicians and scientists unprecedented opportunities to understand behaviors and diseases with more clarity and to intervene in complex behaviors and their antecedents in ways that would have been pragmatically impossible to achieve using traditional research methodologies [4].
