Incorporating Health Behavior Theory into mHealth: an Examination of Weight Loss, Dietary, and Physical Activity Interventions Jessica K. Salwen-Deremer 1 & Alyssa S. Khan 2 & Seth S. Martin 3,4 & Breanna M. Holloway 5 & Janelle W. Coughlin 3,4 # Springer Nature Switzerland AG 2019 Abstract Health behavior interventions are effective for many modifiable lifestyle behaviors. In some cases, remotely-delivered behavioral interventions, particularly those that include some form of contact with a clinician and strong behavioral strategies, have been shown to be as effective as traditional in-person interventions; they are also more flexible, disseminable, and cost-effective. With ubiquitous increases in mobile phone use, opportunities for remote delivery of health interventions (mHealth) have grown exponentially, particularly in the use of behavioral smartphone applications. Despite research suggesting that mHealth interven- tions can be effective at initiating and maintaining behavior changes, many mHealth interventions are not theoretically-based, and evidence-based behavioral strategies are not often adapted into the mobile format. Thus, there is a need for clear summaries of behavioral change theories and examples of theoretically driven behavioral strategies to unify and improve the field of mHealth. The authors review the existing literature on theories of behavior change, intervention, and systems for evaluating theoretical content. Specifically, the authors briefly summarize both traditional and contemporary theories of behavior change, evidence- based behavioral strategies, and the methods for evaluating the degree to which they are included in existing mHealth behavioral interventions, with an emphasis on weight loss, dietary, and physical activity interventions. Authors also include examples of integration of theory into both research and clinical practice. This research highlights the importance of incorporation of theory into behavior change interventions. The authors suggest specific theoretical considerations for the development of mHealth interventions within collaborative, interdisciplinary environments, and recommend future research areas. Keywords mHealth . Health behavior . Lifestyle . Technology . Weight loss . Physical activity Unhealthy lifestyle behaviors, such as poor diet, sedentarism, and smoking, account for the majority of premature deaths worldwide (Lim et al. 2013; Mokdad et al. 2004) and have quickly become the leading cause of disability adjusted life years (Murray et al. 2013). Further, 75% of healthcare costs in the US are linked with chronic preventable diseases such as obesity, diabetes mellitus, heart disease, and cancer, which have maladaptive health behaviors among their root causes (Prevention 2004). Health behavior change interventions have been shown to be effective for a number of lifestyle behaviors, including smoking cessation, weight loss/dietary changes, medication adherence, physical activity, and diabetes mellitus manage- ment (Krebs et al. 2010; Norris et al. 2001; Park et al. 2014; Parsons et al. 2007; Whittaker et al. 2016). These in- terventions are most effective when they are based on behav- ioral science theories and incorporate evidence-based behav- ioral strategies, such as self-monitoring and goal-setting (Glanz and Bishop 2010; Webb et al. 2010). Although most research on gold-standard behavioral interventions is based on in-person delivery models (Diabetes Prevention Program Research Group et al. 2009; The Look AHEAD Research Group and Wing 2010; Sacks et al. 2001), researchers have recently shown that these interventions are also effective when delivered remotely (e.g., telephone calls with a behav- ioral interventionist) and with the assistance of technology that is responsive to behavior change (Appel et al. 2011; * Jessica K. Salwen-Deremer jessica.k.salwen-deremer@hitchcock.org 1 Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA 2 Milken Institute School of Public Health, George Washington University, Washington, DC, USA 3 Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 4 Johns Hopkins University School of Medicine, Baltimore, MD, USA 5 University of Maryland, Baltimore County, Baltimore, MD, USA Journal of Technology in Behavioral Science https://doi.org/10.1007/s41347-019-00118-6