Our review of the studies of health behavior interventions delivered via mobile technologies reveals a paucity of discussion regarding the health behavior theories or models that provide the basis of the intervention. Even among those studies that provided a theoretical basis, very few attempted to evaluate any of the theoretical components hypothesized to be affected by the intervention. Studies in the smoking and weight loss areas tended to use a theoretical model for their intervention, drawing predominately from Social Cognitive Theory or its variants (e.g., self-efficacy). Among these studies are extensive and thoughtful examples of using theory to guide mobile intervention development (e.g., [31
]). In contrast, most of the mobile interventions studied in the treatment adherence and disease management areas did not report a theoretical basis for intervention development, but this may be the result of reliance on evidence based clinical guidelines (e.g., diabetes management) or on the simplicity of the intervention delivered (e.g., appointment reminders).
A simple reminder intervention, however, is an excellent example for the necessity of theory to guide intervention. Text messaging reminders to attend appointments, take medications, exercise, etc. appear relatively straightforward and are consistent with the “cue to action” component of many health behavior theories, but the history of health behavior theories cautions us about assuming that behavior change is straightforward or that innovative modes of delivery alone are sufficient to produce behavior change. In the 1950s, mobile neighborhood tuberculosis screenings were innovative, but the added salience and convenience did not substantially increase screenings. Researchers studying uptake of these neighborhood screenings learned that the perceived threat (severity and susceptibility) of tuberculosis, the perceived benefit of being screened, and the perceived barriers to getting screened contributed to a person obtaining these screenings. These findings led to the Health Belief Model [85
] and the various health behavior theories and models that followed. Therefore, a greater reliance on health behavior theories to guide mobile technology intervention development, even for apparently simple interventions, should result in interventions that address more comprehensively the potential mechanisms of behavior change, resulting in more effective interventions.
While mobile technology applications of health behavior interventions should be guided by our current health behavior models, it is important to acknowledge that these current models appear inadequate to answer many of the intervention development questions likely to arise as interventions better leverage the interactive capabilities of mobile technologies. Some have argued that these theories are inadequate even for traditional interventions [17
], but they are particularly limited at informing just-in-time intervention adaptations. Dunton and Atienza have noted that the increasing availability of time-intensive information (i.e., longitudinal data obtained at an intensive frequency) allows for the intra-individual tailoring of interventions but that current health behavior theories are based on delineating between, not within-person differences [86
]. Boorsboom and colleagues have argued that between-person theories do not imply, test, or support causal accounts valid at the individual level [87
]. As a result, these theories and models have been used with considerable success to tailor health behavior interventions based on pre-intervention factors, but typically have not been used to adapt the intervention to the individual over the course of the intervention [88
]. As these interventions become deliverable on mobile devices, the content of interventions can be adapted for each individual not only initially but also over time based on his/her prior outcome data, prior responses to specific intervention outputs, current environmental and/or social context, and a range of other variables that might influence the optimal intervention based on the current state of the individual (i.e., ecological momentary interventions) [90
In addition to adapting the content
of the intervention over time, mobile technology applications of health behavior interventions also have the ability to adapt the timing
of the interventions based on these same data. For example, following a prompt to exercise, how long should the program wait before prompting again and how should the timing and content of the follow-up prompt be tailored based on prior pattern of responses to prompts? In this review, only a few studies adjusted the timing of the interventions, usually during the transition from behavior change to maintenance (e.g., [23
]), but these timing adjustments were set a priori across all participants and were not dynamically determined at the individual level.
The development of time-intensive, interactive, and adaptive health behavior interventions via mobile technologies demands more intra-individual dynamic regulatory processes than represented in our current health behavior theories. These theories do not preclude the possibility that some conceptual components change over time, and some concepts such as reciprocal determinism in Social Cognitive Theory are explicitly dynamic in nature [14
]. Moreover, a rich behavior change process–outcome literature describes dynamic interactions in face-to-face behavioral interventions that could be applied to behavioral interventions via mobile technologies [91
]. Adoption of dynamical system models for mobile health behavior interventions does not require that our current health behavior theories and models be discarded, but the predominately static, linear nature of these theories appears to be a poor fit with the intra-individual dynamics of future mobile technology interventions.
Control systems engineering [92
] may provide the dynamical system models needed to transform our current health behavior theories into the dynamic theories required for the time-intensive, interactive, and adaptive health behavior interventions delivered via mobile technologies. Control systems engineering examines how to influence dynamical systems to achieve a desired outcome. These dynamical systems are multivariate, time-varying processes, often nonlinear in nature, in which variables that can be manipulated (e.g., system input variables) lead to changes in outcomes of interest (e.g., system output variables) adjusting for exogenous effects (e.g., disturbance variables). Control engineering approaches have been proposed for adaptive interventions to determine, for example, when and how much to change the frequency of family counseling to prevent the development of conduct disorder in children [93
There are a number of practical advantages of using dynamical systems to inform adaptive, time-intensive interventions delivered via mobile technologies. Control systems engineering principles and procedures are mature and provide a robust computational framework for modeling, simulation, and systematic decision making over time. These control system principles and procedures fit well with the rich longitudinal data and real-time intervention adaptations potentially available via mobile technologies. Interventions based on theories enriched by control system models also provide engineers and health behavior researchers with a common language for collaborating on new interventions delivered via mobile technologies [94
Perhaps more importantly, these feedback control systems have the potential to reshape health behavior theories and improve our understanding of human behavior. Feedback regulatory processes are core to neurobiology. Basic neuronal processes are time-intensive, adaptive interactions of excitatory and inhibitory synaptic processes that have been modeled at the single neuron level [95
]. The communalities between brain circuitry and control system engineering principles are so compelling that some brain processes are described via engineering feedback systems (e.g., [96
]). Since the brain appears to regulate itself and other organs using feedback control processes, it seems reasonable that the brain also regulates human behavior via similar feedback control processes. Optimal feedback control systems have been applied to basic sensorimotor control [97
] and to reinforcement learning models [98
]. Therefore, the application of feedback control systems to current health behavior theory concepts provides not only potential theoretical models that are amenable to the time-intensive, interactive nature of mobile health behavior interventions but also theoretical models that are more consistent with the putative neurobiological and environmental processes that regulate these behaviors.
One significant example of regulatory feedback for disease management processes is in diabetes where frequent adjustments to diet and insulin dose are determined based on blood glucose levels. Control engineering approaches have been applied to the glucose–insulin closed-loop system [99
], and this application illustrates the potential of this approach to produce the desired outcome in systems that possess measurement error, model uncertainty, noise, and lagged effects. This work could serve as a basis for developing mobile technologies for diabetes management that take advantage of advanced closed-loop control system models to automate and better regulate blood glucose for insulin-dependent patients.
Detailed descriptions of applications of control systems engineering to other health behavior interventions such as substance use have been described elsewhere [93
], but to illustrate the application of control systems engineering to mobile health behavior interventions, we provide a simple model for intervening on smoking urges depicted in which represents an individual’s smoking behavior in terms of a production–inventory model [101
]. Using a fluid analogy, smoking urge is accumulated as an inventory that is built up by an “inflow” of negative emotional stimuli (e.g., sadness, irritation) and external stimuli (e.g., presence of others smoking, drinking coffee) and depleted by an urge “outflow” resulting from smoking events and the salutary effects of positive affect and dosages of intervention components such as medications and psychosocial interventions. In a control systems conceptualization, smoking urge (y
)) is the controlled variable, while smoking activity represents a manipulated variable (u1
)) whose action serves to reduce the urge to smoke. Environmental stimuli and positive and negative affect represent external disturbance variables (d1
)) whose changes build up or deplete the smoking urge inventory. The dose of intervention components represents additional manipulated variables (u2
)) that can be selected adaptively by a set of decision rules or mathematical algorithms (referred to as the intervention controller in ) based on assessed participant response. A differential equation drawn from the general principle of conservation of matter can be used to describe the relationship between these variables [102
Illustration of control systems dynamical model for smoking urge
, and Ku2
represent coefficients that quantify the rate of change in urge based on changes in manipulated variables (intervention dosages, smoking activity) and disturbance variables (negative and positive affect, environmental stimuli). The inherent dynamics of smoking behavior in constitutes a closed-loop feedback system in which increases in smoking urge precipitate a decision to smoke (the relationship between urge and smoking activity is denoted by the “urge controller” box in ).
The potential of mobile technologies to generate the intensive longitudinal data sets reflecting the behavior of a smoker over time and observed in different settings and contexts is important for building a dynamical system model such as the individual smoking behavior model illustrated above. Mobile devices can collect information in real time about the controlled, manipulated, and disturbance variables, as represented by sensor locations in . Model estimation techniques such as system identification [103
] and functional data analysis [104
] can be used to estimate the Kd1
, Kd 2
, and Ku2
coefficients from these data. These modeling techniques also can be applied to reverse-engineer the functional relationship underlying the urge controller which reflects the actions of an individual smoker. With a dynamical systems model, it becomes possible to apply control systems engineering to develop algorithms that use real-time assessments and predicted responses from the model to adaptively decide on the timing and dose of the intervention components. Mobile technology is an enabler to advanced control algorithms such as model predictive control [105
] that employ formal optimization methods to decide on current and future doses of intervention components while satisfying clinical practice preferences and restrictions [100
]. The use of these control technologies in a smoking cessation intervention as shown in parallels the closed-loop control system for diabetes management described previously. The optimization process leads to specific intervention decisions (u2
)) for providing behavioral strategies and/or medication prompts to replace smoking as a means of reducing smoking urge.
In addition to being sensitive to threshold (or mean) levels of key variables, a control system framework can adapt to and consider variability of key controlling or explanatory variables. Most theories of health behavior change to date have focused on mean levels of predictive variables, but with the advent of newer data technologies, we can develop models that focus on lability or stability of key controlling factors. For many health behaviors, maintaining a stable level or tighter range for some key variables may be as or more important than increasing or decreasing mean levels.
Novel dynamic health behavior theories and their application to health behavior interventions via mobile technologies will require empirical validation. Adaptive treatment methodologies has been applied to interventions that adjust over the course of weeks or months, not hours or days, but these methodologies can be applied to mobile technology interventions [106
]. Other methodological considerations for adaptive interactive technologies applied to eHealth also may be appropriate to consider for mHealth interventions [107
]. The opportunity via mobile devices to collect intensive context- and time-dependent (longitudinal) data and to systematically vary intervention components enables researchers to test not only these components but also the theoretical concepts and dynamic models that underlie them.
The application of mobile technologies to health behavior interventions is an exciting and rapidly growing field. The ability to provide frequent, time-intensive interventions in the context of the behavior holds considerable promise but also poses many challenges to our current health behavior theories and models. To meet these challenges, our current health behavior theories and models need to expand from elucidating between-person differences to explaining within-person changes over time and to evolve to incorporate dynamic feedback control systems to “close the loop.” Health behavior interventions delivered via mobile technologies offer not only the impetus to transform our current theories into more dynamic feedback control models but also the potential to provide the intensive longitudinal data necessary to test and improve our theoretical intervention models. To date, only a subset of mobile health behavior interventions have been theory-based, but a theory-driven iterative model of mobile intervention development holds promise for improving not only our mobile health behavior interventions but also our theoretical and empirical understanding of health behavior change.