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Using this article: Berardi, V., Carretero-González, R., Klepeis, N. E., Ghanipoor Machiani, S., Jahangiri, A., Bellettiere, J., & Hovell, M. (2018). Computational model for behavior shaping as an adaptive health intervention strategy. Translational Behavioral Medicine, 8(2), 183-194. https://doi.org/10.1093/tbm/ibx049 Provide a general description of the study. Who were the participants, describe the intervention in paragph form Adaptive behavioral interventions that automatically adjust in real-time to participants’ changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated. Behavior shaping in health interventions In the USA, the leading causes of death and disease are modifiable behavioral factors such as tobacco use, poor diet, physical inactivity, alcohol consumption, and avoidable injuries [1, 2]. Studies have indicated that preventable, extrinsic factors contribute >70%-90% of lifetime cancer risks [3]. Consequently, enormous gains to public health are achievable through behavior-altering interventions. Most behavioral interventions, though, generate limited, short-lived effects [4], partly due to the reliance on episodic assessments of behavior captured by tools such as counseling sessions, ecological momentary assessments, surveys, and discrete direct observations. In contrast, recent advances in mobile technology have enabled the development of just-in-time, adaptive interventions (JITAIs) that have the potential to improve upon the shortcomings associated with traditional trials [5, 6]. JITAIs typically use assessment technology that is capable of observing and recording behavior in a natural environment on a near-continuous basis over a long period of time. By pairing intensive data collection with analytic systems capable of real-time decision making, JITAIs enable interventions to be provided on an ongoing basis and automatically adapt in response to participants’ varying behaviors, environmental contexts, and past history. This process is hypothesized as an ongoing two-way “conversation” between patients and providers. While still in the preliminary stages, adaptive interventions have been implemented to, for example, encourage physical activity [7], assist with in-home living for older adults [8], and manage HIV medication adherence [9]. The implementation of JITAIs can be enhanced by consideration of the precise mechanisms by which interventions should adapt in response to participants’ behavior. Often, adaptation strategies are developed in an ad hoc fashion with little consideration of theoretical underpinnings, despite findings indicating that adherence to established theory can increase the efficacy of behavioral interventions [10]. Current behavioral theories provide little insight into this regard because they rarely consider behavior as a dynamic entity [11]. Introducing theoretically sound, responsive adaptation strategies will probably lead to more effective interventions and, furthermore, can generate results that will allow the underlying theories to be refined. One possible adaptation strategy with a theoretical foundation is behavior shaping, defined as the process whereby a targeted behavior is gradually cultivated via the differential reinforcement of successive approximations to the target [12]. When implementing this procedure, the range of behaviors that are reinforced narrows with time (Fig. 1A). The shaping process can lead to complex behaviors that would otherwise not be emitted as quickly, or at all. In a traditional shaping scenario, a practitioner must discriminate which behaviors are sufficiently similar to the target behavior in order to receive reinforcement and determine the optimal time to discontinue the reinforcement associated with crude approximations. The withholding of reinforcement produces a temporary extinction condition that might occasion novel or differential rates of behavior that are appropriate for shaping. This process typically occurs in a controlled environment such as classroom or training center. Proficiency in performing these tasks arbitrates the ultimate effectiveness of shaping routines and specialists such as teachers and coaches do this as an art. JITAIs, though, offer the opportunity to precisely gauge behavior on a nearly constant basis and to continually assess its similarity to a target behavior. This enables shaping procedures to be automatically implemented in a much wider variety of contexts than has typically been possible [13].Many behaviors are reliably shaped throughout society. For instance, the entire educational system can be viewed as a high-level shaping procedure where successively close approximations to proficiency in certain disciplines are reinforced as individuals proceed through each grade. Subsequent to these formal shaping protocols, the environment continues to shape behavior, albeit under less predictable schedules. For example, education is reinforced by the admission to college and subsequent employment. In contrast, behavior-shaping routines that would be deployed in JITAIs, at least during this preliminary stage, are likely to be rudimentary and not have the benefit of a host of strong, supporting contingencies. In some cases, such as when attempting to shape tobacco-smoking cessation, the opposite may even be true and environmental factors could discourage the target behavior. The extent to which simple shaping procedures deployed within a natural environment would be successful in producing elevated levels of healthy human behavior is an open question that this manuscript begins the process of addressing. Outside of the JITAI domain, the effectiveness of behavior shaping in humans has been reliably demonstrated in areas ranging from promoting motor activity in patients recovering from strokes [14] to improving dental treatment acceptance among children [15] to the management of cellular service consumption [16]. For individuals with autism, shaping has been used to promote socioemotional functioning [17], to aid in toilet training [18], to increase the duration of sustained attention [19], and to develop social-cueing skills [20]. The latter of these examples utilized automatic behavior assessment features similar to those that are required for JITAIs. Behavior shaping and computational models Using computational models to assess the effectiveness of behavior shaping routines in JITAIs is an attractive preliminary approach since it allows for the components of complex systems to be efficiently isolated and manipulated. Methodologies can be explored, tweaked, and sometimes abandoned without the complications associated with their real-world counterparts. The ultimate aim of the procedure presented herein is to leverage the insight gained within simplified, digital domains to develop increasingly more realistic controlled laboratory experiments and subsequent real-world trials, which will all share a common theoretical underpinning. This will allow behavior shaping programs to be designed with a degree of theoretical fidelity [10] that has been absent in this area thus far. There is a rich history of implementing digital shaping programs within reinforcing learning models that are popular in the realm of artificial intelligence (AI) research. For example, behavior shaping routines have been implemented in reinforcement learning schemes within computational models of bicycle riding [21] and navigating a rod around obstacles [22]. In addition to experiments occurring in a virtual environment, shaping has also been included within the reinforcement learning protocol for robots learning to simulate foraging and other survival behaviors [23, 24]. The shaping protocols in these reinforcement learning models typically consist of having the agent preliminarily complete a simplified version of the full target task and demonstrating that this priming increases the rate at which the target behavior is acquired. In contrast to the hypothesized implementation of shaping within JITAIs, these shaping routines have only a rudimentary temporal component and do not adapt over time, which limits their generalizability to the JITAI domain. Due to the shortcomings of the existing computational shaping routines discussed above, this paper aims to develop computational models that are suitable for a JITAI framework. This is accomplished by modifying McDowell’s evolutionary model of behavior dynamics [25] by incorporating behavior shaping. In accordance with Darwinist principles, the McDowell model emits a stream of behaviors chosen from a population via a system of selection, reproduction, and mutation, a process that may be equivalent to reinforcement learning [26]. As will be detailed below, this is an abstract model that considers a digital organism emitting generic behaviors. The absence of specificity regarding behaviors and targets is an attractive feature since, as opposed to the behavior-specific AI reinforcement learning tasks described above, it enables findings to be generalized to many different JITAIs. As described in Table 1, the McDowell computational model has consistently produced results that agree with many material world experimental findings. In the case of temporally adaptive behavior shaping routines, the appropriate real-world experiments required for model comparison have not yet been performed. The results outlined within this paper lay the groundwork for the development of such experiments that will allow the consistency of computational and material-world findings to be assessed in order to inform behavior-shaping JITAIs.

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