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Response disequilibrium is the perturbation of unconstrained behavior with a contingency. For example, the imposition of advertisements before or after viewing TikTok videos. The purpose of this laboratory analogue was to determine the effects of two such response disequilibrium conditions: (1) which required participants to view 5 s increments of advertisements to access 2 s increments of TikTok videos and (2) which required participants to view 5 s increments of TikTok videos to access 15 s increments of advertisements. The disequilibrium condition in (1) is called a response deficit due to the restricted access to TikTok videos relative to baseline while (2) is called a response excess due to the overabundance of advertisements relative to baseline. Additionally, participants had access to a third activity that was freely available throughout. Participants could browse images of TikTok video thumbnails while in deficit and excess. As predicted, participants increased their viewing of advertisements when TikTok was in deficit and decreased their viewing of TikTok when advertisements were in excess. Furthermore, some participants substituted TikTok with browsing during the excess of advertisements. This investigation has implications for a behavioral economic analysis of social media use and the contingency management of time spent on these platforms.

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Disequilibrium theory is an approach to reinforcement that reconsiders the putative response strengthening prowess of stimuli. This disequilibrium approach-the pinnacle of the response deprivation hypothesis-reliably predicts changes in behavior without reference to a response strengthening process. While the strengthening model of reinforcement has received renewed and critical appraisal in behavior analysis, its appraisers have not fully considered the role that a disequilibrium conceptualization might play in their respective theories of reinforcement. In this essay we celebrate William Timberlake's legacy by elucidating the assumptions of disequilibrium theory and by exploring its predictions and implications within behavior analysis. We treat the disequilibrium approach to reinforcement as the theory of reinforcement in behavior analysis, and in doing so, we distinguish disequilibrium conditions from motivating operations and explore future directions regarding the potential to predict generalization and maintenance outcomes. The disequilibrium approach to reinforcement is not a mere deprivation operation used for the purposes of establishing a stimulus as a "reinforcer," as it is a general theory of behavior.

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The disequilibrium approach to reinforcement and punishment, derived from the probability-differential hypothesis and response deprivation hypothesis, provides a number of potentially useful mathematical models for practitioners. The disequilibrium approach and its accompanying models have proven effective in the prediction and control of behavior, yet they have not been fully espoused and integrated into clinical practice. The purpose of this tutorial is to detail the disequilibrium approach and adapt its mathematical models for use as a tool in applied settings. The disequilibrium models specify how to arrange contingencies and predict the effects of those contingencies. We aggregate these models, and provide them as a single tool, in the form of a Microsoft Excel® spreadsheet that calculates the direction and magnitude of behavior change based on baseline measures and a practitioner's choice of intervention parameters. How practitioners take baseline measures and select intervention parameters in accordance with disequilibrium models is explicated. The proposed tool can be accessed and downloaded for use at https://osf.io/knf7x/.

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Failure to change risk behaviors following myocardial infarction (MI) increases the likelihood of recurrent MI and death. Lower-socioeconomic status (SES) patients are more likely to engage in high-risk behaviors prior to MI. Less well known is whether propensity to change risk behaviors after MI also varies inversely with SES. We performed a systematized literature review addressing changes in risk behaviors following MI as a function of SES. 2160 abstracts were reviewed and 44 met eligibility criteria. Behaviors included smoking cessation, cardiac rehabilitation (CR), medication adherence, diet, and physical activity (PA). For each behavior, lower-SES patients were less likely to change after MI. Overall, lower-SES patients were 2 to 4 times less likely to make needed behavior changes (OR's 0.25-0.56). Lower-SES populations are less successful at changing risk behaviors post-MI. Increasing their participation in CR/secondary prevention programs, which address multiple risk behaviors, including increasing PA and exercise, should be a priority of healthy lifestyle medicine (HLM).

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Deterding et al. (Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, USA 15: 9-15, 2011) report a recent rise in popularity of video game inspired software designed to address issues in a variety of areas, including health, energy conservation, education, and business. These applications have been based on the concept of gamification, which involves a process by which nongame activities are designed to be more like a game. We provide examples of how gamification has been used to increase health-related behavior, energy consumption, academic performance, and other socially-significant behavior. We argue that behavior analytic research and practice stands to benefit from incorporating successful elements of game design. Lastly, we provide suggestions for behavior analysts regarding applied and basic research related to gamification.