by Jesse Dallery, Ph.D. & Hypatia Bolivar, M.S.
University of Florida Behavioral Health and Technology (BHaT) Research Clinic
Advances in technology could be a game changer for behavior analysis. Consider the rise of mobile health apps, electronic medication dispensers, accelerometers to detect physical activity, and emerging technologies to detect substance use (Meredith et al., 2014). Some researchers are calling for a new healthcare model that enables “automated hovering,” or real-time monitoring, of patients’ health behavior while they engage in everyday activities (Asch et al., 2012; Topol, 2012). In addition to revolutionizing healthcare, technology-based tools could revolutionize behavior analysis by providing unprecedented, continuous monitoring of health behavior in naturalistic settings (Dallery, Kurti, & Erb, 2015).
Monitoring alone, however, is just one part of the equation to promote socially significant behavior change. The other part is behavioral technology. Integrating digital and behavioral technology could permit delivery of motivational antecedents and consequences, or “automated nudging.” Indeed, influencing choices related to health represents the single greatest opportunity to reduce death and disease in the developed world (Schroeder, 2007).
In practice, however, there is a relatively large gap between the use of digital technology and behavioral technology. In a major review of mobile-health interventions, Kaplan and Stone (2013) noted, “many application developers seem unaware that there is a basic science of behavior change” (p. 491).
…influencing choices related to health represents the single greatest opportunity to reduce death and disease in the developed world.
Some interventions, however, illustrate that the gap is narrowing (see Dallery et al., 2015 for more information). For example, Ecological Momentary Interventions (EMIs; Heron & Smith, 2010; McClernon & Choudhury, 2013) can deliver “just-in-time” treatment, which means delivering interventions in high-risk situations that are assessed using sensors and/or user-input (e.g., when eating a meal, experiencing anxiety, or craving a cigarette). Burns et al. (2011) developed a mobile phone- and Internet-based application, called Mobilyze!, to treat depression using behavioral activation therapy. Environmental variables were measured from 38 sensors, most of which were housed in the mobile phone (e.g., GPS, accelerometer, missed call count, ambient light). Combined with real-time self-reports of mood, a machine-learning algorithm identified mood states from sensor values. Based on the sensor data, Mobilyze! provided behavioral activation treatment recommendations via phone and a website.
Another example is a mobile phone-based intervention to support recovering alcoholics (Gustafson et al., 2014). The intervention included GPS detection of areas that were identified as high risk for drinking and automated treatment delivery. If a risky area was entered, the program delivered a prompt and asked the participant if he/she was in need of therapeutic support.
…many application developers seem unaware that there is a basic science of behavior change.
A second class of interventions, technology-based contingency management (CM), is evolving rapidly (Dallery et al., 2015). Technology-based CM involves a monitoring system to measure behavior, a delivery system to deliver consequences, and (when possible) a user-authentication protocol to identify the end user. One example is Internet-based CM to promote smoking cessation. Smokers use web cameras to record themselves blowing into carbon monoxide (CO) monitors twice per day, typically for about seven weeks, which provides evidence of smoking status (i.e., by meeting CO cutpoints). The CM delivery system also involves technology. Desirable consequences (monetary incentives, graphed progress, and social feedback) are delivered immediately to individuals who met CO cutpoints for abstinence using an automated, web-based program. Internet-based CM has been modified to include group contingencies (Meredith et al., 2011), where small groups of smokers must collectively achieve cessation goals to receive desirable consequences, and deposit contracts, in which an up-front deposit could be recouped based on evidence of abstinence (Jarvis & Dallery, 2017; Dallery et al., 2008).
Recently, mobile phones have been used to deliver CM to promote smoking cessation and alcohol abstinence (Hertzberg et al., 2013; Alessi & Petry, 2013). Technology-based CM has also been applied to diabetics’ adherence to glucose monitoring (Raiff & Dallery, 2010) and to promote physical activity (Donlin-Washington et al., 2014; Kurti & Dallery, 2013; Van Camp & Hayes, 2012). Kurti et al. (2016) provide an excellent review of technology-based CM to promote health behavior.
The promise of technology-based CM is that it will be accessible to anyone, regardless of geographic or socioeconomic barriers. A recent sign of such progress is the first nationwide deployment of technology-based CM for smoking cessation (Dallery et al., 2017). Smokers from 26 states enrolled, which is a larger area than typical CM clinical trials involving one, relatively constrained geographic region.
Technological tools provide unprecedented ways to improve a range of socially significant behavior. But, their use will depend on whether behavior analysts change their behavior in response to these advances.
We started this essay by saying that technology could be a game changer for behavior analysis. Technological tools provide unprecedented ways to improve a range of socially significant behavior. But, their use will depend on whether behavior analysts change their behavior in response to these advances. Using new tools will depend on a number of variables, and especially the reinforcers. These reinforcers might include collaborating with experts in medicine and technology, new funding opportunities, reaching a broader scientific audience, solving new scientific problems, and most fundamentally, reducing the behavioral causes of death, disease, and suffering.
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Jesse Dallery, Ph.D. is a Professor in the Department of Psychology at the University of Florida, a Licensed Psychologist in the state of Florida, and Deputy Director of the Treatment Development and Implementation Core at the Center for Technology and Behavioral Health at Dartmouth. Jesse received his Ph.D. in Clinical Psychology at Emory University in 1999, and completed a postdoctoral fellowship at the Johns Hopkins University School of Medicine in Behavioral Pharmacology. Jesse’s research focuses on integrating information technologies with behavioral interventions for cigarette smoking and other health-related behavior (e.g., physical activity, medication adherence). Jesse also conducts translational research on choice and decision making in the human laboratory, with a special emphasis on quantitative models of operant behavior. He has published over seventy articles in peer-reviewed journals, and he has received grant support from the National Institutes of Health and from the National Science Foundation. Jesse is a former Associate Editor for The Behavior Analyst, Special Topics Associate Editor (substance abuse) for the Journal of Applied Behavior Analysis, and Associate Editor for Behavioural Processes.
Hypatia Bolivar, M.S. received her B.S. in Psychology from the University of Florida in 2012 and her M.S. in Psychology from the University of Florida in 2015. She is currently a doctoral student in the Behavior Analysis area of the Psychology program at the University of Florida. She has several years of experience working with individuals with intellectual and developmental disabilities, and she is currently working towards becoming a Board Certified Behavior Analyst. Her primary research and clinical interests are in the areas of relapse, health and fitness, and behavioral gerontology.