A New Frontier: Integrating Behavioral and Digital Technology to Promote Health Behavior.

Modifiable behavioral risk factors such as cigarette smoking, physical inactivity, and obesity contribute to over 40 % of premature deaths in the USA. Advances in digital and information technology are creating unprecedented opportunities for behavior analysts to assess and modify these risk factors. Technological advances include mobile devices, wearable sensors, biomarker detectors, and real-time access to therapeutic support via information technology. Integrating these advances with behavioral technology in the form of conceptually systematic principles and procedures could usher in a new generation of effective and scalable behavioral interventions targeting health behavior. In this selective review of the literature, we discuss how technological tools can assess and modify a range of antecedents and consequences of healthy and unhealthy behavior. We also describe practical, methodological, and conceptual advantages for behavior analysts that stem from the use of technology to assess and treat health behavior.

The ABCs of incentive-based treatment in health care: a behavior analytic framework to inform research and practice.

Behavior plays an important role in health promotion. Exercise, smoking cessation, medication adherence, and other healthy behavior can help prevent, or even treat, some diseases. Consequently, interventions that promote healthy behavior have become increasingly common in health care settings. Many of these interventions award incentives contingent upon preventive health-related behavior. Incentive-based interventions vary considerably along several dimensions, including who is targeted in the intervention, which behavior is targeted, and what type of incentive is used. More research on the quantitative and qualitative features of many of these variables is still needed to inform treatment. However, extensive literature on basic and applied behavior analytic research is currently available to help guide the study and practice of incentive-based treatment in health care. In this integrated review, we discuss how behavior analytic research and theory can help treatment providers design and implement incentive-based interventions that promote healthy behavior.

A mobile-phone-based breath carbon monoxide meter to detect cigarette smoking.

INTRODUCTION: Mobile phones hold considerable promise for delivering evidence-based smoking cessation interventions that require frequent and objective assessment of smoking status via breath carbon monoxide (Breath CO) measurement. However, there are currently no commercially available mobile-phone-based Breath CO meters. We developed a mobile-phone-based Breath CO meter prototype that attaches to and communicates with a smartphone through an audio port. We then evaluated the reliability and the validity of Breath CO measures collected with the mobile meter prototype and assessed the usability and acceptability of the meter. METHODS: Participants included 20 regular smokers (≥10 cigarettes/day), 20 light smokers (<10 cigarettes/day), and 20 nonsmokers. Expired air samples were collected 4 times from each participant: twice with the mobile meter and twice with a commercially available Breath CO meter. RESULTS: Measures calculated by the mobile meter correlated strongly with measures calculated by the commercial meter (r = .96, p < .001). Additionally, the mobile meter accurately distinguished between smokers and nonsmokers. The area under the receiver-operating characteristic curve for the mobile meter was 94.7%, and the meter had a combined sensitivity and specificity of 1.86 at an abstinence threshold of ≤6 ppm. Responses on an acceptability survey indicated that smokers liked the meter and would be interested in using it during a quit attempt. CONCLUSIONS: The results of our study suggest that a mobile-phone-based Breath CO meter is a reliable, valid, and acceptable device for distinguishing between smokers and nonsmokers.