Sometimes people weigh the pros and cons of an action and arrive at an explicit decision (“Tomorrow I will sign up for a fitness program”). Such deliberate origins of a behavioral intention are only one side of the coin. The other side can be characterized by a subconscious process of being moved by multiple stimuli, social, cognitive, and affective ones, into a more or less stable intention to change one’s behavior. Among the involved constructs are affective outcome expectancies (“When I exercise, I feel better afterward”). Anticipating either positive affect or unpleasant feelings constitute positive or negative outcome expectancies that may be represented also as distal mental images or episodes. When assessing such feelings by questionnaire, they become conscious. Thus, verbal assessment procedures often turn affect into reflection. In a similar way, diffuse risk perceptions and affect-loaded self-efficacy are other precursors of a behavioral intention that turn into cognitive appraisals when explicit questions are being asked. One of the mechanisms to change self-efficacy addresses affective and somatic states which means controlling or reinterpreting feelings of nervousness before trying a challenging task (Warner & French, 2020). Bandura’s social-cognitive theory has always been considering the pivotal role of affect (Luszczynska & Schwarzer, 2020). In terms of outcome expectancies, affect-based interventions (Conner et al., 2020) have been designed that modify how people feel about performing a behavior (affective attitudes) and how people might feel about performing a behavior (anticipated affect). Interventions based on the reflective-impulsive model (RIM; Deutsch & Strack, 2020) mainly focus on changes of associations in the impulsive system.

According to many theorists, health behavior change is understood as a process that can be subdivided into various phases. A good example is the model of action phases (Rubicon model, Keller et al., 2020) that makes a distinction between motivation and volition. There is a shift in mindset when people have struggled through pros and cons in a motivational phase and arrived at a behavioral intention, followed by a volitional phase in which they face barriers and challenges while attempting to translate their intention into action. Further, people may succeed in adopting a physical exercise regimen but keeping track of it may be hard, and eventually, they may relapse into sedentary behaviors. Thus, intervention projects must consider such phases and need to address not only motivation but also adverse impulses and negative affect that arise at later points. The health action process approach (Schwarzer & Hamilton, 2020) is a theoretical framework that places particular importance on such phases.

Future interventions may benefit from drawing on advanced information and communication technologies as reflected by eHealth or mHealth applications. The advanced technology allows for immediate feedback loops that keep users online, monitoring lapses as well as progress and providing resource information that is individually tailored (just-in-time adaptive interventions: JITAI). More frequent diagnostics allow the reclassification of individuals into concurrent stages of change, tailored for individual needs. Combining treatment components such as self-efficacy, planning, and action control in the volitional phase of behavior change seems to be appropriate in the context of advanced technology-driven interventions, and micro-randomized trials seem to be promising to evaluate such projects. Monitoring interventions develop goal-setting and action-control strategies and help clients to make use of the information that is derived from monitoring (Webb & de Bruin, 2020).