One of the authors’ recommendations is to pay more attention to affective mechanisms and dual process models. It is without doubt important that exercise and leisure time PA should be associated with pleasant (or at least not unpleasant!) affective experiences. Whilst the authors are correct in that some cognitive approaches are still frequently utilized in the PA promotion literature, such approaches have been heavily criticized (e.g., Sniehotta et al., 2014). In fact, other salient conceptual frameworks with a heavy cognitive component do acknowledge the role of positive affective experiences. For instance, self-determination theory (Ryan & Deci, 2017) identifies intrinsic motivation, engaging in an activity because of the inherent pleasure and fun the activity provides, as the most adaptative form of motivation. Furthermore, interventions using this theory highlight, amongst other things, the important role of affective experiences when communicating about behavior change (e.g., acknowledging negative feelings, emphatic listening; Teixeira et al., 2020). Other integrative frameworks of behavioral regulation developed for PA, such as the Action Control Framework (Rhodes & Gray, 2018) integrate affective processes (e.g., reflective affect, manipulated affect through volitional regulation, and reflexive affect) with cognitive mechanisms (e.g., intention formation). In short, the criticism that affective mechanisms are neglected in contemporary literature on the psychology of PA is potentially overstated. Where the problem might lie is that many interventions do not give affect the attention it deserves. The authors of the Target Article offer some good suggestions for supervised interventions that could prevent negative automatic affective valuations. These suggestions could be augmented by other examples that harness digital solutions and have the potential for upscaling. As an example, Conroy and Kim (2021) successfully tested a mobile application for conditioning associations between physical activity and pleasure.

Other potential fruitful avenues for future research on PA promotion might focus on promoting flexible habits. I use this term differently from how the authors use it. I mean habits that are built not on strict routines (e.g., going for a walk on the same time, in the same place, and following the same cue to walk), but flexible routines that allow variations in behavioral execution, and hence are less susceptible to unexpected obstacles. For instance, Beshears et al. (2021) showed that paying exercisers to exercise without a time constraint within any given day, led to more gym visits than paying exercises to exercise only within a two-hour window; this difference was evident even when the incentives were removed. We know from the habits literature that building strong habits promotes sustained behavior, including PA, but sometimes strict habits can become too inflexible and highly routinized, hence undermining the affective experience of exercise. For instance, variety has been proposed as an important ingredient of exercise motivation (Sylvester et al., 2018). Striking the right balance between consistency and flexibility in habit formation could be a key research priority for future research on the psychology of PA promotion. Habit is just one of many behavior change techniques. The recently proposed compendium of self-enactable techniques to change and self-manage motivation and behavior proposes over a hundred of such techniques (Knittle et al., 2021). Whilst not dismissing the role of the wider environment (e.g., social, physical, cultural) on individual behavior, changing such environments takes time and concerted effort. The compendium proposed by Knittle et al. (2021) could be a useful resource to assist people in identifying what they can do on their own to manage their motivation and behavior, including PA.

Lastly, and very briefly, in terms of methodological advances, besides the suggestion given by the authors of the Target Article for intensive longitudinal designs (which are useful for theory development and refinement), we should consider new ways of developing and implementing field interventions. One such promising framework from the field of behavioral science is the Multiphase Optimization Strategy (MOST; Collins, 2018). MOST designs can help PA researchers to fine-tune intervention components (e.g., through factorial experiments) so that only components that are most efficacious, cost-effective, and parsimonious (or whatever the trial optimization criteria are), are filtered through and subsequently used in randomized controlled trials. Currently, most of our PA interventions are multi-component and these components have been compiled based on personal preference, convenience, and/or past evidence, without being subjected to a rigorous selection procedure.