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The decision to exercise is not only bound to rational considerations but also automatic affective processes. The affective–reflective theory of physical inactivity and exercise (ART) proposes a theoretical framework for explaining how the automatic affective process (type‑1 process) will influence exercise behavior, i.e., through the automatic activation of exercise-related associations and a subsequent affective valuation of exercise. This study aimed to empirically test this assumption of the ART with data from 69 study participants. A single-measurement study, including within-subject experimental variation, was conducted. Automatic associations with exercise were first measured with a single-target implicit association test. The somato-affective core of the participants’ automatic valuation of exercise-related pictures was then assessed via heart rate variability (HRV) analysis, and the affective valence of the valuation was tested with a facial expression (FE; smile and frown) task. Exercise behavior was assessed via self-report. Multiple regression (path) analysis revealed that automatic associations predicted HRV reactivity (β = −0.24, p = .044); the signs of the correlation between automatic associations and the smile FE score was in the expected direction but remained nonsignificant (β = −0.21, p = .078). HRV reactivity predicted self-reported exercise behavior (β = −0.28, p = .013) (the same pattern of results was achieved for the frown FE score). The HRV-related results illustrate the potential role of automatic negative affective reactions to the thought of exercise as a restraining force in exercise motivation. For better empirical distinction between the two ART type‑1 process components, automatic associations and the affective valuation should perhaps be measured separately in the future. The results support the notion that automatic and affective processes should be regarded as essential aspects of the motivation to exercise.
Recent research indicates that affective responses during exercise are an important determinant of future exercise and physical activity. Thus far these responses have been measured with standardized self-report scales, but this study used biometric software for automated facial action analysis to analyze the changes that occur during physical exercise. A sample of 132 young, healthy individuals performed an incremental test on a cycle ergometer. During that test the participants’ faces were video-recorded and the changes were algorithmically analyzed at frame rate (30 fps). Perceived exertion and affective valence were measured every two minutes with established psychometric scales. Taking into account anticipated inter-individual variability, multilevel regression analysis was used to model how affective valence and ratings of perceived exertion (RPE) covaried with movement in 20 facial action areas. We found the expected quadratic decline in self-reported affective valence (more negative) as exercise intensity increased. Repeated measures correlation showed that the facial action mouth open was linked to changes in (highly intercorrelated) affective valence and RPE. Multilevel trend analyses were calculated to investigate whether facial actions were typically linked to either affective valence or RPE. These analyses showed that mouth open and jaw drop predicted RPE, whereas (additional) nose wrinkle was indicative for the decline in affective valence. Our results contribute to the view that negative affect, escalating with increasing exercise intensity, may be the body’s essential warning signal that physiological overload is imminent. We conclude that automated facial action analysis provides new options for researchers investigating feelings during exercise. In addition, our findings offer physical educators and coaches a new way of monitoring the affective state of exercisers, without interrupting and asking them.