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The current body of evidence suggests that in healthy participants, implicit attitudes towards physical activity explain variance in exercise behaviour beyond explicit cognitive processes. However, such relationships have not been examined in psychiatric patients, although this may contribute to a better understanding of the motivational and volitional resources needed to self-regulate their exercise behaviour. Therefore, the present cross-sectional study aimed to assess implicit attitudes towards exercise among psychiatric in-patients, and to correlate these implicit attitudes with their physical activity levels. Patients (N = 101) showing a psychiatric disorder, but no severe cognitive impairment, were directly recruited from psychiatric clinics. Their physical activity levels were assessed using both accelerometers and self-reports. Additionally, patients reported psychiatric symptoms and performed a single-target implicit association test (ST-IAT) with exercise employed as the target category. Of all patients, 39% showed a preference for exercise, whereas 13% showed an aversion towards exercise. The implicit attitudes of the remaining participants were equally strong for both concepts. Based on correlational analysis (correcting for age, sex, psychiatric symptoms severity, and ST-IAT sequence), no association was found between ST-IAT score, or self-reported and objectively assessed physical activity. Consequently, the link between exercise behaviour and implicit attitudes towards physical activity found in healthy participants could not be observed in psychiatric patients.
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.
This article introduces a new theory, the Affective-Reflective Theory (ART) of physical inactivity and exercise. ART aims to explain and predict behavior in situations in which people either remain in a state of physical inactivity or initiate action (exercise). It is a dual-process model and assumes that exercise-related stimuli trigger automatic associations and a resulting automatic affective valuation of exercise (type-1 process). The automatic affective valuation forms the basis for the reflective evaluation (type-2 process), which can follow if self-control resources are available. The automatic affective valuation is connected with an action impulse, whereas the reflective evaluation can result in action plans. The two processes, in constant interaction, direct the individual towards or away from changing behavior. The ART of physical inactivity and exercise predicts that, when there is an affective-reflective discrepancy and self-control resources are low, behavior is more likely to be governed by the affective type-1 process. This introductory article explains the underlying concepts and main theoretical roots from which the ART of physical inactivity and exercise was developed (field theory, affective responses to exercise, automatic evaluation, evaluation-behavior link, dual-process theorizing). We also summarize the empirical tests that have been conducted to refine the theory in its present form.
Dropping Out or Keeping Up?
(2016)
The aim of this study was to examine how automatic evaluations of exercising (AEE) varied according to adherence to an exercise program. Eighty-eight participants (24.98 years ± 6.88; 51.1% female) completed a Brief-Implicit Association Task assessing their AEE, positive and negative associations to exercising at the beginning of a 3-month exercise program. Attendance data were collected for all participants and used in a cluster analysis of adherence patterns. Three different adherence patterns (52 maintainers, 16 early dropouts, 20 late dropouts; 40.91% overall dropouts) were detected using cluster analyses. Participants from these three clusters differed significantly with regard to their positive and negative associations to exercising before the first course meeting (η2p = 0.07). Discriminant function analyses revealed that positive associations to exercising was a particularly good discriminating factor. This is the first study to provide evidence of the differential impact of positive and negative associations on exercise behavior over the medium term. The findings contribute to theoretical understanding of evaluative processes from a dual-process perspective and may provide a basis for targeted interventions.
Dropping Out or Keeping Up?
(2016)
The aim of this study was to examine how automatic evaluations of exercising (AEE) varied according to adherence to an exercise program. Eighty-eight participants (24.98 years ± 6.88; 51.1% female) completed a Brief-Implicit Association Task assessing their AEE, positive and negative associations to exercising at the beginning of a 3-month exercise program. Attendance data were collected for all participants and used in a cluster analysis of adherence patterns. Three different adherence patterns (52 maintainers, 16 early dropouts, 20 late dropouts; 40.91% overall dropouts) were detected using cluster analyses. Participants from these three clusters differed significantly with regard to their positive and negative associations to exercising before the first course meeting (η2p = 0.07). Discriminant function analyses revealed that positive associations to exercising was a particularly good discriminating factor. This is the first study to provide evidence of the differential impact of positive and negative associations on exercise behavior over the medium term. The findings contribute to theoretical understanding of evaluative processes from a dual-process perspective and may provide a basis for targeted interventions.
Models employed in exercise psychology highlight the role of reflective processes for explaining behavior change. However, as discussed in social cognition literature, information-processing models also consider automatic processes (dual-process models). To examine the relevance of automatic processing in exercise psychology, we used a priming task to assess the automatic evaluations of exercise stimuli in physically active sport and exercise majors (n = 32), physically active nonsport majors (n = 31), and inactive students (n = 31). Results showed that physically active students responded faster to positive words after exercise primes, whereas inactive students responded more rapidly to negative words. Priming task reaction times were successfully used to predict reported amounts of exercise in an ordinal regression model. Findings were obtained only with experiential items reflecting negative and positive consequences of exercise. The results illustrate the potential importance of dual-process models in exercise psychology.
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.
I Can See It in Your Face.
(2019)
The purpose of this study was to illustrate that people’s affective valuation of exercise can be identified in their faces. The study was conducted with a software for automatic facial expression analysis and it involved testing the hypothesis that positive or negative affective valuation occurs spontaneously when people are reminded of exercise. We created a task similar to an emotional Stroop task, in which participants responded to exercise-related and control stimuli with a positive or negative facial expression (smile or frown) depending on whether the photo was presented upright or tilted. We further asked participants how much time they would normally spend for physical exercise, because we assumed that the affective valuation of those who exercise more would be more positive. Based on the data of 86 participants, regression analysis revealed that those who reported less exercise and a more negative reflective evaluation of exercise initiated negative facial expressions on exercise-related stimuli significantly faster than those who reported exercising more often. No significant effect was observed for smile responses. We suspect that responding with a smile to exercise-related stimuli was the congruent response for the majority of our participants, so that for them no Stroop interference occurred in the exercise-related condition. This study suggests that immediate negative affective reactions to exercise-related stimuli result from a postconscious automatic process and can be detected in the study participants’ faces. It furthermore illustrates how methodological paradigms from social–cognition research (here: the emotional Stroop paradigm) can be adapted to collect and analyze biometric data for the investigation of exercisers’ and non-exercisers’ automatic valuations of exercise.