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Exercise or not?
(2023)
Objective: Individuals’ decisions to engage in exercise are often the result of in-the-moment choices between exercise and a competing behavioral alternative. The purpose of this study was to investigate processes that occur in-the-moment (i.e., situated processes) when individuals are faced with the choice between exercise and a behavioral alternative during a computerized task. These were analyzed against the background of interindividual differences in individuals’ automatic valuation and controlled evaluation of exercise.
Method: In a behavioral alternatives task 101 participants were asked whether they would rather choose an exercise option or a behavioral alternative in 25 trials. Participants’ gaze behavior (first gaze and fixations) was recorded using eye-tracking. An exercise-specific affect misattribution procedure (AMP) was used to assess participants’ automatic valuation of exercise before the task. After the task, self-reported feelings towards exercise (controlled evaluation) and usual weekly exercise volume were assessed. Mixed effects models with random effects for subjects and trials were used for data analysis.
Results: Choosing exercise was positively correlated with individuals’ automatic valuation (r = 0.20, p = 0.05), controlled evaluation (r = 0.58, p < 0.001), and their weekly exercise volume (r = 0.43, p < 0.001). Participants showed no bias in their initial gaze or number of fixations towards the exercise or the non-exercise alternative. However, participants were 1.30 times more likely to fixate on the chosen alternative first and more frequently, but this gaze behavior was not related to individuals’ automatic valuation, controlled evaluation, or weekly exercise volume.
Conclusion: The results suggest that situated processes arising from defined behavioral alternatives may be independent of individuals’ general preferences. Despite one’s best general intention to exercise more, the choice of a non-exercise alternative behavior may seem more appealing in-the-moment and eventually be chosen. New psychological theories of health behavior change should therefore better consider the role of potentially conflicting alternatives when it comes to initiating physical activity or exercise.
There is an ongoing debate about how to test and operationalize self-control. This limited understanding is in large part due to a variety of different tests and measures used to assess self-control, as well as the lack of empirical studies examining the temporal dynamics during the exertion of self-control. In order to track changes that occur over the course of exposure to a self-control task, we investigate and compare behavioral, subjective, and physiological indicators during the exertion of self-control. Participants completed both a task requiring inhibitory control (Go/No-Go task) and a control task (two-choice task). Behavioral performance and pupil size were measured during the tasks. Subjective vitality was measured before and after the tasks. While pupil size and subjective vitality showed similar trajectories in the two tasks, behavioral performance decreased in the inhibitory control-demanding task, but not in the control task. However, behavioral, subjective, and physiological measures were not significantly correlated. These results suggest that there is a disconnect between different measures of self-control with high intra- and interindividual variability. Theoretical and methodological implications for self-control theory and future empirical work are discussed.
Background: The COVID-19 pandemic has highlighted the importance of scientific endeavors. The goal of this systematic review is to evaluate the quality of the research on physical activity (PA) behavior change and its potential to contribute to policy-making processes in the early days of COVID-19 related restrictions.
Methods: We conducted a systematic review of methodological quality of current research according to PRISMA guidelines using Pubmed and Web of Science, of articles on PA behavior change that were published within 365 days after COVID-19 was declared a pandemic by the World Health Organization (WHO). Items from the JBI checklist and the AXIS tool were used for additional risk of bias assessment. Evidence mapping is used for better visualization of the main results. Conclusions about the significance of published articles are based on hypotheses on PA behavior change in the light of the COVID-19 pandemic.
Results: Among the 1,903 identified articles, there were 36% opinion pieces, 53% empirical studies, and 9% reviews. Of the 332 studies included in the systematic review, 213 used self-report measures to recollect prepandemic behavior in often small convenience samples. Most focused changes in PA volume, whereas changes in PA types were rarely measured. The majority had methodological reporting flaws. Few had very large samples with objective measures using repeated measure design (pre and during the pandemic). In addition to the expected decline in PA duration, these studies show that many of those who were active prepandemic, continued to be active during the pandemic.
Conclusions: Research responded quickly at the onset of the pandemic. However, most of the studies lacked robust methodology, and PA behavior change data lacked the accuracy needed to guide policy makers. To improve the field, we propose the implementation of longitudinal cohort studies by larger organizations such as WHO to ease access to data on PA behavior, and suggest those institutions set clear standards for this research. Researchers need to ensure a better fit between the measurement method and the construct being measured, and use both objective and subjective measures where appropriate to complement each other and provide a comprehensive picture of PA behavior.
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.
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.
Background: Healthy university students have been shown to use psychoactive substances, expecting them to be functional means for enhancing their cognitive capacity, sometimes over and above an essentially proficient level. This behavior called Neuroenhancement (NE) has not yet been integrated into a behavioral theory that is able to predict performance. Job Demands Resources (JD-R) Theory for example assumes that strain (e.g. burnout) will occur and influence performance when job demands are high and job resources are limited at the same time. The aim of this study is to investigate whether or not university students’ self-reported NE can be integrated into JD-R Theory’s comprehensive approach to psychological health and performance.
Methods: 1,007 students (23.56 ± 3.83 years old, 637 female) participated in an online survey. Lifestyle drug, prescription drug, and illicit substance NE together with the complete set of JD-R variables (demands, burnout, resources, motivation, and performance) were measured. Path models were used in order to test our data’s fit to hypothesized main effects and interactions.
Results: JD-R Theory could successfully be applied to describe the situation of university students. NE was mainly associated with the JD-R Theory’s health impairment process: Lifestyle drug NE (p < .05) as well as prescription drug NE (p < .001) is associated with higher burnout scores, and lifestyle drug NE aggravates the study demands-burnout interaction. In addition, prescription drug NE mitigates the protective influence of resources on burnout and on motivation.
Conclusion: According to our results, the uninformed trying of NE (i.e., without medical supervision) might result in strain. Increased strain is related to decreased performance. From a public health perspective, intervention strategies should address these costs of non-supervised NE. With regard to future research we propose to model NE as a means to reach an end (i.e. performance enhancement) rather than a target behavior itself. This is necessary to provide a deeper understanding of the behavioral roots and consequences of the phenomenon.
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.