Veranstaltungsprogramm

Physical inactivity is a conundrum: While the beneficial effects of regular physical activity (PA) are generally accepted and widely known (WHO, 2022), a large and potentially growing number of people is insufficiently active (Guthold et al., 2018) – despite the wealth and sophistication of conceptual and intervention approaches to promote physical activity (see Ntoumanis et al, 2018). Here, we introduce two novel approaches that address the resulting calls for “rethinking physical activity research” (Das & Horton, 2012). The first contribution (Schüler et al.) introduces the ProPELL framework (“Promoting Physical Exercise in Lab and Life”), which applies best-practices in intervention design to physical exercise and activity interventions. The ProPELL framework highlights the importance of developing effective and scalable interventions by combining insights about dynamically interacting physiological and psychological processes from lab and field research. The second contribution (Bieleke et al.) presents the first implementation of the ProPELL framework in a multicenter, explorative research program. The current start-up phase of the research program revolves around an 8-week jump-training designed for physically inactive individuals, embedded in a longitudinal design with physiological, psychological, and behavioral measurement bursts in lab and life. The third contribution (Stähler et al.) zooms into one of the ongoing lab studies of the ProPELL research program, showing that eight weeks of training rendered participants’ perception of physical effort more positive, even in a task unrelated to the training. These effects persisted over time, indicating that regular exercise might sustainably boost the value individuals assign to physical effort. The fourth contribution (Ljubic et al.) focuses to the PAAM model (“Physical Activity Adoption and Maintenance,” Strobach et al., 2020), which emphasizes the interplay of implicit (e.g., habitual, affective) and explicit (e.g., intentional) processes involved in becoming and staying physically active over time. The authors introduce a novel research framework to empirically test the model’s assumptions using innovative assessment tools. In the fifth contribution (Jekauc et al.), a set of eight hypotheses about the explicit and implicit processes regulating PA is derived from the PAAM model. These hypotheses were tested in a longitudinal study conducted across five countries, with the results providing sound support of most hypotheses and serving as an impetus for further research on the model. In summary, the ProPELL framework and the PAAM model help to rethink PA research by advancing our knowledge about the determinants of PA and leveraging this knowledge to improve interventions designed to improve PA levels.

Physical inactivity is a serious threat to public health and individual well-being, estimated to cause almost 500 million new cases of preventable, non-communicable diseases between 2020 and 2030 and burdening societies around the world with costs of more than US$ 300 billion (WHO, 2022). Due to its increasing prevalence and its global spread, physical inactivity is considered a pandemic that needs to be addressed as quickly and as effectively as possible (Kohl et al., 2012). Despite their number and level of sophistication, however, currently available interventions seem uncapable of reversing or even stopping this trend (e.g., Hallal & Pratt, 2020; Pratt et al., 2020), which has prompted calls for “rethinking our approach to physical activity” (Das & Horton, 2012). Here, we provide an overview of the current state of physical activity interventions, identify their strengths and weaknesses, and introduce ProPELL (“Promoting Physical Exercise in Lab and Life”) as a novel conceptual framework for developing more effective interventions. According to the ProPELL framework, three challenges stand out when it comes to developing physical activity interventions. First, we need to better integrate physiological and psychological systems, considering how the associated regulatory processes interact dynamically with each other and with the physical and social environment. This requires multidisciplinary approaches that combine dedicated expertise on physical exercise and performance (sport and training science), cognitive and emotional processes (psychological science and neuroscience), and model complex patterns and high resolution data (computer and information science). Second, we need to combine experimental and observational measures that capture the complexity of psychological and physiological parameters at various degrees of spatiotemporal resolutions. This requires the design of controlled training trials, embedded in longitudinal studies with measurement bursts in lab (internal validity) and life settings (external validity). Third, we need to take make sure that interventions are scalable both horizontally (e.g., across age and health groups) and vertically (e.g., across individual and community levels). This requires a focus not only on the effectivity of interventions, but also on whether they can be adapted flexibility to heterogeneous requirements, are acceptable to people with various socio-demographic characteristics, and feasible in complex real-world settings. In summary, the ProPELL framework leverages best-practice approaches in intervention design (e.g., Rothman & Sheeran, 2020) to rethink exercise and physical activity interventions and fight the global inactivity pandemic.

The ProPELL framework (“Promoting Physical Exercise in Lab and Life”) aims to advance physical activity interventions by leveraging three core principles: (1) the integrative analysis of dynamically interacting physiological and psychological processes, (2) the combination of experimental and observational assessments in lab and life, and (3) the development of horizontally and vertically scalable trainings. Implementing, applying, and evaluating this framework constitutes a long-term research goal that requires a multi-phase research project. Here, we introduce Phase 1 of the ProPELL research project, which has been launched in 2022 at the University of Konstanz, Germany. At its core lies a randomized controlled physical training intervention that targets individuals with low levels of exercise and physical activity. The training consists of various high-intensity, low-volume jump exercises to promote neuromuscular and cardiovascular functioning. It is carried out three times a week over a period of eight weeks (i.e., 24 sessions in total) and gradually ramped up in terms of intensity and volume. In line the principle of scalability, each session takes only about 15 minutes and requires no special equipment or exercise experience. To take the multimethodological principle into account, the training is embedded in a 22-week longitudinal study in which multiple sites cooperate to collect physiological (e.g., neuromuscular, cardiovascular, and brain functioning), psychological (e.g., motivational, cognitive, and emotional functioning), and behavioral data (e.g., sports, exercise, and activity levels) in both lab and life. These data represent different temporal resolutions as well as various physiological and psychological systems, including continuous heart rate and activity assessments (with sports watches and heart rate sensors), ambulatory assessments of behavioral and psychological factors (using triggered electronic questionnaires combined with activity trackers), and repeated measurement bursts of neuropsychological processes (e.g., MRT and fNIRS). Consistent with the principle of an integrative physiological-psychological analysis, visual analytics tools will be used to investigate interdependent high-dimensional sequential patterns of these physiological and psychological processes that interact dynamically at different temporal levels. In summary, Phase 1 of the ProPELL research project aims to provide crucial insights into the core assumptions of the ProPELL framework, laying the foundation for subsequent phases of developing novel exercise and physical activity interventions.

Physical exercise requires effort. However, effort feels costly, and people try to minimize its exertion (Hull, 1943). The costly nature of physical exercise might be one reason why so many people fail to exercise sufficiently (Kohl et al., 2012). Crucially, this reasoning does not account for the observation that effort is sometimes also treated as a reward (Inzlicht et al., 2018): Repeated coupling of effort with rewarding outcomes can turn effort itself into a reward (Eisenberger, 1992). So far, this idea has not been utilized to promote long-term physical activity in the general public. We address this gap by investigating how regular physical exercise alters the value of physical effort (VoPE). This study is part of the controlled training study ProPELL with three measurement bursts (pre-intervention, post-intervention, and follow-up). Only sedentary individuals could participate. Participants were assigned to an eight-week high-intensity jump training (TG) or the control group (CG). Here, we present interim results from the first wave of data collection (N = 30 participants). Each measurement session consisted of two bike ergometer tasks. The first task assessed how participants value effort during pre-determined performance levels. These levels were adapted according to the individual aerobic thresholds determined via spiroergometry a few days before each measurement. The aim was to standardize relative effort levels across participants, in order to compare the VoPE across groups and measurement points. The second ergometer task assessed participants’ voluntary choice of effort exertion. Here, the aim was to measure the preference for effort based on actual behavior (watts) and to compare it between groups. For both tasks, participants’ perceived effort (RPE) and VoPE (“How much do you like to exert yourself right now?”) were assessed. Participants in TG and CG did not differ in RPE. After the training period, the VoPE was higher in the TG than in the CG. This group difference remained significant for the follow-up measurement. In the second task, no performance differences were found between TG and CG and between measurement points. We showed that after eight weeks of regular jump training, originally sedentary individuals perceived effort as more valuable, even in a task that is unrelated to the training. Importantly, this increase in the value of effort was stable over time. Increased valuation of effort did not translate into changes in performance over a free-choice task.

Die positiven Effekte körperlicher Aktivität auf die Gesundheit sind umfassend belegt (Warburton & Bredin, 2017). Zudem ist die Kenntnis bzgl. der positiven gesundheitlichen Effekte regelmäßiger körperlicher Aktivität gesellschaftlich weit verbreitet (Lamprecht et al., 2014; O’Donovan & Shave, 2007). Trotz dieser Kenntnis und institutioneller Bemühungen ist der Anteil an körperlich aktiv lebenden Menschen in westlichen Industrienationen von 68.4 % im Jahr 2001 auf 57.7 % im Jahr 2016 gesunken (Guthold et al., 2018). In diesem Zusammenhang drängt sich die Frage auf, ob sich Personen explizit gegen regelmäßige körperliche Aktivität entscheiden oder ob und wie implizite Prozesse die Bildung von Verhalten mitbeeinflussen. Rhodes und de Brujin (2013) berichten in ihrer Metaanalyse, dass von den 3.899 in der Analyse berücksichtigten Personen 78 % angaben, regelmäßig körperlich aktiv sein zu wollen. Jedoch gelang es nur knapp der Hälfte der befragten Personen, ihre Intention regelmäßig in das entsprechende intendierte bewegungsbezogene Verhalten umzusetzen (Rhodes & de Bruijn, 2013). Zur Erklärung dieser Intentions-Verhaltenslücke (Sheeran, 2002) wird u.a. auf Modelle der Verhaltensänderung (Pfeffer & Wegner, 2020) zurückgegriffen, deren Annahmen nur teilweise empirisch überprüft wurden. Ziel der geplanten Studie ist die empirische Überprüfung des „Physical Activity Adoption and Maintenance“-Modells (PAAM; Strobach et al., 2020). Das PAAM-Modell nimmt an, dass vorangegangene körperliche Aktivität Auswirkungen auf explizite (Intentionen) und implizite (Gewohnheiten, Affekte) Prozesse ausübt und dass das Zusammenspiel zwischen diesen und weiteren expliziten Konstrukten (Exekutive Funktionen, dispositionelle Selbstkontrolle) künftiges Aktivitätsverhalten vorhersagen kann. Ziel des Vortrags wird es sein, einen Überblick zum intendierten Forschungsansatz zu vermitteln, mittels dessen die Validität des PAAM-Modells empirisch überprüft werden soll. Zur validen Erfassung der im Modell beinhalteten Konstrukte wird im Rahmen der geplanten Studie auf innovative Erhebungsmöglichkeiten zurückgegriffen. So ist bspw. der Einsatz von sensor-getriggerten ambulatorischen Erhebungen über Akzelerometer in Verbindung mit Smartphones geplant. Diese Kombination ermöglicht u.a. die Erhebung von Affekten unmittelbar vor, während und nach der jeweiligen körperlichen Aktivität in der natürlichen Lebenswelt der Versuchsperson.

In line with dual-process theories, the Physical Activity Adoption and Maintenance (PAAM) model assumes that physical activity behavior (PA) is regulated by explicit and implicit processes (Strobach et al., 2020). In the present study, theoretical assumptions of this model were empirically tested. The following eight hypotheses were derived from the model: 1) past behavior, 2) intention, and 3) habit have a positive effect on future physical activity. Furthermore, we expect 4) intention and 5) habit to mediate at least partially the association between past PA behavior and future PA behavior, whereas 6) affect and habit act as a mediation chain between past PA and future PA. In addition, we propose that 7) trait self-regulation and 8) affect moderate the intention-behavior-relationship. In order to test these hypotheses, data were collected via an online survey in English, German, and Italian at two times of measurement four weeks apart. The sample consisted of 422 participants (Mage= 25.3, SDage= 10.1; 74.2 % female) recruited from Germany, Switzerland, Italy, the USA, and Canada. The results of hierarchical linear regression analyses mostly support the assumptions of the PAAM model. Hypotheses 1-3 were supported, as past behavior, intention and habit each had statistically significant effects on future PA. Additionally, intentions (4) and habits (5) significantly mediated the effects of past behavior on future PA. Moreover, the effect of past PA also had a significant effect on future PA through a mediation chain via affect and habit (6). A significant moderation effect of affect on the intention-behavior relationship (8) was found. However, the hypothesis that trait self-regulation moderates this relationship (7) could not be confirmed. The results mainly support the assumptions of the PAAM model. Further long-term longitudinal studies with a duration of several month and additional times of measurement (e.g., weekly) are needed to examine the utility of the theory in the long-term process of the adoption and maintenance of PA.