# The effort hypothesis at the heart of the virtuous circle

Michel Audiffren, Nathalie André

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Michel Audiffren, Nathalie André. The effort hypothesis at the heart of the virtuous circle. EXCLI Journal: Experimental and Clinical Sciences, 2025, 24, pp.1524-1554. 10.17179/excli2025-8937. hal-05360812

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Review article:

THE EFFORT HYPOTHESIS AT THE HEART OF THE
VIRTUOUS CIRCLE

Michel Audiffren*, Nathalie André

Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, CNRS,
Poitiers, France

* Corresponding author: Michel Audiffren, UMR 7295 CeRCA, Centre de Recherches sur la Cognition et l'Apprentissage, CNRS – Université de Poitiers, Bât A5 – TSA 21103, 5 rue Théodore Lefebvre, 86073 Poitiers Cedex, France.
E-mail: michel.audiffren@univ-poitiers.fr

https://dx.doi.org/10.17179/excli2025-8937

This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/).

ABSTRACT

This article updates the “virtuous circle” model, which links physical exercise with cognition. This model, which originally focused on connectivity between the salience network (SN) and central executive network (CEN), now also incorporates the default mode network (DMN). It describes a bidirectional dynamic: exercise enhances exec-utive functions (i.e., inhibition, flexibility, updating, planning, and problem-solving), which in turn strengthen long-term exercise adherence. This virtuous circle leads to cognitive, physiological, and motivational benefits through synergistic mechanisms induced by exercise such as the *effort hypothesis* (effort as an investment), the neurotrophic hypothesis, the cardiovascular hypothesis, the inflammatory hypothesis and the glucocorticoid hy-pothesis. These mechanisms improve connectivity within large-scale neuronal networks, thereby consolidating behavioral regulation. Compared with other behavior change models (e.g., regulation, dual-process, stage-based, and integrative models), the virtuous circle model is notable in light of its circular nature and emphasis on sustain-ability. In this theoretical framework, adherence to exercise is defined as an evolving strength of the attitude–behavior link, which is shaped by three interconnected processes: immediate motivation (pleasure, mood improve-ment, social interaction, and rewards), which initiates engagement; sustained effort, which enhances executive control, reduces perceived costs, and fosters habit formation; and behavior-driven attitude change, through cogni-tive dissonance and effort justification, which aligns beliefs with actions. Recent longitudinal studies have sup-ported the reciprocal associations among exercise, cognition, and brain health, although further trials are needed. This model highlights the fact that early adoption of the virtuous circle promotes the development of health-pro-ective habits, thereby slowing both physical and cognitive aging. In contrast, sedentary lifestyles foster a vicious circle that accelerates decline.

**Keywords:** Adherence, brain networks, cognitive control, executive functions, exercise, functional connectivity
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**Figure 1: Graphical abstract:** The virtuous circle linking exercise and cognition. The right side of the virtuous circle indicates the causal link between chronic exercise and changes in connectivity within and between three large-scale neuronal networks involved in cognition: the central executive network, the default-mode network, and the salience network. These changes in connectivity lead to an increase in willpower, i.e., the capacity to maintain cognitive control despite of high costs. The left side of the virtuous circle indicates the causal link between changes in connectivity that lead to an increase in willpower and adherence to exercise. The arrows linking behavior (exercising) and attitude (beliefs about exercise) illustrate the strength of the relationship between these two variables with respect to exercise adherence.

# 1. INTRODUCTION

Since its publication in 2019, the seminal paper presenting the virtuous circle model linking exercise and cognition (Audiffren and André, 2019) has received increasing attention, with 75 citations in indexed journals published in English (source: Web of Science 2025). Notably, 20 of these references highlight the value of the virtuous circle model as a key theoretical framework supporting the bidirectional relationship between exercise and cognitive control (e.g., Ludyga et al., 2021; Oliveira et al., 2022; Zhou et al., 2023; Zhao et al., 2024; Gerber et al., 2025). In contrast, many other studies have expanded on these insights by considering only the unidirectional causal link from exercise to cognition (e.g., Chen et al., 2020; Honma et al., 2021; Dai et al., 2024). Finally, other studies have referred to the virtuous circle model by focusing on the relationship between self-control and healthy behaviors, such as regular exercise (e.g., Pesce et al., 2021; Schmengler et al., 2022; Maltagliati et al., 2024).

The virtuous circle model consists of these two causal relationships and assumes a dynamic and bidirectional relationship between behavior (i.e., exercising) and the brain functions associated with cognitive control and executive functions (see Figure 1). Cognitive control is
defined as a set of cognitive processes that regulate behavior when automatic routines are not
available to cope with the situation or are not appropriate for reaching the planned goal (Pessig-
lione et al., 2025). Executive functions (EFs) are a set of higher-order cognitive abilities that
operationalize cognitive control. They include processes such as working memory, inhibitory
control, cognitive flexibility, planning, and problem-solving (Diamond, 2013). The link be-
tween cognitive control and EFs is hierarchical and functional: Cognitive control sets the goals
and priorities to cope with the situation, while EFs implement the strategies necessary to
achieve them.

The *effort hypothesis* describes one mechanism that underlies and maintains the virtuous circle. It implies, on the one hand, the mechanisms that mediate the causal relationship between exercise and three large-scale neuronal networks regulating cognitive control and, on the other hand, the mechanisms supporting the causal relationship between gains in cognitive control and the maintenance of exercise over time. Overall, the *effort hypothesis* assumes that maintaining the dynamic relationship between exercise and cognitive control changes the connectivity within and between three large-scale neuronal networks involved in cognition in such a way that the ability to exert cognitive control is enhanced and facilitates the induction and maintenance of behavior. These three large-scale neuronal networks are presented in section 5.

The principal aim of the present paper is to update our knowledge of the mechanisms in-
volved in the *effort hypothesis* in light of the recent advances made in the fields of social psy-
chology, cognitive psychology and neuroscience. A secondary objective of this paper is to up-
date the model by considering the role of self-referential thoughts in the capacity to sustain
cognitive control during a cognitive or physical task (see section 5) and in the capacity to sustain
regular and long-term engagement in physical activity through attitudes (see section 8.3). Self-
referential thoughts are mental processes in which attention and reflection are directed toward
oneself. They notably include the evaluation of one's own emotions, beliefs, experiences, and
intentions; the recollection of personal events (autobiographical memory); the projection of
oneself into the future (prospection, imagining scenarios); and the construction and mainte-
nance of the sense of identity or “self.”

## 2. THE VIRTUES OF EFFORT

The *effort hypothesis* is one mechanism that maintains the virtuous circle by linking exercise to improved functional connectivity in the brain, increasing cognitive control and maintaining behavior. To understand and adequately use the *effort hypothesis* within the virtuous circle model, clarification on how effort is conceptualized is needed. Effort has often been viewed as inherently aversive, reducing the appeal of rewards or desirable goals (Hull, 1943; Lewis, 1965). Today, many theories postulate that effort is inherently (or biologically) aversive or unpleasant (e.g., Kool et al., 2010; Inzlicht et al., 2014; Cheval and Boisgontier, 2021) and that benefits or rewards must be sufficiently enough to overcome effort aversion. However, empirical evidence does not show that individuals are disinclined to exert effort (i.e., that they are inherently lazy), but that it is the assessment of costs versus benefits that governs engagement in effortful tasks. Individuals are likely to maintain effort as long as the outcomes justify the expenditure, in accordance with the principles of least effort and optimization of resources (Gendolla et al., 2012, 2019). Therefore, the *effort hypothesis* postulates that readiness to engage in effort is determined by cost–benefit analysis (or computation) rather than the capacity to overcome the unpleasantness of effort. The consequence is that people naturally try to avoid exerting effort that seems unnecessary or wasted, not to avoid exerting effort itself. In this way, the brain constantly evaluates costs and benefits related to effort exertion to manage resources efficiently and maintain allostasis, i.e., the process by which the organism anticipates, adapts, and dynamically adjusts its physiological systems, such as the nervous, endocrine, and immune
systems, in response to stressors and environmental challenges (McEwen and Wingfield, 2003). This perspective highlights an important virtue of effort: it represents a rational investment of resources directed toward meaningful outcomes rather than wasted energy. The *effort hypothesis* suggests that encouraging people to value effort requires highlighting situations where the rewards of investing energy clearly outweigh the costs.

Effort can be positively valued when it is associated with meaningful outcomes, such as skill acquisition, physical fitness, or personal satisfaction. Laboratory studies show that exerting effort is often experienced as neutral or even positive (Clay et al., 2022), illustrating that effort is both costly and rewarding (e.g., Inzlicht et al., 2018). Motivational intensity theory further supports the claim that effort expenditure aligns with perceived outcome value (Brehm and Self, 1989; Gendolla et al., 2012, 2019). In this way, effort exertion can be seen as a pathway to growth, achievement, and fulfillment, virtues that emerge precisely because energy is invested rather than withheld. Reconceptualizing effort as costly rather than inherently aversive frames humans and many animals as rational agents who make optimized decisions: effort is avoided when unjustified, but willingly undertaken when the expected benefits outweigh the costs.

### 3. FOUNDATIONS OF THE VIRTUOUS CIRCLE MODEL

The virtuous circle model builds on the following broad stream of research. Cross-sectional, longitudinal, and epidemiological studies have demonstrated associations between physical activity and cognition (e.g., Rockwood and Middleton, 2007; Engeroff et al., 2018). Complementary evidence from randomized controlled trials (RCTs) has validated the effects of chronic exercise on EFs and on the brain regions involved in effort-based decision making (e.g., Colcombe and Kramer, 2003; Colcombe et al., 2006). Longitudinal work further highlights the role of self-control in shaping health behavior (e.g., Moffitt et al., 2011; Fergusson et al., 2013; Richmond-Rakerd et al., 2021). Finally, RCTs indicate that improvements in executive functioning, self-control, and effortful control enhance adherence to exercise (e.g., McAuley et al., 2011; Best et al., 2014; Savikangas et al., 2021).

The selective causal relationship between exercise and EFs was hypothesized more than twenty years ago by Arthur Kramer and his collaborators through the *selective improvement hypothesis* (Kramer et al., 1999), which was subsequently renamed the *executive-control hypothesis* (Colcombe and Kramer, 2003). The *selective improvement hypothesis* proposed that exercise leads to selective improvement in tasks including a substantial frontal-lobe-dependent executive control component, rather than generalized benefits in all domains of cognition. More precisely, according to these authors, tasks requiring executive control exhibit the greatest aging-related declines and, simultaneously, show the greatest improvements in performance as a result of exercise.

Two main distinctions separate the *selective improvement hypothesis* from the *effort hypothesis*. First, the *selective improvement hypothesis* focuses only on the right part of the virtuous circle, i.e., the effects of chronic effects of exercise on executive control, whereas the *effort hypothesis* embraces the whole virtuous circle; i.e., it also includes the mechanisms explaining that long-term changes in connectivity within and between these three large-scale neuronal networks caused by exercise determine our future health behaviors (see Figure 1). Second, as mentioned, the *selective improvement hypothesis* focuses only on executive control, whereas the *effort hypothesis* includes more broadly the chronic effects of exercise on the connectivity within and between three large-scale neuronal networks involved in executive control, effort-based decision making and internally focused thoughts.
4. CHRONIC EFFECTS OF EXERCISE ON COGNITIVE AND BRAIN HEALTH

The causal link between chronic exercise and cognitive health has been clearly established in children, middle-aged adults, and aging people through numerous meta-analyses of randomized controlled trials (e.g., Falck et al., 2019; Sanders et al., 2019; Xue et al., 2019; Zhang et al., 2023; Akalp et al., 2024; Ye et al., 2024; Singh et al., 2025). Five main plausible biological mechanisms leading to the benefits of chronic exercise on brain health have been identified (see Figure 2).

<figure><img src="image_3.png" /><figcaption>**Figure 2:** The different mechanisms mediating the chronic effects of exercise on cognitive and brain health.</figcaption></figure>

The cardiovascular fitness hypothesis suggests that cardiovascular adaptations to aerobic exercise (i.e., a gain in the efficiency of the respiratory and circulatory systems at providing oxygenated blood to relevant musculature during active moments) ameliorate cerebral blood flow, brain perfusion, and brain oxygenation and, consequently, cognitive and brain functions (e.g., Pentikäinen et al., 2017; Thomas et al., 2020). The mediating effect of cardiovascular fitness on the relationship between aerobic exercise and cognition has been tested several times in RCTs and the results are inconclusive (e.g., Etnier et al., 2006; Smiley-Oyen et al., 2008; Albinet et al., 2016; Billinger et al., 2017), whereas the results of cross-sectional and longitudinal studies are more convincing (e.g., Barnes et al., 2003; Aberg et al., 2009; Vidoni et al., 2012; Boots et al., 2015). Additional intervention studies are needed to conclusively demonstrate the contribution of this mechanism to the positive effects of exercise on cognition.

The glucocorticoid hypothesis posits a progressive downregulation of the hypothalamo-pituitary-adrenal (HPA) axis with aging (Sapolsky et al., 1986; Garrido, 2011). Stress activates the HPA axis, beginning with corticotrophin-releasing hormone (CRH) secretion from the hypothalamus, followed by adrenocorticotropic hormone (ACTH) release from the pituitary, and subsequent glucocorticoid (cortisol) secretion from the adrenal cortex. Glucocorticoids mobilize energy and provide inhibitory feedback on CRH and ACTH release (Sapolsky et al., 1986). Chronic stress and aging may impair this feedback, causing HPA hyperactivity and elevated cortisol levels. A second assumption in this context is that chronic exercise mitigates these detrimental effects (Chen et al., 2017), either by buffering cortisol secretion (Lalanza et al., 2012; Hare et al., 2014) or by upregulating medial prefrontal dopamine, which enhances negative feedback of the HPA axis (Diorio et al., 1993; Herman et al., 2003). Moreover, chronic stress,
but not exercise, downregulates mineralocorticoid and glucocorticoid receptors in the hippo-
campus (McEwen et al., 1968; Sapolsky et al., 1986) and impairs adult neurogenesis in the
dentate gyrus (Schoenfeld and Gould, 2012). While the *glucocorticoid hypothesis* is plausible,
it remains to be fully validated in humans.

The *neurotrophic hypothesis* proposes that chronic exercise stimulates the release of neu-
rotrophic factors that support brain plasticity through hippocampal neurogenesis, angiogenesis,
and long-term potentiation (LTP). Exercise promotes neuronal proliferation, differentiation,
and integration in the dentate gyrus (Brown et al., 2003; van Praag, 2008; Itoh et al., 2011;
Farioli-Vecchioli and Tirone, 2015; Liu and Nusslock, 2018; El-Sayes et al., 2019; Voss et al.,
2019a; Lambertus et al., 2024). It also enhances brain angiogenesis, as documented in rodent
treadmill studies (Stevenson et al., 2020; Zang et al., 2023; Maheu et al., 2025). LTP, a cellular
mechanism underlying learning and memory (Paoletti et al., 2013), is facilitated by chronic
exercise (Radahmadi et al., 2016; Moore and Loprinzi, 2020; Vints et al., 2022). Neurotrophic
factors such as brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-
1), and vascular endothelial-derived growth factor (VEGF) mediate these effects (Cotman et
al., 2007; Voss et al., 2013; Canivet et al., 2015; Canivet et al., 2017). BDNF, which is primarily
synthesized in glutamatergic neurons, is central to exercise-dependent neuroplasticity, neuro-
genesis, and LTP (Poo, 2001; Binder and Scharfman, 2004; Lu et al., 2014), whereas IGF-1
and VEGF increase peripherally during exercise, cross the blood–brain barrier, and stimulate
neurogenesis and angiogenesis (Trejo et al., 2001; Fabel et al., 2003; Lopez-Lopez et al., 2004).
Overall, neurotrophic factors are critical to learning, memory, and cognitive health (Bibel and
Barde, 2000; Huang and Reichardt, 2001) and have received strong support from animal stud-
ies, although only indirect evidence in humans has been reported (Erickson et al., 2011).

The *neuroinflammatory hypothesis* posits that aging induces changes in both the central nervous system (CNS) and peripheral immune cells, resulting in chronic low-grade inflammation, characterized by elevated proinflammatory cytokines and linked to neurodegenerative diseases (Bauer et al., 2024). In the aging brain, microglia adopt a proinflammatory phenotype, releasing cytokines such as TNF-α and facilitating T-cell infiltration, thereby sustaining neuroinflammation and cognitive decline (Bauer et al., 2024). Systemic inflammation further exacerbates this process, as cytokines cross the blood–brain barrier or signal via the vagus nerve, activating glial cells and promoting pathogenic events such as beta-amyloid aggregation and tau phosphorylation, key features of Alzheimer’s disease. Thus, aging is tightly associated with neuroinflammation arising from both CNS and systemic changes. Conversely, regular exercise exerts anti-neuroinflammatory effects through multiple mechanisms. It shifts microglia toward an anti-inflammatory state (Dimitrov et al., 2017; Chauquet et al., 2024), elevates beneficial cytokines and metabolites such as IL-6, which exert anti-inflammatory effects during exercise (Pedersen et al., 2001; Febbraio et al., 2002; Petersen and Pedersen, 2005; Han et al., 2023), and strengthens the blood–brain barrier, limiting peripheral immune infiltration (Hu et al., 2024). Collectively, physical activity emerges as a potent strategy to counteract age-related neuroinflammation, a hypothesis that is strongly supported by animal studies, although further validation in humans is needed.

The *effort hypothesis* assumes that practicing regular exercise selectively activates neural
circuits involved in effort-based decision-making and, through repetition, strengthens connec-
tivity within three large-scale neuronal networks in a specific way, facilitating the deployment
of effort despite situational constraints. In this article, effort is defined as the mechanism
through which the brain makes decisions about the resources that need to be mobilized to reach
a goal, including energy and cognitive control (Sanders, 1983; Hockey, 1997; Gendolla and
Wright, 2009; André et al., 2019). Overall, the *effort hypothesis* describes the mechanisms ini-
tiating the dynamics of the virtuous circle. It includes three interrelated assumptions: (1)
regularly practicing effortful physical exercises associated with intrinsic and/or extrinsic rewards leads to an improvement in the ability to exert executive control and, more generally, to exert efforts to reach a goal despite costs such as fatigue and pain often linked to exercise; (2) these improvements in the cognitive domain are underpinned by long-term changes in connectivity within and between three large-scale neuronal networks involved in cognition and described later in this article; and (3) these improvements in executive and effortful control lead to a facilitation of stopping unhealthy behaviors (e.g., smoking) and adhering to healthy behaviors (e.g., exercising). The following section describes each of these three assumptions more precisely.

The abovementioned five hypotheses can be synergistic, and all of them participate in the maintenance of the virtuous circle. For instance, increased brain plasticity induced by chronic exercise may facilitate changes in connectivity within and between the three large-scale neuronal networks involved in executive functions, effort-based decision making and self-referential thoughts. Similarly, better cardiovascular fitness may decrease the costs of exercise and subsequently facilitate the engagement of effort.

## 5. THE SALIENCE NETWORK IN THE HEART OF THE EFFORT HYPOTHESIS

The *effort hypothesis* is based on a large-scale brain network framework, called the triple-network model (Menon, 2011), which describes how three core intrinsic connectivity networks interact to support cognition, emotion, and behavior. It focuses on the dynamic switching between these networks to enable adaptive responses to environmental and internal demands. The three core networks involved in the triple-network model include (see Figure 1) the salience network (SN), central executive network (CEN) and default mode network (DMN). The first two networks are clearly activated during tasks that require attention to external stimuli (i.e., task-positive networks), whereas the DMN exhibits reduced activity during the same tasks (i.e., the task-negative network).

The SN refers to a set of brain regions featuring two principal cortical hubs: the dorsal anterior cingulate cortex (dACC) and the ventral anterior insula (vAI). This network was discovered by William Seeley from the University of California (Seeley et al., 2007). Its main functions include detecting and filtering salient internal and external stimuli, initiating switching between the other two networks, and allocating attentional and cognitive resources according to the stimulus relevance. The vAI plays a central role in detecting salient events in a bottom-up manner and in orchestrating switches between the DMN and CEN to optimize access to attentional and working memory resources (Menon, 2023). Evidence across diverse tasks identifies the vAI as a causal signaling hub, promoting activation of the CEN while suppressing activity within DMN regions (Sridharan et al., 2008; Moran et al., 2013; Manoliu et al., 2014; Chand and Dhamala, 2016). In other respects, converging evidence indicates that the dACC integrates the costs and benefits of an ongoing task to determine the level of cognitive control and energy allocated to its completion (e.g., Kennerley et al., 2006; Shenhav et al., 2013; Klein-Flügge et al., 2016).

The CEN, also known as the frontoparietal network, is anchored mainly in the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC). Its primary role is to support high-level cognitive functions called executive functions, which include working memory, inhibitory control, cognitive flexibility, planning, problem-solving, and reasoning (Diamond, 2013).

The DMN is a collection of distributed and interconnected brain regions, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and angular gyrus (Menon, 2023). The DMN is generally deactivated when attention is directed toward external stimuli, but becomes active in their absence, supporting internally oriented processes such as self-
reflection, mind wandering, daydreaming, recalling personal memories, and imagining the future. Initially identified through its reduced activity during externally focused, cognitively demanding tasks rather than through task-related activation, the DMN is linked to attentional lapses and difficulties disengaging from internal thoughts. It has been widely recognized as supporting five key cognitive domains: self-referential thinking, social cognition, episodic memory, language and semantic memory, and mind wandering (Menon, 2023).

According to the triple-network model, the SN detects external events that are behaviorally relevant. This detection triggers a suppression of the DMN and alters its temporal interactions with cognitive control systems, including the SN itself, the CEN, and the dorsal attention network (DAN). These dynamic changes enable focused attention and working memory processes, which in turn support goal-directed behavior (Menon, 2023). The DAN is a large-scale task-positive brain network that plays a central role in top-down, goal-directed attentional control (Corbetta and Shulman, 2002; Fox et al., 2006). It enables the voluntary allocation of attention to relevant sensory information and the selection of spatial or feature-based targets. The network is bilaterally organized and primarily involves two key hubs: the intraparietal sulcus/superior parietal lobule (IPS/SPL) in the posterior parietal cortex and the frontal eye field (FEF) in the frontal cortex. Together, these regions coordinate attentional orientation, visual search, and the modulation of sensory processing in early visual cortices.

The distinction between task-positive and task-negative networks should be interpreted with caution, as it has been observed, for example, that the PCC, a major hub of the DMN, can be activated during a cognitive task, reflecting learning processes, retrieval of prior knowledge, internal context setting, and the integration of new information, which helps configure brain networks for efficient task execution (e.g., Spreng et al., 2010; Small et al., 2003). These features challenge the oversimplified view of the DMN as purely “task-negative” and highlights its flexible role depending on cognitive demands.

Functional magnetic resonance imaging (fMRI) studies have indicated that attentional lapses are linked to increased activity within the DMN alongside reduced activation of the CEN. For example, Fassbender et al. (2009) reported that greater variability in reaction time correlates with a failure to deactivate the ventromedial prefrontal cortex (VMPFC), a key DMN region, as the task difficulty increases. Similarly, sustained attention deficits in individuals with traumatic brain injury have been associated with elevated DMN activation (Bonnelle et al., 2011). Mind wandering has also been connected to increased DMN activity and diminished CEN engagement (Esposito et al., 2014; Gergelyfi et al., 2021). Complementing these findings, Weber et al. (2022) demonstrated that DMN activation is stronger during periods of low effort than during periods of high effort, thus indicating that DMN activity is dynamically modulated according to effort demands. All these studies clearly show that inappropriate activation of the DMN during the execution of a cognitive task is associated with difficulties in maintaining focus, that is, in effectively engaging EFs. Inappropriate DMN activation occurs in cases such as mental fatigue, aging, psychopathologies (e.g., ADHD, schizophrenia), and nervous system injuries (e.g., traumatic brain injury, Alzheimer's disease). According to the *effort hypothesis*, the regular practice of physical exercises that require mental effort deployment can, in contrast, help develop willpower, the ability to exert mental effort and cognitive control despite adverse conditions (e.g., fatigue, extreme environments, likely failure); in other words, it can improve the regulation of DMN deactivation when it is needed for task performance.

The SN is believed to play a key role in the self-regulatory aspects of physical performance, rather than in its purely motor components. Specifically, during exercises that require stamina, the SN, particularly the dACC, is believed to regulate the willingness to initiate and sustain goal-directed physical effort (Winterer et al., 2002). The *effort hypothesis* posits that exercise training requiring sustained mental effort induces long-term changes in brain connectivity, both
within and between the three networks of the triple-network model, which enhance the capacity to maintain performance despite increasing feelings of fatigue, boredom, and pain. More precisely, exercise training may stabilize a motivational context essential for maintaining goal-directed action sequences through reinforcement learning (Holroyd and Yeung, 2012), particularly within the SN.

## 6. EMPIRICAL EVIDENCE SUPPORTING FUNCTIONAL CONNECTIVITY CHANGES WITH PHYSICAL TRAINING

In fMRI, functional connectivity (FC) refers to the statistical dependence between blood oxygenation level-dependent (BOLD) signal time series from distinct brain regions. Regions that show correlated activity over time are considered functionally connected, regardless of direct structural links. When the activity of two brain regions is positively correlated, their activities are synchronized and the FC is high (see Figure 3A). In contrast, when the activity of two brain regions is inversely correlated (i.e., negatively correlated), their activities are in the opposite phase and indicate an inverse functional relationship, thus suggesting that these regions may participate in brain networks with antagonistic functions (e.g., the DMN vs. the CEN) (see Figure 3B). Finally, when the activity between two brain regions is not correlated, no consistent temporal relationship is evident (see Figure 3C); when one region's activity increases or decreases, the other does not show a predictable change. In other words, the two brain regions are considered functionally disconnected.

<figure><img src="image_4.png" /><figcaption>**Figure 3:** The three patterns of correlation between two brain regions belonging to two brain networks A and B. A: Positive correlation = activities of networks A and B are synchronized. B: negative correlation = activities of networks A and B are in the opposite phase. C: no correlation = activities of networks A and B are dissociated and independent.</figcaption></figure>
Resting-state FC (rs-FC) measures spontaneous correlations in brain activity when no explicit task is performed, whereas task-based FC measures correlations that occur specifically during the performance of a cognitive or motor task. Within-network FC refers to the spontaneous correlation between the activity of a pair of brain regions belonging to the same network (e.g., the DLFC and PPC of the CEN), whereas between-network FC refers to the spontaneous correlation between two brain regions belonging to two different networks (e.g., the vAI of the SN and the PCC of the DMN).

The *effort hypothesis* posits that regular physical training induces long-lasting modifications in brain network connectivity, particularly by strengthening the functional integration and dynamic interactions among the SN, DMN, and CEN. These neural adaptations underlie enhanced willpower, cognitive resilience, sustained attention, and executive control, supporting the concept that exercise is a potent intervention for maintaining and improving brain health across the lifespan. More precisely, the *effort hypothesis* predicts a higher rs-FC with exercise training within and between task-positive networks, such as the SN, CEN, and DAN, and, to a lesser extent, within the DMN. With respect to changes in rs-FC between task-positive networks and the DMN, the pattern of results is more difficult to predict, as the DMN comprises a very large set of brain regions, some of which can be activated during cognitive tasks. For such changes in inter-network connectivity between task-positive and task-negative networks, region-to-region predictions would need to be established, a task that lies beyond the scope of this article.

In this section, we aim to provide arguments in support of the *effort hypothesis* on the basis of cross-sectional, longitudinal, and interventional studies that have examined the effects of regular physical activity practice on rs-FC between brain regions of interest. The studies reported in this section were identified from four review articles (Bray et al., 2021; Won et al., 2021; Huang et al., 2022; Moore et al., 2022) and a bibliographic search. Only studies including regions of interest of the SN, CEN, DAN and DMN are presented.

Eight cross-sectional studies and two longitudinal studies using resting-state fMRI that satisfied our inclusion criteria were identified. The characteristics and main results of these studies are summarized in Table 1. None of these studies specifically tested the *effort hypothesis*, and all of the results presented in Table 1 are inconsistent and cannot be used to support or refute the *effort hypothesis*. Only the study by Raichlen et al. (2016) clearly supports the *effort hypothesis* concerning task-positive networks and shows that endurance athletes have higher within-network connectivity within the CEN than non-athletes. Three other studies support higher within-network functional connectivity within the DMN in individuals with higher cardiorespiratory fitness (Boraxbekk et al., 2016; Ikuta and Loprinzi, 2019; Ikuta et al., 2019).

In other respects, thirty-seven interventional studies measuring rs-FC and including a physical component in the intervention have been identified. The characteristics of these studies are presented in Supplementary Information, S1. In this section, we discuss only the results from the 19 RCTs examining the changes in rs-FC induced by exercise within and between the SN, CEN, DAN, and DMN. As in the case of the cross-sectional studies, none of the interventional studies specifically tested the *effort hypothesis*.
<table><caption>**Table 1:** Main results of the cross-sectional studies examining the association between physical activity or physical fitness levels and functional connectivity within or between brain regions belonging to large-scale neuronal networks. * Longitudinal study</caption><thead><tr><td>Study</td><td>Sample characteristics</td><td>Type of FC</td><td>Main results in relation with the effort hypothesis</td></tr></thead><tbody><tr><td>Boyne et al. (2018)</td><td>n = 119<br>Age range: 18-85 years</td><td>Resting-state</td><td>The greater the distance covered in the six-minute walk test, the greater the FC between the MLR and the right superior frontal gyrus + ACC.</td></tr><tr><td>Gust et al. (2022)</td><td>n = 222<br>(42 young adults, 25-35 years; 180 older adults, 60+ years)</td><td>Resting state</td><td>Fitness (VO<sub>2</sub> peak) was unrelated to within-network FC of the FPN and DMN in both age groups.</td></tr><tr><td>Ikuta and Loprinzi (2019)</td><td>n = 284<br>Age range: 8-85 years</td><td>Resting-state</td><td>Higher cardiorespiratory fitness (extrapolated VO<sub>2</sub> max) was associated with greater parahippocampal but not hippocampal interhemispheric FC.</td></tr><tr><td>Ikuta et al. (2019)</td><td>n = 366<br>Age range: 8-85 years</td><td>Resting-state</td><td>Vigorous-intensity physical activity (M = 1145 METs/week) was associated with greater right and left hippocampal-or-bitofrontal FC.</td></tr><tr><td>Liu et al. (2018)</td><td>Tai chi group: n = 26<br>Control group: n = 25<br>Mean age: 64.57 ± 2.59 years</td><td>Resting-state</td><td>Weaker resting-state FC between the DLPFC and the MFG for the Tai chi group only.</td></tr><tr><td>Peven et al. (2019)</td><td>n = 50<br>Mean age: 25.22 ± 5.17 years</td><td>Task-related<br>Stroop task</td><td>Higher cardiorespiratory fitness (VO<sub>2</sub> peak) was associated with reduced FC between several brain areas evoked by the incongruent condition of the task: right supramarginal gyrus-left OFG, right MTG-left OFG, left angular gyrus-left OFG, and right MTG-lingual gyrus.</td></tr></tbody></table>
<table><thead><tr><td>Study</td><td>Sample characteristics</td><td>Type of FC</td><td>Main results in relation with the effort hypothesis</td></tr></thead><tbody><tr><td>Raichlen et al. (2016)</td><td>Distance runners: n = 11<br>Controls: n = 11<br>Mean age: 21.10 ± 1.89 years</td><td>Resting-state</td><td>Endurance athletes had greater FC between the right superior frontal / SMA and the left anterior seed of the FPN, between the left superior / midfrontal region and left anterior seed of the FPN, and between right superior / midfrontal region and right parietal seed of the FPN.</td></tr><tr><td>Stillman et al. (2018)</td><td>n = 50<br>Age range: 20-38 years</td><td>Resting-state</td><td>Higher cardiorespiratory fitness (VO2 max) levels were associated with stronger FC between the following brain regions: right anterior hippocampus with right frontal pole, bilateral ITG / MTG, right precentral gyrus; left anterior hippocampus with left brain stem / posterior parahippocampal gyrus and left frontal pole / MPFC / ACC; right posterior hippocampus with right precuneus, left frontal pole / ACC, left superior frontal / paracingulate gyrus.</td></tr><tr><td>Voss et al. (2016)</td><td>n = 225<br>(36 young adults, 22 ± 1 years; 189 older adults, 65 ± 4.4 years)</td><td>Resting-state</td><td>Cardiorespiratory fitness (VO2 max) has a positive relationship with within-FC of four networks (14 DMN pairs, 9 DAN pairs, 2 CEN pairs, 1 SN pair) and a negative relationship with within-FC of the same four networks (14 DAN pairs, 2 CEN pairs, 1 DMN pair, 1 SN pair).</td></tr><tr><td>Boraxbekk et al. (2016) *</td><td>n = 308<br>Age range: 25-80</td><td>Resting-state</td><td>Positive association between current PA and the within-network FC of the posterior DMN, specifically the anterior parts of the PCC</td></tr><tr><td>Dorsman et al. (2020) *</td><td>n = 212<br>Mean age: 73.3 ± 6.2 years</td><td>Resting-state</td><td>Within-subject increases in PA scores was positively associated with greater FC within the subcortical network and between the FPN and subcortical network. There was no association between increases in PA scores and FC involving DMN.</td></tr></tbody></table>

Abbreviations: ACC = anterior cingulate cortex; CEN = Central executive network; DAN = Dorsal attention network; DLPFC = dorsolateral prefrontal cortex; DMN = default-mode network; FPN = fronto-parietal network; ITG = inferior temporal gyrus; MFG = middle frontal gyrus; MPFC = medial prefrontal cortex; MLR = midbrain locomotor region (i.e., pedunculopontine nucleus + cuneiform nuclei); MTG = middle temporal gyrus; OFG = occipital fusiform gyrus; PA = Physical activity; PCC = Posterior cingulate cortex; SMA = supplementary motor area; SN = salience network.
The theoretical framework of most RCTs reviewed in this section is based on the broad assumption that exercise, or the combination of physical and cognitive training, stimulates brain plasticity, thereby driving changes in functional connectivity (see Supplementary Information, S2). Only three studies proposed more specific hypotheses regarding networks involved in cognitive control (Hsu et al., 2017; Tao et al., 2017; Prehn et al., 2019). Five others focused on the hippocampus and the DMN in relation to memory (Burdette et al., 2010; Eyre et al., 2016; Tao et al., 2016; Flodin et al., 2017; Broadhouse et al., 2020). Finally, four RCTs targeted both the DMN and networks associated with cognitive control (Voss et al., 2010; Pieramico et al., 2012; Suo et al., 2016; Voss et al., 2019b). Notably, 79 % of the RCTs were conducted in older adult samples.

Among the 19 RCTs identified in this context, only four supported the *effort hypothesis*, showing strengthened functional connectivity within the SN (Voss et al., 2019b; Leocadi et al., 2024), within the CEN (Voss et al., 2010), and between the SN and CEN (Balazova et al., 2021). Interestingly, the latter study also demonstrated that this connectivity change correlated with improvements in executive task performance.

Seven studies reported increased connectivity within the DMN (Voss et al., 2010; Pieramico et al., 2012; Eyre et al., 2016; Tao et al., 2016; Broadhouse et al., 2020; Balazova et al., 2021; Zhu et al., 2021). In two of these studies, changes in DMN connectivity were associated with increased memory performance (Pieramico et al., 2012; Eyre et al., 2016), whereas in two others, they were linked to improvements in EFs (Voss et al., 2010; Zhu et al., 2021).

One study revealed no evidence of connectivity changes after six weeks of balance training (Magon et al., 2016). In contrast, three studies reported results inconsistent with previous findings, showing decreased functional connectivity within the DMN (Tozzi et al., 2016; Dimitriadis et al., 2024) and the DAN (Hsu et al., 2017). These interventions consisted of aerobic exercise programs lasting 16, 12, and 24 weeks, respectively.

Finally, four RCTs reported changes in connectivity between the DMN and the CEN, with four showing increases (Suo et al., 2016; Flodin et al., 2017; Prehn et al., 2019; Claus et al., 2023). However, the study by Claus et al. (2023) also reported a decrease. Notably, the increase reported by Suo et al. (2016) was induced by cognitive training and correlated with improvements in memory performance.

Current evidence suggests that regular physical activity can modulate functional connectivity within brain networks, notably the SN, CEN, DAN, and, to a lesser extent, the DMN. Thus, while the *effort hypothesis* is partially supported, future research using standardized protocols and targeted analyses of specific networks is needed to robustly establish the impact of exercise on brain connectivity.

## 7. EXECUTIVE FUNCTIONS (EFS) AS CORE PROCESSES OF BEHAVIORAL CHANGE MODELS

Behavior change and the long-term maintenance of a health behavior are essential components of the virtuous circle model. The purpose of this section is to highlight the role of EFs in this process of behavior change by examining how EFs have been integrated into different behavior change models, and how the virtuous circle model differs from them.

Since the 1980s, EFs have been introduced in theoretical models dedicated to behavioral changes. This new generation of models started with regulatory models with the goal of narrowing the intention-action gap (Leventhal et al., 1980; Kuhl and Beckman, 1985; Heckhausen and Gollwitzer, 1987; Kruglanski et al., 1990; Gollwitzer, 1999). Regulatory theories have focused on complex and multidimensional processes that cannot be reduced to mere motivation or sheer willpower. Some models emphasize the central role of planning (Gollwitzer's Implementation Intention; Heckhausen and Gollwitzer's Rubicon Model), which enables individuals
to transform intentions into concrete plans and to cross the threshold from wanting to acting. Others highlight the importance of sustaining action over time through attention and monitoring (Kuhl and Beckmann's Action Control Theory), or the ability to adapt when facing obstacles and unexpected challenges (Leventhal and collaborators' Commonsense Model). Finally, Kruglanski's goal systems theory reminds us that goals do not operate in isolation but within a dynamic network of associations, activations, and inhibitions. Taken together, these models suggest that EFs rely on an interplay of preparation, persistence, and flexibility, involving not only the initiation of action but also the maintenance of effort and adaptation to both internal and external constraints (see Table 2).

<table><caption>**Table 2:** Executive functions that play an important role in the models of behavior change.</caption><thead><tr><th>Authors</th><th>Theoretical Models</th><th>Constructs related to Central Executive Network</th></tr></thead><tbody><tr><td><strong>Dual-process models</strong><br>Strobach et al. (2020)</td><td>• Physical Activity Adoption and Maintenance Model - PAAM</td><td>Planification, self-regulation</td></tr><tr><td>Brand and Ekkekakis (2017)</td><td>• Affective-Reflective Theory</td><td>Self-control, reflective evaluation</td></tr><tr><td>Strack and Deutsch (2004)</td><td>• Reflective-Impulsive Model (RIM)</td><td>Reflective system: deliberate control, inhibition, planning</td></tr><tr><td>Hofmann, Friese and Strack (2009)</td><td>• Dual Systems Model of Self-Control</td><td>Executive functions (inhibition, working memory, shifting)</td></tr><tr><td><strong>Stages-based models</strong><br>Schwarzer and Luszczynska (2008)</td><td>• Health Action Process Approach - HAPA</td><td>Coping planning, action planning</td></tr><tr><td>Hall and Fong (2007)</td><td>• Temporal self-regulation model</td><td>Self-regulatory capacity (inhibition, flexibility and updating)</td></tr><tr><td>Weinstein, Sandman and Blalock (2008)</td><td>• Precaution Adoption Process model</td><td>Decision-making</td></tr><tr><td><strong>Regulation models</strong><br>Gollwitzer (1999)</td><td>• Implementation Intention</td><td>Planning</td></tr><tr><td>Kuhl and Beckmann (1985)</td><td>• Action control theory</td><td>Attention, monitoring</td></tr><tr><td>Heckhausen and Gollwitzer (1987)</td><td>• Rubicon Model of Action Phases</td><td>Planning</td></tr><tr><td>Leventhal et al. (1980)</td><td>• Commonsense Model of Self-Regulation</td><td>Coping</td></tr><tr><td>Kruglanski et al. (1990s)</td><td>• Goal Systems Theory</td><td>Goal activation, inhibition, cognitive associations</td></tr><tr><td><strong>Integrative models</strong><br>Michie, van Stralen et West (2011)</td><td>• COM-B (Capacity, Opportunity, Motivation-Behavior)</td><td>Attention, decision-making, behavioral regulation, action planning</td></tr><tr><td>Rhodes (2017)</td><td>• Multi-Process Action Control</td><td>Behavioral, affective, cognitive regulation</td></tr><tr><td>Hagger and Chatzisantis (2014)</td><td>• Integrated Behavior Change Model (extension of the Theory of Planned Behavior)</td><td>Action planning</td></tr><tr><td>De Vries (2017)</td><td>• I (Integrated)-Change Model</td><td>Action planning, overcoming barriers, inhibition, flexibility, working memory</td></tr><tr><td>Audiffren and André (2019)</td><td>• Virtuous Circle Model</td><td>Executive functions (inhibition, updating, shifting, working memory, attentional control, self-regulation)</td></tr></tbody></table>
Dual-process models highlight the interaction between reflective and impulsive systems in guiding behavior. The Physical Activity Adoption and Maintenance Model (PAAM) emphasizes the role of planning and self-regulation in sustaining long-term behaviors such as physical activity (Strobach et al., 2020). The Affective-Reflective Theory adds that self-control depends on the interplay between immediate affective reactions and reflective evaluations, showing how emotions shape decisions about engaging in or avoiding certain behaviors (Brand and Ekkekakis, 2018). The Reflective-Impulsive Model (RIM) distinguishes between a reflective system, which is responsible for deliberate control, inhibition, and planning, and an impulsive system that drives automatic responses, often in conflict with reflective goals (Strack and Deutsch, 2004). Similarly, the Dual Systems Model of Self-Control highlights the importance of EFs such as inhibition, working memory, and cognitive shifting in regulating impulses (Hofmann, Friese, and Strack, 2009). Taken together, these models suggest that successful behavior change depends on the dynamic balance between fast, automatic impulses and slower, deliberate control processes, with successful regulation requiring both strong EFs and effective strategies for aligning impulsive tendencies with reflective goals.

Stages-based models conceptualize behavior change as a process that unfolds over time, with individuals progressing through distinct phases on the path from intention to action. The Health Action Process Approach (HAPA) emphasizes both action planning and coping planning, highlighting how individuals prepare for challenges and obstacles before engaging in behavior (Schwarzer and Luszczynska, 2008). The Temporal Self-Regulation Model stresses the role of self-regulatory capacity, particularly inhibition, flexibility, and updating, in determining whether intentions are successfully translated into actions within temporal contexts (Hall and Fong, 2007). Moreover, the Precaution Adoption Process Model focuses on decision-making stages and describes how individuals move from unawareness or indifference to deliberate decisions and the eventual adoption of protective behaviors (Weinstein, Sandman, and Blalock, 2008). Together, these models emphasize that behavior change is not instantaneous but involves sequential stages, requiring both cognitive resources and adaptive planning strategies to successfully progress from intention to sustained action.

Integrative models combine multiple theoretical perspectives to explain behavior change and self-regulation as the result of interactions among cognitive, affective, and contextual processes. The COM-B model frames behavior as emerging from the interplay between capacity, opportunity, and motivation, emphasizing the roles of attention, decision-making, and action planning (Michie, van Stralen, and West, 2011). The Multi-Process Action Control model extends this view by incorporating behavioral, affective, and cognitive regulation, highlighting the diversity of processes that guide action (Rhodes, 2021). The Integrated Behavior Change Model, an extension of the Theory of Planned Behavior, places action planning at the core of intentional behavior change (Hagger and Chatzisarantis, 2014). Similarly, the I-Change Model highlights the need for action planning alongside mechanisms to overcome barriers, as well as EFs such as inhibition, flexibility, and working memory (De Vries, 2017). Finally, the Virtuous Circle Model emphasizes the cyclical role of EFs, including inhibition, updating, shifting, and attentional control, in sustaining self-regulation (or change) over time (Audiffren and André, 2019). Collectively, these models converge on the idea that effective self-regulation requires not only deliberate planning but also the integration of executive capacities, emotional processes, and contextual opportunities, forming a dynamic and adaptive system of behavior regulation.

The virtuous circle differs from other models in three ways: First, it is not limited to the intention-action transition but emphasizes the sustainability of behavior over time. Moreover, it is a circular model in which behavior itself becomes an important lever for change. Finally,
it proposes an original hypothesis, the *effort hypothesis*, which considers effort as a useful in-
vestment rather than a cost that people systematically try to avoid.

Despite their theoretical diversity, all these models converge on the idea that EFs constitute a fundamental foundation for behavioral change. Together, they show that behavior change relies on a combination of EFs, emotional regulation, and strategies adapted to different goals. The next section details the mechanisms of action of the virtuous circle model.

8. ADHERENCE TO EXERCISE WITHIN THE VIRTUOUS CIRCLE

Behavior change is a dynamic process that unfolds through successive, transient modifica-
tions. In the study of behavior change and health promotion, exercise adherence is a central
concept, as it captures these evolving adjustments. Unlike compliance, which measures behav-
ior in isolation, adherence reflects the reciprocal alignment of attitudes and behaviors. In other
words, engaging in exercise can foster positive attitudinal adjustments, whereas favorable atti-
tudes reinforce behavioral maintenance. The virtuous circle model conceptualizes adherence as
the strength of the attitude–behavior link, positioning exercise not only as an outcome but also
as a determinant of change (André et al., 2024). From this perspective, adherence can be un-
derstood as the degree to which behaviors are consistent with attitude and vice versa. Individu-
als may initially exercise despite negative attitudes—perceiving the activity as tedious—but
engagement can gradually foster supportive beliefs. Conversely, favorable attitudes toward
physical activity, which some call “intention strength” (e.g., Conner and Norman, 2024), facil-
itate repeated participation, capturing the dynamic processes underlying behavioral transfor-
mation.

Exercise can strengthen adherence through three interrelated mechanisms. This conceptu-
alization has practical implications for rehabilitation, reconditioning, performance-based train-
ing, and preventive health programs. Rather than treating attitudes (or intention strength) solely
as precursors, interventions can leverage exercise behavior itself as a driver of adherence. In
this framework, exercise becomes an active determinant of change rather than a passive out-
come.

8.1. *Sustained effort*

Long-term adherence requires sustained physical and cognitive effort. As discussed, chronic exercise improves cognitive control, which relies on EFs, self-control, and self-regulation to regulate thoughts, emotions, and actions (e.g., Audiffren et al., 2022). Enhanced cognitive control in turn facilitates adherence by reducing the cognitive cost of effortful decisions and increasing the reward value of continued engagement. The virtuous circle model illustrates the bidirectional relationship between exercise and cognition: exercise strengthens executive functioning, which in turn improves self-regulation and reduces the appeal of sedentary alternatives. Repeated exercise also induces physiological and psychological adaptations—enhanced cardiovascular fitness, greater willpower, and increased valuation of effort—that reinforce engagement (e.g., Feil et al., 2021). Effective strategies include implementation intentions (planning where, when, and how to exercise), self-monitoring (tracking steps, heart rate, or perceived exertion), goal setting and problem-solving to overcome barriers, and the use of contextual cues to build routines (e.g., Gollwitzer, 1999; Sheeran et al., 2005). These strategies transform exercise from a cognitively costly choice into habitual, semiautomatic behavior, strengthening long-term adherence. This repeated link enhances executive control processes, establishing a virtuous cycle in which improved cognitive control reduces the perceived cost of effort, further supporting adherence.

Recently, additional evidence has shown the effects of exercise on brain connectivity (Mor-
ris et al., 2022; Ai et al., 2023). These two studies were carried out with older adults and
revealed that adherence to a healthy lifestyle is associated with specific brain characteristics. On the one hand, Morris et al. (2022) demonstrated that higher nodal rs-FC within the DMN, CEN, and DAN predicted exercise adherence, whereas rs-FC between the ACC and the supplementary motor area (SMA), as well as between the right AI and the left temporoparietal /temporo-occipital junction, predicted changes in sedentary time in the walking group. On the other hand, Ai et al. (2023) reported that compared with an adherent lifestyle group, a nonadherent lifestyle group exhibited greater positive correlations and fewer anticorrelations between the DMN and other task-positive networks (e.g., the DAN). Overall, a multimodal model combining structural, functional, and clinical measures achieved the strongest prediction, suggesting that adherence emerges from the interaction of executive control, self-reflection, and attentional regulation.

Sustained effort and adherence reinforce each other: exercise enhances executive control and self-regulation, which lowers the perceived cost of effort and make continued engagement more rewarding. Neurobiological evidence shows that adherence is supported by strengthened cortical and functional connectivity in networks linked to control, attention, and self-reflection, highlighting a virtuous cycle that promotes long-term lifestyle change.

**8.2. Immediate motivation**

High dropout rates in exercise interventions often result from the immediate costs of exercise (e.g., fatigue, discomfort, time, financial burden) outweighing delayed benefits (e.g., health improvements, weight loss). The initiation of exercise requires proximal motivators, such as increased arousal, pleasant bodily sensations (warmth, relaxation), enhanced mood, achievement of meaningful goals, and social interaction (Lee et al., 2016). While these effects are short-term, they are crucial for initiating and reinforcing motivation. Behavior change techniques (BCTs), such as rewards, graded tasks, feedback, and biofeedback, enhance short-term motivation across populations, including cancer patients, individuals with obesity, and those experiencing cognitive decline (e.g., Carey et al., 2019; Howlett et al., 2019). Digital tools—wearable devices and AI-based applications—further support motivation by providing real-time feedback during sessions (e.g., Michaelsen and Esch, 2022). By focusing on immediate reinforcers, practitioners can promote adherence at the level of each exercise session, laying the foundation for longer-term commitment.

**8.3. Behavior-driven attitude change**

Attitudes toward exercise often evolve more slowly than do behaviors. Relying solely on attitudinal change as a precursor may limit intervention effectiveness. Behavior itself can drive attitude change through mechanisms such as cognitive dissonance (Festinger, 1957) and commitment theory (Kiesler, 1971; Joule and Beauvois, 1998). Cognitive dissonance arises when individuals act inconsistently with their beliefs; for example, a person who perceives exercise as unnecessary but nonetheless participates may later adjust their beliefs to justify the behavior. Commitment theory suggests that repeated, effortful engagement strengthens psychological investment and aligns attitudes with behavior. Effort is central to this process on the basis of two mechanisms: (a) effort justification (Aronson and Mills, 1959), whereby individuals assign greater value to outcomes requiring substantial effort, legitimizing the cost invested; and (b) the effort heuristic, where people assume that effortful actions yield superior outcomes, enhancing perceived benefits and satisfaction. Consequently, exercise acts not only as a health intervention but also as a catalyst for attitudinal change, progressively reinforcing adherence.

Another way in which behavior can drive attitude change might be through the effect of exercise on changes in rs-FC within the DMN. For instance, self-efficacy belongs to self-beliefs and self-concepts that are associated with the rs-FC of several regions within the DMN (e.g.,
Davey et al., 2019; Dixon and Gross, 2021; Wang et al., 2022). Self-efficacy refers to an individual's confidence in his or her ability to perform activities of a given intensity and duration, whether under optimal conditions or when facing obstacles and adversity. This confidence not only influences exercise-related choices and behaviors, but is also strongly linked to adherence to exercise routines, physical adaptability, and psychological well-being (Medrano-Ureña et al., 2020). For instance, McAuley et al. (2011) demonstrated that individuals who begin an exercise program with stronger executive functioning and greater use of self-regulatory strategies tend to develop stronger beliefs in their exercise abilities, which subsequently promotes higher adherence. The connectivity changes induced by chronic exercise may reflect an increase in positive self-representations and a decrease in negative self-representations. In the future, it would be interesting to test the hypothesis that regular exercise induces changes in functional connectivity within the DMN, which in turn underlies lasting changes in attitudes toward exercise.

These three pathways—immediate motivation, sustained effort, and behavior-driven attitude change—are deeply interconnected. Immediate reinforcers sustain participation in individual sessions; repeated sessions enhance executive functioning and self-regulation; and accumulated effort reshapes attitudes, reinforcing long-term adherence. Adherence should thus be understood as the evolving strength of the link between attitudes and behaviors. Recognizing exercise as both an outcome and a determinant deepens theoretical models of behavior change and informs interventions promoting sustainable engagement. Given its effortful nature, exercise induces attitudinal change via cognitive dissonance and effort justification. Repeated engagement under conditions of free choice, perceived personal cost, and consistent effort strengthen commitment, and align attitudes with behavior. By emphasizing behavior-oriented approaches, practitioners can catalyze a self-reinforcing cycle where repeated activity fortifies the attitude-behavior link, ensuring sustainable adherence.

With respect to the virtuous circle model, the relationship between exercise and EFs or effortful control has been studied more than the relationship between executive functioning and behavior and its maintenance (i.e., exercise adherence). Since 2019, several articles have strongly reviewed this link and provided more evidence (Boat and Cooper, 2019; Gray-Burrows et al., 2019; Strobach et al., 2020; Frye and Shapiro, 2021; Gürdere et al., 2023). Three main relationships can be drawn from these reviews: (1) Cognitive control and EFs help individuals engage in healthy behavior and stop unhealthy behavior; (2) Cognitive control and EFs are more important in the adoption phase and may be less important in the maintenance phase; and (3) Cognitive control and EFs can help move intention into action.

Exercise adherence emerges from the dynamic interaction of immediate motivation, sustained effort, and behavior-driven attitude change. Immediate rewards, such as positive feelings and social interaction, encourage early engagement, while repeated effort strengthens self-regulation and gradually turns exercise into a habit (Gardner and Rebar, 2019). Over time, these behaviors reshape attitudes through cognitive dissonance and commitment, creating a self-reinforcing cycle that consolidates long-term adherence.

## 9. CONCLUSIONS AND PERSPECTIVES

The main objective of this article was to update the model of the virtuous circle linking physical exercise and cognition published in March 2019. The first version of the model considered only changes in functional connectivity induced by regular physical exercise between the SN and the CEN. The new version of the model now integrates the DMN, as it plays a fundamental role in the ability to remain engaged in an activity both in the present moment and over the long term.

This updated version of the model also specifies five neurobiological mechanisms that may synergistically contribute to maintaining the virtuous circle, whereas the previous version
focuses mainly on the *neurotrophic hypothesis* and the *effort hypothesis*, with the latter remain-
ing the mechanism that makes the greatest contribution to the virtuous circle. For instance, the
*cardiovascular hypothesis*, which is grounded in the bidirectional interplay between the heart
and the brain (i.e., the brain-heart axis), highlights the pivotal role of regular engagement in
structured exercise, whether through high-intensity interval training, moderate aerobic activity,
or resistance training, which exerts a regulatory effect on autonomic responses, enhances the
production of BDNF, and fosters neurovascular coupling (e.g., Omole et al., 2025). Collec-
tively, these adaptations contribute to improved cognitive and cardiovascular resilience and fa-
cilitate the maintenance of exercise. Similarly, the *neurotrophic hypothesis* may be seen as re-
inforcing the *effort hypothesis*, insofar as neurotrophic factors released through exercise facili-
tate structural and functional connectivity changes within networks underlying cognitive con-
trol and positive self-representational beliefs.

Furthermore, two additional bidirectional relationships may contribute to this virtuous cy-
cle: the relationship between exercise and sleep quality (e.g., Qian et al., 2025), and the rela-
tionship between the brain and the gut (e.g., Cutuli et al., 2023). On the one hand, exercise
improves sleep quality (e.g., Zhou et al., 2025); in turn, good sleep quality is associated with
energy restoration and the consolidation of memory traces (e.g., Porkka-Heiskanen and Ka-
linchuk, 2011; Weighall and Kellar, 2023), thereby facilitating further engagement in exercise.
On the other hand, exercise positively impacts the bidirectional communication between the
brain and the gut, referred to as the gut-brain axis, which relies primarily on the direct and
indirect interactions of the gut microbiota with the CNS, the autonomic nervous system, the
enteric nervous system, and the HPA axis. This complex interplay involves multiple overlap-
ping pathways, including neuroendocrine and immune mechanisms, and plays a pivotal role in
neuronal development, brain function, cognitive regulation, and the aging process, subse-
quently participating in the maintenance of a virtuous circle between a behavior (i.e., exercis-
ing) and health.

The present article also reviews new experimental evidence supporting the relationship be-
tween the chronic effects of exercise and functional connectivity within and between the three
major neural networks mentioned above. In the perspectives section presented in the previous
article on the virtuous circle model, a lack of RCTs examining the effects of chronic exercise
on the rs-FC of large-scale neural networks involved in cognitive control was mentioned. To
date, only six new RCTs have been published; however, precise predictions regarding the net-
works positively impacted by chronic exercise have not been provided (see Supplementary In-
formation, S2). Two articles targeted specific brain regions, the hippocampus (Broadhouse et
al., 2020) and the orbitofrontal cortex (Claus et al., 2023), but neither refer to the three networks
of the triple-network model in the rationale used to formulate their main hypothesis. However,
three RCTs clearly supported the virtuous circle model (Voss et al., 2019b; Balazova et al.,
2021; Leocadi et al., 2024; see section 6). In the future, researchers seeking to conduct RCTs
to examine the chronic effects of exercise on rs-FC are encouraged to formulate very precise
hypotheses concerning the networks involved, the type of connectivity (within-network or be-
tween-network), the direction of the effects (increased or decreased connectivity), and the the-
oretical rationale leading to these predictions.

Furthermore, the present article clarified the difference between the virtuous circle model
and other behavior change models. In particular, the virtuous circle model proposes that atti-
tudes and behavior (i.e., effort in exercise) are two key ways to initiate and stabilize health
behavior (e.g., engaging in regular exercise). It emphasizes that the deployment of effort de-
pends on the short-term benefits associated with regular physical activity; thus, people are will-
ing to invest effort in physical activity as long as this effort is justified. This feature implies that
any intervention involving physical activity must take these aspects into account to increase
participants' adherence.

Over the past five years, several longitudinal studies with large samples have examined the bidirectional relationships between physical activity and cognitive and brain health, using different statistical approaches: cross-lagged panel models (Zhao et al., 2021; Hofman et al., 2023; Mitchell et al., 2025), latent growth curve models (Stenling et al., 2022), and mixed-model analyses (Maasakkers et al., 2021). Three of these investigations confirmed reciprocal associations between exercise and cognition. For example, Zhao et al. (2021) reported that physical performance predicted later cognitive outcomes, whereas cognition also predicted subsequent physical performance in 4,232 older Chinese adults. Similarly, Maasakkers et al. (2021) reported in 1,276 Irish participants that each additional hour of sedentary behavior per day was linked to lower global cognition scores over time. Conversely, greater declines in immediate and delayed recall were related to slightly greater amounts of sedentary behavior and TV viewing after four years. By analyzing 2,888 adults from the United Kingdom, Mitchell et al. (2025) reported that improvements in verbal memory increased the likelihood of being classified as mid-active during the next wave, whereas becoming physically active was associated with a modest improvement in subsequent memory scores. This reciprocal association appeared stronger for men. In contrast, the two remaining studies supported only a unidirectional effect. Stenling et al. (2022) reported that higher baseline physical activity was associated with a lower decline in episodic memory among 1,722 Swedish adults, whereas in 4,365 Dutch participants, Hofman et al. (2023) reported that greater brain volume at baseline predicted higher levels of physical activity at follow-up. Taken together, this emerging longitudinal evidence using structural equation modeling provides promising insights into the dynamic links between exercise and cognition; however, further development is warranted.

The virtuous circle described herein contributes to the development of healthy habits that, in turn, facilitate its maintenance (e.g., Feil et al., 2021). The mechanisms of action outlined above (i.e., sustained effort, immediate motivation, and changes in attitudes) promote the formation of habits. The earlier in the lifespan the virtuous cycle is initiated, the more solidly health-promoting habits that facilitate long-term maintenance are established. The virtuous circle that maintains engagement in regular physical exercise slows physical and cognitive aging. In contrast, a sedentary lifestyle fosters a vicious circle that tends to accelerate aging. Considering the theoretical framework of the virtuous circle model and *effort hypothesis* may, on the one hand, support the development of intervention programs aimed at rehabilitation and reconditioning—particularly for patients suffering from chronic fatigue, anhedonia, or apathy. Specifically, the implementation of exercise programs should be systematically combined with therapeutic patient education to strengthen the links between exercise behavior and a positive attitude toward physical activity. Beyond rehabilitation, the virtuous circle may also guide motivational interventions across various clinical contexts. When coupled with AI-assisted monitoring, they could enhance adherence, optimize feedback, and sustain the long-term benefits of physical activity.

Conflict of interest

The authors declare that they have no con-
flicts of interest related to this work.

Using Artificial Intelligence (AI)

AI (ChatGPT) was used for the concep-
tion of Figure 3 and translation of sentences
from French to English.
Authors contribution

Conceptualization = MA, NA; Writing -
Original Draft = MA, NA; Writing - Review
and Editing = MA, NA; Visualization = MA;
Funding acquisition = MA.

REFERENCES

Aberg MA, Pedersen NL, Torén K, Svartengren M, Bäckstrand B, Johnsson T, et al. Cardiovascular fitness is associated with cognition in young adulthood. Proc Natl Acad Sci U S A. 2009;106(49):20906-11. doi: 10.1073/pnas.0905307106.

Ai M, Morris TP, Zhang J, Noriega de la Colina A, Tremblay-Mercier J, Villeneuve S, et al. Resting-state MRI functional connectivity as a neural correlate of multidomain lifestyle adherence in older adults at risk for Alzheimer's disease. Sci Rep. 2023;13:7487. doi: 10.1038/s41598-023-32714-1.

Akalp K, Ferreira JP, Soares CM, Ribeiro MJ, Teixeira AM. The effects of different types of exercises on cognition in older persons with mild cognitive impairment: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2024;126:105541. doi: 10.1016/j.archger.2024.105541.

Albinet CT, Abou-Dest A, André N, Audiffren M. Executive functions improvement following a 5-month aquaerobics program in older adults: Role of cardiac vagal control in inhibition performance. Biol Psychol. 2016;115:69-77. doi: 10.1016/j.biopsycho.2016.01.010.

André N, Grousset M, Audiffren M. A behavioral perspective for improving exercise adherence. Sports Med Open. 2024;10:56. doi: 10.1186/s40798-024-00714-8.

Aronson E, Mills J. The effect of severity of initiation on liking for a group. J Abnorm Soc Psychol. 1959;59(2):177-181. doi: 10.1037/h0047195.

Audiffren M, André N. The exercise-cognition relationship: A virtuous circle. J Sport Health Sci. 2019; 8:339-347.

Audiffren M, André N, Baumeister RF. Training willpower: reducing costs and valuing effort. Front Neurosci. 2022;16:699817. doi: 10.3389/fnins.2022.699817.

Balazova Z, Marecek R, Novakova L, Nemcova-Elfmarkova N, Kropacova S, Brabenec L, et al. Dance intervention impact on brain plasticity: A randomized 6-month fMRI study in non-expert older adults. Front Aging Neurosci. 2021;13:724064. doi: 10.3389/fnagi.2021.724064.

Barnes DE, Yaffe K, Satariano WA, Tager IB. A longitudinal study of cardiorespiratory fitness and cognitive function in healthy older adults. J Am Geriatr Soc. 2003;51(4):459-65. doi: 10.1046/j.1532-5415.2003.51153.x.

Bauer ME, Pawelec G, Paganelli R. Neuroimmunology and ageing - the state of the art. Immun Ageing. 2024;21(1):5. doi: 10.1186/s12979-024-00411-w.

Best JR, Nagamatsu LS, Liu-Ambrose T. Improvements to executive function during exercise training predict maintenance of physical activity over the following year. Front Hum Neurosci. 2014;8:353. doi: 10.3389/fnhum.2014.00353.

Bibel M, Barde YA. Neurotrophins: Key regulators of cell fate and cell shape in the vertebrate nervous system. Genes Dev. 2000;14(23):2919-37. doi: 10.1101/gad.841400.

Billinger SA, Vidoni ED, Morris JK, Thyfault JP, Burns JM. Exercise test performance reveals evidence of the cardiorespiratory fitness hypothesis. J Aging Phys Act. 2017;25(2):240-46. doi: 10.1123/japa.2015-0321.

Binder DK, Scharfman HE. Brain-derived neurotrophic factor. Growth Factors 2004;22(3):123-31. doi: 10.1080/08977190410001723308.

Boat R, Cooper SB. Self-control and exercise: A review of the bi-directional relationship. Brain Plast. 2019;5(1):97-104. doi: 10.3233/bpl-190082.

Bonnelle V, Leech R, Kinnunen KM, Ham TE, Beckmann CF, De Boissezon X, et al. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci. 2011;31(38):13442-51. doi: 10.1523/JNEUROSCI.1163-11.2011.

Boots EA, Schultz SA, Oh JM, Larson J, Edwards D, Cook D, et al. Cardiorespiratory fitness is associated with brain structure, cognition, and mood in a middle-aged cohort at risk for Alzheimer's disease. Brain Aging Behav. 2015;9(3):639-49. doi: 10.1007/s11682-014-9325-9.

Boraxbekk CJ, Salami A, Wåhlin A, Nyberg L. Physical activity over a decade modifies age-related decline in perfusion, gray matter volume, and functional connectivity of the posterior default-mode network-A multimodal approach. Neuroimage. 2016;131:133-41. doi: 10.1016/j.neuroimage.2015.12.010.

Brand R, Ekkekakis P. Affective-Reflective Theory of physical inactivity and exercise. Ger J Exerc Sport Res. 2018;48:48-58. doi: 10.1007/s12662-017-0477-9.
Bray NW, Pieruccini-Faria F, Bartha R, Doherty TJ,
Nagamatsu LS, Montero-Odasso M. The effect of
physical exercise on functional brain network connec-
tivity in older adults with and without cognitive im-
pairment. A systematic review. Mech Ageing Dev.
2021;196:111493. doi: 10.1016/j.mad.2021.111493.

Brehm J, Self E. The intensity of motivation. Annu
Rev Psychol. 1989;40:109-131. doi: 10.1146/an-
nurev.ps.40.020189.000545.

Broadhouse KM, Singh MF, Suo C, Gates N, Wen W,
Brodaty H, et al. Hippocampal plasticity underpins
long-term cognitive gains from resistance exercise in
MCI. Neuroimage Clin. 2020;25:102182. doi:
10.1016/j.nicl.2020.102182.

Brown J, Cooper-Kuhn CM, Kempermann G, Van Praag H, Winkler J, Gage FH, et al. Enriched environment and physical activity stimulate hippocampal but not olfactory bulb neurogenesis. Eur J Neurosci. 2003;17(10):2042-46. doi: 10.1046/j.1460-9568.2003.02647.x.

Burdette JH, Laurienti PJ, Espeland MA, Morgan A,
Telesford Q, Vechlekar CD, et al. Using network sci-
ence to evaluate exercise-associated brain changes in
older adults. Front Aging Neurosci. 2010;2:23. doi:
10.3389/fnagi.2010.00023.

Canivet A, Albinet CT, André N, Pylouster J, Rodríguez-Ballesteros M, Kitzis A, et al. Effects of BDNF polymorphism and physical activity on episodic memory in the elderly: a cross sectional study. Eur Rev Aging Phys Act. 2015;12:15. doi: 10.1186/s11556-015-0159-2.

Canivet A, Albinet CT, Rodríguez-Ballesteros M,
Chicherio C, Fagot D, André N, et al. Interaction be-
tween BDNF polymorphism and physical activity on
inhibitory performance in the elderly without cogni-
tive impairment. Front Hum Neurosci. 2017;11:541.
doi: 10.3389/fnhum.2017.00541.

Carey RN, Connell LE, Johnston M, Rothman AJ, de Bruin M, Kelly MP, et al. Behavior Change Techniques and Their Mechanisms of Action: A Synthesis of Links Described in Published Intervention Literature. Ann Behav Med. 2019;53(8):693-707. doi: 10.1093/abm/kay078.

Chand GB, Dhamala M. Interactions among the brain default-mode, salience, and central-executive networks during perceptual decision-making of moving dots. Brain Connect. 2016;6(3):249-54. doi: 10.1089/brain.2015.0379.

Chauquet S, Willis EF, Grice L, Harley SBR, Powell
JE, Wray NR, et al. Exercise rejuvenates microglia
and reverses T cell accumulation in the aged female
mouse brain. Aging Cell. 2024;23(7):e14172. doi:
10.1111/acel.14172.

Chen C, Nakagawa S, An Y, Ito K, Kitaichi Y,
Kusumi I. The exercise-glucocorticoid paradox: How
exercise is beneficial to cognition, mood, and the
brain while increasing glucocorticoid levels. Front
Neuroendocrinol. 2017;44:83-102. doi:
10.1016/j.yfrne.2016.12.001.

Chen FT, Etnier JL, Chan KH, Chiu PK, Hung TM,
Chang YK. Effects of Exercise Training Interventions
on Executive Function in Older Adults: A Systematic
Review and Meta-Analysis. Sports Med. 2020;50(8):
1451-67. doi: 10.1007/s40279-020-01292-x.

Cheval B, Boisgontier MP. The theory of effort mini-
mization in physical activity. Exerc Sport Sci Rev.
2021;49(3):168-178. doi:
10.1249/JES.0000000000000252.

Claus J, Upadhyay N, Maurer A, Klein J, Scheef L,
Daamen M, et al. Physical activity alters functional
connectivity of orbitofrontal cortex subdivisions in
healthy young adults: A longitudinal fMRI study.
Healthcare (Basel). 2023;11(5):689. doi:
10.3390/healthcare11050689.

Clay G, Mlynski C, Korb FM, Goschke T, Job V. Rewarding cognitive effort increases the intrinsic value of mental labor. Proc Natl Acad Sci U S A. 2022;119 (5):e2111785119. doi: 10.1073/pnas.2111785119.

Colcombe S, Kramer AF. Fitness effects on the cogni-
tive function of older adults: a meta-analytic study.
Psychol Sci. 2003;14(2):125-30. doi: 10.1111/1467-9280.t01-1-01430.

Colcombe SJ, Erickson KI, Scalf PE, Kim JS, Prakash
R, McAuley E, et al. Aerobic exercise training in-
creases brain volume in aging humans. J Gerontol A
Biol Sci Med Sci. 2006;61(11):1166-70. doi:
10.1093/gerona/61.11.1166.

Conner M, Norman P. Understanding the intention-behavior gap: The role of intention strength. Front Psychol. 2022;13:923464. doi: 10.3389/fpsyg.2022.923464.

Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201-15. doi: 10.1038/nrn755.

Cotman CW, Berchtold NC, Christie LA. Exercise builds brain health: key roles of growth factor cascades and inflammation. Trends Neurosci.
2007;30(9):464-72. doi: 10.1016/j.tins.2007.06.011.
Cutuli D, Decandia D, Giacovazzo G, Coccurello R. Physical exercise as disease-modifying alternative against Alzheimer's disease: A gut-muscle-brain partnership. Int J Mol Sci. 2023;24(19):14686. doi: 10.3390/ijms241914686.

Dai S, Dong Y, Shi H, Jin J, Gan Y, Li X, et al. Aerobic exercise prevents and improves cognitive dysfunction caused by morphine withdrawal via regulating endogenous opioid peptides in the brain. Psychopharmacology (Berl). 2024;241(12):2525-37. doi: 10.1007/s00213-024-06698-3.

Davey C, Fornito A, Pujol J, Breakspear M, Schmaal L, Harrison B. Neurodevelopmental correlates of the emerging adult self. Dev Cogn Neurosci. 2019;36:epub ahead of print. doi: 10.1016/j.dcn.2019.100626.

De Vries H. An integrated approach for understanding health behavior: The I-Change Model as an example. Psychol Behav Sci Int J. 2017;2(2):555585. doi: 10.19080/pbsij.2.2.

Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135-68. doi: 10.1146/annurev-psych-113011-143750.

Dimitriadis SI, Castells-Sánchez A, Roig-Coll F, Dacosta-Aguayo R, Lamonja-Vicente N, Torán-Monserrat P, et al. Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment. Geroscience. 2024;46(1):573-596. doi: 10.1007/s11357-023-00946-8.

Dimitrov S, Hulteng E, Hong S. Inflammation and exercise: Inhibition of monocytic intracellular TNF production by acute exercise via β2-adrenergic activation. Brain Behav Immun. 2017;61:60-68. doi: 10.1016/j.bbi.2016.12.017.

Diorio D, Viau V, Meaney MJ. The role of the medial prefrontal cortex (cingulate gyrus) in the regulation of hypothalamic-pituitary-adrenal responses to stress. J. Neurosci. 1993;13:3839-47. doi: 10.1523/JNEUROSCI.13-09-03839.1993.

Dixon ML, Gross JJ. Dynamic network organization of the self: Implications for affective experience. Curr Opin Behav Sci. 2021;39:1-9. doi: 10.1016/j.cobeha.2020.11.004.

Dorsman KA, Weiner-Light S, Staffaroni AM, Brown JA, Wolf A, Cobigo Y, et al. Get Moving! Increases in physical activity are associated with increasing functional connectivity trajectories in typically aging adults. Front Aging Neurosci. 2020;12:104. doi: 10.3389/fnagi.2020.00104.

El-Sayes J, Harasym D, Turco CV, Locke MB, Nelson AJ. Exercise-induced neuroplasticity: A mechanical model and prospects for promoting plasticity. The Neuroscientist. 2019;25(1):65-85. doi: 10.1177/1073858418771538.

Engeroff T, Ingmann T, Banzer W. Physical activity throughout the adult life span and domain-specific cognitive function in old age: A systematic review of cross-sectional and longitudinal data. Sports Med. 2018; 48(6):1405-36. doi: 10.1007/s40279-018-0920-6.

Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, Chaddock L, et al. Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A. 2011;108(7):3017-22. doi: 10.1073/pnas.1015950108.

Esposito F, Otto T, Zijlstra FR, Goebel R. Spatially distributed effects of mental exhaustion on resting-state fMRI networks. PLoS One. 2014;9(4):e94222. doi: 10.1371/journal.pone.0094222.

Etnier JL, Nowell PM, Landers DM, Sibley BA. A meta-regression to examine the relationship between aerobic fitness and cognitive performance. Brain Res Rev. 2006;52(1):119-30. doi: 10.1016/j.brainresrev.2006.01.002.

Eyre HA, Acevedo B, Yang H, Siddarth P, Van Dyk K, Ercoli L, et al. Changes in neural connectivity and memory following a Yoga intervention for older adults: A pilot study. J Alzheimers Dis. 2016;52(2): 673-84. doi: 10.3233/JAD-150653.

Fabel K, Fabel K, Tam B, Kaufer D, Baiker A, Simmons N, et al. VEGF is necessary for exercise-induced adult hippocampal neurogenesis. Eur J Neurosci. 2003; 18(10):2803-12. doi: 10.1111/j.1460-9568.2003.03041.x.

Falck RS, Davis JC, Best JR, Crockett RA, Liu-Ambrose T. Impact of exercise training on physical and cognitive function among older adults: a systematic review and meta-analysis. Neurobiol Aging. 2019;79: 119-30. doi: 10.1016/j.neurobiolaging.2019.03.007.

Farioli-Vecchioli S, Tirone F. Control of the cell cycle in adult neurogenesis and its relation with physical exercise. Brain Plast. 2015;1(1):41-54. doi: 10.3233/BPL-150013.

Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, et al. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 2009;1273:114-28. doi: 10.1016/j.brainres.2009.02.070.
Febbraio MA, Pedersen BK. Muscle-derived interleukin-6: mechanisms for activation and possible biological roles. FASEB J 2002;16:1335-47. doi: 10.1096/fj.01-0876rev.

Feil K, Allion S, Weyland S, Jekauc D. A Systematic Review Examining the Relationship Between Habit and Physical Activity Behavior in Longitudinal Studies. Front Psychol. 2021;12:626750. doi: 10.3389/fpsyg.2021.626750.

Fergusson DM, Boden JM, Horwood LJ. Childhood self-control and adult outcomes: results from a 30-year longitudinal study. J Am Acad Child Adolesc Psychiatry. 2013;52(7):709-17.e1. doi: 10.1016/j.jaac.2013.04.008.

Festinger L. A theory of cognitive dissonance. Stanford, CA: Stanford University Press, 1957.

Flodin P, Jonasson LS, Riklund K, Nyberg L, Boraxbekk CJ. Does aerobic exercise influence intrinsic brain activity? An aerobic exercise intervention among healthy old adults. Front Aging Neurosci. 2017;9:267. doi: 10.3389/fnagi.2017.00267.

Fox MD, Corbetta M, Snyder AZ, Vincent JL, Rai-chle ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A. 2006;103(26):10046-51. doi: 10.1073/pnas.0604187103.

Frye WS, Shapiro SK. The role of executive functioning on the intention-behaviour relationship of health behaviours: a temporal self-regulatory perspective. Psychol Health. 2021;36(5):612-627. doi: 10.1080/08870446.2020.1767778.

Gardner B, Rebar A. Habit formation and behavior change. Oxford Research Encyclopedia of Psychology; 2019: from https://oxfordre.com/psychology/view/10.1093/acrefore/9780190236557.001.0001/acrefore-9780190236557-e-129.

Garrido P. Aging and stress: past hypotheses, present approaches and perspectives. Aging Dis. 2011;2(1):80-99.

Gendolla GHE, Wright RA. Effort. In: Sander D, Scherer KR (eds): The Oxford companion to emotion and the affective sciences (pp 134-135). New York, NY: Oxford University Press, 2009.

Gendolla GHE, Wright RA, Richter M. Effort intensity: Some insights from the cardiovascular system. In: Ryan RM (ed): The Oxford handbook of human motivation (pp 420-438). New York, NY: Oxford University Press, 2012.

Gendolla GHE, Wright RA, Richter M. Advancing issues in motivation intensity research: Updated insights from the cardiovascular system. In: Ryan RM (ed): The Oxford Handbook of Human Motivation (2nd ed.) (pp 373-392). New York, NY: Oxford University Press, 2019.

Gerber M, Cheval B, Cody R, Colledge F, Hohberg V, Klimentidis YC, et al. Psychophysiological foundations of human physical activity behavior and motivation: theories, systems, mechanisms, evolution, and genetics. Physiol Rev. 2025;105(3):1213-1290. doi: 10.1152/physrev.00021.2024.

Gergelyfi M, Sanz-Arigita EJ, Solopchuk O, Dricot L, Jacob B, Zénon A. Mental fatigue correlates with depression of task-related network and augmented DMN activity but spares the reward circuit. Neuroimage. 2021;243:118532. doi: 10.1016/j.neuroimage.2021.118532.

Gollwitzer PM. Implementation intentions: Strong effects of simple plans. Am Psychol. 1999;54(7):493-503. doi: 10.1037/0003-066X.54.7.493.

Gray-Burrows K, Taylor N, O'Connor D, Sutherland E, Stoet G, Conner M. A systematic review and meta-analysis of the executive function-health behaviour relationship. Health Psychol Behav Med. 2019;7(1):253-268. doi: 10.1080/21642850.2019.1637740.

Gürdere C, Strobach T, Pastore M, Pfeffer I. Do executive functions predict physical activity behavior? A meta-analysis. BMC Psychol. 2023;11(1):33. doi: 10.1186/s40359-023-01067-9.

Gust CJ, Moe EN, Seals DR, Banich MT, Andrews-Hanna JR, Hutchison KE, et al. Associations between age and resting state connectivity are partially dependent upon cardiovascular fitness. Front Aging Neurosci. 2022;14:858405. doi: 10.3389/fnagi.2022.858405.

Hagger MS, Chatzisarantis NLD. An integrated behavior change model for physical activity. Exerc Sport Sci Rev. 2014;42(2):62-69. doi: 10.1249/JES.0000000000000008.

Hall PA, Fong GT. Temporal Self-Regulation Theory: A Model for Individual Health Behavior. Health Psychol Rev. 2007;1(1):6-52. doi: 10.1080/17437190701492437.

Han H, Zhao Y, Du J, Wang S, Yang X, Li W, et al. Exercise improves cognitive dysfunction and neuroinflammation in mice through Histone H3 lactylation in microglia. Immun Ageing. 2023;20(1):63. doi: 10.1186/s12979-023-00390-4.
Hare BD, Beierle JA, Toufexis DJ, Hammack SE, Falls WA. Exercise associated changes in the corticosterone response to acute restraint stress: evidence for increased adrenal sensitivity and reduced corticosterone response duration. Neuropsychopharmacology. 2014; 39(5):1262-69. doi: 10.1038/npp.2013.329.

Heckhausen H, Gollwitzer PM. Thought contents and cognitive functioning in motivational versus volitional states of mind. Motivation Emotion. 1987;11(2):101-120. doi: 10.1007/BF00992338.

Herman JP, Figueiredo H, Mueller NK, Ulrich-Lai Y, Ostrander MM, Choi DC, et al. Central mechanisms of stress integration: hierarchical circuitry controlling hypothalamo-pituitary-adrenocortical responsiveness. Front. Neuroendocrinol. 2003;24:151-80. doi: 10.1016/j.yfrne.2003.07.001.

Hockey GR. Compensatory control in the regulation of human performance under stress and high workload; a cognitive-energetical framework. Biol Psychol. 1997;45(1-3):73-93. doi: 10.1016/s0301-0511(96)05223-4.

Hofman A, Rodriguez-Ayllon M, Vernooij MW, Croll PH, Luik AI, Neumann A, et al. Physical activity levels and brain structure in middle-aged and older adults: a bidirectional longitudinal population-based study. Neurobiol Aging. 2023;121:28-37. doi: 10.1016/j.neurobiolaging.2022.10.002.

Hofmann W, Friese M, Strack F. Impulse and self-control from a dual-systems perspective. Pers Soc Psychol Rev. 2009;13(2):104-116. doi: 10.1177/1088868309341587.

Holroyd CB, Yeung N. Motivation of extended behaviors by anterior cingulate cortex. Trends Cogn Sci. 2012;16(2):122-8. doi: 10.1016/j.tics.2011.12.008.

Honma M, Murakami H, Yabe Y, Kuroda T, Futamura A, Sugimoto A, et al. Stopwatch training improves cognitive functions in patients with Parkinson's disease. J Neurosci Res. 2021;99(5):1325-1336. doi: 10.1002/jnr.24812.

Howlett N, Trivedi D, Troop NA, Chater AM. Are physical activity interventions for healthy inactive adults effective in promoting behavior change and maintenance, and which behavior change techniques are effective? A systematic review and meta-analysis. Transl Behav Med. 2019;9(1):147-157. doi: 10.1093/tbm/iby010.

Hsu CL, Best JR, Wang S, Voss MW, Hsiung RGY, Munkacsy M, et al. The impact of aerobic exercise on fronto-parietal network connectivity and its relation to mobility: An exploratory analysis of a 6-month randomized controlled trial. Front Hum Neurosci. 2017; 11:344. doi: 10.3389/fnhum.2017.00344. Erratum in: Front Hum Neurosci. 2017;11:449. doi: 10.3389/fnhum.2017.00449.

Hu J, Huang B, Chen K. The impact of physical exercise on neuroinflammation mechanism in Alzheimer's disease. Front Aging Neurosci. 2024;16:1444716. doi: 10.3389/fnagi.2024.1444716.

Huang EJ, Reichardt LF. Neurotrophins: Roles in neuronal development and function. Annu. Rev. Neurosci. 2001;24:677-736. doi: 10.1146/anurev.neuro.24.1.677.

Huang X, Zhao X, Cai Y, Wan Q. The cerebral changes induced by exercise interventions in people with mild cognitive impairment and Alzheimer's disease: A systematic review. Arch Gerontol Geriatr. 2022;98: 104547. doi: 10.1016/j.archger.2021.104547.

Hull C. Principles of behavior. New York, NY: Appleton-Century, 1943.

Ikuta T, Loprinzi PD. Association of cardiorespiratory fitness on interhemispheric hippocampal and parahippocampal functional connectivity. Eur J Neurosci. 2019;50(2):1871-1877. doi: 10.1111/ejn.14366.

Ikuta T, Frith E, Ponce P, Loprinzi PD. Association of physical activity on the functional connectivity of the hippocampal-orbitofrontal pathway. Phys Sportsmed. 2019;47(3):290-294. doi: 10.1080/00913847.2018.1549461.

Inzlicht M, Schmeichel BJ, Macrae CN. Why self-control seems (but may not be) limited. Trends Cogn Sci. 2014;18(3):127-133. doi: 10.1016/j.tics.2013.12.009.

Inzlicht M, Shenhav A, Olivola CY. The effort paradox: Effort is both costly and valued. Trends Cogn Sci. 2018;22(4):337-349. doi: 10.1016/j.tics.2018.01.007.

Itoh T, Imano M, Nishida S, Tsubaki M, Hashimoto S, Ito A, et al. Exercise increases neural stem cell proliferation surrounding the area of damage following rat traumatic brain injury. J. Neural Transm. 2011;118(2): 193-202. doi: 10.1007/s00702-010-0495-3.
Joule RV, Beauvois JL. La Soumission librement con-
sentie. Paris: Presses Universitaires de France, 1998.

Kennerley SW, Walton ME, Behrens TE, Buckley
MJ, Rushworth MF. Optimal decision making and the
anterior cingulate cortex. Nat Neurosci.
2006;9(7):940-947. doi: 10.1038/nn1724.

Kiesler CA. The psychology of commitment: experi-
ments linking behavior to belief. New York: Aca-
demic Press, 1971.

Klein-Flügge MC, Kennerley SW, Friston K, Best-
mann S. Neural signatures of value comparison in hu-
man cingulate cortex during decisions requiring an ef-
fort-reward trade-off. J Neurosci. 2016;36(39):10002-
15. doi: 10.1523/JNEUROSCI.0292-16.2016.

Kool W, McGuire J, Rosen Z, Botvinick M. Decision
making and the avoidance of cognitive demand. J Exp
Psychol Gen. 2010;139:665-682. doi:
10.1037/a0020198.

Kramer AF, Hahn S, Cohen NJ, Banich MT,
McAuley E, Harrison CR, et al. Ageing, fitness and
neurocognitive function. Nature.
1999;400(6743):418-19. doi: 10.1038/22682.

Kruglanski AW. Lay epistemic theory in social-cogni-
tive psychology. Psychol Inq. 1990;1(3):181-197.
doi.org/10.1207/s15327965pli0103_1.

Kuhl J, Beckmann J. Action control: From cognition to behavior. Heidelberg, Berlin, New York: Springer, 1985.

Lalanza JF, Sanchez-Roige S, Gagliano H, Fuentes S,
Bayod S, Camins A, et al. Physiological and behav-
ioural consequences of long-term moderate treadmill
exercise, Psychoneuroendocrinology. 2012;37(11):
1745-1754. doi: 10.1016/j.psyneuen.2012.03.008.

Lambertus M, Geiseler S, Morland C. High-intensity
interval exercise is more efficient than medium inten-
sity exercise at inducing neurogenesis. J Physiol.
2024; 602(24):7027-7042. doi: 10.1113/JP287328.

Lee HH, Emerson JA, Williams DM. The exercise-af-
fect-adherence pathway: An evolutionary perspective.
Front Psychol. 2016;7:1285. doi:
10.3389/fpsyg.2016.01285.

Leocadi M, Canu E, Sarasso E, Gardoni A, Basaia S,
Calderaro D, et al. Dual-task gait training improves
cognition and resting-state functional connectivity in
Parkinson's disease with postural instability and gait
disorders. J Neurol. 2024;271(4):2031-2041. doi:
10.1007/s00415-023-12151-w.

Leventhal H, Meyer D, Nerenz D. The common sense
model of illness danger. In: Rachman S (ed): Medical
psychology (Vol. II, pp 7-30). New York, NY: Per-
gamon Press, 1980.

Lewis M. Psychological effect of effort. Psychol Bull.
1965;64(3):183-190. doi: 10.1037/h0022224.

Lopez-Lopez C, LeRoith D, Torres-Aleman I. Insulin-
like growth factor I is required for vessel remodeling
in the adult brain. Proc Natl Acad Sci U S A.
2004;101(26):9833-9838. doi:
10.1073/pnas.0400337101.

Liu PZ, Nusslock R. Exercise-mediated neurogenesis
in the hippocampus via BDNF. Front Neurosci.
2018;12:52. doi: 10.3389/fnins.2018.00052.

Liu Z, Wu Y, Li L, Guo X. Functional connectivity
within the executive control network mediates the ef-
fects of long-term Tai Chi exercise on elders' emotion
regulation. Front Aging Neurosci. 2018;10:315. doi:
10.3389/fnagi.2018.00315.

Lu B, Nagappan G, Lu Y. BDNF and synaptic plastic-
ity, cognitive function, and dysfunction. Handb Exp
Pharmacol. 2014;220:223-50. doi: 10.1007/978-3-
642-45106-5_9.

Ludyga S, Pühse U, Gerber M, Mücke M. Muscle
strength and executive function in children and ado-
lescents with autism spectrum disorder. Autism Res.
2021;14(12):2555-2563. doi: 10.1002/aur.2587.

Maasakkers CM, Claassen JAHR, Scarlett S, Thijssen
DHJ, Kenny RA, Feeney J, et al. Is there a bidirec-
tional association between sedentary behaviour and
cognitive decline in older adults? Findings from the
Irish Longitudinal Study on Ageing. Prev Med Rep.
2021;23: 101423. doi: 10.1016/j.pmedr.2021.101423.

Magon S, Donath L, Gaetano L, Thoeni A, Radue
EW, Faude O, et al. Striatal functional connectivity
changes following specific balance training in elderly
people: MRI results of a randomized controlled pilot
study. Gait Posture. 2016;49:334-339. doi:
10.1016/j.gaitpost.2016.07.016.

Maheu MG, James N, Clark Z, Yang A, Patel R,
Beaudette SM, et al. Running to remember: The ef-
fects of exercise on perineuronal nets, microglia, and
hippocampal angiogenesis in female and male mice.
Behav Brain Res. 2025;484:115478. doi:
10.1016/j.bbr.2025.115478.

Maltagliati S, Fessler L, Yu Q, Zhang Z, Chen Y, Du-
puy O, et al. Effort minimization: A permanent, dy-
namic, and surmountable influence on physical activ-
ity. J Sport Health Sci. 2024;14:100971. doi:
10.1016/j.jshs.2024.100971.
Manoliu A, Riedl V, Zherdin A, Mühlau M, Schwerthöffer D, Scherr M, et al. Aberrant dependence of default mode/central executive network interactions on anterior insular salience network activity in schizophrenia. Schizophr Bull. 2014;40(2):428-437. doi: 10.1093/schbul/sbt037.

McAuley E, Mullen SP, Szabo AN, White SM, Wójcicki TR, Mailey EL, et al. Self-regulatory processes and exercise adherence in older adults: executive function and self-efficacy effects. Am J Prev Med. 2011;41(3):284-290. doi: 10.1016/j.amepre.2011.04.014.

McEwen BS, Wingfield JC. The concept of allostasis in biology and biomedicine. Horm Behav. 2003;43 (1):2-15. doi: 10.1016/S0018-506X(02)00024-7.

McEwen BS, Weiss JM, Schwartz LS. Selective retention of corticosterone by limbic structures in rat brain. Nature. 1968; 220:911-912. doi: 10.1038/220911a0.

Medrano-Ureña MDR, Ortega-Ruiz R, Benítez-Sillero JD. Physical Fitness, Exercise Self-Efficacy, and Quality of Life in Adulthood: A Systematic Review. Int J Environ Res Public Health. 2020;17(17):6343. doi: 10.3390/ijerph17176343.

Menon V. Large-scale brain networks and psycho-pathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483-506. doi: 10.1016/j.tics.2011.08.003.

Menon V. 20 years of the default mode network: A review and synthesis. Neuron. 2023;111(16):2469-2487. doi: 10.1016/j.neuron.2023.04.023.

Michaelsen MM, Esch T. Functional mechanisms of health behavior change techniques: A conceptual review. Front Psychol. 2022;13:725644. doi: 10.3389/fpsyg.2022.725644.

Michie S, van Stralen MM, West R. The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6(1):42. doi: 10.1186/1748-5908-6-42.

Mitchell JJ, Hamer M, James SN, Norris T, Jefferis BJ, Wannamethee SG, et al. Bidirectional associations of Physical Activity and Cognitive Function in Mid-life Adults: A Longitudinal Analysis across 26 years follow-up. Am J Epidemiol. 2025; kwaf144;epub ahead of print. doi: 10.1093/aje/kwaf144.

Moffitt TE, Arseneault L, Belsky D, Dickson N, Han-cox RJ, Harrington H, et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc Natl Acad Sci U S A. 2011;108(7):2693-2698. doi: 10.1073/pnas.1010076108.

Moore D, Loprinzi PD. Exercise influences episodic memory via changes in hippocampal neurocircuitry and long-term potentiation. Eur J Neurosci. 2021;54 (8):6960-6971. doi: 10.1111/ejn.14728.

Moore D, Jung M, Hillman CH, Kang M, Loprinzi PD. Interrelationships between exercise, functional connectivity, and cognition among healthy adults: A systematic review. Psychophysiology. 2022;59(6):e14014. doi: 10.1111/psyp.14014.

Moran LV, Tagamets MA, Sampath H, O'Donnell A, Stein EA, Kochunov P, et al. Disruption of anterior insula modulation of large-scale brain networks in schizophrenia. Biol Psychiatry. 2013;74(6):467-474. doi: 10.1016/j.biopsych.2013.02.029.

Morris TP, Burzynska A, Voss M, Fanning J, Salerno EA, Prakash R, et al. Brain structure and function predict adherence to an exercise intervention in older adults. Med Sci Sports Exerc. 2022;54(9):1483-1492. doi: 10.1249/MSS.0000000000002949.

Oliveira GTA, Elsangedy HM, Vivas A, Fontes EB. Can discrepancies between impulsive and reflective processes be associated with movement behavior among the elderly? The facilitating role of inhibitory control. Psychol. Sport Exerc. 2022;63:102272. doi: 10.1016/j.psychsport.2022.102272.

Omole JG, Okon IA, Udom GJ, Aziakpono OM, Agbana RD, Aturamu A, et al. Neurophysiological mechanisms underlying cardiovascular adaptations to exercise: A narrative review. Physiol Rep. 2025;13 (13):e70439. doi: 10.14814/phy2.70439.

Paoletti P, Bellone C, Zhou Q. NMDA receptor subunit diversity: impact on receptor properties, synaptic plasticity and disease. Nat Rev Neurosci. 2013;14(6):383-400. doi: 10.1038/nrn3504.

Pedersen BK, Steensberg A, Schjerling P. Muscle-derived interleukin-6: possible biological effects. J Physiol 2001;536:329-337. doi: 10.1111/j.1469-7793.2001.0329c.xd.

Pentikäinen H, Ngandu T, Liu Y, Savonen K, Komulainen P, Hallikainen M, et al. Cardiorespiratory fitness and brain volumes in men and women in the FINGER study. Age Ageing. 2017;46(2):310-313. doi: 10.1093/ageing/afw191.

Pesce C, Lakes KD, Stodden DF, Marchetti R. Foster-ing self-control development with a designed inter-vention in physical education: A two-year class-ran-domized trial. Child Dev. 2021;92(3):937-958. doi: 10.1111/cdev.13445.
Pessiglione M, Blain B, Wiehler A, Naik S. Origins and consequences of cognitive fatigue. Trends Cogn Sci. 2025;29(8):730-749. doi: 10.1016/j.tics.2025.02.005.

Petersen AMW, Pedersen, BK. The anti-inflammatory effect of exercise. J Appl Physiol 2005;98:1154-1162. doi: 10.1152/japplphysiol.00164.2004.

Peven JC, Litz GA, Brown B, Xie X, Grove GA, Watt JC, et al. Higher cardiorespiratory fitness is associated with reduced functional brain connectivity during performance of the Stroop Task. Brain Plast. 2019;5(1): 57-67. doi: 10.3233/BPL-190085.

Pieramico V, Esposito R, Sensi F, Cilli F, Mantini D, Mattei PA, et al. Combination training in aging individuals modifies functional connectivity and cognition, and is potentially affected by dopamine-related genes. PLoS One. 2012;7(8):e43901. doi: 10.1371/journal.pone.0043901.

Poo MM. Neurotrophins as synaptic modulators. Nat Rev Neurosci. 2001;2(1):24-32. doi: 10.1038/35049004.

Porkka-Heiskanen T, Kalinchuk AV. Adenosine, energy metabolism and sleep homeostasis. Sleep Med Rev. 2011;15(2):123-135. doi: 10.1016/j.smrv.2010.06.005.

Prehn K, Lesemann A, Krey G, Witte AV, Köbe T, Grittner U, et al. Using resting-state fMRI to assess the effect of aerobic exercise on functional connectivity of the DLPFC in older overweight adults. Brain Cogn. 2019;131:34-44. doi: 10.1016/j.bandc.2017.08.006.

Qian S, Yong C, Li S, Peng J, Li X, Ye Q, et al. Bidirectional relationships between sleep quality and cognitive function in older Chinese adults: Observational analysis of SAWA trial. Am J Health Promot. 2025;39 (6):880-889. doi: 10.1177/08901171251326311.

Radahmadi M, Hosseini N, Alaei H. Effect of exercise, exercise withdrawal, and continued regular exercise on excitability and long-term potentiation in the dentate gyrus of hippocampus. Brain Res. 2016;1653:8-13. doi: 10.1016/j.brainres.2016.09.045.

Raichlen DA, Bharadwaj PK, Fitzhugh MC, Haws KA, Torre GA, Trouard TP. Alexander GE. Differences in resting state functional connectivity between young adult endurance athletes and healthy controls. Front Hum Neurosci. 2016;10:610. doi: 10.3389/fnhum.2016.00610.

Rhodes RE. Multi-Process Action Control in Physical Activity: A Primer. Front Psychol. 2021;12:797484. doi: 10.3389/fpsyg.2021.797484.

Richmond-Rakerd LS, Caspi A, Ambler A, d'Arbeloff T, de Bruine M, Elliott M, et al. Childhood self-control forecasts the pace of midlife aging and preparedness for old age. Proc Natl Acad Sci U S A. 2021;118(3): e2010211118. doi: 10.1073/pnas.2010211118.

Rockwood K, Middleton L. Physical activity and the maintenance of cognitive function. Alzheimers De-ment. 2007;3(2 Suppl):S38-44. doi: 10.1016/j.jalz.2007.01.003.

Sanders AF. Towards a model of stress and human performance. Acta Psychol. 1983;53:61-97. doi: 10.1016/0001-6918(83)90016-1.

Sanders LMJ, Hortobagyi T, la Bastidevan, Gemert S, van der Zee EA, van Heuvelen MJG. Dose-response relationship between exercise and cognitive function in older adults with and without cognitive impairment: A systematic review and meta-analysis. PLoS ONE 2019;14(1):e0210036. doi: 10.1371/journal.pone.0210036.

Sapolsky RM, Krey LC, McEwen BS. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocr Rev. 1986;7(3):284-301. doi: 10.1210/edrv-7-3-284.

Savikangas T, Törmäkangas T, Tirkkonen A, Alen M, Fielding RA, Kivipelto M, et al. The effects of a physical and cognitive training intervention vs. physical training alone on older adults' physical activity: A randomized controlled trial with extended follow-up during COVID-19. PLoS ONE 2021;16(10):e0258559. doi: 10.1371/journal.pone.0258559.

Schmengler H, Peeters M, Stevens GWJM, Kunst AE, Delaruelle K, Dierckens M, et al. Socioeconomic inequalities in adolescent health behaviours across 32 different countries - The role of country-level social mobility. Soc Sci Med. 2022;310:115289. doi: 10.1016/j.socscimed.2022.115289.

Schoenfeld TJ, Gould E. Stress, stress hormones, and adult neurogenesis. Exp Neurol. 2012;233(1):12-21. doi: 10.1016/j.expneurol.2011.01.008.

Schwarzer R, Luszczynska A. How to overcome health-compromising behaviors: The health action process approach. Eur Psychol. 2008;13(2):141-151. doi: 10.1027/1016-9040.13.2.141.
Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27(9):2349-2356. doi: 10.1523/JNEUROSCI.5587-06.2007.

Sheeran P, Webb TL, Gollwitzer PM. The interplay between goal intentions and implementation intentions. Pers Soc Psychol Bull. 2005;31(1):87-98. doi: 10.1177/0146167204271308.

Shenhav A, Botvinick MM, Cohen JD. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013;79(2):217-240. doi: 10.1016/j.neuron.2013.07.007.

Singh B, Bennett H, Miatke A, Dumuid D, Curtis R, Ferguson T, et al. Effectiveness of exercise for improving cognition, memory and executive function: a systematic umbrella review and meta-meta-analysis. Br J Sports Med. 2025;59:866-876. doi: 10.1136/bjsports-2024-108589.

Small DM, Gitelman DR, Gregory MD, Nobre AC, Parrish TB, Mesulam MM. The posterior cingulate and medial prefrontal cortex mediate the anticipatory allocation of spatial attention. Neuroimage. 2003;18(3): 633-641. doi: 10.1016/s1053-8119(02)00012-5.

Smiley-Oyen AL, Lowry KA, Francois SJ, Kohut ML, Ekkekakis P. Exercise, fitness, and neurocognitive function in older adults: the “selective improvement” and “cardiovascular fitness” hypotheses. Ann Behav Med. 2008;36(3):280-291. doi: 10.1007/s12160-008-9064-5.

Spreng RN, Stevens WD, Chamberlain JP, Gilmore AW, Schacter DL. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage. 2010;53(1):303-317. doi: 10.1016/j.neuroimage.2010.06.016.

Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci USA. 2008;105(34):12569-12574. doi: 10.1073/pnas.0800005105.

Stenling A, Eriksson Sörman D, Lindwall M, Machado L. Bidirectional Within- and Between-Person Relations Between Physical Activity and Cognitive Function. J Gerontol B Psychol Sci Soc Sci. 2022;77(4):704-709. doi: 10.1093/geronb/gbab234.

Stevenson ME, Miller CC, Owen HA, Swain RA. Aerobic exercise increases sprouting angiogenesis in the male rat motor cortex. Brain Struct Funct. 2020;225 (8):2301-2314. doi: 10.1007/s00429-020-02100-y.

Stillman CM, Uyar F, Huang H, Grove GA, Watt JC, Wollam ME, et al. Cardiorespiratory fitness is associated with enhanced hippocampal functional connectivity in healthy young adults. Hippocampus. 2018;28 (3):239-247. doi: 10.1002/hipo.22827.

Strack F, Deutsch R. Reflective and impulsive determinants of social behavior. Pers Soc Psychol Rev. 2004;8 (3): 220-247. doi: 10.1207/s15327957pspr0803_1.

Strobach T, Englert C, Jekauc D, Pfeffer I. Predicting adoption and maintenance of physical activity in the context of dual-process theories. Perform Enhancement Health. 2020;8(1):100162. doi: 10.1016/j.peh.2020.100162.

Suo C, Singh MF, Gates N, Wen W, Sachdev P, Brodaty H, et al. Therapeutically relevant structural and functional mechanisms triggered by physical and cognitive exercise. Mol Psychiatry. 2016;21(11):1633-1642. doi: 10.1038/mp.2016.19.

Tao J, Liu J, Egorova N, Chen X, Sun S, Xue X, et al. Increased hippocampus–medial prefrontal cortex resting-state functional connectivity and memory function after Tai Chi Chuan practice in elder adults. Front Aging Neurosci. 2016;8:25. doi: 10.3389/fnagi.2016.00025.

Tao J, Chen X, Egorova N, Liu J, Xue X, Wang Q, et al. Tai Chi Chuan and Baduanjin practice modulates functional connectivity of the cognitive control network in older adults. Sci Rep. 2017;7:41581. doi: 10.1038/srep41581.

Thomas BP, Tarumi T, Sheng M, Tseng B, Womack KB, Cullum CM, et al. Brain Perfusion Change in Patients with Mild Cognitive Impairment After 12 Months of Aerobic Exercise Training. J Alzheimers Dis. 2020;75(2):617-631. doi: 10.3233/JAD-190977.

Tozzi L, Carballedo A, Lavelle G, Doolin K, Doyle M, Amico F, et al. Longitudinal functional connectivity changes correlate with mood improvement after regular exercise in a dose-dependent fashion. Eur J Neurosci. 2016;43(8):1089-1096. doi: 10.1111/ejn.13222.

Trejo JL, Carro E, Torres-Aleman I. Circulating insulin-like growth factor I mediates exercise-induced increases in the number of new neurons in the adult hippocampus. J Neurosci. 2001;21(5):1628-1634. doi: 10.1523/JNEUROSCI.21-05-01628.2001.

van Praag H. Neurogenesis and exercise: Past and future directions. Neuromolecular Med. 2008;10(2):128-140. doi: 10.1007/s12017-008-8028-z.
Vidoni ED, Honea RA, Billinger SA, Swerdlow RH, Burns JM. Cardiorespiratory fitness is associated with atrophy in Alzheimer's and aging over 2 years. Neurobiol Aging. 2012;33(8):1624-1632. doi: 10.1016/j.neurobiolaging.2011.03.016.

Vints WAJ, Levin O, Fujiyama H, Verbunt J, Masiulis N. Exerkines and long-term synaptic potentiation: Mechanisms of exercise-induced neuroplasticity. Front Neuroendocrinol. 2022;66:100993. doi: 10.1016/j.yfrne.2022.100993.

Voss MW, Prakash RS, Erickson KI, Basak C, Chaddock L, Kim JS, et al. Plasticity of brain networks in a randomized intervention trial of exercise training in older adults. Front Aging Neurosci. 2010;2:32. doi: 10.3389/fnagi.2010.00032.

Voss MW, Erickson KI, Prakash RS, Chaddock L, Kim JS, Alves H, et al. Neurobiological markers of exercise-related brain plasticity in older adults. Brain Behav Immun. 2013;28:90-99. doi: 10.1016/j.bbi.2012.10.021.

Voss MW, Weng TB, Burzynska AZ, Wong CN, Cooke GE, Clark R, et al. Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging. Neuroimage. 2016;131: 113-125. doi: 10.1016/j.neuroimage.2015.10.044.

Voss MW, Soto C, Yoo S, Sodoma M, Vivar C, van Praag H. Exercise and hippocampal memory systems. Trends Cogn Sci. 2019a;23(4):318-333. doi: 10.1016/j.tics.2019.01.006.

Voss MW, Sutterer M, Weng TB, Burzynska AZ, Fanning J, Salerno E, et al. Nutritional supplementation boosts aerobic exercise effects on functional brain systems. J Appl Physiol. 2019b;126(1):77-87. doi: 10.1152/japplphysiol.00917.2017.

Wang X, Chen Q, Li Y, Ding K, Qiu J. The brain functional connectivity in the default mode network is associated with self-efficacy in young adults. Brain Imaging Behav. 2022;16(1):107-117. doi: 10.1007/s11682-021-00480-1.

Weber S, Aleman A, Hugdahl K. Involvement of the default mode network under varying levels of cognitive effort. Sci Rep. 2022;12(1):6303. doi: 10.1038/s41598-022-10289-7.

Weighall A, Kellar I. Sleep and memory consolidation in healthy, neurotypical children, and adults: a summary of systematic reviews and meta-analyses. Emerg Top Life Sci. 2023;7(5):513-524. doi: 10.1042/ETLS20230110.

Weinstein ND, Sandman PM, Blalock SJ. The Precaution Adoption Process Model. In: Glanz K, Rimer BK, Viswanath K (eds): Health Behavior and Health Education: Theory, Research, and Practice (4th ed, pp 123-147). San Francisco, CA: Jossey-Bass, 2008.

Winterer G, Adams CM, Jones DW, Knutson B. Volition to action--an event-related fMRI study. Neuroimage. 2002;17(2):851-858. doi: 10.1006/nimg.2002.1232.

Won J, Callow DD, Pena GS, Gogniat MA, Kommula Y, Arnold-Nedimala NA, et al. Evidence for exercise-related plasticity in functional and structural neural network connectivity. Neurosci Biobehav Rev. 2021;131: 923-940. doi: 10.1016/j.neubiorev.2021.10.013.

Xue Y, Yang Y, Huang T. Effects of chronic exercise interventions on executive function among children and adolescents: a systematic review with meta-analysis. Br J Sports Med. 2019;53(22):1397-1404. doi: 10.1136/bjsports-2018-099825.

Ye M, Song T, Xia H, Hou Y, Chen A. Effects of aerobic exercise on executive function of healthy middle-aged and older adults: A systematic review and meta-analysis. Int J Nurs Stud. 2024;160:104912. doi: 10.1016/j.ijnurstu.2024.

Zang Q, Wang S, Qi Y, Zhang L, Huang C, Xiu Y, et al. Running exercise improves spatial learning and memory ability and enhances angiogenesis in the cerebral cortex via endogenous nitric oxide. Behav Brain Res. 2023;439:114243. doi: 10.1016/j.bbr.2022.114243.

Zhang M, Jia J, Yang Y, Zhang L, Wang X. Effects of exercise interventions on cognitive functions in healthy populations: A systematic review and meta-analysis. Ageing Res Rev. 2023;92:102116. doi: 10.1016/j.arr.2023.102116.

Zhao X, Jin L, Sun SB. The Bidirectional Association Between Physical and Cognitive Function Among Chinese Older Adults: A Mediation Analysis. Int J Aging Hum Dev. 2021;92(2):240-263. doi: 10.1177/0091415020940214.

Zhao X, Lai X, Huang S, Li Y, Dai X, Wang H, et al. Long-term protective effects of physical activity and self-control on problematic smartphone use in adolescents: A longitudinal mediation analysis. Ment. Health Phys. Act. 2024;26:100585. doi: 10.1016/j.mhpa.2024.100585.

Zhou X, Kong Y, Yu B, Shi S, He H. Effects of exercise on sleep quality in general population: Meta-analysis and systematic review. Sleep Med. 2025;125:1-13. doi: 10.1016/j.sleep.2024.10.036.
Zhou Y, Stubbs RJ, Finlayson G. A neurocognitive perspective on the relationship between exercise and reward: Implications for weight management and drug addiction. Appetite. 2023;182:106446. doi: 10.1016/j.appet.2022.106446.

Zhu L, Xiong X, Dong X, Zhao Y, Kawczyński A, Chen A, et al. Working memory network plasticity after exercise intervention detected by task and resting-state functional MRI. J Sports Sci. 2021;39(14):1621-1632. doi: 10.1080/02640414.2021.