Sedentary behaviors, defined as any waking behavior in a reclining, sitting, or lying position that requires an energy expenditure lower than 1.5 Metabolic Equivalent Task (Sedentary Behavior Research Network, 2012), are associated with a wide range of detrimental health consequences, including adverse metabolic conditions (Hamilton et al., 2007), depression (Teychenne et al., 2010), and cognitive decline (Olanrewaju et al., 2020). Adults spend about 77% of their waking time being sedentary (Diaz et al., 2016) and workplace settings account for a large amount of this daily time (Saidj et al., 2015). To mitigate the detrimental consequences associated with such patterns of activity at work, reducing sedentary behaviors during leisure time seems particularly important (Patel et al., 2010). However, the main precursors underlying leisure-time sedentary behaviors remain poorly investigated.
Explaining Sedentary Behaviors Through Sociocognitive Models
In the past decade, a growing number of studies investigated the motivational precursors of sedentary behaviors (Biddle, 2011). Most of these studies were anchored within sociocognitive models, which are based on the premise that imagined end states (expectancies, goals) are proximal variables of behaviors (Brand & Cheval, 2019). These models suggest that attitudes, intentions, and self-perceptions (e.g., perceived competence) orient individuals’ actions toward or away from specific behaviors.
Previous work showed that positive affective attitudes (i.e., perceiving sedentary behaviors as something pleasant) (Busschaert et al., 2016) and positive instrumental attitudes (i.e., perceiving sedentary behaviors as something useful) (Prapavessis et al., 2015) toward sedentary behaviors were associated with greater time spent in sedentary behaviors. By contrast, higher intention to reduce sedentary behaviors was negatively associated with time spent in sedentary behaviors (Maher & Conroy, 2015; Maher & Dunton, 2020). Besides motivation toward sedentary behaviors, positive affective and instrumental attitudes toward physical activity (Ham et al., 2013), higher intention to be physically active (He et al., 2010; but see Maher & Conroy, 2015 for null findings), and higher perceived competence to adopt a more active lifestyle (Bai et al., 2015; Quartiroli & Maeda, 2014) were associated with lower time spent in sedentary behaviors. However, the associations between these motivational precursors and sedentary behaviors were only of small to medium magnitude, suggesting that others motivational variables may drive sedentary behaviors (Rollo et al., 2016). In this line, additional theoretical perspectives, such as the dual-process models (Rhodes et al., 2019), have been mobilized to explain sedentary behaviors.
Explaining Sedentary Behaviors Through Dual-process Models
Dual-process models contend that behaviors are governed by both controlled and automatic motivational processes (Strack & Deutsch, 2004). Controlled processes are slow, initiated intentionally, require cognitive resources and effort, and operate within conscious awareness. The key aforementioned sociocognitive constructs are assumed to be “plugged” in this controlled dimension (Conroy & Berry, 2017). Conversely, automatic processes are fast, initiated unintentionally, require relatively less cognitive resources and effort, occur outside conscious awareness (e.g., habits, automatic affective reactions, approach-avoidance tendencies).
Despite the incidental enactment of sedentary behaviors (Spence et al., 2017), explained by the profusion of attention-grabbing cues in our modern environment (Levine, 2015), the influence of automatic motivational processes remains overlooked. Indeed, only a few studies have mobilized dual-process models to explain sedentary behaviors (Chevance et al., 2017; Conroy et al., 2013; Maher & Conroy, 2015, 2016; Maher & Dunton, 2019). Results showed that higher habit strength for sedentary behaviors (i.e., association between a contextual cue in the environment and a behavioral response, Gardner, 2015) was associated with higher time spent in sedentary behaviors (Conroy et al., 2013; Maher & Conroy, 2015, 2016; Maher & Dunton, 2019). Unlike results observed for the controlled motivational precursors, habit strength for physical activity was not associated with time spent in sedentary behaviors (Maher & Conroy, 2015). One study assessed automatic affective attitudes toward sedentary behaviors (Chevance et al., 2017), using the Single Category Implicit Association Test, a reaction time-based task (Karpinski & Steinman, 2006; Rebar et al., 2015), but did not observed association with time spent in sedentary behaviors.
Besides habits and automatic affective reactions, other automatic processes could be investigated, such as approach-avoidance tendencies (Cheval et al., 2014, 2015; Zenko & Ekkekakis, 2019). Indeed, avoiding stimuli depicting sedentary behaviors triggered larger evoked-related potentials in the medial frontal cortex and frontocentral cortex, which are related to conflict monitoring and inhibition, respectively (Cheval et al., 2018). Consistent with the idea that sedentary behaviors are difficult to avoid (Cheval et al., 2020; Cheval et al., 2017), these results suggest that approach-avoidance tendencies could play an important role in the regulation of sedentary behaviors. However, no study has yet assessed the association between automatic approach-avoidance tendencies and sedentary behaviors.
Importantly, previous studies rarely distinguished between leisure-time and working-time sedentary behaviors (Conroy et al., 2013; Maher & Conroy, 2015, 2016; Maher & Dunton, 2019). This lack of distinction is understandable in older populations who are retired (Maher & Conroy, 2016; Maher & Dunton, 2019), but is questionable among active adults (Conroy et al., 2013; Maher & Conroy, 2015). Students and workers may hardly control the time spent in sedentary behaviors in the workplace or at the university because such contexts often constrain to engage in prolonged sitting activities (Saidj et al., 2015; Vandelanotte et al., 2013). In this line, while specific motivational precursors are expected to drive leisure-time sedentary behaviors (Owen et al., 2011), previous work may have blurred such associations by merging leisure time and working time. Moreover, as theorized by socioecological models (Owen et al., 2011), other variables, related to the individual and one’s environment, could also contribute to better explain sedentary behaviors.
Explaining Sedentary Behaviors Through Socioecological Models
Socioecological models are based on the premise that behaviors are jointly driven by multiple determinants (Glass & McAtee, 2006), which can be classified as intrapersonal, interpersonal, and environmental factors (O’Donoghue et al., 2016). Intrapersonal variables refer to demographic (e.g., gender, age) and physical factors (e.g., body mass index), as well as motivational and socio-professional factors (e.g., leisure time, working time, physical activity at work). Interpersonal variables include familial determinants, such as, the number of children. Alongside with built environmental determinants (e.g., accessibility to facilities), natural environmental factors can refer, to the days of the week or to weather conditions. Hence, far from competing with models focusing on motivational variables, the socioecological model integrates the aforementioned motivational precursors by considering individuals as actors amidst broader networks (Rhodes et al., 2019; Sniehotta et al., 2017).
To date, the application of the socioecological models to sedentary behaviors has mainly focused on demographic and physical precursors (see Chastin et al., 2015; O’Donoghue et al., 2016; Rhodes et al., 2012 for reviews). For example, being a male, older (e.g., Saidj et al., 2015), or having a greater BMI (e.g., Vandelanotte et al., 2009) were associated with higher time spent in sedentary behaviors. However, results were mixed regarding the influence of socio-professional variables on leisure-time sedentary behaviors. In line with the idea than an increase in physical activity may be subsequently compensated by an decrease in energy expenditure (Melanson, 2017), previous work revealed that greater level of physical activity at work was associated with higher sedentary behaviors during leisure time (e.g., Stamatakis et al., 2014). However, other studies did not reveal such relationship (Tigbe et al., 2011; Vandelanotte et al., 2013). Regarding interpersonal variables, having less children was associated with higher time spent in sedentary behaviors during leisure time (Van Uffelen et al., 2012). For environmental variables, higher time spent in leisure-time sedentary behaviors was observed on weekend days (Thorp et al., 2012), and on cloudy and rainy days (Chan & Ryan, 2009).
While socioecological models emphasize the importance to map these different levels of influence on sedentary behaviors, previous work has mostly examined these variables in isolation (Buck et al., 2019; De Craemer et al., 2018). Although studies have jointly investigated the effects of motivational precursors with others variables (e.g., sex, body mass index, day of the week) (e.g., Conroy et al., 2013), no study has integrated socio-professional, interpersonal, and environmental factors alongside with controlled and automatic motivational variables to predict leisure-time sedentary behaviors. Importantly, recent findings suggested that demographic, physical, socio-professional, interpersonal, and environmental factors could exert a greater influence on sedentary behaviors than motivational variables (Buck et al., 2019). However, no study has directly compared the predictive validity of these variables within the same sample.
The Current Study: An Integrative Approach
The current study aimed to investigate the predictive validity of motivational (controlled and automatic), demographic, physical, socio-professional, interpersonal, and environmental precursors of leisure-time sedentary behaviors. Therefore, this study provides an integrative approach contributing to provide better understanding of the relative weight of motivational precursors in the regulation of sedentary behaviors (Fig. 1). To this end, 135 healthy workers’ leisure-time sedentary behaviors were monitored for one week using an accelerometer and associations with aforementioned precursors were examined.
We hypothesized that both controlled and automatic motivational determinants predict leisure-time sedentary behaviors (H1). Specifically, higher controlled (H1a) (i.e., attitudes, intention, perceived competence) and automatic (H1b) (i.e., habit strength and approach-avoidance tendencies) motivation to reduce sedentary behaviors and to be physically active should negatively predict leisure-time sedentary behaviors. We also expected that demographic, physical, socio-professional, interpersonal, and environmental variables should predict leisure-time sedentary behaviors (H2). Finally, we compared the strength of the associations between these variables and leisure-time sedentary behaviors. We did not formulate an priori hypothesis on the relative weight of these precursors, although recent work suggests that the association between motivational precursors and leisure-time sedentary behaviors may be weaker than associations with the other variables (Buck et al., 2019).