Development of the Generic Multifaceted Automaticity Scale (GMAS) and preliminary validation for physical activity

Bonaiuto

et al. 2021

Front. Psychol.

Self Cite

ObjectivesThe aims of this research were (1) to compare the levels of physical activity of eHealth users and non-users, (2) to determine the effects of these technologies on motivations, and (3) to establish the relationship that could exist between psychological constructs and physical activity behaviors.MethodsThis cross-sectional study involved 569 adults who responded to an online questionnaire during confinement in France. The questions assessed demographics, usage of eHealth for exercise and physical activity, and behavioral levels. The questionnaire also measured the constructs of Social Cognitive Theory, the Theory of Planned Behavior, and automaticity facets toward eHealth for exercise and physical activity.ResultsParticipants who were users of eHealth for exercise and physical activity presented significantly higher levels of vigorous physical activity and total physical activity per week than non-users (p < 0.001). The chi-square test showed significant interactions between psychological constructs toward eHealth (i.e., self-efficacy, behavioral attitudes, intentions, and automaticity) and physical activity levels (all interactions were p < 0.05). Self-efficacy was significantly and negatively correlated with walking time per week. Concerning the automaticity facets, efficiency was positive and significantly correlated with vigorous physical activity levels per week (p < 0.05). Then, regressions analyses showed that self-efficacy and automaticity efficiency explained 5% of the variance of walking minutes per week (ß = −0.27, p < 0.01) and vigorous physical activity per week (ß = 0.20, p < 0.05), respectively.ConclusionThis study has shown that people during confinement looked for ways to stay active through eHealth. However, we must put any technological solution into perspective. The eHealth offers possibilities to stay active, however its benefits and the psychological mechanisms affected by it remains to be demonstrated: eHealth could be adapted to each person and context.

Section: Introductionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 88%

See 1 more Smart Citation

Exercise and Physical Activity eHealth in COVID-19 Pandemic: A Cross-Sectional Study of Effects on Motivations, Behavior Change Mechanisms, and Behavior

Bonaiuto

et al. 2021

Front. Psychol.

Self Cite

“… 18 A discriminant validity study indicates that the 4 items of the SRBAI fail to capture this facet of automaticity, contrary to those 3 SRHI items. 28 Following the stem “Behavior X is something I do…,” participants used a 5-point Likert scale ranging from 1 ( totally not agree ) to 5 ( totally agree ) to characterize 3 affirmations (“that makes me feel weird if I do not do it”; “that would require effort not do it”; “that I would find hard not to do”). Internal consistency was satisfactory for all scales (0.72 < α < 0.93).…”

Section: Studymentioning

confidence: 99%

Intention and automaticity toward physical and sedentary screen-based leisure activities in adolescents: A profile perspective

Journal of Sport and Health Science

Chevance

Boiché

2018

Self Cite

PurposePhysical activity (PA) and sedentary behavior (SB) are increasingly considered independent health behaviors. Additionally, current research suggests that both controlled and automatic determinants account for their adoption. The purpose of this article was to identify intention–automaticity profiles toward PA and screen-based SB and to examine how those profiles are associated with different behavioral patterns.MethodTwo cross-sectional studies based on self-report questionnaires were conducted with French high school students (Study 1: n = 198; Study 2: n = 185).ResultsIn all, 4 distinct motivational profiles appeared. The first 3 clusters emerged in both studies: “PA” (high levels of automaticity and intention for PA, low levels of automaticity and intention for screen-based SB); “screen” (high levels of automaticity and intention for screen-based SB, low levels of automaticity and intention for PA), and “mixed” (high levels of all variables), whereas the fourth cluster was observed only in Study 2: “high control” (below-mean levels of automaticity, high levels of intention toward both PA and screen-based SB). Adolescents with a screen profile displayed the least healthy behavioral pattern, whereas those in the PA profile demonstrated the most favorable behaviors.ConclusionFuture research is needed to extend these results to other populations using complementary assessment methods of automatic psychological processes and PA and SB behaviors.

“…In the present study, we examine whether automaticity and its facets, assessed by the Generic Multifaceted Automaticity Scale (GMAS; Boiché et al, 2016) for active commuting and screen-based sedentary behaviours, were positively correlated with objective measures of behaviours as validation criteria. We expected that the lack of intentionality and lack of control for active commuting would correlate with moderate physical activity measured by accelerometry.…”

mentioning

confidence: 99%

Automaticity facets applied to screen-time sedentary behaviours and active commuting measured by accelerometers

Health Psychology and Behavioral Medicine

Servajean

Niçaise

et al. 2020

Self Cite

Background: The time adults spend sitting in front of screens is a health risk factor. In contrast, walking and cycling to and from work, also known as active commuting, could promote physical activity and improve population health. Objective: This study investigated automatic properties role in explaining active commuting and screen-based sedentary behaviours. The stable, daily conditions for carrying out active commuting and screen-based sedentary behaviour are most likely to develop automatic properties. These characteristics mean performing behaviours via external cues (i.e. lack of intentionality), with an unpleasant emotional experience of not carrying out a set routine (i.e. lack of controllability), and without paying much attention (i.e. efficiency). Method: This article describes findings of a prospective and correlational study in which 128 people participated. First, participants responded to questions assessed using the Generic Multifaceted Automaticity Scale (GMAS), which measured the automatic properties of screen-based sedentary behaviour and active commuting. The following week, both behaviours were assessed by daily logs to document active commuting and screen-based sedentary behaviour events, and by an accelerometer, worn for seven days, as an objective criterion. Confirmatory factor analyses, bivariate correlations, and multiple linear regressions were computed for the associations between the GMAS scores and objective criterion measures of screen-based sedentary behaviours and active commuting. Results: Automaticity facets displayed different relationships with screen-based sedentary behaviours and active commutingpeople with higher lack of intentionality and lack of controllability for active commuting present higher levels of moderate physical activity. In contrast, the lack of controllability of screen-based sedentary behaviours was a significant predictor of sedentary screen time. Conclusions: The multidimensional approach to automaticity could be useful in determining more precisely the features that need to be addressed to promote the adoption of active commuting and limit the time spent sitting in front of screens.