Automaticity of E-health for Exercise and Physical Activity.

Background: The development of Smartphones goes hand in hand with the growth of e-health for exercise and physical activity - de ned as digital, online, or internet tools intended to help people practice exercise or physical activity. Research over the last decade suggests that interaction with these technologies occurs through habitual processes (Larose, 2010), characterized by more or less automatic thinking (Bayer & Campbell, 2012). The concept of automaticity represents a cognitive process that lacks intentionality, lacks con- trol, is wholly or partially unconscious, and is highly e cient (Bargh, 1994). So, it is likely that the use of e-health for exercise and physical activity could be highly in uenced by habit automaticity, as these technologies have become embedded in everyday life and underlying cognition (Bayer, Dal Cin, Campbell, & Panek, 2016). Consequently, one might expect an e ect of automaticity on physical activity levels (Gardner et al., 2011). In this research, we sought to determine the relationship between Automaticity (Boich e, Marchant, Nicaise, & Bison, 2016) using e-health and physical activity levels. Method: An online survey was conducted during the rst wave of the Covid-19 pandemic in France. A total of 569 adults participated in the study, of which 299 (58%) were eHealth users for exercise and physical activity. E-health users responded to a questionnaire on auto- maticity measured by nine items of the Generic Multifaceted Automaticity Scale, a validated scale in French (GMAS; Boich e et al., 2016). This instrument assesses three dimensions of automaticity ( =.78)- lack of intentionality( =.64), lack of control ( =.66), and e ciency ( =.72). Also, physical activity behavior was measured using the International Physical Activity Short Form (IPAQ-SF; Craig et., 2003). Results: Correlations indicated a signi cant and positive relation between e ciency and vigorous MET Minutes (the amount of energy expended during a minute while at rest) per week (r= .21, p < .01). Regressions showed that e ciency explained 5% of the variance of vigorous physical activity per week ( = .20, p < .05). Conclusion: eHealth use can be characterized by its automaticity that, in turn, could have a positive in uence on physical activity levels. Similarly, these tools o ered on Smart- phones could play an essential role in promoting physical activity. However, we must put any technological solution into perspective. eHealth o ers possibilities to stay active, but its bene ts, and the psychological mechanisms they a ect, remain to be demonstrated. References Bayer, J. B., Dal Cin, S., Campbell, S. W., and Panek, E. (2016). Consciousness and self- regulation in mobile communication. Hum. Commun. Res. 42, 71{97. doi: 10.1111/hcre.12067 Boich e, J., Marchant, G., Nicaise, V., and Bison, A. (2016). Development of the generic multifaceted automaticity scale (GMAS) and preliminary validation for physical activity. Psychol. Sports Exerc. 25, 60{67. doi: 10.1016/j.psychsport. 2016.03.003