Using self-determination theory to understand motivation for walking: Instrument development and model testing using Bayesian structural equation modelling

Abstract

Objective: The motivational processes underpinning walking behaviour are not well understood. This study aimed to develop walking-specific motivation measures drawn from selfdetermination theory (SDT), assess the psychometric properties of the measures, incorporating Baysesian structural equation modelling (BSEM), and examine how these variables relate to walking behaviour.Method: Participants (n=298; mean age=41.69; S.D.=11.06; male =57) completed the Behavioural Regulations in Walking Questionnaire (BRWQ), Psychological Needs Satisfaction for Walking Scale (PNSWS) and the IPAQ-long form, from which measures of workplace, transport and leisure walking were extracted. BSEM was used to test the hypothesized factor structures of the BRWQ and PNSWS. Internal reliabilities were assessed using the composite reliability coefficient.Convergent and discriminant validity were assessed by examining the relationships between the variables in relation to established theory.Results: BSEM showed excellent fit for the BRWQ and PNSWS measurement models. The scales demonstrated good internal consistency. The associations within and between the BRWQ and PNSWS subscales were generally as expected. The relationship between the BRWQ subscales and walking for transport and leisure were also generally as expected, but there were no significant relationships for walking at work. Two PNSWS subscales were significantly related to walking for leisure, but no significant relationships were evident for walking for transport and at work. Conclusions:There is preliminary evidence for the acceptable psychometric properties of instruments to measure SDT constructs in walking, and the findings highlight the advantages of BSEM. The findings also suggest that the motivational processes underpinning walking may vary by type of walking. SELF-DETERMINATION THEORY AND WALKING 3

Motivational and implicit processes contribute to incidental physical activity

“…These findings build on the limited research that has investigated the relationship between motivation and incidental physical activity (Keatley et al ., ). Previous studies have found that autonomous motivation is associated with higher levels of intentional physical activity than controlled motivation, although controlled motivation has also been associated with engagement in health‐related behaviours (Chatzisarantis & Hagger, ; Chatzisarantis et al ., ; Hagger & Chatzisarantis, ; Hagger et al ., ; Niven & Markland, ; Webber et al ., ). Our finding here for incidental physical activity suggests that autonomous motivation and controlled motivation both contribute to behaviour engagement.…”

“…In particular, autonomous motivation has been associated with greater exercise engagement over time (Chatzisarantis & Hagger, ; Chatzisarantis, Hagger, Biddle, Smith, & Wang, ), and intentions to exercise (Hagger & Chatzisarantis, ; Hagger, Chatzisarantis, Culverhouse, & Biddle, ). However, recent research has also linked controlled motivation to engagement in health‐related behaviours, particularly when external pressures remain (Caudwell & Keatley, ; Niven & Markland, ). A recent systematic review of 53 exercise studies provided some support for the influence of controlled motivation on physical activity behaviour (Teixeira et al ., ).…”

“…Based on previous research in the exercise domain (e.g., Chatzisarantis & Hagger, ; Chatzisarantis et al ., ), we predicted that autonomous motivation would be positively associated with levels of incidental physical activity. In view of recent physical activity research (e.g., Caudwell & Keatley, ; Niven & Markland, ; Teixeira et al ., ), we further predicted that controlled motivation would also be positively associated with levels of incidental physical activity. In line with physical activity research from a dual processes perspective (e.g., Berry et al ., ; Cheval, Sarrazin, & Pelletier, ; Conroy et al ., ), we also expected that there would be a positive association between the implicit processes (implicit attitudes, attentional bias, approach‐avoid bias) and levels of incidental physical activity.…”

Structural validation of three German versions of behavioral and motivational scales in high-risk sports

“…Contrary to the SEAS analyses, the present analyses used BSEM, which allows more complex, and thus more realistic, models to be specified. BSEM was recently used by Niven and Markland (2016) to establish motivational factors in walking and was favored over the independent clusters model using a maximum likelihood approach to confirmatory factor analysis (ML-CFA ICM). The authors criticized the ML-CFA ICM approach because "ICM approach channels unspecified covariation between indicators through their factors, upwardly biasing interfactor correlations" (Niven & Markland, 2016, p. 97).…”

“…The analytical methods used in this study should also be seen as another strength of this article. It has been shown that BSEM is a more appropriate method than traditional CFA methods as it better reflects the complexities of reality (Niven & Markland, 2016;Stenling et al, 2015).…”