Línea Estratégica: EDIFICIOS EQUIPAMIENTOS/INSTALACIONES E INDUSTRIA

There is a considerable number of theories, models and frameworks in AST research. I found twelve whilst reviewing the literature on this topic. Current

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approaches are diverse, ranging from a focus on individual variables to environmental conceptualisations.

An emphasis on the individual is at the core of behavioural economics, social cognitive theory, theory of planned behaviour and habit strength, and a conceptual framework connecting preference, location choice and behaviour, all of which have been used in AST (Faulkner et al., 2010; Yang et al., 2010; Mendoza et al., 2011; Murtagh et al., 2012). Individual-focused models are common in health behavioural science and often highlight cognitive processes as an underlying mechanism of behaviour change (Jeffery, 2004). Behaviour is still recognised as influenced by environmental factors, but the effects of such distal factors are assumed to be mediated by the proximal factors specified by the model (e.g. intentions, attitudes) (Sutton, 2001). Unlike many distal factors, the proximal factors are hypothesised to be changeable (e.g. through provision of information) and can then be the basis of health behaviour interventions (Sutton, 2001). Other advantages of individually- focussed models include: the specificity of the content of cognitions, which allows the generation of items for self-report (i.e. I intend to walk with my child to school every day); high degree of standardisation of measures; and applicability to other domains or behaviours (e.g. environmental psychology, social psychology) (Sutton, 2001). On the downside, a focus on the individual may neglect the fact that even a very individualised behaviour can be strongly affected by policy or social context (Glanz et al., 2008). The appeal to internal variables has been said to obscure our

understanding of behaviour because it multiplies the variables to be explained, and diverts attention from potentially relevant contextual events (Chiesa, 1994). This point will be expanded shortly.

Multi-level environmental approaches in AST comprise: the social ecological model (Martin et al., 2009), the ANGELO framework (Pont et al., 2009); a conceptual framework of a primary-aged child travel’s behaviour (McMillan, 2005); a conceptual framework for the environmental determinants of active travel in children (Panter et al., 2008b); a model of children’s active travel (M-CAT) (Pont et al., 2011b); a

behavioural model of school transportation (Mitra, 2013); an ecological and cognitive active commuting framework (Sirard and Slater, 2008); and an integrated,

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school travel (Hodgson et al., 2012). All of these models incorporate psychological variables (e.g. perceptions about environment, beliefs).

A strength of ecological models is the emphasis given to multiple levels of influence which broadens options for interventions. Unfortunately, they share a general lack of clarity about important hypothesised influences and their interactions, and a difficulty in identifying critical factors for each behavioural application (Glanz et al., 2008). In behaviour change literature, a distinction is often made between factors underlying the adoption or initiation of new behaviours and those responsible for their

maintenance (Marcus et al., 2000). Despite the complexity of some of the existing ATS models, few of them address this issue in an explicit manner. The M-CAT hypothesises that events occurring during the journey to school, positive (e.g. socialising with other children, increased fitness) or negative (e.g. road dangers), form the beginning of a feedback loop in which parental and child perceptions are affected, ultimately impacting on ATS (Pont et al., 2011a). On the other hand,

Hodgson et al stress the importance of establishing behavioural habits, or associative responses to environmental cues (e.g. weather), in the maintenance of ATS; they suggest ways to break habits include strategies such as prompting a review of pros and cons for each travel option, environmental changes, or to help people remember the reasons for their choices (Hodgson et al., 2012). These approaches highlight the need to make ATS pleasant and rewarding so that families choose ATS in the long- term, as well as the use of verbal, social or otherwise environmental cues to

encourage ATS.

Anticipating the development of an intervention, the main criticism that I wish to make about existing AST theories/models/frameworks is the difficulty in putting theory into practice, even if the theory is supported by robust evidence. Studies often conclude that future research needs to take cognitive (or otherwise psychological) factors into account, but it is not obvious which cognitive factors must be targeted nor how. This problem is echoed by Panter et al. (2010): “although our findings suggest that

changing parental perceptions may be an important intervention strategy, how this could be achieved is currently unknown. The provision of more supportive

environments for active commuting might be particularly appropriate as this may itself result in changes in attitudes or perceptions” (Panter et al., 2009, p.7).

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In the same vein, others have found that parental self-efficacy, i.e. the belief in their child’s ability to actively travel to school, was significantly associated with AST

(Mendoza et al., 2010; Lu et al., 2015). In one study, the authors concluded that AST interventions should aim to improve this parental psychological construct but gave no indication of how this could be achieved (Mendoza et al., 2010). In another study, it was found that both children’s and parents’ self-efficacy with respect to AST were significantly associated with the occurrence of the behaviour (Lu et al., 2015). Four strategies were recommended to boost child’s self-efficacy and therefore AST: community-based interventions to secure neighbourhood safety, by involving schools, families, and communities; increased exposure to supportive role models and positive peer influence; boosting parental self-efficacy, although authors had insufficient data to make specific proposals on this point; and reducing physical and social environmental constraints (Lu et al., 2015). It is fair to say that the range of suggestions to increase self-efficacy had considerable heterogeneity. In addition to the above, others have proposed strategies as diverse as self-management

techniques or using rewards to increase self-efficacy (e.g. (Williams and French, 2011; Prestwich et al., 2014)), making it hard to conceive the scope of a “self-efficacy intervention”.

A further example is provided by Murtagh et al. (2012), who found that perceived behavioural control predicted intentions, which in turn predicted AST (Murtagh et al., 2012). Ways suggested to increase perceived behavioural control (and consequently, intentions and AST) were the promotion of personal mastery experiences (e.g.

successful performance of behaviour following guidance), vicarious experiences, verbal persuasion and emotional arousal (e.g. stressing the benefits of performing a behaviour and the risks of not doing so) (Murtagh et al., 2012). It is unclear, however, how these strategies were inferred from the cognitive variable at issue.

Perceived behavioural control, for example, has been identified as increasing when targeted by a number of diverse behaviour change strategies (e.g. (van Dam et al., 2003; Hardeman et al., 2005; Pavlin et al., 2006)). Moreover, it is unclear what is gained by targeting the perceived behavioural control (or intention) of performing AST rather than targeting the behaviour itself, or the conditions in which behaviour occurs. The same techniques are administered in other fields without the addition of cognitive concepts (Martin, 2007; Miltenberger, 2011). Murtagh et al. suggest that the inclusion of perceived behavioural control explains how behaviour changes come

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about (“via increases in perceived behavioural control”) (Murtagh et al., 2012, p.1)). However, how contextual variables lead to cognitive changes (e.g. verbal

descriptions → perceived behavioural control), and how these psycho-cognitive changes determine behaviour (e.g. perceived behavioural control → AST) are questions that remain to be answered (Chiesa, 1994).