Results A four-item automaticity subscale ( the ‘ Self-Report Behavioural Automaticity Index ’ ; ‘ SRBAI ’ ) was found to be reliable and sensitive to two hypothesised effects of habit on behaviour : a habit-behaviour correlation , and a moderating effect of habit on the intention-behaviour relationship .
Conclusion The SRBAI offers a parsimonious measure that adequately captures habitual behaviour patterns .
The SRBAI may be of particular utility in predicting future behaviour and in studies tracking habit formation or disruption
Background The twelve-item Self-Report Habit Index ( SRHI ) is the most popular measure of energy-balance related habits .
This measure characterises habit by automatic activation , behavioural frequency , and relevance to self-identity .
Previous empirical research suggests that the SRHI may be abbreviated with no losses in reliability or predictive utility .
Drawing on recent theorising suggesting that automaticity is the ‘ active ingredient ’ of habit-behaviour relationships , we tested whether an automaticity-specific SRHI subscale could capture habit-based behaviour patterns in self-report data .
How well do cognitive and environmental variables predict active commuting?
Background In recent years , there has been growing interest in theoretical studies integrating cognitions and environmental variables in the prediction of behaviour related to the obesity epidemic . This is the approach adopted in the present study in reference to the theory of planned behaviour . More precisely , the aim of this study was to determine the contribution of cognitive and environmental variables in the prediction of active commuting to get to and from work or school . Methods A prospect i ve study was carried out with 130 undergraduate and graduate students ( 93 females ; 37 males ) .  Environmental , cognitive and socio-demographic variables  were evaluated at baseline by question naire . Two weeks later , active commuting ( walking/bicycling ) to get to and from work or school was self-reported by question naire . Hierarchical multiple regression analyses were performed to predict intention and behaviour . Results The model predicting behaviour based on cognitive variables explained more variance than the model based on environmental variables ( 37.4 % versus 26.8 % ; Z = 3.86 , p < 0.001 ) . Combining cognitive and environmental variables with socio-demographic variables to predict behaviour yielded a final model explaining 41.1 % ( p < 0.001 ) of the variance . The significant determinants were intention , habit and age . Concerning intention , the same procedure yielded a final model explaining 78.2 % ( p < 0.001 ) of the variance , with perceived behavioural control , attitude and habit being the significant determinants . Conclusion The results showed that cognitive variables play a more important role than environmental variables in predicting and explaining active commuting . When environmental variables were significant , they were mediated by cognitive variables . Therefore , individual cognitions should remain one of the main focuses of interventions promoting active commuting among undergraduate and graduate students
Results A four-item automaticity subscale ( the ‘ Self-Report Behavioural Automaticity Index ’ ; ‘ SRBAI ’ ) was found to be reliable and sensitive to two hypothesised effects of habit on behaviour : a habit-behaviour correlation , and a moderating effect of habit on the intention-behaviour relationship .
Conclusion The SRBAI offers a parsimonious measure that adequately captures habitual behaviour patterns .
The SRBAI may be of particular utility in predicting future behaviour and in studies tracking habit formation or disruption
Background The twelve-item Self-Report Habit Index ( SRHI ) is the most popular measure of energy-balance related habits .
This measure characterises habit by automatic activation , behavioural frequency , and relevance to self-identity .
Previous empirical research suggests that the SRHI may be abbreviated with no losses in reliability or predictive utility .
Drawing on recent theorising suggesting that automaticity is the ‘ active ingredient ’ of habit-behaviour relationships , we tested whether an automaticity-specific SRHI subscale could capture habit-based behaviour patterns in self-report data .