Data processing and statistical analysis

Returned questionnaires were given a sequential identification number for the purposes of data entry. All topic questions were coded 1-5; demographic questions were given appropriate codes. Coded data from the questionnaires was entered directly onto an SPSS spreadsheet for subsequent analysis, (SPSS Inc., Chicago, Il, USA), by one operator then checked by a second independent operator to ensure accuracy.

Participant characteristics

Descriptive information regarding participant characteristics and place of work was produced and compared using cross tabulations and t tests.

Domain questions

As questions were coded as strongly agree = 1 to strongly disagree = 5, scores from positively phrased questions were inverted so that the higher score related to a more favourable response, thus keeping the coding consistent throughout. Mean domain scores were calculated from each set of domain-specific

questions, a high score indicating that the constructs within the domains were not acting as barriers to performing the behaviour (discuss and advise PA with obese pregnant women). A low score indicated that it was more likely the constructs within the specific domains were acting as barriers to performing the behaviour. The internal consistency of the questions within each domain was assessed by calculating Chronbach’s alpha; a cut-off of 0.5 was deemed sufficient for preliminary research, as previously described (Amemori et al., 2011). Effectively, an alpha of less than 0.5 was deemed to be an indication that there was variability in how individual midwives had responded to the different questions within that domain. Where this was the case the domains were

examined to see how alpha improved if individual questions were removed from the analysis. Subsequently, 9 questions were excluded from further analysis. Removal brought Chronbach’s alpha up to a more robust value for most

domains. The ‘environment, context resource’ domain was an exception to this. Despite removal of one of the questions α remained low. Results for this domain

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are still presented in the tables, however they are not interpreted and no conclusions are drawn from them.

The mean domain scores for each Trust and staff groups were calculated and compared using t tests and analysis of variance.

Associations

Associations between respondent characteristics and mean domain scores were initially investigated using cross tabulations. Further associations between the ‘behaviour’ (discuss and advise PA with obese pregnant women), the

domains and demographic characteristics were calculated using Pearson’s correlation coefficient and defined as low (0.0 to 0.39), moderate (0.40 to 0.69) and high (0.70 to 1.0).

Influencing factors

Factor analysis was performed to identify and describe the underlying components influencing the behaviour. Categorical principle component analysis (CATPCA) was used to perform data reduction as the variable of interest, (discuss and advise PA with obese pregnant women), was reported on a categorical scale of 1-5. The analysis was performed in SPSS with different combinations of domains until a meaningful solution, with clearly defined dimensions, was obtained based on goodness of fit.

The question response distributions were tested for normality and the behaviour was found to be positively skewed, i.e. high scores for the behaviour ‘discuss and advise PA with obese pregnant women’ were more probable than low scores. Since the outcome variable of interest was categorical the probability of midwives scoring 1, 2, 3, 4 or 5 for this question was investigated using an ordinal regression model with a complementary log-log link (McCullagh and Nelder, 1989). The complementary log-log link was chosen since it is better suited than other standard link functions to situations in which the data are skewed towards higher values. Ordinal regression produced estimated coefficients for the predictor variables; these in turn were used to produce the Wald statistic, (the square of the ratio of the coefficient to its standard error). A significant result means that the null hypothesis, (that this value will be equal to zero), can be rejected, indicating that covariates with a significant value are associated with the behaviour (discuss/advise PA with obese pregnant women).

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The analysis was repeated systematically using every combination of the covariates

A test for parallel lines was performed; this tests the assumption that the relationships between the covariates and log odds of being in a given category for ‘behaviour’, (that is, scoring 1, 2, 3, 4 or 5), or the category above is the same for all categories. This would mean that the results are a set of parallel lines, one for each category of the behaviour. The difference between the -2 Log likelihood values for Staff Group 1, (community and antenatal midwives), was found to be 2.04, p=0.879, indicating that there was no difference between the slope coefficients of the parallel lines.

As part of the ordinal regression analysis SPSS performs a ‘goodness of fit measure’ for the model. The observed and expected cell counts are used in this calculation; if these are similar with no significant difference then the model fits well. For Staff Group 1 Pearson’s chi-squared = 111.15, on 142 degrees of freedom, p=0.974, indicating the model fitted well. These calculations were repeated for the whole sample and the other sub-divided groups and the same patterns and conclusions were found.

The regression model was then used to estimate the effect on the behaviour, (discuss and advise obese pregnant regarding PA), if the scores for the influencing domains were increased, so mimicking the effect of a successful training intervention or policy/practice change, (that is, decrease barriers and increase the facilitating factors that encourage the midwives to perform the behaviour).

Free text comments

Free text responses were typed verbatim directly onto an Excel spreadsheet. These comments were then coded thematically using the TDF domains by two independent reviewers (CMcP and RB). Once each comment was aligned to one or more domains the link was either categorised as facilitating the

behaviour or acting as a barrier against performing the behaviour. The number of barriers related to each domain was subtracted from the number of

facilitators to give an overall score which is presented graphically and compared to the quantitatively produced question mean domain scores.

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