Krølner et al. BMC Public Health 2012, 12:191 http://www.biomedcentral.com/1471-2458/12/191 classes, by having larger number of participating schools and pupils in each intervention group, or by randomly assigning schools to intervention group [12,13]. Other studies have included process measures [25,39,89]. We contribute to this field by developing and applying a the- ory-and evidence based process evaluation model, and by using both qualitative and quantitative methods and dif- ferent data sources [33,51]. This will enable us to make a systematic, thorough evaluation of the implementation of each intervention component. Items on usage of the school FV programme and motives and barriers for usage have been incorporated in the computer tailoring module to monitor the pupils’ access to and usage of the FV pro- gramme throughout the school year thereby supplement- ing information from the baseline and 1st follow-up questionnaires. These data can be merged with the other dataset by the pupils’ unique identification number. Together with the measurements of determinants of FV intake (proximal outcomes of the intervention), the pro- cess evaluation data will guide the decomposition of effects of each intervention component. As in the Pro Children project [55] we used source triangulation to measure availability in home, school and the local area. Whereas availability in the leisure time was only mea- sured by questionnaires to pupils and parents in the Pro Children study, the Boost study also measured it by ques- tionnaires to coaches and managers of sports-and youth clubs although only in the intervention group. Finally, the project group decided the selection of FV to be deliv- ered to the schools throughout the school year which means that the dose and types of FV delivered to schools are well described. Strength and limitations of the Boost study Merits of the Boost study are 1) the successfully rando- mised controlled design, 2) the random sample of munici- palities and schools, 3) the high response rates and large sample sizes of schools and pupils, 4) the systematic the- ory-and evidence-based planning procedure of the study, 5) the thorough and comprehensive process evaluation, 6) the triangulation of sources, methods and theories, 7) the use of two outcome measures for FV intake (habi- tual intake and amount), 8) the participatory approach (inclusion of the teachers in the planning phase), and 9) the measurements of proximal outcomes (determinants of FV intake). Limitations of the Boost intervention are that outcome measures are based on self-reports, lower response rates among parents, the risk of contamination as some pupils from control schools may attend the intervention youth- and sports clubs and the lack of control group for the lei- sure time component. Municipalities and schools were randomly selected. High response rates among principals and pupils at baseline minimised the risk of selection Page 21 of 25 bias. However, when we analyse matched teenager-parent data, selection bias may be introduced as a consequence of lower parental response rates. The FFQ and 24-hour recall measurements were based on the validated Pro Children instrument [77]. The dietary data are based on self-reports and may be over-or under- estimated depending on recall bias and social desirability bias. To prevent social desirability bias among pupils we introduced the survey by telling them that it was not an exam and that there were no right or wrong answers. We encouraged them to answer as honest as possible. Most measurements of determinants and sociodemographic fac- tors in the Boost study were based on the validated Pro Children questionnaires [76] and HBSC questionnaires [78,94]. New items were also developed some of which are not fully validated. Each intervention tool was designed to change specific determinants of FV intake. By means of addressing these determinants as proximal outcomes related to each of the components of the intervention, we may be able to decompose the effects of the multi-component interven- tion, so that the effect of each of these dimensions becomes apparent. As pupil-and parent questionnaires had to cover many determinants and included a detailed 24-hour recall questionnaire, we had to include only one or few measurements of each of these determinants to keep the questionnaire short. This may compromise the precision of each item and may introduce information bias. As the intervention targets availability we prioritised to have detailed measurements of this multidimensional concept to capture the influences of different aspects of availability (visibility, accessibility, variety, quantity, qual- ity), which is a unique feature of this study. We adhered to the Intervention Mapping protocol and addressed modifiable and important determinants such as availability and taste preferences. We did not address genetic factors such as bitterness taste sensitivity as they are non-modifiable and the findings from research of the importance of bitter taste sensitivity for FV intake and taste preferences among children and adults are inconclusive [95-99]. We agree with other researchers that it is a time con- suming task to follow the Intervention Mapping protocol [57,100] but we found the effort worthwhile. The Interven- tion Mapping protocol distinguish itself by guiding researchers to make well-considered, explicit theory-and evidence-based decisions in every step of the process of intervention development, implementation, and evaluation [51]. We did not have time to create matrices on adoption and implementation which might have contributed with valuable insights on how to support teachers in imple- menting the interventions at schools. Creation of these matrices post intervention may qualify the process evalua- tion. Finally, the sustainability of the Boost intervention is