abcd.RdThis function generates an acyclic behavior change diagram (ABCD) from a specification in a google sheet or .csv file. An ABCD is a logic model that illustrates the assumptions underlying a behavior change intervention. Specifically, the ABCD shows the assumed causal and structural assumptions, thereby showing what is assumed to cause what (e.g. which elements of the intervention are assumed to influence which aspects of the target population's psychology?) and what is assumed to consist of what (e.g. which determinants are assumed to contain which specific aspects of the target population's psychology?).
abcd(specs, specCols = c("bcps", "cnds", "apps", "sdts", "dets", "pobs", "behs"), omitColOrder = c("cnds", "behs", "bcps", "apps"), localBackup = NULL, title = "Acyclic Behavior Change Diagram\n\n", outputFile = NULL, outputWidth = 3000, outputHeight = 1500, includeColNames = TRUE, maxLabelLength = 30, silent = FALSE, returnGraphOnly = FALSE, regExReplacements = list(c("\\\"", "`"), c("\\'", "`"), c("\\\\", "/"))) # S3 method for abcdiagram print(x, width = x$input$width, height = x$input$height, title = DiagrammeR::get_graph_name(x$output$graph), ...)
| specs | The specifications: either a google sheets URL, the path to a local file, a character vector with both, or a matrix or data frame |
|---|---|
| specCols | The order of the columns. This character vector specified the order of the elements of an ABCD. In the default order, from left to right, these are (see below for definitions and more details):
|
| omitColOrder | If not all seven columns are available in the specifications, this character vector specifies which columns are assumed to be omitted. |
| localBackup | Whether to write the specifications to a local backup |
| title | The title of the diagram |
| outputFile | If specified, the ABCD is written to this file using DiagrammeR::export_graph. |
| outputWidth, outputHeight | If an |
| includeColNames | Whether to include the column names as titles/legend for the entities in each 'column' of the ABCD. |
| maxLabelLength | At which width to word wrap the labels. |
| silent | Whether to suppress ( |
| returnGraphOnly | Whether to return the full results object or only the DiagrammeR::DiagrammeR graph. |
| regExReplacements | A list of pairs of regular expressions that will be applied to the specifications before generating the ABCD. This can be used to sanitize problematic characters (e.g. ', " and ). |
| x | The ABCD object to print (as generated by a call to |
| width, height | Width and height to use when printing the ABCD. |
| … | Any additional arguments are passed on to
|
A list consisting of an input, intermediate, and
output list, where the ABCD is stored in the output list
as a DiagrammeR::DiagrammeR called graph.
Specifically, a full ABCD is a model that shows the following elements:
Behavior Change Principles (BCPs): The specific psychological principles engaged to influence the relevant sub-determinants, usually selected using the determinants to which the sub-determinants 'belong'. These are also known as methods of behavior change in the Intervention Mapping framework, or behavior change techniques, BCTs, in the Behavior Change Wheel approach. For a list of 99 BCPs, see Kok et al. (2016).
Conditions for effectiveness: The conditions that need to be met for a Behavior Change Principle (BCP) to be effective. These conditions depend on the specific underlying Evolutionary Learning Processes (ELPs) that the BCP engages (Crutzen & Peters, 2018). If the conditions for effectiveness (called parameters for effectiveness in the Intervention Mapping framework) are not met, the method will likely not be effective, or at least, not achieve its maximum effectiveness.
Applications: Since BCP's describe aspects of human psychology in general, they are necessarily formulated on a generic level. Therefore, using them in an intervention requires translating them to the specific target population, culture, available means, and context. The result of this translation is the application of the BCP. Multiple BCPs can be combined into one application; and one BCP can be applied in multiple applications (see Kok, 2014).
Sub-determinants: Behavior change interventions
engage specific aspects of the human psychology (ideally, they
specifically, target those aspects found most important in
predicting the target behavior, as can be established with
CIBER plots. These aspects are
called sub-determinants (the Intervention Mapping framework
references Change Objectives, which are sub-determinants
formulated according to specific guidelines). In some
theoretical traditions, sub-determinants are called beliefs.
Determinants: The overarching psychological constructs that are defined as clusters of specific aspects of the human psychology that explain humans' behavior (and are targeted by behavior change interventions). Psychological theories contain specific definitions of such determinants, and make statements about how they relate to each other and to human behavior. There are also theories (and exists empirical evidence) on how these determinants can be changed (i.e. BCPs), so althought the sub-determinants are what is targeted in an intervention, the selection of feasible BCPs requires knowing to which determinants those sub-determinants belong.
Performance objectives: The specific sub-behaviors that often underlie (or make up) the ultimate target behavior. These are distinguished from the overarching target behavior because the relevant determinants of these sub-behaviors can be different: for example, the reasons why people do or do not buy condoms can be very different from the reasons why they do or do not carry condoms or why they do or do not negotiate condom use with a sexual partner.
Behavior: The ultimate target behavior of the intervention, usually an umbrella that implicitly contains multiple performance objectives.
For details, see Peters et al. (2019).
Crutzen, R., & Peters, G.-J. Y. (2018). Evolutionary learning processes as the foundation for behaviour change. Health Psychology Review, 12(1), 43–57. https://doi.org/10.1080/17437199.2017.1362569
Kok, G. (2014). A practical guide to effective behavior change: How to apply theory- and evidence-based behavior change methods in an intervention. European Health Psychologist, 16(5), 156–170. https://doi.org/10.31234/osf.io/r78wh
Kok, G., Gottlieb, N. H., Peters, G.-J. Y., Mullen, P. D., Parcel, G. S., Ruiter, R. A. C., … Bartholomew, L. K. (2016). A taxonomy of behavior change methods: an Intervention Mapping approach. Health Psychology Review, 10(3), 297–312. https://doi.org/10.1080/17437199.2015.1077155
Peters, G.-J. Y., et al. (2019) The core of behavior change: introducing the Acyclic Behavior Change Diagram to report and analyze interventions.
### Using 'print' to prevent pkgdown() from choking ### Partial acyclic behavior change diagram of only ### one performance objective (sub-behavior) ### (using the 'abcd_specs_single_po_without_conditions' ### dataset in this package) print(behaviorchange::abcd(behaviorchange::abcd_specs_single_po_without_conditions)); ### Acyclic behavior change diagram including multiple ### sub-behaviors (performance objectives) ### (using the 'abcd_specs_complete' dataset in this ### package) print(behaviorchange::abcd(behaviorchange::abcd_specs_complete));