Theoretical background
The WHAAM application is based on the concepts and methods of functional behavioural assessment (FBA). The FBA is strongly linked to the above-mentioned ABA, bearing in mind that both are founded on the principle of Skinner’s operant conditioning.5,6 Unlike the first stimulus-response (S-R) proposed by Watson7 at the beginning of behavioural analysis, the operant conditioning studies the observed human behaviour and considers the possibilities between antecedent, the behaviour itself, and its consequence. The old S-R model was therefore replaced by a stimulus, response, stimulus (S-R-S) model. According to this new perspective, the reasons that a behaviour is maintained can not only be derived from its external form or topography but also by discovering the conditions which trigger and maintain it. Carr,8 in a renowned scientific paper about self-injurious behaviour, proposed three general classes of possible functions sustained by behaviour: positive reinforcement, negative reinforcement and sensory or automatic consequences of the behaviour. In the last few decades, these functions have been deeply investigated and some researchers thought to consider new additional functions, such as tangible,9 control10 or access to stereotypy.11 FBA is defined by Gresham et al.12 as a collection of methods for gathering information about antecedents, behaviours and consequences in order to determine the reason (function) of the behaviour. It differs from experimental functional analysis because it is easily applied in natural settings (home, school, etc.), it needs less training to use it and it does not require direct manipulation of dependent variables to identify the main function sustained by a problem behaviour. The FBA is therefore a descriptive assessment involving indirect and direct observations and the measurement of the target behaviour.13 Questionnaires, rating scales and interviews are examples of indirect methods because they do not require a direct observation of the target behaviour and third party people, such as relatives or teachers, can administer them. The direct observation of behaviour is instead carried out in a natural setting by descriptive assessments and/or systematic recording. While descriptive methods like antecedent behaviour consequence (ABC) charts and narrative recording provide qualitative information about a targeted behaviour, identifying the variables that may trigger or maintain it,14 systematic direct observation of behaviour provides quantitative information about frequency, intensity or duration of a targeted behaviour during a defined time interval. The WHAAM application15,16 is intended to promote the application of FBA methods to reduce the occurrence of problem behaviours in relation to attention deficit hyperactivity disorder (ADHD) or to replace them with positive ones. According to the Diagnostic and Statistical Manual of Mental Disorders-5,17 ADHD is a persistent pattern of inattention and/or hyperactivity–impulsivity that interferes with functioning or development, has symptoms presenting in two or more settings, and negatively impacts directly on social, academic or occupational functioning. For instance, children with ADHD are often rejected by peers and few are popular with their peers18,19 and they are at greater risk of substance abuse, school dropout, delinquency, academic problems and other psychological disorders.20 In the FBA context, the WHAAM application is a successful example of how the most recent technologies can support FBA by adopting an EBP. In the following section, the system’s functions will be described, a case study will be presented highlighting the statistical relevance of the behavioural interventions carried out and the satisfaction of the users of the technology will be evaluated.
Overview of the WHAAM application features
Starting from the theoretical background of the FBA, the WHAAM application21 features are inspired by the multimodal treatment of ADHD. The most common approaches to manage ADHD symptoms includes behavioural interventions, the involvement of people included in the major settings of life in psycho-educational activities and the analysis of the medical history of the patient. In particular, the WHAAM application provides users with features for creating a network of stakeholders (parents, teachers and caregivers) close to the child with ADHD. This is to promote their collaboration during the whole functional behaviour process using both the web and the mobile sides of the application. The website allows users to sign in using their personal identification to access the following features:
inclusion of data related to a patient (i.e. diagnosis, medical therapy, information about schools attended and crucial life events in his personal history);
invitation of caregivers to become members of the child’s private network;
use of instant messaging to promote collaboration and sharing data and information between the network’s member;
identification and description of a child’s specific behavioural problem (target behaviour) which the network wants to change;
planning the collection of baseline data;
creating a hypothesis about the main function sustaining the target behaviour;
creating an intervention plan, describing in detail the strategies to be applied and who will apply them within the network;
the evaluation of the effect size of the intervention, applying statistical algorithms.
The mobile application is available for Android operating systems. It provides the following features:
viewing the case data;
viewing the target behaviour descriptions;
access to the observation sessions assigned to the logged user;
ongoing observation of the targeted behaviour through the recording of the behaviour frequencies or duration;
collection of qualitative data about the target behaviour through ABC charts.
The WHAAM application supports the FBA process in six steps. The first step is the creation of a child’s record. In order to let the users choose what they want to disclose, the personal information of the child can be omitted, using only a nickname, gender and the date of birth as the required information. Additional data such as diagnoses, medications, school information and other general events characterizing the child’s life can be added in the section of ‘case data’. This section provides health professionals with information intended to support the understanding of the child target behaviours. According to the Italian Personal Data Protection Code,22 data encryption of personal records and a credentials’ authentication system guarantee the personal data protection from intrusive accesses. During the second step, people who have an important role in the life contexts of the child with ADHD can be invited to become a member of the child’s private networks. Thus, they will become active participants in all the phases of the FBA processes, putting into practice the tasks assigned to them, sharing knowledge and information. The third step is creating the accurate description of a target behaviour in order to start the functional assessment process. To help with the clarification of a target behaviour in a recognised, measurable way, the WHAAM application provides users with a set of predefined common ADHD behaviour definitions. It is however possible and helpful to write a specific behavioural definition in an operational, objective and concise way. It needs to be objective and concise because the reader has to have a very clear picture of the specific behaviour they are to observe. The description includes the place and the setting in which the target behaviour occurs. The fourth step is defining the settings for the data collection phases. The WHAAM application supports an AB single case research design, allowing users to compare baseline and intervention data. AB single-case design generally starts with a baseline (phase A) in order to observe the dependent variable as it appears. Once the baseline is established, the observation continues while implementing the intervention (phase B) in order to compare the two time series and testing the hypothesised change. The fifth step is collecting quantitative and qualitative data using the WHAAM mobile app. The user, who plays the role of observer, gathers the frequency or the duration of a targeted behaviour according to the agreed data collection settings. Every member of the child’s network can contribute qualitative data of a behaviour by filling in ABC charts. Data collected during this step is available to the child’s network directly through the WHAAM web application. Health professionals analyse all the gathered observations to formulate a hypothesis about the main function that is sustaining the target behaviour and then to create an intervention plan intended to reduce or eliminate the inappropriate behaviour. The intervention plan consists of a set of behavioural strategies defined by health professionals and assigned to specific members of the child’s network. During the intervention phase, new data are collected for further comparisons. The final step is in the evaluation of intervention. Health professionals can produce a visual analysis of the gathered data accessing the scatter plots representing the observation sessions. Moreover, the Parker et al.23 TAU-U test is automatically calculated by the application to show the effect of the treatment on the occurrences of the target behaviour in the AB designs. TAU-U is a non-parametric statistic, which estimates the treatment effect considering both the non-overlap between phases and the trend change in phase B. This effect is estimated controlling the natural trend of phase A. The use of Kendall TAU permits us to estimate the phases monotonicity without assuming any trend function (e.g. linear). TAU-U showed to be a robust statistic with autocorrelated data overcoming some limitations of other nonoverlap techniques. The formula r = sin (0.5 πτ) is used to convert TAU-U in Pearson’s r effect size index.24 Following Cohen,25 absolute value of r between 0 and 0.09, 0.10 and 0.29, 0.30 and 0.49 and greater or equal to 0.50 is evidence of a null, small, medium and large effect size, respectively.
The WHAAM application has been extensively tested in Italy, Portugal and United Kingdom during its development. The next section will describe the methodological constraints surrounding the use of the WHAAM application and two illustrative case studies supported by it.