Disruptive Behaviors and Intellectual Disability: Creating a New Script.

Background: Terms currently used to describe the so-called challenging and disruptive behaviors (CBDs) of children with intellectual disabilities (ID) have different connotations depending on guiding contextual frameworks, such as academic and cultural settings in which they are used. A non-judgmental approach, which does not attempt to establish existing categorical diagnoses, but which describes in a neutral way, is missing in the literature. Therefore, we tried to describe CDBs in youth with ID in an explorative study. Methods: Interviews with families investigated the CDBs of five youth with Down syndrome. At home, families tracked youth's sleep/wake behaviors and physical activity. Youth were observed in a summer school classroom. The collected information and suggested explanatory models for observed CDBs were reviewed with the families. Results: We grouped CDBs as challenging, if they were considered to be reactive or triggered, or unspecified, if no such explanatory model was available. A third category was created for light-hearted CDBs: goofy, acknowledging the right to laugh together with peers. We found some relationships between sleep, physical activity, and CDBs and developed an explorative approach, supporting a child-centered perspective on CDBs. Conclusion: The controversial discussions on terminology and management of CDBs in the literature demonstrate the need for a non-judgmental approach. Such an explorative approach, allowing non-professionals to not label, has been missing. The fact that, up to now, the light-hearted behaviors of an individual with ID have not been integrated in commonly-used behavioral checklists as their natural right, proves our concept and indicates that a paradigm change from judgment-based to exploratory-driven approaches is needed.

Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a 'restless' child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Journal of Psychiatric Research). To develop the PG-PL, seven research assistants annotated three original Fidgety Philip cartoons. Their annotations were analyzed with descriptive statistics. To review the PG-PL's performance, the same seven research assistants annotated 12 snapshots with free hand annotations, followed by using the PG-PL, each time in randomized sequence and on two separate occasions. After achieving satisfactory inter-observer agreements, the PG-PL annotation software was used for reviewing videos where the same seven research assistants annotated 12 one-minute long video clips. The video clip annotations were finally used to develop a machine learning algorithm for automated movement detection (Journal of Psychiatric Research). These data together demonstrate the value of the PG-PL for manually annotating human movement patterns. Researchers are able to reuse the data and the first version of the machine learning algorithm to further develop and refine the algorithm for differentiating movement patterns.

Is Fidgety Philip's ground truth also ours? The creation and application of a machine learning algorithm.

BACKGROUND: Behavioral observations support clinical in-depth phenotyping but phenotyping and pattern recognition are affected by training background. As Attention Deficit Hyperactivity Disorder, Restless Legs syndrome/Willis Ekbom disease and medication induced activation syndromes (including increased irritability and/or akathisia), present with hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors), we first developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting. METHODOLOGY & RESULTS: The PG-PL was applied for annotating 12 1-min sitting-videos (inter-observer agreements >85%->97%) and these manual annotations were used as a ground truth to develop an automated algorithm using OpenPose, which locates skeletal landmarks in 2D video. We evaluated the algorithm's performance against the ground truth by computing the area under the receiver operator curve (>0.79 for the legs, arms, and feet, but 0.65 for the head). While our pixel displacement algorithm performed well for the legs, arms, and feet, it predicted head motion less well, indicating the need for further investigations. CONCLUSION: This first automated analysis algorithm allows to start the discussion about distinct phenotypical characteristics of H-behaviors during structured behavioral observations and may support differential diagnostic considerations via in-depth phenotyping of sitting behaviors and, in consequence, of better treatment concepts.

"Emplotted Narratives" and Structured "Behavioral Observations" Supporting the Diagnosis of Willis-Ekbom Disease/Restless Legs Syndrome in Children with Neurodevelopmental Conditions.

BACKGROUND: Willis-Ekbom disease/restless legs syndrome (WED/RLS) seems to be a frequent cause of intractable chronic insomnia (ICI) but is under-recognized in children/adolescents with neurodevelopmental conditions (NDCs), as many patients do not have the ability to express the underlying "urge-to-move". In light of this, we aim to develop a protocol for behavioral observations supporting the diagnosis of WED/RLS. METHODS: We investigated 26 pediatric patients (age 1-16 years, median 8) with NDCs, ICI and evidence of familial WED/RLS employing (1) "emplotted narratives" for description of the various "urge-to-move" presentations and (2) self-description and "behavioral observations" during a "suggested clinical immobilization test" (SCIT). RESULTS: Parental narratives reflected typical WED/RLS-related "urge-to-move" symptoms during day-, bed-, and nighttime in all patients. Fifteen out of 26 patients could describe the "urge-to-move" during the SCIT. Ten out of 26 patients, unable to describe their symptoms due to cognitive disabilities, showed patterns of "relieving-movements" upon observation. Sensory processing abnormalities were reported in all patients, with tactile sensitivities (26/26) (including shifted pain threshold) as the most common sensory domain. CONCLUSION: "Emplotted narratives" and structured "behavioral observations" support recognition of familial WED/RLS associated movement patterns and provide a useful tool for the diagnosis of WED/RLS in children with NDCs in a clinical office setting.