Is parental propensity to risk associated with their child's medically-attended injuries? A cross-sectional study.

BACKGROUND: Societal expectations about safety influence parents' risk perceptions and children's risky play opportunities. This study examined parents' propensity to take risks themselves and their propensity to accept risks for their child, sex-related differences in parents' propensity to accept risks for their child, and the association between parents' propensity to accept risks for their child and that child's medically-attended injury history. METHODS: A total of 467 parents attending a pediatric hospital with their 6-12-year-old child completed a questionnaire about their risk propensity for themselves and for their child and reported their child's injury history. RESULTS: Parents' risk propensity for themselves was significantly higher than for their child, and fathers' risk propensity for themselves was higher than mothers'. Linear regressions showed that fathers reported significantly more propensity to accept risks for their child than mothers, but parents did not differentiate between their sons and daughters. A binary logistic regression showed that parents' propensity to accept risks for their child was a significant predictor of pediatric medically-attended injury. CONCLUSIONS: Parents were more comfortable in taking risks for themselves than for their child. While fathers were more comfortable with their children engaging in risks than mothers, child's sex was not related to parents' propensity to accept risks for their child. Pediatric injury was predicted by parents' propensity to accept risks for their child. Further research investigating injury type and severity related parent risk propensity is needed to determine how parents' attitudes toward risk might relate to severe injury.

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.