Self-injurious behaviour is purportedly common in autism, but prevalence rates have not yet been synthesised meta-analytically. In the present study, data from 14,379 participants in thirty-seven ...papers were analysed to generate a pooled prevalence estimate of self-injury in autism of 42% (confidence intervals 0.38–0.47). Hand-hitting topography was the most common form of self-injury (23%), self-cutting topography the least common (3%). Sub-group analyses revealed no association between study quality, participant intellectual disability or age and overall prevalence rate of self-injury. However, females obtained higher prevalence rates than males (
p
= .013) and hair pulling and self-scratching were associated with intellectual disability (
p
= .008 and
p
= .002, respectively). The results confirm very high rates of self-injury in autism and highlight within group risk-markers.
Haptic Choice Blindness Steenfeldt-Kristensen, Catherine; Thornton, Ian M.
i-Perception,
01/2013, Volume:
4, Issue:
3
Journal Article
Peer reviewed
Open access
Choice blindness is the failure to notice a mismatch between intention and outcome when making decisions. It is unknown whether choice blindness occurs when participants have extended interaction ...with real objects. Here, we examined the case when objects could be touched but not seen. Participants examined pairs of common, everyday objects inside a specially constructed box where a silent turntable was used to switch objects between initial choice and later justification. For similar pairs of objects, we found detection rates of around 22%, consistent with previous studies of choice blindness. For pairs consisting of more distinctive exemplars, the detection rate rose to 70%. Our results indicate that choice blindness does occur after haptic interaction with real objects, but is strongly modulated by similarity.
Background: Previous research has identified a number of child and behavioural characteristics that are associated with self-injurious behaviour (SIB) in those with an intellectual disability and ...developmental delay. However, to date, few studies have explored the unique contribution of each risk marker to the presence of SIB, nor have any studies translated these risk markers into a clinical algorithm that can distinguish between the presence or absence of SIB. Therefore, the aim of the current study was to develop a model that classifies the presence or absence of SIB in children who have these known risk markers, as identified by the Self-injury, Aggression and Destruction-Screening Questionnaire (SAD-SQ). Method: The study utilised existing data from previous studies that had recruited individuals with a confirmed or suspected intellectual disability or developmental delay and who had used the SAD-SQ as a measure in their studies. These data formed the training sample (N=1540) which was used to develop the risk model to predict presence or absence of SIB. Eight possible predictor variables were entered into the model. Binary logistic regression was used to identify the most predictive risk markers for SIB. These risk markers formed the final risk model and subsequent risk algorithm. The algorithm was then applied to a new test sample of children (N=320) and receiver operating characteristic (ROC) curve analysis used to assess the algorithm’s predictive accuracy. Results: Diagnosis of autism, presence of health conditions, repetitive behaviour, impulsivity and age were predictive of SIB. Gender, diagnosis of a genetic syndrome and level of ability were the least predictive of SIB, and therefore, excluded from the final risk model. At the optimum cut-off of 0.28, the risk algorithm had a sensitivity of 77% and a specificity of 58%. When applied to the test sample at the same cut-off, the algorithm had a sensitivity of 87% and a specificity of 34% with positive and negative predictive values of 64.3% and 66.7%, respectively. ROC analysis provided an area under the receiver operating curve (AUC) value of .608 which is considered moderate. Analysis of the most severe cases of SIB did not alter the accuracy of the model. Conclusion: The SAD-SQ is a sensitive screening tool that offers a simple and reliable way of screening those at risk of developing SIB in individuals with a suspected or confirmed intellectual disability or developmental delay. These results are discussed in relation to clinical and theoretical implications and areas for future research.
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