| Autor: |
Mourad J; REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium.; Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium.; Department of Psychology, University of Namur, 5000 Namur, Belgium.; Transition Institute, University of Namur, 5000 Namur, Belgium., Daniels K; REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium.; Department of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium., Bogaerts K; REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium.; Health Psychology, Faculty of Psychology and Educational Sciences, University of Leuven, 3000 Leuven, Belgium., Desseilles M; Department of Psychology, University of Namur, 5000 Namur, Belgium.; Transition Institute, University of Namur, 5000 Namur, Belgium., Bonnechère B; REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium.; Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium.; Department of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium. |
| Abstrakt: |
In this perspective paper, we propose a novel tech-driven method to evaluate body representations (BRs) in autistic individuals. Our goal is to deepen understanding of this complex condition by gaining continuous and real-time insights through digital phenotyping into the behavior of autistic adults. Our innovative method combines cross-sectional and longitudinal data gathering techniques to investigate and identify digital phenotypes related to BRs in autistic adults, diverging from traditional approaches. We incorporate ecological momentary assessment and time series data to capture the dynamic nature of real-life events for these individuals. Statistical techniques, including multivariate regression, time series analysis, and machine learning algorithms, offer a detailed comprehension of the complex elements that influence BRs. Ethical considerations and participant involvement in the development of this method are emphasized, while challenges, such as varying technological adoption rates and usability concerns, are acknowledged. This innovative method not only introduces a novel vision for evaluating BRs but also shows promise in integrating traditional and dynamic assessment approaches, fostering a more supportive atmosphere for autistic individuals during assessments compared to conventional methods. |
