Examining the Effectiveness of Human-Robotic Interaction on the Executive Functions of Children with Autism Spectrum Disorders

Farrokhi, Hossein; Aghamohammadian Sharbaaf, Hamidreza; Seyedzadeh Dalooyi, Seyed Iman; Zolfaghari, Hamid

Volume 13 - Articles-1402 MEJDS (2023) 13: 71 | Back to browse issues page

Research code: 49645

Ethics code: IR.UM.REC.1401.177

Mendeley

Zotero

RefWorks

Farrokhi H, Aghamohammadian Sharbaaf H, Seyedzadeh Dalooyi S I, Zolfaghari H. Examining the Effectiveness of Human-Robotic Interaction on the Executive Functions of Children with Autism Spectrum Disorders. MEJDS 2023; 13 :71-71
URL: http://jdisabilstud.org/article-1-3018-en.html

Examining the Effectiveness of Human-Robotic Interaction on the Executive Functions of Children with Autism Spectrum Disorders

Hossein Farrokhi¹

, Hamidreza Aghamohammadian Sharbaaf ^*²

, Seyed Iman Seyedzadeh Dalooyi¹

, Hamid Zolfaghari¹

1- PhD in Psychology, Faculty of Education Sciences and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran
2- Professor of the Faculty of Education Sciences and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract: (906 Views)

Abstract
Background & Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social interactions and communication along with repetitive patterns of behavior, interests, or activities. It is a chronic and debilitating psychiatric condition of children with cognitive impairment. One of the damaged areas in these children is executive functions, a topic of great interest for investigation in autism. The psychological components of executive functions include attention, memory, and response inhibition problems. Technological interventions show remarkable results in improving social communication skills in children with ASD. Therefore, developing and implementing new interventions focusing on cognitive functions can be helpful. This study aimed to investigate the effectiveness of human–robot interaction on executive functions (working memory, sustained attention, and response inhibition) in people with ASD.
Methods: The research method was quasi–experimental with a pretest–posttest design and a control group. The study's statistical population comprised all children aged 6 to 10 years referred to counseling and psychology clinics in Mashhad City, Iran, diagnosed with ASD in 2017. The study sample included 32 children selected through available sampling and were randomly assigned to the intervention and control groups. The inclusion criteria were as follows: having a diagnosis of ASD, not participating in other treatment programs at the same time, not receiving individual counseling or using psychiatric drugs, living with their parents, providing written consent form of parents, and willingness of children to cooperate and participate in the intervention. The exclusion criteria were as follows: not attending more than two training sessions and suffering from a physical or mental illness that occurred during the sessions. The Structured Clinical Interview for DSM–5 Disorders: Clinician Version (First, 2015) and Childhood Autism Rating Scale (Schopler et al., 1980) were used to select the sample group. The study data were gathered via a Computerized N–Back Task (Jaeggi et al., 2010) and the Integrated Visual and Auditory (IVA) test (Arble et al., 2014). In the intervention group, human–robotic interaction group training was conducted in twelve 60–minute sessions, three times a week. However, the control group did not receive any intervention. Descriptive (mean and standard deviation) and inferential statistics (analysis of covariance and the independent t test) were used at a significance level of 0.05 in SPSS version 21 software to describe and analyze the data.
Results: Findings showed that after removing the effect of the pretest, there was a significant difference between the average posttest scores of the experimental group and the control group in the variables of execution speed (p<0.001), sustained visual attention (p<0.001), and visual response inhibition (p<0.001). Also, based on the effect size, 68%, 50%, and 31% of the variable score changes were respectively due to human–robotic interaction intervention.
Conclusion: Based on the findings of this study, human–robotic interaction intervention effectively improves the executive functions of children with ASD. Therefore, this method and other existing interventions can be useful and effective.

Keywords: Human–Robotic interaction, Executive functions, Autism spectrum disorder

Full-Text [PDF 547 kb] (305 Downloads)

Type of Study: Original Research Article | Subject: Psychology

References

1. Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr. 2020;9(S1):S55–65. [DOI]

2. Cavalli G, Galeoto G, Sogos C, Berardi A, Tofani M. The efficacy of executive function interventions in children with autism spectrum disorder: a systematic review and meta-analysis. Expert Rev Neurother. 2022;22(1):77–84. [DOI]

3. Alloway T, Lepere A. Sustained attention and working memory in children with autism spectrum disorder. Int J Disabil Dev Educ. 2021;68(1):1–9. [DOI]

4. Tonizzi I, Giofrè D, Usai MC. Inhibitory control in autism spectrum disorders: meta-analyses on indirect and direct measures. J Autism Dev Disord. 2022;52(11):4949–65. [DOI]

5. Salimi Z, Jenabi E, Bashirian S. Are social robots ready yet to be used in care and therapy of autism spectrum disorder: A systematic review of randomized controlled trials. Neurosci Biobehav Rev. 2021;129:1–16. [DOI]

6. DiPietro J, Kelemen A, Liang Y, Sik-Lanyi C. Computer- and robot-assisted therapies to aid social and intellectual functioning of children with autism spectrum disorder. Medicina. 2019;55(8):440. [DOI]

7. Holeva V, Nikopoulou VA, Papadopoulou M, Vrochidou E, Papakostas GA, Kaburlasos VG. Toward robot-assisted psychosocial intervention for children with autism spectrum disorder (ASD). In: International Conference on Social Robotics [Internet]. Cham: Springer International Publishing; 2019. [DOI]

8. Taheri A, Meghdari A, Alemi M, Pouretemad H. Impacts of social robots in education and rehabilitation of children with autism in Iran. Journal of Mechanical Engineering. 2020;52(8):2329–54. [Persian] [Article]

9. Kumazaki H, Yoshikawa Y, Yoshimura Y, Ikeda T, Hasegawa C, Saito DN, et al. The impact of robotic intervention on joint attention in children with autism spectrum disorders. Mol Autism. 2018;9(1):46. [DOI]

10. Ghiglino D, Chevalier P, Floris F, Priolo T, Wykowska A. Follow the white robot: efficacy of robot-assistive training for children with autism spectrum disorder. Res Autism Spectr Disord. 2021;86:101822. [DOI]

11. Jegarian M, Bakuoie F, Bahrami M, Pouretemad HR. Human-socially assistive robot interaction for improving joint attention in children with autism. Journal of Mechanical Engineering. 2018 48(3): 29–35. [Persian]

12. Krejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. 1970;30(3):607–10. [DOI]

13. First MB. Structured clinical interview for the DSM (SCID). In: Cautin RL, Lilienfeld SO; editors. The encyclopedia of clinical psychology. First edition. Wiley; 2015. [DOI]

14. Shabani A, Masoumian S, Zamirinejad S, Hejri M, Pirmorad T, Yaghmaeezadeh H. Psychometric properties of structured clinical interview for DSM‐5 disorders‐clinician version (SCID‐5‐CV). Brain Behav. 2021;11(5):e01894. [DOI]

15. Schopler E, Reichler RJ, DeVellis RF, Daly K. Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). J Autism Dev Disord. 1980;10(1):91–103. [DOI]

16. Vakilizadeh N, Abedi A, Mohseni Ezhiyeh A. Investigating validity and reliability of early screening for autistic traits-Persian version (ESAT-PV) in toddlers. Archives of Rehabilitation. 2017;18(3):182–93. [Persian] [Article]

17. Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B. The concurrent validity of the N -back task as a working memory measure. Memory. 2010;18(4):394–412. [DOI]

18. Klatzky RL, Giudice NA, Marston JR, Tietz J, Golledge RG, Loomis JM. An N-Back Task using vibrotactile stimulation with comparison to an auditory analogue. Behavior Research Methods. 2008;40(1):367–72. [DOI]

19. Miller KM, Price CC, Okun MS, Montijo H, Bowers D. Is the N-Back task a valid neuropsychological measure for assessing working memory? Arch Clin Neuropsychol. 2009;24(7):711–7. [DOI]

20. Hossaini Khah K, Nikdel F, Nasser N. the effectiveness of training self-regulation strategies on processing efficiency and working memory function of high school girl students. Research in Cognitive and Behavioral Sciences. 2019;8(2):33–48. [Persian] [Article]

21. Arble E, Kuentzel J, Barnett D. Convergent validity of the integrated visual and auditory continuous performance test (IVA+Plus): associations with working memory, processing speed, and behavioral ratings. Arch Clin Neuropsychol. 2014;29(3):300–12. [DOI]

22. Wang LJ, Lee SY, Tsai CS, Lee MJ, Chou MC, Kuo HC, et al. Validity of visual and auditory attention tests for detecting ADHD. J Atten Disord. 2021;25(8):1160–9. [DOI]

23. Salamati A, Hosseini S A, Haghgou H. Effectiveness of vestibular stimulation on visual attention in children with attention deficit hyperactivity disorder. Archives of Rehabilitation. 2014;15(3):18–25. [Persian] [Article]

24. Desideri L, Di Santantonio A, Varrucciu N, Bonsi I, Di Sarro R. Assistive technology for cognition to support executive functions in autism: a scoping review. Adv Neurodev Disord. 2020;4(4):330–43. [DOI]

25. Taheri A, Meghdari A, Alemi M, Pouretemad HR. Teaching music to children with autism: a social robotics challenge. Scientia Iranica. 2019;26(Special Issue on: Socio-Cognitive Engineering):40–58. [Persian] [DOI]

26. Pasqualotto A, Mazzoni N, Bentenuto A, Mulè A, Benso F, Venuti P. Effects of cognitive training programs on executive function in children and adolescents with autism spectrum disorder: a systematic review. Brain Sci. 2021;11(10):1280. [DOI]