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A systematic review of observational, naturalistic, and neurophysiological outcome measures of nonpharmacological interventions for autism

Abstract

Objective:

Naturalistic and neurophysiological assessments are relevant as outcome measures in autism intervention trials because they provide, respectively, ecologically valid information about functioning and underlying neurocognitive mechanisms. We conducted a systematic review to highlight which specific neurophysiological techniques, experimental tasks, and naturalistic protocols have been used to assess neural and behavioral functioning in autism intervention studies.

Methods:

Studies were collected from four electronic databases between October 2019 and February 2020: MEDLINE (via PubMed), PsycINFO, LILACS, and Web of Science, and were included if they used structured observational, naturalistic, or neurophysiological measures to assess the efficacy of a nonpharmacological intervention for ASD.

Results:

Fourteen different measures were used by 64 studies, with the Autism Diagnostic Observation Schedule the most frequently used instrument. Thirty-seven different coding systems of naturalistic measures were used across 51 studies, most of which used different protocols. Twenty-four neurophysiological measures were used in 16 studies, with different experimental paradigms and neurophysiological components used across studies.

Conclusions:

Cross-study variability in assessing the outcomes of autism interventions may obscure comparisons and conclusions about how different behavioral interventions affect autistic social communication and underlying neurophysiological mechanisms.

Autism; intervention; outcome measures; neurophysiology; naturalistic assessments


Introduction

Autism spectrum disorder (ASD) is a developmental condition characterized by difficulties in social communication and interaction, restricted interests, and repetitive behaviors.11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Arlington: American Psychiatric Publishing; 2013. These symptoms persist throughout life in most individuals.22. Steinhausen HC, Mohr Jensen C, Lauritsen MB. A systematic review and meta-analysis of the long-term overall outcome of autism spectrum disorders in adolescence and adulthood. Acta Psychiatr Scand. 2016;133:445-52. Many autistic individuals also have mood and anxiety disorders33. Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry. 2008;47:921-9. and impairments in cognitive44. Demetriou EA, Lampit A, Quintana DS, Naismith SL, Song YJ, Pye JE, et al. Autism spectrum disorders: a meta-analysis of executive function. Mol Psychiatry. 2018;23:1198-204. and adaptive55. Chatham CH, Taylor KI, Charman T, Liogier D'ardhuy X, Eule E, Fedele A, et al. Adaptive behavior in autism: minimal clinically important differences on the Vineland-II. Autism Res. 2018;11:270-83. skills. Autistic symptoms and associated difficulties can result in reduced autonomy and impaired quality of life.66. Posar A, Visconti P. Long-term outcome of autism spectrum disorder. Turk Pediatri Ars. 2019;54:207-12. The severity of autistic symptoms varies considerably across individuals.77. Charman T, Loth E, Tillmann J, Crawley D, Wooldridge C, Goyard D, et al. The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation. Mol Autism. 2017;8:27. Correspondingly, adaptive functioning skills and the amount of daily life support required by autistic people vary from minimal to substantial.88. Tillmann J, San José Cáceres A, Chatham CH, Crawley D, Holt R, Oakley B, et al. Investigating the factors underlying adaptive functioning in autism in the EU-AIMS Longitudinal European Autism Project. Autism Res. 2019;12:645-57. There is also a relationship between ASD severity and intellectual ability, with lower intelligence quotient (IQ) related to more severe autistic symptoms in many cases.88. Tillmann J, San José Cáceres A, Chatham CH, Crawley D, Holt R, Oakley B, et al. Investigating the factors underlying adaptive functioning in autism in the EU-AIMS Longitudinal European Autism Project. Autism Res. 2019;12:645-57.,99. de Giambattista C, Ventura P, Trerotoli P, Margari M, Palumbi R, Margari L. Subtyping the autism spectrum disorder: comparison of children with high functioning autism and Asperger syndrome. J Autism Dev Disord. 2019;49:138-50.

Difficulties with social interaction include reduced ability to initiate social interactions, engage in relationships, and maintain social reciprocity.1010. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Primers. 2020;6:5. Communication problems include delayed or atypical speech and language development, difficulties in nonverbal communication, and low responsiveness in situations of shared attention.1010. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Primers. 2020;6:5.,1111. Mundy P, Sigman M, Kasari C. A longitudinal study of joint attention and language development in autistic children. J Autism Dev Disord. 1990;20:115-28. Restricted and repetitive behaviors (RRBs) include inflexibility and perseveration in interests and activities (also called insistence on sameness), motor stereotypies, and repetitive speech, routines, and rituals,1010. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Primers. 2020;6:5. as well as hyper- or hypo-reactivity to sensory input or unusual interests in sensory aspects of the environment.11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Arlington: American Psychiatric Publishing; 2013. These symptoms are associated with difficulties in social perception and cognition, as well as atypical patterns of neurophysiological activity during social processing.1212. Dawson G, Webb SJ, McPartland J. Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev Neuropsychol. 2005;27:403-24.

13. Elsabbagh M, Divan G, Koh YJ, Kim YS, Kauchali S, Marcín C, et al. Global prevalence of autism and other pervasive developmental disorders. Autism Res. 2012;5:160-79.

14. Elsabbagh M, Johnson MH. Autism and the social brain: the first-year puzzle. Biol Psychiatry. 2016;80:94-9.

15. Neuhaus E, Jones EJ, Barnes K, Sterling L, Estes A, Munson J, et al. The relationship between early neural responses to emotional faces at age 3 and later autism and anxiety symptoms in adolescents with autism. J Autism Dev Disord. 2016;46:2450-63.
-1616. Shephard E, Milosavljevic B, Mason L, Elsabbagh M, Tye C, Gliga T, et al. Neural and behavioural indices of face processing in siblings of children with autism spectrum disorder (ASD): a longitudinal study from infancy to mid-childhood. Cortex. 2020;127:162-79. Other neurocognitive alterations, such as reduced neural connectivity, have also been associated with ASD.1717. Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19:659-67.

18. Dickinson A, Varcin KJ, Sahin M, Nelson CA 3rd, Jeste SS. Early patterns of functional brain development associated with autism spectrum disorder in tuberous sclerosis complex. Autism Res. 2019;12:1758-73.

19. Oldehinkel M, Mennes M, Marquand A, Charman T, Tillmann J, Ecker C, et al. Altered connectivity between cerebellum, visual, and sensory-motor networks in autism spectrum disorder: results from the EU-AIMS longitudinal European autism project. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:260-70.
-2020. Shephard E, Bedford R, Milosavljevic B, Gliga T, Jones EJ, Pickles A, et al. Early developmental pathways to childhood symptoms of attention-deficit hyperactivity disorder, anxiety and autism spectrum disorder. J Child Psychol Psychiatry. 2019;60:963-74. These neurocognitive atypicalities seem to begin early in life, likely as a result of an altered trajectory of neural and behavioral development.2121. Johnson MH. Autism as an adaptive common variant pathway for human brain development. Dev Cogn Neurosci. 2017;25:5-11.

ASD seems to emerge from an interaction between pre-existing neurodevelopmental vulnerabilities (e.g., genetics) and the prenatal environment.2222. Jones EJ, Gliga T, Bedford R, Charman T, Johnson MH. Developmental pathways to autism: a review of prospective studies of infants at risk. Neurosci Biobehav Rev. 2014;39:1-33.,2323. Kasari C. Assessing change in early intervention programs for children with autism. J Autism Dev Disord. 2002;32:447-61. However, the postnatal environment is crucial in terms of managing ASD symptoms, coping with functional and neurocognitive difficulties, and ensuring quality of life.2121. Johnson MH. Autism as an adaptive common variant pathway for human brain development. Dev Cogn Neurosci. 2017;25:5-11.,2222. Jones EJ, Gliga T, Bedford R, Charman T, Johnson MH. Developmental pathways to autism: a review of prospective studies of infants at risk. Neurosci Biobehav Rev. 2014;39:1-33. Based on this concept, several intervention programs have been designed to enhance the development of autistic children, some of them focusing on social communication and parent-child interaction2424. Provenzani U, Fusar-Poli L, Brondino N, Damiani S, Vercesi M, Meyer N, et al. What are we targeting when we treat autism spectrum disorder? A systematic review of 406 clinical trials. Autism. 2020;24:274-84.

25. Fletcher-Watson S, Petrou A, Scott-Barrett J, Dicks P, Graham C, O'Hare A, et al. A trial of an iPad™ intervention targeting social communication skills in children with autism. Autism. 2016;20:771-82.

26. Herbrecht E, Kievit E, Spiegel R, Dima D, Goth K, Schmeck K. Become related: FIAS, an intensive early intervention for young children with autism spectrum disorders. Psychopathology. 2015;48:162-72.
-2727. Abujadi C, Croarkin PE, Bellini BB, Brentani H, Marcolin MA. Intermittent theta-burst transcranial magnetic stimulation for autism spectrum disorder: an open-label pilot study. Braz J Psychiatry. 2018;40:309-11. and others focusing on sensory issues,2828. Karmiloff-Smith A, Casey BJ, Massand E, Tomalski P, Thomas MS. Environmental and genetic influences on neurocognitive development: the importance of multiple methodologies and time-dependent intervention. Clin Psychol Sci. 2014;2:628-37. RRBs,2929. Dawson G, Rogers S, Munson J, Smith M, Winter J, Greenson J, et al. Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model. Pediatrics. 2010;125:e17-23. or co-occurring difficulties like anxiety.3030. Green J, Pickles A, Pasco G, Bedford R, Wan MW, Elsabbagh M, et al. Randomised trial of a parent-mediated intervention for infants at high risk for autism: longitudinal outcomes to age 3 years. J Child Psychol Psychiatry. 2017;58:1330-40.,3131. Sgandurra G, Lorentzen J, Inguaggiato E, Bartalena L, Beani E, Cecchi F, et al. A randomized clinical trial in preterm infants on the effects of a home-based early intervention with the 'CareToy System'. PLoS One. 2017;12:e0173521. In autistic adolescents and adults, interventions based on social cognition training, cognitive-behavioral therapy, mindfulness, and applied behavior analysis have also been developed to target social communication skills and cognitive and emotional functioning.3232. Whitehouse AJ, Varcin KJ, Alvares GA, Barbaro J, Bent C, Boutrus M, et al. Pre-emptive intervention versus treatment as usual for infants showing early behavioural risk signs of autism spectrum disorder: a single-blind, randomised controlled trial. Lancet Child Adolesc Health. 2019;3:605-15.

Given the range of intervention programs developed for autism, it is challenging for parents, clinicians, and public policy makers to select a particular intervention (or set of interventions) for real-world implementation outside of the research context. This problem is further compounded by the variability in response to treatment reported across intervention trials in autism.3333. Weitlauf AS, Sathe N, McPheeters ML, Warren ZE. Interventions targeting sensory challenges in autism spectrum disorder: a systematic review. Pediatrics. 2017;139:e2017034.,3434. Raulston TJ, Hansen SG, Machalicek W, McIntyre LL, Carnett A. Interventions for repetitive behavior in young children with autism: a survey of behavioral practices. J Autism Dev Disord. 2019;49:3047-59. This variability likely reflects the heterogeneity of the ASD phenotype as well as differences in characteristics of intervention studies, such as the age of the child when the intervention was delivered and the type and intensity of treatment approach.3333. Weitlauf AS, Sathe N, McPheeters ML, Warren ZE. Interventions targeting sensory challenges in autism spectrum disorder: a systematic review. Pediatrics. 2017;139:e2017034.,3434. Raulston TJ, Hansen SG, Machalicek W, McIntyre LL, Carnett A. Interventions for repetitive behavior in young children with autism: a survey of behavioral practices. J Autism Dev Disord. 2019;49:3047-59.

Another factor that likely contributes to inconsistent findings concerning the efficacy of autism interventions is variability in the assessments used as outcome measures.3535. Clarke C, Hill V, Charman T. School based cognitive behavioural therapy targeting anxiety in children with autistic spectrum disorder: a quasi-experimental randomised controlled trail incorporating a mixed methods approach. J Autism Dev Disord. 2017;47:3883-95.,3636. Delli CK, Polychronopoulou SA, Kolaitis GA, Antoniou AS. Review of interventions for the management of anxiety symptoms in children with ASD. Neurosci Biobehav Rev. 2018;95:449-63. For instance, to assess the effect of interventions on social communication skills, some studies have used observational instruments3737. Bishop-Fitzpatrick L, Minshew NJ, Eack SM. A systematic review of psychosocial interventions for adults with autism spectrum disorders. J Autism Dev Disord. 2013;43:687-94.,3838. Landa RJ. Efficacy of early interventions for infants and young children with, and at risk for, autism spectrum disorders. Int Rev Psychiatry. 2018;30:25-39. and others have used rating scales3939. Zachor DA, Ben-Itzchak E. Variables affecting outcome of early intervention in autism spectrum disorder. J Pediatr Neurol. 2017;15:129-33.

40. Schiltz HK, McVey AJ, Dolan BK, Willar KS, Pleiss S, Karst JS, et al. Changes in depressive symptoms among adolescents with ASD completing the PEERS. ®Social Skills Intervention. J Autism Dev Disord. 2018;48:834-43.

41. Shum K, Cho WK, Lam LM, Laugeson EA, Wong WS, Law LS. Learning how to make friends for Chinese adolescents with autism spectrum disorder: a randomized controlled trial of the Hong Kong Chinese version of the PEERS® intervention. J Autism Dev Disord. 2019;49:527-41.
-4242. Smith IM, Flanagan HE, Garon N, Bryson SE. Effectiveness of community-based early intervention based on pivotal response treatment. J Autism Dev Disord. 2015;45:1858-72. or naturalistic measures.4343. Schertz HH, Odom SL, Baggett KM, Sideris JH. Mediating parent learning to promote social communication for toddlers with autism: effects from a randomized controlled trial. J Autism Dev Disord. 2018;48:853-67.,4444. Sun IY, Varanda CA, Fernandes FD. Stimulation of executive functions as part of the language intervention process in children with autism spectrum disorder. Folia Phoniatr Logop. 2017;69:78-83. This cross-study variability in outcome measure selection has been highlighted in previous narrative and systematic reviews.3636. Delli CK, Polychronopoulou SA, Kolaitis GA, Antoniou AS. Review of interventions for the management of anxiety symptoms in children with ASD. Neurosci Biobehav Rev. 2018;95:449-63.,4545. Anagnostou E, Jones N, Huerta M, Halladay AK, Wang P, Scahill L, et al. Measuring social communication behaviors as a treatment endpoint in individuals with autism spectrum disorder. Autism. 2015;19:622-36.

46. McConachie H, Parr JR, Glod M, Hanratty J, Livingstone N, Oono IP, et al. Systematic review of tools to measure outcomes for young children with autism spectrum disorder. Health Technol Assess. 2015;19:1-506.
-4747. Scahill L, Aman MG, Lecavalier L, Halladay AK, Bishop SL, Bodfish JW, et al. Measuring repetitive behaviors as a treatment endpoint in youth with autism spectrum disorder. Autism. 2015;19:38-52. For example, Anagnostou4545. Anagnostou E, Jones N, Huerta M, Halladay AK, Wang P, Scahill L, et al. Measuring social communication behaviors as a treatment endpoint in individuals with autism spectrum disorder. Autism. 2015;19:622-36. found that 37 different outcome measures of social communication abilities had been used in clinical trials of autism interventions, but concluded that only six of them were appropriate. Likewise, McConachie4646. McConachie H, Parr JR, Glod M, Hanratty J, Livingstone N, Oono IP, et al. Systematic review of tools to measure outcomes for young children with autism spectrum disorder. Health Technol Assess. 2015;19:1-506. reported that 188 different outcome measures had been used to assess progress and outcomes in core autism symptoms associated with interventions in children under 6 years of age. Analyzing variability in the selection of outcome measures in clinical trials, Provenzani3636. Delli CK, Polychronopoulou SA, Kolaitis GA, Antoniou AS. Review of interventions for the management of anxiety symptoms in children with ASD. Neurosci Biobehav Rev. 2018;95:449-63. found 327 different outcome measures of core ASD characteristics and co-occurring psychiatric symptoms, 69% of which were used only once.

However, those previous reviews focused on the use of observational instruments or parent-report rating scales as outcome measures. Although parent-report rating scales provide easy, quick, and efficient measurements of behaviors, they are also easily biased by factors such as the rater’s reading level or linguistic skills, learning disabilities, psychological maturity, emotional awareness, psychopathology, and other general contextual factors.4848. Myers K, Winters NC. Ten-year review of rating scales. II: Scales for internalizing disorders. J Am Acad Child Adolesc Psychiatry. 2002;41:634-59. Moreover, parents are rarely blinded to intervention group status. This is problematic because recent work has shown that parents tend to report reductions in their child’s autistic symptoms even in the absence of active intervention.4949. Jones RM, Carberry C, Hamo A, Lord C. Placebo-like response in absence of treatment in children with autism. Autism Res. 2017;10:1567-72. Thus, rating scales may not be the most appropriate outcome measures for clinical trials testing interventions for autism.

Observational measures such as the Autism Diagnostic Observation Schedule (ADOS)1010. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Primers. 2020;6:5. provide more objective and standardized measures of autistic symptoms. However, there is evidence to suggest they may not be sufficiently sensitive to detect intervention-related changes in behavior.5050. Carruthers S, Charman T, El Hawi N, Kim YA, Randle R, Lord C, et al. Utility of the autism diagnostic observation schedule and the brief observation of social and communication change for measuring outcomes for a parent-mediated early autism intervention. Autism Res. 2021;14:411-25. Furthermore, many observational measures, such as the ADOS, are expensive and require high levels of training in administration and coding, which limits the utility of these measures for low-resource settings where interventions are urgently needed.

A promising alternative method of evaluating outcomes of autism interventions is to assess the child’s functioning in everyday situations, using naturalistic measures. Naturalistic measures are ecologically valid behavioral observation methods used to assess behaviors of interest during a social interaction between the child and another person. The interaction is videotaped and coded offline using a predefined coding system.5151. Carey AL, Rentscher KE, Mehl MR. Naturalistic observation of social interactions. In:Sweeny K, Robbins ML, Cohen LM, editors. The Wiley encyclopedia of health psychology. Vol. 4. Hoboken: Wiley-Blackwell; 2020. p. 373-83. Naturalistic measures are particularly relevant to evaluating the outcomes of autism interventions because they provide a more realistic assessment of the individual’s social communication functioning.5151. Carey AL, Rentscher KE, Mehl MR. Naturalistic observation of social interactions. In:Sweeny K, Robbins ML, Cohen LM, editors. The Wiley encyclopedia of health psychology. Vol. 4. Hoboken: Wiley-Blackwell; 2020. p. 373-83.,5252. Aspland H, Gardner F. Observational measures of parent-child interaction: an introductory review. Child Adolesc Ment Health. 2003;8:136-43.

Another approach to assessing outcomes of autism interventions is to examine changes in neurophysiological activity associated with the neural systems underlying social-communicative functioning. Neurophysiological measures may be more objective than observer- or parent-evaluated symptom assessments, less susceptible to response bias, and are informative as to the underlying mechanisms of symptom improvements, thereby enhancing comprehension of intervention outcomes,5353. Patrick CJ, Iacono WG, Venables NC. Incorporating neurophysiological measures into clinical assessments: fundamental challenges and a strategy for addressing them. Psychol Assess. 2019;31:1512-29. although the clinical salience of neurophysiological measures is less clear. No prior systematic review has examined the extent to which naturalistic measures have been used as outcome measures in intervention studies, and only one review investigated neurophysiological outcome measures, such as electroencephalography (EEG).5454. Stavropoulos K. Using neuroscience as an outcome measure for behavioral interventions in autism spectrum disorders (ASD): a review. Res Autism Spectr Disord. 2017; 25:62-73.

Therefore, we aimed to conduct a systematic review of naturalistic and neurophysiological outcome measures that have been used in studies of nonpharmacological interventions for ASD. We highlighted which specific neurophysiological techniques, experimental tasks, and naturalistic protocols have been used to assess neural and behavioral functioning, and the consistency with which those measures have been employed across intervention studies. For completeness and comparison, we also reviewed the specific observational outcome measures that have been used across intervention studies. We focused the review on nonpharmacological intervention studies, since pharmacological treatments usually target co-occurring difficulties, such as aggression, rather than autism itself. The information produced by this review will be useful for future intervention studies that aim to assess observational, naturalistic, and neurophysiological outcomes in ASD.

Methods

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and pre-registration

The protocol for this systematic review was pre-registered on PROSPERO (CRD42019137004). The methodology and reporting of outcomes are consistent with the PRISMA statement.

Search strategy and selection criteria

A systematic literature search was carried out to identify studies that assessed the efficacy of nonpharmacological interventions for ASD on structured observational, naturalistic, and neurophysiological outcome measures. Studies were collected from four electronic databases between October 2019 and February 2020: MEDLINE (via PubMed), PsycINFO, LILACS, and Web of Science. The searches included full-length articles written in Portuguese, English, or Spanish that were accepted for publication in peer-reviewed journal up to February 2020. Indexed terms were selected from the MeSH and DeCS databases, and the electronic search was conducted according to the PICO (Patient, Intervention, Comparison, and Outcome) methodology. The search query used in all four databases was: (“Autistic Disorder” OR “Autism Spectrum Disorders” OR “autism spectrum disorder” OR “ASD” OR “autism”) AND (“Social Communication Disorders, Therapy” OR “Autistic Disorder, Therapy” OR “Autism Spectrum Disorders, Therapy” OR “Communication, Therapy” OR “Social Skill, Therapy” OR “Social Behaviors, Therapy” OR “intervention” OR “treatment” OR “therapy” NOT “Drug Therapies” NOT “Therapy, Drug” NOT “Pharmacotherapy”) AND (“Social Skill” OR “Communication” OR “Verbal Behaviors” OR “Nonverbal Communications” OR “asd symptom severity” OR “social impairment” OR "communication impairment” OR “social communication skills” OR “social communication ability” OR “social cognition” OR “diagnostic techniques, neurologic” OR “neuroimaging, functional” OR “neuroimaging” OR “electrophysiology” OR “electroencephalography” OR “EEG” OR “nirs” OR “near-infrared spectroscopy” OR “fmri” OR “functional magnetic resonance imaging” OR “magnetic resonance imaging” OR “MRI”).

In the screening phase, the titles and abstracts of all studies retrieved by the electronic search were evaluated. Studies were selected for full-text reading if they reported the use of a nonpharmacological intervention for ASD in the title or in the abstract. Reporting of the use of an observational, naturalistic, or neurophysiological outcome measure in the title or abstract was not considered as an inclusion criterion in the screening stage because we believed that studies might include these measures but not report them in the abstract. During full-text reading, studies were included if they used structured observational, naturalistic, or neurophysiological measures to assess the efficacy of a nonpharmacological intervention for ASD. Studies were excluded if they were systematic reviews, case studies, studies that used interventions that involved ingestion of any substance, studies with interventions for conditions other than ASD or for children “at risk” for ASD, studies with interventions targeting symptoms not specifically related to ASD (such as sleep problems, depression, tone of voice, catatonia) or parental well-being, studies that did not use at least one structured observational, naturalistic, or neurophysiological measure, or studies with n<10.

Data extraction, coding, and synthesis

Study eligibility and data extraction were conducted independently by three investigators (PBGG, FMS, and LS), with each paper reviewed by two authors. Disagreements were resolved by discussion between the two investigators. Extracted information included: study population/design, participant characteristics (age, sex, socioeconomic status, race/ethnicity), clinical characteristics (IQ, language skills), study objectives, intervention description, measures used for clinical characterization (diagnostic measures), outcome measures at pre- and post-intervention assessments, and a descriptive summary of the results. A narrative synthesis of the findings was then created, with studies grouped according to the type of outcome measure used. The descriptive summary of results is not presented here because this review focused on outcome measures and assessment methods, rather than trial outcomes.

Results

After full-text reading, 92 non-duplicate studies met the inclusion criteria5555. Aldred C, Green J, Adams C. A new social communication intervention for children with autism: pilot randomised controlled treatment study suggesting effectiveness. J Child Psychol Psychiatry. 2004;45:1420-30.

56. Aldred C, Green J, Emsley R, McConachie H. Brief report: mediation of treatment effect in a communication intervention for pre-school children with autism. J Autism Dev Disord. 2012;42:447-54.

57. Almirall D, DiStefano C, Chang YC, Shire S, Kaiser A, Lu X, et al. Longitudinal effects of adaptive interventions with a speech-generating device in minimally verbal children with ASD. J Clin Child Adolesc Psychol. 2016;45:442-56.

58. Amatachaya A, Jensen MP, Patjanasoontorn N, Auvichayapat N, Suphakunpinyo C, Janjarasjitt S, et al. The short-term effects of transcranial direct current stimulation on electroencephalography in children with autism: a randomized crossover controlled trial. Behav Neurol. 2015;2015:928631.

59. Arabi M, Saberi Kakhki A, Sohrabi M, Soltani Kouhbanani S, Jabbari Nooghabi M. Is visuomotor training an effective intervention for children with autism spectrum disorders? Neuropsychiatr Dis Treat. 2019;15:3089-102.

60. Bauminger N. The facilitation of social-emotional understanding and social interaction in high-functioning children with autism: intervention outcomes. J Autism Dev Disord. 2002;32:283-98.

61. Ben Itzchak E, Lahat E, Burgin R, Zachor AD. Cognitive, behavior and intervention outcome in young children with autism. Res Dev Disabil. 2008;29:447-58.

62. Ben-Itzchak E, Zachor DA. Change in autism classification with early intervention: predictors and outcomes. Res Autism Spec Disord. 2009;3:967-76.

63. Ben-Sasson A, Lamash L, Gal E. To enforce or not to enforce? The use of collaborative interfaces to promote social skills in children with high functioning autism spectrum disorder. Autism. 2013;17:608-22.

64. Bölte S, Feineis-Matthews S, Leber S, Dierks T, Hubl D, Poustka F. The development and evaluation of a computer-based program to test and to teach the recognition of facial affect. Int J Circumpolar Health. 2002;61 Suppl 2:61-8.

65. Boyd BA, Watson LR, Reszka SS, Sideris J, Alessandri M, Baranek GT, et al. Efficacy of the ASAP intervention for preschoolers with ASD: a cluster randomized controlled trial. J Autism Dev Disord. 2018;48:3144-62.

66. Byford S, Cary M, Barrett B, Aldred CR, Charman T, Howlin P, et al. Cost-effectiveness analysis of a communication-focused therapy for pre-school children with autism: results from a randomised controlled trial. BMC Psychiatry. 2015;15:316.

67. Carr D, Felce J. “Brief report: increase in production of spoken words in some children with autism after PECS teaching to Phase III”. J Autism Dev Disord. 2007;37:780-7.

68. Carter AS, Messinger DS, Stone WL, Celimli S, Nahmias AS, Yoder P. A randomized controlled trial of Hanen's 'More Than Words' in toddlers with early autism symptoms. J Child Psychol Psychiatry. 2011;52:741-52.

69. Chan AS, Cheung MC, Sze SL, Leung WW. Seven-star needle stimulation improves language and social interaction of children with autistic spectrum disorders. Am J Chin Med. 2009;37:495-504.

70. Chan AS, Sze SL, Siu NY, Lau EM, Cheung MC. A chinese mind-body exercise improves self-control of children with autism: a randomized controlled trial. PLoS One. 2013;8:e68184.

71. Choque Olsson N, Flygare O, Coco C, Görling A, Råde A, Chen Q, et al. Social skills training for children and adolescents with autism spectrum disorder: a randomized controlled trial. J Am Acad Child Adolesc Psychiatry. 2017;56:585-92.

72. Corbett BA, Swain DM, Coke C, Simon D, Newsom C, Houchins-Juarez N, et al. Improvement in social deficits in autism spectrum disorders using a theatre-based, peer-mediated intervention. Autism Res. 2014;7:4-16.

73. Corbett BA, Key AP, Qualls L, Fecteau S, Newsom C, Coke C, et al. Improvement in social competence using a randomized trial of a theatre intervention for children with autism spectrum disorder. J Autism Dev Disord. 2016;46:658-72.

74. Deckers A, Muris P, Roelofs J, Arntz A. A group-administered social skills training for 8- to 12- year-old, high-functioning children with autism spectrum disorders: an evaluation of its effectiveness in a naturalistic outpatient treatment setting. J Autism Dev Disord. 2016;46:3493-504.

75. Dolan BK, Van Hecke AV, Carson AM, Karst JS, Stevens S, Schohl KA, et al. Brief report: assessment of intervention effects on in vivo peer interactions in adolescents with autism spectrum disorder (ASD). J Autism Dev Disord. 2016;46:2251-9.

76. Estes A, Munson J, Rogers SJ, Greenson J, Winter J, Dawson G. Long-Term outcomes of early intervention in 6-year-old children with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2015;54:580-7.

77. Faja S, Aylward E, Bernier R, Dawson G. Becoming a face expert: a computerized face-training program for high-functioning individuals with autism spectrum disorders. Dev Neuropsychol. 2008;33:1-24.

78. Furukawa K, Okuno H, Mohri I, Nakanishi M, Eyberg SM, Sakai S. Effectiveness of child-directed interaction training for young Japanese children with autism spectrum disorders. Child Fam Behav Ther. 2018;40:166-86.

79. Gengoux GW, Berquist KL, Salzman E, Schapp S, Phillips JM, Frazier TW, et al. Pivotal response treatment parent training for autism: findings from a 3-month follow-up evaluation. J Autism Dev Disord. 2015;45:2889-98.

80. Goods KS, Ishijima E, Chang YC, Kasari C. Preschool based JASPER intervention in minimally verbal children with autism: pilot RCT. J Autism Dev Disord. 2013;43:1050-6.

81. Gordon K, Pasco G, McElduff F, Wade A, Howlin P, Charman T. A communication-based intervention for nonverbal children with autism: what changes? Who benefits? J Consult Clin Psychol. 2011;79:447-57.

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-141141. Zlomke KR, Jeter K, Murphy J. Open-trial pilot of parent-child interaction therapy for children with autism spectrum disorder. Child Fam Behav Ther. 2017;39:1-18. (Figure 1, and Table S1, available as online-only supplementary material.). A total of 75 different measures were used, consisting of 14 structured observational measures (Table 1), 37 naturalistic measures with coding systems grouped into seven categories (Table 2), and 24 neurophysiological measures (Table 3). Fifty-six percent (k = 43) of the instruments/methods were used only once to assess intervention outcomes. Few studies (k = 4) combined more than one type of these measures to assess intervention outcomes. One study used a structured observational measure only, 20 used naturalistic measures only, and three used neurophysiological measures only. Most studies (k = 69) combined one or more of these types of measure with rating scales or cognitive assessment. Demographic information for the samples of each study included can be found in Table S2, available as online only supplementary material.

Figure 1
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart. ASD = autism spectrum disorder.
Table 1
Structured observational outcome measures used to assess autism intervention effects
Table 2
Naturalistic outcome measures used to assess autism intervention effects
Table 3
Neurophysiological outcome measures used to assess autism intervention effects

Structured observational measures

Structured observational measures are those in which an examiner uses a structured script and a standardized set of materials to interact with a child and classify a behavior or ability as present or absent. Fourteen different measures were used by 64 studies, with ADOS142142. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30:205-23.,143143. Lord C, Petkova E, Hus V, Gan W, LuF, MartinDM, et al. A multisite study of the clinical diagnosis of different autism spectrum disorders. Arch Gen Psychiatry. 2012;69:306-13. being the most frequently used instrument (k = 26), followed by the Early Social Communication Scales (ESCS)144144. Mundy P, Delgado C, Block J, Venezia M, Hogan A, Seibert J. A Manual for the Early Social Communication Scales (ESCS) [Internet]. 2003 [cited 2022 Jan 15]. education.ucdavis.edu/sites/main/files/file-attachments/escs_manual_2003_2013.pdf
education.ucdavis.edu/sites/main/files/f...
(k = 10). Together, both instruments represented 56% of the structured observational measures used.

Naturalistic measures

Fifty-one studies used naturalistic procedures as outcome measures of the effect of autism interventions. As can be seen in Table 2, video-recorded parent-child interaction was the most frequently used procedure (k = 24), but some studies also used teacher-child interactions (k = 2), examiner-child interaction (k = 8), family-child interactions during dinnertime (k = 1), child observation during normal classroom routines (k = 3), and child interactions with autistic or non-autistic peers (k = 13). A range of specific behaviors were coded from these interactions, including social communication skills, attachment-related behaviors, play behavior, joint attention, joint engagement, and RRBs. Thirty-seven different coding systems were used to quantify specific behaviors, and were rarely used in more than one study. Further, they were often not described in detail (Table 2).

Neurophysiological measures

Neurophysiological measures were used by 16 studies and could be grouped into the following broad categories (Table 3): event-related electrophysiological activity derived from EEG (event-related EEG, k = 6), resting-state EEG (k = 3), event-related blood oxygenation level-dependent (BOLD) neural activity measured with functional magnetic resonance imaging (event-related functional magnetic resonance imaging [fMRI], k = 3), resting-state fMRI (k = 2), and white matter microstructure measured with diffusion tensor imaging (DTI) (k = 1). Specific measures used within the event-related EEG category were event-related potential (ERP) components indexing error-monitoring (error-related negativity [ERN] and error positivity, k = 2), ERP components indexing early attentional and inhibitory processes (P50, N1, N2, P3) (k = 1), ERP components reflecting face processing (P1, N170, and N250 components in one study and the P3 in another study), and oscillatory power measured during working memory and self-referential processing (k = 1). Of note, none of the studies measuring these components used the same cognitive task to elicit event-related EEG activity (Table 3). Likewise, the three event-related fMRI studies examined BOLD activity in different regions or networks of the brain during three different cognitive tasks (reading network during sentence reading, fusiform gyrus during face processing, superior temporal sulcus during biological motion perception).

All four resting-state EEG studies used a different specific index of activity (inter-hemispheric coherence, oscillatory power, hemispheric asymmetry, peak alpha frequency), as did the two resting-state fMRI studies (functional connectivity in the reading network in one study, functional connectivity in frontotemporal networks in the other). Only one study examined structural brain metrics (using DTI) (Table 3). Most studies used neurophysiological measures combined with rating scales or observational or neurocognitive tools, but two studies used EEG only111111. Ortiz-Sanchez P, Mulas F, Abad-Mas L, Roca P, Gandia-Beneto R. [Randomised controlled study of inter-hemispheric electroencephalographic coherence following assisted therapy with dolphins in children with autism spectrum disorders]. Rev Neurol. 2018;66:S65-S70.,123123. Sokhadze EM, Baruth JM, Sears L, Sokhadze GE, El-Baz AS, Casanova MF. Prefrontal neuromodulation using rTMS improves error monitoring and correction function in autism. Appl Psychophysiol Biofeedback. 2012;37:91-102. and two others used fMRI only107107. Maximo JO, Murdaugh DL, O'Kelley S, Kana RK. Changes in intrinsic local connectivity after reading intervention in children with autism. Brain Lang. 2017;175:11-7.,108108. Murdaugh DL, Maximo JO, Cordes CE, O'Kelley SE, Kana RK. From word reading to multisentence comprehension: Improvements in brain activity in children with autism after reading intervention. Neuroimage Clin. 2017;16:303-12. No study using near infrared spectroscopy (NIRS) or functional NIRS was retrieved from the databases, although the search strategy included a term that covered this technique.

Discussion

To our knowledge, this is the first systematic review to synthesize structured observational, naturalistic, and neurophysiological assessments that have been used as outcome measures of interventions for ASD. Consistent with a recent review focused on the variability of outcome measures in autism intervention trials,3636. Delli CK, Polychronopoulou SA, Kolaitis GA, Antoniou AS. Review of interventions for the management of anxiety symptoms in children with ASD. Neurosci Biobehav Rev. 2018;95:449-63. our results revealed extensive variability in outcome measures employed to assess the efficacy of nonpharmacological interventions across studies. This was especially true for naturalistic and neurophysiological methods, with 61 different naturalistic and neurophysiological methods of assessment used across 66 studies. Since each assessment instrument has a different framework, content, and rationale for its construction, comparing the efficacy of interventions assessed with different outcome measures is a challenging task.3535. Clarke C, Hill V, Charman T. School based cognitive behavioural therapy targeting anxiety in children with autistic spectrum disorder: a quasi-experimental randomised controlled trail incorporating a mixed methods approach. J Autism Dev Disord. 2017;47:3883-95. Future intervention studies in autism should therefore consider including assessments that have been used in previous trials, perhaps as secondary outcome measures if a new primary outcome measure is needed, to facilitate the comparison of different interventions across studies.

In this regard, structured observational instruments may represent the best choice for outcome measures since they have most frequently been used in the literature, facilitating cross-study comparisons of the efficacy of autism interventions. In terms of specific structured observational instruments, the ADOS has most often been used in previous intervention studies. However, this assessment tool is expensive (both in terms of purchasing the materials and in training individuals in administration and coding) and may not be sensitive to intervention-related changes in behavior.5050. Carruthers S, Charman T, El Hawi N, Kim YA, Randle R, Lord C, et al. Utility of the autism diagnostic observation schedule and the brief observation of social and communication change for measuring outcomes for a parent-mediated early autism intervention. Autism Res. 2021;14:411-25. On the other hand, the ADOS is one of few autism assessment tools that is available in a range of non-English languages, such as Brazilian Portuguese145145. Pacífico MC, de Paula CS, Namur VS, Lowenthal R, Bosa CA, Teixeira MCTV. Preliminary evidence of the validity process of the Autism Diagnostic Observation Schedule (ADOS): translation, cross-cultural adaptation and semantic equivalence of the Brazilian Portuguese version. Trends Psychiatry Psychother. 2019;41:218-26. and Polish.146146. Chojnicka I, Pisula E. Adaptation and validation of the ADOS-2, Polish Version. Front Psychol. 2017;8:1916. The unavailability of assessment instruments in languages other than English is a major barrier to conducting autism intervention studies in non-English-speaking countries. However, it should be noted that instruments that measure reductions of autism symptoms, such as the ADOS, have been criticized as intervention outcome measures by the autistic community. This is because these are diagnostic assessment tools and there is the risk that, with reduced symptom severity on these measures, autistic individuals may no longer meet the diagnostic criteria for autism and, consequently, may no longer receive support that is dependent on a diagnosis.147147. Leadbitter K, Buckle KL, Ellis C, Dekker M. Autistic self-advocacy and the neurodiversity movement: implications for autism early intervention research and practice. Front Psychol. 2021;12:635690. For this reason, instruments that assess autistic people acting and interacting in their own environment have been suggested as key to better understanding the real-life impact of interventions on autistic individuals.147147. Leadbitter K, Buckle KL, Ellis C, Dekker M. Autistic self-advocacy and the neurodiversity movement: implications for autism early intervention research and practice. Front Psychol. 2021;12:635690.

Naturalistic outcome measures may be helpful in this regard. Our results showed that 51 of the included studies used such measures, which consisted of observation and coding techniques to assess autistic individuals’ behavior while interacting with another person (caregiver, teacher, examiner or peers) or in their own real-life environment (e.g., in the classroom). However, the studies varied greatly in the specific procedures and coding systems adopted during naturalistic assessments. The most frequently used coding systems were the Dyadic Communication Measure for Autism (DCMA),5555. Aldred C, Green J, Adams C. A new social communication intervention for children with autism: pilot randomised controlled treatment study suggesting effectiveness. J Child Psychol Psychiatry. 2004;45:1420-30. which was used in three studies, and the Playground Observation of Peer Engagement (POPE),9393. Kasari C, Rotheram-Fuller E, Locke J, Gulsrud A. Making the connection: randomized controlled trial of social skills at school for children with autism spectrum disorders. J Child Psychol Psychiatry. 2012;53:431-9. which was also used in three studies. A more standardized approach to the use of naturalistic outcome measures, as well as rigorous and widely available training resources, is needed. It will also be important for future work to establish the extent to which specific measures of social communication abilities are comparable across different naturalistic paradigms and coding procedures (e.g., DCMA vs. POPE).

In addition to the variation in the coding systems used in naturalistic outcome measures, the interaction partner varied considerably across studies. The majority of naturalistic assessments involved interactions with the autistic child’s parent or caregiver, or with the child’s teacher. However, it is arguably most relevant to assess the child’s social communication skills when interacting with peers, since difficulties in social interactions and communication with individuals of the same age are considered when determining level of the impairment associated with autism.11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Arlington: American Psychiatric Publishing; 2013. The POPE9393. Kasari C, Rotheram-Fuller E, Locke J, Gulsrud A. Making the connection: randomized controlled trial of social skills at school for children with autism spectrum disorders. J Child Psychol Psychiatry. 2012;53:431-9. is one measure that does assess children’s interaction behavior with peers in a playground setting. Since this was also one of the more frequently used measures across studies, it may be most appropriate for use in future autism intervention studies in terms of comparability to previous intervention work.

It is also important to consider recent findings indicating that autistic individuals do not show social communication impairments when interacting with other autistic people. These findings suggest that social communication difficulties in autism might actually reflect a mismatch of interaction styles between non-autistic and autistic people in the majority of naturalistic interaction assessments.148148. Davis R, Crompton CJ. What do new findings about social interaction in autistic adults mean for neurodevelopmental research?Perspect Psychol Sci. 2021;16:649-53.,149149. Edey R, Cook J, Brewer R, Johnson MH, BirdG, PressC. Interaction takes two: typical adults exhibit mind-blindness towards those with autism spectrum disorder. J Abnorm Psychol. 2016;125:879-85. Despite this evidence for the importance of autistic-autistic interactions, our results showed that only one study assessed social communication skills of autistic children while interacting with other autistic children as an outcome measure.6363. Ben-Sasson A, Lamash L, Gal E. To enforce or not to enforce? The use of collaborative interfaces to promote social skills in children with high functioning autism spectrum disorder. Autism. 2013;17:608-22. This finding highlights the need for further studies using naturalistic measures of autistic-autistic interactions. Indeed, including such measures could highlight which specific aspects of social communication are most challenging for autistic people during interactions with both autistic and non-autistic individuals; this, in turn, may lead to new intervention targets.148148. Davis R, Crompton CJ. What do new findings about social interaction in autistic adults mean for neurodevelopmental research?Perspect Psychol Sci. 2021;16:649-53.,150150. Crompton CJ, Hallett S, Ropar D, Flynn E, Fletcher-Watson S. 'I never realised everybody felt as happy as I do when I am around autistic people': a thematic analysis of autistic adults' relationships with autistic and neurotypical friends and family. Autism. 2020;24:1438-48.

Concerning neurophysiological outcome measures, our findings showed that the use of these assessments in autism intervention trials has grown in the last 10 years. However, they are still a minority choice for evaluating the outcome of behavioral interventions for ASD. Furthermore, neurophysiological outcome measures vary widely in the specific techniques and methods of quantification used, which makes it difficult to understand how different interventions affect neurophysiological activity or how and whether different neurophysiological patterns influence intervention outcomes.151151. Anagnostou E, Taylor MJ. Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Mol Autism. 2011;2:4. Although there are limitations to the use of neurophysiological indices as outcome measures of interventions (e.g., it would be difficult to establish their clinical relevance if intervention-related changes were found in these but not in clinical assessments), neurophysiological measures still provide important information about neural mechanisms related to the autistic phenotype. For example, the N170 ERP component is a robust index of face processing (an important aspect of social cognition) and has been shown to be fairly reliably altered in at least some autistic individuals.152152. Kang E, Keifer CM, Levy EJ, Foss-Feig JH, McPartland JC, Lerner MD. Atypicality of the N170 event-related potential in autism spectrum disorder: a meta-analysis. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:657-66. Indeed, the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) proposed the N170 component as a candidate biomarker for ASD.153153. McPartland JC, Bernier RA, Jeste SS, Dawson G. , Nelson CA, Chawarska K, et al. The autism biomarkers consortium for clinical trials (ABC-CT): scientific context, study design, and progress toward biomarker qualification. Front Integr Neurosci. 2020;14:16. Yet, only one intervention study in the current review included the N170 as a neurophysiological outcome measure.7777. Faja S, Aylward E, Bernier R, Dawson G. Becoming a face expert: a computerized face-training program for high-functioning individuals with autism spectrum disorders. Dev Neuropsychol. 2008;33:1-24.

The only neurophysiological measure that was used in more than one study was the ERN component, which was examined in two studies from the same group.122122. Sokhadze E, Baruth J, Tasman A, Mansoor M, Ramaswamy R, Sears L, et al. Low-frequency repetitive transcranial magnetic stimulation (rTMS) affects event-related potential measures of novelty processing in autism. Appl Psychophysiol Biofeedback. 2010;35:147-61.,123123. Sokhadze EM, Baruth JM, Sears L, Sokhadze GE, El-Baz AS, Casanova MF. Prefrontal neuromodulation using rTMS improves error monitoring and correction function in autism. Appl Psychophysiol Biofeedback. 2012;37:91-102. While the ERN is a robust index of self-regulatory ability, specifically of error monitoring,154154. Holroyd CB, Coles MG. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychol Rev. 2002;109:679-709. there is little evidence to indicate it is reliably altered in autism or should be used as an intervention target.155155. Hüpen P, Groen Y, Gaastra GF, Tucha L, Tucha O. Performance monitoring in autism spectrum disorders: a systematic literature review of event-related potential studies. Int J Psychophysiol. 2016;102:33-46. Further, the ERN was measured during different experimental paradigms across the two studies, which limits cross-study comparisons of intervention effects on error monitoring. The most commonly used experimental paradigm, employed in six studies, was the resting state. However, each of those studies used a different method to quantify resting-state EEG or fMRI activity. Furthermore, evidence indicates that the stability of resting-state measures over time is low, as indicated by poor test-retest reliability.156156. Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage. 2013;83:550-8.,157157. Guo CC, Kurth F, Zhou J, Mayer EA, Eickhoff SB, Kramer JH, et al. One-year test-retest reliability of intrinsic connectivity network fMRI in older adults. Neuroimage. 2012;61:1471-83. It is therefore not clear whether resting-state activity is an appropriate method of detecting pre/post-intervention changes in neurophysiology. In contrast to resting-state functional activity, structural neural measures appear to have good test-retest reliability158158. Lin Q, Dai Z, Xia M, Han Z, Huang R, Gong G, et al. A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults. Sci Data. 2015;2:150056. and be sensitive to the short-term changes in behavior that are tested in nonpharmacological psychosocial interventions.159159. Davidson RJ, McEwen BS. Social influences on neuroplasticity: stress and interventions to promote well-being. Nat Neurosci. 2012;15:689-95. Yet, structural brain metrics (white matter microstructure assessed with DTI) were investigated in only one autism intervention study.117117. Saaybi S, AlArab N, Hannoun S, Saade M, Tutunji R, Zeeni C, et al. Pre- and post-therapy assessment of clinical outcomes and white matter integrity in autism spectrum disorder: pilot study. Front Neurol. 2019;10:877. Future research using neurophysiological outcome measures should select measures that have been extensively examined in ASD, such as the N170, which have been shown to be reliably associated with the condition and have good test-retest reliability.

Future directions for the selection of outcome measures in autism intervention trials should be in line with autistic individuals’ desires and needs.147147. Leadbitter K, Buckle KL, Ellis C, Dekker M. Autistic self-advocacy and the neurodiversity movement: implications for autism early intervention research and practice. Front Psychol. 2021;12:635690. Along these lines, future trials could prioritize naturalistic assessments as outcome measures, particularly if they quantify autistic-autistic communication and interaction as well as autistic-non-autistic interactions. However, further work is needed to standardize naturalistic assessment protocols and coding systems. Qualitative interviews with autistic participants receiving an intervention and their caregivers should also be conducted to evaluate the acceptability and usefulness of the intervention for autistic people. For trials that include neurophysiological measures, paradigms and quantification procedures need to be standardized, and more ecologically valid situations (such as live social interaction) should be considered.

Some limitations of the current review should be noted. We decided not to include an analysis on risk of bias across studies because we did not focus on results related to the efficacy of different interventions. Therefore, although previous reviews have reported that the quality of autism trials is generally low,3636. Delli CK, Polychronopoulou SA, Kolaitis GA, Antoniou AS. Review of interventions for the management of anxiety symptoms in children with ASD. Neurosci Biobehav Rev. 2018;95:449-63. we did not examine how study quality may have influenced outcome measure selection (or vice versa) in the present review. We also did not analyze the quality and psychometric properties of individual outcome measures, nor the robustness of different naturalistic and neurophysiological methods. Magnetoencephalography (MEG) was not explicitly entered as a search term, though we believe this neurophysiological measure would have been covered by other terms in the search. We did not discuss which neurophysiological and naturalistic outcome measures may be most appropriate for use with different intervention protocols. Instead, we provide a broad overview of the pattern of utilization of these different outcome measures, which could be used to guide future outcome measure selection in terms of choosing measures that have most commonly been used in different studies, or with specific interventions.

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Publication Dates

  • Publication in this collection
    24 June 2022
  • Date of issue
    Sep-Oct 2022

History

  • Received
    03 Sept 2021
  • Accepted
    12 Nov 2021
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