Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium

doi:10.21203/rs.3.rs-2447849/v1

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Research Article

Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium

https://doi.org/10.21203/rs.3.rs-2447849/v1

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published 28 Aug, 2023

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Background

Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these “supra-modal” traits in the autistic population.

Methods

Leveraging a combined sample of 3,868 autistic youth drawn from 12 distinct data sources (ages 3–18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a “general response pattern” factor for each supra-modal construct and determine the added value of “modality-specific response pattern” scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO and SEEK (sub)constructs.

Results

All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses unambiguously supported the validity of a supra-modal HYPER construct (ω_H = .800), whereas a coherent supra-modal HYPO construct was not supported (ω_H = .611), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ω_H = .799; 4/7 modalities). Within each sensory construct, modality-specific subscales demonstrated substantial added value beyond the supra-modal score. Meta-analytic correlations varied by construct, although sensory features tended to correlate most strongly with other domains of core autism features and co-occurring psychiatric symptoms. Certain subconstructs within the HYPO and SEEK domains were also associated with lower adaptive behavior scores.

Limitations:

Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to parent-report of observable behaviors and excluded multisensory items that reflect many “real-world” sensory experiences.

Conclusion

Psychometric issues may limit the degree to which some measures of supra-modal HYPO/SEEK can be interpreted. Depending on the research question at hand, modality-specific response pattern scores may represent a valid alternative method of characterizing sensory reactivity in autism.

autism

integrative data analysis

meta-analysis

sensory features

sensory seeking

hyporeactivity

hyperreactivity

item response theory

sensitivity

responsiveness

measurement

Differences in behavioral responses to sensory inputs from the environment have been associated with autism spectrum disorder (hereafter “autism”) since the first clinical descriptions of the condition (6, 7). Sensory phenotypes are present across multiple modalities (e.g., auditory, visual, tactile) and include differences in sensory reactivity and modulation, multisensory integration, and certain aspects of perception (8–14). With regard to sensory reactivity, these features are frequently parsed into three specific behavioral “response patterns”: hyperreactivity (HYPER; i.e., excessive and/or defensive reactions to stimuli that most individuals find innocuous), hyporeactivity (HYPO; i.e., diminished or absent responses to sensory stimuli that most individuals would respond to), and sensory seeking (SEEK; i.e., unusually strong fascination with or craving of sensory stimulation, often accompanied by repeatedly seeking out specific sensory inputs (15–17)). Notably, these response patterns are not mutually exclusive, and many individuals express behaviors characteristic of multiple sensory response patterns, even within the same modality. Sensory reactivity differences are extremely common in autistic individuals: the point prevalence of a child displaying differences in any of the three response patterns (i.e., HYPER, HYPO, or SEEK in any modality) was recently estimated to be 74% using large-scale population-based data from the Autism and Developmental Disabilities Monitoring Network (18), and 70.9–88.3% of autistic youth in two large samples (from the United States and Australia, respectively) were determined to have sensory reactivity differences of at least “mild” severity (19, 20).

Although sensory reactivity differences are prevalent in many childhood-onset neurodevelopmental and neuropsychiatric conditions (e.g., attention deficit hyperactivity disorder [ADHD], anxiety, obsessive-compulsive disorder, Tourette syndrome, Williams syndrome (21–26)), and all of these clinical groups can be differentiated from neurotypical controls in terms of sensory reactivity differences (see also (27)), a recent meta-analysis suggests that autistic individuals demonstrate higher average levels of HYPER (with findings mixed and inconclusive for HYPO and SEEK) when compared to individuals with other clinical conditions (28). Moreover, many qualitative and quantitative studies have linked specific sensory features of autism to functional impairment, reduced activity participation, and lower quality of life (e.g., 29–39), further emphasizing the importance of research into the sensory aspects of the autism phenotype. However, it is worth noting that not all sensory features of autism are inherently impairing or pathological, and some (particularly within the SEEK domain) are viewed positively by autistic people themselves (40–48).

Although recognition of the sensory features of autism has grown noticeably in recent years (28), relatively little published research in this area has evaluated structural relationships between different domains of sensory reactivity or tested the validity of existing theoretical subdimensions to describe this aspect of the autism phenotype (e.g., 49–54). The majority of studies examining sensory features in autism have utilized caregiver-report questionnaires such as the Sensory Profile (SP (55, 56)), the Sensory Experiences Questionnaire (SEQ (57–59)), and the Sensory Processing–3 Dimensions: Inventory (SP-3D:I (60, 61)), which are most often scored by generating supra-modal (i.e., combining multiple sensory modalities) HYPER, HYPO, and SEEK “response pattern scores” that aggregate items within a single response pattern across all assessed sensory modalities. Although these supra-modal constructs are consistent with the major conceptual models of sensory features (62–64), empirical support for the practice of combining responses to stimuli across multiple sensory modalities into a single “overall response pattern [HYPER, HYPO, or SEEK]” construct (as would be operationalized by a total score on all HYPER items, for instance; see (65)) is relatively limited. When examining the factor structures of existing sensory questionnaires, models that consider supra-modal response pattern factors in isolation tend to be inadequate, demonstrating very poor overall fit to empirical data (e.g., 66). Thus, in order to successfully explain the factor structure of the HYPER, HYPO, and SEEK constructs, previous studies have needed to utilize more complex models that include not only supra-modal response pattern factors but also modality-specific response pattern factors that account for the additional shared (co)variance between items within a given sensory modality (e.g., 49,52,67). As these models represent bifactor structures with variance attributable to both supra-modal and modality-specific constructs (68, 69), summed supra-modal response pattern scores may only be clearly interpretable as measures of HYPER, HYPO, or SEEK if the strength of the modality-general factor is much stronger than the modality-specific factors (69–72).

In contrast to studying HYPER, HYPO, and SEEK at the supra-modal level, a minority of studies (e.g., 25,73–80) have investigated these sensory constructs in a modality-specific manner by calculating response pattern scores that are limited to a single sensory modality (e.g., “Auditory HYPER,” which reflects the sum score of only the HYPER items within the Auditory modality). As psychophysical and neural measures of sensory function (e.g., detection thresholds, psychometric function parameters, evoked potential amplitudes) are frequently limited to a single sensory modality, some researchers theorize that the modality-specific subconstructs represented by these measures will correlate more strongly with sensory reactivity measures that are limited to that same sensory modality rather than collapsed across modalities (e.g., visual evoked potential amplitudes may be expected to correlate moreso with a measure of Visual HYPER than with general HYPER). To our knowledge, studies to date have not formally tested these hypotheses to determine whether or not modality-specific response pattern scores demonstrate any empirical advantages over conceptually broader supra-modal response pattern scores when correlated with psychophysical or neurophysiological measures of sensory function.

Determining the most appropriate “level of analysis” (supra-modal versus modality-specific versus some combination of the two) for these sensory constructs has major implications for other areas of sensory autism research, as this decision will impact whether modality-specific or supra-modal sensory constructs are assessed by diagnostic/phenotyping instruments, targeted by clinical interventions, correlated with other individual differences, explained with neuroscientific or psychological models (e.g., multiple forms of sensory reactivity having a shared underlying mechanism or cause versus separate mechanisms [or even multiple mechanisms] being proposed for each form of sensory reactivity), and even incorporated into the diagnostic criteria for autism. Thus, additional research is needed to more conclusively determine whether sensory reactivity differences in autism are most appropriately studied at the level of a response pattern score (HYPER, HYPO, or SEEK) combined across modalities (e.g., 28,65,81–84), at the level of modality-specific response pattern scores (e.g., 74,76–79,85), or some combination of the two (e.g., interpreting both types of scores; favoring one level of analysis at different points in a study based on the research question or the specific construct(s) being studied).

Purpose

To address this critical gap in research on sensory features in autism, the present study sought to quantitatively investigate the latent structure of caregiver-reported sensory features across a large and heterogeneous group of autistic children. By pooling data from multiple independent research groups and the National Database for Autism Research (NDAR (86)), we compiled a cohort of several thousand autistic children to be analyzed within the methodological framework of integrative data analysis (IDA (87, 88)). The IDA approach has recently gained popularity within autism research, going beyond small sample studies to yield insights about the latent structure of core and associated autism features (54, 89–94), the psychometric properties of widely-used measures (54, 91, 95–100), and the associations between autism features and other related clinical and demographic variables (92, 98, 101–103). Utilizing modern psychometric techniques such as item response theory (IRT (104, 105)) and bifactor modeling (68, 69, 106), the current IDA sought to rigorously evaluate the latent structure of sensory features in autism across multiple measures. Our specific aims were to:

derive psychometrically sound metrics of HYPER, HYPO, and SEEK within modalities;
derive psychometrically sound supra-modal metrics of HYPER, HYPO, and SEEK;
evaluate whether modality-specific HYPER, HYPO, and SEEK metrics (e.g., Auditory HYPER, Tactile HYPO, Visual SEEK) provide added-value to the supra-modal metrics; and
estimate meta-analytic associations between psychometrically derived sensory constructs and other clinical and demographic variables in the autistic population.

Participants

Data used in the current investigation were obtained from nine separate research groups within the Autism Sensory Research Consortium (https://tinyurl.com/ASRCoverview): University of North Carolina (n = 104), Vanderbilt University Medical Center 1 [PI: CJC] (n = 181), University of California San Francisco (n = 35), Syracuse University (n = 55), University of California Los Angeles (n = 67), Thomas Jefferson University (n = 93), University of Reading (n = 37), Kennedy Krieger Institute (n = 47), and Vanderbilt University Medical Center 2 [PI: TGW/MTW] (n = 114). Although no systematic review of the broader literature was undertaken, the included cases represented the full population of individual-participant data (including unpublished data) gathered by researchers within the Autism Sensory Research Consortium that could legally be shared with the first author’s institution. These data were further pooled with (a) all eligible non-overlapping data available in NDAR (n = 741 [15 collections; NDAR study 1160]), (b) data from a large online cohort including participants drawn from the Kennedy Krieger Institute-based Interactive Autism Network (IAN (107)) and various statewide and local autism advocacy groups (referred to as the Sensory Experiences Project [SEP] sample; n = 1285 (19)), and (c) data from individuals recruited from Simons Powering Autism Research for Knowledge (SPARK (108)) research match (project number RM0035_Woynaroski; n = 1107), resulting in a total of 3866 unique participants across all data sources (see Supplemental Table S1 for individual sample demographics). In accordance with IRB approved protocols for each primary study, informed consent was obtained from parents or legal guardians of each participant, and when relevant, assent was obtained from participants as well at the time of data collection. The institutional review board at Vanderbilt University Medical Center approved the secondary analysis of pooled data from these studies.

All participants included in the current study were between the ages of 3 years 0 months and 18 years 0 months and had clinical diagnoses of autism spectrum disorder according to DSM-5 criteria (or equivalent DSM-IV-TR diagnoses). Notably, we chose to restrict our analyses to autistic children as a way of protecting against both non-normal latent trait distributions and differential item functioning according to autism diagnostic status (109, 110). An additional criterion for inclusion in the current study was the accessibility of cross-sectional item-level data from one of the following sensory questionnaires: the Sensory Profile 1 (SP1 (55)), the Short Sensory Profile 1 (SSP1 (111)), the Sensory Profile 2 (SP2 (56)), the Short Sensory Profile 2 (SSP2 (56, 112)), the Sensory Experiences Questionnaire, version 2.1 (SEQ-2.1 (57, 113)), or the Sensory Experiences Questionnaire, version 3.0 (SEQ-3.0 (49, 58)). Although other caregiver questionnaires such as the SP-3D:I were originally considered for inclusion in analyses as well, they were ultimately not included due to a very small number of individuals in our dataset (< 7% of the sample) with usable data on these measures. Broader inclusion/exclusion criteria for participation in contributing studies varied across samples; no participants represented in extant datasets were excluded from the current study due to any additional clinical characteristics (e.g., language level, cognitive skills/IQ), demographic factors (with the exception of chronological age < 3 or > 18), co-occurring medical/psychiatric conditions, or receipt of specific interventions/services.

Constructs And Measures

All participants in the current study had usable data on one or more of the primary study questionnaires, including the SP1/SSP1, SP2/SSP2, SEQ-2.1, or SEQ-3.0, and these measures were used to operationalize the sensory (sub)constructs of interest (see Supplemental Methods for additional details regarding the chosen sensory questionnaires and their use in the autistic population). In the current study, analogous items on the SP1 and SP2 (and their short forms) as well as analogous items on the SEQ-2.1 and SEQ-3.0 were combined into single items for the purpose of cross-dataset analysis. Notably, as the SP1/SSP1 are scored in the opposite direction of the remaining questionnaires, these measures were reverse-scored (i.e., such that scores of “5” represent more frequent behaviors) in order to keep item-level scoring consistent between all items in the study.

In addition to measures of sensory features, we also examined a number of putative demographic and clinical correlates, including age, sex at birth, cognitive ability, adaptive behavior, core autism features, and co-occurring psychiatric symptoms. Cognitive ability was assessed using verbal, nonverbal, and full-scale intelligence quotients (VIQ, NVIQ, and FSIQ, respectively [derived from many instruments]), their developmental quotient (DQ) analogs, and a binary indicator of intellectual disability status (FSIQ < 70 or NVIQ < 70 if no FSIQ available). Adaptive behavior was measured via summary scores (Communication [COM] domain, Daily Living Skills [DLS] domain, Socialization [SOC] domain, and Adaptive Behavior Composite [ABC]) from the Vineland Adaptive Behavior Scales (VABS (114)), including the first, second, and third editions of the measure. Core autism features were assessed using the Social Responsiveness Scale –School Age (SRS (115, 116)) total raw score, as well as the Repetitive Behavior Scale–Revised (RBS-R (117)) repetitive sensory motor (RSM), self-injurious behavior (SIB), and “ritualistic/sameness/compulsive” behavior (RSC) subscales (the latter being the sum of the RBS-R ritualistic/sameness and compulsive subscales due to a high intercorrelation between the two in the current sample; see Supplemental Methods). Co-occurring psychiatric symptoms (based on multiple measures) were summarized using the trait domains of “internalizing symptoms” (INT), “externalizing symptoms” (EXT), and “total psychiatric symptoms” (TOT), as well as features of ADHD (ADHD). See Supplemental Methods for additional information on the measures and scores used in the current study.

Sensory Item Selection

Before statistical analyses were undertaken, items from the four primary sensory questionnaires (SP1/SSP1, SP2/SSP2, SEQ-2.1, SEQ-3.0) were first subjected to a qualitative review by the first author to remove items that were multisensory in nature (e.g., SEQ-3.0 item 95: “How often does your child avoid playing with toys or novel objects that make a lot of noise and light up at the same time?”) or appeared to assess non-sensory behaviors (e.g., SSP1 item 27: “Has difficulty paying attention.”). The remaining items were then sorted into modality × response pattern “sensory subconstructs” (e.g., Auditory HYPO) by the first author, and this sorting process was reviewed iteratively by four additional experts (authors RS, GTB, CJC, and TGW) until a consensus classification was reached. See Fig. 1 and Supplemental Methods for additional information on this process. A full list of items and their theoretical classifications can be found in Supplemental Table S2.

[FIGURE 1 HERE]

Data Analysis

Sensory Subconstruct Refinement and Empirical Item Removal

The first aim of this study was to develop psychometrically sound indicators of each sensory subconstruct (i.e., the combination of modality and response pattern, e.g., Visual HYPO, Auditory HYPER, Tactile SEEK). In order to develop these single-subconstruct scales, we started with all items theoretically classified as belonging to that subconstruct and empirically removed items until the resulting set of items conformed to a unidimensional structure, as defined below. In doing this, we aimed to generate a set of well-differentiated sensory constructs that could then be fit to a bifactor model (with a general factor representing the supra-modal response pattern and specific factors for each modality-specific subconstruct), allowing us to test the hierarchical structure of each sensory response pattern without poorly-related items inflating estimates of general factor saturation.

For the subconstruct refinement portion of the study, we opted to use data only from the subset of autistic children who provided data on both (a) one version of the SP and (b) one version of the SEQ (n = 930). However, as relatively few individuals in this pre-defined group of 930 had completed the SP2 or SSP2 (n = 26), we expanded this exploratory sample to encompass all other children in the full sample who had completed any version of the SP2 (n = 267). This resulted in a final sample of 1197 individuals, which we refer to as the “calibration sample”.

All data analyses were conducted in the R statistical computing environment, version 4.1.0 (118). Subscale item refinement was conducted in the calibration sample using an iterative process based on hierarchical item clustering with the ICLUST algorithm (119, 120), as implemented in the psych R package (121) (see Supplemental Methods for additional details). Once no more items met the criteria for removal, we evaluated the resulting scale for unidimensionality and reliability by fitting it to a graded response model (GRM (122); a type of IRT model) and assessing that model for global fit and composite score reliability (see Supplemental Methods for model specifications and specific psychometric criteria used to judge fit). Poor model fit or low reliability was followed up with examination of local misfit and iterative item removal with the goal of further improving model fit. In cases where a single-subconstruct scale demonstrated inadequate unidimensionality and/or insufficient reliability that could not be corrected by further item removal, that construct was deemed not sufficiently measurable with a subscale, and the subconstruct of interest was then operationalized using the single item from the available pool deemed most global or nonspecific by the first author. This process was repeated for each subconstruct until there was at least one unidimensional scale or single item available to assess all subconstructs of interest for the current investigation.

Structural Evaluation of Supra-modal Sensory Response Pattern Constructs

The second aim of the present study was to better understand the structure of supra-modal sensory “response pattern” constructs (i.e., HYPER, HYPO, SEEK). After we had generated viable indicators (either a unidimensional scale or a single item) for each modality-specific subconstruct within a given response pattern (e.g., Visual, Auditory, Tactile modalities within HYPER), we then sought to examine the higher-order factor structure of these indicators by conducting a scale-level factor analysis of these scores (or in some cases, items) in our full sample of 3866 autistic children (see Supplemental Methods for full model specifications and fit criteria). If these models did not demonstrate adequate fit, we subsequently re-fit the models without the single-item sensory constructs (i.e., only conducting a CFA of the continuous EAP scores). If either unidimensional factor model (i.e., the original model or the re-fit model) fit the covariance structure of the subscales adequately, the response pattern of interest was deemed to have some degree of evidence for a supra-modal factor.

Bifactor Modeling of Sensory Constructs

To further assess the validity of the supra-modal HYPER, HYPO, and SEEK constructs, we additionally examined the item-level latent structure for each response pattern using confirmatory bifactor IRT models. This was done using the same pool of items found to produce the best-fitting subscale-level model (i.e., if single-item constructs were removed from the subscale-level CFA, they were not included in this stage of the structural evaluation). Approximate simple structure (i.e., items loading on expected modality factors) was first confirmed using exploratory graph analysis (EGA (123, 124); see Supplemental Methods for technical details), and once this structure was confirmed, item-level data were fit to a bifactor GRM (125, 126). Model fit was assessed with the same criteria for adequate fit as used for the unidimensional GRMs (see Supplemental Methods).

After confirming the adequate fit of a bifactor GRM, bifactor model-based indices (70, 71, 127) were calculated to determine the appropriateness of interpreting supra-modal response pattern scores. Bifactor indices that were examined included coefficient omega total (ω_T; model-based total score reliability), coefficient omega subscale (ω_S; model-based subscale score reliability), coefficient omega hierarchical (ω_H; proportion of variance in total score accounted for by general factor), coefficient omega hierarchical subscale (ω_HS; proportion of variance in subscale score accounted for by general factor), explained common variance of the general factor (ECV_G), and explained common variance of the specific factor for each subscale (ECV_SS). We defined bifactor structures with ω_H ≥ .80 (70) or the combination of ω_H ≥ .70 and ECV_G ≥ .60 (128) as demonstrating evidence of a strong general factor, and thus, a valid and interpretable higher-order construct (129).

A third aim of the study was to determine whether modality-specific subconstruct scores provide substantial “added value” over the supra-modal response pattern scores in characterizing the sensory features of autism. In the current study, we specifically addressed this question using recently-proposed psychometric decision rules based on combinations of bifactor model-based statistics (127). Modality-specific subconstruct scores (i.e., the set of items representing one subconstruct such as Tactile SEEK) with low reliability (ω_S ≈ .60) were considered to have sufficient added value with ω_HS ≥ .25 or ECV_SS ≥ .45; sensory subconstructs with high reliability (ω_S ≈ .80), were considered to have sufficient added value with ω_HS ≥ .20 or ECV_SS ≥ .30 (127). With these analyses, we could determine whether the HYPER, HYPO, and SEEK constructs should be interpreted (a) only at the supra-modal level (i.e., consideration of HYPER as a unitary construct), (b) only for individual modalities (i.e., consideration of Auditory HYPER and Tactile HYPER as distinct modality-specific subconstructs), or (c) as a combination of the two (analogous to interpretation of FSIQ as well as VIQ/NVIQ on an intelligence test).

Demographic and Clinical Correlates of Sensory Reactivity

Modeling Procedures. The fourth aim of this study was to evaluate correlations between sensory reactivity and demographic and clinical characteristics. Based on the previous bifactor analyses, EAP factor scores were calculated for all interpretable modality-specific subconstructs and supra-modal constructs (130) and then examined using random-effects IDA models (87), which use random effects to account for the heterogeneity of effect sizes among the different study samples. Correlates examined in these models included chronological age, sex (female versus male), cognitive scores (VIQ/VDQ, NVIQ/NVDQ, and FSIQ/FSDQ), intellectual disability status (which was defined as FSIQ < 70 [or NVIQ < 70 in cases where FSIQ was missing], excluding DQ scores), psychiatric symptom scores (internalizing symptoms, externalizing symptoms, total psychiatric symptoms, ADHD features), VABS scores (COM, DLS, SOC, ABC), and core autism features (SRS total score, RBS-R scores [RSM, SIB, and RSC]). Models were not fit for combinations of sensory outcomes and putative correlates with fewer than 100 observed cases. The random-effects IDA models were specified as Bayesian hierarchical linear models with a standardized subconstruct score regressed on the correlate of interest; additional random intercept and slope terms were also added to account for between-study mean differences in the outcome and effect sizes. Weakly-informative priors were placed on all model parameters. These models were fit using the brms R package (131, 132). Supplemental Table S3 contains additional details regarding IDA model and prior specification, including computational specifications.

To summarize the strength of each IDA-based association, standardized effect sizes (Cohen’s d for categorical predictors and the linear correlation r for continuous predictors) were calculated based on the standardized regression slopes, and posterior distributions were summarized using the median and 95% highest-density credible interval (CrI (133)). Effect size posterior distributions were tested against interval null hypotheses that the effects were too small to be practically significant (d = [-0.2, 0.2] and r = [-.1, .1], respectively (134)). It is important to note that such interval null-hypothesis tests have been found to demonstrate substantially lower false-positive rates that traditional frequentist tests of a point-null hypothesis (135), thereby playing the role of a multiple testing correction in the current study (though see also (136, 137) for Bayesian perspectives on multiple testing). These interval null hypotheses were assessed using a Bayesian hypothesis testing procedure based on the region of practical equivalence (ROPE (133)) and the ROPE Bayes factor (BF_ROPE (138, 139)). From the posterior distribution of effect sizes, we calculated the following indices: (a) P_ROPE, the posterior probability that the null hypothesis is true (i.e., the summary effect size is too small to be practically meaningful), (b) log(BF_ROPE), a measure of evidence that the summary effect size falls within versus outside the ROPE. Values of log(BF_ROPE) greater than 1.1 and 2.3 (i.e., log(3) and log(10)), respectively, provide moderate and strong evidence that the true effect size lies outside the ROPE (i.e., evidence that the effect is large enough to be practically meaningful), and log(BF_ROPE) values less than − 1.1 and − 2.3, respectively, provide moderate and strong evidence that the effect size lies within the ROPE (i.e., the effect is practically equivalent to zero). If log(BF_ROPE) value lies between − 1.1 and 1.1, the evidence for or against the interval null hypothesis is deemed inconclusive (140). These Bayesian indices were calculated using the bayestestR R package (141).

From each IDA model, we also calculated several heterogeneity indices, including τ² (the variance of the random slope parameter in standard deviation units), I² (the percentage of variance in the slope parameter due to between-study heterogeneity), and the intraclass correlation coefficient (ICC; the proportion of total variance accounted for by both the random slope and intercept terms). Lastly, we calculated the 95% prediction interval of r or d (142, 143), which includes the range of values likely to be sampled from a new study of similar size to the ones included in the current analyses.

Demographic and clinical characteristics of the sample are displayed in Table 1, and characteristics of each contributing sample can be found in Supplemental Table S1. Children in the combined sample had a mean age of 8.41 (SD = 3.36) years and were predominantly male (79.5%) and White/Caucasian (75.5%; 84.4% of those with non-missing data). The mean full-scale IQ/DQ of children with available data (n = 1028) was 92.1 (SD = 24.5), with FSIQ/DQ scores ranging from 12.0–153.0 (FSIQ: M ± SD [min–max] = 98.98 ± 19.59 [32.0–153.0]; FSDQ: M ± SD [min–max] = 59.08 ± 18.45 [12.0–124.0]).

Table 1

*Participant Demographics and Broader Characteristics for Calibration Sample and Full Sample*
	Calibration Sample	Full Sample
Sample Size	1197	3866
Age (years; M ± SD (n), min–max)	8.36 ± 3.29 (1197), 3.00–17.97	8.41 ± 3.36 (3866), 3.00–17.99
Sex (male / female; n [%])	870 (72.7%) / 326 (27.3%)	3072 (79.5%) / 793 (20.5%)
Ethnicity
Hispanic or Latino (n [%])	53 (4.4%)	284 (7.3%)
Not Hispanic or Latino (n [%])	575 (48.0%)	1957 (50.6%)
Not Reported or Unknown (n [%])	569 (47.5%)	1625 (42.0%)
Race
White (n [%])	783 (65.4%)	2919 (75.5%)
American Indian or Alaska Native (n [%])	7 (0.6%)	41 (1.1%)
Asian (n [%])	33 (2.8%)	106 (2.7%)
Black or African American (n [%])	41 (3.4%)	109 (2.8%)
Native Hawaiian or Other Pacific Islander (n [%])	2 (0.2%)	4 (0.1%)
More than One Race (n [%])	100 (8.4%)	279 (7.2%)
Not Reported or Unknown (n [%])	231 (19.3%)	408 (10.6%)
Sensory Measures Administered
Sensory Profile 1/ Short Sensory Profile 1 (n [%])	905 (75.6%)	1573 (40.0%)
Sensory Profile 2/ Short Sensory Profile 2 (n [%])	293 (24.5%)	293 (7.6%)
Sensory Experiences Questionnaire Version 2.1 (n [%])	420 (35.1%)	433 (11.2%)
Sensory Experiences Questionnaire Version 3.0 (n [%])	510 (42.6%)	2498 (64.6%)
Cognitive Scores
Intelligence Quotient (IQ; n [%])	503 (42.0%)	1042 (27.0%)
Developmental Quotient (DQ; n [%])	125 (10.4%)	247 (6.4%)
Neither (n [%])	569 (47.5%)	2577 (66.7%)
Full-scale IQ/DQ (M ± SD (n), min–max)	91.3 ± 25.8 (414), 12.0-153.0	92.1 ± 24.5 (1028), 12.0-153.0
Verbal IQ/DQ (M ± SD (n), min–max)	83.9 ± 30.6 (451), 8.0-153.0	88.8 ± 27.9 (1038), 8.0-160.0
Nonverbal IQ/DQ (M ± SD (n), min–max)	92.4 ± 25.5 (600), 16.7–148.0	93.0 ± 26.0 (1193), 6.0-160.0
Adaptive Functioning
Vineland Adaptive Behavior Composite (M ± SD (n), min–max)	71.0 ± 16.6 (367), 22.0-121.0	69.7 ± 16.1 (1472), 20.0-126.0
Vineland Communication (M ± SD (n), min–max)	76.6 ± 17.5 (472), 22.0-133.0	73.3 ± 19.6 (1590), 20.0-133.0
Vineland Daily Living Skills (M ± SD (n), min–max)	74.6 ± 19.2 (339), 19.0-125.0	72.3 ± 19.1 (1449), 19.0-138.0
Vineland Socialization (M ± SD (n), min–max)	71.3 ± 15.5 (471), 32.0-127.0	68.8 ± 17.1 (1589), 20.0-127.0
Psychiatric Symptoms
Total Psychiatric Symptoms (T-score; M ± SD (n), min–max)	64.1 ± 11.4 (298), 31.0-111.0	63.6 ± 10.4 (671), 26.0-111.0
Internalizing Symptoms (T-score; M ± SD (n), min–max)	59.3 ± 10.7 (392), 33.0–98.0	59.9 ± 11.9 (765), 0.0-106.0
Externalizing Symptoms (T-score; M ± SD (n), min–max)	57.3 ± 11.5 (393), 32.0-112.0	56.5 ± 12.2 (766), 0.0-112.0
Note. The calibration sample was used to perform the empirically-driven item reduction for each subconstruct scale, whereas the full sample was used to examine correlates of each sensory construct. Cognitive scores (varied standardized measures of both IQ and DQ; see methods section for additional details) and adaptive behavior scores (derived from the Vineland Adaptive Behavior Scales; Sparrow, 2011) are presented on a standard score metric (normative sample M = 100, SD = 15). Psychiatric symptom scores (varied standardized measures; see methods section for additional details) are presented on a T-score metric (normative sample M = 50, SD = 10). Full demographics for each subsample can be found in Supplemental Table S1.

[Table 1 HERE]

Sensory Subconstruct Refinement

Results of the scale refinement process for each subconstruct are presented in Table 2. Within the HYPER domain, items were originally sorted into six modalities (Auditory, Visual, Tactile/Somatosensory, Olfactory, Gustatory, and Vestibular/Proprioceptive [Movement]), each of which produced a unidimensional subconstruct scale that met our a priori requirements for reliability and model fit. Notably, during the scale refinement process, adequately-fitting bifactor structures were also found for both Auditory HYPER (7 items, 1 specific factor; Supplemental Table S4) and Tactile HYPER (13 items, 4 specific factors; Supplemental Table S5), meeting all of our a priori psychometric requirements aside from unidimensionality. For these two sensory subconstructs, unidimensional scales (4 items each) were utilized in the cross-modality structural analysis of HYPER, although subsequent single-modality models were built using the more reliable general factor scores from these bifactor models, as the latter included a broader item pool that was inclusive of more information about the sensory subconstruct of interest.

Table 2

*Results of Scale Refinement Process for Each Single-modality Sensory Subconstruct*
	Initial n_items	Final n_items	Final Model Fit			ρ_xx / ω_T	Scale Retained?
HYPER Constructs	Initial n_items	Final n_items	TLI_C2	RMSEA_C2	SRMR	ρ_xx / ω_T	Scale Retained?
Auditory (Bifactor)	10	7	0.989	0.051	0.046	0.893 / 0.945	YES
Unidimensional Model	10	4	0.998	0.030	0.028	0.875 / 0.898	YES^a
Visual	9	4	0.996	0.046	0.021	0.877 / 0.928	YES
Tactile (Bifactor)	18	13	0.985	0.043	0.047	0.916 / 0.946	YES
Unidimensional Model	18	4	0.978	0.053	0.038	0.718 / 0.713	YES^a
Olfactory	3	3	—	—	0.017	0.751 / 0.840	YES
Gustatory	9	4	1.005	0.000	0.017	0.849 / 0.859	YES
Vestibular/Proprioceptive [Movement]	10	4	1.002	0.000	0.030	0.757 / 0.891	YES
HYPO Constructs
Auditory	5	3	—	—	0.024	0.891 / 0.904	YES (Speech)
Visual	3	3	—	—	0.024	0.656 / 0.631	NO
Tactile/Somatosensory	9	3	—	—	0.022	0.837 / 0.863	YES (Pain/Temperature)
Olfactory	2	2	—	—	—	—	NO
Gustatory	1	1	—	—	—	—	NO
SEEK Constructs
Auditory	3	3	—	—	0.010	0.729 / 0.623	NO
Visual	12	6	1.002	0.000	0.027	0.834 / 0.859	YES
Tactile	10	4	1.011	0.000	0.039	0.746 / 0.727	YES
Olfactory	5	3	—	—	0.045	—	NO
Gustatory	5	3	—	—	0.044	—	NO
Oral Tactile	5	4	0.985	0.088	0.026	0.848 / 0.938	YES
Vestibular/Proprioceptive [Movement]	10	4	0.993	0.042	0.029	0.831 / 0.818	YES
Note. HYPER = hyperreactivity; HYPO = hyporeactivity; SEEK = sensory seeking. Bolded values indicate instances wherein a scale did not meet a priori-specified criteria for psychometric adequacy (i.e., TLI_C2 > 0.97, RMSEA_C2 < 0.089, SRMR < 0.05 [or < 0.033 for 3-item composites], ρ_xx > 0.7, and ω_T > 0.7). n_items = number of items on the scale; TLI_C2 = C₂-statistic based Tucker-Lewis index; TLI_C2 = C₂-statistic based root mean square error of approximation; SRMR = limited-information standardized root-mean-square residual; ρ_xx = marginal reliability; ω_T = omega total (composite score reliability).
^a As a bifactor model of these constructs demonstrated adequate fit and stronger reliability, unidimensional Auditory and Tactile HYPER scores were retained for use in bifactor HYPER model only.

Within the HYPO domain, items were originally sorted into five modalities (Auditory, Visual, Tactile/Somatosensory, Olfactory, and Gustatory), although two of these modalities (Olfactory and Gustatory) ultimately contained fewer than the minimum of three items needed for a subconstruct scale. Thus, these two subconstructs were operationalized with single items rather than multi-item composites (Olfactory: SEQ-3.0 item 69: “How often does your child seem to be unaware of strong or unpleasant smells that most other people notice?”; Gustatory: SEQ-3.0 item 74: “How often does your child have trouble distinguishing between different types of tastes or flavors?”). From the Auditory HYPO items, a three-item composite met our criteria for an adequate subconstruct scale; however, after examining the content of the items (Supplemental Table S2), it was noted that these three items measured hyporeactivity to speech in particular, rather than auditory stimuli more generally. Thus, to ensure that reactivity to both speech and nonspeech auditory stimuli were both captured in the analysis of HYPO constructs, an additional single item (SEQ-2.1 item 4/SEQ-3.0 item 4: “How often does your child ignore or tune-out loud noises?”) was included to capture non-speech Auditory HYPO. Similarly, although a reliable three-item scale could be derived from the Tactile HYPO items, this scale only contained items assessing hyporeactivity to pain and temperature. Thus, an additional single item (SP1 item 46/SP2 item 26: “Doesn't seem to notice when face or hands are messy”) was chosen as the single indicator for Tactile HYPO unrelated to pain or temperature. Lastly, although the Visual HYPO scale contained three items and fit a unidimensional model adequately (SRMR = .024), the scale demonstrated subpar reliability (ω = .656, ρ_xx = .631) and, thus, was not retained. We therefore used a single item (SEQ-2.1 item 10/SEQ-3.0 items 22/23: “Is your child slow to notice new objects or toys in the room, or slow to look at objects that are placed or held near him/her?”) to capture Visual HYPO in our structural analyses.

Within the SEEK domain, items were originally sorted into seven modalities (Auditory, Visual, Tactile/Somatosensory, Olfactory, Gustatory, Oral Tactile, and Vestibular/Proprioceptive [Movement]). Notably, based on discussions during the item sorting, the SEEK response pattern encompassed the additional modality of Oral Tactile, which contained items from the SP/SEQ “Taste/Smell” sections that specifically describe a child mouthing or licking nonfood objects without necessarily seeking out the taste of those objects. Of the SEEK subconstructs, unidimensional scales were derived from four (Visual, Tactile, Oral Tactile, and Vestibular/Proprioceptive [Movement]). A three-item Auditory SEEK scale was found to demonstrate adequate fit (SRMR = .010) but subpar reliability (ω = .623, ρ_xx = .729); thus, it was replaced with a single-item indicator of Auditory SEEK (SEQ-2.1 item 36a/SEQ-3.0 item 7: “How often does your child seem fascinated with sounds?”). Additionally, three-item Olfactory and Gustatory SEEK scales demonstrated inadequate model fit (Olfactory: SRMR = .045; Gustatory: SRMR = .044); therefore, these constructs were replaced with single items as well (Olfactory: SEQ-2.1 item 36c/SEQ-3.0 item 67: “How often does your child seem fascinated with particular smells?”; Gustatory: SEQ-3.0 item 64: “How often does your child crave foods with a strong taste or flavor (such as spicy, sour, or bitter foods)?”).

[Table 2 HERE]

Structural Evaluation Of Supra-modal Sensory Constructs

HYPER

To examine the higher-order structure of HYPER constructs, expected a posteriori (EAP) factor scores were derived from unidimensional models for each modality (i.e., Auditory, Visual, Tactile/Somatosensory, Olfactory, Gustatory, and Vestibular/Proprioceptive) and fit to a unidimensional CFA model using MLR estimation. This CFA model fit the data adequately (χ²(9) = 75.3, CFI = .974, RMSEA = .044, SRMR = .024), and all single-modality factor scores loaded with moderate to moderately large magnitudes onto the higher-order HYPER factor (λ = 0.445–0.638). The item-level latent structure of the 23 HYPER items was then examined using EGA, with the community structure recreating the six hypothesized modality-specific factors. A bifactor GRM with specific factors for each modality demonstrated largely adequate global fit (C₂(228) = 380.9, TLI_C2 = 0.985, RMSEA_C2 = .036, SRMR = .053); thus, the bifactor coefficients from this model were interpreted to determine the strength of the general HYPER factor.

Factor loadings and bifactor coefficients from the HYPER model are presented in Table 3 and Fig. 2A. Coefficient omega total indicated that the overall HYPER sum score was highly reliable (ω_T = .988), and coefficient omega hierarchical indicated that the general factor saturation was sufficient to justify interpretation of the total score (ω_H = .800). However, despite the adequate ω_H value, the general factor explained a relatively low proportion of common variance (ECV_G = .436, i.e., 43.6%), suggesting that modality-specific subconstruct factors were responsible for a slight majority (56.4%) of reliable common variance in HYPER items (see also ECV_G and ECV_SS values in Table 3 for the proportions of common variance in each subconstruct accounted for by general and specific factors, respectively). Moreover, based on the guidelines of Dueber & Toland (2021), five of six modality-specific HYPER subconstructs (all except Tactile/Somatosensory) demonstrated added value beyond that provided by the total HYPER score (i.e., ω_S > .80 and ECV_SS ≥ .30 Table 3). Thus, we concluded that both the HYPER total score and subconstruct scores were interpretable. EAP scores for both the HYPER general factor (calculated from the bifactor GRM) and modality-specific HYPER subconstruct factors (calculated from unidimensional GRMs or from bifactor GRMs in the case of Auditory and Tactile HYPER) were, therefore, examined in relation to demographic and clinical correlates.

Table 3

*Factor Loadings and Bifactor Coefficients for Hyperreactivity (HYPER) Items*
Item	G-HYPER	Auditory	Visual	Tactile	Gustatory	Olfactory	Movement	h²	I-ECV
SP1 Q1/SP2 Q1	0.513	0.699	—	—	—	—	—	0.752	0.350
SP1 Q2/SP2 Q2	0.469	0.683	—	—	—	—	—	0.687	0.320
SEQ2 Q1/SEQ3 Q1	0.469	0.753	—	—	—	—	—	0.786	0.279
SEQ3 Q9	0.611	0.475	—	—	—	—	—	0.599	0.622
SEQ2 Q8/SEQ3 Q15	0.571	—	0.594	—	—	—	—	0.679	0.480
SP1 Q10/SP2 Q15	0.563	—	0.677	—	—	—	—	0.775	0.409
SP1 Q14/SP2 Q13	0.649	—	0.601	—	—	—	—	0.783	0.538
SP1 Q15	0.565	—	0.599	—	—	—	—	0.677	0.471
SP1 Q30/SP2 Q16/ SEQ2 Q15/SEQ3 Q49	0.449	—	—	0.449	—	—	—	0.404	0.500
SP1 Q36/SP2 Q18	0.608	—	—	0.261	—	—	—	0.438	0.844
SEQ2 Q16/SEQ3 Q38	0.564	—	—	0.100	—	—	—	0.328	0.970
SP1 Q33	0.650	—	—	0.324	—	—	—	0.527	0.801
SEQ2 Q22/SEQ3 Q59	0.451	—	—	—	0.695	—	—	0.686	0.297
SP1 Q55/SP2 Q44	0.465	—	—	—	0.786	—	—	0.834	0.259
SP1 Q56/SP2 Q45	0.454	—	—	—	0.721	—	—	0.727	0.284
SEQ3 Q70	0.482	—	—	—	0.393	—	—	0.387	0.600
SEQ3 Q61	0.619	—	—	—	—	0.685	—	0.851	0.450
SEQ3 Q66	0.543	—	—	—	—	0.713	—	0.803	0.367
SEQ3 Q73	0.547	—	—	—	—	0.413	—	0.469	0.637
SP1 Q18	0.482	—	—	—	—	—	0.701	0.724	0.321
SP1 Q19	0.462	—	—	—	—	—	0.680	0.677	0.316
SP1 Q20	0.408	—	—	—	—	—	0.637	0.572	0.291
SEQ3 Q83	0.464	—	—	—	—	—	0.621	0.600	0.359
Bifactor Coefficients
ω_T/ω_S	0.986	0.911	0.938	0.743	0.908	0.851	0.906
ω_H/ω_HS	0.800	0.552	0.470	0.142	0.596	0.443	0.601
ECV_G	0.436	0.381	0.475	0.773	0.326	0.460	0.322
ECV_SS	—	0.619	0.525	0.227	0.674	0.540	0.678
Note. Factor loadings are derived from full-information maximum likelihood confirmatory bifactor analysis, equivalent to a bifactor graded response model. SP = Sensory Profile; SEQ = Sensory Experiences Questionnaire; h² = communality; I-ECV = item explained common variance; G-HYPER = General HYPER domain factor; ω_T = Coefficient omega total (total score reliability); ω_S = Coefficient omega subscale (subscale reliability); ω_H = Coefficient omega hierarchical (general factor saturation; ≥ 0.8 indicates strong general factor); ω_HS = Coefficient omega hierarchical subscale (specific factor saturation; ≥ 0.20/0.25 support the added value of a specific factor under conditions of high/low reliability, respectively); ECV_G = General factor explained common variance; ECV_SS = Specific factor explained common variance for a subscale (≥ 0.30/0.45 support the added value of a specific factor under conditions of high/low reliability, respectively).

[Table 3 and FIGURE 2 HERE]

HYPO

To examine the higher-order structure of the HYPO constructs, we used WLSMV estimation to fit a CFA model to the EAP scores for the two multi-item HYPO constructs (Auditory-Speech and Tactile-Pain/Temperature), as well as five single-item HYPO indicators for each sensory modality (i.e., Auditory [non-speech], Visual, Tactile, Olfactory, and Gustatory). The CFA model fit the data adequately (χ²(14) = 87.7, CFI = .960, RMSEA = .037, SRMR = .038), and all standardized factor loadings were moderate in magnitude (λ = 0.416–0.516). The item-level latent structure of the 11 HYPO items was then examined using EGA, with the Walktrap algorithm indicating a two-factor solution with hyporeactivity to speech in one community and all other items in the second community. Although the hyporeactivity to pain/temperature items did not form their own community, this structure nevertheless provided support for the separation of HYPO pain/temperature and HYPO speech subconstructs. The 11 items were then fit to a bifactor GRM with one general factor and two specific factors (Speech HYPO and Pain/temperature HYPO); all subconstructs represented by single items were specified to load only onto the general factor.

Factor loadings and bifactor coefficients for the HYPO model are presented in Table 4 and Fig. 2B. This model demonstrated adequate fit on all indices other than SRMR (C₂(38) = 58.6, TLI_C2 = 0.987, RMSEA_C2 = .033, SRMR = .065) and high total score reliability (ω_T = .905). However, additional bifactor coefficients did not support the interpretation of the general HYPO factor, which fell below both a priori guidelines for general factor saturation (ω_H = .611) and proportion of explained common variance (ECV_G = .413; see also ECV_G and ECV_SS values in Table 4 for the proportions of common variance in each subscale accounted for by general and specific factors, respectively). Moreover, both the Speech HYPO (ω_S = .929, ω_HS = 0.710, ECV_SS = .781) and Pain/temperature HYPO (ω_S = .845, ω_HS = .579, ECV_SS = .715) subconstructs demonstrated large proportions of specific-factor variance, indicating substantial added value over a general HYPO score. Thus, as HYPO subconstruct scores but not the supra-modal HYPO score met our guidelines for interpretability, only the Speech HYPO and Pain/temperature HYPO EAP scores (calculated from unidimensional GRMs) were examined in our analysis of clinical/demographic correlates.

Table 4

*Factor Loadings and Bifactor Coefficients for Hyporeactivity (HYPO) Items*
Item	G-HYPO	Speech	Pain/Temp	h²	I-ECV
SEQ2 Q3/SEQ3 Q8	0.446	0.725	—	0.724	0.275
SP1 Q6/SP2 Q6	0.344	0.726	—	0.646	0.183
SP1 Q7/SP2 Q7	0.416	0.848	—	0.892	0.194
SEQ2 Q19/SEQ3 Q53	0.413	—	0.690	0.647	0.264
SP1 Q42/SP2 Q23/SP2 Q24	0.441	—	0.870	0.951	0.205
SEQ3 Q56	0.447	—	0.478	0.428	0.466
SEQ2 Q10/SEQ3 Q22/SEQ3 Q23 [Visual]	0.541	—	—	0.292	1.000
SP1 Q46/SP2 Q26 [Tactile]	0.470	—	—	0.221	1.000
SEQ2 Q4/SEQ3 Q4 [Auditory]	0.554	—	—	0.307	1.000
SEQ3 Q69 [Olfactory]	0.445	—	—	0.198	1.000
SEQ3 Q74 [Gustatory]	0.448	—	—	0.201	1.000
Bifactor Coefficients
ω_Τ/ω_S	0.905	0.929	0.845
ω_Η/ω_ΗS	0.611	0.710	0.579
ECV_G	0.413	0.219	0.285
ECV_SS	—	0.781	0.715
Note. Factor loadings are derived from full-information maximum likelihood confirmatory bifactor analysis, equivalent to a bifactor graded response model. SP = Sensory Profile; SEQ = Sensory Experiences Questionnaire; h² = communality; I-ECV = item explained common variance; G-HYPO = General HYPO domain factor; ω_T = Coefficient omega total (total score reliability); ω_S = Coefficient omega subscale (subscale reliability); ω_H = Coefficient omega hierarchical (general factor saturation; ≥ 0.8 indicates strong general factor); ω_HS = Coefficient omega hierarchical subscale (specific factor saturation; ≥ 0.20/0.25 support the added value of a specific factor under conditions of high/low reliability, respectively); ECV_G = General factor explained common variance; ECV_SS = Specific factor explained common variance for a subscale (≥ 0.30/0.45 support the added value of a specific factor under conditions of high/low reliability, respectively).

[Table 4 HERE]

SEEK

To examine the higher-order structure of the SEEK constructs, we fit a CFA model to the Visual, Tactile, Oral Tactile, and Vestibular/Proprioceptive [Movement] EAP scores, as well as three single-item SEEK indicators for each additional sensory modality (i.e., Auditory, Olfactory, and Gustatory). The initial CFA model (WLSMV estimation) fit the data poorly (χ²(14) = 361.9, CFI = .896, RMSEA = .080, SRMR = .057); therefore, this model was not interpreted. We then fit a model that excluded the three single-item SEEK indicators, only examining the four EAP subconstruct factor scores. This CFA model (MLR estimation) demonstrated good fit to the data (χ²(2) = 13.7, CFI = .994, RMSEA = .039, SRMR = .013), and all factor loadings were moderate to moderately large in magnitude (λ = 0.439–0.685). The 18 SEEK items (excluding all single-item indicators) were then examined using EGA, with the resulting community structure recreating the hypothesized modality-specific subconstructs. These items were then fit to a bifactor GRM with one general factor and four modality-based specific factors.

Factor loadings and bifactor coefficients for the SEEK model are presented in Table 5 and Fig. 2C. The model demonstrated adequate fit for all indices other than SRMR (C₂(117) = 232.9, TLI_C2 = 0.982, RMSEA_C2 = .044, SRMR = .058), and total score reliability was very high (ω_T = .969). Notably, the general factor saturation of this model was just below the a priori threshold for interpretability (ω_H = .799), and while the explained common variance was still relatively low (ECV_G = .531; see also ECV_G and ECV_SS values in Table 5 for the proportions of common variance in each subscale accounted for by general and specific factors, respectively), this suggested that a composite SEEK score could potentially be interpretable. However, as we excluded three modality-specific subconstructs that fit poorly in the higher-order SEEK CFA, the omega hierarchical estimate from the 18-item bifactor structure likely overestimated the true general factor saturation of a scale consisting of all seven modalities (i.e., the construct captured by the supra-modal score on the SP or SEQ SEEK composite). Thus, as this overestimate did not exceed the a priori-specified threshold for general factor interpretability, we chose to not interpret the SEEK general factor score. Supporting our decision to interpret SEEK at the single-modality level only, additional bifactor indices suggested that all four modality-specific SEEK subscale scores demonstrated added value over the total score (Visual: ω_S = .934, ω_HS = .686, ECV_SS = .747; Tactile: ω_S = .751, ω_HS = .255, ECV_SS = .380; Oral: ω_S = .840, ω_HS = .241, ECV_SS = .337; Movement: ω_S = .876, ω_HS = .287, ECV_SS = .344). Thus, only the EAP scores for the four SEEK subconstructs (calculated from unidimensional GRMs) were examined in our analysis of clinical/demographic correlates.

Table 5

*Factor Loadings and Bifactor Coefficients for Sensory Seeking (SEEK) Items (Excluding Single-item Indicators)*
Item	G-SEEK	Visual	Tactile	Oral Tactile	Movement	h²	I-ECV
SEQ2 Q9/SEQ3 Q17	0.691	0.409	—	—	—	0.645	0.740
SP1 Q97/SP2 Q80/SP2 Q81	0.593	0.296	—	—	—	0.439	0.801
SEQ3 Q19	0.656	0.310	—	—	—	0.527	0.817
SEQ3 Q27	0.552	0.558	—	—	—	0.616	0.494
SEQ3 Q29	0.526	0.487	—	—	—	0.513	0.538
SEQ3 Q30	0.572	0.486	—	—	—	0.563	0.581
SP1 Q45/SP1 Q45/SP2 Q21/SP2 Q25	0.598	—	0.364	—	—	0.490	0.729
SEQ2 Q36f/SEQ3 Q45	0.551	—	0.688	—	—	0.777	0.390
SEQ3 Q37	0.436	—	0.413	—	—	0.360	0.527
SEQ3 Q50	0.672	—	0.131	—	—	0.469	0.963
SEQ2 Q25/SEQ3 Q62	0.441	—	—	0.796	—	0.827	0.235
SP1 Q64	0.433	—	—	0.856	—	0.920	0.203
SP1 Q65	0.421	—	—	0.772	—	0.774	0.229
SEQ3 Q71	0.525	—	—	0.706	—	0.774	0.356
SP1 Q24/SP1 Q25/SP2 Q27	0.611	—	—	—	0.483	0.606	0.615
SEQ2 Q27/SEQ3 Q76	0.624	—	—	—	0.358	0.518	0.752
SP1 Q84/SP2 Q60	0.599	—	—	—	0.160	0.385	0.933
SP1 Q26	0.683	—	—	—	0.651	0.890	0.524
Bifactor Coefficients
ω_Τ/ω_S	0.969	0.876	0.751	0.934	0.840
ω_Η/ω_ΗS	0.799	0.287	0.255	0.686	0.241
ECV_G	0.531	0.656	0.620	0.253	0.663
ECV_SS	—	0.344	0.380	0.747	0.337
Note. Factor loadings are derived from full-information maximum likelihood confirmatory bifactor analysis, equivalent to a bifactor graded response model. SP = Sensory Profile; SEQ = Sensory Experiences Questionnaire; h² = communality; I-ECV = item explained common variance; G-SEEK = General SEEK domain factor; ω_T = Coefficient omega total (total score reliability); ω_S = Coefficient omega subscale (subscale reliability); ω_H = Coefficient omega hierarchical (general factor saturation; ≥ 0.8 indicates strong general factor); ω_HS = Coefficient omega hierarchical subscale (specific factor saturation; ≥ 0.20/0.25 support the added value of a specific factor under conditions of high/low reliability, respectively); ECV_G = General factor explained common variance; ECV_SS = Specific factor explained common variance for a subscale (≥ 0.30/0.45 support the added value of a specific factor under conditions of high/low reliability, respectively).

[Table 5 HERE]

Demographic And Clinical Correlates

Modeling

Figure 3 displays a summary of all bivariate IDA model-based effect sizes estimating relations between EAP sensory scores and identified clinical and demographics correlates (i.e., r for continuous variables and d for binary variables). Additional model-based statistics, including effect size credible intervals, posterior probabilities of the interval null hypothesis (P_ROPE), log(BF_ROPE) values, predictive intervals, and heterogeneity estimates are presented in Supplemental Tables S6-S7. Of 240 correlations examined (with interval null hypothesis tests used to control the false-discovery rate; ,135), 47 (19.6%) demonstrated strong evidence for a practically significant effect (log(BF_ROPE) > 2.3), 30 (12.5%) demonstrated moderate evidence for a practically significant effect (1.1 < log(BF_ROPE) < 2.3), 70 (29.2%) demonstrated strong evidence for a trivially small effect (log(BF_ROPE) < -2.3), and 27 (11.2%) demonstrated moderate evidence for a trivially small effect (-2.3 < log(BF_ROPE) < -1.1). The remaining 66 correlations (27.5%) provided inconclusive evidence for or against the presence of a practically significant effect (-1.1 < log(BF_ROPE) < 1.1). Additionally, of the 26 examined Cohen’s d values, two (7.7%) demonstrated moderate evidence for a practically significant effect (1.1 < log(BF_ROPE < 2.3), 17 (65.4%) demonstrated strong evidence for a trivially small effect (log(BF_ROPE) < -2.3), four (15.4%) demonstrated moderate evidence for a trivially small effect (-2.3 < log(BF_ROPE) < -1.1), and three (11.5%) were inconclusive (-1.1 < log(BF_ROPE) < 1.1). Heterogeneity varied greatly between models (ICC range 0.012–0.823), with random effects typically accounting for approximately 10–20% of total score variance (Supplemental Tables S6-S7).

[FIGURE 3 HERE]

Correlations with Demographic Variables

Caregiver-reported sensory reactivity demonstrated relatively few significant correlations with demographic variables (i.e., age, sex), and almost all log(BF_ROPE) values for these models moderately or strongly favored the null hypothesis of a trivially small effect. However, younger age was significantly associated with higher Speech HYPO (r = − .151, CrI_95% [-.214, − .094]), as well as a higher degree of SEEK in the Tactile (r = − .152, CrI_95% [-.205, − .099]), Oral (r = − .172, CrI_95% [-.250, − .107]), and Vestibular/Proprioceptive [Movement] (r = − .231, CrI_95% [-.323, − .133]) modalities, though these effects were small in magnitude.

Correlations with Cognition and Adaptive Functioning

No sensory variable demonstrated practically significant associations with VIQ, NVIQ, or FSIQ when measured continuously. However, moderate group differences were found between individuals with and without intellectual disability for both Pain/Temperature HYPO (d = 0.521, CrI_95% [0.083, 0.959]) and Visual SEEK (d = 0.527, CrI_95% [0.164, 0.875]) such that individuals with intellectual disability were reported to have higher scores on both constructs. Adaptive skills, as measured by multiple VABS subscales, demonstrated small yet practically significant negative associations with Pain/Temperature HYPO (VABS COM: r = − .220, CrI_95% [-.344, − .089]; VABS DLS: r = − .195, CrI_95% [-.323, − .076]; VABS SOC: r = − .210, CrI_95% [-.294, − .123]; VABS ABC: r = − .234, CrI_95% [-.342, − .115]), Speech HYPO (VABS SOC: r = − .195, CrI_95% [-.318, − .079]), Tactile SEEK (VABS COM: r = − .185, CrI_95% [-.293, − .078]), Oral SEEK (VABS DLS: r = − .197, CrI_95% [-.306, − .075]), and Movement SEEK (VABS SOC: r = − .160, CrI_95% [-.227, − .097]; VABS ABC: r = − .155, CrI_95% [-.220, − .088]).

Correlations with Core Autism Features and Psychiatric Symptoms

Although not associated with cognitive or adaptive behavior scores to a practically meaningful degree, most subconstructs in the HYPER response pattern displayed small to moderate associations across domains of core autism features (SRS, RBS-R RSM/SIB/RSC), ADHD symptoms, and broad measures of caregiver-reported psychiatric symptoms; these relations ranged from small to large in magnitude (Fig. 3; Supplemental Table S6). Speech HYPO, Tactile SEEK, and Movement SEEK also demonstrated significant relations with the majority of caregiver-reported symptom domains. Other SEEK domains demonstrated somewhat more selective patterns of correlations, with Visual SEEK correlating significantly with core autism features (SRS, RBS-R RSM), and Oral Tactile SEEK correlating significantly with ADHD features and total psychiatric symptoms. Notably, the majority of non-significant correlations between sensory reactivity and core autism features or psychiatric symptoms were inconclusive rather than trivially small, and thus it is likely the case that many of the true population correlations do not fall within r = [-.1, .1].

Despite the recent elevation of sensory reactivity differences to the status of a diagnostic criterion for autism (15, 16), there has been relatively little empirical work examining the underlying latent structure of these core sensory features within the autistic population (see also 49). By analyzing caregiver-reported sensory reactivity differences in a heterogeneous cross-sectional sample of nearly 4,000 autistic children, the current study sought to investigate the hierarchical structure of sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and seeking (SEEK) across the full spectrum of children captured under the label of autism spectrum disorder. Utilizing modern psychometric techniques, we developed structural models of HYPER, HYPO, and SEEK in individual sensory modalities, subsequently testing whether each construct is most appropriately studied at the level of a single supra-modal sensory response pattern (e.g., an overall SEEK score) or separately for each modality within the sensory response patterns (e.g., separate scores for Visual SEEK, Auditory SEEK, and Tactile SEEK). Of the three sensory response patterns included within current autism diagnostic criteria (15, 16), only HYPER demonstrated unambiguous evidence of an interpretable supra-modal construct, whereas supra-modal HYPO scores as currently operationalized were found to have limited construct validity. The evidence for supra-modal SEEK scores was more ambiguous, as we were unable to generate an adequately-fitting higher-order model of SEEK that included all relevant sensory modalities, but once modalities measured with only single items (i.e., Auditory, Olfactory, and Gustatory) were removed, the model containing the four remaining modalities (Visual, Tactile, Oral Tactile, and Movement) demonstrated an adequately fitting higher-order structure and borderline acceptable general factor saturation. Although our findings did not conclusively support the construct validity of a SEEK composite that includes all seven standard modalities (i.e., those operationalized by the SP or SEQ SEEK response pattern scores), the more limited “General SEEK'' construct described here (consisting of only Visual, Tactile, Oral Tactile, and Movement items) may be a useful supra-modal aspect of the sensory autism phenotype if replicated in future studies. Additionally, irrespective of the construct validity of supra-modal scores, nearly all modality-specific sensory subconstructs demonstrated added value over and above their respective response pattern scores, indicating that modality-specific HYPER, HYPO, and SEEK scores are able explain additional individual differences in sensory reactivity to a greater degree than a single supra-modal HYPER, HYPO or SEEK score. These findings have implications for researchers interested in characterizing, explaining, or intervening on sensory reactivity in autistic individuals, as they suggest that some of the supra-modal response pattern scores used in these areas may have previously unrealized psychometric limitations. HYPER, HYPO or SEEK scores that are limited to one modality (i.e., single-modality subconstruct scores) could potentially prove advantageous in some contexts, although additional research is necessary to determine the extent to which these measures demonstrate incremental clinical or practical utility over established supra-modal HYPER, HYPO, or SEEK scores.

The current study also provided a wealth of information about the measurement of sensory reactivity in autistic youth based on caregiver-report questionnaires. Using the most frequently employed caregiver-report sensory measures in the autism literature (the SP and SEQ), we attempted to generate unidimensional scales to operationalize each combination of modality × response pattern as its own unique sensory subconstruct. However, based on a priori psychometric criteria, we were unable to generate acceptable unidimensional scales for three of five HYPO modalities (Visual, Olfactory, Gustatory) and three of seven SEEK modalities (Auditory, Olfactory, Gustatory), necessitating the use of single-item indicators of these subconstructs in later structural analyses. Moreover, within the HYPO domain, the two subconstructs that did produce sufficiently reliable scales reflected fairly specific subsets of the total item pool (e.g., hyporeactivity to pain and temperature rather than all somatosensory stimuli), suggesting that they did not fully operationalize the “Auditory HYPO” and “Tactile HYPO” constructs that we had originally intended to measure. Thus, despite broadband sensory reactivity measures such as the SP, SEQ, and SP-3D:I typically including HYPER, HYPO, and SEEK items for each sensory modality, a sizable minority of “modality × response pattern” subconstructs demonstrated inadequate construct validity within this large autistic sample. In the current study, is unclear whether this finding stems from instrument-specific measurement issues (i.e., inadequate construct coverage within the specific questionnaires from which items were drawn) as opposed to more general issues regarding the theoretical definition of the construct or its ability to be reliably operationalized as a set of observer-reported questionnaire items. As both sets of issues are likely to contribute in different cases, we suggest some sensory subconstructs (particularly within the HYPO and SEEK domains) are (a) underrepresented in existing questionnaires (i.e., more questions are needed), (b) poorly defined (e.g., Visual HYPO items have unclear relations with specific aspects of visual perception), (c) difficult for caregivers to report on reliably (e.g., few indicators of Gustatory SEEK are present in most children, limiting the pool of potential items available to capture this construct), and/or (d) of potentially limited theoretical relevance when predicting clinical outcomes (e.g., Olfactory HYPO may be unlikely to substantially influence the expression of other core features of autism). Future work should attempt to evaluate which, if any, of these poorly operationalized sensory subconstructs are relevant to autism research and clinical practice and if so, how they can be reliably measured.

As the majority of work examining sensory constructs in both autism and other clinical populations has utilized supra-modal scores from the SP, SEQ, SP-3D:I, or similar measures (e.g., 27,28,81,144,145), our findings signal the need for sensory autism research to broaden the ways in which sensory reactivity differences are characterized, potentially shifting away from the field’s nearly exclusive reliance upon supra-modal HYPER, HYPO, and SEEK scores for this purpose. Single-modality measures of HYPER, HYPO, and/or SEEK represent a viable alternative method of assessing these constructs and may be particularly useful when substantive research hypotheses include associations with other sensory constructs in a single modality (e.g., tactile detection thresholds, visual evoked potentials). Moreover, there is a great need to develop more comprehensive measures of modality-specific HYPER, HYPO, and SEEK subconstructs, either by expanding upon the item banks employed in the current study, adapting questionnaires used in other fields (e.g., 146), or developing and validating entirely novel measures (e.g., 85). By more densely sampling each subconstruct of interest, these measures would ostensibly increase the reliability, validity, conceptual breadth, and clinical utility of modality-specific response pattern scales compared to the short and relatively general item pools currently included in longer broadband sensory measures. Importantly, we are not suggesting that researchers entirely abandon the study of supra-modal sensory constructs—particularly HYPER, for which we have found some empirical support for the supra-modal response pattern—as there is certainly value in the investigation of these higher-order constructs as well. Rather, in future studies where both supra-modal and modality-specific subconstruct scores could feasibly be interpreted, we strongly recommend that researchers use contextual factors to determine which “level of analysis” is most appropriate or informative to answer the substantive research question(s) at hand.

For researchers who do choose to characterize sensory reactivity at the single-modality level going forward, it is notable that the “level of analysis” chosen to study a problem will likely frame the ways in which sensory features of autism are conceptualized and studied more broadly as an aspect of autism’s heterogeneity (e.g., 147). In particular, when developing clinical interventions for sensory reactivity in autism, a focus on modality-specific sensory subconstruct outcomes may motivate clinicians and researchers to investigate the efficacy of intervention modalities that are more focused on specific subconstructs rather than sensory reactivity in general (e.g., the use of sound generators to treat hyperacusis, a specific type of Auditory HYPER (148)). Personalized interventions that seek to assess an autistic child’s specific pattern of sensory reactivity differences and ameliorate challenges associated with each domain could also be assessed within this framework, using modality-specific assessments of each sensory response pattern to monitor the effectiveness of each putative “active ingredient” of the intervention. A shift in measurement practices will also allow researchers to associate these single-modality behavioral subconstructs with psychophysical and/or neurophysiological measures within a given modality (e.g., 74,75), informing theories of the neurocognitive underpinnings of certain types of sensory reactivity in autism (e.g., 39,149,150). Though we do not claim a single-modality perspective to be advantageous in all cases or for all research questions (particularly for those focused on “real-world” multisensory contexts), we believe that a greater diversity of theoretical approaches and frameworks within sensory autism research is needed to make optimal progress towards improving the lives of autistic people within this line of work.

In addition to our examination of the latent structure of sensory reactivity and assessment of evidence to support each “level of analysis,” we also employed random-effects integrative data analysis to estimate the meta-analytic associations between all sufficiently interpretable sensory subconstructs (i.e., General HYPER, six HYPER subconstructs, two HYPO subconstructs, and four SEEK subconstructs) and relevant demographic and clinical correlates (i.e., age, sex, cognitive abilities, adaptive functioning, core autism features, and co-occurring psychiatric symptoms). In general, the majority of associations were modest in size (with a few exceptions, e.g., large correlations between RBS-R RSM/Movement SEEK and RBS-R RSC/General HYPER), and Bayes factor tests indicated that many of the observed effects (particularly associations with age, sex, adaptive functioning, and cognitive abilities), were small enough to be practically equivalent to zero. Notably, none of the assessed sensory variables were significantly associated with sex or cognitive abilities as continuously quantified, although intellectual disability status was associated with moderately higher levels of Pain/temperature HYPO and Visual SEEK. Significant negative associations were also observed between certain HYPO/SEEK scores and adaptive behavior scores (with Pain/temperature HYPO demonstrating the most consistent associations); however, these effects were relatively small in magnitude (|r|s < .234).

In line with the classification of sensory reactivity differences as a core diagnostic criterion for autism classified under restricted/repetitive behaviors and interests, most sensory subconstructs correlated moderately with one or more of the RBS-R subscales (i.e., RSM [lower-order repetitive behaviors] and/or RSC [higher-order repetitive behaviors]). Notably, the largest summary effect observed in the current study was the correlation between Movement SEEK and the RBS-R RSM subscale, although this was likely driven to some extent by overlapping item content (e.g., both SEQ 2.1 Item 27/SEQ 3.0 Item 76 and RBS-R item 4 contain jumping and spinning in circles as exemplars). Nevertheless, core autism features (as operationalized by both the SRS total score and multiple RBS-R subscales) demonstrated practically meaningful positive correlations with the majority of sensory constructs considered in the current study. Associated psychiatric symptoms also demonstrated small to moderate correlations with almost all HYPER subconstructs and several modality-specific HYPO and SEEK subconstructs, suggesting that outside of other core autism features, sensory reactivity (particularly HYPER) is most robustly related to transdiagnostic psychiatric symptomatology. Notably, as the sensory subconstructs of Tactile and Movement SEEK demonstrated practically significant positive correlations with externalizing symptoms and features of ADHD but not internalizing symptoms, these two domains may be reflective of an underlying liability for dysregulated or impulsive behavior (see also (80)). These two domains of seeking also showed negative correlations with age, potentially suggesting that these traits decrease over time as children develop increased capacity to regulate their motor impulses with age (e.g., 151,152). Although cross-sectional correlations such as those explored in the current study are insufficient to determine causal relationships between sensory reactivity and other clinical constructs (153), the present findings can nevertheless be useful in generating hypotheses for future targeted investigations of the causal interplay between sensory constructs and other core/associated features of autism.

Despite only two HYPO subconstructs (Speech HYPO and Pain/Temperature HYPO) being considered within the analysis of meta-analytic correlates, it is notable that these two domains of sensory reactivity diverged strongly in terms of their correlations with non-sensory variables. Speech HYPO demonstrated practically significant positive correlations with all core autism features (i.e., SRS, RBS-R subscales) and domains of psychiatric symptoms, as well as practically significant negative correlations with age and VABS socialization. Notably, a child not responding to their name or other speech stimuli is frequently considered a core feature of autism outside of the sensory domain, conceptualized as a failure to orient attention to socially salient stimuli (e.g., 154–156). Thus, it is notable that observed hyporeactivity to speech could feasibly be present in the absence of underlying differences in sensory reactivity (e.g., due to differences in broader social or attentional processes). Future studies, particularly those that include multi-method assessments of both social-communicative and sensory factors, may be necessary to determine whether the underlying causes of Speech HYPO are indeed sensory in nature, thereby investigating the appropriateness of classifying this subconstruct as a sensory reactivity difference.

Unlike the Speech HYPO domain, the Pain/Temperature HYPO subconstruct was reported at moderately higher levels in autistic children with a categorical label of intellectual disability, and this trait was also significantly negatively associated with all domains of the VABS, though no correlations with core autism features nor psychiatric symptoms reached the threshold for practical significance. Though these results seemingly indicate that insensitivity to pain and temperature covary with intellectual disability and reduced adaptive functioning in the autistic population, we strongly caution against overinterpretation of these findings due to the substantial limitations of quantifying response to pain in autism based on solely reports from caregivers (157, 158). Although a co-occurring diagnosis of intellectual disability or more significant impairments in adaptive behavior may be more common in individuals with additional rare neurological conditions that truly include insensitivity to pain as a symptom (e.g., congenital insensitivity to pain with anhidrosis (159)), it is also quite possible that proxy reporters such as caregivers underestimate the pain or discomfort of autistic children who are not able to communicate their internal states in typical ways (158). With the recent development of methods to better capture the internal pain experiences of autistic individuals with intellectual disability and/or limited language (160), additional work is greatly needed to determine whether caregiver reports of seeming insensitivity to pain correspond with self-reports of pain experience in this population, providing more conclusive evidence for or against the claim that autistic individuals with more significant cognitive impairments are truly less sensitive to pain and temperature than autistic individuals who are more cognitively-able (versus this difference being driven by atypical communication of pain or distress).

Overall, the findings of the current study with regard to studied HYPO subconstructs suggest that Pain/Temperature HYPO and Speech HYPO represent theoretically distinct aspects of the autism phenotype with almost completely non-overlapping correlates and divergent future directions relevant to construct validation. Therefore, for applied researchers hoping to investigate these aspects of the autism phenotype using caregiver-report questionnaires, we strongly recommend that these two HYPO subconstructs in particular be studied at the single-modality level, as the nuanced associations between modality-specific variables and external correlates may be obscured by the use of supra-modal HYPO scores that combine subconstructs into a single variable when assessing individual differences. Notably, it is currently unclear whether these HYPO subconstructs demonstrate equally divergent patterns of external correlations when measured using other techniques (e.g., clinician observation (83, 84)), and this remains an important avenue for future research.

Strengths And Limitations

The current study had a number of strengths, including its very large sample size, representation of autistic children and adolescents across a wide range of ages and developmental levels, sensory phenotyping of all major modalities and response patterns with widely used caregiver-report measures, and state-of-the-art statistical approaches that allowed for the pooling of partially overlapping sensory item scores and evaluation of between-dataset effect heterogeneity. However, it was not without limitations. Most notably, the studies that comprised the dataset utilized vastly different methods; each had substantially different inclusion/exclusion criteria, geographic locations, and assessment batteries. To allow for maximal pooling of similar data across studies, we combined measures of the same construct (e.g., different versions of the same questionnaire, standard scores on different measures of an ostensibly similar construct such as FSIQ or internalizing symptoms into single variables, potentially introducing additional heterogeneity due to noninvariance between the different measures or measure versions. For sensory constructs, this pooling was also done at the item level to allow for different versions of the same measure (i.e., SP1 and SP2, SEQ-2.1 and SEQ-3.0) to be calibrated on the same latent scale using IRT. Though many items on different questionnaire versions were extremely similar, version-specific changes in anchor wording, item stems, or order effects could theoretically have resulted in noninvariance of the two homologous items, again increasing overall heterogeneity. Nevertheless, the random-effects IDA model utilized in the current study allowed for the heterogeneity of each effect to be quantified (i.e., using ICCs and prediction intervals), thereby helping to contextualize both the population summary effect and the range of possible effects observable under different study conditions. The measurement of many sensory subconstructs was also a limitation, as despite the large initial item pool, a number of subconstructs had relatively few initial indicators and were, therefore, difficult to form into viable unidimensional scales from the start. For constructs that could not be operationalized in the current study using a psychometrically adequate unidimensional scale, we opted to use an ad-hoc single item indicator such that these subconstructs would still remain in each supra-modal bifactor model. However, it is unclear whether the use of single-item indicators partially contributed to the psychometric inadequacy of the higher-order HYPO and SEEK constructs, and future studies in which all modality-specific subconstructs are adequately captured are necessary to rule out poor subconstruct measurement as a potential cause of supra-modal construct invalidity for sensory response pattern scores.

Another major limitation of the current study was the fact that multisensory items were removed from the questionnaires before psychometric analyses were undertaken. Although this choice greatly simplified the bifactor models due to the lack of specific-factor cross-loadings, it is notable that “real-world” sensory experiences are inherently multisensory in nature (161). By removing items containing multiple sensory modalities, we may have inadvertently excluded a number of relevant sensory behaviors in real-world contexts from the measurement models, limiting the content validity of the supra-modal constructs operationalized by the general HYPER, HYPO, and SEEK factors. Though it remains unknown whether these items would have been retained in our models based on psychometric criteria or excluded due to misfit, future studies are warranted to investigate the utility and properties of sensory reactivity bifactor (or indeed more complex hierarchically-structured) models that include multisensory items in addition to single-modality subconstructs.

As an additional limitation, the questionnaires used in the current study were all based on caregiver-report of a child’s behavior; even in cases where autistic individuals were capable of self-reporting on their own sensory experiences (or provided such data), this information was not included in the current investigation. As sensory percepts are fundamentally subjective experiences, reports solely based on the observations of untrained proxy reporters (i.e., caregivers) may be capturing only the most extreme and/or distressing sensory reactivity differences, potentially also introducing confounding according to the child’s language or communication ability (see also (162)). Moreover, it is quite possible that our conclusions regarding the inadequacy of supra-modal HYPO and SEEK scores (and/or the adequacy of supra-modal HYPER scores) are limited to caregiver-reported sensory measures, and additional work is needed to test the appropriateness of such scores using other measurement methods, including self-report (e.g., 163,164), clinician-rated behavioral observation (e.g., 83,84,165,166), and parent/caregiver interview (e.g., 83) tools. Ideally, further studies of autistic youth and adults capable of self-report should attempt to utilize multimodal sensory measurements that include both self- and informant-reports simultaneously ((e.g., 167) see also (83) for a measure combining clinician observation with caregiver interview), therefore allowing both an individual’s internal experience and observed behavior to contribute to their ratings of sensory reactivity.

Considering the statistical limitations of the study, it is worth noting that all associations between caregiver-reported sensory reactivity differences and clinical/demographic variables were estimated using models that did not control for other relevant demographic or clinical variables (e.g., age, sex, IQ/DQ, or language level). Thus, many of the meta-analytic correlation estimates in our current study likely overestimate the strength of hypothesized causal effects (i.e., associations in which the sensory construct is theorized to be causally upstream of the correlate of interest (168)) due to the presence of (often substantial) residual confounding (153). On the other hand, the current study only examined unconditional, linear associations between variables; therefore, it is also possible that the strength of any nonlinear relationship was underestimated. Future work should attempt to quantify the conditional linear and nonlinear effects of various sensory predictors on relevant clinical outcomes over and above other potentially confounding variables (e.g., 102). Lastly, it is notable that the current investigation relied solely on cross-sectional data, limiting our ability to draw conclusions regarding the predictive validity of sensory reactivity for other relevant outcomes. Although some studies have begun to demonstrate the predictive utility of sensory reactivity in autistic children and other populations such as infants at elevated likelihood to develop autism (e.g., 169–174), these studies have largely used supra-modal response pattern scores; therefore, additional large-scale, longitudinal studies are necessary to determine which single-modality sensory subconstructs (or combinations thereof) can be utilized as clinically-relevant predictors of core and frequently co-occurring features of autism.

The past decade has seen a substantial rise in the number of studies examining the sensory aspects of autism (28), but to date, relatively little published work has examined the latent structure or construct validity of proposed sensory (sub)constructs, particularly those that span multiple sensory modalities. By compiling a large dataset of richly-phenotyped autistic children, we conducted an integrative data analysis that specifically investigated the hierarchical structure of the three canonical sensory “response patterns” (i.e., HYPER, HYPO, and SEEK). Although much research to date has focused on the examination of response patterns that span multiple modalities, the current study demonstrates that some of these supra-modal construct scores (in particular those purported to tap hyporeactivity and to a lesser extent, sensory seeking) are contaminated to a substantial degree by modality-specific variance, making these supra-modal scores difficult to interpret when such variance is not explicitly partialed out (e.g., in the context of a latent variable model). Depending upon the nature of the research question (e.g., if assessing sensory correlates within the same modality or a mechanism of change that is likely to work at the level of a single modality rather than the supra-modal level), modality-specific subconstruct scores may be preferable, or at least represent a viable alternative to supra-modal scores for characterizing individual differences in sensory reactivity in autistic children and adolescents, although additional research is needed to further develop modality-specific sensory measures beyond the limited subsets of items available in broadband inventories currently in use. We therefore recommend that applied researchers studying the sensory aspects of autism tailor the sensory reactivity construct(s) they hope to measure to their specific research questions, rather than exclusively and uncritically relying on supra-modal response pattern scores.

Using integrative data analysis models, we also examined meta-analytic bivariate associations between single-modality sensory subconstructs and various other clinical outcomes, with other measures of core autism features (e.g., subscales of the RBS-R) and psychiatric symptoms demonstrating particularly strong relations with most aspects of sensory reactivity. Although the empirically derived sensory subconstruct measures in the current study correlated meaningfully with other clinical outcomes, there remains a great need to expand existing measures and/or develop novel measures that sample each modality-specific subconstruct (and when relevant, multiple distinct aspects or subdimensions of that subconstruct) in greater detail, as well as to explicitly investigate caregiver-reported reactivity in multisensory contexts that were not tested in the current study. Notably, the field of sensory research remains ripe for cross-disciplinary collaboration between clinical and behavioral scientists, occupational therapists, psychologists, neuroscientists, and autistic individuals themselves (e.g., 175–179), as a synthesis of clinical, behavioral, neuroscientific, and lived experience perspectives on sensory reactivity within and across modalities is likely to produce valid and useful assessments of specific aspects of the autism phenotype, their underlying psychological and neural mechanisms, and their unique clinical correlates. Basic and applied research into the sensory features of autism has immense potential to improve the lives of many autistic individuals across the lifespan, but in order to realize this potential, systematic efforts must be made to rigorously define all sensory constructs of interest and develop psychometrically valid measures of such constructs for use in both research and clinical practice.

Adaptive Behavior Composite (ABC)

Attention-Deficit/Hyperactivity Disorder (ADHD)

Confirmatory Factor Analysis (CFA)

Communication (COM)

Comparative Fit Index (CFI)

Credible Interval (CrI)

Daily Living Skills (DLS)

Expected A Posteriori (EAP)

Explained Common Variance (ECV)

Graded Response Model (GRM)

Hyperreactivity (HYPER)

Hyporeactivity (HYPER)

Full-scale Developmental Quotient (FSDQ)

Full-scale Intelligence Quotient (FSIQ)

Integrative Data Analysis (IDA)

Internalizing Symptoms (INT)

Item Response Theory (IRT)

Nonverbal Developmental Quotient (NVDQ)

Nonverbal Intelligence Quotient (NVIQ)

Region of Practical Equivalence [to zero] (ROPE)

Repetitive Behavior Scale–Revised (RBS-R)

Robust Maximum Likelihood (MLR)

Root Mean Square Error of Approximation (RMSEA)

ROPE Bayes Factor (BF_ROPE)

Repetitive Sensorimotor Behavior (RSM)

Ritutalistic/Sameness/Compulsive Behavior (RSC)

Self-injurious Behavior (SIB)

Sensory Experiences Questionnaire, version 2.1 (SEQ-2.1)

Sensory Experiences Questionnaire, version 3.0 (SEQ-3.0)

[Short] Sensory Profile 1 ([S]SP1)

[Short] Sensory Profile 2 ([S]SP2)

Sensory Processing–Three Dimensions Scale: Inventory (SP-3D:I)

Sensory Seeking (SEEK)

Socialization (SOC)

Social Responsiveness Scale (SRS)

Standardized Root Mean Square Residual (SRMR)

Tucker-Lewis Index (TLI)

Verbal Developmental Quotient (VDQ)

Verbal Intelligence Quotient (VIQ)

Vineland Adaptive Behavior Scales (VABS)

Ethics approval and consent to participate

In accordance with IRB-approved protocols for each primary study, informed consent was obtained from parents or legal guardians of each participant, and when relevant, assent was obtained from participants as well at the time of data collection. The institutional review board at Vanderbilt University Medical Center approved the secondary analysis of pooled data from these studies. All study procedures complied with the ethics code outlined in the Declaration of Helsinki and its later amendments.

Consent for publication

Not applicable.

Availability of data and materials

Individual-participant data analyzed in the current study cannot be shared due to conditions of the data transfer agreements needed to procure these data from other institutions; however, derivative data such as covariance matrices and fitted model objects are available from the corresponding author upon request. Approved researchers will be able to obtain the SPARK and NDAR datasets (SPARK: RM0035_Woynaroski; NDAR: 1160) described in this study by applying at https://base.sfari.org and https://nda.nih.gov/, respectively. The remainder of research materials can be obtained from the corresponding author upon request.

Competing interests

ZJW has received consulting fees from Roche, Autism Speaks, and the May Institute. He also serves on the family advisory committee of the Autism Speaks Autism Care Network Vanderbilt site and on the autistic researcher review board of the Autism Intervention Research Network on Physical Health (AIR-P). KM is a member of Autistica’s Network Steering Committee. GTB is the author of the Sensory Experiences Questionnaire, version 2.1 and Sensory Experiences Questionnaire, version 3.0, which are freely available and for which she receives no royalties. SHM has a patent with Invention ID: D12847, Tech ID: C12847, Title: Kinematic and Morphometric Analysis of Digitized Handwriting Tracings. The remaining authors have no conflicts of interest to disclose.

Funding

This work was supported by a grant from the Nancy Lurie Marks Family Foundation, as well as the National Institute on Deafness and Other Communication Disorders (grants F30-DC019510; R01-DC020186), National Institute of General Medical Sciences (grant T32-GM007347), and Autism Science Foundation. Primary studies from which data were drawn were funded by grants from the National Institute of Mental Health (grants R01-MH102272; R01-MH101536; R00-MH107719; R21-MH101321; R21-MH109158; K01-MH090232; K08-MH112871; K23-MH083890), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grants P50-HD105352; R01-HD082814; R01-HD042168; R21-HD100869; U54-HD083211; U54-HD079123), the National Institute for Biomedical Imaging and Bioengineering (grant P41-EB031771), the United States Department of Defense (grant AR1-40197), an American Recovery and Reinvestment Act Supplement (grant A10-0589), the Simons Foundation Autism Research Initiative (grants 345389; 571124), MQ and Autistica (grant MQF17/6), Autism Speaks (treatment grant 3797), the Hill Collaboration for Environmental Medicine, the Wallace Research Foundation (grant P0511051), and Syracuse University (‘CUSE Innovative and Interdisciplinary Research Grant).

Authors’ contributions (CRediT Statement)

Zachary J. Williams: conceptualization (lead); data curation (lead); formal analysis (lead); methodology (lead); project administration (equal); software (lead); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Roseann Schaaf: conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation (equal); project administration (supporting); resources (equal); supervision (supporting); writing – reviewing and editing (equal). Karla K. Ausderau: investigation (equal); writing – reviewing and editing (equal). Grace T. Baranek: conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). D. Jonah Barrett: data curation (supporting); formal analysis (supporting); software (supporting); visualization (supporting); writing – reviewing and editing (supporting). Carissa J. Cascio: conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Rachel L. Dumont: data curation (supporting); investigation (equal); writing – reviewing and editing (supporting). Ekomobong E. Eyoh: data curation (supporting); investigation (equal); writing – reviewing and editing (supporting). Michelle D. Failla: data curation (supporting); investigation (equal); writing – reviewing and editing (supporting). Jacob I. Feldman: conceptualization (supporting); data curation (supporting); investigation (equal); writing – original draft (supporting); writing – reviewing and editing (supporting). Jennifer H. Foss-Feig: investigation (equal); writing – reviewing and editing (equal). Heather L. Green: conceptualization (supporting); funding acquisition (supporting); writing – reviewing and editing (supporting). Shulamite A. Green: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Jason L. He: data curation (supporting); investigation (equal); writing – reviewing and editing (equal). Elizabeth A. Kaplan-Kahn: investigation (equal); writing – reviewing and editing (supporting). Bahar Keçeli-Kaysılı; data curation (supporting); investigation (equal); writing – reviewing and editing (supporting). Keren MacLennan: investigation (equal); writing – reviewing and editing (supporting). Zoe Mailloux conceptualization (supporting); funding acquisition (supporting); writing – reviewing and editing (supporting). Elysa J. Marco: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (supporting). Lisa E. Mash: investigation (equal); writing – reviewing and editing (supporting). Elizabeth P. McKernan: investigation (equal); writing – reviewing and editing (supporting). Sophie Molholm: investigation (equal); resources (equal); writing – reviewing and editing (equal). Stewart H. Mostofsky: investigation (equal); resources (equal); writing – reviewing and editing (equal). Nicolaas A. J. Puts: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Caroline E. Robertson: conceptualization (supporting); funding acquisition (supporting); writing – reviewing and editing (equal). Natalie Russo: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Nicole Shea: investigation (equal); writing – reviewing and editing (supporting). John Sideris: data curation (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); writing – reviewing and editing (equal). James S. Sutcliffe: investigation (equal); writing – reviewing and editing (equal). Teresa Tavassoli: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Mark T. Wallace: investigation (equal); resources (equal); writing – reviewing and editing (equal). Ericka L. Wodka: conceptualization (supporting); data curation (supporting); funding acquisition (supporting); investigation (equal); resources (equal); writing – reviewing and editing (equal). Tiffany G. Woynaroski: conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); project administration (equal); resources (equal); supervision (lead); writing – original draft (supporting); writing – reviewing and editing (equal).

Acknowledgements

One of the primary datasets was collected from Simons Foundation Powering Autism Research for Knowledge (SPARK) Research Match (Project Number RM0035_Woynaroski). The authors are tremendously grateful to all of the individuals and families enrolled in SPARK, the SPARK clinical sites, and SPARK staff. They further appreciate being granted access to demographic and phenotypic data on SFARI Base. Approved researchers will be able to obtain the SPARK data set described in this study by applying at https://base.sfari.org. Additional data and/or research tools used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR; study number 1160). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. We also acknowledge two large registries in recruitment for one of the samples, including the Interactive Autism Network (IAN) Project at the Kennedy Krieger Institute, Baltimore, Maryland, and The Neurodevelopmental Disorders Research Center Autism Subject Registry at The University of North Carolina at Chapel Hill. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDAR. The authors would also like to thank the many participants, families, researchers, and administrative staff involved in generating, organizing, compiling, and sharing the data used in this project. The authors would like to especially thank Dr. Pratik Mukherjee for his contribution of data to the integrative data analysis project.

Bottema-Beutel K, Kapp SK, Lester JN, Sasson NJ, Hand BN. Avoiding ableist language: Suggestions for autism researchers. Autism Adulthood. 2021 Mar;3(1):18–29.
Bury SM, Jellett R, Spoor JR, Hedley D. “It defines who I am” or “It’s something I have”: What language do [autistic] Australian adults [on the autism spectrum] prefer? J Autism Dev Disord [Internet]. 2020 Feb 28 [cited 2023 Jan 2]; Available from: http://link.springer.com/10.1007/s10803-020-04425-3
Keating CT, Hickman L, Leung J, Monk R, Montgomery A, Heath H, et al. Autism-related language preferences of English-speaking individuals across the globe: A mixed methods investigation. Autism Res [Internet]. 2022 Dec 6 [cited 2023 Jan 2]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.2864
Kenny L, Hattersley C, Molins B, Buckley C, Povey C, Pellicano E. Which terms should be used to describe autism? Perspectives from the UK autism community. Autism. 2016;20(4):442–62.
Taboas A, Doepke K, Zimmerman C. Short report: Preferences for identity-first versus person-first language in a US sample of autism stakeholders. Autism [Internet]. 2022 Oct 13 [cited 2023 Jan 2]; Available from: https://doi.org/10.1177/13623613221130845
Asperger H. “Autistic psychopathy” in childhood. In: Frith U, editor. Autism and Asperger syndrome. New York, NY, US: Cambridge University Press; 1991. p. 37–92.
Kanner L. Autistic disturbances of affective contact. Nerv Child. 1943;2:217–50.
Feldman JI, Dunham K, Cassidy M, Wallace MT, Liu Y, Woynaroski TG. Audiovisual multisensory integration in individuals with autism spectrum disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2018;95:220–34.
Hazen EP, Stornelli JL, O’Rourke JA, Koesterer K, McDougle CJ. Sensory symptoms in autism spectrum disorders. Harv Rev Psychiatry. 2014 Apr;22(2):112–24.
He JL, Williams ZJ, Harris AD, Powell H, Schaaf R, Tavassoli T, et al. A working taxonomy for describing the sensory differences of autism [Internet]. Molecular Autism. In press [cited 2022 Sep 18]. Available from: https://psyarxiv.com/jmv6k/
Mottron L, Dawson M, Soulières I, Hubert B, Burack J. Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. J Autism Dev Disord. 2006 Jan;36(1):27–43.
Proff I, Williams GL, Quadt L, Garfinkel SN. Sensory processing in autism across exteroceptive and interoceptive domains. Psychol Neurosci. 2022 Jun;15(2):105–30.
Robertson CE, Baron-Cohen S. Sensory perception in autism. Nat Rev Neurosci. 2017;18(11):671–84.
Schaaf RC, Lane AE. Toward a best-practice protocol for assessment of sensory features in ASD. J Autism Dev Disord. 2015 May;45(5):1380–95.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders [Internet]. 5th ed. Arlington, VA: American Psychiatric Association Publishing; 2013. Available from: https://doi.org/10.1176/appi.books.9780890425596
American Psychiatric Association. Diagnostic and statistical manual of mental disorders [Internet]. 5th ed., text rev. Washington, DC: American Psychiatric Association Publishing; 2022. 1050 p. Available from: https://doi.org/10.1176/appi.books.9780890425787
Baranek GT, Little LM, Diane Parham L, Ausderau KK, Sabatos-DeVito MG. Sensory features in autism spectrum disorders. In: Volkmar FR, Rogers SJ, Paul R, Pelphrey KA, editors. Handbook of autism and pervasive developmental disorders [Internet]. 4th ed. John Wiley & Sons, Ltd; 2014 [cited 2022 Jan 22]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118911389.hautc16
Kirby AV, Bilder DA, Wiggins LD, Hughes MM, Davis J, Hall‐Lande JA, et al. Sensory features in autism: Findings from a large population‐based surveillance system. Autism Res. 2022 Apr;15(4):751–60.
Ausderau KK, Furlong M, Sideris J, Bulluck J, Little LM, Watson LR, et al. Sensory subtypes in children with autism spectrum disorder: Latent profile transition analysis using a national survey of sensory features. J Child Psychol Psychiatry. 2014 Aug;55(8):935–44.
Lane AE, Simpson K, Masi A, Grove R, Moni MA, Montgomery A, et al. Patterns of sensory modulation by age and sex in young people on the autism spectrum. Autism Res [Internet]. 2022 [cited 2022 Jun 23]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.2762
Ghanizadeh A. Sensory processing problems in children with ADHD, a systematic review. Psychiatry Investig. 2011 Jun;8(2):89–94.
Glod M, Riby DM, Rodgers J. Sensory processing in Williams syndrome: A narrative review. Rev J Autism Dev Disord. 2020 Mar 1;7(1):32–45.
Houghton DC, Stein DJ, Cortese BM. Exteroceptive sensory abnormalities in childhood and adolescent anxiety and obsessive-compulsive disorder: A critical review. J Am Acad Child Adolesc Psychiatry. 2020 Jan 1;59(1):78–87.
Isaacs D, Riordan H. Sensory hypersensitivity in Tourette syndrome: A review. Brain Dev. 2020 Oct;42(9):627–38.
Lazerwitz MC, Rowe MA, Trimarchi KJ, Garcia RD, Chu R, Steele MC, et al. Brief Report: Characterization of sensory over-responsivity in a broad neurodevelopmental concern cohort using the Sensory Processing Three Dimensions (SP3D) Assessment. J Autism Dev Disord [Internet]. 2022 Sep 30 [cited 2023 Jan 2]; Available from: https://doi.org/10.1007/s10803-022-05747-0
Schwarzlose RF, Tillman R, Hoyniak CP, Luby JL, Barch DM. Sensory over-responsivity: A feature of childhood psychiatric illness associated with altered functional connectivity of sensory networks. Biol Psychiatry. 2023 Jan 1;93(1):92–101.
van den Boogert F, Klein K, Spaan P, Sizoo B, Bouman YHA, Hoogendijk WJG, et al. Sensory processing difficulties in psychiatric disorders: A meta-analysis. J Psychiatr Res. 2022 Jul;151:173–80.
Ben-Sasson A, Gal E, Fluss R, Katz-Zetler N, Cermak SA. Update of a meta-analysis of sensory symptoms in ASD: A new decade of research. J Autism Dev Disord. 2019;49(12):4974–96.
Bagby MS, Dickie VA, Baranek GT. How sensory experiences of children with and without autism affect family occupations. Am J Occup Ther. 2012 Jan 1;66(1):78–86.
Caniato M, Zaniboni L, Marzi A, Gasparella A. Evaluation of the main sensitivity drivers in relation to indoor comfort for individuals with autism spectrum disorder. Part 2: Influence of age, co-morbidities, gender and type of respondent on the stress caused by specific environmental stimuli. Energy Rep. 2022 Nov 1;8:2989–3001.
Elwin M, Ek L, Kjellin L, Schröder A. Too much or too little: Hyper- and hypo-reactivity in high-functioning autism spectrum conditions. J Intellect Dev Disabil. 2013 Sep;38(3):232–41.
Jones EK, Hanley M, Riby DM. Distraction, distress and diversity: Exploring the impact of sensory processing differences on learning and school life for pupils with autism spectrum disorders. Res Autism Spectr Disord. 2020 Apr;72:Article 101515.
Landon J, Shepherd D, Lodhia V. A qualitative study of noise sensitivity in adults with autism spectrum disorder. Res Autism Spectr Disord. 2016 Dec;32:43–52.
Lin LY, Huang PC. Quality of life and its related factors for adults with autism spectrum disorder. Disabil Rehabil. 2019 Apr 10;41(8):896–903.
Little LM, Ausderau K, Sideris J, Baranek GT. Activity participation and sensory features among children with autism spectrum disorders. J Autism Dev Disord. 2015 Sep 1;45(9):2981–90.
Reynolds S, Bendixen RM, Lawrence T, Lane SJ. A pilot study examining activity participation, sensory responsiveness, and competence in children with high functioning autism spectrum disorder. J Autism Dev Disord. 2011 Nov;41(11):1496–506.
Schaaf RC, Toth-Cohen S, Johnson SL, Outten G, Benevides TW. The everyday routines of families of children with autism: Examining the impact of sensory processing difficulties on the family. Autism. 2011 May 1;15(3):373–89.
Scheerer NE, Boucher TQ, Bahmei B, Iarocci G, Arzanpour S, Birmingham E. Family experiences of decreased sound tolerance in ASD. J Autism Dev Disord. 2022 Sep;52(9):4007–21.
Williams ZJ, He JL, Cascio CJ, Woynaroski TG. A review of decreased sound tolerance in autism: Definitions, phenomenology, and potential mechanisms. Neurosci Biobehav Rev. 2021 Feb 1;121:1–17.
Ashburner J, Bennett L, Rodger S, Ziviani J. Understanding the sensory experiences of young people with autism spectrum disorder: A preliminary investigation. Aust Occup Ther J. 2013 Jun;60(3):171–80.
Howe FEJ, Stagg SD. How sensory experiences affect adolescents with an autistic spectrum condition within the classroom. J Autism Dev Disord. 2016 May 1;46(5):1656–68.
Jones RSP, Quigney C, Huws JC. First-hand accounts of sensory perceptual experiences in autism: a qualitative analysis. J Intellect Dev Disabil. 2003 Jun;28(2):112–21.
Kapp SK, Steward R, Crane L, Elliott D, Elphick C, Pellicano E, et al. ‘People should be allowed to do what they like’: Autistic adults’ views and experiences of stimming. Autism. 2019 Oct;23(7):1782–92.
Kirby AV, Dickie VA, Baranek GT. Sensory experiences of children with autism spectrum disorder: In their own words. Autism. 2015 Apr 1;19(3):316–26.
MacLennan K, O’Brien S, Tavassoli T. In our own words: The complex sensory experiences of autistic adults. J Autism Dev Disord. 2022 Jul;52(7):3061–75.
Robertson AE, Simmons DR. The sensory experiences of adults with autism spectrum disorder: A qualitative analysis. Perception. 2015 May;44(5):569–86.
Sibeoni J, Massoutier L, Valette M, Manolios E, Verneuil L, Speranza M, et al. The sensory experiences of autistic people: A metasynthesis. Autism. 2022 Jul 1;26(5):1032–45.
Smith RS, Sharp J. Fascination and isolation: A grounded theory exploration of unusual sensory experiences in adults with Asperger syndrome. J Autism Dev Disord. 2013 Apr;43(4):891–910.
Ausderau KK, Sideris J, Furlong M, Little LM, Bulluck J, Baranek GT. National survey of sensory features in children with ASD: Factor structure of the Sensory Experience Questionnaire (3.0). J Autism Dev Disord. 2014 Apr;44(4):915–25.
Grove R, Begeer S, Scheeren AM, Weiland RF, Hoekstra RA. Evaluating the latent structure of the non-social domain of autism in autistic adults. Mol Autism. 2021 Mar 3;12(1):Article 22.
Parks K, Schulz S, McDonnell CG, Anagnostou E, Nicolson R, Kelley E, et al. Sensory processing in ASD and ADHD: A confirmatory factor analysis [Internet]. PsyArXiv; 2020 [cited 2022 Jun 24]. Available from: https://psyarxiv.com/myjbq/
Tillmann J, Uljarevic M, Crawley D, Dumas G, Loth E, Murphy D, et al. Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: A factor mixture modelling approach. Mol Autism. 2020 Aug 31;11(1):Article 67.
Weiland RF, Polderman TJ, Hoekstra RA, Smit DJ, Begeer S. The Dutch Sensory Perception Quotient-Short in adults with and without autism. Autism. 2020 Nov 1;24(8):2071–80.
Williams ZJ, Failla MD, Gotham KO, Woynaroski TG, Cascio C. Psychometric evaluation of the Short Sensory Profile in youth with autism spectrum disorder. J Autism Dev Disord. 2018 Dec;48(12):4231–49.
Dunn W. Sensory Profile: User’s manual. San Antonio, TX: Psychological Corporation; 1999. 146 p.
Dunn W. Sensory Profile 2: User’s manual. Bloomington, MN.: Pearson; 2014. 268 p.
Baranek GT. Sensory Experiences Questionnaire (SEQ), version 2.1. Chapel Hill, NC, USA: University of North Carolina at Chapel Hill; 1999.
Baranek GT. Sensory Experiences Questionnaire (SEQ), version 3.0. Chapel Hill, NC, USA: University of North Carolina at Chapel Hill; 2009.
Baranek GT, David FJ, Poe MD, Stone WL, Watson LR. Sensory Experiences Questionnaire: Discriminating sensory features in young children with autism, developmental delays, and typical development. J Child Psychol Psychiatry. 2006;47(6):591–601.
Schoen SA, Miller LJ, Green KE. Pilot study of the Sensory Over-Responsivity Scales: Assessment and Inventory. Am J Occup Ther. 2008 Jul 1;62(4):393–406.
Schoen SA, Miller LJ, Sullivan J. The development and psychometric properties of the Sensory Processing Scale Inventory: A report measure of sensory modulation. J Intellect Dev Disabil. 2017 Jan 2;42(1):12–21.
Dunn W. The impact of sensory processing abilities on the daily lives of young children and their families: A conceptual model. Infants Young Child. 1997 Apr;9(4):23–35.
Lane AE. Practitioner Review: Effective management of functional difficulties associated with sensory symptoms in children and adolescents. J Child Psychol Psychiatry. 2020;61(9):943–58.
Miller LJ, Anzalone ME, Lane SJ, Cermak SA, Osten ET. Concept evolution in sensory integration: A proposed nosology for diagnosis. Am J Occup Ther. 2007 Mar 1;61(2):135–40.
Lefebvre A, Tillmann J, Cliquet F, Amsellem F, Maruani A, Leblond C, et al. Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways. Autism Res [Internet]. 2022 Dec 4 [cited 2023 Jan 2]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.2861
Su CT, Parham LD. Validity of sensory systems as distinct constructs. Am J Occup Ther. 2014 Sep 1;68(5):546–54.
Williams ZJ, Feldman JI, Woynaroski TG. Examining the hierarchical structure of parent-reported sensory features in autism using bifactor models. In: International Society for Autism Research Annual Meeting [Internet]. Virtual Meeting; 2021. Available from: https://www.autism-insar.org/resource/resmgr/docs/annualmeeting/Abstract_Book_INSAR2021Virtu.pdf
Markon KE. Bifactor and hierarchical models: Specification, inference, and interpretation. Annu Rev Clin Psychol. 2019 May 7;15(1):51–69.
Reise SP. The rediscovery of bifactor measurement models. Multivar Behav Res. 2012 Sep 1;47(5):667–96.
Rodriguez A, Reise SP, Haviland MG. Applying bifactor statistical indices in the evaluation of psychological measures. J Pers Assess. 2016;98(3):223–37.
Rodriguez A, Reise SP, Haviland MG. Evaluating bifactor models: Calculating and interpreting statistical indices. Psychol Methods. 2016;21(2):137–50.
Williams ZJ. A bifactor model of the autism spectrum disorder phenotype. J Am Acad Child Adolesc Psychiatry. 2019 Oct;58(10):1019–21.
Dwyer P, Wang X, De Meo-Monteil R, Hsieh F, Saron CD, Rivera SM. Defining clusters of young autistic and typically developing children based on loudness-dependent auditory electrophysiological responses. Mol Autism. 2020 Jun 15;11(1):Article 48.
Dwyer P, Takarae Y, Zadeh I, Rivera SM, Saron CD. A multidimensional investigation of sensory processing in autism: Parent- and self-report questionnaires, psychophysical thresholds, and event-related potentials in the auditory and somatosensory modalities. Front Hum Neurosci. 2022 May 10;16:Article 811547.
Dwyer P, Vukusic S, Williams ZJ, Saron CD, Rivera SM. “Neural noise” in auditory responses in young autistic and neurotypical children. J Autism Dev Disord [Internet]. 2022 Nov 25 [cited 2022 Nov 25]; Available from: https://doi.org/10.1007/s10803-022-05797-4
Foss-Feig JH, Heacock JL, Cascio CJ. Tactile responsiveness patterns and their association with core features in autism spectrum disorders. Res Autism Spectr Disord. 2012 Jan 1;6(1):337–44.
Jones CRG, Happé F, Baird G, Simonoff E, Marsden AJS, Tregay J, et al. Auditory discrimination and auditory sensory behaviours in autism spectrum disorders. Neuropsychologia. 2009 Nov;47(13):2850–8.
Sapey-Triomphe LA, Lamberton F, Sonié S, Mattout J, Schmitz C. Tactile hypersensitivity and GABA concentration in the sensorimotor cortex of adults with autism. Autism Res. 2019;12(4):562–75.
Tavassoli T, Brandes-Aitken A, Chu R, Porter L, Schoen S, Miller LJ, et al. Sensory over-responsivity: Parent report, direct assessment measures, and neural architecture. Mol Autism. 2019 Dec;10(1):Article 4.
Wodka EL, Puts NAJ, Mahone EM, Edden RAE, Tommerdahl M, Mostofsky SH. The role of attention in somatosensory processing: A multi-trait, multi-method analysis. J Autism Dev Disord. 2016 Oct 1;46(10):3232–41.
Ben-Sasson A, Hen L, Fluss R, Cermak SA, Engel-Yeger B, Gal E. A meta-analysis of sensory modulation symptoms in individuals with autism spectrum disorders. J Autism Dev Disord. 2009 Jan;39(1):1–11.
Feldman JI, Cassidy M, Liu Y, Kirby AV, Wallace MT, Woynaroski TG. Relations between sensory responsiveness and features of autism in children. Brain Sci. 2020 Nov;10(11):Article 775.
Siper PM, Kolevzon A, Wang AT, Buxbaum JD, Tavassoli T. A clinician-administered observation and corresponding caregiver interview capturing DSM-5 sensory reactivity symptoms in children with ASD: Sensory Assessment for Neurodevelopmental Disorders. Autism Res. 2017 Jun;10(6):1133–40.
Unwin KL, Barbaro J, Uljarevic M, Hussain A, Chetcuti M, Lane AlisonE. The Sensory Observation Autism Rating Scale (SOAR): Developed using the PROMIS® framework. Autism Res [Internet]. 2022 Dec 24 [cited 2023 Jan 2]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.2881
Egelhoff K, Lane AE. Brief report: Preliminary reliability, construct validity and standardization of the Auditory Behavior Questionnaire (ABQ) for children with autism spectrum disorders. J Autism Dev Disord. 2013 Apr 1;43(4):978–84.
Hall D, Huerta MF, McAuliffe MJ, Farber GK. Sharing heterogeneous data: The National Database for Autism Research. Neuroinformatics. 2012 Oct;10(4):331–9.
Curran PJ, Hussong AM. Integrative data analysis: The simultaneous analysis of multiple data sets. Psychol Methods. 2009;14(2):81–100.
Hussong AM, Curran PJ, Bauer DJ. Integrative data analysis in clinical psychology research. Annu Rev Clin Psychol. 2013 Mar 28;9(1):61–89.
Frazier TW, Ratliff KR, Gruber C, Zhang Y, Law PA, Constantino JN. Confirmatory factor analytic structure and measurement invariance of quantitative autistic traits measured by the Social Responsiveness Scale-2. Autism. 2014 Jan 1;18(1):31–44.
Hatch B, Nordahl CW, Schwichtenberg AJ, Ozonoff S, Miller M. Factor structure of the Children’s Sleep Habits Questionnaire in young children with and without autism. J Autism Dev Disord. 2021 Sep 1;51(9):3126–37.
Sturm A, Huang S, Kuhfeld M. Advancing methodologies to improve RRB outcome measures in autism research: Evaluation of the RBS-R. Psychol Assess. 2022;34:30–42.
Uljarević M, Frazier TW, Phillips JM, Jo B, Littlefield S, Hardan AY. Mapping the Research Domain Criteria social processes constructs to the Social Responsiveness Scale. J Am Acad Child Adolesc Psychiatry. 2020 Nov 1;59(11):1252-1263.e3.
Uljarević M, Jo B, Frazier TW, Scahill L, Youngstrom EA, Hardan AY. Using the big data approach to clarify the structure of restricted and repetitive behaviors across the most commonly used autism spectrum disorder measures. Mol Autism. 2021 May 27;12(1):Article 39.
Zheng S, Kaat AJ, Farmer C, Kanne S, Georgiades S, Lord C, et al. Extracting latent subdimensions of social communication: A cross-measure factor analysis. J Am Acad Child Adolesc Psychiatry. 2021 Jun 1;60(6):768-782.e6.
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(2):270–83.
Kuhfeld M, Sturm A. An examination of the precision of the Autism Diagnostic Observation Schedule using item response theory. Psychol Assess. 2018;30(5):656–68.
Magiati I, Lerh JW, Hollocks MJ, Uljarevic M, Rodgers J, McConachie H, et al. The measurement properties of the Spence Children’s Anxiety Scale-parent version in a large international pooled sample of young people with autism spectrum disorder. Autism Res. 2017;10(10):1629–52.
Sturm A, Kuhfeld M, Kasari C, McCracken JT. Development and validation of an item response theory-based Social Responsiveness Scale short form. J Child Psychol Psychiatry. 2017;58(9):1053–61.
Wei T, Chesnut SR, Barnard-Brak L, Richman D. Psychometric analysis of the Social Communication Questionnaire using an item-response theory framework: Implications for the use of the lifetime and current forms. J Psychopathol Behav Assess. 2015;37(3):469–80.
Williams ZJ, Everaert J, Gotham KO. Measuring depression in autistic adults: Psychometric validation of the Beck Depression Inventory–II. Assessment. 2021 Apr 1;28(3):858–76.
Burrows CA, Grzadzinski RL, Donovan K, Stallworthy IC, Rutsohn J, St. John T, et al. A data driven approach in an unbiased sample reveals equivalent sex ratio of autism spectrum disorder associated impairment in early childhood. Biol Psychiatry. 2022 Oct 15;92(8):654–62.
Kaat AJ, Shui AM, Ghods SS, Farmer CA, Esler AN, Thurm A, et al. Sex differences in scores on standardized measures of autism symptoms: A multisite integrative data analysis. J Child Psychol Psychiatry. 2021 Jan;62(1):97–106.
Zheng S, Kaat A, Farmer C, Thurm A, Burrows CA, Kanne S, et al. Bias in measurement of autism symptoms by spoken language level and non-verbal mental age in minimally verbal children with neurodevelopmental disorders. Front Psychol. 2022 Jul 29;13:Article 927847.
Cai L, Choi K, Hansen M, Harrell L. Item response theory. Annu Rev Stat Its Appl. 2016;3(1):297–321.
Thomas ML. Advances in applications of item response theory to clinical assessment. Psychol Assess. 2019 Dec;31(12):1442–55.
Reise SP, Bonifay W, Haviland MG, Irwing P, Booth T, Hughes DJ. Bifactor modeling and the evaluation of scale scores. In: The Wiley handbook of psychometric testing [Internet]. Wiley Blackwell; 2018 [cited 2022 Jan 23]. p. 675–707. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118489772.ch22
Daniels AM, Rosenberg RE, Anderson C, Law JK, Marvin AR, Law PA. Verification of parent-report of child autism spectrum disorder diagnosis to a web-based autism registry. J Autism Dev Disord. 2012 Feb 1;42(2):257–65.
Feliciano P, Daniels AM, Snyder LG, Beaumont A, Camba A, Esler A, et al. SPARK: A US cohort of 50,000 families to accelerate autism research. Neuron. 2018;97(3):488–93.
Kaat AJ, Farmer C. Commentary: Lingering questions about the Social Responsiveness Scale short form. A commentary on Sturm et al. (2017). J Child Psychol Psychiatry. 2017 Sep;58(9):1062–4.
Reise SP, Rodriguez A, Spritzer KL, Hays RD. Alternative approaches to addressing non-normal distributions in the application of IRT models to personality measures. J Pers Assess. 2018 Jul 4;100(4):363–74.
McIntosh DN, Miller LJ, Shyu V. Development and validation of the Short Sensory Profile. In: Dunn W, editor. Sensory Profile manual. San Antonio, TX: Psychological Corporation; 1999. p. 59–73.
Simpson K, Adams D, Alston-Knox C, Heussler HS, Keen D. Exploring the sensory profiles of children on the autism spectrum using the Short Sensory Profile-2 (SSP-2). J Autism Dev Disord. 2019 May;49(5):2069–79.
Lee H, Chen YJ, Sideris J, Watson LR, Crais ER, Baranek GT. Sensory features of young children from a large community sample: Latent factor structures of the Sensory Experiences Questionnaire (Version 2.1, Short Form). Am J Occup Ther. 2022 May 31;76(3):Article 7603205140.
Sparrow SS. Vineland Adaptive Behavior Scales. In: Kreutzer JS, DeLuca J, Caplan B, editors. Encyclopedia of clinical neuropsychology [Internet]. New York, NY: Springer; 2011 [cited 2022 Apr 21]. p. 2618–21. Available from: https://doi.org/10.1007/978-0-387-79948-3_1602
Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, et al. Validation of a brief quantitative measure of autistic traits: Comparison of the Social Responsiveness Scale with the Autism Diagnostic Interview-Revised. J Autism Dev Disord. 2003 Aug 1;33(4):427–33.
Constantino JN, Gruber CP. Social Responsiveness Scale–Second Edition (SRS-2): Manual. 2nd ed. Torrance, CA: Western Psychological Services; 2012.
Bodfish JW, Symons FJ, Parker DE, Lewis MH. Varieties of repetitive behavior in autism: Comparisons to mental retardation. J Autism Dev Disord. 2000 Jun 1;30(3):237–43.
R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org/
Cooksey RW, Soutar GN. Coefficient beta and hierarchical item clustering: An analytical procedure for establishing and displaying the dimensionality and homogeneity of summated scales. Organ Res Methods. 2006 Jan;9(1):78–98.
Revelle W. ICLUST: A cluster analytic approach to exploratory and confirmatory scale construction. Behav Res Methods Instrum. 1978 Sep;10(5):739–42.
Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research [Internet]. Evanston, Illinois: Northwestern University; 2022. Available from: https://CRAN.R-project.org/package=psych
Samejima F. Estimation of latent ability using a response pattern of graded scores. Psychometrika. 1969;34(1):1–97.
Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS One. 2017;12(6):e0174035.
Golino HF, Shi D, Christensen AP, Garrido LE, Nieto MD, Sadana R, et al. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychol Methods. 2020 Jun;25(3):292–320.
Gibbons RD, Bock RD, Hedeker D, Weiss DJ, Segawa E, Bhaumik DK, et al. Full-information item bifactor analysis of graded response data. Appl Psychol Meas. 2007 Jan 1;31(1):4–19.
Toland MD, Sulis I, Giambona F, Porcu M, Campbell JM. Introduction to bifactor polytomous item response theory analysis. J Sch Psychol. 2017;60:41–63.
Dueber DM, Toland MD. A bifactor approach to subscore assessment. Psychol Methods [Internet]. 2021 Dec 23 [cited 2022 Nov 14]; Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/met0000459
Reise SP, Scheines R, Widaman KF, Haviland MG. Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educ Psychol Meas. 2013 Feb 1;73(1):5–26.
Revelle W, Wilt J. The general factor of personality: A general critique. J Res Personal. 2013 Oct;47(5):493–504.
Curran PJ, Hussong AM, Cai L, Huang W, Chassin L, Sher KJ, et al. Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis. Dev Psychol. 2008;44(2):365–80.
Bürkner PC. brms: An R package for Bayesian multilevel models using Stan. J Stat Softw. 2017 Aug 29;80(1):1–28.
Bürkner PC. Advanced Bayesian multilevel modeling with the R package brms. R J. 2018;10(1):395.
Kruschke JK. Rejecting or accepting parameter values in Bayesian estimation. Adv Methods Pract Psychol Sci. 2018 Jun;1(2):270–80.
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, N.J: L. Erlbaum Associates; 1988. 567 p.
Blume JD, Greevy RA, Welty VF, Smith JR, Dupont WD. An introduction to second-generation p-values. Am Stat. 2019 Mar 29;73(sup1):157–67.
Gelman A, Hill J, Yajima M. Why we (usually) don’t have to worry about multiple comparisons. J Res Educ Eff. 2012 Apr 1;5(2):189–211.
Sjölander A, Vansteelandt S. Frequentist versus Bayesian approaches to multiple testing. Eur J Epidemiol. 2019 Sep 1;34(9):809–21.
Liao JG, Midya V, Berg A. Connecting and contrasting the Bayes factor and a modified ROPE Procedure for testing interval null hypotheses. Am Stat. 2021 Jul 3;75(3):256–64.
Makowski D, Ben-Shachar MS, Chen SHA, Lüdecke D. Indices of effect existence and significance in the Bayesian framework. Front Psychol. 2019 Dec 10;10:Article 2767.
Wagenmakers EJ, Wetzels R, Borsboom D, van der Maas HLJ. Why psychologists must change the way they analyze their data: The case of psi: Comment on Bem (2011). J Pers Soc Psychol. 2011 Mar;100(3):426–32.
Makowski D, Ben-Shachar MS, Lüdecke D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian Framework. J Open Source Softw. 2019;4(40):Article 1541.
Graham PL, Moran JL. Robust meta-analytic conclusions mandate the provision of prediction intervals in meta-analysis summaries. J Clin Epidemiol. 2012 May;65(5):503–10.
IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open. 2016;6(7):Article e010247.
Burns CO, Dixon DR, Novack M, Granpeesheh D. A systematic review of assessments for sensory processing abnormalities in autism spectrum disorder. Rev J Autism Dev Disord. 2017 Sep;4(3):209–24.
Glod M, Riby DM, Honey E, Rodgers J. Psychological correlates of sensory processing patterns in individuals with autism spectrum disorder: A systematic review. Rev J Autism Dev Disord. 2015 Jun 1;2(2):199–221.
Carson TB, Qiu Y, Liang L, Medina AM, Ortiz A, Condon CA, et al. Development and validation of a paediatric version of the Khalfa Hyperacusis Questionnaire for children with and without autism. Int J Audiol [Internet]. 2022 Sep 2 [cited 2022 Nov 14]; Available from: https://doi.org/10.1080/14992027.2022.2113827
Uljarević M, Baranek G, Vivanti G, Hedley D, Hudry K, Lane A. Heterogeneity of sensory features in autism spectrum disorder: Challenges and perspectives for future research. Autism Res. 2017;10(5):703–10.
Amir I, Lamerton D, Montague ML. Hyperacusis in children: The Edinburgh experience. Int J Pediatr Otorhinolaryngol. 2018 Sep;112:39–44.
Mikkelsen M, Wodka EL, Mostofsky SH, Puts NAJ. Autism spectrum disorder in the scope of tactile processing. Dev Cogn Neurosci. 2018 Jan 1;29:140–50.
Simmons DR, Robertson AE, McKay LS, Toal E, McAleer P, Pollick FE. Vision in autism spectrum disorders. Vision Res. 2009 Nov;49(22):2705–39.
Baranek GT, Carlson M, Sideris J, Kirby AV, Watson LR, Williams KL, et al. Longitudinal assessment of stability of sensory features in children with autism spectrum disorder or other developmental disabilities. Autism Res. 2019 Jan;12(1):100–11.
Bezdjian S, Tuvblad C, Wang P, Raine A, Baker LA. Motor impulsivity during childhood and adolescence: A longitudinal biometric analysis of the go/no-go task in 9- to 18-year-old twins. Dev Psychol. 2014;50(11):2549–57.
Rohrer JM. Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Adv Methods Pract Psychol Sci. 2018 Mar 1;1(1):27–42.
Dawson G, Meltzoff AN, Osterling J, Rinaldi J, Brown E. Children with autism fail to orient to naturally occurring social stimuli. J Autism Dev Disord. 1998;28(6):479–85.
Dawson G, Toth K, Abbott R, Osterling J, Munson J, Estes A, et al. Early social attention impairments in autism: Social orienting, joint attention, and attention to distress. Dev Psychol. 2004;40(2):271–83.
Hedger N, Dubey I, Chakrabarti B. Social orienting and social seeking behaviors in ASD. A meta analytic investigation. Neurosci Biobehav Rev. 2020 Dec 1;119:376–95.
Moore DJ, Failla MD. Pain in autism spectrum disorders. In: Volkmar FR, editor. Encyclopedia of autism spectrum disorders [Internet]. 2nd ed. Cham: Springer; 2021 [cited 2022 Jul 31]. p. 3255–60. Available from: http://link.springer.com/10.1007/978-3-319-91280-6_102488
Moore DJ. Acute pain experience in individuals with autism spectrum disorders: A review. Autism. 2015 May;19(4):387–99.
Zhang M, Cao X, Li N, Duan G, Zhang X. Autism spectrum disorder in a boy with congenital insensitivity to pain with anhidrosis: A case report. BMC Pediatr. 2022 Dec;22(1):Article 126.
Fitzpatrick R, McGuire BE, Lydon HK. Improving pain‐related communication in children with autism spectrum disorder and intellectual disability. Paediatr Neonatal Pain. 2022 Mar;4(1):22–32.
Murray MM, Lewkowicz DJ, Amedi A, Wallace MT. Multisensory processes: A balancing act across the lifespan. Trends Neurosci. 2016 Aug 1;39(8):567–79.
Rossow T, MacLennan K, Tavassoli T. The relationship between sensory reactivity differences and mental health symptoms in preschool-age autistic children. Autism Res. 2021;14(8):1645–57.
Brown C, Tollefson N, Dunn W, Cromwell R, Filion D. The Adult Sensory Profile: Measuring patterns of sensory processing. Am J Occup Ther. 2001 Jan 1;55(1):75–82.
Millington E, Brown L, McMahon H, Robertson AE, Simmons D. Children’s Glasgow Sensory Questionnaire (C-GSQ): Validation of a simplified and visually aided questionnaire [Internet]. PsyArXiv; 2021 [cited 2023 Jan 2]. Available from: https://psyarxiv.com/f6bg2/
Baranek GT. Sensory Processing Assessment for Young Children (SPA). Chapel Hill, NC, USA: University of North Carolina at Chapel Hill; 1999.
Ramappa S, Anderson A, Jung J, Chu R, Cummings KK, Patterson G, et al. An observed assessment of sensory responsivity in autism spectrum disorders: Associations with diagnosis, age, and parent report. J Autism Dev Disord [Internet]. 2022 Aug 4 [cited 2023 Jan 2]; Available from: https://doi.org/10.1007/s10803-022-05653-5
Keith JM, Jamieson JP, Bennetto L. The importance of adolescent self-report in autism spectrum disorder: Integration of questionnaire and autonomic measures. J Abnorm Child Psychol. 2019 Apr;47(4):741–54.
Bradshaw J, Schwichtenberg AJ, Iverson JM. Capturing the complexity of autism: Applying a developmental cascades framework. Child Dev Perspect. 2022;16(1):18–26.
Baranek GT, Woynaroski TG, Nowell S, Turner-Brown L, DuBay M, Crais ER, et al. Cascading effects of attention disengagement and sensory seeking on social symptoms in a community sample of infants at-risk for a future diagnosis of autism spectrum disorder. Dev Cogn Neurosci. 2018 Jan 1;29:30–40.
Carpenter KLH, Baranek GT, Copeland WE, Compton S, Zucker N, Dawson G, et al. Sensory over-responsivity: An early risk factor for anxiety and behavioral challenges in young children. J Abnorm Child Psychol. 2019 Jun 1;47(6):1075–88.
Chen Y, Sideris J, Watson LR, Crais ER, Baranek GT. Developmental trajectories of sensory patterns from infancy to school age in a community sample and associations with autistic traits. Child Dev [Internet]. 2022 Mar 2 [cited 2022 May 31]; Available from: https://onlinelibrary.wiley.com/doi/10.1111/cdev.13745
Damiano-Goodwin CR, Woynaroski TG, Simon DM, Ibañez LV, Murias M, Kirby A, et al. Developmental sequelae and neurophysiologic substrates of sensory seeking in infant siblings of children with autism spectrum disorder. Dev Cogn Neurosci. 2018 Jan 1;29:41–53.
Feldman JI, Raj S, Bowman SM, Santapuram P, Golden AJ, Daly C, et al. Sensory responsiveness is linked with communication in infant siblings of children with and without autism. J Speech Lang Hear Res. 2021 Jun 4;64(6):1964–76.
Jatkar A, Garrido D, Zheng S, Silverman G, Elsayed H, Huguely Davis P, et al. Toddlers at elevated likelihood for autism: Exploring sensory and language treatment predictors. J Early Interv [Internet]. 2022 Feb 4 [cited 2023 Jan 2]; Available from: https://doi.org/10.1177/10538151211067227
Cascio CJ, Woynaroski T, Baranek GT, Wallace MT. Toward an interdisciplinary approach to understanding sensory function in autism spectrum disorder. Autism Res. 2016;9(9):920–5.
Keating CT. Participatory autism research: How consultation benefits everyone. Front Psychol. 2021;12:Article 713982.
Miller LJ, Marco EJ, Chu RC, Camarata S. Sensory processing across the lifespan: A 25-year initiative to understand neurophysiology, behaviors, and treatment effectiveness for sensory processing. Front Integr Neurosci. 2021;15:Article 652218.
Rosenau KA, Hotez E. Promoting interdisciplinary and participatory autism research. Pediatrics. 2022 Mar 1;149(Supplement 4):Article e2020049437P.
Scott KE, Schulz SE, Moehrle D, Allman BL, Oram Cardy JE, Stevenson RA, et al. Closing the species gap: Translational approaches to studying sensory processing differences relevant for autism spectrum disorder. Autism Res. 2021 Jul;14(7):1322–31.

Competing interest reported. ZJW has received consulting fees from Roche, Autism Speaks, and the May Institute. He also serves on the family advisory committee of the Autism Speaks Autism Care Network Vanderbilt site and on the autistic researcher review board of the Autism Intervention Research Network on Physical Health (AIR-P). KM is a member of Autistica’s Network Steering Committee. GTB is the author of the Sensory Experiences Questionnaire, version 2.1 and Sensory Experiences Questionnaire, version 3.0, which are freely available and for which she receives no royalties. SHM has a patent with Invention ID: D12847, Tech ID: C12847, Title: Kinematic and Morphometric Analysis of Digitized Handwriting Tracings. The remaining authors have no conflicts of interest to disclose.

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Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium

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Journal Publication

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Abstract

Background

Methods

Results

Limitations:

Conclusion

Figures

Introduction

Purpose

Methods

Participants

Constructs And Measures

Sensory Item Selection

[FIGURE 1 HERE]

Data Analysis

Sensory Subconstruct Refinement and Empirical Item Removal

Structural Evaluation of Supra-modal Sensory Response Pattern Constructs

Bifactor Modeling of Sensory Constructs

Demographic and Clinical Correlates of Sensory Reactivity

Results

[Table 1 HERE]

Sensory Subconstruct Refinement

[Table 2 HERE]

Structural Evaluation Of Supra-modal Sensory Constructs

HYPER

HYPO

[Table 4 HERE]

SEEK

[Table 5 HERE]

Demographic And Clinical Correlates

Modeling

[FIGURE 3 HERE]

Correlations with Demographic Variables

Correlations with Cognition and Adaptive Functioning

Correlations with Core Autism Features and Psychiatric Symptoms

Discussion

Strengths And Limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1