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Trajectories of Positive Affect in Autistic Individuals during the Transition to Adulthood

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Title: Trajectories of Positive Affect in Autistic Individuals during the Transition to Adulthood
Language: English
Authors: James B. McCauley (ORCID 0000-0001-8009-6484), Elaine B. Clarke, Hillary K. Schiltz (ORCID 0000-0001-7861-6049), Catherine Lord (ORCID 0000-0001-5633-1253)
Source: Autism: The International Journal of Research and Practice. 2025 29(1):118-129.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Sponsoring Agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
National Institute of Mental Health (NIMH) (DHHS/NIH)
Contract Number: R01HD081199
R01MH081873
Document Type: Journal Articles
Reports - Research
Descriptors: Autism Spectrum Disorders, Students with Disabilities, Individualized Transition Plans, Longitudinal Studies, Mental Health, Interpersonal Competence, Positive Attitudes, Affective Behavior, Caregiver Attitudes, Caregivers, Cognitive Ability, Self Evaluation (Individuals), Learning Trajectories, Quality of Life
Geographic Terms: North Carolina, Illinois (Chicago), Michigan
DOI: 10.1177/13623613241263902
ISSN: 1362-3613
1461-7005
Abstract: Longitudinal analyses have revealed informative patterns about health, mental health, adaptive skills, autism symptoms, and social skills during the transition to adulthood for autistic individuals. This study expands on these findings by investigating the trajectories of positive affect from age 15 to 30 years using a heterogeneous cohort (n = 253) of individuals first referred for autism at an early age. Group-based trajectory modeling was used to identify commonalities in trajectories using both caregiver-reported and self-reported positive affect. We analyzed differences between these trajectory groups on demographic and behavioral measures, as well as indices of adult functioning characterized for either higher and lower cognitive abilities. Caregiver-reported values revealed four different patterns of stability and change, and self-reported values revealed three distinct patterns of stability and change with variable intercepts. These trajectory groups differed by autism severity, intelligence quotient, daily living skills, and different indices of adult outcomes, including social relationships, work, and activity engagement. There were some differences in trajectory shape by reporter, with agreement between caregiver-report and self-report being limited after age 23 years. The results of this study have implications for how we measure subjective indices of experience across the spectrum of cognitive abilities present in autism.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1454596
Database: ERIC
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  Value: <anid>AN0181802395;f9d01jan.25;2024Dec24.01:41;v2.2.500</anid> <title id="AN0181802395-1">Trajectories of positive affect in autistic individuals during the transition to adulthood </title> <p>Longitudinal analyses have revealed informative patterns about health, mental health, adaptive skills, autism symptoms, and social skills during the transition to adulthood for autistic individuals. This study expands on these findings by investigating the trajectories of positive affect from age 15 to 30 years using a heterogeneous cohort (n = 253) of individuals first referred for autism at an early age. Group-based trajectory modeling was used to identify commonalities in trajectories using both caregiver-reported and self-reported positive affect. We analyzed differences between these trajectory groups on demographic and behavioral measures, as well as indices of adult functioning characterized for either higher and lower cognitive abilities. Caregiver-reported values revealed four different patterns of stability and change, and self-reported values revealed three distinct patterns of stability and change with variable intercepts. These trajectory groups differed by autism severity, intelligence quotient, daily living skills, and different indices of adult outcomes, including social relationships, work, and activity engagement. There were some differences in trajectory shape by reporter, with agreement between caregiver-report and self-report being limited after age 23 years. The results of this study have implications for how we measure subjective indices of experience across the spectrum of cognitive abilities present in autism. Recent research has revealed informative patterns about health, mental health, self-help skills, autism symptoms, and social skills during the transition to adulthood for autistic individuals. This study expands on these findings by examining how positive affect (e.g. excited) changes from age 15 to 30 years using a group of individuals first referred for autism at an early age. We also examined the agreement between caregiver-report and self-report on positive affect. We found different patterns of stability and change in positive affect across the transition to adulthood that related to differences in autism severity, cognitive abilities, self-help skills, as well as social and work participation in adulthood. The agreement between caregiver-report and self-report was strong in adolescence but became much weaker after the individuals were 23 years. These results have implications for how we measure happiness, positive emotions, or other internal experiences of autistic individuals.</p> <p>Keywords: adults; autism spectrum disorders; developmental trajectories; quality of life</p> <p>Historically, research on autism spectrum disorder (ASD) in adulthood has leveraged a deficit-based approach, focusing on challenges obtaining and maintaining "normative" adult outcomes—in other words, indicators associated with adult success in the general population. Although these indicators highlight that many autistic adults have difficulties gaining employment, living independently, and maintaining friendships and romantic relationships ([<reflink idref="bib3" id="ref1">3</reflink>]; [<reflink idref="bib6" id="ref2">6</reflink>]; [<reflink idref="bib31" id="ref3">31</reflink>]; [<reflink idref="bib33" id="ref4">33</reflink>]), some argue the achievement of one or more of these outcomes is not necessary for leading a fulfilling adult life ([<reflink idref="bib30" id="ref5">30</reflink>]) and therefore does not capture a full picture of adulthood. For example, there is some evidence that autistic adults do not derive as much satisfaction from employment as their non-autistic adults ([<reflink idref="bib4" id="ref6">4</reflink>]), and some autistic adults report differing social desires and/or needs in terms of friends and romantic relationships from adults in the general population ([<reflink idref="bib9" id="ref7">9</reflink>]). In light of these concerns and recent calls for autism research to move away from traditional deficit-based approaches of scientific inquiry ([<reflink idref="bib38" id="ref8">38</reflink>]), this study examines positive affect (a component of subjective well-being; see [<reflink idref="bib24" id="ref9">24</reflink>]) using longitudinal data from early adolescence to adulthood as a strength-based indicator of adult outcomes for autistic individuals.</p> <p>From research in the general population, we know individuals have long periods of stability in subjective well-being punctuated by fluctuations before and after pivotal changes, shifting life roles, and transitions commonly experienced by young adults ([<reflink idref="bib22" id="ref10">22</reflink>]). However, the bulk of autism research and service allocation efforts focus exclusively on childhood ([<reflink idref="bib14" id="ref11">14</reflink>]). Given increasing numbers of autistic individuals entering adulthood each year (e.g. [<reflink idref="bib13" id="ref12">13</reflink>]), as well as dramatic increases in the number of individuals receiving an autism diagnosis for the first time as adults ([<reflink idref="bib11" id="ref13">11</reflink>]), the gaps in our current understanding of autistic adulthood are concerning. It remains unclear how the social restructuring, shifting expectations and demands, and health-related changes associated with adulthood may impact the subjective well-being or emotional experience of autistic adults. Notably, cross-sectional research highlights changes in negative aspects of well-being (e.g. anxiety, depression, etc.) across adulthood in autism ([<reflink idref="bib18" id="ref14">18</reflink>]; [<reflink idref="bib46" id="ref15">46</reflink>]), with longitudinal research indicating that some risk factors during childhood may heighten risk for later subjective well-being ([<reflink idref="bib51" id="ref16">51</reflink>]). There is a need for more longitudinal research to bolster these findings by investigating within-person patterns of positive aspects of well-being over time. Specifically, in neurotypical adults, positive emotions can help regulate negative emotions and promote resilience after negative experiences ([<reflink idref="bib28" id="ref17">28</reflink>]; [<reflink idref="bib45" id="ref18">45</reflink>]). Understanding how positive emotions vary across time in autistic adults would broaden our understanding of their overall experience beyond negative impacts or experiences. By examining stability and change in a well-characterized longitudinal cohort of individuals with autism, this study tests both how and for whom positive affect changes in adolescence and early adulthood among autistic adults with a childhood diagnosis of ASD.</p> <p>The heterogeneity of autism is mirrored by the considerable variability seen in adulthood outcomes, including well-being, employment, housing, and relationships, among this population (e.g. [<reflink idref="bib29" id="ref19">29</reflink>]; [<reflink idref="bib34" id="ref20">34</reflink>]). Some autistic individuals achieve independence and "normative" adult success, others lead lives with many positive aspects despite limited achievement of these adult outcomes, and still others have substantial daily care needs that greatly limit their adult pursuits. Several demographic and phenotypic characteristics such as autism traits, cognitive ability, and gender have been linked to variation in subjective and objective outcomes among autistic adults ([<reflink idref="bib20" id="ref21">20</reflink>]; [<reflink idref="bib24" id="ref22">24</reflink>]; [<reflink idref="bib33" id="ref23">33</reflink>]; [<reflink idref="bib42" id="ref24">42</reflink>]), suggesting there are systematic and predictable differences in adulthood autism experiences. For example, higher verbal intelligence quotient (IQ) has been linked with a greater number of "normative" outcomes (e.g. employment status, independent living, and friendships ([<reflink idref="bib24" id="ref25">24</reflink>])). As another example, recent research highlights the critical role of gender on the life experiences of autistic women ([<reflink idref="bib42" id="ref26">42</reflink>]), and one longitudinal study of employment outcomes in autism found autistic women were more likely than their male counterparts to experience decreases in employment activities over time ([<reflink idref="bib40" id="ref27">40</reflink>]). Although the more "objective" adult outcomes described above may not fully capture autistic adulthood experiences ([<reflink idref="bib30" id="ref28">30</reflink>]), employment and social connection are sensible benchmarks of adult life to explore <emph>alongside</emph> subjective experiences for the vast majority of adults, regardless of diagnostic and developmental history. The financial stability associated with employment is an economic necessity for most adults, and many autistic individuals of all ages express a desire for more social connections ([<reflink idref="bib2" id="ref29">2</reflink>]; [<reflink idref="bib43" id="ref30">43</reflink>]) and experience loneliness when such connections are lacking ([<reflink idref="bib47" id="ref31">47</reflink>]). Given that the classification of "normative" or "objective" outcomes is imperfect—subjective metrics can add to our understanding of how autistic individuals are experiencing adulthood. Past research has found subjective metrics of perceived stress, quality of life ratings, and well-being to have informative associations with adult adaptive behaviors and experiences (e.g. [<reflink idref="bib25" id="ref32">25</reflink>]; [<reflink idref="bib33" id="ref33">33</reflink>]), but we need to better understand how positive emotions vary across time as they relate to experiences and living situations. Therefore, this study will consider how characteristics such as autism symptoms, IQ, and gender, as well as attainment of normative adult outcomes are associated with trajectories of positive emotions.</p> <p>One of the many challenges of researching autistic adult outcomes, especially those that are subjective, is measurement. Some have critiqued the ways in which academic literature has conceptualized well-being in autistic adults ([<reflink idref="bib16" id="ref34">16</reflink>]). There are also concerns with respect to informants. For example, it may be difficult for caregivers to accurately report on internal experiences of their child. Nonetheless, the use of caregiver-report may be a necessity in some cases. Given the broad heterogeneity of autism, caregiver-report measures, although imperfect, provide insight into the lived experiences of more cognitively impacted adults, many of whom have high daily care needs and/or limited language. As such, the use of caregiver-report allows us to capture well-being across the full autism spectrum. Collecting both caregiver and self-reported aspects of well-being in tandem, when possible, allows us to examine meaningful similarities and differences in perceptions of subjective well-being trajectories across reporters.</p> <hd id="AN0181802395-2">Study aims</hd> <p>Our aims for the current project were as follows:</p> <p></p> <ulist> <item> Characterize trajectories of self- and caregiver-reported positive affect to understand patterns of change from adolescence into adulthood among autistic individuals.</item> <p></p> <item> Identify demographic and phenotypic differences between derived trajectory groups. Specifically, we aimed to identify if childhood cognitive ability and autism features distinguished trajectories, and whether the derived trajectory groups would differ on metrics of adult functioning in the domains of work, activities, living situations, and daily living skills (DLSs), as previous studies have found these to be related to measures of subjective well-being (e.g. [<reflink idref="bib24" id="ref35">24</reflink>]; [<reflink idref="bib33" id="ref36">33</reflink>]).</item> <p></p> <item> Examine the similarities and differences between self-report and caregiver-report positive affect across age to understand the influence of reporter on the measurement of positive affect.</item> </ulist> <hd id="AN0181802395-3">Method</hd> <p></p> <hd id="AN0181802395-4">Participants</hd> <p>The current sample draws from a longitudinal cohort of 253 individuals with autism (<emph>n</emph> = 198) or non-ASD developmental delays (<emph>n</emph> = 55) and their families. The sample was recruited from three sites in the United States: North Carolina, the greater Chicago area, and Michigan. Families from Chicago and North Carolina (<emph>n</emph> = 213) were referred to clinics for potential ASD or non-ASD developmental delays when their children were under 3 years old. Families from Michigan (<emph>n</emph> = 40) subsequently joined the study when their children were approximately 9 years old and have been followed at the same times as the North Carolina and Chicago recruits.</p> <p>The primary analyses included data from 157 participants whose primary caregivers completed the Positive and Negative Affect Scales (PANAS; [<reflink idref="bib49" id="ref37">49</reflink>]) at least once (m = 6.52 instances surveyed, SD = 4.42) when the participant was between ages 15 and 29 years. Our analyses also included self-report data from 60 participants who completed the PANAS at least once (m = 5.39, SD = 3.64) between ages 15 and 29 years.</p> <p>Although the majority of participants in this study have a diagnosis of autism, it is important to note that 38 participants (24%) in the caregiver-reported sample and 18 participants (30%) in the self-reported sample had not received a diagnosis of autism, but had been frequently assessed for autism. Many of these individuals have been in the longitudinal sample since age 2 years and were initially referred for developmental delays. They are retained in the current analyses due to similarities in developmental milestones and adult outcomes (see [<reflink idref="bib20" id="ref38">20</reflink>]) and to increase statistical power. Multiple conditions—including intellectual disability, attention-deficit hyperactivity disorder, and learning disorders—are present in this group. Due to concerns that these individuals may skew the overall pattern of results in this article, all main analyses were conducted with the autism-only sample and compared to the main analyses reported below. When analyses were conducted with autism-only sample alone, only minor variations in the pattern of results occurred (see Supplementary File 1). In addition, the differences between participants in the current 157 family subsamples and the total 253 family longitudinal samples are reported in Table 1.</p> <p>Table 1. Participant demographics in the current subsample and total longitudinal sample.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="left">Current sample (<italic>n</italic> = 157)</th><th align="left">Total sample (<italic>n</italic> = 253)</th><th /></tr></thead><tbody><tr><td rowspan="2">Participant race</td><td>White</td><td>127</td><td>182</td><td rowspan="2">χ2 (1, 253) = 15.83, <italic>p</italic><italic><</italic> 0.001</td></tr><tr><td>Black Asian American/Pacific Islander or Mixed Race</td><td>31</td><td>71</td></tr><tr><td rowspan="2">Participant gender</td><td>Male</td><td>124</td><td>203</td><td rowspan="2">χ2 (1, 253) = 0.50, <italic>p</italic> = 0.48</td></tr><tr><td>Female</td><td>33</td><td>50</td></tr><tr><td rowspan="2">Caregiver education</td><td><4-Year College Degree</td><td>89</td><td>116</td><td rowspan="2">χ2 (1, 253) = 4.30, <italic>p</italic> = 0.04</td></tr><tr><td>⩾4-Year College Degree</td><td>68</td><td>137</td></tr><tr><td rowspan="2">Participant ethnicity</td><td>Non-Hispanic/Latino</td><td>150</td><td>242</td><td rowspan="2">χ2 (1, 250) = 0.01, <italic>p</italic> = 0.98</td></tr><tr><td>Hispanic/Latino</td><td>5</td><td>8</td></tr><tr><td rowspan="2">Urbanicity</td><td>Urban</td><td>102</td><td>88</td><td rowspan="2">χ2 (1, 247) = 1.28, <italic>p</italic> = 0.26</td></tr><tr><td>Rural</td><td>50</td><td>159</td></tr><tr><td rowspan="2">Diagnostic status</td><td>Ever ASD</td><td>119</td><td>198</td><td rowspan="2">χ2 (1, 253) = 0.34, <italic>p</italic> = 0.56</td></tr><tr><td>Never ASD</td><td>38</td><td>55</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181802395-5">Procedure</hd> <p>Participants and their caregivers completed both in-person testing and questionnaire packets. Questionnaires were mailed to caregivers seven times during the study period (years 15, 18, 20, 23, 25, and 29). At approximately age 9 years, participants completed extensive face-to-face testing, including the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; [<reflink idref="bib21" id="ref39">21</reflink>]) and standardized developmentally appropriate IQ tests (see [<reflink idref="bib1" id="ref40">1</reflink>]) with trained clinicians. At approximately age 14 years, caregivers completed the Vineland Adaptive Behavior Scales, Second Edition (VABS-II; [<reflink idref="bib35" id="ref41">35</reflink>]) as a phone interview with trained clinicians. In most cases, the term caregiver in this study refers to a biological parent of the participant, but there were nine total foster or adoptive parents in the total sample. Guardians and autistic individuals over age 18 years who were their own legal guardians gave written consent as required by the relevant institutional review boards (IRBs) prior to visits. All study procedures were approved by the relevant IRBs.</p> <hd id="AN0181802395-6">Measures</hd> <p></p> <hd id="AN0181802395-7">Positive affect</hd> <p>The PANAS ([<reflink idref="bib49" id="ref42">49</reflink>]) is widely used measure of mood and emotion. Consisting of 20 items, 10 of which measure positive affect (e.g. enthusiastic, proud) and 10 of which measure negative affect (e.g. irritable, guilty), the PANAS was designed to efficiently capture an individual's possible range of positive and negative emotions. Each item is rated on a 5-point Likert-type scale from 1 (<emph>very slightly</emph>) or 5 (<emph>extremely</emph>) based on how much the participant has felt a particular feeling/emotion (e.g. irritable) in the past month. Extensive factor analyses of the PANAS have confirmed that positive affect and negative affect are separate constructs that are relatively uncorrelated ([<reflink idref="bib17" id="ref43">17</reflink>]). In other words, an individual can experience both positive and negative emotions simultaneously, and the PANAS captures both ranges of emotion. This two-factor structure appears consistent across different populations (e.g. [<reflink idref="bib44" id="ref44">44</reflink>]). Both affect scales have good internal consistency as well as good convergent and discriminant validity ([<reflink idref="bib17" id="ref45">17</reflink>]). For this study, caregiver- and self-report scores for only the positive affect scale were analyzed. Participants and/or their primary caregiver completed the PANAS at seven time points: when participants were approximately 15, 18, 20, 23, 25, 27, and 29 years of age.</p> <hd id="AN0181802395-8">IQ</hd> <p>Participant cognitive ability was measured via assessments selected from a standardized hierarchy including the Wechsler Abbreviated Scale of Intelligence (WASI; [<reflink idref="bib50" id="ref46">50</reflink>]), Differential Ability Scales (DAS-II; [<reflink idref="bib8" id="ref47">8</reflink>]), and the Mullen Scales of Early Learning ([<reflink idref="bib27" id="ref48">27</reflink>]). The selection of cognitive assessment was primarily determined by expressive language capabilities, age, and previous performance on IQ tests, with the most cognitively and linguistically demanding assessment attempted first, with some changes in assessment depending on if basal or ceilings were achieved. Ratio IQs were calculated from age equivalents (AEs) when raw scores fell outside deviation score ranges (see [<reflink idref="bib1" id="ref49">1</reflink>]). A best estimate of IQ was then generated for each participant that reflected the highest performance on the most age-appropriate test the team could complete. Non-verbal and verbal IQ from age 9 years (<emph>n</emph> = 140) was compared across PANAS trajectory groups.</p> <hd id="AN0181802395-9">Autism features and diagnoses</hd> <p>Autism symptomology was measured using calibrated severity scores (CSS; i.e. autism features) from the ADOS-II ([<reflink idref="bib21" id="ref50">21</reflink>]). CSS scores range from 1 to 10, with higher scores reflecting more autism features. These scores were utilized from approximately age 9 years as 89% of the participants in this study had a complete assessment done at this time, with the remainder having one at 18 or 21 years. Clinicians made best-estimate diagnoses of ASD or other developmental disabilities at each in-person visit.</p> <hd id="AN0181802395-10">DLSs</hd> <p>Clinicians administered the VABS-II ([<reflink idref="bib35" id="ref51">35</reflink>]) to caregivers via phone interview when participants were approximately 14 years old. The VABS-II is a standardized structured interview assessing a range of adaptive functioning including communication, DLSs, socialization, and motor skills in comparison to other same-aged individuals. DLS AE scores (<emph>n</emph> = 139), assessing personal, domestic, and community skills, were compared across PANAS trajectory groups.</p> <hd id="AN0181802395-11">Objective adult outcomes</hd> <p>Consistent with previous research using this sample ([<reflink idref="bib24" id="ref52">24</reflink>]), adult outcomes are defined separately for participants with IQ above or below 70. Adult outcome data reflects participant status at age 26 years. For participants with IQ > 70, we defined three outcomes: (<reflink idref="bib1" id="ref53">1</reflink>) independent employment or age-appropriate education, (<reflink idref="bib2" id="ref54">2</reflink>) having at least one friend, and (<reflink idref="bib3" id="ref55">3</reflink>) living independently. Most of this information was acquired through semi-structured interviews with the adults (when possible) and with a caregiver using a modified version of the Social-Emotional Functioning Interview (SEF-S and SEF-I; [<reflink idref="bib32" id="ref56">32</reflink>]). In addition, participants regularly updated information about living situations and employment through demographic questionnaires. For participants with IQ < 70, we defined three outcomes: (<reflink idref="bib1" id="ref57">1</reflink>) supported, non-supported, or voluntary work activities outside the home, (<reflink idref="bib2" id="ref58">2</reflink>) having any non-family social contacts, and (<reflink idref="bib3" id="ref59">3</reflink>) having an average age equivalence score above 8 years on the personal and domestic subscales of the Vineland-II. Skills at this level included self-care (toileting, dressing oneself, basic hygiene) and the ability to complete some household chores. The information on adult outcomes for participants with an IQ < 70 was acquired through semi-structured interview with a caregiver using the modified SEF-I. In addition, we acquired some information from item-level analysis of the Vineland-II (caregiver-reported).</p> <hd id="AN0181802395-12">Analysis plan</hd> <p>Analyses were performed using Stata, version 17. To identify subgroups within trajectories of positive affect across time, group-based trajectory modeling was performed using the traj plugin for Stata ([<reflink idref="bib15" id="ref60">15</reflink>]). Group-based trajectory modeling identifies clusters of individuals within existing data using posterior probabilities in a two-stage process. Successive models of different class numbers were compared by modeling each trajectory group with intercept only models. A single class model was tested first which was then compared to models with increasing class size up to 6 classes (See Supplementary File 2). Bayesian information criteria (BIC) were used to identify the best-fitting model by comparing successive models on changes in BIC and by examining the smallest group size (see Supplementary File 2 for BIC values across models). After selection of the number of classes, models were tested to determine the best-fitting shape of each trajectory over time (intercept only, linear, quadratic, cubic), and posterior probabilities were examined to determine adequate model fit. Group-based trajectory modeling uses maximum likelihood estimation, and therefore, individuals with missing values are included within models. This procedure was done first for the caregiver-reported data, and then again used to develop and compare models for self-reported data.</p> <p>After final trajectory model selection for both the caregiver-reported and self-reported data, demographic, childhood, and adulthood characteristics were tabulated by trajectory class to understand the composition of groups selected. Multinomial logistic regression was used to statistically compare trajectory groups on these factors. When examining the objective adult outcomes, separate tables were constructed based on cognitive ability in adulthood.</p> <p>Associations between caregiver-reported and self-reported PANAS data were calculated via correlations at each time point. Cross-tabulation between membership in self- and caregiver-reported PANAS trajectory classes was calculated to examine commonalities across data structures.</p> <hd id="AN0181802395-13">Missing data</hd> <p>A subsample of 157 individuals was included in the caregiver-trajectory analyses. This subsample differed from the initial longitudinal sample on race (χ<sups>2</sups> (<reflink idref="bib1" id="ref61">1</reflink>, 253) = 18.05, <emph>p</emph><emph><</emph> 0.001) and caregiver education (χ<sups>2</sups> (<reflink idref="bib1" id="ref62">1</reflink>, 253) = 4.30, <emph>p</emph> = 0.04), but not on gender, urbanicity, nor diagnostic status. There were 60 individuals with enough self-reported data to be included in the self-report trajectory analyses. The number of available cases with positive affect data at each age was variable (see Supplementary File 3). In addition, there were 24 individuals without enough data from adulthood assessments to be included in the caregiver-reported class profiles for adult outcomes and 8 individuals with partially missing data in the self-reported class profiles.</p> <hd id="AN0181802395-14">Community involvement statement</hd> <p>There was no community involvement in the reported study.</p> <hd id="AN0181802395-15">Results</hd> <p></p> <hd id="AN0181802395-16">Caregiver-reported PANAS trajectories</hd> <p>Comparing models set to intercept functions, a four-class trajectory model was found to be the best overall fit for class number. Comparing the fit of trajectory shapes within the four-class model revealed one intercept only trajectory and three linear trajectories. Illustrated in Figure 1, the classes were labeled "Class 1: Lower Stable" (8.20% of sample), that was typified by a low intercept only trajectory, "Class 2: Lower Decreasing" (34.32% of sample), described by a relatively low intercept that decreased in positive affect across age, "Class 3: Higher Decreasing" (34.62% of sample) that had a higher intercept that also decreased linearly across age, and "Class 4: Higher Increasing" (18.24% of sample), that had a similar intercept as Class 3 but increased in positive affect across age.</p> <p>Graph: Figure 1. Trajectories of Caregiver-Reported PANAS Positive Total Scores from ages 15 to 29 years.</p> <p>Table 2 contains the caregiver-reported class membership profiles for demographic and childhood data for all in the current subsample. The Higher Decreasing and Higher Increasing classes contained more participants who never received an autism diagnosis compared to the classes with lower intercepts. Compared to Class 3: Higher Decreasing, there were significantly higher autism features at age 9 years in Class 1: Lower Stable and significantly lower IQ and Vineland DLS Age Equivalence in Class 2: Lower Decreasing. There were no statistically significant differences between Classes 3 (Higher Decreasing) and 4 (Higher Increasing) on demographic or childhood data.</p> <p>Table 2. Caregiver-reported PANAS trajectory class membership profiles—total sample.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Lower stable(<italic>n</italic> = 13)</th><th align="left">Lower decreasing(<italic>n</italic> = 56)</th><th align="left">Higher decreasing(<italic>n</italic> = 60)</th><th align="left">Higher increasing(<italic>n</italic> = 28)</th></tr></thead><tbody><tr><td>Ever ASD</td><td>92.30%<xref ref-type="table-fn" rid="tfn1">*</xref></td><td>87.5%<xref ref-type="table-fn" rid="tfn1">*</xref></td><td>66.67%</td><td>64.29%</td></tr><tr><td>Male</td><td>92.30%</td><td>82.14%</td><td>76.67%</td><td>71.43%</td></tr><tr><td>White</td><td>84.61%</td><td>78.57%</td><td>81.67%</td><td>82.14%</td></tr><tr><td>Caregiver education > high school</td><td>92.30%</td><td>80.36%</td><td>71.66%</td><td>89.29%</td></tr><tr><td>Lower cognitive ability in adulthood</td><td>58.33%(<italic>n</italic> = 7; 1 missing data)</td><td>72.34%(<italic>n</italic> = 47; 9 missing data)</td><td>45.10%(<italic>n</italic> = 51; 9 missing data)</td><td>43.48%(<italic>n</italic> = 10; 5 missing data)</td></tr><tr><td>FSIQ best estimate @ 9</td><td>55.38 (33.12)</td><td>48.84<xref ref-type="table-fn" rid="tfn1">*</xref> (35.80)</td><td>68.93 (36.86)</td><td>70.18 (33.75)</td></tr><tr><td>Autism features @ 9</td><td>6.31<xref ref-type="table-fn" rid="tfn1">*</xref> (2.72)</td><td>6.09 (2.74)</td><td>5.40 (2.98)</td><td>5.57 (3.10)</td></tr><tr><td>VABS DLS age equivalence in years @ 18</td><td>9.67 (4.83)</td><td>8.09<xref ref-type="table-fn" rid="tfn1">*</xref> (4.31; 6 missing)</td><td>11.39 (4.79; 3 missing)</td><td>12.59 (5.26; 2 missing)</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>p</emph> < 0.05 in multinomial logistic model with Class 3 (Higher Decreasing) as reference group.</p> <p>Table 3 contains the caregiver-reported class membership profiles for adults with lower cognitive abilities that had requisite data in adulthood. There were significantly fewer individuals with friends, peers, or acquaintances in Class 2: Lower Decreasing as compared to Classes 3: Higher Decreasing and 4: Higher Increasing, and significantly fewer individuals who were occupied during the day in Class 2: Lower Decreasing compared to Class 3: Higher Decreasing. Classes 3: Higher Decreasing and 4: Higher Increasing had higher probabilities of meeting more than one of the defined adult outcomes compared to Class 2: Lower Decreasing.</p> <p>Table 3. Caregiver-reported PANAS trajectory class membership profiles in autistic adults with lower cognitive abilities.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Lower stable(<italic>n</italic> = 7)</th><th align="left">Lower decreasing(<italic>n</italic> = 34)</th><th align="left">Higher decreasing(<italic>n</italic> = 23)</th><th align="left">Higher increasing(<italic>n</italic> = 10)</th></tr></thead><tbody><tr><td>% with activities during the day</td><td>85.71%</td><td>29.41%<xref ref-type="table-fn" rid="tfn2">*</xref></td><td>61.90%</td><td>60.00%</td></tr><tr><td>% with friends, peers, or acquaintances</td><td>57.14%</td><td>28.41%<xref ref-type="table-fn" rid="tfn2">*</xref></td><td>66.67%</td><td>80.00%</td></tr><tr><td>% with DLS age equivalency >8</td><td>14.29%<xref ref-type="table-fn" rid="tfn2">*</xref></td><td>41.18%</td><td>65.22%</td><td>50.00%</td></tr><tr><td>% meeting all outcomes</td><td>14.29%</td><td>8.82%<xref ref-type="table-fn" rid="tfn2">*</xref></td><td>26.09%</td><td>30.00%</td></tr><tr><td>% meeting none</td><td>14.29%</td><td>32.35%<xref ref-type="table-fn" rid="tfn2">*</xref></td><td>8.70%</td><td>10.00%</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>p</emph> < 0.05 in multinomial logistic model with Class 3 (Higher Decreasing) as reference group.</p> <p>Table 4 contains the caregiver-reported class membership profiles for adults with higher cognitive abilities with adulthood data. Individuals in Class 2: Lower Decreasing had lower frequencies of full-time work, living independently, and having a true friend as compared to Class 3: Higher Decreasing. There were no statistically significant differences between Class 3: Higher Decreasing and 4: Lower Decreasing on adult outcome criteria.</p> <p>Table 4. Caregiver-reported PANAS trajectory class membership profiles in autistic adults with higher cognitive abilities.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Lower stable(<italic>n</italic> = 5)</th><th align="left">Lower decreasing(<italic>n</italic> = 13)</th><th align="left">Higher decreasing(<italic>n</italic> = 28)</th><th align="left">Higher increasing(<italic>n</italic> = 13)</th></tr></thead><tbody><tr><td>% with independent work</td><td>60%</td><td>69.23%<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>75%</td><td>100%</td></tr><tr><td>% living independently</td><td>20%</td><td>23.08%<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>39.29%</td><td>61.54%</td></tr><tr><td>% with true friend</td><td>20%</td><td>38.46%<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>57.14%</td><td>69.23%</td></tr><tr><td>% meeting all outcomes</td><td>20%</td><td>15.38%<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>32.14%</td><td>53.85%</td></tr><tr><td>% meeting none</td><td>40%</td><td>23.08%</td><td>21.43%</td><td>0%</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>p</emph> < 0.05 in multinomial logistic model with Class 3 (Higher Decreasing) as reference group.</p> <hd id="AN0181802395-17">Self-reported PANAS trajectories</hd> <p>A three-class model was the overall best fit for the self-reported data. Comparing trajectory shapes over time revealed one class comprised of a lower intercept and decreasing linear trajectory ("Class 1: Lower Decreasing," 33.6% of sample), one class comprised of a higher intercept with a decreasing linear trajectory ("Class 2: Higher Decreasing," 27% of sample), and one class comprised of a higher intercept and no observable change ("Class 3: Stable High," 39.4% of sample), as seen in Figure 2.</p> <p>Graph: Figure 2. Trajectories of self-reported PANAS positive total scores from ages 18 to 27 years.</p> <p>Table 5 contains the self-reported class membership profiles for childhood data and adult outcome data. There were no significant differences in demographic characteristics or IQ between groups. Class 1: Lower Decreasing had significantly higher ADOS autism features as assessed at 9 years old. Class 2: Higher Decreasing had significantly higher daily living skills as measured at 18 years. In adulthood, Class 2 had a higher proportion of individuals with a full-time position, and Class 1 had a significant lower proportion of individuals living independently and who had a true friend.</p> <p>Table 5. Self-reported PANAS trajectory class membership profiles.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Lower decreasing(<italic>n</italic> = 21)</th><th align="left">Higher decreasing(<italic>n</italic> = 12)</th><th align="left">Higher stable(<italic>n</italic> = 27)</th></tr></thead><tbody><tr><td>Ever ASD</td><td>85.71%</td><td>66.67%</td><td>59.25%</td></tr><tr><td>Male</td><td>76.19%</td><td>83.33%</td><td>77.78%</td></tr><tr><td>White</td><td>90.47%</td><td>100%</td><td>85.18%</td></tr><tr><td>Caregiver education > high school</td><td>66.67%</td><td>91.67%</td><td>85.18%</td></tr><tr><td>FSIQ best estimate @ 9</td><td>89.65 (21.49)</td><td>105.83 (18.97)</td><td>97.03 (19.05)</td></tr><tr><td>Autism features @ 9</td><td>6.15<xref ref-type="table-fn" rid="tfn4">*</xref> (2.66)</td><td>4.41 (2.74)</td><td>4.07 (2.82)</td></tr><tr><td>VABS DLS age equivalence in years @ 18</td><td>12.50 (3.02)</td><td>17.13<xref ref-type="table-fn" rid="tfn4">*</xref> (3.37)</td><td>14.37 (3.07; 1 missing)</td></tr><tr><td>% with independent work</td><td>50%<xref ref-type="table-fn" rid="tfn4">*</xref>(<italic>n</italic> = 8; 5 missing data)</td><td>90.90%(<italic>n</italic> = 10; 1 missing data)</td><td>79.16%(<italic>n</italic> = 19; 3 missing data)</td></tr><tr><td>% living independently</td><td>12.50%<xref ref-type="table-fn" rid="tfn4">*</xref>(<italic>n</italic> = 2; 5 missing data)</td><td>54.54%(<italic>n</italic> = 6; 1 missing data)</td><td>45.83%(<italic>n</italic> = 11; 3 missing data)</td></tr><tr><td>% with true friend</td><td>31.25%<xref ref-type="table-fn" rid="tfn4">*</xref>(<italic>n</italic> = 5; 5 missing data)</td><td>45.45%(<italic>n</italic> = 5; 1 missing data)</td><td>62.50%(<italic>n</italic> = 15; 3 missing data)</td></tr><tr><td>% meeting all outcomes</td><td>6.25%<xref ref-type="table-fn" rid="tfn4">*</xref>(<italic>n</italic> = 1; 5 missing data)</td><td>45.45%(<italic>n</italic> = 5; 1 missing data)</td><td>37.50%(<italic>n</italic> = 9; 3 missing data)</td></tr><tr><td>% meeting none</td><td>43.75%<xref ref-type="table-fn" rid="tfn4">*</xref>(<italic>n</italic> = 7; 5 missing data)</td><td>9.09%(<italic>n</italic> = 1; 1 missing data)</td><td>16.67%(<italic>n</italic> = 4; 3 missing data)</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>p</emph> < 0.05 in multinomial logistic model with Class 3 (Higher Stable) as reference group.</p> <hd id="AN0181802395-18">Caregiver- and self-reported PANAS agreement</hd> <p>Correlations between self-reported and caregiver-reported positive affect totals were variable across ages. There was significant agreement at age 18 years (<emph>r</emph> = 0.44, <emph>p</emph> = 0.01, <emph>n</emph> = 34), age 20 years (<emph>r</emph> = 0.61, <emph>p</emph> = 0.001, <emph>n</emph> = 28), and age 23 years (<emph>r</emph> = 0.60, <emph>p</emph> = 0.001, <emph>n</emph> = 29), but correlations between raters were weak at age 25 years (<emph>r</emph> = 0.09, <emph>p</emph> = 0.68, <emph>n</emph> = 22) and age 27 years (<emph>r</emph> = 0.11, <emph>p</emph> = 0.65, <emph>n</emph> = 20). There were not enough data from individuals under age 18 years and above age 27 years to meaningfully assess agreement. The cross-tabulation between self- and caregiver-reported PANAS trajectory classes for individuals with self-reported data is reported in Table 6. In general, there appears to be more correspondence in the trajectory groups when looking "higher" or "lower" intercept, but a few individuals who were either in a lower intercept in the caregiver-reported groups but a higher intercept in the self-reported groups (<emph>n</emph> = 6), and some who were in the higher intercept caregiver-reported groups but lower intercept group (<emph>n</emph> = 8). When looking at patterns of change across time, there was little overlap between the caregiver- and self-reported trajectory groups.</p> <p>Table 6. Cross-tabulation between caregiver- and self-reported trajectory classes.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="left" colspan="4">Caregiver-reported trajectory classification</th></tr><tr><th /><th /><th align="left">Lower stable</th><th align="left">Lower decreasing</th><th align="left">Higher decreasing</th><th align="left">Higher increasing</th></tr></thead><tbody><tr><td rowspan="3">Self-reported trajectory classification</td><td>Lower decreasing</td><td>5</td><td>8</td><td>7</td><td>1</td></tr><tr><td>Higher decreasing</td><td>0</td><td>1</td><td>6</td><td>5</td></tr><tr><td>Higher stable</td><td>0</td><td>5</td><td>13</td><td>9</td></tr></tbody></table> </ephtml> </p> <p>5 Numbers represent cases present in both classes.</p> <hd id="AN0181802395-19">Discussion</hd> <p>In this study, we leveraged a strength-based approach to examine longitudinal patterns of positive affect—an important component of positive subjective well-being—from late adolescence into adulthood for autistic adults using both caregiver-reported and self-reported values. Our data revealed informative patterns of stability and change using group-based trajectory analyses on caregiver- and self-reported PANAS data that were related to multiple demographic and phenotypic characteristics. We also found variability in agreement between caregivers and individuals across age. These analyses enrich understanding of mental health, vocational, and adaptive behavior trajectories during the transition to adulthood (e.g. [<reflink idref="bib7" id="ref63">7</reflink>]; [<reflink idref="bib23" id="ref64">23</reflink>]; [<reflink idref="bib37" id="ref65">37</reflink>]).</p> <hd id="AN0181802395-20">Positive affect trajectories</hd> <p>Trajectory analysis based on caregiver-reported positive affect, representing autistic individuals of a wide range of cognitive abilities, revealed four different patterns of change. Two of the identified trajectory groups had lower intercepts and minimal negative change, and two had higher intercepts with opposite patterns of linear change. In a study on neurotypical young adults, there exists some evidence that positive affect (as measured on the PANAS) has an overall linear positive growth, but with significant variance in individual slopes ([<reflink idref="bib48" id="ref66">48</reflink>]). In our analysis, there were significant differences between the classes with higher and lower intercepts, notably the classes with lower positive affect intercepts had a higher proportion of individuals with an autism diagnosis, higher mean autism features, and lower mean daily living skills age equivalence scores. In addition, there were differences in IQ measured at age 9 years between classes, although IQ did not determine class membership as wide variation existed within all classes. Given the higher proportion of autistic individuals characterized by lower intercepts and stable or decreasing trajectories compared to the non-autistic individuals in the sample, there appear to be substantive differences in positive affect by diagnostic groups evident by late adolescence, which persist into adulthood. In accordance with other research, these differences may be related to distinct social experiences ([<reflink idref="bib39" id="ref67">39</reflink>]), mental health challenges ([<reflink idref="bib10" id="ref68">10</reflink>]), and, in the school-aged years, peer victimization experiences ([<reflink idref="bib19" id="ref69">19</reflink>]). More in-depth examinations of experiences that influence positive affect ratings, as well as how these may vary based on diagnostic status, are warranted.</p> <p>Past research has described poorer "normative" outcomes (e.g. employment, friendships, housing) for autistic adults with lower cognitive abilities, but there is little evidence that intellectual ability impacts metrics of subjective well-being ([<reflink idref="bib33" id="ref70">33</reflink>]). It is critical to disentangle assumptions about quality of life and well-being from intellectual ability for autistic adults—positive emotions are not limited by IQ itself but rather are more likely influenced by experience. The majority of work on well-being in autistic adults has only included individuals capable of self-report, thereby excluding many individuals diagnosed in earlier childhood that proportionally skew toward individuals with lower cognitive abilities ([<reflink idref="bib12" id="ref71">12</reflink>]). We urgently need valid and reliable measures that capture subjective experience or utilize behavioral observation for autistic individuals with lower cognitive abilities or minimal language to understand how to promote positive life experiences more broadly in the autism population. Ultimately, the debate concerning which outcomes we assess as normative or ideal for autistic adults is secondary to the goal of promoting happiness and quality of life (see [<reflink idref="bib41" id="ref72">41</reflink>]), but the current tools remain limited and inequitable.</p> <p>Self-reported PANAS trajectories, describing patterns only among individuals with higher cognitive abilities, revealed three distinct patterns of change from adolescents to young adulthood. Two of these patterns were linear and decreasing but with significant differences in their intercepts. The third pattern was best represented by a high intercept and stability across ages. Looking at class membership, individuals with higher intercept of positive emotions had lower autism symptoms, and higher frequency of working, living independently, and having friendships in adulthood when compared to the lower intercept class. Only one measured characteristic distinguished the stable trajectory from the high intercept decreasing trajectory: individuals in the decreasing trajectory group had higher daily living skills measured at 18 years old. This finding is intriguing as it suggests that individuals with higher skills at 18 years were more likely to decline in positive emotions across young adulthood, even when the relative frequencies of work, living independently, and friendships were similar between the two high intercept classes. Similarly, while findings revealed several differences between the caregiver-reported classes with lower intercepts from the classes with higher intercepts on metrics of adult outcomes, there were no discernible differences on available metrics between the two caregiver-reported classes with higher intercepts but divergent linear trajectories. While it is possible that higher daily living skills could influence the demands and expectations that are placed on an individual, thereby influencing trajectories of well-being, the classes may also be capturing other underlying personality differences within the sample. We were also unable to measure self-determination or similar concepts that could moderate the relationship between skills and outcome attainment. Adults who attain their goals might experience increases in positive affect more than adults who are meeting outcomes but who may want other experiences or more control over their work, living, or social arrangements.</p> <p>Overall, from the caregiver-reported class profiles, autistic individuals with lower cognitive abilities appear to have a higher intercept and overall higher mean across time on positive emotions when they have activities during the day, social relationships, and higher levels of daily living skills. For autistic individuals with higher cognitive abilities, the frequency of work, independent living, and true friendships were related to membership in trajectories with higher levels of positive emotions across time using both caregiver-reported and self-reported data. These findings corroborate other research on autistic adults—social and community opportunities are highly influential to positive experiences (e.g. [<reflink idref="bib5" id="ref73">5</reflink>]).</p> <hd id="AN0181802395-21">Caregiver- and self-reported agreement</hd> <p>The agreement between caregiver-reported and self-reported values across time contributes to our understanding of the measurement of well-being and other subjective metrics. Our trajectory analyses revealed similarities in trajectory intercept between the self- and caregiver-reported class assignments, with some minor variation in trajectory shape. For individuals who could self-report, there was a high correlation between self- and caregiver-reported positive affect until around age 25 years. This suggests that there may be limitations in caregiver-report as autistic adults are able to gain independence, and thus caregiver-report may have limited utility after the transition to adulthood. For adults with lower cognitive abilities, caregiver-reports were the only available method in this study to understand well-being across time. Measurement and data analytic choices have often led to exclusion of individuals with lower cognitive abilities as there are limited available procedures that apply to this population, particularly when self-report is used as an inclusion criterion. Therefore, surrogate or proxy-reporting is an important tool for including more of the autism population in research. Proxy-reporting is not without drawbacks; in that, it might misrepresent the experiences of autistic adults. For example, there are concerns that proxy-report is liable to observer bias ([<reflink idref="bib26" id="ref74">26</reflink>]) in addition to concerns that caregiver decision-making and reporting can lessen the autonomy of an autistic person ([<reflink idref="bib36" id="ref75">36</reflink>]). These concerns exist across the spectrum of cognitive abilities. When individuals can report for themselves, it should be the priority to utilize this information, and when they cannot, we need to attempt to understand their experience from the best available source, which in most cases is a family caregiver who provides them support. However, this relates again to the problem of equity—as individuals enter adulthood, there are sequentially more limitations for studying autistic individuals with a wide range of cognitive abilities using the same measurement tools and theoretical perspectives.</p> <hd id="AN0181802395-22">Strengths and limitations</hd> <p>The strengths of this study included a longitudinal sample that has been well-characterized since childhood and the representation of data from multiple sources and across the transition into adulthood. However, there are also limitations to the study design. As with all longitudinal studies, attrition impacts the generalization of the results. Across 30 years of the study, drop out has been related to race and caregiver education, which limits our ability to understand how these factors influence trajectories of well-being. In addition, it is important to emphasize that trajectory groups are derived from available data, and therefore may not be reflective of the experiences of individuals beyond this study. We also had smaller sample sizes when examining differences between trajectory groups on adult outcomes, limiting our ability to detect certain group-level differences by these outcomes. Finally, all data presented here represent pre–COVID-19 pandemic levels of positive affect, and therefore, we may expect these trajectories to change with the inclusion of data representative of this time period.</p> <hd id="AN0181802395-23">Conclusion</hd> <p>Autistic individuals have variable trajectories of positive affect from adolescence into adulthood. While factors such as social relationships and activities distinguished the overall levels of positive affect, intellectual ability does not consistently relate to different patterns of change. Caregiver-reported positive affect values and self-reported values have some concordance in adolescence and young adulthood, but our findings suggest caution is warranted in using caregiver-report on subjective values further into adulthood if self-report is available. Future work should expand on methods to improve or create alternatives to caregiver-report for more impacted individuals who are unable to report on their own internal experiences of well-being via traditional methods. Autistic individuals, parents, and professionals should prioritize the individual goals and perspectives of the adult where possible, and future work is needed to identify and promote meaningful opportunities to engage in activities for autistic adults with lower cognitive abilities.</p> <hd id="AN0181802395-24">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613241263902 for Trajectories of positive affect in autistic individuals during the transition to adulthood by James B McCauley, Elaine B Clarke, Hillary K Schiltz and Catherine Lord in Autism</p> <hd id="AN0181802395-25">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-2-aut-10.1177_13623613241263902 for Trajectories of positive affect in autistic individuals during the transition to adulthood by James B McCauley, Elaine B Clarke, Hillary K Schiltz and Catherine Lord in Autism</p> <hd id="AN0181802395-26">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-3-aut-10.1177_13623613241263902 for Trajectories of positive affect in autistic individuals during the transition to adulthood by James B McCauley, Elaine B Clarke, Hillary K Schiltz and Catherine Lord in Autism</p> <p>The authors thank the participants and families involved in this study for their time and efforts.</p> <ref id="AN0181802395-27"> <title> References </title> <blist> <bibl id="bib1" idref="ref40" type="bt">1</bibl> <bibtext> Anderson D. 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All other authors have no conflicts of interest to declare.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the National Institute of Child Health and Human Development R01 HD081199 (PI: C.L.) and the National Institute of Mental Health R01MH081873 (PI: C.L.)</bibtext> </blist> <blist> <bibtext> James B McCauley</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-8009-6484 Hillary K Schiltz</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-7861-6049 Catherine Lord</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0001-5633-1253</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By James B McCauley; Elaine B Clarke; Hillary K Schiltz and Catherine Lord</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib31" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib33" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib30" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib38" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib24" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib22" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib14" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib13" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib11" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib18" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib46" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib51" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib28" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib45" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib29" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib34" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib20" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib42" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib40" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib43" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib47" firstref="ref31"></nolink> <nolink nlid="nl22" bibid="bib25" firstref="ref32"></nolink> <nolink nlid="nl23" bibid="bib16" firstref="ref34"></nolink> <nolink nlid="nl24" bibid="bib49" firstref="ref37"></nolink> <nolink nlid="nl25" bibid="bib21" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib35" firstref="ref41"></nolink> <nolink nlid="nl27" bibid="bib17" firstref="ref43"></nolink> <nolink nlid="nl28" bibid="bib44" firstref="ref44"></nolink> <nolink nlid="nl29" bibid="bib50" firstref="ref46"></nolink> <nolink nlid="nl30" bibid="bib27" firstref="ref48"></nolink> <nolink nlid="nl31" bibid="bib32" firstref="ref56"></nolink> <nolink nlid="nl32" bibid="bib15" firstref="ref60"></nolink> <nolink nlid="nl33" bibid="bib23" firstref="ref64"></nolink> <nolink nlid="nl34" bibid="bib37" firstref="ref65"></nolink> <nolink nlid="nl35" bibid="bib48" firstref="ref66"></nolink> <nolink nlid="nl36" bibid="bib39" firstref="ref67"></nolink> <nolink nlid="nl37" bibid="bib10" firstref="ref68"></nolink> <nolink nlid="nl38" bibid="bib19" firstref="ref69"></nolink> <nolink nlid="nl39" bibid="bib12" firstref="ref71"></nolink> <nolink nlid="nl40" bibid="bib41" firstref="ref72"></nolink> <nolink nlid="nl41" bibid="bib26" firstref="ref74"></nolink> <nolink nlid="nl42" bibid="bib36" firstref="ref75"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Trajectories of Positive Affect in Autistic Individuals during the Transition to Adulthood
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22James+B%2E+McCauley%22">James B. McCauley</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8009-6484">0000-0001-8009-6484</externalLink>)<br /><searchLink fieldCode="AR" term="%22Elaine+B%2E+Clarke%22">Elaine B. Clarke</searchLink><br /><searchLink fieldCode="AR" term="%22Hillary+K%2E+Schiltz%22">Hillary K. Schiltz</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7861-6049">0000-0001-7861-6049</externalLink>)<br /><searchLink fieldCode="AR" term="%22Catherine+Lord%22">Catherine Lord</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5633-1253">0000-0001-5633-1253</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2025 29(1):118-129.
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  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)<br />National Institute of Mental Health (NIMH) (DHHS/NIH)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R01HD081199<br />R01MH081873
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Transition+Plans%22">Individualized Transition Plans</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+Competence%22">Interpersonal Competence</searchLink><br /><searchLink fieldCode="DE" term="%22Positive+Attitudes%22">Positive Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+Behavior%22">Affective Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Caregiver+Attitudes%22">Caregiver Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Caregivers%22">Caregivers</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Evaluation+%28Individuals%29%22">Self Evaluation (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Trajectories%22">Learning Trajectories</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+Life%22">Quality of Life</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22North+Carolina%22">North Carolina</searchLink><br /><searchLink fieldCode="DE" term="%22Illinois+%28Chicago%29%22">Illinois (Chicago)</searchLink><br /><searchLink fieldCode="DE" term="%22Michigan%22">Michigan</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613241263902
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Longitudinal analyses have revealed informative patterns about health, mental health, adaptive skills, autism symptoms, and social skills during the transition to adulthood for autistic individuals. This study expands on these findings by investigating the trajectories of positive affect from age 15 to 30 years using a heterogeneous cohort (n = 253) of individuals first referred for autism at an early age. Group-based trajectory modeling was used to identify commonalities in trajectories using both caregiver-reported and self-reported positive affect. We analyzed differences between these trajectory groups on demographic and behavioral measures, as well as indices of adult functioning characterized for either higher and lower cognitive abilities. Caregiver-reported values revealed four different patterns of stability and change, and self-reported values revealed three distinct patterns of stability and change with variable intercepts. These trajectory groups differed by autism severity, intelligence quotient, daily living skills, and different indices of adult outcomes, including social relationships, work, and activity engagement. There were some differences in trajectory shape by reporter, with agreement between caregiver-report and self-report being limited after age 23 years. The results of this study have implications for how we measure subjective indices of experience across the spectrum of cognitive abilities present in autism.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1454596
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1454596
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613241263902
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 118
    Subjects:
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Students with Disabilities
        Type: general
      – SubjectFull: Individualized Transition Plans
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Mental Health
        Type: general
      – SubjectFull: Interpersonal Competence
        Type: general
      – SubjectFull: Positive Attitudes
        Type: general
      – SubjectFull: Affective Behavior
        Type: general
      – SubjectFull: Caregiver Attitudes
        Type: general
      – SubjectFull: Caregivers
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Self Evaluation (Individuals)
        Type: general
      – SubjectFull: Learning Trajectories
        Type: general
      – SubjectFull: Quality of Life
        Type: general
      – SubjectFull: North Carolina
        Type: general
      – SubjectFull: Illinois (Chicago)
        Type: general
      – SubjectFull: Michigan
        Type: general
    Titles:
      – TitleFull: Trajectories of Positive Affect in Autistic Individuals during the Transition to Adulthood
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: James B. McCauley
      – PersonEntity:
          Name:
            NameFull: Elaine B. Clarke
      – PersonEntity:
          Name:
            NameFull: Hillary K. Schiltz
      – PersonEntity:
          Name:
            NameFull: Catherine Lord
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
ResultId 1