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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: Child Neuropsychol. 2021 Jan 1;27(4):447–467. doi: 10.1080/09297049.2020.1862076

Neurocognitive Predictors of Adaptive Functioning Trajectories among Youth with Spina Bifida

Adrien M Winning 1, Alexa Stern 1, Joseph R Rausch 2, Meredith Starnes 1, Grayson N Holmbeck 1
PMCID: PMC8035277  NIHMSID: NIHMS1657701  PMID: 33386061

Abstract

Objective:

Little is known about how youth with spina bifida (SB) acquire adaptive functioning skills across development. Therefore, the current study examined: (1) trajectories of adaptive functioning in youth with SB as they transitioned from childhood into adolescence, and (2) neuropsychological functioning as a potential risk factor for long-term adaptive functioning difficulties.

Methods:

Participants (n = 131 youth with SB) were recruited as part of a larger ongoing longitudinal study. Growth curves were used to examine changes over time across six adaptive functioning skills: communication, self-direction, functional academics, social, self-care, and home living skills. Additionally, youth’s attention and executive functioning (i.e., working memory, planning/organizational skills, cognitive flexibility, inhibition) were assessed via questionnaires and performance-based assessments, and entered as predictors in the models.

Results:

Youth’s communication, self-direction, functional academics, self-care, and home living skills increased over time across age, whereas youth’s social skills did not. Scaled scores for youth’s social, communication, self-direction, and functional academics skills were generally within normal limits, whereas those for self-care and home living skills fell in the borderline range. Better attention and executive functioning predicted a higher intercept for many adaptive functioning abilities at 11.5 years old, above and beyond the influence of IQ. However, these variables did not predict growth in adaptive functioning.

Conclusion:

Results indicate that youth with SB acquire skills across development to better meet the demands of daily life. However, youth with poorer neurocognitive functioning may demonstrate adaptive functioning deficits in early childhood and benefit from timely intervention.

Keywords: spina bifida, children, adolescents, adaptive functioning, executive functioning, attention

Spina bifida (SB) is a complex chronic medical condition resulting from the failed closure of the embryonic neural tube during the first month of pregnancy and has significant consequences for the central nervous system (Copp et al., 2015). Specifically, SB is associated with physical and cognitive impairments (e.g., bladder and bowel incontinence, motor and sensory neurological deficits, hydrocephalus, paralyzed lower extremities; Copp et al., 2015), the severity of which varies based on spinal cord lesion level and neurological complications (e.g., Chiari II malformations resulting in shunt revisions and infections; Copp et al., 2015; Fletcher et al., 2005). This constellation of primary impairments can then have downstream effects on children’s academic, psychosocial, and functional outcomes (Holmbeck & Devine, 2010; Holmbeck & Faier-Routman, 1995; Holmbeck et al., 2003). Functional abilities, in particular, are often compromised in youth with SB, such that they display deficits in managing the demands of daily life compared to their typically developing peers (Copp et al., 2015; Holmbeck et al., 2003). Characterizing changes in adaptive functioning over time (i.e., overall increases or decreases), as well as isolating risk factors for slower growth in such functioning, are critical to the development of targeted interventions for this population.

The American Association on Intellectual and Developmental Disabilities (AAIDD) characterizes adaptive functioning as abilities across three distinct domains: conceptual, social, and practical (Schalock et al., 2010). The conceptual domain includes skills such as literacy, language, and self-direction, whereas the social domain includes skills related to interacting with others (e.g., social problem solving; Schalock et al., 2010). Further, the practical domain includes skills of daily living, such as managing self-care and navigating transportation and health-care (Schalock et al., 2010).

Previous research on adaptive functioning in the conceptual domain suggests that youth with SB have a complex language profile, exceling in vocabulary but struggling with comprehension and language flexibility (Copp et al., 2015; Dennis, Landry, Barnes, & Fletcher, 2006; Taylor et al., 2010). Youth also display difficulties with verbosity (Taylor et al., 2010). In addition to these communication difficulties, self-direction (i.e., skills related to self-control, making decisions, completing tasks, and following directions; Harrison & Oakland, 2003) may also be challenging for youth with SB (Coster & Haltiwanger, 2004; Warchausky, Kaufman, Schutt, Evitts, & Hurvitz, 2017). Finally, with regard to functional academics (i.e., basic reading, writing, and arithmetic abilities), youth with SB display a distinct cognitive phenotype characterized by relative strengths and weakness in the information processing systems underlying content domains (Dennis et al., 2006). As such, they are prone to difficulties mastering academic skills across multiple content domains, including reading comprehension, numerical estimation (e.g., estimating size or length), and complex arithmetic (Barnes et al., 2002; Dennis et al., 2006).

With respect to the social domain of adaptive functioning, youth with SB experience overall poorer social competence than their able-bodied peers, and are likely to be socially immature, dependent on their parents, and experience social isolation (Blum, Resnick, Nelson, & St. Germaine, 1991; Holmbeck & Devine, 2010). Compared to their peers, youth with SB also report less security and closeness in their friendships (Devine, Holmbeck, Gayes, & Purnell, 2012). Though most research is cross-sectional, the few existing longitudinal studies suggest that social functioning deficits in SB are maintained from childhood into adolescence (Holmbeck & Devine, 2010; Holmbeck et al., 2010).

According to the AAIDD, the practical domain is the third and final adaptive functioning domain and includes skills related to daily living (e.g., self-care, navigating transportation and health-care; Schalock et al., 2010). Compared to the conceptual and social domains of adaptive functioning, literature in the practical domain remains sparse. However, preliminary evidence suggests that children and adolescents with SB do experience difficulties in this area, especially with tasks such as home living or self-care (Andren & Grimby, 2000). While cross-sectional work has found that most youth with SB are independent in eating, dressing, bathing, and hygiene (Buran et al., 2004; Börjeson & Lagergren, 1990), a majority of those with hydrocephalus and a spinal lesion level above L2 continue to require assistance with self-care tasks (Verhoef et al., 2006). Research examining home living in SB has almost exclusively focused on adults, finding that most adults continue to live at home and require assistance with the tasks of daily living (e.g., cooking, cleaning; Andren & Grimby, 2000; Castree & Walker, 1981; Oakeshott & Hunt, 2003). Overall, there is a need to better understand the progression of practical skill development among youth with SB.

Preliminary evidence suggests that adaptive functioning deficits are present in youth with SB as early as six months of age (e.g., poorer cognitive, language, and motor skills; Lomax-Bream, Barnes, Copeland, Taylor, & Landry, 2007) and endure into adulthood, resulting in a failure to reach certain developmental milestones (e.g., poor employment outcomes, low rates of independent living and community participation; Mukherjee, 2007; Oakeshott & Hunt, 2003). A few studies have separately examined changes in the conceptual, social, and practical domains of adaptive functioning over time, finding that difficulties in these domains are maintained from childhood into adolescence (Davis, Shurtleff, Walker, Seidel, & Duguay, 2006; Dennis et al., 2006; Holmbeck et al., 2010). Additional longitudinal research is needed to examine adaptive functioning over time across all three AAIDD domains.

Such deficits in adaptive functioning may be partially due to underlying neurocognitive impairments. Though youth with SB demonstrate a complex neuropsychological profile (Brown et al., 2008; Dennis et al., 2006; Rose & Holmbeck, 2007; Wasserman & Holmbeck, 2016), previous research indicates that youth with SB are especially vulnerable to difficulties with attention (e.g., focused and selective attention) and executive functioning (e.g., working memory, planning, organizing, sequencing; Brown et al., 2008; Fletcher et al., 1996; Rose & Holmbeck, 2007; Tuminello, Holmbeck, & Olson, 2012). Given that many adaptive functioning skills depend on higher order cognitive processes, executive functioning and attention deficits may preclude youth with SB from being able to effectively complete certain adaptive functioning tasks.

Importantly, the Ecological Model of Adaptation for Adolescents with SB identifies neuropsychological functioning as a risk factor for poor functional independence (Heffelfinger et al., 2008) and data from previous work provides support for this model. Specifically, better executive functioning and attention are predictive of greater autonomy development (Tuminello et al., 2012), more adaptive outcomes (Heffelfinger et al., 2008; Loss, Yeates, & Enrile, 1998; Rose & Holmbeck, 2007), and the achievement of developmental milestones among youth with SB (Zukerman, Devine, & Holmbeck, 2011). A few studies have documented associations between executive functioning and/or attention and specific adaptive functioning skills (i.e., language, academic, and social skills; Rose & Holmbeck, 2007; Wasserman & Holmbeck, 2016). Although one study found significant associations between executive functioning and adaptive functioning across all three AAIDD domains (i.e., conceptual, social, practical), this study was cross-sectional and did not examine the influence of attention (Warschausky et al., 2017). Thus, additional research delineating the impact of cognitive dysfunction on all adaptive functioning domains is needed to better understand: (1) specific cognitive processes that are implicated with distinct adaptive functioning skills, and (2) which individuals with SB may be most at-risk for long-term difficulties in adaptive functioning.

The present study aimed to extend the existing literature by characterizing developmental trajectories of adaptive functioning in children with SB as they transition into adolescence. Examining the transition from childhood to adolescence is especially important as this is a time when maladaptive pathways may emerge or be modified by protective processes (Cicchetti & Rogosch, 2002). Moreover, this study examined neurocognitive functioning (i.e., executive functioning and attention) as a potential risk factor for poorer adaptive functioning outcomes. We hypothesized that skills in the conceptual (i.e., communication, self-direction, and functional academics) and practical (i.e., self-care, home living) domain would improve over time. However, those in the social domain were expected to remain stable over time due to previous SB research documenting a flat trajectory for social acceptance across development (i.e., lack of significant change in slope; Holmbeck et al., 2010). Given that social acceptance likely relies on effectively utilizing social skills, it was expected that the social skills trajectory in this sample would be relatively flat (Merrell & Gimpel, 2012). Additionally, it was hypothesized that poorer executive functioning and attention would be predictive of poorer adaptive functioning and less growth over time across all three skill domains (see Figure 1). By utilizing a multi-method, multi-informant, and longitudinal study design that spans three time points, this study addresses many of the methodological shortcomings of past research.

Figure 1.

Figure 1.

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Conceptual Model: Executive Functioning and Attention as Predictors of Change Over Time in Youth Adaptive Functioning.

Methods

Participants

Participants were recruited as part of a larger, ongoing longitudinal study examining neuropsychological functioning and family adjustment in youth with SB (e.g., Devine et al., 2012). Families of youth with SB were initially recruited from four hospitals and a statewide SB association in the Midwest. Families were approached during regularly scheduled clinic visits and/or sent recruitment letters. Interested families were screened in person or via phone by a trained member of the research team. Eligible children: (a) were diagnosed with SB (types included myelomeningocele, lipomeningocele, and myelocystocele); (b) were ages 8–15; (c) were proficient in English or Spanish; (d) had the involvement of at least one primary caregiver; and (e) were living within 300 miles of the laboratory (to allow for data collection at participants’ homes).

Of the 246 families approached for recruitment, 163 initially agreed to participate in the study. However, ultimately 21 families were excluded as they were unable to be contacted or later declined participation, and two did not meet inclusion criteria. Thus, the final sample of participants included 140 families of children with SB (53.6% female, 53.5% Caucasian, Mage = 11.40 at Time 1). Those who declined participation did not significantly differ from those who agreed to participate based on SB type (i.e., myelomeningocele versus other), χ2 (1) = 0.0002, p > .05, shunt status, χ2 (1) = 0.003, p > .05, or occurrence of shunt infections χ2 (1) = 1.08, p > .05. Following the baseline assessment, families were contacted again for follow-up time points every two years.

The current project included a subset of 131 families who had at least one parent complete a measure of the child’s adaptive functioning during at least one of the first three time points (Time 1 [T1] = baseline; Time 2 [T2] = two year follow-up; Time 3 [T3] = four year follow-up). There were no significant differences in youth IQ, gender, race, lesion level, or family socioeconomic status between the larger sample (i.e., the 140 study participants) and this subsample. However, youth included in the current analyses were significantly younger than those who were not, t(9.61) = −3.72, p < .01. Also, there was a decrease in sample size over time, which was primarily due to approximately 25% of youth becoming over 18 years of age at Time 3 and completing a “young adult” assessment protocol with no parental involvement in the data collection (i.e., this protocol does not include adaptive functioning data; the sample sizes with adaptive functioning data at each time point are as follows: T1 = 123 families, T2 = 104 families, T3 = 75 families). Over half of the families had complete data at all three time points (N = 66, 50.4%); however, the remaining families had incomplete data at one or two time points (NTime 1 only = 20, 15.3%; NTime 1 & Time 2 = 31, 23.7%; NTime 1 & Time 3 = 6, 4.6%; NTime 2 &/or Time 3 = 8, 6.1%). Attrition analyses indicated that families who did not have data at T2 and T3 did not significantly differ from those who did with respect to gender, IQ, age, lesion level, or adaptive functioning at T1.

At T1, youth were on average 11.26 years old (SD = 2.40), most had a diagnosis of myelomeningocele (82.4%), and a little over half were female (52.7%) and Caucasian (53.4%). Individuals with myelomeningocele, lipomeningocele, and myelocystocele were included in the study to obtain a sample representative of the larger SB population and improve generalizability of results. Differences in adaptive functioning based on SB type (i.e., myelomeningocele versus other) across the three time points were minimal (i.e., only 4 significant differences out of 18 analyses), such that those with myelomeningocele demonstrated significantly worse functional academics, t(19.74) = −3.63, p < .01, home living t(113) = −3.41, p < .01, self-care, t(113) = −2.62, p < .05, and self-direction skills, t(113) = −2.32, p < .05, only at T1. With regard to attention and executive functioning, youth with myelomeningocele demonstrated worse attention, t(118) = −2.06, p < .05, working memory, t(119) = −2.57, p < .05, cognitive flexibility, t(105) = −2.35, p < .05, and planning and organizational skills, t(118) = −3.39, p < .01. Additional information regarding child demographic characteristics is displayed in Table 1.

Table 1.

Child Demographic and Condition-Related Characteristics at Time 1

n (%) or M (SD)
Gender: female 69 (52.7)
Age 11.26 (2.40)
Race/Ethnicity
 Caucasian 70 (53.4)
 African-American/Black 17 (13.0)
 Hispanic 36 (27.5)
 Other 8 (6.1)
Family SES 39.48 (15.91)
IQ 86.38 (19.64)
Spina bifida type
 Myelomeningocele 108 (82.4)
 Lipomeningocele 13 (9.9)
 Myelocystocele 2 (1.5)
 Unknown/Not reported 8 (6.1)
Lesion level
 Thoracic 21 (16.0)
 Lumbar 63 (48.1)
 Sacral 39 (29.8)
 Unknown/Not reported 8 (6.1)
Shunt present 99 (75.6)
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Note. Demographic information is based on a sample of 131 youth with spina bifida (SB). SES = socioeconomic status; IQ = intelligence quotient.

Procedure

The current study was approved by university- and medical center-based Institutional Review Boards. Prior to data collection, parents provided informed consent and children provided informed consent (over 18 years of age) or assent (under 18). Parents also completed releases of information allowing the research team to obtain data from medical charts, health professionals, and teachers. Next, trained research assistants (i.e., undergraduate and graduate students) collected data in families’ homes during visits that lasted approximately three hours. Specifically, data collection consisted of two separate three-hour home visits at T1, as well as one three-hour home visit at T2 and T3. At least one research assistant was bilingual for home visits in which families primarily spoke Spanish. Families were compensated with $150, a t-shirt, and a pen at each time point.

During the home visit, family members independently completed questionnaires. Though most questionnaires were offered in English and Spanish, those that were only available in English were translated and back translated into Spanish by research assistants who were native Spanish speakers. Research assistants read questionnaires aloud to participants as needed (e.g., requested by participant, reading difficulties). In addition to questionnaires, trained research assistants also administered a battery of neuropsychological assessments to youth with SB. The current study used parent- and teacher-reported questionnaire data, as well as performance-based neuropsychological data.

Measures

Demographics and SB Characteristics.

Parents reported on family and youth demographic information at T1, including age, gender, race/ethnicity, income, education level, and employment status. Family socioeconomic status (SES) was measured using the Hollingshead Index of Socioeconomic Status, in which higher scores indicate higher SES (Hollingshead, 1975). Detailed information about youth’s SB (i.e., type of SB, shunt status, lesion level) was collected via parent-report on the Medical History Questionnaire (MHQ; Holmbeck et al., 2003) and hospital medical chart abstractions at T1.

Youth IQ.

Youth were administered the Vocabulary and Matrix Reasoning subtests of the Wechsler Abbreviated Scale of Intelligence (WASI) at T1, which were then used to compute an estimated Full Scale IQ (FSIQ) that served as a proxy for general intellectual functioning in the current study (Wechsler, 1999). The WASI is a well-validated measure of intellectual functioning in children and both scales have established reliability for individuals aged 6 to 16 years (Vocabulary α = .89, Matrix Reasoning α = .92; Wechsler, 1999).

Youth Adaptive Functioning.

Mothers and fathers completed portions of the Adaptive Behavior Assessment-Second Edition (ABAS-II; Harrison & Oakland, 2003) at T1, T2, and T3 to rate their child’s ability to perform a variety of daily tasks. The ABAS-II assesses nine skill areas, which can be combined into three composites that mirror the AAIDD domains: conceptual, social, and practical. Only six of the nine skill areas were assessed at T1, T2, and T3 in the larger study, including communication, self-direction, functional academics, social, self-care, and home living. Therefore, analyses in the current study focused on change over time at the skill-level rather than the domain-level (i.e., conceptual, social, and practical). However, to reduce the number of analyses and chance of Type 1 error, analyses focused on change over time at the domain-level when examining executive functioning and attention as predictors. These domain-level composite scores were created by summing the raw scores for skills within the conceptual (i.e., communication, self-direction, functional academics) and practical domains (i.e., self-care, home living). The social domain only included one skill area (i.e., social). Notably, the ABAS-II provides norm-referenced scores for each of the skill areas (i.e., scaled scores), which are presented to aid with interpretation of results. Raw scores were used for all data analyses (i.e., skill-level and domain-level) as they allow for greater variability in scores. The ABAS-II has demonstrated high internal consistency (rs range from .85–.99) and high test–retest reliability (rs range from .80–.90). Internal consistency was adequate in this sample across reporters and time (α = .89-.96).

Youth Neurocognitive Functioning.

Neurocognitive functioning in youth with SB was assessed using questionnaires (i.e., teacher-report) and performance-based neuropsychological assessments that were administered by trained research assistants at T1. Both questionnaires and performance-based assessments were used to obtain a comprehensive view of youth neurocognitive functioning (i.e., optimal and typical functioning; Holmbeck, Li, Schurman, Friedman, & Coakley, 2002; Toplak et al., 2013). Guided by previous literature highlighting common neurocognitive deficits in youth with SB (Brown et al., 2008; Dennis et al., 2006; Rose & Holmbeck, 2007), this study evaluated attention and the following domains of executive functioning that may impact adaptive functioning: (1) working memory, (2) planning and organizational skills, (3) cognitive flexibility, and (4) inhibition. Questionnaire measures were reverse-scored to be in the same direction as performance-based measures and, as such, higher scores indicated stronger attention and executive functioning abilities.

Attention.

Teachers completed the Swanson, Nolan, and Pelham Teacher Rating Scale-Version IV (SNAP-IV; Swanson, 1992). The 18 items on this measure correspond with the DSM-IV (American Psychiatric Association, 1994) criteria for Attention-Deficit/ Hyperactivity Disorder (ADHD). Only the Inattention subscale was included in the current study, which demonstrated adequate internal consistency (α = .94). Additionally, teachers completed the Teacher Report Form (TRF; Achenbach & Rescorla, 2001), which assesses behavioral and emotional problems in youth. This measure has established reliability and validity, and is widely used for children aged 6–18 years old (Achenbach & Rescorla, 2001). In the current study, only the Attention Problems subscale was used (i.e., assesses inattention, hyperactivity, and impulsivity), which demonstrated adequate internal reliability (α = .85). Finally, the Number Detection subtest (ND) from the Cognitive Assessment System (CAS; Naglieri & Das, 1997) was used as a performance-based measure of attentional ability. The CAS battery is designed to measure non-verbal cognitive processing in children ages 5–17 years old. During this task, examinees must locate and underline specific numbers on a page that contains distractors (i.e., the same numbers presented in a different font). The Number Detection subtest of the CAS has high internal consistency (α = .77) and test-retest reliability (r = .77; Naglieri & Das, 1997).

Working Memory.

Teachers completed the Working Memory subscale of the Behavior Rating Inventory of Executive Functioning (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000). On this measure, teachers were instructed to circle whether their child has never, sometimes, or often demonstrated a particular behavior during the past six months. Internal consistency was adequate for the Working Memory subscale in the current sample (α = .95). In addition to the BRIEF, the Digit Span subtest from the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV; Wechsler, 2003) was used as a performance-based assessment of working memory in youth. The WISC-IV is designed to measure cognitive ability in children ages 6–16 years old. The Digit Span subtest is comprised of two tasks: Digit Span Forward and Digit Span Backward. During Digit Span Forward, the examinee is verbally presented with a list of numbers by the interviewer and is then asked to repeat the sequence aloud. During Digit Span Backward, the examinee is also verbally presented with a list of numbers by the interviewer, but then is asked to repeat the sequence aloud in the reverse order. The Digit Span subtest has good internal consistency (r = .87) and test-retest reliability (r = .83; Williams, Weiss, & Rolfhus, 2003).

Planning and Organizational Skills.

Teachers completed the Plan/Organize and Organization of Materials subscales of the BRIEF (Gioia et al., 2000). These subscales measure one’s ability to plan and organize during problem solving, as well as organize one’s environment and materials. Internal consistency was adequate in the current sample for the Plan/Organize (α = .92) and Organization of Materials subscales (α = .92). Moreover, the Planned Connections (PCn) subtest from the CAS (Naglieri & Das, 1997) was used as a performance-based assessment of planning and organizational skills in youth. This subtest consists of eight items, in which examinees must connect numbers in sequential order (items 1–6 on subtest) and then connect both numbers and letters (i.e., alternating) in sequential order (items 7–8 on subtest). Notably, the Planned Connections subtest of the CAS has high internal consistency (α = .77) and test-retest reliability (r = .73; Naglieri & Das, 1997).

Cognitive Flexibility.

Teachers completed the Shift subscale of the BRIEF (Gioia et al., 2000). This subscale measures one’s ability to adjust to changes in routine or task demands. Internal consistency was adequate in the current sample (α = .91).

Inhibition.

Teachers completed the Inhibition subscale of the BRIEF (Gioia et al., 2000). This subscale measure’s one’s ability to inhibit impulsive responses. Internal consistency was adequate in the current sample (α = .92).

Data Analytic Plan

Preliminary Analyses.

Prior to hypothesis testing, the psychometric properties of all included measures were evaluated and descriptive statistics were calculated. To minimize the number of analyses and probability of Type 1 error, data reduction techniques were employed when appropriate. Specifically, Pearson correlations were used to examine associations between measures with two informants (e.g., mother and father report) or methodologies (e.g., teacher report and performance-based assessment). Measures were collapsed across informants for significant correlations in which r ≥ .40 (Holmbeck et al., 2002). Similarly, for constructs with three or more informants or methodologies, alpha coefficients were calculated, where total scale scores from different respondents/methodologies were treated as if they were items in a scale. If α ≥ .60, the respondent-specific measures were aggregated into a composite score. Analyses were conducted separately for measures that were not significantly correlated. The following variables were aggregated into composites: adaptive functioning (mother and father report on ABAS-II), attention (SNAP-IV-Inattention, TRF-Attention Problems, CAS-ND), working memory (BRIEF-Working Memory, WISC-IV Digit Span), and planning and organizational skills (BRIEF-Plan/Organize, BRIEF-Organization of Materials, CAS-PCn). After creating composite scores, all variables were examined for outliers and then skewness. Any value that was greater than three standard deviations from the mean and was not part of the normal distribution was considered an outlier (Cohen, Cohen, West, & Aiken, 2003). Consistent with guidelines outlined by West and colleagues (1995), variables were considered skewed if their skewness value was greater than 2.0.

Primary Analyses.

As noted, there were slight inconsistencies in the sample from T1 to T3 (e.g., some families participated at T1 and T2, whereas others participated at T1 and T3). To obtain the largest possible sample, participants with incomplete data were retained in the current study. Growth curves using a linear mixed-effects model (PROC MIXED in SAS; SAS Institute Inc.) were used to examine changes in adaptive functioning over time. To define time, participant’s age was used as the predictor variable as this provided greater insight into how adaptive functioning changes across development, as opposed to the arbitrary time points of the study’s assessment schedule. The mixed-effects models that were tested were individual change models that allowed for linear change and for fixed and random effects for the individual intercepts and slopes across age. In cases where the variance for the slope random effects was estimated to be zero, the model was re-fit to exclude the slope random effect. To account for data missing at random across time, a maximum likelihood estimation (MLE) procedure was used (Rubin, 1976; Schafer & Graham, 2002). Separate mixed-models were run for each adaptive functioning skill area (i.e., communication, self-direction, functional academics, social, self-care, and home living) to determine change for the total sample across age.

After identifying average change in the six adaptive functioning skills across age, predictors were entered into the mixed-models. Specifically, attention and executive functioning variables were examined as predictors of change across age (i.e., intercept and slope growth parameters). To limit the number of analyses, as well as Type 1 error, composite scores were used for adaptive functioning (i.e., conceptual, social, and practical domain composites) when examining predictors. Given the cognitive variability among individuals with SB and to best interpret our findings, analyses were run with and without IQ included as a covariate.

Results

Preliminary Results

Composite scores were created based on the previously discussed aggregation criteria for youth adaptive functioning at T1, T2, and T3 (see Table 2 for correlation coefficients), as well as for youth attention (α = .78), working memory (r = .40), and planning and organizational skills (α = .67; see Table 3 for additional information). Outliers were identified across 15 variables and were subsequently corrected by changing the score to one unit greater than the next highest value, as recommended by Cohen and colleagues (2003). None of the variables were skewed based on the criteria previously discussed (see Data Analytic Plan; West et al., 1995).

Table 2.

Description of Composite Scores Created for Youth Adaptive Functioning at Time 1, Time 2, and Time 3

Composite Scores Reporters & Measures T1 (baseline) T2 (2 year follow-up) T3 (4 year follow-up)
r M (SD) r M (SD) r M (SD)
Conceptual Domain
 Communication skills ABAS-II: Communication subscale (M, F) .68 9.79 (3.21) .61 9.68 (3.10) .73 10.23 (2.52)
 Self-Direction skills ABAS-II: Self-Direction subscale (M, F) .61 6.93 (3.73) .54 7.20 (4.05) .68 7.83 (3.59)
 Functional Academics skills ABAS-II: Functional Academics subscale (M, F) .82 8.17 (4.02) .62 7.81 (3.95) .75 8.15 (3.90)
Social Domain
 Social skills ABAS-II: Social subscale (M, F) .44 8.21 (3.30) .45 8.07 (3.73) .64 8.73 (3.42)
Practical Domain
 Self-Care skills ABAS-II: Self-Care subscale (M, F) .85 5.45 (3.72) .74 5.53 (4.12) .68 5.81 (3.78)
 Home Living skills ABAS-II: Home Living subscale (M, F) .67 4.40 (3.75) .74 4.41 (4.02) .81 5.10 (4.20)
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Note. M = Mother Report, F = Father Report. ABAS-II = Adaptive Behavior Assessment System-Second Edition. Pearson correlations represent associations between Mother and Father Report on the ABAS-II. Means and standard deviations are based on scaled scores from the ABAS-II that have a normative mean of 10 and a standard deviation of 3 (lower scores denote poorer functioning). While raw scores from the ABAS-II were used in all data analyses, scaled scores are presented to aid with interpretation of results. All ps < .05.

Table 3.

Description of Composite Scores Created for Youth Neurocognitive Functioning at Time 1

Composite Scores Reporters & Measures M(SD) α/r
Working Memory 1. BRIEF: Working Memory subscale (T) 67.31 (18.16) r = .40
2. WISC-IV: Digit Span subtest 7.49 (3.17)
Planning & Organization 1. BRIEF: Plan/Organize subscale (T) 64.85 (14.47) α = .67
2. BRIEF: Organization of Materials subscale (T) 67.11 (21.08)
3. CAS: Planned Connections subtest 6.20 (3.54)
Cognitive Flexibility 1. BRIEF: Shift subscale (T) 58.52 (15.84) -
Inhibition 1. BRIEF: Inhibit subscale (T) 52.90 (12.01) -
Attention 1. TRF: Attention Problems subscale (T) 56.71 (5.82) α = .78
2. SNAP-IV: Inattention subscale (T) 1.21 (0.78)
3. CAS: Number Detection subtest 6.16 (3.32)
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Note. Time 1 = baseline assessment. T = Teacher Report. BRIEF = Behavior Rating Inventory of Executive Functioning, WISC-IV = Wechsler Intelligence Scale for Children – 4th Edition, CAS = Cognitive Assessment System, TRF = Teacher Report Form, SNAP-IV = Swanson, Nolan, and Pelham – 4th Edition. Pearson correlations (r) are presented for measures with two informants or methodologies, whereas Cronbach’s Alpha (α) is presented for measures with three informants or methodologies. To aid with interpretation of results, T-scores are presented for the BRIEF and TRF, scaled scores are presented for the WISC-IV and CAS, and the raw mean score is presented for the SNAP-IV. Composite scores for attention, working memory, and planning and organization were created using z-scores.

Scaled scores for the six adaptive functioning skill areas are presented in Table 2. Per manual guidelines, the following classifications were used to interpret adaptive skill performance: scaled scores ≥ 15 indicated superior performance, 13–14 indicated above average performance, 8–12 indicated average performance, 6–7 indicated below average performance, 4–5 indicated borderline performance, and ≤ 3 indicated extremely low performance (Harrison & Oakland, 2003). Results were consistent across all three time points (i.e., T1, T2, T3), such that parents’ ratings of youth’s communication, functional academics, and social skills were largely in the average range compared to same aged peers. Home living and self-care skills were in the borderline range, and self-direction skills were in the below average range. Notably, parents’ ratings indicated less than average adaptive skill performance (i.e., below average, borderline, or extremely low performance) for a number of children at T1 with regard to communication (24, 20%), functional academics (52, 42%), home living (98, 80%), self-care (85, 69%), self-direction (59, 48%), and social skills (45, 37%). These percentages were similar at T2 and T3.

Change Over Time in Adaptive Functioning

For all growth curve analyses, participant age was entered as the predictor variable to better understand how adaptive functioning changes across development in youth with SB. Notably, participant age was centered at 11.5 years old, such that 11.5 was subtracted from the age variable at T1, T2, and T3. This value was chosen as it is the midpoint of the age range at T1 (i.e., age range at T1 = 8–15 years). Using data from three time points (T1-T3), separate growth models were conducted to examine change over time in each of the six adaptive functioning domains (i.e., communication, functional academics, home living, self-care, self-direction, social skills). As expected, youth’s adaptive functioning improved over time in five of the six skill areas. Specifically, youth’s communication, functional academics, home living, self-care, and self-direction skills significantly increased with age. However, youth’s social skills did not significantly change over time. Results for the growth models examining outcomes across age are summarized in Table 4.

Table 4.

Change in Adaptive Functioning Outcomes Across Age

Adaptive Functioning Outcome No Predictor
Communication 0.638***
Self-Direction 1.706***
Functional Academics 1.449***
Social 0.306
Self-Care 1.367***
Home Living 1.796***
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Note. Table presents coefficients from growth models and indicates change in slope for each unit change of 1 year in age.

*

p < .05

**

p ≤ .01

***

p ≤ .001.

Neurocognitive Predictors of Adaptive Functioning Trajectories

Next, the six adaptive functioning skills were aggregated to create domain-level composite scores for the conceptual (i.e., communication, self-direction, functional academics) and practical domains (i.e., self-care, home living). The social domain only included one skill area (i.e., social). Cronbach’s α and Pearson bivariate correlations met predetermined aggregation criteria for the conceptual (T1 α = .85, T2 α = .89, T3 α = .81) and practical (T1 r = .79, T2 r = .79, T3 r = .66) composites, respectively. These three composite scores were entered as outcome variables into the models, whereas the five neurocognitive functioning variables (i.e., attention, working memory, planning and organizational skills, cognitive flexibility, and inhibition) were entered as predictors. Separate growth models were used to examine the relationship between each neurocognitive and adaptive functioning variable.

Without controlling for IQ, neurocognitive functioning variables did not significantly predict change across age in any adaptive functioning domains. Instead, neurocognitive variables predicted the intercept for adaptive functioning abilities at 11.5 years old. Specifically, better attention, working memory, planning and organizational skills, cognitive flexibility, and inhibition predicted a higher intercept for adaptive functioning across all domains. When IQ was included in the models as a covariate, neurocognitive functioning variables still did not predict change across age in any adaptive functioning domains. However, attention and many domains of executive functioning remained significant predictors of the intercept for adaptive functioning abilities at 11.5 years old. In particular, better attention and executive functioning (i.e., working memory, planning and organizational skills, and cognitive flexibility) continued to predict a higher intercept for the conceptual domain of adaptive functioning. Similarly, better attention and executive functioning (i.e., planning and organizational skills, cognitive flexibility) continued to predict a higher intercept for the practical domain of adaptive functioning. For the social domain of adaptive functioning, only one aspect of executive functioning (i.e., cognitive flexibility) remained a significant predictor, with better cognitive flexibility predictive of more social skills at 11.5 years old. Specific results for the growth models examining neurocognitive predictors of adaptive functioning outcomes across age are summarized in Table 5.

Table 5.

Neurocognitive Predictors of Change in Adaptive Functioning Across Age

Growth Analyses without Controlling for IQ
Adaptive Functioning Outcome Working Memory Planning & Organization Cognitive Flexibility Inhibition Attention
Intercept Slope Intercept Slope Intercept Slope Intercept Slope Intercept Slope
Conceptual 16.254*** 0.512 13.888*** −0.778 11.038*** −1.160 9.676** −0.424 16.044*** 0.043
Social 1.822* 0.260 1.722* 0.017 2.519*** −0.046 1.520* 0.230 1.672* 0.259
Practical 8.935** 0.607 11.349*** −0.223 8.397** −0.761 5.804* −0.415 10.789*** 0.828
Growth Analyses Controlling for IQ
Adaptive Functioning Outcome Working Memory Planning & Organization Cognitive Flexibility Inhibition Attention
Intercept Slope Intercept Slope Intercept Slope Intercept Slope Intercept Slope
Conceptual 9.044** 0.140 7.399* −0.515 6.763* −1.109 4.428 −0.403 9.817** −0.201
Social 0.794 0.158 1.361 0.059 1.787* −0.089 0.674 0.164 0.891 0.201
Practical 4.118 0.317 9.808** −0.069 5.410* −0.761 2.920 −0.442 8.095** 0.586
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Note. Intercept and slope columns present coefficients from growth models. Slope column indicates change in slope across age for each 1-unit change in neurocognitive functioning.

*

p < .05

**

p ≤ .01

***

p ≤ .001.

Discussion

This study attempted to address gaps in the existing literature by examining changes in adaptive functioning as individuals with SB transitioned from childhood into adolescence. Additionally, this study sought to delineate the impact of neuropsychological functioning, namely attention and executive functioning, on skill attainment. As hypothesized, findings indicated that youth with SB acquired skills across most adaptive functioning domains as they age, including communication, self-direction, functional academics, self-care, and home living skills. These findings are generally consistent with limited longitudinal research on youth with SB suggesting that youth become more autonomous over time (Jacobson et al., 2013).

Interestingly, within the conceptual domain, youth’s communication, self-direction, and functional academics skills were in the low average to average range at all time points, indicating that individuals with SB are performing at about the same level as normative samples (i.e., skills in these areas were typical for age). Findings regarding functional academics were not surprising given that youth with SB typically demonstrate relative strengths in associative processing (i.e., data driven tasks like word recognition; Dennis et al., 2006), which are the focus of many items on the ABAS-II functional academics subtest (e.g., “able to measure in length and height,” “reads and obeys common signs”). However, youth’s relatively average performance with regard to communication and self-direction skills was unexpected considering past research highlighting diminished functioning in these areas (Coster & Haltiwanger, 2004; Dennis et al., 2006; Taylor et al., 2010).

One potential explanation for this set of findings could be that the measure used to assess these domains of adaptive functioning (i.e., ABAS-II) may not have been sensitive to the specific difficulties faced by this population. For example, youth with SB tend to struggle with more subtle aspects of language and communication (e.g., comprehension, flexibility; Copp et al., 2015; Taylor et al., 2010), rather than exhibiting blatant aphasic disturbances. Although the present study did reveal some difficulties with self-direction, they were fairly minimal as youth’s performance fell in the low average range. Previous research has often lumped “self-direction” in with other constructs such as “initiative,” which may have led to an underestimation of youth’s skills in this area (Coster & Haltiwanger, 2004). Youth with SB tend to be passive and rely on adults for guidance and, in turn, this lack of initiative may instead have been interpreted as a lack of self-direction (Holmbeck et al., 2003).

Findings in the practical domain were consistent with previous work (Andren & Grimby, 2000; Verhoef et al., 2006), revealing that youth’s home living and self-care skills fell in the borderline range at all time points (i.e., skills in these areas were poorer than expected for age) and were significant areas of weakness. Indeed, youth with SB often experience physical limitations, such as paralysis of the lower extremities, which may negatively impact their ability to master home living skills (e.g., sweeping the floor, taking out the trash; Copp et al., 2015). Not only could these physical limitations also help to explain youth’s difficulties mastering self-care skills (e.g., independently getting out of bed), but difficulties in this skill area may also be reflective of the complex self-management requirements common among those with SB. More specifically, many youth with SB must manage a catheterization and bowel program, which may make certain self-care tasks (e.g., using restroom independently) more difficult to master. Overall, deficits in these skill areas may have a pervasive impact on the lives of youth with SB, leading to difficulties with establishing future autonomy.

Youth’s mastery of social skills did not significantly change over time (i.e., there was not a significant increase or decrease in skills across age), which was expected in the context of the existing literature. This lack of growth mirrors a study by Holmbeck and colleagues (2010) finding that youth with SB demonstrated deficits in social acceptance compared to peers and these differences were maintained via a lack of change in either slope over time (i.e., trajectories were flat and parallel). Of note, this same study also found that deficits with regard to number of friendships were maintained over time in the SB group despite longitudinal change, indicating that trajectories may differ across social dimensions (Holmbeck et al., 2010). Nevertheless, these past findings suggest that youth with SB move along the same developmental progression as their peers from pre- (ages 8–9) to middle/late adolescence (ages 14–18), even if they lag slightly behind throughout this developmental period. In contrast to reports that youth with SB are socially immature, isolated, and have fewer friendships than typically developing youth (Holmbeck & Devine, 2010), youth’s social skills fell in the average range at all time points in the current study, indicating that youth with SB were performing in line with the general population. Given disparate findings regarding social functioning in this population (Stiles-Shields et al., 2019), it is possible that youth thrive in some social domains (e.g., friendship quality, basic social skills like saying “thank you” when receiving a gift) and struggle in others (e.g., acceptance from others, number of friendships). Therefore, additional research characterizing developmental changes across multiple dimensions of social functioning is warranted.

Findings were also partially consistent with hypotheses that neuropsychological functioning is a significant predictor of adaptive functioning in youth with SB. More specifically, while attention and executive functioning did not predict growth across the adaptive functioning domains, they did consistently predict the intercept (i.e., youth’s level of functioning at 11.5 years old). Across all adaptive functioning domains, both better executive functioning and attention were predictive of better functional abilities at age 11.5 when not controlling for IQ. Results were relatively similar when IQ was included as a covariate, as many of the initial effects of attention and executive functioning remained. However, associations with inhibition were reduced, such that inhibition was no longer a significant predictor for any of the three adaptive functioning domains. Additionally, only cognitive flexibility remained a significant predictor of the intercept for the social domain.

These results parallel previous work linking executive functioning/attention with functional independence, academic functioning, and social outcomes in youth with SB (Heffelfinger et al., 2008; Rose & Holmbeck, 2007; Wasserman & Holmbeck, 2016), suggesting that those with poorer cognition may demonstrate skill deficits across all AAIDD domains of adaptive functioning. However, given that cognitive functioning did not predict growth in most models, neurologically vulnerable youth may also develop and acquire skills at the same rate as those with more intact cognitive abilities. These findings extend the current literature, underscoring the importance of monitoring cognitive functioning in at-risk youth from an early age. If or once difficulties are detected, initiating early intervention services may help promote independence in children who are lagging behind higher functioning peers with SB. Given that attention problems and executive dysfunction impact adaptive functioning beyond the influence of IQ, interventions aimed at remediating weaknesses in these areas may be beneficial (Stubberud, Langenbahn, Levine, Stanghelle, & Schanke, 2014). Moreover, supportive technologies may help youth compensate for existing cognitive challenges. For example, children with planning and organizational difficulties may benefit from visual schedules or alarms (i.e., on watches or smart phones) to master self-care or home living tasks.

The current study had a number of strengths. First, the study addressed multiple gaps in the literature and expanded knowledge about adaptive functioning in youth with SB; novel risk factors were also identified. Second, data from multiple reporters (e.g., parents and teachers) were used to capture youth’s functioning across both home and school environments. Relatedly, performance-based neuropsychological assessments were employed to evaluate youth’s cognitive abilities, which is recommended to capture functioning in structured and unstructured settings (Toplak et al., 2013). Third, the study’s longitudinal design allowed for an examination of developmental changes in adaptive functioning from ages 8 to 18 years old, as well as temporal relationships between risk variables and adaptive functioning outcomes.

That being said, a few limitations should be acknowledged. This study did not examine potential condition-related moderators, such as spinal lesion level, that are known to influence functioning (Fletcher et al., 2005). Additional, research is needed to better understand the interplay between condition-related and environmental factors that can influence adaptive functioning in youth with SB. Moreover, it is important to note that the adaptive functioning measure used for this study inherently introduced possible ceiling effects. More specifically, the ABAS-II assesses individuals’ competence in skills that the general population is thought to master by adulthood. For example, items on the ABAS-II ask about writing first/last name and using a fork to eat solid food for the functional academics and self-care domains, respectively. Given the unique profile of youth with SB (i.e., individuals are often able to perform basic skills and struggle when task complexity increases), as well as the ABAS-II’s lack of specific questions regarding SB and its management, this measure may not be sensitive to the more nuanced anomalies in functioning present in this population. Indeed, those with SB must manage numerous self-management tasks (e.g., catheterization, bowel programs) that are not fully captured on the ABAS-II. As such, the development of SB-specific measures in the realm of adaptive functioning may be an important direction for future research.

Despite the aforementioned limitations, the current study addressed multiple gaps in the existing SB literature. In accordance with the AAIDD model, youth’s skills were examined across all three adaptive functioning domains, with findings suggesting that youth with SB acquire numerous skills to meet the demands of daily life as they age. Youth are generally able to complete everyday tasks related to communication, self-direction, functional academics, and social interactions with others at the same level as their peers, but struggle with tasks related to self-care and home living (e.g., cleaning, food preparation). However, it is important to note that a substantial subset of children demonstrated difficulties in each of the six adaptive functioning skill areas (i.e., communication, self-direction, functional academics, social, self-care, and home living). Therefore, interventions aimed at improving autonomy, especially during the transition from adolescence to early adulthood, may be of utmost importance for this population (Stiles-Shields et al., 2018). Adaptive skills are likely amenable to change, yet it is also important to tailor interventions to meet the specific needs of each individual with SB. This involves a consideration of each individual’s ability level in the context of environmental needs. For instance, gaining complete independence with self-care and home living skills may be an appropriate goal for some youth with SB, whereas others may be lower functioning and benefit more from enhanced communication skills to effectively convey their needs to caregivers.

Impairments in executive functioning and attention can negatively impact functional outcomes and therefore are important to monitor during routine clinic visits for all children with SB. Creating structured checklists to capture each individual’s cognitive risk factors (e.g., presence of shunts) and developmental course (e.g., whether or not they are meeting milestones), in turn, may help healthcare providers efficiently connect families with necessary supportive services in a timely manner. Indeed, given variability in the cognitive presentation of SB due to condition-related factors (e.g., presence of shunts; Copp et al., 2015), all youth with SB may not need supportive services. However, by using a structured checklist, children who are at elevated risk for cognitive difficulties and/or are falling behind in school could be flagged and referred for a neuropsychological assessment. Moreover, functional outcomes may be related to the synergistic effects of condition-related and environmental factors (e.g., SES, access to support services and resources). While family and parenting variables (e.g., family resources and support system, parenting styles) were not included in the current analyses, these factors may serve as important mechanisms for intervention. Thus, additional research that examines other risk and protective factors in the context of adaptive functioning is needed to improve care for these families.

Funding

This work was supported by grants from the National Institute of Nursing Research and the Office of Behavioral and Social Sciences Research (R01 NR016235), National Institute of Child Health and Human Development (R01 HD048629), and the March of Dimes Birth Defects Foundation (12-FY13-271). The authors would like to thank the families who generously participated in this work.

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