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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Sci Stud Read. 2020 Oct 8;25(5):436–451. doi: 10.1080/10888438.2020.1826950

Sustained attention and behavioral ratings of attention in struggling readers

Kelly T Macdonald 1, Marcia A Barnes 2, Jeremy Miciak 1, Greg Roberts 3, Kelly K Halverson 1, Sharon Vaughn 3, Paul T Cirino 1
PMCID: PMC8411923  NIHMSID: NIHMS1632475  PMID: 34483643

Abstract

Attention is correlated with reading, but the extent to which behavioral ratings and sustained attention relate to reading skills is unclear. We assessed 245 4th and 5th grade struggling readers (mean age = 10.3 years) on behavioral ratings of attention, sustained attention, and reading over a school year. Contributions of behavioral ratings and sustained attention were considered cross-sectionally and longitudinally in the context of other important predictors of reading. Results suggest that sustained measures and behavioral ratings assess distinct, yet overlapping, aspects of attention. Both types of attention accounted for unique variance in comprehension, but not word reading accuracy or fluency, when evaluated cross-sectionally. Results also support the role of behavioral ratings of attention in fluency and in comprehension growth. Findings suggest that multidimensional assessment of attention is useful when considering its relation to reading, and highlights the need to integrate conceptualizations of attention that arise from different theoretical approaches.

Keywords: sustained attention, behavioral ratings of attention, reading difficulties

Attention is related to achievement, including reading (McGrath et al., 2011; Willcutt et al., 2010). While a number of theories have implicated attention in the reading process (Perfetti, 1991; Van den Broek, Young, Tzeng, & Linderholm, 1999; van de Sande, Segers, & Verhoeven, 2013), attention is often considered broadly without considering the theoretical implications for how different attentional assessments and processes relate to one another and differentially impact reading. For instance, many studies have conceived of attention in terms of behavioral ratings (e.g., from parents or teachers), either continuously (Ehm et al., 2016; Hart et al., 2010; Rabiner & Coie, 2000) or by forming groups of Attention-Deficit/Hyperactivity Disorder (ADHD) and reading disability (RD) (Baumgaertel, Wolraich, & Dietrich, 1995; Willcutt et. al., 2010). In contrast, other studies have focused on objective measures such as sustained attention in relation to reading (Sims & Lonigan, 2013; Stern & Shalev, 2013; Valiente, Lemery-Chalfant, & Swanson, 2010). This heterogeneous body of work supports the role of attention in reading, but because attention is complex and has conceptual overlap with related constructs including executive function, self-regulation, and inhibition, identifying key aspects of attention on which to focus is important to clarify their relations to reading.

For reasons described below, the present study selected sustained attention and behavioral ratings of attention as examples of two different theoretical perspectives on attention. Our theoretical understanding of these attention types framed hypotheses about the ways in which they might differentially relate to different types of reading skills (i.e., word reading accuracy, reading fluency, and comprehension). Not only are such relations underexplored theoretically and empirically, but few studies have evaluated the roles of multiple aspects of attention in reading among struggling readers, within a longitudinal context, and with attention considered continuously (Marcus & Berry, 2011). Enhancing understanding at this more nuanced level can inform theoretical models of both reading and attention, and also holds potential to inform intervention efforts for struggling readers that integrate attention as a target. Therefore, the overarching purpose of the current study is to evaluate the relationship between sustained attention and behavioral ratings of attention, and their unique versus complementary contributions to multiple reading outcomes.

Theoretical Models of Attention

The concept and measurement of behavioral ratings of attention grew out of early models of ADHD. Earlier theories of ADHD emphasized cognitive constructs including impulse control and sustained attention (i.e., Douglas, 1980). Over time, various theories have proposed different arguments for the core characteristics of ADHD (i.e., Laufer, Denhoff, & Solomons, 1957; van der Meere et al., 1989; for a review see Barkley, 2006). A widely-accepted framework of ADHD is Barkley’s unifying model (Barkley, 1997), which identifies behavioral disinhibition as the core deficit in ADHD that leads to cognitive impairments in working memory, sustained attention, motor control, and regulation of affect. Thus, although ADHD remains a behavioral diagnosis, cognition has continued to play a role in the conceptualization of the disorder. Nevertheless, the use of behavioral rating scales is required to reach a diagnosis (Swanson et. al., 2006; Conners, 2008), and importantly, ratings of inattention demonstrate stronger links to reading than those of hyperactivity/impulsivity (Cain & Bignell, 2014; Lonigan et. al., 1999; Pham, 2016; Willcutt et al., 2010). Of note, inattention items do tap various attentional processes including sustained attention, selective attention, and inhibition (Swanson et al., 2006).

Sustained attention, in addition to being implicated in theories of ADHD, is also a prominent component of several cognitive frameworks of attention that developed separately from the disorder-specific models (Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991; Cooley & Morris, 1990). Additionally, a number of neural models constrain their focus to sustained attention (Bellgrove, Hawi, Gill, & Robertson, 2006; Langner & Eickhoff, 2013; Pardo, Fox, & Raichle, 1991). For instance, Langner and Eickhoff (2013), synthesizing neuroimaging findings, discussed a multicomponent model of sustained attention characterized by a core network of predominantly right-lateralized regions including dorsomedial, mid- and ventrolateral prefrontal cortex, anterior insula, and parietal areas, as well as subcortical structures.

Thus, both behavioral ratings and sustained attention are theoretically well-situated constructs, and are also commonly assessed. Interestingly, studies that have examined behavioral ratings and sustained attention measures together have found only small to moderate correlations (Egeland, Johansen, & Ueland, 2009; Sims & Lonigan, 2012). For example, Egeland et al. (2009) found small, significant correlations of omissions on a continuous performance test (CPT) with parent and teacher ratings of inattention (r = .25 and .23, respectively) in their sample of 9–16 year olds. Such findings may appear surprising if these measures are taken to assess the same general construct, which is reasonable. For example, in addition to specific items asking about sustained attention, individuals with ADHD consistently demonstrate poorer CPT omissions relative to controls; a recent meta-analysis (Huang-Pollock, Karalunas, Tam, & Moore, 2012) found a medium effect size (Cohen’s d = .62). Despite these findings, it is important to note that associations between cognitive processes and their behavioral manifestations (whether through behavioral ratings or performance on objective tests) are not well understood. Moreover, as noted above, behavioral rating scales likely capture a broader range of attentional processes than the CPT, and therefore these two types of measures are distinct from one another and should not be viewed as interchangeable. However, to the extent that attention measures arise from different theoretical stances and take different approaches to measurement, their moderate relations allow them to have complementary relations to important outcomes including reading. Only by directly including both types of measures can we better understand their similarities and differences, as well as the ways in which they may differentially relate to reading.

Attention and Reading

A number of theoretical arguments for the role of attention in different aspects of the reading process have been proposed. For example, at the word reading level, Perfetti (1991) argued that sustained attention is critical to developing an explicit understanding of grapheme-phoneme relationships. Even for mature readers, attention must be allocated to phonemic structure in order to decode new words (Treiman, 1991). Visual attention theories of reading have posited that the reading deficits among students with word-level reading disabilities may be due, in part, to problems with focusing visual attention (Facoetti, Paganoni, Turatto, Marzola, & Mascetti, 2000), though causal claims are controversial. At the preschool and kindergarten levels, Martinussen et al. (2014) posited that behavioral inattention (as reflected by behavioral ratings) in the classroom interferes with word recognition development due to less time engaged with books and thus less print exposure. A distinct yet related body of work supports the theoretical link between attention and reading skills within a self-regulation framework in younger children (Allan, Hume, Allan, Farrington, & Lonigan, 2012; Blair & Razza, 2007; van de Sande, Segers, & Verhoeven, 2013; Ponitz, McClelland, Matthews, & Morrison, 2009). For example, van de Sande et al. (2013) argued that attentional control allows for efficient phonological processing and decoding, specifically allowing a reader to suppress attention to irrelevant phonological information, shift attention between phonological representations, and switch attention from the structural features of a word to its semantic meaning. In addition to word-level reading accuracy, Pham (2016) argued that impaired attentional processes may also negatively impact reading fluency through increased distractibility, leading to fewer words read and/or reduced accuracy.

Regarding reading comprehension, sustained attention is necessary because of the need to maintain information and make connections within text (Van den Broek, Young, Tzeng, & Linderholm, 1999). Sustained attention also implies that the reader focuses on and maintains activation for relevant information while also suppressing irrelevant information, which prior work has shown to be important for successful reading comprehension (Gernsbacher, 1993). Behavioral ratings of attention in a classroom context also have implications for reading comprehension, for instance, if a student is easily distracted or off-task while reading longer passages.

The theoretical arguments above implicate multiple attention-related processes for reading, with sustained attention and behavioral ratings of attention being two of the most common. As already noted, these two aspects of attention have separable theoretical backgrounds as well, independent of reading. Crucially though, the ways in which specific types of attention differentially relate to various reading outcomes is not well understood. Further understanding how these two aspects of attention differentially contribute to reading can help lay groundwork for how additional components of attention might then also be situated.

In addition to the strong theoretical rationale for the role of attention in reading described above, there are also robust empirical findings that link attention and reading, and these also encompass a range of types of attention. For example, numerous studies have evaluated objective measures of attention and reading-related outcomes in younger samples (Allan et al., 2014; Blair & Razza, 2007; van de Sande et al., 2013; Ponitz et al., 2009). Given the self-regulatory framework of many such studies, though, the specific objective measures have often included attentional control, inhibitory control, and effortful control, rather than sustained attention per se, and these studies have not typically included both objective measures and behavioral ratings. However, Blair and Razza (2007) did consider both approaches to measurement, assessing self-regulation with both performance-based measures and behavior ratings, and found that both measurement methods accounted for unique variance in letter-word knowledge. While these studies provide important evidence for the role of attention in reading, their theoretical underpinnings in self-regulation are somewhat distinct from those of the current study, where the focus is constrained to sustained attention and behavioral ratings of attention. Furthermore, the aforementioned studies were conducted with much younger students (i.e., preschool-1st grade), and it is unclear how such findings translate to late elementary school students.

There are however a small number of studies that have specifically associated sustained attention with reading. For example, Stern and Shalev (2013) found a moderate negative correlation (r = −.43) between sustained attention (measured with a CPT) and a reading comprehension composite. Sims and Lonigan (2013) found that sustained attention predicted aspects of emergent literacy in preschool students. Valiente et al. (2010) also found that CPT performance predicted scores on a reading composite consisting of single word reading and comprehension.

More often, studies of school-age children have linked attention to reading via parent or teacher rating scales of ADHD symptoms (Breslau et al., 2009; Duncan et al., 2007; Claessens & Dowsett, 2014; Miller et al., 2014; Roberts et al., 2015). As noted, these studies have tended to focus on inattentive symptoms, as these have demonstrated consistent relations to reading moreso than symptoms of hyperactivity/impulsivity (Cain & Bignell, 2014; Lonigan et. al., 1999; Pham, 2016; Willcutt et al., 2010). Studies with elementary aged students have consistently supported a relationship between behavioral ratings of attention and reading comprehension. For instance, Pham (2016) found that teacher ratings of attention predicted both reading fluency and reading comprehension in a sample of third and fourth graders. Other studies examining group differences in reading between students with and without ADHD have reported poorer reading comprehension among those with ADHD (Brock & Knapp, 1996; Willcutt et al., 2005). A number of studies have also demonstrated a relationship between behavioral ratings of attention and word reading ability (Dally, 2006; Dittman, 2016; Martinussen et al, 2014), though many of these are at the preschool and kindergarten level. Together, these studies have strongly demonstrated the association of behavioral ratings of attention with reading; however, none compared the relative role of behavioral ratings of attention to that of sustained attention, and they did not consistently evaluate a range of reading outcomes.

We are only aware of one study that evaluated the ways in which sustained attention and behavioral ratings of attention differentially predict reading-related outcomes. Sims and Lonigan (2013) found that both teacher ratings of inattention and CPT omissions predicted significant, unique variance in different aspects of emergent literacy (e.g., phonological awareness, print knowledge) in a preschool sample, with teacher ratings accounting for more variance than CPT omissions. However, it is unclear if similar relations would be found with older children.

Current Study

The present study evaluates both sustained attention and behavioral ratings of attention, their inter-relations, and their relations to reading outcomes, both cross-sectionally and longitudinally, in 4th and 5th grade struggling readers. Our approach is rooted in seminal theories of attention which informed the selection of the specific attention constructs (sustained attention, behavioral ratings of attention) to be considered alongside one another in order to better understand their potentially differential contributions to reading skills. Cross-sectional hypotheses were tested with data collected at the beginning of the school year (both sustained attention and reading measures), with the exception of behavioral ratings of attention, which were obtained at the end of the year so that teachers were more familiar with their students and could provide more informed ratings. Sustained attention was indexed with CPT omissions, as this metric is associated with inattention rather than hyperactivity and omissions have demonstrated stronger relations to ratings of inattention and preliteracy skills than commissions or response variability in prior work (Sims & Lonigan, 2012; 2013). Longitudinal questions considered the roles of attention in growth across reading outcomes from the beginning to end of the year, with end of year behavioral ratings of attention used as a proxy for beginning of year ratings, as in prior studies (Cirino, Miciak, Ahmed, Barnes, Taylor, & Gerst, 2019; McClelland, Acock, & Morrison, 2006; Rabiner & Coie, 2000).

In order to understand the unique roles of sustained and behavioral ratings of attention, it is important to consider their contributions in the context of well-known predictors of reading, specifically working memory (WM) and verbal knowledge. WM is related to both attention and reading, and also assists in the maintenance of connections across text. WM develops from and requires attentional processes (Wass, Scerif, & Johnson, 2012), and is moderately related to phonological processing, word reading, reading fluency, and reading comprehension (Arrington et al., 2014; Cain, Oakhill, & Bryant, 2004). Verbal knowledge addresses language, known to be important for reading, and has previously demonstrated moderate to strong relations to reading comprehension (Denton et al., 2011).

This study uses data from a larger intervention study (Vaughn, Roberts, Miciak, Taylor, & Fletcher, 2019), and uses a similar sample as a different study focused on reading anxiety and reading performance (Macdonald, Cirino, Miciak, & Grills, under review). However, neither of those reports utilized measures of attention, and therefore the aims of the current study are unique. Moreover, we do not make hypotheses about intervention effects in the current study but include intervention condition in our models as a covariate. We propose the following hypotheses:

  1. Given available evidence regarding the relation of sustained attention and behavioral ratings of attention, we expect that sustained attention and behavioral ratings of attention will demonstrate a significant, yet small to moderate, relationship.

  2. Based on the Sims and Lonigan (2013) study with much younger children, we expect that both sustained attention and behavioral ratings of attention will uniquely predict reading (word reading accuracy, oral reading fluency, and reading comprehension) in cross-sectional models (i.e., all variables are measured at the beginning of the school year with the exception of behavioral ratings of attention, measured at the end of the year). Because studies in older elementary school students have consistently demonstrated a relationship between behavioral ratings of attention and reading comprehension, and because most existing evidence linking sustained attention to reading has done so with measures of comprehension, we expect attention to have its largest effects on comprehension.

  3. Using a longitudinal design, we expect that attention will predict growth in reading outcomes from beginning of the year to end of the year. Specifically, we expect that beginning of year sustained attention and end of year behavioral ratings of attention will predict growth in reading outcomes, even in the context of other strong predictors of reading (i.e., WM, verbal knowledge) and demographics. We do not make hypotheses for different component reading skills based on the lack of information in the literature from which to make more specific predictions.

Method

Informed consent was given by parents, and participating students provided their assent. All procedures were approved by the Institutional Review Board of the University of Houston in compliance with recognized standards of the US Federal Policy for the Protection of Human Subjects.

Participants

Schools.

Participants were drawn from nine schools across three school districts in the southwestern United States (three schools each from a large urban district, a small affluent urban district, and a near-urban school district). School demographics and academic profiles varied considerably. For example, mean enrollment for participating schools was 585 students (SD = 119.9, range: 440–728), and 4th grade pass rates of state reading exam ranged from 34% to 99% (M = 75.2%, SD = 22.5).

Students.

We drew the current sample (N = 280) of 4th and 5th grade students from a larger reading intervention study. Participants were identified as struggling readers with a standard score of 85 or below on the Test of Silent Reading Efficiency and Comprehension (TOSREC; Wagner, Torgesen, Rashotte, & Pearson, 2010). As shown in Table 1, our sample was also characterized by below average to average scores across measures of word reading accuracy, oral reading fluency, and reading comprehension. The entire population of 4th and 5th graders at the nine schools were screened (n = 2,570), with 674 students (26%) eligible for the intervention; 280 of these were selected at random, due to resource limitations. Of these 280, 35 did not complete all measures used here, and so were excluded, leaving 245 struggling readers.

Table 1.

Correlation Matrix and Means and Standard Deviations for All Variables

1 2 3 4 5 6 7 8 9 10
1 Sustained Attention ---
2 Word Read Time 1 −0.17 ---
3 Read Fluency Time 1 −0.18 0.61 ---
4 Read Comp Time 1 −0.28 0.35 0.46 ---
5 Vocabulary −0.09 0.33 0.21 0.33 ---
6 WM −0.22 0.13 0.14 0.21 0.26 ---
7 Behavioral Ratings of Attention 0.28 −0.16 −0.28 −0.28 −0.02 −0.16 ---
8 Word Read Time 2 −0.18 0.86 0.63 0.36 0.23 0.15 −0.19 ---
9 Read Fluency Time 2 −0.21 0.61 0.83 0.43 0.21 0.17 −0.29 0.67 ---
10 Read Comp Time 2 −0.30 0.43 0.46 0.62 0.31 0.29 −0.42 0.47 0.50 ---
Mean 55.75 94.26 87.49 88.91 87.87 11.21 2.28 98.25 117.51 90.81
SD 12.53 10.79 28.83 10.11 14.23 3.50 11.63 12.53 36.37 11.02
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Note. Word Read = word reading; Read Fluency = reading fluency; Read Comp = reading comprehension; WM = working memory. Correlations between measures within each composite variable are presented on the diagonal. Correlations from 0.15 to 0.18 = p < 0.05; correlations from 0.19 to 0.23 = p < 0.01; correlations greater than 0.24 = p < 0.001.

The mean age of the included students was 10.3 years (SD = .68 years). Fourty-eight percent of the sample was female. Seventy-six percent of students qualified for free or reduced-price lunch, a proxy for low socioeconomic status (this data was missing for 23 students). The proportion of the sample that had been previously identified by their school as requiring special education services, an umbrella term used to identify students who receive some form of extra support or accommodations for an identified disability, was 14.7%. The proportion of the sample that was classified by their school as limited English proficient (LEP), as defined by statewide assessments of listening, speaking, reading, and writing in English, was 19.6%. The sample was ethnically diverse (44.2% African American, 34.3% Hispanic, 17.2% Caucasian, 2.9% American Indian/Alaskan Native, and 1.3% Asian, and 2% with missing data).

Struggling readers were randomized into either to business-as-usual (BAU) or to a word reading and comprehension intervention (n = 123 of the present sample). The intervention study showed significant effects for the intervention on measures of word reading and reading fluency, though not reading comprehension (see Vaughn et al., 2019). Treatment condition was considered in the analyses.

Measures

Examiners were trained by experienced assessment coordinators, and were unaware of students’ assignment to condition. All but one measure was administered both at pretest and posttest, with the exception being behavioral ratings of attention, given at posttest to allow teachers significant interaction with students to be able to rate classroom attention and hyperactivity. This approach to capturing teacher ratings of attention has been used elsewhere (McClelland, Acock, & Morrison, 2006; Rabiner & Coie, 2000).

Reading Measures.

Single word reading accuracy was assessed with Letter-Word Identification of the Woodcock-Johnson III (WJ-III, Woodcock, McGrew, Mather, & Schrank, 2001). Age-based standard scores were used in analyses. Test-retest reliability for children ages 8–13 ranges from .89 to .96. Oral reading fluency was assessed with AIMSweb Oral Reading Fluency (Shinn & Shinn, 2002); a grade-level passage is read aloud for one minute, and the score is the number of words accurately read. Alternate form reliability ranges from .94 to .96, and test-retest reliability from .92 to .95. For reading comprehension, we used the Gates-MacGinitie Reading Test – Fourth Edition (MacGinitie et al., 2000), which has expository and narrative passages of varying length (3–15 sentences), followed by 3–6 multiple-choice questions. Reliability is high (range .91 to .93). Age-based standard scores were used in analyses.

Attention Measures.

The Conners Continuous Performance Test 3 (CPT-3; Conners, 2014) was used to evaluate sustained attention and inhibition. This measure was chosen to assess sustained attention because the CPT has previously demonstrated relations to reading (Sims & Lonigan, 2013; Stern & Shalev, 2013; Valiente, Lemery-Chalfant, & Swanson, 2010). More specifically, omissions were chosen as the metric of attention because previous work has specifically linked this to behavioral ratings of inattention as well as reading. Students press the space bar for any letter other than X (which appears infrequently). Each letter is displayed for 250 milliseconds and inter-stimulus intervals vary between 1, 2 and 4 seconds; 360-trials are collected over 14 minutes. Omissions (inattention) are missed targets, with split-half reliability in the normative sample of .96 and test-retest reliability of .83.

The Strength and Weaknesses of Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior Scale (SWAN; Swanson et. al., 2006) was rated by students’ classroom teachers as an index of behavioral ratings of attention. The SWAN is an 18-item inventory based on the DSM-IV criteria for ADHD diagnosis, with separate subscales for Hyperactivity/Impulsivity and Inattention. Items are phrased positively in relation to normal behavior expectations, on a 7-point Likert scale. Only the inattention subscale of the SWAN was used for the current analysis, because as noted, inattention is more related to reading than is hyperactivity/impulsivity (e.g., (Dittman 2016; Pham, 2016). Internal consistency is .88 and test-retest reliability is .82 (Arnett et. al., 2013).

Other Predictors.

The Listening Recall subtest of the Working Memory Test Battery for Children (WMTB-C; Pickering & Gathercole, 2001) is a sentence span task wherein students listen to a series of short sentences, determining if each sentence is true or false, and then recalling the last word of each sentence. Each of six levels has six items; four items must be passed to advance to the next span level, and if three consecutive items within a level are failed, the task ends. Scores are based on the total number of correct items, and test-retest reliability is .61. The Verbal Knowledge subtest of the Kaufman Brief Intelligence Test – Second Edition (KBIT-2; Kaufman & Kaufman, 2004) is a measure of receptive vocabulary and general verbal knowledge. Students are presented with an array of six images while the examiner states a word or asks a question. The student responds by pointing to the picture that shows the meaning of the word or answers the question. Age-based standard scores were used in analyses. Test-retest reliability for children ages 4–12 is .88.

Data Analysis

Preliminary analyses evaluated relationships between outcome variables and free/reduced lunch status (a proxy for economic disadvantage), special education eligibility, gender, age, and limited English proficiency status to determine their potential as covariates. One-way ANCOVAs revealed a significant effect of free/reduced lunch status on comprehension, p = 0.014, so this was maintained as a covariate in subsequent analyses. Other covariates included special education eligibility status (related to word reading, p < 0.001, reading fluency, p < 0.001, and reading comprehension, p < 0.001), gender (related to comprehension, p = 0.003), and age (related to word reading skill, p < 0.001). Limited English proficiency status (determined by the schools) was not related to reading achievement so was not included as a covariate. Regression diagnostics demonstrated adequate normality, linearity, homogeneity of variance, and independence, as well as lack of collinearity among predictors. Hypothesis 1 was evaluated with bivariate correlations. Hierarchical multiple regression analyses were used to examine Hypothesis 2, including the contributions of behavioral ratings of attention and sustained attention in step 1, and known predictors of reading (verbal knowledge, WM), and covariates (above) added in step 2. For these hypotheses, beginning of year data was used, except for behavioral ratings of attention (which as noted was only obtained at the end of the year). One way repeated measures (RM) ANCOVAs were used to examine Hypothesis 3, evaluating the relation of behavioral ratings of attention and sustained attention to growth in reading skills (again, in the context of other predictors/covariates). We also included treatment condition as a covariate in all of the models in order to control for intervention effects on reading, as noted above.

Results

Hypothesis 1: Relations of sustained attention and behavioral ratings of attention

Table 1 shows correlations among all predictor and outcome variables, as well as descriptive statistics. Behavioral ratings and sustained attention measures demonstrated a significant, modest relation (r = 0.28, p < 0.001).

Hypothesis 2: Contributions of sustained attention and behavioral ratings of attention to reading skills

Multiple hierarchical regression models were used to test the differential contributions of behavioral and sustained attention to reading skills (all measured at the beginning of the school year, with the exception of end-of-year behavioral ratings of attention) in the context of other reading-related and attention-related variables. Results from the final step of the models (with all covariates) can be found in Table 2.

Table 2.

Summary of Regression Analyses for Variables Predicting Word Reading, Fluency, and Comprehension (N = 245)

Model 1: Word Reading Model 2: Fluency Model 3: Comprehension
B SE b β B SE b β B SE b β
Step 1:
Sustained Attention −0.12 0.06 −0.13* −0.27 0.15 −0.12 −0.18 0.05 −0.22**
Behavioral Ratings of Attention −0.12 0.06 −0.12 −0.58 0.16 −0.23** −0.19 0.05 −0.22**
Intervention Condition −0.01 1.36 <−0.01 3.29 3.55 0.06 −0.60 1.22 −0.03
Adjusted R 2 0.03** 0.08** 0.11**
Step 2:
Sustained Attention −0.04 0.05 −0.04 −0.07 0.12 −0.03 −0.12 0.05 −0.15*
Behavioral Ratings of Attention −0.11 0.06 −0.12 −0.46 0.13 −0.18** −0.15 0.05 −0.18**
Word Reading+ 1.67 0.15 0.62** 0.19 0.06 0.20**
Vocabulary 0.19 0.05 0.24** 0.10 0.11 0.05 0.16 0.04 0.23**
Working Memory −0.01 0.19 <−0.01 −0.20 0.43 −0.02 0.12 0.18 0.04
Age −2.61 0.95 −0.16** 12.75 2.17 0.30** 1.03 0.89 0.07
SESa −3.11 1.36 −0.13* −5.08 3.10 −0.08 −2.96 1.27 −0.14*
SPED Eligibleb −8.23 1.81 −0.27** −2.85 4.22 −0.04 −1.03 1.74 −0.04
Genderc −2.54 1.29 −0.12* −1.84 2.89 −0.03 1.46 1.20 0.07
Adjusted R 2 0.23** 0.47** 0.25**
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Note:

+

Word reading was not included as a predictor for Model 1 since word reading is the outcome variable.

a

Reference = Not economically disadvantaged

b

Reference = Not eligible for special education

c

Reference = Male

*

p < .05.

**

p < .01.

For word reading accuracy, sustained attention (β = −0.12, p = 0.042), but not behavioral ratings (β = −0.12, p = 0.059) predicted reading when entered into the model together and accounted for 3% of the variability in word reading. Intervention condition did not predict word reading so was not retained in step 2. When verbal knowledge, WM, and all demographic covariates were added to the model, neither attention measure accounted for significant variance in word reading. The full model accounted for 23% of the variance in word reading accuracy.

For oral reading fluency, behavioral ratings (β = −0.23, p < 0.001) but not sustained attention (β = −0.12, p = 0.063) predicted reading when entered into the model together and accounted for 8% of the variability in fluency. Intervention condition did not predict fluency so was not retained in step 2. When word reading, verbal knowledge, WM, and all demographic covariates were added to the model, the contribution of behavioral ratings of attention remained significant (β = −0.18, p < 0.001), and this full model accounted for 47% of the variance in oral reading fluency.

For reading comprehension, both sustained attention (β = −0.22, p < 0.001) and behavioral ratings (β = −0.22, p < 0.001) predicted reading when entered into the model together and accounted for 11% of the variability in comprehension performance. Intervention condition did not predict comprehension so was not retained in step 2. When word reading, verbal knowledge, WM, and all demographic covariates were added to the model, the contributions of both sustained attention (β = −0.15, p = 0.014) and behavioral ratings of attention (β = −0.18, p = 0.005) remained significant, and the full model accounted for 25% of variability in reading comprehension.

Hypothesis 3: Contributions of sustained attention and behavioral ratings of attention to reading growth

One way repeated measures ANCOVAs were used to examine the role of beginning of year sustained attention and end of year behavioral ratings of attention in reading growth over the school year for each reading outcome. Results from the final models with all covariates are shown in Tables 3–5. Neither behavioral ratings nor sustained attention were related to growth in word reading, even before the addition of covariates. Similarly, neither type of attention was related to growth in fluency. However, behavioral ratings (though not sustained attention), were significantly related to growth in reading comprehension, and this effect remained significant even when all other variables were added to the model (F = 4.31, p = 0.039). It is important to note that this result should be interpreted with some caution due to lack of time precedence for behavioral ratings of attention (see limitations for further discussion).

Table 3.

Within Subject Effects from Repeated Measures Analysis of Variance Demonstrating the Role of Sustained Attention and Behavioral Ratings of Attention in Word Reading Growth

Effect MS df F p
Time 37.91 1 1.86 0.174
Time × Behavioral Ratings of Attention 11.15 1 0.55 0.460
Time × Sustained Attention 6.93 1 0.34 0.560
Time × Vocabulary 89.24 1 4.38 0.037
Time × Working Memory 46.07 1 2.26 0.134
Time × Intervention Condition 30.99 1 1.52 0.219
Time × SES 20.36 1 1.00 0.318
Time × Age 4.36 1 0.21 0.644
Time × SPED Eligibility 14.24 1 0.70 0.404
Time × Gender 4.35 1 0.21 0.645
Error 20.36 235
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Table 5.

Within Subject Effects for Repeated Measures Analysis of Variance Demonstrating the Role of Sustained Attention and Behavioral Ratings of Attention in Reading Comprehension Growth

Effect MS df F p
Time 6.98 1 0.17 0.683
Time × Behavioral Ratings of Attention 180.25 1 4.31 0.039
Time × Sustained Attention 17.20 1 0.41 0.522
Time × Word Reading 71.30 1 1.71 0.193
Time × Vocabulary 29.03 1 0.69 0.406
Time × Working Memory 93.40 1 2.23 0.136
Time × Intervention Condition 49.04 1 1.17 0.280
Time × SES 26.01 1 0.62 0.431
Time × Age 2.27 1 0.05 0.816
Time × SPED Eligibility 7.99 1 0.19 0.662
Time × Gender 55.45 1 1.33 0.251
Error 41.81 233
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Discussion

The purpose of the present study was to examine the relation of sustained attention with behavioral ratings of attention and their potential differential relations to different aspects of reading both cross-sectionally and longitudinally among struggling readers in 4th-5th grades. Our findings demonstrate a modest relationship between these two attention measures, and show that these aspects of attention are differentially related to reading skills, with both behavioral ratings of attention and sustained attention accounting for unique variance in reading comprehension when evaluated cross-sectionally. Additionally, behavioral ratings demonstrated a significant contribution to fluency, and also predicted growth in comprehension over the course of the year.

The Relationship Between Behavioral Ratings of Attention and Sustained Attention

The small yet significant relationship between behavioral ratings of attention and sustained attention is consistent with previous work comparing different assessment methods of attention to one another (Egeland, Johansen, & Ueland, 2009; Sims & Lonigan, 2012; 2013). This finding either suggests that these two methods of capturing attention assess partially overlapping but distinct aspects of a single multidimensional construct, or that they are different enough to be fundamentally different constructs. Our view is more in line with the former interpretation, and is also consistent with the reference to either as measures of attention throughout the literature. At the same time, we recognize the issues associated with considering attention too broadly, and view the present study as a demonstration of the need to be clear, from both a theoretical and empirical perspective, about the specific construct(s) under study and how they are measured.

The Role of Behavioral Ratings of Attention in Reading

The pattern of findings regarding the role of behavioral ratings of attention in different types of reading skills was largely consistent with our hypotheses. Given that most extant research documenting the relationship between attention and reading in elementary school students has used behavioral rating scales and reading comprehension measures, our findings that behavioral ratings of attention predicted reading comprehension at the beginning of the year as well as growth in comprehension over the course of one year are not surprising. The proportion of the variance in comprehension that was explained by behavioral ratings as well as the magnitude of the relationship between behavioral ratings and reading comprehension was similar to prior work; for example, Pham (2016) reported an additional 11% of the variance in comprehension was explained by behavior ratings, whereas we found that 11% of the variance in comprehension was explained by both behavior ratings and sustained attention. Similarly, we found a correlation of r = −.42 between behavior ratings and comprehension, whereas Pham (2016) reported r = −.32 and Hart et al. (2010) reported r = −.37.

Behavioral ratings simultaneously capture attentional abilities of the child as well as contextual information about the ways in which the child’s attention is impacted by the classroom setting. This combination likely has important implications for reading comprehension because of the need to stay on-task and sustain attention to a passage of text over time, connect the text of the passage with meaning, and answer questions about the text, all while suppressing irrelevant information both within text and extraneously (i.e., distractions in the classroom).

We did not find support for the role of behavioral ratings of attention in untimed word reading. Prior studies that have found such a link have been with younger samples (Dally, 2006; Miller et al., 2014). It is possible that more of this observable attention is needed at these younger ages when students are learning grapheme-phoneme relationships and applying them to new words (Perfetti, 1991), but that as these skills become automatized they demand fewer attentional resources at older ages. Our findings also support the role of behavioral ratings of attention in reading fluency, which is consistent with Pham (2016); it may be that students with attention difficulties have an increased level of distractibility, leading to fewer words read (or slower rate of reading), and/or a higher rate of errors.

The Role of Sustained Attention in Reading

This study is a first step in examining how sustained attention impacts reading outcomes in the context of the more commonly assessed behavioral ratings of attention. Our findings provide mixed evidence for the role of sustained attention in reading. Importantly, this work is the first to show that behavioral ratings of attention and sustained attention each account for unique variance in reading comprehension, even when considering other important predictors of reading (word reading, verbal knowledge, WM). However, in the longitudinal analyses, sustained attention did not predict growth in comprehension.

Sustained attention was also related to untimed word reading, though not timed word reading. However, the effect of sustained attention on untimed word reading did not remain significant when strong and theoretically-meaningful predictors (i.e., verbal knowledge, working memory) were included. It is possible that more specific attention measures, particularly those aligned with visual attention (e.g., visual attention span measure) might have yielded different results (see van den Boer, van Bergen, & deJong, 2015), particularly as these theories focus on hypothesized relation of attention with word-level reading. Additionally, as with behavioral ratings of attention, it is possible that we would have found stronger support for the role of sustained attention in word reading in a younger sample.

Despite inconsistent results for sustained attention, our study provides some evidence for the relationship between sustained measures of attention and reading outcomes, particularly reading comprehension, consistent with prior work. For instance, we found a correlation of r = −.30 between sustained attention and comprehension, whereas Stern and Shalev (2013) reported r = −.43. Given that reading comprehension plays out over time (our measure takes approximately 40 minutes), it is necessary to sustain focus over this time. Sustained attention is also needed to maintain contextually-relevant information in memory (Van den Broek, Young, Tzeng, & Linderholm, 1999) and make connections within the text (Cain & Bignell, 2014) which allows for successful comprehension.

Implications for Attention and Reading

Our study addressed two types of attention, from different theoretical perspectives, as a first step towards understanding their roles in relation to reading. For example, our findings show that an objective measure of sustained attention and behavioral ratings of attention measured through teachers ratings relate differently to reading. However, other attentional processes might also be examined. Single word reading for example may be more stimulus driven and rely more on selective than sustained attention, and may also require fewer of the behaviors on rating scales, given the restricted parameters of decoding measures and their brief duration. On the other hand, performance on a reading fluency task may depend on both task and situational parameters (e.g., a speeded word list may be more associated with selective than other types of attention, whereas timed oral reading fluency may be more associated with behavioral ratings of attention given the context of a classroom and/or social environment). Finally, reading comprehension is likely to tap sustained attention, and behavioral ratings of attention are also likely to impact performance on a lengthy, demanding task wherein more opportunities for distractions and careless mistakes are available.

The present findings also suggest that it may be useful to consider how behavioral aspects of attention situate in conjunction with theoretical models of attention (e.g., Chun et al., 2011; Dennis et al., 2008; Petersen & Posner, 2012), given that they are able to reflect longer-term goal-directed processes (including reading, particularly comprehension). It may be that sustained attention is relevant for understanding mechanistic relations, whereas behavioral ratings of attention act as a marker for problems that need to be addressed. Despite the present study being an important first step, future studies (ideally using pre-registration to replicate and extend upon current findings) are required to more firmly establish the present results.

Future studies might also examine additional theoretically meaningful aspects of attention. One approach toward identifying these is to be guided by a well-elaborated taxonomy of attention. For example, Chun, Golomb, and Turk-Browne (2011) distinguish between internal and external attentional processes, with the distinction being whether attention is driven by internal representations versus external stimuli. As with other frameworks for attention (i.e., Dennis et al., 2008; Petersen & Posner, 2012), the Chun et al. taxonomy describes a bottom-up stimulus-driven network (i.e., posterior system) and a top-down, goal-directed network (i.e., anterior system). It is possible that these different aspects of attention differentially predict reading outcomes. In the context of Chun’s taxonomy, the CPT task has elements requiring both external and internal attention, since it is driven by external stimuli but requires the child to maintain an internal state of sustained attention over time. It would be interesting to contrast findings for attention measured by the CPT with measures of externally-directed attention (i.e., selective attention, visual attention span), and/or with an internally-directed attention task such as a test of mind wandering, which is defined as a failure of executive control over conscious thought, and has been linked to reading comprehension (Feng, D’Mello, & Graesser, 2013; McVay & Kane, 2012; Schooler, 2004).

The role of attention in reading might be leveraged by integrating attention-boosting targets into reading interventions. For example, a student with high ratings of behavioral inattention may benefit from interventions that incorporate goal setting, rapid feedback, and motivational aspects in the classroom and homework settings. Students with objective sustained attentional difficulties may benefit, for example, from more frequent breaks to re-focus. Differential interventions of this sort have not yet been tested, and it is unclear how specific such interventions might be; it is also recognized that interventions directly focused on improving attention per se (as well as related domain general constructs) have not yielded promising results thus far (Barnes et al., 2016; Cirino et al., 2017; Melby-Lervag & Hulme, 2013; Jacob & Parkinson, 2015, but see Peng & Miller, 2016). It is possible that a clearer understanding of the way attentional components more broadly interrelate can more effectively inform the design of future interventions.

Limitations and Future Directions

One limitation was that we only had end-of-year behavioral ratings of attention available. However, this is balanced against the need for teachers to have adequate experience with students. Nonetheless, a better balance could be struck in future studies (e.g., mid-year ratings), and our results should be interpreted with some caution due to lack of time precedence for these ratings. It is also important to note that our behavioral ratings of attention scale likely provided a broader assessment of attention than the sustained attention measure (CPT), since items on the SWAN describe multiple types of attention (i.e., sustained attention, selective attention, inhibition). However, we chose to use this measure because it is strongly rooted in theories of attention and is often the operational measure of attention utilized in studies examining the relationship between attention and reading. Future studies may benefit from aligning self-report measures and objective measures more closely with one another in order to better understand how behavioral ratings and cognitive tests of the same construct differentially relate to reading. Additionally, although the CPT is widely used as an index of sustained attention, it is important to note that this task may measure additional processes as well (i.e., stimulus-specific influences; see Roebuck, Freigang, & Barry, 2016). Thus, future studies may consider utilizing different or multiple objective tasks to evaluate sustained attention. Studies that aim to differentiate between attention type and assessment method would also be of benefit (i.e., objective measurements of behavior such as actigraphs or observational coding sheets; more specific ratings of different aspects of attention under specific situations). A final limitation may be the somewhat truncated distribution of both reading and attention abilities due to the sample comprising all struggling readers, which may have resulted in reduced effect sizes, although the degree of truncation was not so severe as to impede our statistical analyses.

Conclusion

This study builds upon prior work by including sustained attention, behavioral ratings of attention, and multiple reading outcomes in both a cross-sectional and longitudinal fashion. Results highlight the need to more systematically evaluate the structure of attention, their relations to one another, and the different roles each may have in the support of different types of reading, at both theoretical and empirical levels. This study represents one step in this process, focused on sustained and behavioral ratings of attention. Future similar work can then be used to provide a basis for establishing how these processes impact interventions for struggling readers that integrate attention.

Table 4.

Within-Subject Effects from Repeated Measures Analysis of Variance Demonstrating the Role of Sustained Attention and Behavioral Ratings of Attention in Reading Fluency Growth

Effect MS df F p
Time 0.41 1 <0.00 0.961
Time × Behavioral Ratings of Attention 9.44 1 0.06 0.814
Time × Sustained Attention 150.33 1 0.89 0.347
Time × Word Reading 1386.84 1 8.18 0.005
Time × Vocabulary 0.40 1 <0.00 0.961
Time × Working Memory 240.43 1 1.42 0.235
Time × Intervention Condition 5097.74 1 30.08 <0.001
Time × SES 566.21 1 3.34 0.069
Time × Age 121.99 1 0.72 0.397
Time × SPED Eligibility 11.03 1 0.07 0.799
Time × Gender 2083.70 1 12.29 <0.001
Error 169.49 233
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Acknowledgments

This research was supported by Award P50 HD052117, Texas Center for Learning Disabilities, and Award F31 HD098797 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development to the University of Houston. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health.

Footnotes

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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