Technology applications for children with ADHD: assessing the empirical support.
Attention-deficit hyperactivity disorder (ADHD) is a condition that describes children and adolescents who display significant difficulties with inattention, impulse control, and overactive behaviors (American Psychiatric Association, 1994). ADHD affects between 3 and 5 percent of the school-age population, with boys outnumbering girls at about a 2:1 to 5:1 ratio (Barkley, 1998a). ADHD presents significant challenges to educators and has been the focus of considerable effort to develop instructional and treatment strategies. The use of technology as an educational tool offers new options for the expansion and development of these strategies.
Technology offers promise because a number of inherent features are closely associated with characteristics of effective instruction. For example, computers may be used to introduce new material with graphics, words, and sound within game formats, animation, or color. It also can simulate real world situations with images and sounds. The computer allows repeated trials, offers privacy, and organizes content into smaller chunks of information. Software can provide step-by step instruction, wait for responses, offer immediate feedback and reinforcement, and allow students to work at their own pace. These attributes allow the teacher to plan learning activities for students with short attention spans, enable students to be actively involved in learning, and may even increase the student's motivation and confidence (Fitzgerald,1994).
Advances in technology have enabled educators and psychologists to individualize instruction, increase the scope of cognitive training, and study neurological processes of students with ADHD (Plude, 1996). Moreover, specific computer applications may enhance some types of social skills, teach students social decision-making and problem-solving, and provide students opportunities to practice handling problem situations (Muscott & Gifford, 1994). Computerized simulations allow students to experiment with problem solving techniques, make good and bad choices, and see the consequences in a safe environment. Based on the characteristics of students with ADHD and the strengths that technology tools bring to instructional and treatment activities, there is clearly potential to contribute to improved learning and behavior.
The purpose of this article is to review empirical studies that have assessed the efficacy of technology as a tool for students with ADHD. The research is reviewed in five categories: computer-assisted instruction, computer-based cognitive training, biofeedback training, assessment, and behavior modification. Studies were identified through computerized bibliographic searches of the ERIC and PSYCHLIT databases. The following descriptors were used for the literature search: attention deficit hyperactivity disorder (ADHD), attention deficit disorder (ADD), hyperactivity, computer-assisted instruction, computer, technology, EEG biofeedback, EMG biofeedback, continuous performance test, and assessment. An ancestral search was conducted to locate additional studies by reviewing the reference lists of all studies identified. Benefits and limitations of the technologies for students with ADHD are addressed. Finally, recommendations for practice and future research efforts are presented. Table 1 shows a summary table of studies.
Computer Assisted Instruction
Academic difficulties are common among students with ADHD (Reid, Maag, Vasa, & Wright, 1994). For children with ADI-ID computer assisted instruction (CAI) offers great potential (DuPaul & Stoner, 1994). CAI can provide an instructional environment that is highly stimulating, where students receive frequent and immediate performance feedback, instant reinforcement, and continuous opportunities to respond to academic stimuli. All of these attributes have been shown to improve the performance of children with ADHD (Barkley, 1998b). In addition, programs may be able to teach new behaviors, provide practice, and allow automatic recording of information for subsequent retrieval and analysis. CAI can provide instructional modifications which have been proven to be beneficial to students with ADHD, such as step-by-step elaboration of tasks, models of task completion, concrete examples, and shorter assignments (Bender & Bender, 1996). Only two studies have examined the effects of instructional technology with ADHD stu dents.
Ford, Poe, and Cox (1993) examined the effects of different types of CAI on the attending behavior of 21 elementary school children identified as ADHD. The subjects were divided into three groups based on methods of identification: teacher identified only, teacher and Revised Conners' Questionnaire identified, and identified by private practitioners and receiving medication. Using a within-subjects group design, participants were instructed with four software packages: (a) math drill and practice, (b) math instructional game, (c) reading drill and practice, and (d) reading tutorial, drill, and practice. Each package included two formats for comparison: game and nongame format, playing against computer and playing with a partner, animated or nonanimated graphics, and unlimited time to respond or beat the clock competition. The non-attending behaviors on each software package were rated every two minutes during two ten-minute periods by two raters using a prepared checklist. Those behaviors included fidgeting, responding impulsively, out of seat, talking to neighbor, and making inappropriate noises. The authors reported that the attention of the identified children increased significantly on software with a game format, without animated graphics, and with unlimited time to respond. More non-attending behaviors occurred on the reading tutorial and drill and practice software program than on the two math packages.
A number of methodological difficulties limit the conclusions that can be drawn. First, the procedures used to identify participants were suspect. Some subjects were identified as having ADHID based only on teacher report or Conners results. This does not conform to recommended diagnostic procedures (e.g., DuPaul & Stoner, 1994). Moreover, 8 of the participants did not score above the cutpoint in the Revised Conners Questionnaire. There was no effort to control carryover effects of the instructional procedures so order effects may have affected outcomes. Inter-observer agreement was not addressed for the observational data thus the reliability of these data is uncertain. Most importantly, the researchers claimed: (a) that non-attending behaviors increased when the material contained in the non-game drill and practice programs was too easy or too difficult, (b) that fewer non-attending behaviors were observed when students were playing with a partner than when just competing with the computer, and (c) that no increase in nonattending behaviors was observed when the software had a game format. However, as the authors noted, because there were differences in content (i.e., reading vs. math) or task (i.e., syllabication vs. matching a word to its definition), and programs (i.e., tutorial vs. drill and practice) it is impossible to determine if differences were due solely to program format or other factors.
Kleiman, Humphrey, and Lindsay (1981) compared 18 children's performance on arithmetic problems administered by computer with problems given in a standard paper and pencil format using a program that was specially modified for use with students with ADHD. Modifications included individualized level of problem difficulty, a more readable display, self-paced problem-solving, familiar answer format, and motivational features (such as graphic displays and praise statements). Dependent measures included accuracy, number of problems attempted, and rate of problem-solving in the computer format and the paper and pencil format. On average, children did almost twice as many problems on the computer as they did with paper and pencil. In addition, the ADHD group on average also spent more time working on problems on the computer, without any significant loss of accuracy or speed. Informal interviews with the children indicated their strong preference for the computer. Two factors limit the usefulness of this study. Firs t, the specific nature of the problems of these children were not provided in detail, thus there is no way to evaluate how many subjects actually met the criteria for an ADHD diagnosis. Second, and most critically, no statistical tests were conducted to determine whether there were real differences across treatments.
In summary, there is not a great deal of evidence to support the use of CAI activities with students with ADHD. While there is reason for optimism-the results of both studies were positive-due to methodological issues and the paucity of studies no firm conclusions are possible. Ford, Poe, and Cox (1993) do highlight an important consideration for further research, which is that the difficulty level of the material should be considered. Hasselbring and Bottge (1999) have pointed out that much of the research related to the use of computers to increase fluency in the general population has shown mixed results. However, when specific conditions are met positive gains can be demonstrated. These conditions include (1) the terminal skill is acquired before the practice begins, (2) the activity should emphasize rapid responding, and (3) the software should include a management system that monitors student progress. In evaluating the efficacy of technology, the appropriate application of that technology in the instru ctional setting is a primary factor in its success and results should be judged in this context.
It was also positive that these studies focused on the specific design aspects of the software and attempted to investigate the impact of varying formats on students' attention. This type of information is important in designing or evaluating programs specifically intended to meet the needs of students with ADHD
The available empirical research represents a very narrow view of instructional technology. Hasselbring and Bottge (1999) have described an array of instructional technology uses that includes: basic skill acquisition and fluency (tutorial and drill and practice), exploratory technologies (simulation, hypermedia, and World Wide Web), application technologies (word processing and mulitmedia development), and communication technologies. With the exception of the social skills simulation programs discussed in the next section, we found no empirical investigations of the impact of instructional technology used in any of these other ways with students with ADHD.
Computer-assisted Cognitive Training
Students with ADHD typically demonstrate poor cognitive organization skills, and social skill deficits (Barkley, 1998a). Researchers have found that medical and behavioral treatments are relatively ineffective in treating some specific deficits, such as low frustration tolerance, high distractibility, information processing, and emotional lability (Tansey & Bruner, 1983). Computer-assisted cognitive training (CACT) may be a promising approach for helping students with ADHD gain self-control over their behaviors. Some of the advantages of CACT include the ability to produce an individualized treatment program for the specific deficits of the child, simulate several settings in order to enhance the transfer of knowledge, and automatic recording of information for subsequent retrieval and analysis. Three studies have examined the effects of CACT with ADHD students.
Kotwal, Burns, and Montgomery (1996) conducted a case study to assess the use of Captain's Log. The subject in the study was a 13-year-old boy who was diagnosed with ADHD. Captain's Log contains a wide range of cognitive exercises with respect to attention, concentration, memory, eye-hand coordination, and problem solving skills. The boy received 35 sessions over a 3-month period. The authors reported that the Conners Parent Ratings Scale showed a reduction of impulsive-hyperactive behaviors, conduct, and learning problems. Both parent's and teacher's informal reports showed an increase in on-task behaviors and a reduction of disruptive behaviors at home and at school respectively. However, the Conners Teacher Rating Scale suggested both improvement and worsening among those disruptive behaviors. There was no significant change for the Wechsler subtests. EEC testing suggested that both theta and beta brain wave amplitude decreased. A 7-month follow-up demonstrated some deterioration of the treatment effect, b ut reported gains during treatment were generally maintained.
The authors' conclusions must be viewed with caution because of limitations of their study. Because the study was not experimental, other factors (e.g., changes in school program or home life) which were not controlled, may have contributed to the changes reported. The most serious concern is that the authors drew their conclusion based on the mother's and the teacher's informal reports and rating scale data. However, parent and teacher ratings of ADHD children are notorious for improving, sometimes dramatically, between the first and second evaluations even when no treatment has occurred (Barkley, 1992; Diamond & Deane, 1990). Thus, the changes reported may be due to rater effects as opposed to actual behavior changes.
A similar study was conducted by Slate et al. (1998) to determine the efficacy of the Captain's Log for four severely emotionally disturbed children with ADHD. Each child performed the cognitive tasks for 30-minute sessions 4 times a week for 16 weeks. Three of the children improved in mathematics and receptive knowledge of vocabulary and showed improvements in motor speed and response accuracy. Two children improved their daily behaviors observed by the facility staff; however, the data reported indicated that only one child demonstrated a marked difference between pre and post scores on the Conners, and there was little if any improvement in the other three children's behaviors. The data on actual behavior supports this conclusion. Students were awarded points for positive behavior and points were deducted for inappropriate behavior. These data show that only one child demonstrated improvement as measured by points awarded, one child demonstrated little or no change, and, at the time of the post test, the r emaining two children actually lost more points than they were awarded. In addition, the children were receiving other treatments known to improve behavior and learning such as behavior modification and medication during the computerized cognitive training. As a result, we cannot be certain how much the improvement is due to medication or behavior modification, and how much due to the computerized cognitive training.
Many students with special needs leave school without being able to effectively compete for professional jobs that require problem solving and adequate interpersonal skills. Elias, Tobias, and Friedlander (1994) described a Personal Problem Solving Guide with the support of computer technology to teach those skills to students with special needs. The purpose of the training is to develop students' metacognitive abilities, which they need to monitor their own thinking in interpersonal situations and consciously invoke appropriate behaviors. The subject in this study was a seventh-grade student with ADHD. He was referred to the school psychologist for making obscene comments to girls after being teased by them. The authors reported that there was no recurrence of obscene comments or any other behavior problem for the remainder of the school year as a consequence of the computer-based problem solving, decision-making, and conflict resolution training. Unfortunately, other factors could account for the behavior c hange. For example, the principal removed previously assigned punishers. Therefore, it is not possible to infer that the computer-based problem-solving program was responsible for changes. Whether this program would offer any advantages over conventional counseling is also uncertain since the program required the school psychologist to be present and work with the student when the software was used.
In summary, the results of the CACT studies closely parallel those of CAI studies. The studies generally reported positive results; however, these findings must be tempered by the fact that only a few studies have addressed the use of CACT, and there are serious methodological problems. Perhaps the safest stance is guarded optimism. Additionally, as with CAI, the research hits at some of the core issues in the treatment of children with ADHD, such as attention, problem solving, and social skills.
Biofeedback training is an area which is growing in popularity and is receiving increasing media attention. According to Lubar, a leading proponent of biofeedback training, there are now over 2000 professional who are actively using biofeedback training (Condor, 2000) with 700 groups using biofeedback training for children with ADHD (Baron-Faust, 2000). Once limited to medical practitioners, biofeedback training is now becoming an educational issue as there are reports of biofeedback training being used in the schools (Condor, 2000). Thus, educators should be informed as to the empirical support for biofeedback training. There are a number of studies on biofeedback with ADHD. In the next section we discuss the rationale behind biofeedback training. We will discuss studies representative of the major thrust of biofeedback research in detail and note similar studies.
Biofeedback training has two potential applications in the treatment of children with ADHD: one is the reduction of muscle tension levels through electromyographic (EMG) feedback, the other is the operant conditioning of brain-wave activity through electroencephalographic (EEG) feedback (Tansey & Bruner, 1983). EMG biofeedback utilizes an electromyometer to assist children in reducing muscle tension levels. The position of the pointer of a peak-to-peak microvolt meter provides one mode of visual feedback. The children are instructed to calm down so that the meter pointer or the lights will move in the desired direction. Learned reduction of muscle tension through EMG feedback has been claimed to be efficacious in the treatment of hyperactivity (Braud, 1978; Hampstead, 1979). Lubar (1991) has suggested that children with ADHD produce excessive slow wave theta activity and are deficient in beta production. EEG training can address this. During the EEG biofeedback process, the brain electrical activity is measur ed using the EEG. The computer can then detect and classify the different levels of brain electrical activity. This information is displayed using lights and/or sounds (beeps) that can show children how much of each activity they are producing. Children are given feedback about their own brain activities and are trained to increase the desired brain activity and decrease the undesired activity.
There are numerous case studies of biofeedback reporting improved functioning and academic performance. Tansey and Bruner (1983) presented a case study using both EMG and EEG biofeedback using Sensorimotor Rhythm (SMR) training in the treatment of a 10-year-old boy diagnosed with ADHD, developmental reading disorder, and ocular instability. The boy received three 40-minute once-weekly EMG biofeedback training, followed by 20 once-weekly 40-minute EEG biofeedback training. The authors reported that the child's motoric activity level was reduced to below that which had been achieved by past administration of Ritalin. His hyperactivity and distractibility were no longer diagnosable following the EMG biofeedback training. Moreover, the EEG biofeedback training resulted in the improved reading comprehension and in the ability to read without ocular anomalies. These results remained unchanged in a follow-up study after 24 months.
The authors' claims must be tempered by the fact that, besides biofeedback, the subject received other treatments (e.g., positive reinforcement treatments such as verbal praise, contingency contracting, and tangible rewards) all of which are known to be effective for increasing appropriate behavior. Additionally, the biofeedback training itself resulted in increased attention for the child. As a result, it is impossible to determine whether the biofeedback training, behavior modification, or the combination of the two accounted for any changes. Additionally, no objective data were reported on behavioral change. For example, the boy's motoric activity level was reported to be "under control;" however, the criteria used to establish "under control" were not reported, and no observational data were reported. Moreover, this determination appeared to be based solely on parental report as observed by his mother and the therapist, both of whom may be influenced by placebo or Hawthorne effects. Unless the results of training generalize to behavior outside the therapy sessions the training will be of little practical use. Unfortunately, because the generalization data reported were based on subjective impressions and other environmental factors may have affected the child, it is difficult to assess the validity the authors' claims.
Lubar and Lubar (1984) conducted six case studies on long-term biofeedback and academic treatment for attention deficit disorders. Six males ranging from 10 to 19 years old were included in this study. Their symptoms were primarily specific learning disabilities. Each subject received two sessions per week for 10 to 27 months. The sessions consisted of either feedback alone or feedback combined with academic training. During the feedback training, the children were encouraged to increase beta feedback while inhibiting 4- to 8-Hz theta or EMG (electromyographic) activity. The academic training consisted of reading, arithmetic, and spatial tasks to improve their attention. The authors reported that all six children increased SMR or beta and decreased slow EEG and EMG activity, and that combined training produced significant and sustained improvements in their school performance as measured by improved grades and standardized achievement tests.
Perhaps the most serious flaw of this study is that the children were receiving academic training at the same time that they were receiving the biofeedback training-over periods that range from 10 to 27 months. Additionally, because the training occurred across different school years (e.g. 6th to 7th grades) factors in the school environment such as different teachers or increased school services may have affected outcomes. As a result, we cannot determine the relative individual contributions of biofeedback and academic remediation. Another potential weakness of this study is that the authors did not provide any information on how children were diagnosed with ADHD. Thus, it is impossible to assess whether the children's problems were due to ADHD or other factors such as learning disabilities. Because there were no objective behavioral measures reported, it is difficult to conclude that biofeedback can help the children to increase focused attention and decrease hyperkinetic behaviors.
One critical aspect of biofeedback training is generalization. Many studies have reported changes on laboratory attention tasks (e.g., Lubar, Swartwood, Swartwood, & O'Donnell, 1995). However, few studies have investigated whether the effects of biofeedback training will transfer to the classroom settings. For this, Blanton and Johnson (1991) designed a case study to assess the potential of using computer-assisted biofeedback with two6th grade and one 4th grade males identified as having ADHD. They exhibited problem behaviors such as out of seat, failure to pay attention, and failure to complete work assignments. Each student participated in 6 or 720-minute EMG biofeedback sessions. The authors reported that all three students were able to achieve low EMG ratings following biofeedback. However, analysis of the data reported suggests that only one student actually exhibited a marked decrease from baseline levels. Classroom data were reported for only one of the three students. This consisted of a 5-minute obse rvation to assess the transfer effect of biofeedback to the classroom. The reported data showed that the mean level of on-task behavior increased over baseline with more stable levels; however, there was near 100% overlapping data points across baseline and treatment which complicates interpretation. Additionally, the lack of a reversal phase prevented ruling out other outside factors that may have accounted for the observed changes. Others have reported descriptive or survey data suggesting generalization (e.g., Alhambra, Fowler, & Alhambra, 1995; Wadhwani, Radvanski, & Carmody, 1998). However, none of the studies were controlled.
Several group studies have reported positive results on rating scales, measures of intelligence, and achievement tests (e.g., Linden, Habib, & Radojevic, 1996; Lubar et al., 1995; Patrick, 1996). For example, Lubar et al., (1995) reported significant changes in WISC-R scores and behavior ratings on the Attention Deficit Disorder Evaluation Scale for 10 children who successfully completed a neurofeedback training regimen. Patrick (1996) reported significant changes for 11 children on the WISC-3 and the Child Behavior Checklist. Similarly, Linden et al. (1996) reported increases on the composite IQ score of the Kauffman-Brief Intelligence Test and improvements in parent ratings on the IOWA Conners. However, there are methodological concerns with all the studies. Based on the data reported, Patrick (1996) apparently used independent as opposed to dependent t tests which makes results uncertain. In the case of Linden et al. (1996) the overall MANOVA was not significant, and the authors failed to control for famil y-wise error (due to multiple univariate tests). If family wise error were controlled, none of the results would be significant. In the case of Lubar et al. (1995) there is a distinct possibility that changes in behavior ratings were the result of Hawthorne effects. While the group who responded to biofeedback training showed significant positive changes in behavior ratings, there was no significant difference in post-treatment ratings between the responders and the non-responders. This suggests that changes were not the result of biofeedback training. Lubar's IQ data were startling. Some of the children in the responder group made gains of over a standard deviation on verbal or performance scales. One student increased his/her verbal scale score by 25 points and the average increase was 10 points (2/3 of a standard deviation). However, there was no control over pretest administration, which raises the possibility that differences in test administration may have contributed to the differences.
One very serious problem in the group studies is that all studies used a pie-post wait list control group designs which were not double blind and which did not include a placebo treatment group. The fact that both parents and teachers (typically used as a source of behavior ratings) were not blind to treatment raises the possibility of placebo effects. Moreover, there is also the question of whether any observed changes are due to biofeedback training or to the non-contingent adult attention and reinforcement provided during the biofeedback sessions (Barkley, 1992). Only one study has utilized a placebo treatment group. Kaduson and Finnerty (1995) compared EMG biofeedback, cognitive training, and a game control condition in which children played board games (e.g., Monopoly). The results showed that there were no differences across the three conditions on three behavioral rating scales (i.e. Child Behavior Checklist, Conners Parent Rating Scale, Attention Deficit Disorder Evaluation Scale).
Arnold (1999) characterized biofeedback training research as potentially promising. Based on the number of positive case studies and the positive results of other studies, this appears to be a fair assessment. An informed assessment of biofeedback is not possible until randomized, double-blind studies with placebo-control conditions which also utilize direct observations of classroom behavior are conducted. Biofeedback training may be a useful ancillary treatment. However, at this point in time, it could not be recommended as a primary treatment. Time and cost factors should also be considered. The recommended 40 treatment sessions at a cost of approximately one hundred dollars per session represents a large investment of time and money. This is approximately 10 times the cost of a full year of medication.
One of the most thoroughly investigated technologies used with children with ADHD is the continuous performance task (CPT) used in ADHD assessment. CPT tests are widely used by researchers. Although they are not generally used in educational settings, they are often seen in clinical settings. Thus, educators may be presented with the results of CPT tests from an assessment performed by a child psychiatrist, or may be asked by parents to explain to interpret CPT results. Presently there are a number of commercially available CPT tests for which published data are available. These are the Continuous Performance Test (Conners, 1995), the Gordon Diagnostic System (GDS) (Gordon, 1983), and the Test of Variables of Attention (TOVA) Greenberg & Kindschi, 1996). Comparisons between the different tests are impossible because there are no studies which have compared the different CPT tests (Gordon & Barkley, 1998). Additionally, there is very little published data available on the reliability and validity of the TOVA a nd Conners' CPT. For this reason we will focus our discussion on the GDS as there is more information available.
The GDS is a portable, single-component microcomputer-based device that can be used to measure sustained attention and impulse control. It is also a standardized instrument that allows for normative comparisons. The GDS administers three types of tasks: the delay task, the vigilance task, and the distractibility task. The delay task is designed to measure a child's ability to inhibit responding. In this task the subject is instructed to press a button and then wait a set interval of time (6 seconds) before pressing the button again. If the subject waits long enough a light flashes and the counter on the front display shows an increment of one point. If the subject responds too soon no point is earned and the internal timer resets.
The vigilance task presents a series of digits on the display. The subject is instructed to press the response button only after viewing a one which is immediately followed by a nine. The distractibility task presents random distracting digits on either side of the central stimulus digits and the subjects is required to respond to the target stimuli only when it appears in the middle column.
Several studies have investigated the reliability as well as concurrent and discriminant validity of the GDS. Gordon and Mettelman (1988) presented standardization and reliability data for the three primary tasks of the GDS based on 1266 non-referred children between 4 and 16 years of age. The primary GDS scores did not correlate highly with IQ scores in this standardization sample; this is important as previous research suggests that the type of attention problems exhibited by children with ADHD are not related to intelligence. Children performed similarly on both versions of the vigilance task, and GDS variables across tasks are not highly correlated which indicates that each GDS test assesses different aspects of attentional functioning. In addition, test-retest reliability coefficients are high after intervals of less than 45 days and one year which suggests that the GDS results are stable over time.
Several studies have explored the relationship between objective measures of attention and other traditional tests which are used to infer attentional functioning. Grant et al. (1990) examined correlations between computerized measures of impulsivity and sustained attention using the GDS and a battery of intellectual, achievement, and neuropsychological tests. The subjects were 119 boys with the ages ranging from 6 years to 12 years 11 months, who were diagnosed as having ADHD or ADHD with learning disabilities. The results showed that the number of correct responses for vigilance and distractibility tasks of GDS were significantly correlated with scores from the WRAT-R arithmetic subtest, verbal and performance IQs of the WISC-R; however, when alpha levels were adjusted to take into account family wise error (i.e. the significance level was adjusted to .001) only one correlation was significant. The significant correlation was between the number of correct responses for the distractibility task of GDS and fo r the Freedom from distractibility factor of the WISCR which, as the authors noted should be expected.
A similar study conducted by Rasile et al. (1995) examined the relationship between tests of attention from the GDS and other traditional measures of attention based on the performance in 136 college students. All subjects received the Standard Delay, Vigilance, and Distractibility Tests of the GDS; 69 of the subjects received the WAIS-R Digit Span, Arithmetic, and Digit Symbol subtests, followed by the Kagan's Matching Familiar Figure (MFF) test. The other 67 subjects received the Visual Span subtest of the WMS-R, and the Stroop Color and Word Test. The results showed that within-test correlations were strong while between-test correlations were weak among the various GDS scores which is consistent with previous research showing that each GDS test assesses different aspects of attention. The results also indicated that correlations of the GDS tasks and the WAIS-R sub-tests and the MFF were significant. In addition, the GDS standard Delay total correct correlated with the Stroop interference score. However, a ceiling effect on the Vigilance task may have affected its correlations with other tests.
Perhaps the most important aspect of any test is the ability to accurately detect the presence or absence of ADHD. Data supporting the discriminant validity (i.e., the extent to which the GDS can accurately classify children with and without ADHD) are limited. Wherry et al. (1993) examined the discriminant validity of the GDS using teacher ratings as criterion measures. The subjects were 29 boys aged 6 to 13 years. They were categorized into "normals" or "ADHDs" based on their scores from the CBCL-Teacher Report Form (TRE) and ADHD Rating Scale. Results failed to demonstrate the discriminant validity of any GDS scores for both TRF and ADHD Rating Scale. The authors' correctly stressed that interpretation of these negative results must be tempered because of small sample size and hence the relatively low statistical power. However, serious concerns about the GDS in particular (and other CPT tests in general) have been raised.
Researchers using CPT tests have repeatedly demonstrated group differences between children with ADHD and controls (Gordon & Barkley, 1998). But at the individual level the picture is much less clear. First, there is the problem of "false negatives," that is children who have ADHD but whose test results are normal. Barkley and Grodsinsky (1994) reported that the false negative rate for the GDS ranges from 15% to 52%. Thus, a sizable portion of children with ADHD would not test positive and would fail to be diagnosed based on CPT results. Second, there is the problem of correlation between CPT scores and teacher ratings. Several studies have failed to find significant correlations between teacher ratings and CPT scores (e.g., Halperin, Scharma, Greenblatt, & Schwartz, 1991; Lovejoy & Rasmussen, 1990). Other researchers have found that classification decisions based on CPT results do not agree well with decisions based on parent interviews and rating scale results (DuPaul, Anastopoulos, Shelton, Guevremont, & M etevia, 1992). Moreover, when used with children diagnosed as ADHD by rigorous research standards, GDS vigilance test scores were able to correctly identify less than half of the children with ADHD (DuPaul, Anastopoulos, et al., 1992). There have been instances where CPT tests have performed well. Forbes (1998) found that the TOVA could correctly identify 80% of children with ADHD; however, he also reported a high rate of false positives (28% of non-ADHD children would be identified). Thus, the ecological validity of CPT tests is suspect (Barkley, 1991). Additionally, when factors such as age, sex, and vocabulary skills were controlled for, CPT tests did not discriminate between children with ADHD and controls (Werry, Elkind, & Reeves, 1987).
In summary, these studies suggest that the use of CPT tests is limited. Moreover, there is a strong consensus that CPT tests can not be used as a substitute for more commonly used assessment techniques such as rating scales. CPT test results may be useful as an additional source of information to reach a final diagnostic decision. Because CPT tests are presented as an objective measure of attention, there may be a tendency to accept the results on face value (DuPaul & Stoner, 1994). However, interpretation of CPT test results demands caution. Gordon and Barkley (1998) present three important points regarding interpretation of CPT test results. First, the results of CPT tests in isolation should not be considered diagnostic of ADHD. Second, an abnormal CPT score, in conjunction with other measures, may be indicative of ADHD. Third, a normal CPT test score does not rule out ADHD.
Behavior modification is an important component in treatment of ADHD (Barkley, 1998b). However, many behavior modification techniques require considerable teacher time and effort and thus may be difficult for teachers to implement in the classroom. One effective strategy is response cost (DuPaul & Stoner, 1994). A study using an experimental device to communicate response-cost fines was actually superior to medication in terms of reducing off-task behavior (Rapport, Murphy, & Bailey, 1982). For this reason, Gordon and colleagues (Gordon et al., 1991) introduced the Attention Training System (ATS) (marketed by Gordon Systems, Inc. 1987) which allows for implementation of a response cost program in a way that teachers find manageable within the general education or special education classroom. The ATS is a battery-operated, electronic device with a counter and a red light. It is placed on a child's desk and displays cumulative points earned for on-task behavior. Points are awarded at set intervals (e.g., every minute). The points can be exchanged later for other reinforcers such as free-time activities or toys. When the child exhibits an inappropriate behavior (e.g., goes off-task) the teacher activates a small remote control device by pressing a button that causes a red light on the ATS to shine for 15 seconds and a point to be deducted from the accumulated total. The effectiveness of this system for use with children with ADHD has been documented in several studies.
Gordon, Thomason, Cooper, and Ivers, (1991) employed a within-subjects design to examine the effectiveness of the ATS in a clinical setting. This study involved six children with ADHD between the ages of 6 and 9 years. None of the subjects were on stimulant medication during the study. The number of off-task behaviors was recorded during an 11 week training phase and 2 weeks at the outset and 2 weeks at the end of the study. The results showed that attention to task improved markedly from baseline to the training phases and then deteriorated once the ATS was removed in five of the six cases. However, because the study was conducted in a clinic setting, the results cannot be generalized to classroom settings.
DuPaul, Guevremont, and Barkley (1992) conducted a study to investigate the efficacy of the ATS in a special education classroom setting. Participants were two boys identified as having ADHD. A multiple-baseline design across settings (i.e., reading and language) was employed to evaluate each child's behavior and academic performance. Dependent measures included teacher ratings, behavioral observations, and academic performance measures. The results of the study indicated that both children improved their task-related attention and reduced levels of ADHD behaviors as a function of the ATS. However, the effects on academic productivity were equivocal. Reading task completion rates improved under ATS conditions, but accuracy rates were not consistently affected. It is important to note that the participating teacher, aide, and students reported a clear preference for the ATS as opposed to the classwide token reinforcement program during independent seatwork sessions. Because the study took place in a special ed ucation classroom with a small student-teacher ratio, the results can not be generalized to larger mainstream classrooms.
One important question regarding the use of the ATS is whether it provides any additional benefits beyond the use of medication alone. Evans and colleagues (Evans et al., 1995) evaluated the effects of the ATS as an adjunct to stimulant medication usage in further reducing distractibility and off-task behavior in an 11-year -old boy with ADHD (physician-identified) in a self-contained special education classroom. The experimental design utilized in this study was an ABAB design across three periods of the day using three subject areas (reading, social studies, and science). The ATS, as an adjunct to the use of psychostimulant medication, led to a reduction in off-task behavior across all three settings. The specific effects of the ATS cannot be determined because it was combined with the medication; however, the results suggest that the ATS did improve behavior over and above medication alone.
In summary, there is good evidence that the use of the ATS system is effective in decreasing the off-task behaviors in children with ADHD. A major advantage of the ATS is that it does not require extensive involvement of teachers and is relatively unobtrusive. However, the ATS program alone may not be sufficient to maintain behavior change; it may be more effective in combination with medication or other interventions (Evans, et al. 1995). Another limitation of the ATS it can not be utilized to address other areas of functioning (e.g., peer relations, impulse control) and in certain school settings (e.g., cafeteria, playground) (DuPaul, Guevremont, & Barkley, 1992). Additionally, further research is needed to examine the potential uses of the ATS in mainstream classrooms.
The most obvious conclusion that can be drawn from this review is that the amount of research aimed at investigating the use of technology with students with ADHD is (with the exception of CPT) extremely limited and scattered. While some promising findings were presented in the literature, very little empirical data exist to support claims of effectiveness or to guide effective implementation of technology. Three major concerns arise from this review. First, much of the available research has serious methodological shortcomings. Second, the available research is limited to very narrow areas. Third, available research provides little practical guidance for implementing technology for children with ADHD.
Methodological problems in the studies seriously limit any conclusions that can be drawn. There are only a handful of well-controlled experimental studies that support the effectiveness of technology for students with ADHD, and these were limited to narrow implementations (i.e., the ATS and CPT tests). The majority of studies were quasi-experimental at best or were very limited case studies. The main methodological concerns lie in three areas: (1) the lack of rigorous experimental studies, (2) subject selection procedures, and (3) outcome measures. For some studies confounding variables make it uncertain to what extent the positive results reported in these studies are due to other factors and how much, if any, can be attributed to the computer-based training. For example, in many studies students also received behavior modification (Elias, et al., 1994; Slate et al., 1998; Tansey & Bruner, 1983) or changes in the academic environment (Elias, et al., 1994; Kotwal, et al., 1996; Lubar, 1984). Subject selection is also problematic. Current best practice in ADHD diagnosis requires a multi-stage, multi-informant assessment procedure (DuPaul & Stoner, 1994). Few studies met this requirement. In some studies it is uncertain whether an ADHD diagnosis was warranted because of lack of description of diagnostic criteria (Elias et al., 1994), overreliance on report data (Ford et al., 1993; Wherry et al., 1993), or the identification process had not been completed (Kleiman, et al., 1981; Lubar & Lubar, 1984). The situation is further complicated by the fact that, over the time span covered by this review, the diagnostic criteria for ADHD have changed three times. Thus, comparisons across studies may be difficult. Another serious problem lies in the area of outcome measures. Only a few studies reported objective, reliable observational or academic data (e.g., DuPaul, Guevremont, & Barkley, 1992; Evans et al., 1995; Gordon & Thomason, 1991; Kleiman et al., 1981). Others used rating scale data or report data (Elias, et al., 199 4; Ford et al., 1993; Kotwal et al., 1996; Tansey & Bruner, 1983) which is subjective. While promising results are described in many of the studies noted above, any optimism must be guarded because of the methodological shortcomings.
Scope of Research
Another factor that has prevented a clearer understanding of the impact of technology on students with ADHD is the limited nature of the interventions used in these studies. The two applications that have demonstrated efficacy-the ATS and CPT tests-while useful, are quite limited. Others such as EEG training, which are intended to offer broad improvements in behavior or academics have not demonstrated generalizability. The applications which are most likely to be seen in the classroom, CAI and CACT have hardly been touched upon. Additionally, some important computer applications, such as the use of the word processor, have not been evaluated with children with ADHD. Given that children with ADHD commonly experience difficulty with handwriting and resistance to written language tasks (DuPaul & Stoner, 1994) this omission is glaring.
Additional research is needed in order to more clearly understand the power and limitations of technology applications for students with ADHD. One critical area is in how to best match the characteristics and needs of students with ADHD and the attributes of computer software. In addition, there needs to be further research and development of software and hardware specifically designed for assisting students with ADHD in academics, behavioral, and social skills. There is also a pressing need for research on the marriage of cooperative learning and computer-based training for ADHD students with the emerging computer networks and Internet technologies.
While the studies examined in this paper indicated that technology offers a promising avenue for a variety of educational interventions for students with ADHD there are several unanswered questions: (1) How should computers and other forms of instruction be integrated to maximize effectiveness of computer-based instruction? (2) How can teachers and learners use technology most efficiently to produce optimal results? (3) How can teachers select appropriate software and hardware for students with ADHD? (4) What are the short- or long-term effects of such software or hardware on maintaining student attention and academics? (5) How can computer program be designed to train students with ADHD in behavioral and social skills?
We would stress that computer technology, like other forms of technology, is neither a panacea nor a cure for the academic, behavioral, and social problems of students with ADHD. Practical limitations constrain the use of computer technology in the schools. There are limited numbers of computers and little software for students with ADHD. This means that access to technology for children with ADHD will necessarily be limited. Although hardware and software access is improving with the advancement of technology and competition, schools pay more attention to amassing greater numbers of computers than more powerful platforms. This may limit the extent to which they can access newer applications (Okolo, Bahr, & Rieth, 1993). In addition, some software for students with special needs contain age-inappropriate activities, complicated operations, and animated games, which may distract the students' attention and violate the purpose of instruction (Bender & Bender, 1996). There is also shortage of trained personnel w ho can efficiently employ the new technologies. To use computers effectively in the resource rooms, teachers must receive adequate and appropriate training. In addition, teachers must spend the time to familiarize themselves with new software, schedule computer access time, select and evaluate appropriate software, assist students with troubleshooting, and monitor student performance on the computer(Okolo, Bahr, & Rieth, 1993). Thus, the computer-based training leads to more challenges for special educators.
Although in theory computer-based technologies offer great promise for children with ADHD, this potential has yet to be demonstrated empirically. Given that an estimated 1-2 million children may have ADHD, this represents a major void in our knowledge of how best to treat these children. To achieve this goal, researchers need to identify effective technologies, establish viable service delivery systems, and promote large-scale implementation.
Table 1 Summary of Research and Reported Outcomes Computer Assisted Instruction Authors Participants Ford, Poe & Cox 21 elementary (1993) children - 7 third- grade, 14 fourth- grade Kleiman, 18 children Humphrey, & undergoing Lindsay ( 1981) assessment for ADHD Authors Methodologies Ford, Poe & Cox Within-subjects design (1993) comparing format in 4 software packages Kleiman, Comparison of arithmetic Humphrey, & problems administered by Lindsay ( 1981) computer and in paper and pencil format Authors Dependent Measures Ford, Poe & Cox Non-attending behaviors (1993) rated every two minutes using prepared checklist Kleiman, Accuracy, number of Humphrey, & problems attempted, rate of Lindsay ( 1981) problem solving, child preferences Authors Reported Outcomes Ford, Poe & Cox Attention increased for game format (1993) without animated graphics and unlimited response time. More non- attending time on reading tutorial and drill and practice than math packages. Kleiman, No statistical tests. Reported Humphrey, & twice as many problems attempted Lindsay ( 1981) and strong preference for computer. Computer-Assisted Cognitive Training Authors Participants Kotwal, Burns, 13 old boy & Montgomery diagnosed with (1996) ADHD Slate, et al. 4 children (1998) diagnosed with ADHD and other emotional and behavior disorders. All were receiving medication Elias, Tobias, & Seventh-grade Friedlander student with ADHD (1994) Authors Methodologies Kotwal, Burns, Case study using & Montgomery "Captain's Log" software (1996) (cognitive exercises) over 35 sesions Slate, et al. "Captain's Log" software (1998) used for 30-minute sessions 4 times a week for 16 weeks. Elias, Tobias, & Case study using Personal Friedlander Problem Solving Guide (1994) Authors Dependent Measures Kotwal, Burns, Conners Parent Ratings & Montgomery Scale (1996) Conners, Teacher Rating WISC III subtests, EEG testing. Slate, et al. WISC-III, WRAT-3, (1998) PPVT-R, TMT, IVA, CECL, TRF, Conners Rating Scale7 EEG, behavioral point system Elias, Tobias, & Number of reported Friedlander behavior problems (1994) Authors Reported Outcomes Kotwal, Burns, Conners Parent Rating Scale showed & Montgomery significant behavior changes; (1996) Conners Teacher Rating scale equivocal; No significant changes for WISCIII Scale, informal reports, subtests; EEG decrease in theta and beta amplitude. Slate, et al. 3 of 4 children showed improvement (1998) in mathematics, receptive knowledge of vocabu lary, motor speed and response accuracy; 2 of 4 children improved observed daily behaviors; 1 child marked improvement on Conners. Elias, Tobias, & No problems reported after Friedlander intervention (1994) Biofeedback Training Authors Participants Tansey & Bruner 10-year old boy diagnosed with (1983) ADHD, reading disorder and ocular instability Lubar and Lubar Six males from 10-19 years old with (1984) learning disabilities and referral for ADHD Blanton and Johnson Three male students one 4th grad (1991) and two 6th grade identified with ADHD Lubar et al. 10 children (9 male) with ADHD from (1995) 8-19 years old Patrick 11 children ages 8-14 with ADHD (1996) diagnosis Linden, et al. Children aged 5-15, 12 with Add / (1996) ADHD diagnosis and 6 children with LD diagnosis Kaduson and Finnerty 63 children with ADHD (5 female) (1995) ages 8-12 Authors Methodologies Tansey & Bruner Case study - EMG and EEG (1983) biofeedback using Sensorimotor Rhythm training Lubar and Lubar Case study - SMR training in (1984) conjunction with academic training Blanton and Johnson Case study - 6 of 7 20-minute EMG (1991) biofeedback sessions Lubar et al. Pre-post assessment - Neurofeedback (1995) training regimen Patrick Pre-post assessment (1996) Linden, et al. Pre-post assessment (1996) Kaduson and Finnerty Comparison of EMG biofeedback, (1995) cognitive training and game control condition Authors Dependent Measures Tansey & Bruner Parental report, therapist report, (1983) EMG readings, grade report Lubar and Lubar SMR, EEG activity; (1984) Grades and standardized achievement test scores Blanton and Johnson Observation of on-task behavior (1 (1991) student only); EMG ratings Lubar et al. WISC-R and behavior ratings on (1995) Attentiion Deficit Disorder Evaluation Scale Patrick WISC-3 and Child Behavior Checklist (1996) Linden, et al. Composite IQ score of (1996) Kauffman-Brief Intelligence Test; Parent ratings on IOWA Conners Kaduson and Finnerty Child Behavior Checklist, Conners (1995) Parent Rating Scale, Attention Deficit Disorder Evaluation Scale Authors Reported Outcomes Tansey & Bruner Motor activity level reduced to (1983) levels previously achieved with Ritalin. Hyperactivity and distract ibility no longer diagnosable. Improved reading comprehension. Unchanged after 24 months. Lubar and Lubar Increased SMR or beta and decreased (1984) EEG and EMG activity. Improved grades and standardized achievement tests. Blanton and Johnson Low EMG ratings following (1991) biofeedback - only one exhibited marked decrease below baseline levels. Observation of one student showed mean level of on task behavior increased over baseline. Lubar et al. Significant changes in WISC-R (1995) scores and in behavior. Patrick Significant changes in WISC-3 (1996) scores and in behavior. Linden, et al. Significant changes in IQ scores (1996) and behavior. Kaduson and Finnerty No differences across three (1995) conditions Computerized Assessment Authors Participants Gordon & Mettleman 1266 randomly selected normal (1988) children ages 4-16 Grant et al. 119 boys ages 6 years to 12 years (1990) 11 months diagnosed as ADHD or ADHD & LD Rasile et al. 136 college age students (1995) Whery et al. 29 boys age 6 to 13 years - Normal (1993) and ADHD Forbes 146 children (36 female) aged 6-12 (1998) referral for ADHd Authors Methodologies Gordon & Mettleman Correlation between GDS and IQ, (1988) inter-task correlation and test-retest Grant et al. Correlation between GDS and (1990) intellectual, achievement, and neuropsychological tests Rasile et al. Correlation between GDS Tests and (1995) Kagan's Matching Familiar Figure test (69 subjects), Visual Span subtest of WMS-R Whery et al. Discriminant Validity of GDS - (1993) classification of normal or ADHD Forbes Comparison with diagnosis (1998) Authors Dependent Measures Gordon & Mettleman GDS, Slosson IQ and WISC-R (1988) Grant et al. WRAT-R and WISC-R (1990) Rasile et al. Standard Delay, Vigilance, and (1995) Distractibility Tests of GDS, Kagan's Matching Familiar Figure test, Visual Span subtest of WMS-R, Whery et al. GDS, CBCL-Teacher Report Form and (1993) ADHD Rating Scale Forbes TOVA (1998) Authors Reported Outcomes Gordon & Mettleman NO significant correlation with IQ (1988) scores. No significant correlation across tasks. High test-retest reliability Grant et al. Significant correlations between (1990) GDS and WRAT arithmetic subtest and with Freedom from Distractibility factor of the WISC-R. Others not significant Rasile et al. Strong within test correlations and (1995) weak between test correlations. Significant correlations between GDS scores and WAIS-R and MFF. Significant correlation between GDS Standard Delay and Stroop Color and Word Test. Whery et al. Failure to demonstrate discriminant (1993) validity Forbes Correctly identified 80% of (1998) children with ADHD; also reported high rate of false positives (28% of non-ADHD children) Behavior Modification Authors Participants Gordon, Six children ages 6 to Thomason, 9 years with ADHD Cooper and Ivers (1991) DuPaule, Two boys identified Guevremont, with ADHD Barkley (1992) Evans et al. 1 eleven year old boy (1995) with ADHD Authors Methodologies Gordon, Within-subjects design - Thomason, ATS Cooper and Ivers (1991) DuPaule, Multiple-baseline across Guevremont, settings (reading and Barkley (1992) language) with ATS Evans et al. Single subject design (1995) (ABA) - ATS coupled with medication in three subject areas (reading, social studies, and science) Authors Dependent Measures Gordon, Number of off-task Thomason, behaviors Cooper and Ivers (1991) DuPaule, Teacher ratings, behavioral Guevremont, observations, and academic Barkley (1992) performance measures Evans et al. Behavioral observation (1995) Authors Reported Outcomes Gordon, Attention to task improved from Thomason, baseline to training phases and Cooper and deteriorated when ATS removed in 5 Ivers (1991) of 6 cases DuPaule, Improved task-related attention and Guevremont, reduced levels of ADHD behaviors. Barkley (1992) Equivocal effects on academic productivity. Teachers, aides and students indicated preference for ATS. Evans et al. ATS and medication improved (1995) behavior over medication alone. Note: ADHD = Attention DeficitHyperactivity Disorder; LD = Leaming Disabilities; WISC-R = Wechsler Intelligence Scale for Children-Revised; WISC-III = Wechsler Intelligence Scale for Children - Third Addition; WAIS = Wechsler Adult Intelligence Scale; WRAT-R = Wide Range Achievement Test - Revised; WRAT-3 = Wide Range Acheivement Test - Third Edition; PPVT-R = Peabody Picture Vocabulary Test - Revised; TMT = Trail Making Test; IVA = Intermediate Visual and Auditory Continuous Performance Test; CBCL = Child Behavior Checklist; TRF = Teacher Report Form; EEG = electroencephalographic feedback; EMG = electromyometer feedback; SMR=Sensorimolor Rhythm; GOS = Gordon Diagnostic System; WMS-R = Wechsler Memory Scale - Revised; CPT = Continuous Performance Test; TOVA = Test of Variables of Attention; ATS = Attention Training System.
Alhambra, M., Fowler, T., & Alhambra, A. (1995). EEG biofeedback: A new treatment option of ADD/ADHD. Journal of Neurotherapy, 1, 39-43.
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington. DC: Author.
Arnold, L. E. (1999). Treatment alternatives for attention-deficit/hyperactivity disorder. Journal of Attention Disorders, 3, 30-48.
Barkley, R. A. (1991). The ecological validity of laboratory and analogue assessment methods of ADHD symptoms. Journal of Abnormal Child Psychology, 19, 149-178.
Barkley, R. A. (1992). Is EEG biofeedback treatment effective for ADHD children? CH.A.D.D.er Box, 5(3), 5-11.
Barkley, R. A. (1998a). ADHD and the nature of self-control. New York: Guilford.
Barkley, R. A. (1998b). Attention deficit hyperactivity disorder: A handbook for diagnosis and treatment. New York: The Guilford Press.
Barkley, R. A. & Grodsinsky, G. (1994). Are tests of frontal lobe function useful in the diagnosis of attention deficit disorders? Clinical Neuropsychologist, 8, 121-139.
Baron-Faust, R. (February 12, 2000) Biofeedback widens its role in medicine. CNN.Com.Health.
Bender, W. N., & Bender R. L. (1996). Computer-Assisted instruction for Students at Risk for ADHD, Mild Disabilities, or Academic Problems. Boston: Allyn & Bacon.
Blanton, J. & Johnson, L. J. (1991). Using computer assisted biofeedback to help children with attention deficit disorder to gain self-control. Journal of Special Education Technology, 11, 49-56.
Braud, L. W. (1978). The effects of EMG biofeedback and progressive relaxation upon hyperactivity and its behavioral concomitants. Biofeedback and Self-Regulation, 3, 69-89.
Condor, B. (May 14, 2000). Getting feedback on attention deficit disorder. Chicago Tribune Internet Edition.
Conners, C. K. (1995). The Conners Continuous Performance Test. North Tonawanda, NY: MultiHealth Systems.
Diamond, J. M. & Deane, F. P. (1990). Conners Teacher's Questionnaire: Effects and implications of frequent administration. Journal of Clinical Child Psychology, 19, 202-204.
DuPaul, G. J., Anastopoulos, A. D., Shelton, T. L., Guevremont, D. C., & Metevia, L. (1992). Multimethod assessment of attention-deficit hyperactivity disorder: The diagnostic utility of clinic-based tests. Journal of Clinical Child Psychology, 21, 394-402.
DuPaul, G. J., Guevremont, D. C., & Barkley, R. A. (1992). Behavioral treatment of attention-deficit hyperactivity disorder in the classroom. Behavior Modification, 16, 204-225.
DuPaul, G. J. & Stoner, G. (1994). ADHD in the schools. New York: Guilford.
Elias, M. J., Tobias, S. E., & Friedlander, B. 5. (1994). Enhancing skills for everyday problem solving, decision making, and conflict resolution in special needs students with the support of computer-based technology. Special Services in the Schools, 8(2), 33-52.
Evans, J. H., Ferre, L., Ford, L.A., & Green, J. L. (1995). Decreasing attention deficit hyperactivity disorder symptoms utilizing an automated classroom reinforcement device. Psychology in the Schools, 32, 210-219.
Fitzgerald, G. E. (1994). Using the computer with students with emotional and behavioral disorders. Technology and Disability, 3, 87-99.
Forbes, G. B. (1998). Clinical utility of the Test of Variables of Attention (TOVA) in the diagnosis of attention-deficit/hyperactivity disorder. Journal of Clinical Psychology, 54, 461-476.
Ford, M. J., Poe, V., & Cox, J. (1993). Attending behaviors of ADHD children in math and reading using various types of software. Journal of Computing in Childhood Education, 4(2), 183-196.
Gordon, M. (1983). The Gordon Diagnostic System. DeWitt, NY: Gordon Systems.
Gordon, M., & Barkley, R. A. (1998). Tests and observational measures. in R. A. Barkley (Ed.). Attention deficit hyperactivity disorder: A handbook for diagnosis and treatment (2nd ed.). (pp. 294-311), New York: Guilford Press.
Gordon, M., & Mettelman, B. B. (1988). The assessment of attention: Standardization and reliability of a behavior-based measure. Journal of Clinical Psychology, 44, 682-690.
Gordon, M., Thomason, D., Cooper, S., & Ivers, C. (1991). Nonmedical treatment of ADHD/Hyperactivity: the attention training system. Journal of School Psychology, 29, 151-159.
Grant, M., Ilai, D., Nussbaum, N. L., & Bigler, E. D. (1990). The relationship between continuous performance tasks and neuropsychological tests in children with attention-deficit hyperactivity disorder. Perceptual and Motor Skills, 70, 435-445.
Greenberg, L. M. & Kindschi, C. L. (1996). T. O. V. A. Test of Variables of Attention. St. Paul, MN: TOVA Research Foundation.
Halperin, J. M., Scharma, V., Greenblatt, E., & Schwartz, S. T. (1991). Assessment of the continuous performance test: Reliability and validity in a non-referred sample. Psychological Assessment, 3, 603-608.
Hampstead, W. J. (1979). The effects of EMG-assisted relaxation training with hyperkinetic children: A behavioral alternative. Biofeedback and Self-Regulation, 4, 113-125.
Hasselbring, T. S. & Bottge, B. A. (1999). Planning and implementing technology programs in inclusive settings. In J.D. Lindsey (Ed.), Technology and Exceptional Individuals (pp. 91-113). Austin, TX: Pro-Ed.
Kaduson, H. G. & Finnerty, K. (1995). Self-control game interventions for attention deficit hyperactivity disorder. International Journal of Play Therapy, 4, 15-29.
Kleiman, G., Humphrey, M., & Lindsay, P. H. (1981). Microcomputers and hyperactive children. Creative Computing, 7, 93-94.
Kotwal, D. B., Bums, W. J., & Montgomery, D. D. (1996). Computer-assisted cognitive training for ADHD: a case study. Behavior Modification, 20, 85-96.
Linden, M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on the cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and Self-Regulation, 21, 35-49.
Lovejoy, M. C., & Rasmussen, N. H. (1990). The validity of vigilance tasks in differential diagnosis of children referred for attention and learning problems. Journal of Abnormal Child Psychology, 28, 671-681.
Lubar, J. F.(1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-Regulation, 16(3), 201-225.
Lubar, J. O. & Lubar, J. F. (1984). Electroencephalographic biofeedback of SMR and beta for treatment of attention deficit disorders in a clinical setting. Biofeedback and Self-Regulation, 9, 1-23.
Lubar, J. F., Swartwood, M. O., Swartwood, J. N., & O'Donnell, P. H. (1995). Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Biofeedback and Self-Regulation, 20, 83-99.
Muscott, H. S., & Gifford, T. (1994). Virtual reality and social skills training for students with behavioral disorders: applications, challenges and promising practices. Education and Treatment of Children, 17, 417-434.
Okolo, C. M., & Rieth, H. J. (1993). A retrospective view of computer-based instruction. Journal of Special Education Technology, 12, 1-27.
Patrick, G. J. (1996). Improved neuronal regulation in ADHD: An application of fifteen sessions of photic-driven EEG neurotherapy. Journal of Neurotherapy, 1, 27-36.
Plude, D. (1996). New Technology: A Biological Understanding of Attention Deficit Hyperactivity Disorder and its Treatment. Journal of Neurotherapy, 1, 10-14.
Rapport, M. D., Murphy, H. A., & Bailey, J. S. (1982). Ritalin vs. response cost in the control of hyperactive children: A within-subject comparison. Journal of Applied Behavior Analysis, 15, 205-216.
Rasile, D. A., Burg, J. S., Burright, R. G., & Donovick, P. J. (1995). The relationship between performance on the Gordon diagnostic system and other measures of attention. International Journal of Psychology, 30, 35-45.
Reid, R., Maag, J. W., Vasa, S. F., & Wright, G. (1994) Who are the children with ADHD: A school-based survey. Journal of Special Education, 28, 117-137.
Slate, S. E., Meyer, T. L., Burns, W. J., & Montgomery, D. D. (1998). Computerized cognitive training for severely emotionally disturbed children with ADHD. Behavior Mod Modification, 22(3), 415-437.
Tansey, M. A., & Bruner, R. L. (1983). EMG and EEG biofeedback training in the treatment of a 10-year-old hyperactive boy with a developmental reading disorder. Biofeedback and Self-Regulation, 8, 25-37.
Wadhwani, S., Radvanski, D.C., & Carmody, D. P. (1998). Neurofeedback training in a case of attention deficit hyperactivity disorder. Journal of Neurotherapy, 3, 42-49.
Werry, J. S., Elkind, G. S., & Reeves, J. C. (1987). Attention deficit, conduct, oppositional, and anxiety disorders in children: III. Laboratory differences. Journal of Abnormal Child Psychology, 15, 409-428.
Wherry, J. N., Paal, N. Jolly, J., Adam, B., Holloway, C., Everett, B., & Vaught, L. (1993). Concurrent and discriminant validity of the Gordon diagnostic system: a preliminary study. Psychology in the School, 30, 29-36.
Corresponding Author Address: Robert Reid, Ph.D, Dept. of Special Education and Communication Disorders, 202 Barkley Memorial Center, University of Nebraska, Lincoln, NE 68583-0732.
|Printer friendly Cite/link Email Feedback|
|Author:||Xu, Chunzhen; Reid, Robert; Steckelberg, Allen|
|Publication:||Education & Treatment of Children|
|Date:||May 1, 2002|
|Previous Article:||A tool for identifying preschoolers' deficits in social competence: the Preschool Taxonomy of Problem Situations.|
|Next Article:||Developing a writing package with student graphing of fluency.|