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Self-Monitoring with Goal-Setting: Decreasing Disruptive Behavior in Children with Attention-Deficit/Hyperactivity Disorder

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Title: Self-Monitoring with Goal-Setting: Decreasing Disruptive Behavior in Children with Attention-Deficit/Hyperactivity Disorder
Language: English
Authors: McKenna, Kara, Bray, Melissa A. (ORCID 0000-0002-3437-3505), Fitzmaurice, Brenna (ORCID 0000-0003-2427-2193), Choi, Dowon, DeMaio, Erin, Bray, Clark R., Bernstein, Carly
Source: Psychology in the Schools. 2023 60(12):5167-5188.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 22
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Descriptors: Goal Orientation, Self Management, Intervention, Student Behavior, Behavior Problems, Elementary School Students, Attention Deficit Hyperactivity Disorder, Behavior Modification, Program Effectiveness
DOI: 10.1002/pits.23026
ISSN: 0033-3085
1520-6807
Abstract: This study sought to investigate the effects of a self-monitoring (SM) with goal-setting (GS) intervention on students' disruptive behavior. A multiple baseline A-B-BC design was implemented across five elementary school-aged participants diagnosed with attention-deficit/hyperactivity disorder (ADHD) to examine the use of a behavioral intervention combining SM and GS techniques to decrease disruptive behavior. The results of this study suggest that SM with GS appears to be an effective intervention package for decreasing the disruptive behavior of students with ADHD and that these behavioral decreases sustain after intervention completion. Results also suggest moderate benefits of using a SM with GS intervention over a SM intervention. Teacher ratings suggest that the SM with GS package is moderately acceptable for classroom use.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1399673
Database: ERIC
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  Value: <anid>AN0173485977;pis01dec.23;2023Nov10.04:13;v2.2.500</anid> <title id="AN0173485977-1">Self‐monitoring with goal‐setting: Decreasing disruptive behavior in children with attention‐deficit/hyperactivity disorder </title> <p>This study sought to investigate the effects of a self‐monitoring (SM) with goal‐setting (GS) intervention on students' disruptive behavior. A multiple baseline A‐B‐BC design was implemented across five elementary school‐aged participants diagnosed with attention‐deficit/hyperactivity disorder (ADHD) to examine the use of a behavioral intervention combining SM and GS techniques to decrease disruptive behavior. The results of this study suggest that SM with GS appears to be an effective intervention package for decreasing the disruptive behavior of students with ADHD and that these behavioral decreases sustain after intervention completion. Results also suggest moderate benefits of using a SM with GS intervention over a SM intervention. Teacher ratings suggest that the SM with GS package is moderately acceptable for classroom use.</p> <p>Practitioner points: Combining self‐monitoring (SM) and goal‐setting (GS) was successful in reducing disruptive behavior of students with attention‐deficit/hyperactivity disorder (ADHD).Combining SM and GS was somewhat more effective than using SM alone to reduce disruptive behaviors of students with ADHD.Teachers found the intervention acceptable and reported a willingness to use it to reduce disruptive behaviors in their classrooms.</p> <p>Keywords: attention deficit hyperactivity disorder; classroom intervention; disruptive behavior; elementary; goal setting; self‐monitoring</p> <hd id="AN0173485977-2">INTRODUCTION</hd> <p>Teachers face the challenging task of helping students gain appropriate academic skills while simultaneously addressing various behavioral needs in the classroom. As a function of their hyperactive or impulsive symptoms, children diagnosed with attention‐deficit/hyperactivity disorder (ADHD) often demonstrate disruptive patterns of behavior that negatively impact the classroom learning environment (American Psychiatric Association, [<reflink idref="bib3" id="ref1">3</reflink>]; DuPaul & Stoner, [<reflink idref="bib23" id="ref2">23</reflink>]). For ADHD‐related behavioral challenges, school‐based interventions are recommended as an integral part of treatment planning for these students (Fried et al., [<reflink idref="bib30" id="ref3">30</reflink>]).</p> <p>One school‐based intervention, self‐monitoring (SM), has been shown to be effective for several challenges that arise within the classroom, particularly to remediate several academic difficulties and inattentive symptoms associated with ADHD (Falkenberg & Barbetta, [<reflink idref="bib27" id="ref4">27</reflink>]; Gureasko‐Moore et al., [<reflink idref="bib34" id="ref5">34</reflink>]; Harris et al., [<reflink idref="bib35" id="ref6">35</reflink>]; McDougall et al., [<reflink idref="bib47" id="ref7">47</reflink>]; Meyer & Kelley, [<reflink idref="bib48" id="ref8">48</reflink>]; Shimabukuro et al., [<reflink idref="bib57" id="ref9">57</reflink>]). Studies have also shown SM interventions to effectively decrease disruptive behaviors, which are often the most prominent symptoms of ADHD (Barry & Messer, [<reflink idref="bib6" id="ref10">6</reflink>]; Lam et al., [<reflink idref="bib43" id="ref11">43</reflink>]; Slattery et al., [<reflink idref="bib58" id="ref12">58</reflink>]). In addition, SM has also been a user‐friendly intervention within the school setting; in their study, Amato‐Zech et al. ([<reflink idref="bib2" id="ref13">2</reflink>]) found that teachers rated the SM intervention as effective and easy to implement.</p> <p>Goal‐setting (GS) interventions, in which students or teachers track student progress toward specific goals, have also demonstrated positive effects in the school setting, such as increasing reading fluency and early number skills (Burns et al., [<reflink idref="bib11" id="ref14">11</reflink>]; Codding et al., [<reflink idref="bib15" id="ref15">15</reflink>]). However, while GS interventions have displayed positive effects on several aspects of academic performance, little research has been conducted using GS to ameliorate the disruptive behavior of children with ADHD in school.</p> <hd id="AN0173485977-3">LITERATURE REVIEW</hd> <p></p> <hd id="AN0173485977-4">Attention‐deficit/hyperactivity disorder</hd> <p>ADHD is one of the most commonly diagnosed disorders in school‐age children and persists into adulthood for more than half of those diagnosed (Caye et al., [<reflink idref="bib13" id="ref16">13</reflink>]; Thomas et al., [<reflink idref="bib61" id="ref17">61</reflink>]). ADHD is characterized as a multidimensional disorder, as individuals vary in symptom presentation and severity of symptoms. This heterogeneity of presentation is captured by diagnostic classifications of primarily inattentive, hyperactive‐impulsive, or combined presentations (American Psychiatric Association, [<reflink idref="bib3" id="ref18">3</reflink>]).</p> <p>Not only is ADHD multidimensional in terms of symptom presentation, severity, and comorbidity, but treatment response rates also differ among individuals, which can cause difficulties in targeting effective interventions for the treatment of symptoms (Hinshaw, [<reflink idref="bib36" id="ref19">36</reflink>]; Langberg et al., [<reflink idref="bib44" id="ref20">44</reflink>]; Murray et al., [<reflink idref="bib50" id="ref21">50</reflink>]). In addition, treatment outcomes vary across several factors, including gender; socioeconomic status; symptom severity; and comorbidity with other disorders such as anxiety disorders, oppositional defiant disorder, and conduct disorder (Bax et al., [<reflink idref="bib7" id="ref22">7</reflink>]; Hinshaw, [<reflink idref="bib36" id="ref23">36</reflink>]; Langberg et al., [<reflink idref="bib44" id="ref24">44</reflink>]; Murray et al., [<reflink idref="bib50" id="ref25">50</reflink>]). Therefore, planning an appropriate treatment to ameliorate ADHD symptoms can be complex.</p> <p>The difficulty of finding an effective treatment for ADHD can have long‐term consequences beyond the observable behavior deficits. Individuals diagnosed with ADHD are more likely to develop antisocial personality disorder and substance abuse disorder in adulthood (American Psychiatric Association, [<reflink idref="bib3" id="ref26">3</reflink>]). Further, the symptoms experienced in childhood often continue as students age, leading to negative life consequences such as higher rates of school suspensions, expulsions, and dropouts; riskier sexual behaviors; higher rates of incarceration; and difficulties with professional achievement and employment retention (Fried et al., [<reflink idref="bib30" id="ref27">30</reflink>]; Gordon & Fabiano, [<reflink idref="bib32" id="ref28">32</reflink>]; Spiegel & Pollak, [<reflink idref="bib60" id="ref29">60</reflink>]).</p> <p>According to Barkley's ([<reflink idref="bib4" id="ref30">4</reflink>], [<reflink idref="bib5" id="ref31">5</reflink>]) theory, ADHD can best be characterized as a deficiency in executive functioning and self‐regulation. Executive functions are the mental abilities needed to sustain problem‐solving and organize behavior toward a goal‐directed action (Barkley, [<reflink idref="bib5" id="ref32">5</reflink>]). A negative relationship between executive functioning skills and ADHD diagnoses has been established, with previous studies indicating that the colinear relationship between the two may be a function of assessing the same underlying constructs (Barkley, [<reflink idref="bib5" id="ref33">5</reflink>]; Chmielewski et al., [<reflink idref="bib14" id="ref34">14</reflink>]; Ezpeleta & Granero, [<reflink idref="bib26" id="ref35">26</reflink>]).</p> <p>Barkley's theory further examines executive functions through an extended progression in which executive functioning is shown to develop first in self‐direction, then to affect near‐term goal achievement, social networking and friendship, and culminating in working with others to accomplish shared goals (Barkley, [<reflink idref="bib5" id="ref36">5</reflink>]). In this way, a deficiency in one of the areas of executive functioning will impact the others in a "cascade of secondary deficits" (Barkley, [<reflink idref="bib5" id="ref37">5</reflink>], p. 421). Therefore, it is likely that the deficits demonstrated interfere with the abilities to coordinate future‐directed behavior and accomplish long‐term goals. Further, because children with ADHD demonstrate deficits in internal self‐control, their behavior relies more on the external environment (Barkley, [<reflink idref="bib5" id="ref38">5</reflink>]). This reliance on external stimuli has several impacts on behavior, such as deficits in an individual's ability to exercise self‐control, delay gratification, and initiate goal‐directed action (Barkley, [<reflink idref="bib5" id="ref39">5</reflink>]).</p> <p>School professionals are in need of effective strategies that can help their students learn to manage their behavior (Alderman & MacDonald, [<reflink idref="bib1" id="ref40">1</reflink>]). As previously discussed, ADHD disrupts self‐regulation, motivation, inhibition, and other executive functions, so it follows that effective intervention strategies should include external management of executive functioning deficits. In keeping with Barkley's theoretical underpinnings of ADHD as an executive functioning deficit, an effective behavioral intervention will focus on improving students' self‐motivation and internal behavior management systems (Kofler et al., [<reflink idref="bib41" id="ref41">41</reflink>]). Self‐management interventions can be one method of providing these strategies to students, as these interventions can directly target the inhibition deficits demonstrated by students with ADHD.</p> <hd id="AN0173485977-5">Self‐management interventions</hd> <p>Self‐management, or self‐regulation, skills are those used to maintain or alter behaviors (Hoff & DuPaul, [<reflink idref="bib37" id="ref42">37</reflink>]). Individuals who demonstrate self‐regulation can independently execute several executive functions, such as GS, organization, SM, and self‐evaluation of progress (Zimmerman, [<reflink idref="bib63" id="ref43">63</reflink>]). The purpose of using self‐management interventions in the school setting is to increase students' abilities to control their behaviors through increased self‐awareness to improve their educational or academic outcomes (Briesch et al., [<reflink idref="bib9" id="ref44">9</reflink>]). Self‐management intervention procedures can stand alone to effect changes in the targeted behavior. The success of self‐management without the need for additional procedures is likely the reason that it is used as an intervention strategy for academic skills deficits as well as social and behavioral problems (Shapiro & Cole, [<reflink idref="bib56" id="ref45">56</reflink>]).</p> <p>There are several benefits to using self‐management interventions over teacher‐managed techniques in the school setting. Self‐management allows students to control and oversee their own behavior change. This enables teachers to remain focused on the instruction of the whole class, as significantly less of their time is devoted to behavioral intervention (Briesch et al., [<reflink idref="bib9" id="ref46">9</reflink>]; Fantuzzo et al., [<reflink idref="bib28" id="ref47">28</reflink>]). Additionally, by placing the responsibility of behavior change on the individual, self‐management interventions may increase a student's sense of self‐efficacy, which in turn can increase the likelihood that behavioral change will generalize and continue after termination of the intervention (Barry & Messer, [<reflink idref="bib6" id="ref48">6</reflink>]; Bruhn et al., [<reflink idref="bib10" id="ref49">10</reflink>]; Slattery et al., [<reflink idref="bib58" id="ref50">58</reflink>]). Research has demonstrated that self‐management interventions effectively improve several classroom behaviors, including disruptive behavior in children with ADHD (Bruhn et al., [<reflink idref="bib10" id="ref51">10</reflink>]; Reid et al., [<reflink idref="bib53" id="ref52">53</reflink>]; Smith et al., [<reflink idref="bib59" id="ref53">59</reflink>]).</p> <hd id="AN0173485977-6">Self‐monitoring</hd> <p>In SM, students are required to observe and record their behavior. SM interventions cue students to assess their behavior through some form of an external signal (Briesch et al., [<reflink idref="bib9" id="ref54">9</reflink>]), often auditory, including recorded tones and teacher prompts. However, Amato‐Zech et al. ([<reflink idref="bib2" id="ref55">2</reflink>]) found an appropriate substitute for an audio cue using a vibrating timer called the MotivAider. The MotivAider can be worn on a student's waistband, emitting a vibration to cue SM of behavior. In their study, the researchers determined the MotivAider to be an effective and unobtrusive cueing method for students, and it received high levels of reported acceptability from both teachers and students.</p> <p>SM in the absence of other self‐management techniques has been demonstrated as a strong enough intervention to bring about targeted behavior change, including when used for students with ADHD in the improvement of academic difficulties and disruptive behaviors (Falkenberg & Barbetta, [<reflink idref="bib27" id="ref56">27</reflink>]; Gureasko‐Moore et al., [<reflink idref="bib34" id="ref57">34</reflink>]; Harris et al., [<reflink idref="bib35" id="ref58">35</reflink>]; Lam et al., [<reflink idref="bib43" id="ref59">43</reflink>]; McDougall et al., [<reflink idref="bib47" id="ref60">47</reflink>]; Meyer & Kelley, [<reflink idref="bib48" id="ref61">48</reflink>]; Shimabukuro et al., [<reflink idref="bib57" id="ref62">57</reflink>]). Additionally, Briesch and Chafouleas ([<reflink idref="bib8" id="ref63">8</reflink>]) found that SM as an intervention is as, if not more, effective than teacher monitoring in decreasing disruptive and other off‐task behaviors. These findings suggest that when provided a mechanism to manage their own behavior, children with ADHD can successfully improve their self‐regulatory skills.</p> <p>Besides their effectiveness, SM procedures in the classroom also have practical benefits over more externally managed interventions. Students asked to monitor their behavior are not only able to address observable behaviors but also internal thoughts and motivations, such as negative self‐talk or impulses to act (Cole & Bambara, [<reflink idref="bib18" id="ref64">18</reflink>]; Gee et al., [<reflink idref="bib31" id="ref65">31</reflink>]). Researchers have also found that students' accuracy when recording their behavior does not impact the effects of SM interventions (DuPaul & Stoner, [<reflink idref="bib22" id="ref66">22</reflink>]; Harris et al., [<reflink idref="bib35" id="ref67">35</reflink>]; Ritter et al., [<reflink idref="bib54" id="ref68">54</reflink>]).</p> <p>External management techniques, in which a teacher or other school staff member manages a student's behavior, also have several drawbacks. These techniques are often ineffective at changing behavior due to inconsistent presentation of rewards and consequences (Shapiro & Cole, [<reflink idref="bib56" id="ref69">56</reflink>]). Additionally, these techniques do not lead to long‐term behavior change because students are unlikely to develop the skills needed to change or maintain their behavior. Instead, students are more likely to become dependent on teacher management and feedback to elicit the desired behavioral or academic outcomes (Shapiro & Cole, [<reflink idref="bib56" id="ref70">56</reflink>]). Not only are these techniques viewed negatively by teachers, as they take time away from instruction, but they often utilize steps that are difficult to implement or implemented incorrectly and are abandoned when results are not demonstrated quickly. Teacher attitudes toward a classroom intervention largely contribute to its success, so it is crucial to look for procedures that will be effective, easy to institute, and minimally disruptive (Shapiro & Cole, [<reflink idref="bib56" id="ref71">56</reflink>]).</p> <hd id="AN0173485977-7">Goal‐setting</hd> <p>GS is an essential aspect of self‐regulation, as creating and monitoring goals leads students to take ownership of behavior by developing a plan for change, thinking about their behavior, and determining how effective they are at bringing about the desired level of change (Zimmerman et al., [<reflink idref="bib64" id="ref72">64</reflink>]). In this way, GS can increase a student's ability to self‐monitor because it clearly defines a relationship between current levels and desired levels of performance (Copeland & Hughes, [<reflink idref="bib20" id="ref73">20</reflink>]).</p> <p>Not surprisingly, students who set academic goals demonstrate more improvement than those who do not (Burns et al., [<reflink idref="bib11" id="ref74">11</reflink>]; Williams‐Diehm et al., [<reflink idref="bib62" id="ref75">62</reflink>]). Furthermore, GS interventions have been shown to produce positive educational outcomes by improving students' academic performances in the areas of reading, writing, mathematics, spelling, and work completion, as well as the time‐on‐task behavior of students with and without ADHD (Burns et al., [<reflink idref="bib11" id="ref76">11</reflink>]; Codding et al., [<reflink idref="bib16" id="ref77">16</reflink>]; Figarola et al., [<reflink idref="bib29" id="ref78">29</reflink>]; Graham et al., [<reflink idref="bib33" id="ref79">33</reflink>]; Konrad et al., [<reflink idref="bib42" id="ref80">42</reflink>]).</p> <p>Additionally, interventions with GS components have high levels of student‐reported preference and demonstrate maintenance and generalization of gains over time (Lee & Tindal, [<reflink idref="bib45" id="ref81">45</reflink>]; Moore et al., [<reflink idref="bib49" id="ref82">49</reflink>]). Furthermore, visuals can contribute to positive change by helping individuals track their progress toward goal attainment (Moore et al., [<reflink idref="bib49" id="ref83">49</reflink>]). Figarola et al. ([<reflink idref="bib29" id="ref84">29</reflink>]) incorporated a self‐graphing component to their intervention, in which early elementary students with ADHD and learning disabilities effectively graphed their own mathematics progress data, demonstrating that not only can self‐graphing be used to enhance GS and monitoring, but even young students can participate in their GS processes.</p> <p>Despite its promotion of self‐regulatory skills and positive effects, Briesch and Chafouleas ([<reflink idref="bib8" id="ref85">8</reflink>]) found that GS is not often used as a component of self‐management interventions. Additionally, the researchers found that in the few interventions utilizing GS, these strategies incorporated goals set by adults and were not student‐managed (Briesch & Chafouleas, [<reflink idref="bib8" id="ref86">8</reflink>]). Therefore, based on the lack of research on GS as an effective component of self‐management interventions, this study proposes to examine its effectiveness in conjunction with a proven self‐management component, SM, to explore the efficacy of this combined intervention.</p> <p>While SM interventions have been shown to reduce the disruptive behavior of children with ADHD, it is hypothesized that the addition of student‐developed GS to address specific disruptive behavior targets will lead to further decreases in disruptive behavior for this population. Therefore, we investigated the use of SM and GS techniques to decrease disruptive behavior in elementary school students with ADHD.</p> <p>This work sought to answer the following four research questions: (<reflink idref="bib1" id="ref87">1</reflink>) does an intervention utilizing GS and SM procedures decrease the disruptive behavior of children with ADHD in the elementary classroom setting?; (<reflink idref="bib2" id="ref88">2</reflink>) does the addition of student‐developed GS to an intervention utilizing SM procedures introduce further decreases in disruptive behaviors than that of SM alone?; (<reflink idref="bib3" id="ref89">3</reflink>) do intervention effects maintain at follow‐up?; (<reflink idref="bib4" id="ref90">4</reflink>) is the intervention acceptable for use in the school setting?</p> <hd id="AN0173485977-8">METHODS</hd> <p></p> <hd id="AN0173485977-9">Participants and setting</hd> <p>Participants were recruited from three elementary schools of a regional school district in Southern Connecticut during the 2012–2013 school year. Students were recruited based on teacher reports of disruptive classroom behaviors. All third through sixth‐grade teachers were provided with recruitment forms to recommend students who demonstrated disruptive behavior in their classrooms based on a provided definition of disruptive behavior. Based on these criteria, eight elementary school students in grades three through six were recruited.</p> <p>Inclusionary criteria for study participation included the presence of a medical diagnosis of ADHD documented in educational records and the demonstration of disruptive classroom behavior as reported by teachers and confirmed through behavioral observation. The presentation type of ADHD was not recorded in the educational record and was not further ascertained by the researcher for the purposes of this study. As a result, the presentation type (inattentive, hyperactive‐impulsive, combined) was not known.</p> <p>Written parental permission was obtained for the eight recruited students to access their educational records to ascertain the presence of ADHD diagnoses from a medical professional. The researcher then observed students with medical diagnoses to corroborate teacher reports of disruptive behavior. Three of the eight recruited students were excluded because they were not diagnosed with ADHD.</p> <p>After students were determined to meet inclusionary criteria, written parental permission was obtained for participation in the study. Written teacher consent was also collected from the five teachers of the included students. All teachers were Caucasian females who taught in the general education setting. Student assent forms were then collected from the five included students.</p> <p>Based on the inclusionary criteria and a 100% response rate, those five students, three fourth‐grade students and two sixth‐grade students, participated in this study. Four of the five participants were male, and all participants identified as Caucasian according to school records. Participant demographics are summarized in Table 1. All students received support services to address difficulties related to ADHD in the school setting; four participants received special education services, and one received an accommodation plan under Section 504 of the Rehabilitation Act. All accommodations received by the participants were academic in nature, meaning that no student had behavioral interventions in place as a result of their special education or 504‐related services.</p> <p>1 Table Participant demographics.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Participant</th><th>Gender</th><th>Age</th><th>Grade</th><th>Ethnicity</th><th>Class subject</th></tr></thead><tbody valign="top"><tr><td>1</td><td>Male</td><td align="char" char=".">11</td><td>6th</td><td>Caucasian</td><td>Mathematics</td></tr><tr><td>2</td><td>Male</td><td align="char" char=".">9</td><td>4th</td><td>Caucasian</td><td>Mathematics</td></tr><tr><td>3</td><td>Female</td><td align="char" char=".">11</td><td>6th</td><td>Caucasian</td><td>Language Arts</td></tr><tr><td>4</td><td>Male</td><td align="char" char=".">9</td><td>4th</td><td>Caucasian</td><td>Language Arts</td></tr><tr><td>5</td><td>Male</td><td align="char" char=".">9</td><td>4th</td><td>Caucasian</td><td>Mathematics</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>: The class subject variable describes during which subject the intervention occurred within the general education classroom.</p> <p>Participant 1 was an 11‐year‐old male sixth‐grade student. He received special education services under the eligibility category of Other Health Impairment‐ADD/ADHD. Participant 2 was a 9‐year‐old male fourth‐grade student. He received special education services under the eligibility category of Other Health Impairment‐ADD/ADHD. Unfortunately, Participant 2 was terminated early from the study based on his disenrollment from school. Participant 3 was an 11‐year‐old female student enrolled in sixth grade. She also received special education services under the eligibility category of Other Health Impairment‐ADD/ADHD. Participant 4 was a 9‐year‐old male fourth‐grade student who received educational accommodations to address symptoms of ADHD through a Section 504 Accommodation Plan. Participant 5 was a 9‐year‐old male student in the fourth grade. He received special education services under the eligibility category of Other Health Impairment‐ADD/ADHD.</p> <hd id="AN0173485977-10">Design</hd> <p>Across participants, a multiple baseline A‐B‐BC design was implemented to determine the differential treatment effects between a self‐management intervention comprised solely of SM and a self‐management intervention consisting of a package of SM and GS. Observations were conducted in each participants' general education classroom to measure the percentage of intervals in which the participant demonstrated disruptive behavior. Disruptive behaviors were measured through observations at baseline, intervention, and fading phases. Observations also occurred at a 7‐week follow‐up to determine the presence of maintenance effects from the intervention. Except for the baseline, each phase lasted approximately 2 weeks. While a minimum of five observations was attempted, the actual number of observations per phase varied due to severe weather, school schedule interruptions, and student absences.</p> <hd id="AN0173485977-11">Dependent variable</hd> <p>For this study, disruptive behavior was operationally defined as any off‐task behavior that disrupts classroom activities and negatively impacts student learning. As in Kehle et al. ([<reflink idref="bib40" id="ref91">40</reflink>]), seven discrete categories of behavior, touching; vocalizing; aggression; playing; disorienting; making noise; and out of seat, were merged into one variable, called "disruptive behavior," and the presence of any one of these factors was recorded as an instance of disruptive behavior. As students were asked to develop an individual goal pertaining to one of the discrete categories used to define disruptive behavior as part of the SM with GS intervention phase, each discrete behavior was listed separately on the observation form. Participant goals are summarized in Table 2. This was done to measure the number of intervals in which each discrete category of disruptive behavior was observed and to compare the chosen target behavior between phases. The operational definitions for each discrete category were defined according to Romanczyk et al. ([<reflink idref="bib55" id="ref92">55</reflink>]).</p> <p>2 Table Student chosen goals based on target behavior.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Target behavior</th><th>Student chosen goal</th></tr></thead><tbody valign="top"><tr><td>Participant 1: Playing</td><td>Playing with objects in desk</td></tr><tr><td>Participant 2: Playing</td><td>Playing with "stuff"</td></tr><tr><td>Participant 3: Playing</td><td>Playing with "things" from desk</td></tr><tr><td>Participant 4: Vocalizing</td><td>Shouting out</td></tr><tr><td>Participant 5: Vocalizing</td><td>Talking when they shouldn't be</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>: Students chose their goals based on the target behavior. Playing = the child using their hand to play with their own or community property, so that such behavior is incompatible with learning. Vocalizing = any nonpermitted "audible" behavior emanating from the mouth.</p> <hd id="AN0173485977-12">OUTCOME MEASURES</hd> <p></p> <hd id="AN0173485977-13">Direct observations</hd> <p>Observations of each participant's disruptive behavior were conducted over a 20‐min time period using a 15‐s partial‐interval time sampling procedure. Participants were observed during the academic activity in which they most often demonstrated disruptive behavior, as reported by their teachers. Observations occurred during baseline, intervention, and fading phases, and again at the 7‐week follow‐up. The observers, seven volunteer undergraduate students trained in this procedure, conducted observations individually. Observers were naïve to study phases and participants' target disruptive behaviors throughout the study.</p> <hd id="AN0173485977-14">Interobserver agreement</hd> <p>Agreement on the observed frequency of target disruptive behaviors between two observers were collected in at least 10% of total observations for each participant. The observer agreement was calculated using seven observers. The observation pairs were not the same for each observation period and all seven observers did not assess each of the participants. The criterion for interobserver agreement was set at 80% and was calculated by dividing the total number of agreements by the total number of agreements and disagreements and multiplying by 100. This agreement level was set based on the conventional standard of the field which is believed to show an acceptable amount of variability in the observational recordings (Kazdin, [<reflink idref="bib39" id="ref93">39</reflink>], pp. 72–73).</p> <p>Interobserver agreements were collected across 10.53% of observations for Participant 1. The mean agreement percentage was 96.25%. Interobserver agreements were collected across 42.86% of observations for Participant 2. The mean agreement percentage was 86.25%. Interobserver agreements were collected across 10% of observations for Participant 3. The mean agreement percentage was 96.88%. Interobserver agreements were collected across 13.63% of observations for Participant 4. The mean agreement percentage was 92.50%. Interobserver agreements were collected across 27.27% of observations for Participant 5. The mean agreement percentage was 94.79%.</p> <hd id="AN0173485977-15">Means/standard deviations</hd> <p>Means and standard deviations of each participant's observed intervals of disruptive target behaviors were calculated for the following phases of the study: baseline, SM intervention, SM with GS intervention, and follow‐up. The means and standard deviations for these phases are presented in Table 3.</p> <p>3 Table Observed frequency of target behavior of all four phases.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th align="left">Baseline</th><th>SM</th><th align="left">SM + GS</th><th align="left">Follow‐up</th></tr><tr valign="bottom"><th>Target behavior</th><th align="left"><italic>M</italic> (SD)</th><th>Range</th><th align="left"><italic>M</italic> (SD)</th><th>Range</th><th align="left"><italic>M</italic> (SD)</th><th>Range</th><th align="left"><italic>M</italic> (SD)</th><th>Range</th></tr></thead><tbody valign="top"><tr><td>Participant 1: Playing</td><td align="char" char="(">6.00 (4.69)</td><td>2–4</td><td align="char" char="(">2.75 (3.59)</td><td>0–8</td><td align="char" char="(">4.50 (5.07)</td><td>1–12</td><td align="char" char="(">2.67 (4.62)</td><td>0–8</td></tr><tr><td>Participant 2: Playing</td><td align="char" char="(">13.75 (11.70)</td><td>1–26</td><td align="char" char="(">16.67 (11.23)</td><td>7–29</td><td align="char" char="(">10.33 (7.51)</td><td>3–18</td><td /><td /></tr><tr><td>Participant 3: Playing</td><td align="char" char="(">7.67. (8.45)</td><td>0–24</td><td align="char" char="(">5.00 (8.00)</td><td>1–17</td><td align="char" char="(">0.00 (0.00)</td><td>0–0</td><td align="char" char="(">2.33 (2.08)</td><td>0–4</td></tr><tr><td>Participant 4: Vocalizing</td><td align="char" char="(">3.80 (2.94)</td><td>0–8</td><td align="char" char="(">2.67 (0.58)</td><td>2–3</td><td align="char" char="(">3.00 (3.56)</td><td>0–8</td><td align="char" char="(">4.00 (4.24)</td><td>1–7</td></tr><tr><td>Participant 5: Vocalizing</td><td align="char" char="(">4.90 (4.04)</td><td>0–15</td><td align="char" char="(">7.00 (6.00)</td><td>0–12</td><td align="char" char="(">5.67 (6.03)</td><td>0–12</td><td align="char" char="(">4.50 (6.36)</td><td>0–9</td></tr></tbody></table> </ephtml> </p> <p>3 Abbreviations: Baseline, baseline phase; follow‐up, follow‐up phase; SM, self‐monitoring phase; SM + GS, self‐monitoring with goal‐setting phase.</p> <hd id="AN0173485977-16">Effect sizes</hd> <p>In conjunction with visual analysis, non‐regression analysis was used to determine effect sizes for each participant. Effect sizes are used to evaluate the effectiveness of an intervention. One such method is the Approach 1: No Assumptions Method of calculating the standard mean difference (SMD), originally presented by Busk and Serlin ([<reflink idref="bib12" id="ref94">12</reflink>]). It is a simple and user‐friendly calculation, providing data that can be easily interpreted and compared across studies. This approach can be used in all studies, regardless of whether the intervention causes an increase or decrease in the dependent variable. Therefore, overlapping data need not be removed (Olive & Franco, [<reflink idref="bib51" id="ref95">51</reflink>]).</p> <p>For the results of this study, an effect size value is reported as Cohen's <emph>d</emph>, which was calculated by finding the standard difference between means. The calculated effect size values were interpreted based on Cohen ([<reflink idref="bib17" id="ref96">17</reflink>]), which categorize meaningful effect size values into small (<emph>d</emph> = 0.2), medium (<emph>d</emph> = 0.5), and large (<emph>d</emph> = 0.8). All calculated effect sizes were considered in the context of the present study and its participants.</p> <p>Several SMD values were calculated based on the intervention design to address the research questions put forth at the beginning of the present study. First, the mean of the baseline data was compared to the mean of the SM with GS intervention data to investigate the effectiveness of this package on participants' disruptive behaviors. Second, an effect size was calculated in which the mean of the SM intervention data was compared to the mean of the SM with GS intervention data to investigate the additive effects of GS on participants' disruptive behaviors. Finally, the mean of the baseline data was compared to the mean of the data from follow‐up to investigate the maintenance of any disruptive behavioral improvements.</p> <hd id="AN0173485977-17">Visual analysis</hd> <p>Visual analysis was used to examine the data for magnitude and rate of change. First, data were analyzed to determine the presence of changes in level across phases. For this criterion, the mean of each participant's observed target disruptive behavior was graphed for each phase of the intervention to visually inspect average levels of changes across intervention elements. Changes in trend were next inspected to determine if a change in the direction of observed target disruptive behavior occurred after the phase change. The split‐middle technique was employed to assess the trend. Next, the latency of change was examined to determine whether or not quick behavior changes occurred after intervention phase changes. Finally, the consistency of behavior was assessed to investigate variability in observed target disruptive behavior. The standard deviations for each phase were utilized to assess consistency.</p> <hd id="AN0173485977-18">Teacher acceptability</hd> <p>Teachers completed the Behavior Intervention Rating Scale (BIRS; Elliott & Treuting, [<reflink idref="bib24" id="ref97">24</reflink>]) upon student completion of the intervention to measure levels of acceptability and perceived effectiveness of the intervention. Results from this measure were used to determine attitudes toward the interventions. All five teachers were asked to complete the BIRS during the fading phase of the study. Four out of five teachers returned completed forms.</p> <p>The BIRS has been used to assess teachers' ratings of treatment acceptability in the school setting and has also been used to assess the treatment acceptability of ADHD interventions (Colton & Sheridan, [<reflink idref="bib19" id="ref98">19</reflink>]; Curtis et al., [<reflink idref="bib21" id="ref99">21</reflink>]; Elliott & Treuting, [<reflink idref="bib24" id="ref100">24</reflink>]; Erchul et al., [<reflink idref="bib25" id="ref101">25</reflink>]; Pisecco et al., [<reflink idref="bib52" id="ref102">52</reflink>]). This measure is an extension and revision of the Intervention Rating Profile for Teachers (IRP‐15), a 15‐item scale measuring treatment acceptability (Martens et al., [<reflink idref="bib46" id="ref103">46</reflink>]). Nine additional items were added to the BIRS to assess treatment effectiveness and timeliness of effect. The BIRS is rated on a six‐point Likert‐type scale with anchor points ranging from one, indicating "Strongly disagree," to six, indicating "Strongly agree." Item scores are summed to generate an overall score, with higher scores indicating more acceptability of the intervention.</p> <p>The reliability and validity of the BIRS have been demonstrated by Elliott and Treuting ([<reflink idref="bib24" id="ref104">24</reflink>]). Validation analyses found that the BIRS breaks into three distinct scales: acceptability, effectiveness, and timeliness of effect. The overall measure's internal consistency is demonstrated by an <emph>α</emph> coefficient of.97. Comparisons between the three scales of the BIRS and the Semantic differential, an established measure previously used in treatment acceptability research, suggest correlations of.78 with acceptability,.67 with effectiveness, and.52 with timeliness of effect.</p> <hd id="AN0173485977-19">Means/standard deviations/acceptability percentages</hd> <p>Means and standard deviations were calculated for teacher ratings on the BIRS (Elliott & Treuting, [<reflink idref="bib24" id="ref105">24</reflink>]) for general acceptability as well as the subscales of acceptability, effectiveness, and timeliness of effect. Acceptability percentages were calculated by dividing the sum of all respondents' rating points by the total possible rating points for each scale. This was calculated to provide a common comparison measure of teacher ratings across scales, as each subscale contains a different number of rating statements.</p> <hd id="AN0173485977-20">PROCEDURE</hd> <p></p> <hd id="AN0173485977-21">Baseline</hd> <p>A concurrent multiple baseline design was utilized for this study; therefore, baseline data collection for all five participants started simultaneously. During baseline, teachers were instructed to manage behavior as usual, without providing additional management or ignoring behavior that would typically be addressed in the classroom. SM and GS strategies were not used during this period. The length of the baseline phase was staggered for each participant, with each subsequent participant receiving more time in the baseline phase than the previous participant. This design aimed to attempt to control for external events that might interfere with accurate data interpretation.</p> <hd id="AN0173485977-22">Self‐monitoring</hd> <p>After completing baseline data collection, participants received training in SM procedures from the student investigator during three 20‐min sessions. The investigator used a script to ensure that each participant received the same training. This script was followed word‐for‐word to ensure integrity. During the first session, participants met individually with the student investigator to discuss their target disruptive behaviors. Participants were introduced to the SM procedures and materials, learning how to observe and record their behavior. At this time, participants were introduced to the MotivAider as a cue for SM. Students were taught how to turn the MotivAider on and off and were also taught to use the SM data sheet in conjunction with the MotivAider vibration. The remaining two training sessions focused on helping participants become comfortable wearing and using the MotivAider. Participants were also given the opportunity to practice recording their behavior during these sessions.</p> <p>After completing the training sessions, participants started to self‐monitor their target disruptive behaviors in class during the specified activities in which they had the most difficulty, as determined by teacher reports. Teachers reminded students to wear the MotivAider, which was programmed to vibrate at random intervals averaging 3 min. Upon feeling the vibration, students assessed their own behavior to determine if they had been demonstrating disruptive behaviors and recorded whether or not they engaged in their target disruptive behaviors by circling yes or no on their record forms. Each participant met with the student investigator daily to review their record form and troubleshoot any problems with the MotivAider.</p> <hd id="AN0173485977-23">SM with GS</hd> <p>After the SM phase, participants received training in GS procedures and additional SM training as a booster. Training occurred during two 20‐min sessions. During the first session, each participant met individually with the student investigator to discuss his or her target disruptive behavior and to create a goal for desired behavior change. Then, with the support of the student investigator, participants independently reflected on their behavior during the SM phase and determined a specific number of the target disruptive behavior that served as their behavioral goal to work toward during the SM with GS intervention phase. Participants also reviewed observation and recording procedures to ensure continued understanding and proficiency. During the second session, participants were introduced to a self‐graphing procedure that was used to track goal attainment, as they were required to graph the frequency of their disruptive behavior with the help of the student investigator.</p> <p>After completing the training sessions, participants continued to self‐monitor their disruptive behavior in class, now only focusing on their target behavior. In addition, they continued to meet daily with the student investigator to review their record forms and graph each day's target disruptive behavior frequency, as determined by the self‐report data collected by each participant on his or her yes/no record form.</p> <hd id="AN0173485977-24">Fading</hd> <p>Upon completing the SM with GS intervention phase, participants experienced a 2‐week fading procedure. Participants continued wearing the MotivAider, completing self‐recording and self‐graphing procedures, and attending daily meetings with the student investigator. Each day, the average time interval between vibrations was increased by 1 min. By the end of the fading phase, the average time interval between vibrations increased from 3 to 13 min. At the end of the 2 weeks, participants were no longer provided MotivAiders but could retain the record and graphing forms if desired. Participants no longer met with the investigator after the completion of the fading phase.</p> <hd id="AN0173485977-25">Follow‐up</hd> <p>Direct observations of participants' disruptive behaviors were conducted 7 weeks after the completion of the fading phase of the intervention to determine the presence of maintenance effects. Observation procedures for this phase were identical to those in all previous phases.</p> <hd id="AN0173485977-26">RESULTS</hd> <p>Visual analyses and descriptive statistics were used to examine the efficacy and utility of a behavioral intervention including SM and GS components. The following results are organized by the four research questions' corresponding hypotheses.</p> <hd id="AN0173485977-27">1 Hypothesis</hd> <p> <emph>The use of a behavioral intervention combining SM and GS techniques would decrease disruptive behavior in elementary school students with ADHD in the classroom, as measured by direct observation.</emph> </p> <p>Data analysis of the number of intervals for which participants demonstrated target disruptive behavior was displayed to compare baseline and SM + GS (Table 4). Effect sizes using Busk and Serlin's ([<reflink idref="bib12" id="ref106">12</reflink>]) Approach 1: No Assumptions Method were calculated for this comparison as well and are summarized in Table 4. The effect size was calculated by finding the difference between the mean of each participant's baseline phase and the mean of each of their SM + GS phases and dividing by the standard deviation of the baseline phase. Each participant's data were also examined through visual analysis. During the baseline phase, Participant 1 demonstrated a mean of 6.00 observed intervals of disruptive playing behaviors (SD = 4.69, range = 2–14). During the SM + GS phase, Participant 1 demonstrated a mean of 4.50 observed intervals of disruptive playing behaviors (SD = 5.07, range = 1–12). An effect size of 0.32 suggests a small improvement in Participant 1's observed disruptive playing behaviors during the SM + GS intervention over baseline levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 1's level of disruptive playing behavior decreased, there was a stable nonchange in trend to a decrease in trend, the behavior decreased upon phase change, and consistency worsened.</p> <p>4 Table Effect sizes of intervention components.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Target behavior</th><th align="left">SM + GS0002</th><th align="left">GS0003</th><th align="left">Follow‐up0004</th></tr></thead><tbody valign="top"><tr><td>Participant 1: Playing</td><td align="char" char=".">0.32</td><td align="char" char=".">−0.49</td><td align="char" char=".">0.71</td></tr><tr><td>Participant 2: Playing</td><td align="char" char=".">0.29</td><td align="char" char=".">0.56</td><td /></tr><tr><td>Participant 3: Playing</td><td align="char" char=".">0.91</td><td align="char" char=".">0.63</td><td align="char" char=".">0.63</td></tr><tr><td>Participant 4: Vocalizing</td><td align="char" char=".">0.27</td><td align="char" char=".">−0.58</td><td align="char" char=".">−0.07</td></tr><tr><td>Participant 5: Vocalizing</td><td align="char" char=".">−0.19</td><td align="char" char=".">0.21</td><td align="char" char=".">0.10</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Note</emph>. Effect sizes greater than 0.50 are shown in boldface.</item> <item>5 a SM + GS = effect size of frequency of observed target behaviors in the self‐monitoring with goal‐setting intervention compared to baseline frequency.</item> <item>6 b GS = effect size of frequency of observed target behaviors in the self‐monitoring with goal‐setting intervention compared to the self‐monitoring intervention frequency.</item> <item>7 c Follow‐up = effect size of frequency of observed target behaviors in the follow‐up phase compared to baseline frequency.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01dec23/pits23026-fig-0001.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23026-fig-0001.jpg" title="1 Frequency of target behaviors across intervention phases. Each graph displays the frequency of observed intervals of target disruptive behaviors across phases with the dotted lines representing the start of a new phase and the red line representing the average frequency of the behavior across the measured phase. The SM phase represents the self‐monitoring intervention period. The SM + GS phase represents the combination of the self‐monitoring and goal‐setting interventions. GS, goal‐setting; SM, self‐monitoring." /> </p> <p></p> <p>During the baseline phase, Participant 2 demonstrated a mean of 13.75 observed intervals of disruptive playing behaviors (SD = 11.70, range = 1–26). During the SM + GS phase, Participant 2 demonstrated a mean of 10.33 observed intervals of disruptive playing behaviors (SD = 7.51, range = 3–18). An effect size of 0.29 suggests a small improvement in observed disruptive playing behaviors during the SM + GS intervention over baseline levels (see Table 4). Visual analysis of the data from Figure 1 suggests that Participant 2's level of disruptive playing behavior decreased, there was an increase in trend to a decrease in trend, the behavior decreased upon phase change, and consistency improved. Observation agreement was lower for Participant 2 in comparison to the other participants (86.25); this lower value was likely due to relatively small changes in the measured behavior as demonstrated through the effect size (Kazdin, [<reflink idref="bib39" id="ref107">39</reflink>], p. 73). During the baseline phase, Participant 3 demonstrated a mean of 7.67 observed intervals of disruptive playing behaviors (SD = 8.45, range = 0–24). During the SM + GS phase, Participant 3 demonstrated a mean of 0.00 observed intervals of disruptive playing behaviors (SD = 0.00, range = 0–0). An effect size of 0.91 suggests a large improvement in Participant 3's observed disruptive playing behaviors during the SM + GS intervention over baseline levels. Visual analysis of Participant 3's data, presented in Figure 1, suggests that the level of disruptive playing behavior decreased, there was a slight decrease in trend to a stable non‐change in trend, the behavior decreased upon phase change, and consistency improved.</p> <p>During the baseline phase, Participant 4 demonstrated a mean of 3.80 observed intervals of disruptive vocalizing behaviors (SD = 2.94, range = 0–8). During the SM + GS phase, Participant 4 demonstrated a mean of 3.00 observed intervals of disruptive vocalizing behaviors (SD = 3.56, range = 0–8). An effect size of 0.27 suggests a small improvement in Participant 4's observed disruptive vocalizing behaviors during the SM + GS intervention over baseline levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 4's level of disruptive vocalizing behavior decreased, there was an increase in trend for both phases, the behavior decreased upon phase change, and consistency worsened.</p> <p>During the baseline phase, Participant 5 demonstrated a mean of 4.90 observed intervals of disruptive vocalizing behaviors (SD = 4.04, range = 0–15). During the SM + GS phase, Participant 5 demonstrated a mean of 5.67 observed intervals of disruptive vocalizing behaviors (SD = 6.03, range = 0–12). An effect size of −0.19 suggests a small increase in Participant 5's observed disruptive vocalizations during the SM + GS intervention over baseline levels. Visual analysis of the data in Figure 1 suggests that Participant 5's level of disruptive vocalizing behavior increased, there was a stable nonchange in trend to an increase in trend, the behavior decreased upon phase change, and consistency worsened.</p> <hd id="AN0173485977-29">2 Hypothesis</hd> <p> <emph>The addition of student‐developed GS would lead to further decreases in disruptive behavior over SM alone in elementary school students with ADHD.</emph> </p> <p>Data analysis of the number of intervals for which participants demonstrated target disruptive behavior was compared for SM and SM + GS phases and is summarized in Table 4. To examine the possible additive effects of GS, the effect size was calculated by finding the difference between the mean of each participant's SM intervention phase and the mean of the SM + GS intervention phase and dividing it by the standard deviation of the SM phase (Table 4).</p> <p>During the SM phase, Participant 1 demonstrated a mean of 2.75 observed intervals of disruptive playing behaviors (SD = 3.59, range = 0–8). During the SM + GS phase, Participant 1 demonstrated a mean of 4.50 observed intervals of disruptive playing behaviors (SD = 5.07, range = 1–12). An effect size of −0.49 suggests a moderate increase in Participant 1's observed disruptive playing behaviors during the SM + GS intervention over SM intervention phase levels (see Table 4). Visual analysis of the data, presented in Figure 1, suggests that Participant 1's level of disruptive playing behavior increased, there was a decrease in trend for both phases, the behavior increased upon phase change, and consistency worsened.</p> <p>During the SM phase, Participant 2 demonstrated a mean of 16.67 observed intervals of disruptive playing behaviors (SD = 11.23, range = 7–29). During the SM + GS phase, Participant 2 demonstrated a mean of 10.33 observed intervals of disruptive playing behaviors (SD = 7.51, range = 3–18). An effect size of 0.56 in Table 4 suggests a moderate improvement in Participant 2's observed disruptive playing behaviors during the SM + GS intervention over SM levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 2's level of disruptive playing behavior decreased, there was an increase in trend to a decrease in trend, the behavior decreased upon phase change, and consistency improved.</p> <p>During the SM phase, Participant 3 demonstrated a mean of 5.00 observed intervals of disruptive playing behaviors (SD = 8.00, range = 1–17). During the SM + GS phase, Participant 3 demonstrated a mean of 0.00 observed intervals of disruptive playing behaviors (SD = 0.00, range = 0–0). An effect size of 0.63 in Table 4 suggests a moderate improvement in Participant 3's observed disruptive playing behaviors during the SM + GS intervention over SM intervention levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 3's level of disruptive playing behavior decreased, there was a stable nonchange in trend for both phases, the behavior decreased upon phase change, and consistency improved.</p> <p>During the SM phase, Participant 4 demonstrated a mean of 2.67 observed intervals of disruptive vocalizing behaviors (SD = 0.58, range = 2–3). During the SM + GS phase, Participant 4 demonstrated a mean of 3.00 observed intervals of disruptive vocalizing behaviors (SD = 3.56, range = 0–8). An effect size of −0.58 (Table 4) suggests a moderate increase in Participant 4's observed disruptive vocalizing behaviors during the SM + GS intervention over SM intervention levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 4's level of disruptive vocalizing behavior increased, there was a slight increase in trend to an increase in trend, the behavior decreased upon phase change, and consistency worsened.</p> <p>During the SM phase, Participant 5 demonstrated a mean of 7.00 observed intervals of disruptive vocalizing behaviors (SD = 6.00, range = 0–12). During the SM + GS phase, Participant 5 demonstrated a mean of 5.67 observed intervals of disruptive vocalizing behaviors (SD = 6.03, range = 0–12). An effect size of 0.21 in Table 4 suggests a small improvement in Participant 5's observed disruptive vocalizing behaviors during the SM + GS intervention over SM intervention levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 5's level of disruptive vocalizing behavior decreased, there was a decrease in trend to an increase in trend, no change in latency was observed, and consistency did not change.</p> <hd id="AN0173485977-30">3 Hypothesis</hd> <p> <emph>The behavioral improvement from the SM with GS intervention would maintain after intervention completion.</emph> </p> <p>Data analysis of the number of intervals for which participants demonstrated target disruptive behavior was compared for baseline and follow‐up (Table 4). Effect sizes were calculated by finding the difference between the mean of the baseline phase and the mean of the follow‐up phase and dividing by the standard deviation of the baseline phase. Each participant's data were also included for examination through visual analysis.</p> <p>During the baseline phase, Participant 1 demonstrated a mean of 6.00 observed intervals of disruptive playing behaviors (SD = 4.69, range = 2–14). At follow‐up, Participant 1 demonstrated a mean of 2.67 observed intervals of disruptive playing behaviors (SD = 4.62, range = 0–8). An effect size of 0.71 in Table 4 suggested a moderately large improvement in Participant 1's observed disruptive playing behaviors during the follow‐up phase as compared to baseline phase behavior levels. Visual analysis of the data in Figure 1 suggests that Participant 1's level of disruptive playing behavior decreased, there was no change in trend to a decrease in trend, the behavior decreased upon phase change, and there was no change in consistency. Due to Participant 2's withdrawal caused by family relocation, data were not collected for the follow‐up phase.</p> <p>During the baseline phase, Participant 3 demonstrated a mean of 7.67 observed intervals of disruptive playing behaviors (SD = 8.45, range = 0–24). At follow‐up, Participant 3 demonstrated a mean of 2.33 observed intervals of disruptive playing behaviors (SD = 2.08, range = 0–4). An effect size of 0.63 in Table 4 suggests a moderate improvement in Participant 3's observed disruptive playing behaviors at follow‐up over baseline levels. Visual analysis of the data, presented in Figure 1, suggests that Participant 3's level of disruptive playing behavior decreased, there was an increase in trend to a decrease in trend, the behavior decreased upon phase change, and consistency improved.</p> <p>During the baseline phase, Participant 4 demonstrated a mean of 3.80 observed intervals of disruptive vocalizing behaviors (SD = 2.94, range = 0–8). At follow‐up, Participant 4 demonstrated a mean of 4.00 observed intervals of disruptive vocalizing behaviors (SD = 4.24, range = 1–7). An effect size of −0.07 in Table 4 suggests that the frequency of Participant 4's observed disruptive vocalizing behaviors during the follow‐up phase did not differ from the frequency of those behaviors observed during the baseline phase. Visual analysis of the data (Figure 1) suggests that Participant 4's level of disruptive vocalizing behavior increased, there was an increase in trend to a decrease in trend, the behavior decreased upon phase change, and consistency worsened.</p> <p>During the baseline phase, Participant 5 demonstrated a mean of 4.90 observed intervals of disruptive vocalizing behaviors (SD = 4.04, range = 0–13). At follow‐up, Participant 5 demonstrated a mean of 4.50 observed intervals of disruptive vocalizing behaviors (SD = 6.36, range = 0–9). An effect size of 0.10 in Table 4 suggests a very small improvement in Participant 5's observed disruptive vocalizations at follow‐up as compared to observed baseline levels. Visual analysis of the data in Figure 1 suggests that Participant 5's level of disruptive vocalizing behavior decreased, there was no change in trend to a decrease in trend, the behavior decreased upon phase change, and consistency worsened.</p> <hd id="AN0173485977-31">4 Hypothesis</hd> <p> <emph>Teachers would report high levels of acceptability for use of the SM with GS intervention in their classrooms.</emph> </p> <p>Descriptive statistics of teacher acceptability data collected from teacher ratings on the BIRS (Elliott & Treuting, [<reflink idref="bib24" id="ref108">24</reflink>]) were examined to determine teacher attitudes toward the acceptability of the intervention for use in the school setting. Means and standard deviations were calculated to assess teachers' attitudes about the intervention on three subscales: acceptability, effectiveness, and timeliness of effect. The mean and standard deviation were also calculated for an overall general acceptability scale, which includes all three subscales. Additionally, acceptability percentages were calculated to compare teacher ratings across scales.</p> <p>Regarding teachers' attitudes toward the acceptability of the intervention, the mean total score in the BIRS across the four responding teachers for general acceptability was 82.75 (SD = 4.76, acceptable percentage = 57.47). Examination of teacher ratings in the three subcategories of the BIRS indicates that the responding teachers rated the intervention to be highest in acceptability (<emph>M</emph> = 58.00, SD = 3.08), with lower moderate ratings in the areas of effectiveness (<emph>M</emph> = 19.25, SD = 4.60) and timeliness of effect (<emph>M</emph> = 5.50, SD = 1.12).</p> <p>Item analysis of the mean rating across responding teachers for each question indicates that teachers rated the intervention most highly for the following statements: "The intervention was a fair way to handle the child's problem behavior," (<emph>M</emph> = 4.75); "I would be willing to use this in the classroom setting," (<emph>M</emph> = 4.5); and "The intervention would not result in negative side‐effects for the child" (<emph>M</emph> = 4.5). Item analysis also indicates that teachers rated the intervention most poorly for the following statements: "The intervention would quickly improve the child's behavior," (<emph>M</emph> = 2.5) and "The intervention should produce enough improvement in the child's behavior so the behavior no longer is a problem in the classroom," (<emph>M</emph> = 2.5).</p> <hd id="AN0173485977-32">DISCUSSION</hd> <p>The purpose of this study was to examine a behavioral intervention utilizing both SM and GS components to determine its effects on the disruptive behavior of students with ADHD. SM interventions provide the frequent feedback that individuals with ADHD need for decreasing their inattentive, impulsive, and/or hyperactive symptoms. Moreover, goals serve as internal motivators, helping to facilitate the self‐regulation needed to become aware of the difference between current and desired levels of behavior and to focus attention on goal attainment (Graham et al., [<reflink idref="bib33" id="ref109">33</reflink>]). Therefore, by combining SM with GS, students should receive an intervention that provides immediate, external feedback while also providing self‐regulation support for internal motivation.</p> <p>In keeping with these theoretical underpinnings, this study sought to investigate the effects of SM with GS intervention on students' disruptive behavior. It was hypothesized that the use of a behavioral intervention combining SM and GS techniques would decrease disruptive behavior in elementary school students with ADHD. Additionally, that the supplement of student‐developed GS would lead to further decreases in disruptive behavior over SM alone for this population, and that the behavioral improvement would maintain after the intervention. Finally, it was hypothesized that teachers would report high levels of acceptability for the use of this intervention in their classrooms.</p> <hd id="AN0173485977-33">The efficacy of a SM with GS intervention</hd> <p>This study highlights elementary students' with ADHD potential autonomy and self‐regulation during the intervention. The participants were tasked with monitoring their own behavior based on individually set goals, resulting in an effective intervention package for decreasing the disruptive behavior of students with ADHD. The target disruptive behavior of four of the five participants improved from baseline levels after intervention and effect sizes for four participants suggest meaningful changes in behavior. The visual analysis confirmed the data analysis procedures, with decreases in level from baseline to intervention phase observed for all but one participant and a decrease in disruptive behavior appearing with immediacy for all participants. In this, we demonstrate the use of moving students from a passive role to an active role in their own behavioral intervention within the classroom setting.</p> <hd id="AN0173485977-34">The additive effects of adding GS to SM</hd> <p>While the SM with GS intervention was demonstrated to be successful at improving disruptive behavior in students with ADHD, its benefits beyond those of SM were more modest. Three participants demonstrated improvements in observed target disruptive behavior during the SM with GS intervention over SM alone. The effect sizes suggest a significant improvement for three participants. However, the two remaining participants demonstrated moderate increases in disruptive behaviors from the SM intervention to the added GS intervention.</p> <p>There are several possible explanations for the different reactions to the intervention. Previous research has found that individual differences (e.g., gender, socioeconomic status, and comorbidity of diagnoses) moderate how individuals respond to interventions such as ours (American Psychiatric Association, [<reflink idref="bib3" id="ref110">3</reflink>]; Bax et al., [<reflink idref="bib7" id="ref111">7</reflink>]; Hinshaw, [<reflink idref="bib36" id="ref112">36</reflink>]; Langberg et al., [<reflink idref="bib44" id="ref113">44</reflink>]; Murray et al., [<reflink idref="bib50" id="ref114">50</reflink>]). Some reasons for certain moderators' impact on intervention remain unexplained; still, traits such as those reinforced through gender and anxiety symptoms could lead to greater compliance to or trust in the intervention (Hinshaw, [<reflink idref="bib36" id="ref115">36</reflink>]; Langberg et al., [<reflink idref="bib44" id="ref116">44</reflink>]). Additionally, factors resulting from lower socioeconomic status can lead to impacts through limited access to outside intervention and medication (Bax et al., [<reflink idref="bib7" id="ref117">7</reflink>]). Unfortunately, since we either did not measure or have much diversity within these moderators, we cannot determine what specific factors influence the participants' response to intervention. Even with the mixed results, GS gives students control over their intervention, which may result in greater student buy‐in and acceptance of the behavioral intervention—a variable not measured in this study (Lee & Tindal, [<reflink idref="bib45" id="ref118">45</reflink>]; Moore et al., [<reflink idref="bib49" id="ref119">49</reflink>]).</p> <hd id="AN0173485977-35">Maintenance effects</hd> <p>Previously, externally managed interventions have been ineffective in supporting long‐term efficacy in behavior change (Shapiro & Cole, [<reflink idref="bib56" id="ref120">56</reflink>]). This study attempted to shed light on the possible use of internally managed interventions in enacting the long‐term change that prior studies had not achieved. It is difficult to draw conclusions about the maintenance of behavioral improvement after the termination of the intervention package due to the small number of participants. However, overall data suggest that the decreasing effects of the intervention package on students' disruptive behaviors in this study were sustained after intervention completion.</p> <hd id="AN0173485977-36">Intervention acceptability</hd> <p>Ratings on the BIRS suggest that teachers find the SM with GS package to be moderately acceptable. Even so, teachers rated the intervention package as slow to improve student behavior as well as not improving behavior enough to eliminate it in the classroom. Still, teachers reported that the intervention package of SM with GS is an appropriate way to decrease students' disruptive behaviors and that this intervention demonstrates no adverse side effects for students. Furthermore, their ratings also indicate a willingness to use the intervention in their classrooms. Allowing students to set their own goals based on behavioral challenges faced may reduce teachers' responsibility to select the most optimal goals for each student. Teachers are busy all the time, and handing over the reins to students to monitor and set goals for their classroom behavior could reduce the overall teacher workload.</p> <hd id="AN0173485977-37">Limitations</hd> <p>The diagnostic criteria for ADHD suggest that this disorder may best be conceptualized as a spectrum, as symptoms vary among individuals. With three different presentations of symptoms, individuals can vary widely in the symptomatic behavior they demonstrate and the level of impact their behavior has on their functioning (American Psychiatric Association, [<reflink idref="bib3" id="ref121">3</reflink>]). This is one of multiple possible explanations for differences between subjects that were not considered in this study's design, which may have confounded the results. While participants were chosen for this study based on the presence of medical diagnoses of ADHD, no standardized measurement of their ADHD symptoms was completed. Such a measure would have helped ensure that participants demonstrated similar ADHD symptoms. Additionally, response to treatment has been shown to be impacted by several individual factors, such as gender, socioeconomic status, and the comorbidity of ADHD with other disorders, which were outside the scope of this research (Bax et al., [<reflink idref="bib7" id="ref122">7</reflink>]; Hinshaw, [<reflink idref="bib36" id="ref123">36</reflink>]; Langberg et al., [<reflink idref="bib44" id="ref124">44</reflink>]; Murray et al., [<reflink idref="bib50" id="ref125">50</reflink>]).</p> <p>A second confounding variable to this study's results was the GS training. Participants in this study were not trained to accurately record their behavior, as this procedure has been shown to be unnecessary for SM based on the research suggesting that students' recording accuracy does not impact the effects of SM interventions (DuPaul & Stoner, [<reflink idref="bib22" id="ref126">22</reflink>]; Harris et al., [<reflink idref="bib35" id="ref127">35</reflink>]). However, Hoza et al. ([<reflink idref="bib38" id="ref128">38</reflink>]) found that boys with ADHD tend to overestimate their competencies in several areas, including academics and behavior, suggesting that students with ADHD may demonstrate a less accurate awareness of their abilities than their peers. Therefore, it is possible that participants rated themselves more positively than was accurate when asked to determine their progress toward their behavior goals, which may have caused them to become less invested in SM their goals in the classroom. To target this limitation, an added training element to ensure the accuracy of participants' SM would have eliminated this possibility. Another added element could include measures of teacher or parent perspectives which might have allowed us to gather a more accurate picture of the changes in behavior when combined with the student SM data.</p> <p>Another concern of this study is the possible lack of treatment fidelity, which was confounded by the school calendar, weather closures, student and teacher absences, and changes in classroom schedules. However, without a measure of fidelity, we do not know how these confounds interfered with the consistency of intervention provision. A measure between the student investigator and the classroom teachers to track the students' adherence to the treatment would have helped monitor and possibly improve fidelity.</p> <p>Lastly, this study did not measure the student's perceptions of intervention acceptability. While we did measure teacher acceptability to determine if the intervention was a suitable fit in the school setting from a teacher perspective (Colton & Sheridan, [<reflink idref="bib19" id="ref129">19</reflink>]; Curtis et al., [<reflink idref="bib21" id="ref130">21</reflink>]; Elliott & Treuting, [<reflink idref="bib24" id="ref131">24</reflink>]; Erchul et al., [<reflink idref="bib25" id="ref132">25</reflink>]; Pisecco et al., [<reflink idref="bib52" id="ref133">52</reflink>]), the students' perspectives could lead to a greater understanding of the fit of this intervention for their population. Especially since the GS aspect of the intervention may be able to increase buy‐in from students into the intervention (Lee & Tindal, [<reflink idref="bib45" id="ref134">45</reflink>]; Moore et al., [<reflink idref="bib49" id="ref135">49</reflink>]), finding an age‐appropriate measure of student perspectives could allow us to determine if the student's ratings of intervention acceptability are related to increased compliance to the intervention.</p> <hd id="AN0173485977-38">Future research</hd> <p>In light of the small sample of participants in the current study and the limitations outlined above, a replication of procedures that account for these limitations and include a greater number of participants is suggested. Another option would be to use a group design, with the combined SM with GS group being one group, the SM group being another, and lastly the control group would serve a similar purpose to the baseline phase. However, due to the larger sample needed, observational methods may not lend themselves to this design, possibly necessitating different measures. Additionally, some of the limitations of this study lend themselves to questions that may further the current research. As the data seem to suggest that this intervention system varies in effectiveness based on individual differences, it may be beneficial to explore the differences between students with similar and different ADHD symptomologies, genders, socioeconomic status, and comorbidity differences with respect to response to the intervention.</p> <hd id="AN0173485977-39">CONCLUSION</hd> <p>The goal of this study was to add to the literature by building on Barkley ([<reflink idref="bib4" id="ref136">4</reflink>], [<reflink idref="bib5" id="ref137">5</reflink>]) theory of ADHD as an executive functioning deficit through the investigation of a behavioral intervention combining SM and GS for students with ADHD. The results suggest modest, positive outcomes for using this intervention package for students with ADHD. However, the limitations of this study make definitive conclusions about the intervention's effectiveness difficult. These limitations should be addressed in future research.</p> <hd id="AN0173485977-40">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors declare no conflict of interest.</p> <hd id="AN0173485977-41">DATA AVAILABILITY STATEMENT</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p> <ref id="AN0173485977-42"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref40" type="bt">1</bibl> <bibtext> Alderman, M. K., & MacDonald, S. (2015). A self‐regulatory approach to classroom management: Empowering students and teachers. 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Bray and Carly Bernstein</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib23" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib30" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib27" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib34" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib35" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib47" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib48" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib57" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib43" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib58" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib11" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib13" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib61" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib36" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib44" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib50" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib32" firstref="ref28"></nolink> <nolink nlid="nl19" bibid="bib60" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib14" firstref="ref34"></nolink> <nolink nlid="nl21" bibid="bib26" firstref="ref35"></nolink> <nolink nlid="nl22" bibid="bib41" firstref="ref41"></nolink> <nolink nlid="nl23" bibid="bib37" firstref="ref42"></nolink> <nolink nlid="nl24" bibid="bib63" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib56" firstref="ref45"></nolink> <nolink nlid="nl26" bibid="bib28" firstref="ref47"></nolink> <nolink nlid="nl27" bibid="bib10" firstref="ref49"></nolink> <nolink nlid="nl28" bibid="bib53" firstref="ref52"></nolink> <nolink nlid="nl29" bibid="bib59" firstref="ref53"></nolink> <nolink nlid="nl30" bibid="bib18" firstref="ref64"></nolink> <nolink nlid="nl31" bibid="bib31" firstref="ref65"></nolink> <nolink nlid="nl32" bibid="bib22" firstref="ref66"></nolink> <nolink nlid="nl33" bibid="bib54" firstref="ref68"></nolink> <nolink nlid="nl34" bibid="bib64" firstref="ref72"></nolink> <nolink nlid="nl35" bibid="bib20" firstref="ref73"></nolink> <nolink nlid="nl36" bibid="bib62" firstref="ref75"></nolink> <nolink nlid="nl37" bibid="bib16" firstref="ref77"></nolink> <nolink nlid="nl38" bibid="bib29" firstref="ref78"></nolink> <nolink nlid="nl39" bibid="bib33" firstref="ref79"></nolink> <nolink nlid="nl40" bibid="bib42" firstref="ref80"></nolink> <nolink nlid="nl41" bibid="bib45" firstref="ref81"></nolink> <nolink nlid="nl42" bibid="bib49" firstref="ref82"></nolink> <nolink nlid="nl43" bibid="bib40" firstref="ref91"></nolink> <nolink nlid="nl44" bibid="bib55" firstref="ref92"></nolink> <nolink nlid="nl45" bibid="bib39" firstref="ref93"></nolink> <nolink nlid="nl46" bibid="bib12" firstref="ref94"></nolink> <nolink nlid="nl47" bibid="bib51" firstref="ref95"></nolink> <nolink nlid="nl48" bibid="bib17" firstref="ref96"></nolink> <nolink nlid="nl49" bibid="bib24" firstref="ref97"></nolink> <nolink nlid="nl50" bibid="bib19" firstref="ref98"></nolink> <nolink nlid="nl51" bibid="bib21" firstref="ref99"></nolink> <nolink nlid="nl52" bibid="bib25" firstref="ref101"></nolink> <nolink nlid="nl53" bibid="bib52" firstref="ref102"></nolink> <nolink nlid="nl54" bibid="bib46" firstref="ref103"></nolink> <nolink nlid="nl55" bibid="bib38" firstref="ref128"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Self-Monitoring with Goal-Setting: Decreasing Disruptive Behavior in Children with Attention-Deficit/Hyperactivity Disorder
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22McKenna%2C+Kara%22">McKenna, Kara</searchLink><br /><searchLink fieldCode="AR" term="%22Bray%2C+Melissa+A%2E%22">Bray, Melissa A.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3437-3505">0000-0002-3437-3505</externalLink>)<br /><searchLink fieldCode="AR" term="%22Fitzmaurice%2C+Brenna%22">Fitzmaurice, Brenna</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2427-2193">0000-0003-2427-2193</externalLink>)<br /><searchLink fieldCode="AR" term="%22Choi%2C+Dowon%22">Choi, Dowon</searchLink><br /><searchLink fieldCode="AR" term="%22DeMaio%2C+Erin%22">DeMaio, Erin</searchLink><br /><searchLink fieldCode="AR" term="%22Bray%2C+Clark+R%2E%22">Bray, Clark R.</searchLink><br /><searchLink fieldCode="AR" term="%22Bernstein%2C+Carly%22">Bernstein, Carly</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Psychology+in+the+Schools%22"><i>Psychology in the Schools</i></searchLink>. 2023 60(12):5167-5188.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 22
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Problems%22">Behavior Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Modification%22">Behavior Modification</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/pits.23026
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0033-3085<br />1520-6807
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study sought to investigate the effects of a self-monitoring (SM) with goal-setting (GS) intervention on students' disruptive behavior. A multiple baseline A-B-BC design was implemented across five elementary school-aged participants diagnosed with attention-deficit/hyperactivity disorder (ADHD) to examine the use of a behavioral intervention combining SM and GS techniques to decrease disruptive behavior. The results of this study suggest that SM with GS appears to be an effective intervention package for decreasing the disruptive behavior of students with ADHD and that these behavioral decreases sustain after intervention completion. Results also suggest moderate benefits of using a SM with GS intervention over a SM intervention. Teacher ratings suggest that the SM with GS package is moderately acceptable for classroom use.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1399673
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1399673
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/pits.23026
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 5167
    Subjects:
      – SubjectFull: Goal Orientation
        Type: general
      – SubjectFull: Self Management
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Behavior Problems
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Behavior Modification
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
    Titles:
      – TitleFull: Self-Monitoring with Goal-Setting: Decreasing Disruptive Behavior in Children with Attention-Deficit/Hyperactivity Disorder
        Type: main
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            NameFull: McKenna, Kara
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              M: 01
              Type: published
              Y: 2023
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            – Type: issn-print
              Value: 0033-3085
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