Executive Function and Behavior Improvement: An 8-Week Observational Study of a Gamified Reward System for Children

Executive Function and Behavior Improvement: An 8-Week Observational Study of a Gamified Reward System for Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Executive Function and Behavior Improvement: An 8-Week Observational Study of a Gamified Reward System for Children Joseph Raiker, Kevin Bunarjo, Isaac Eaves, Brad Brenner, Robert Henry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6316490/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Provider shortages, lengthy waitlists, and a variety of other challenges associated with implementing traditional evidence-based strategies to reduce parent stress and improve childhood outcomes have resulted in the development of digital solutions intended to overcome many of these challenges. Unfortunately, provider uptake of many of these digital approaches remains low. The current study examines parent perceptions of usability and changes in their child’s behavior, executive functioning, and impairment following eight weeks of use of a digital contingency management system that overcomes limitations of past approaches and integrates a parent application with a child video game. Method: Ninety children between the ages of 6 and 12 were enrolled in this observational study. Parents were asked to use the digital application for eight weeks and complete the Disruptive Behavior Disorders Rating Scale - Modified (DBD-Modified), Child Executive Function Inventory (CHEXI), and Impairment Rating Scale (IRS) every two weeks. Multilevel models (MLM) were used with repeated observations (Level-1) nested within individual parents (Level-2) to examine changes on these outcomes over time. Results: Small to moderate magnitude improvements in behavior (Cohen’s d = 0.57), executive functioning (Cohen’s d = 0.50), and impairment (Cohen’s d = 0.43) following eight weeks of use were observed. Additionally, most parents found the application easy to use (88.7%), would recommend it to a friend (90.4%) and perceived it as reducing stress (59.7%) and arguments with their child (72.6%). Discussion: This study provides preliminary evidence for the potential benefits of a parent-child digital application leveraging gamification for youth with behavioral and executive functioning challenges. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Behavioral challenges and executive functioning deficits in children have been investigated for decades given their centrality to disorders such as ADHD and ODD (Barkley, 1997; for a review, see Wilens & Spencer, 2013). To date, only two evidence-based treatments for these disorders have been found to be both efficacious and effective and they include stimulant medication and behavior therapy (Wolraich et al., 2019). Despite a robust evidence base for their use, however, an average of 28.3% of parents of children with ADHD report not using stimulant medications in the last 12 months and less than two-thirds (Median = 61.8%) report receiving some form of behavioral therapy in the last 12 months highlighting significant unmet treatment need (Danielson et al., 2023). The discrepancy between those in need of services for behavioral challenges and the availability of such services has only intensified over the last several years. For example, the number of children diagnosed with ADHD has risen substantially over the last 6 years with more than 1 million more children receiving a diagnosis in 2022 relative to 2016 (Danielson et al., 2024). Further, workforce surveys such as those distributed by APA reveal extensive wait lists among providers willing to provide such services (APA, 2022). The need for innovative solutions to address growing mental health challenges has never been greater as evidenced by the US surgeon general’s recent proclamation of a youth mental health crisis (OSG, 2021) as well as an even more recent advisory related to the mental health and well-being of parents (OSG, 2024). Notably, this problem extends even beyond those with a diagnosis such as ADHD and ODD and includes even youth without formal diagnoses (e.g., at-risk, typically developing) that might benefit from implementation of evidence-based behavioral strategies before problems begin to emerge. The continued rise - particularly among youth – in mental health challenges has corresponded with increased interest in leveraging emerging technologies to address them. For example, for children with behavior problems, a number of interventions that rely on digital technology and/or serious games have been developed in recent years in hopes of expanding the availability of services as well as addressing underlying hypothesized core deficits such as executive functioning (Doulou et al., 2025; Oh, Choi, Han, & Kim, 2023; Rodrigo-Yanguas, Gonzalez-Tardon, Bella-Fernandez, & Blasco-Fontecilla, 2022; Westwood et al., 2023). For example, EndeavorRX, was the first FDA-cleared prescription video game geared towards families of children with ADHD (Kollins et al., 2020). Similarly, Central Executive Training (CET) was developed shortly thereafter and has demonstrated promise in improving underlying executive functioning deficits as well as behavioral symptoms of ADHD (Kofler et al., 2018; Kofler et al., 2020). Despite this, however, these approaches have garnered substantial skepticism (Evans et al., 2021) or have not been widely adopted by treatment providers to date. Difficulties gaining widespread adoption among providers likely reflects a variety of challenges including lack of reimbursement pathways, knowledge around new and emerging approaches for intervening, as well as overall skepticism related to the adoption of alternate or more novel approaches to the treatment of these disorders. Importantly, many of the technologies described above were developed primarily around hypothesized upstream neurocognitive deficits presumed to underlie behavioral symptoms of disorders such as ADHD and thus relied on entirely different mechanisms of action than those traditionally hypothesized to be responsible for improvements observed with currently available evidence-based treatments such as behavior therapy. For example, the prevailing clinical treatment approaches of stimulant medication and behavioral management training are hypothesized to improve symptoms via neurochemical changes or implementation of operant conditioning procedures such as reward and punishment, respectively. As a result, providers may feel more comfortable recommending or applying traditional approaches rather than transitioning to adopting entirely novel treatments hypothesized to improve other suspected core deficits (e.g., neurocognitive dysfunction) with which they may be less familiar. Given these challenges, others have attempted instead to develop digital analogues of already established evidence-based interventions for disorders such as ADHD and ODD that may be more familiar to providers and result in increased adoption. For example, Fabiano and Pelham’s well-known daily report card (DRC) intervention was recently integrated into an online portal known as DRCO and evaluated amongst teachers (Owens, Lee, Eackles, Medina, Evans, & Reid, 2022). Briefly, the DRC is a well-established intervention in which specific goals are set for a child (e.g., complete 80% of homework; interrupt fewer than 3 times each lesson). At the end of each day, the proportion of goals achieved are reviewed with the child and this information is provided to the parent or guardian. For successful goal achievement, children earn short- (e.g., selecting a toy from a box) and long-term (e.g., getting to participate in a classroom-wide pizza party) rewards. Expectations regarding successful goal achievement are dynamically adjusted following review of data related to how the child is progressing and goals are revised as the child gains success. This is a well-documented intervention for improving behavior (Fabiano et al., 2010; Pyle & Fabiano, 2017) and has been used in both home and school settings. Owens and colleagues (2019; 2022) recently developed and evaluated an online version of this intervention for use in the school setting. Results revealed that while the majority of teachers (55.56%) were willing to adopt an online version of this intervention, only 20% used it for longer than two months. Notably, significant, large-magnitude (Within-subject effect size = 0.65 to 0.86) improvements were noted across multiple domains including behavior problems and social functioning despite only approximately eight weeks of use (Owens et al., 2019). This study highlights the importance of adapting existing interventions for digital use in schools and points to a significant need for similar studies to be conducted for tools that may be equally, if not even more, useful to parents (e.g., reward charts). While emerging technology has substantial potential to fill the gap in delivering services to those most in need, it is important to note that currently most digital interventions (including those reviewed above) are intended to be used on a digital device by only a single stakeholder such as a child playing a videogame individually or a teacher inputting data on a mobile device. Critically, however, behavioral interventions for disorders such as ADHD and ODD rely on not only behavioral changes exhibited by the child (e.g., being more compliant, being more organized) but depend also on changes implemented by others within the child’s immediate environment (e.g., parents or teachers consistently delivering contingencies to increase or decrease behaviors of interest). As a result, digital interventions designed to engage multiple stakeholders (e.g., both parents and children) are likely to confer greater benefit by increasing accountability for everyone involved in a child’s success. In an effort to fill these gaps, commercially available digital applications, [MASKED FOR REVIEW], that reflect analogues of components of existing evidence-based interventions and are intended to be used by both parents and children have been developed. These enhancements are expected to ultimately facilitate improvements in children’s behavior and executive functioning. Specifically, [MASKED FOR REVIEW] leverages existing operant conditioning principles (i.e., response-reward contingencies), automation (e.g., notifying parents when tasks are completed), and gamification of the child application with the goal of improving the consistency and efficacy of the delivery of a common behavioral treatment component referred to as contingency management (i.e., reward charts). The parent application allows parents to assign tasks or expectations to children using a task board. Tasks can include any behavioral expectations the parent has for their children and include things such as completing homework, certain household chores, behaving appropriately with peers and siblings, and any other relevant behaviors parents are interested in improving. Each task can be assigned to be completed a single time, daily, weekly, or monthly depending on expectations. Further, each task is assigned a corresponding coin value by the parent. The parent is able to view which tasks the child has completed (while children can self report on this as well) and approve them as they are completed at which point the child earns the corresponding number of coins to use in a companion videogame. The connected child application is the videogame in which children raise a virtual pet the child names, feeds, pets, and allows to explore various elements throughout the game. Coins earned for task completion are used throughout the videogame to redeem desired items such as clothing articles for the child’s pet. The child can view what tasks have been assigned by their parent in the videogame by accessing the task board. Ultimately, the applications are designed such that children’s access to the video game depends primarily upon their success in the ‘real-world.’ [MASKED FOR REVIEW] use of a token economy system reflects similar real life response-reward contingencies parents are often encouraged to develop and implement as part of existing evidence-based parenting programs (e.g., sticker charts, reward charts). It is well-established that these approaches result in improvements in behavioral functioning (Barkley, 2020; Coelho et al., 2015; Reitman et al., 2001; van Langen et al., 2021). Additionally, emerging evidence among preschoolers indicates that embedding these types of contingency management programs into more intensive treatment options may also improve executive functioning (Landis, Hart, & Graziano, 2018). Aside from understanding whether use of [MASKED FOR REVIEW] is associated with changes in behavioral and executive functioning, it is also critical to understand parent perceptions and preferences of the applications themselves as negative perceptions regarding the applications are unlikely to result in utilization undermining the potential effectiveness and scalability of these applications. The current study is the first to examine changes in parent perceptions of their children’s behavioral and executive functioning over eight weeks of use digital applications designed to be used by both parents and children. It is expected that parents will report significant improvements in both behavioral and executive functioning in their children after eight weeks of use. Further, it is expected that reductions in impairment will be reported following eight weeks of use. Finally, we anticipate that users will find [MASKED FOR REVIEW] acceptable, will enjoy using the applications, and perceive [MASKED FOR REVIEW] as helping across a variety of areas (e.g., stress, accountability) relevant to the implementation of behavioral strategies. Method This study was approved by the Biomedical Research Alliance of New York (BRANY) Institutional Review Board. Participants This study included 90 participants. Participants were parents, 18 years of age or older, of children between the ages of 6 and 12 who created an account, signed up for a paid subscription to use the iPhone app, and verified a task at least once within 48 hours (i.e., used the application). Details regarding the demographic composition of this sample is included in Tables 1 and 2 below. Overall, the sample was composed primarily of parents of male children ( n = 49, 54.40%) and children were approximately 8.92 ( SD = 1.76) years, on average. Nearly three-quarters of the children had been previously diagnosed with at least one mental health disorder ( n = 67, 74.4%) with the most common being ADHD ( n = 17, 18.9%). It is important to note that many of the children with multiple diagnoses also had ADHD. More than one-third of participants were receiving treatment in the form of either medication ( n = 35, 38.90%) or therapy ( n = 34, 37.8%). Parents enrolled in the study were primarily White ( n = 76, 84.4%) and female ( n = 85, 94.40%) with an average age of 37.19 ( SD = 6.31). Approximately 7.78% ( n = 7) of the parents were Hispanic/Latino. Additionally, most were currently married ( n = 66, 73.3%). The majority of parents had attained a Bachelor’s Degree or higher ( n = 48, 53.32%) and were employed full-time ( n = 46, 51.1%) with nearly two-thirds reporting an annual income of $75,000 or higher ( n = 56, 62.22%). Procedure Parents who downloaded the commercially available app, signed up for a free trial of the application (or paid for the annual subscription), and verified at least one task within the first 48 hours of beginning their subscription were invited to participate in the study. Specifically, they were shown a brief modal outlining the study requirements and associated study compensation. After indicating that they were interested in participating, parents were re-directed to Qualtrics to complete the informed consent and corresponding study measures to determine eligibility. Inclusion criteria included that the parent and child were English-speaking, new users of the application, the parent was 18 years of age or older, the family resided in the US and possessed an iPhone, and that the child was between the ages of 6 and 12. Additionally, if a Table 1. Child Demographic Characteristics (n = 90) Characteristic n (%) or M (SD) Age (in years) 8.92 (1.76) Biological Sex Female 40 (44.40%) Male 49 (54.40%) Not Reported 1 (1.11%) Diagnosis Depression Only 1 (1.11%) Anxiety Only 6 (6.67%) ADHD Only 17 (18.9%) ASD Only 2 (2.22%) LD Only 2 (2.22%) Other diagnosis 7 (7.78%) No diagnosis 23 (25.60%) Multiple diagnoses 32 (35.60%) Current Treatment Taking Medication 35 (38.90%) Receiving Therapy 34 (37.8%) Table 2. Parent Demographic Characteristics (n = 90) Characteristic n (%) or M (SD) Age (in years) 37.19 (6.31) Biological Sex Female 85 (94.40%) Male 4 (4.44%) Not Reported 1 (1.11%) Race Black 6 (6.67%) Asian 1 (1.11%) White 76 (84.4%) American Indian or Alaska Native 1 (1.11%) Multiracial 4 (4.44%) None of the above 2 (2.2%) Ethnicity Hispanic/Latino 7 (7.78%) Highest Level of Education Completed High School Diploma or Equivalent 10 (11.1%) Some college 31 (34.4%) Bachelor’s Degree 22 (24.4%) Master’s Degree 24 (26.7%) Doctoral 2 (2.22%) Other 1 (1.11%) Table 2 (continued). Parent Demographic Characteristics (n = 90) Characteristic n (%) or M (SD) Employment Status Self-employed 16 (17.80%) Full-time 46 (51.1%) Part-time 10 (11.1%) Student 2 (2.22%) Other 6 (6.67%) Not employed 9 (10%) Not reported 1 (1.11%) Marital Status Never married 5 (5.56%) Separated 2 (2.22%) Divorced 13 (14.4%) Married 66 (73.3%) Single 2 (2.22%) Not reported 2 (2.22%) Family Income (annually) $150,000 18 (20.00%) Not reported 5 (5.56%) parent signed up to use the application with more than one child, only one child was eligible to participate in this study. To facilitate retention, participants were offered a free subscription to the applications for one year for participating in the study. Additionally, participants were offered a $10.00 gift card for each time point at which they completed the questionnaires for a possible total of $60.00 if questionnaires for all time points were completed by a participant. Participants received half of their earned compensation following the mid-point of the study and the final half of their earned compensation following the final time point of the study. Finally, reminders were provided to participants who did not complete the questionnaires. They were given a 3 day window in which to complete these questionnaires. Intervention All parents who agreed to participate in the study used [MASKED FOR REVIEW], an integrated platform for families that consists of two primary applications. One application is for children and is called the [MASKED FOR REVIEW] and the other application is for parents and is called [MASKED FOR REVIEW]. In the [MASKED FOR REVIEW], children access a video game where they advance through the game using coins they have earned by completing real-life behaviors that are managed and monitored by parents using the parent companion application. The companion applications were designed based on extant evidence-based principles (e.g., operant conditioning) such as reward-response contingencies that have been shown to be beneficial for improving behavior (Fabiano et al., 2010; Pyle et al., 2017; Reitman et al., 2001). Positive reinforcement, in the form of coins, is rewarded to children after their parent has reviewed what tasks they have completed and approved that these were indeed completed, as desired. Children see these coins in the application interface and can redeem the coins for various items throughout the game (e.g., food for their virtual pet, outfits, etc.). Additionally, parents can create custom real-life rewards (e.g., an allowance, going to bed later, getting to pick a family movie) that can also be redeemed for these virtual coins. When parents assign tasks they can select from pre-determined templates of other tasks parents have assigned to children or create completely new tasks based on the needs of them and their family. Parents can select how many coins are rewarded for each task they assign to their child as well as customize other factors related to the task (e.g., what days it needs to be completed). Children can view these tasks and how many coins they will earn in the video game by clicking on the icon corresponding to the task list. Parents who elected to participate in the study were instructed to use the applications daily for eight weeks. No other instructions regarding how to use the applications were provided to participants. Outcomes The following outcomes were collected at baseline and every two weeks until the end of the eight week study period. This frequency of data collection was selected to ensure sufficient data was collected. To account for missing data, multilevel models (MLMs) were utilized with maximum-likelihood (ML) estimation. Behavior Problems Disruptive Behavior Disorders (DBD) Rating Scale - Modified (Pelham et al., 1992): The DBD - Modified is a 30-item questionnaire completed by the parent regarding their child that assesses severity of symptoms related to inattention (9 items), hyperactivity/impulsivity (9 items), and oppositional behavior (8 items). Items related to conduct disorder were removed as they were not relevant to this study. Parents were asked to rate each item with respect to how well it described their child on a Likert scale from 0 to 3 (0: not at all; 1: just a little; 2: pretty much; 3: very much). Items were summed across the total scale as well as each subscale (i.e., ADHD-I, ADHD-HI, and ODD) and changes over time across each of these sum scores were evaluated. Higher scores indicated greater levels of inattention, hyperactivity/impulsivity, and oppositional behavior. Internal consistency across all five time points in this sample was excellent and ranged from Cronbach’s α of 0.93 to 0.95. Executive Functioning Childhood Executive Functioning Inventory (CHEXI; Catale, Meulemans, & Thorell, 2015): The CHEXI is a 24-item questionnaire completed by the parent regarding their child that assesses executive functioning (e.g., working memory, inhibition, planning, and regulation). Parents were asked to rate each item with respect to how well it described their child on a Likert scale from 1 to 5 (1: definitely not true; 2: not true; 3: partially true; 4: true; 5: definitely true). Items were summed across the total scale as well as each subscale (i.e., Working Memory, Inhibition; Planning; Regulation) to evaluate changes over time across each of these sum scores. Notably, consistent with the optimal factor structure identified by Catale and colleagues (2015) and given limitations associated with the current sample size, scores across these four subscales were further reduced to reflect either a Working Memory or Inhibition total score. Items related to working memory and planning contributed to the Working Memory Total score whereas items related to inhibition and regulation contributed to the Inhibition Total score. Higher scores indicated greater levels of difficulties with executive functioning, working memory, and inhibition. Internal consistency across all five time points was excellent in this sample and ranged from Cronbach’s α of 0.92 to 0.96. Impairment Impairment rating scale (IRS): The IRS is a 7-item questionnaire adapted from Leopold et al. (2019) and completed by the parent regarding their child that assesses impairment across recreational activities, daily responsibilities, educational activities, participation in community activities, interactions with adults (e.g., teachers), home life and family. Parents were asked to rate how much their child’s problems have interfered with these areas over the last two weeks on a Likert scale from 0 to 3 (0: not at all; 1: just a little; 2: quite a bit; 3: very much). Scores were totaled across the 7-item IRS to obtain an overall impairment total score. Higher scores indicated greater levels of impairment. Internal consistency across all five time points in this sample was good and ranged from Cronbach’s α of 0.79 to 0.89. Usability Usability survey : A usability survey was developed for the purposes of this study and contained 12 items completed by the parent regarding their experience and their child’s experience with the applications. Parents were asked to rate each item with respect to how much they agreed with it on a Likert scale from 1 to 5 (1: do not agree at all; 2: do not agree; 3: neither agree nor disagree; 4: agree; 5: definitely agree). Proportions of agreement across each item are reported below. Statistical Analysis Plan For categorical variables (e.g., endorsements on the usability survey), data were summarized using percentages. For continuous measures, data were summarized with traditional descriptive statistics including mean and standard deviation. For all inferential statistical tests, an alpha of .05 was used and 95% confidence intervals were calculated. Two-tailed tests without correction for experiment-wise error were used. For each outcome variable, a repeated-measures multilevel model (MLM) was used to capitalize on data collected at each time point as well as overcome limitations associated with missing data. Thus, repeated observations (Level-1) were nested within individual parents (Level-2). MLMs were run in R (R Core Team, 2020) using the lme4 library. For each outcome variable, models included a random slope for time (baseline, 2 weeks, 4 weeks, 6 weeks, 8 weeks). Models were run with and without relevant Level-2 covariates (i.e., child biological sex, parent race/ethnicity, child age, and concomitant treatment with medications or therapy). The covariance across repeated measures was unstructured. If the pattern between the models with and without covariates was similar, simple model results with no covariates are reported with the mean and 95% confidence intervals to ease interpretation. If the models differ significantly, the same information is presented for the model with covariates with the exception of least-squares means being included instead of raw means. To obtain an estimate of the magnitude of change from baseline to Week 8, Cohen’s d effect sizes were computed using the effectsize library from R . Due to technical limitations, these were estimated from the random intercept only model for each outcome. Specifically, estimated marginal means were obtained for each model and pairwise comparisons were conducted for each pair of timepoints to obtain t -ratios. The t -ratio for the comparison between Week 8 and baseline was then used to derive an estimate of the Cohen’s d effect size over time (Craske et al., 2023; Feingold, 2018). Effect size magnitude was interpreted consistent with existing guidelines (i.e., 0.2 or smaller = small, 0.5 = moderate, 0.8 or larger = large; Cohen, 1990). Results Missingness Data for each participant at each time point was evaluated for substantial portions of missingness. Overall, there was very little missing data across scales with most participants with missing data only missing between 1 and 3 items across all measures. In cases where the amount of missing data was 5% or greater, participants' individual item endorsements across the measures were examined to determine whether there was sufficient information reported by the participant to compute a total scale score using mean substitution while minimizing bias. If more than a third of the individual items for a given scale were missing, the scale was treated as though it had not been completed in subsequent analyses and relevant missing data approaches were applied to estimate the total scale score for that measure (e.g., see information on the use of multilevel linear modeling [MLM] below). Ultimately, this threshold resulted in two partially completed scales being dropped for a single participant at baseline, one partially completed scale being dropped for a single participant at the week 2 follow-up, and one partially completed scale being dropped for two participants at the week 8 follow-up. For all other participants, given the low number of missing items across each scale, missing item endorsements were estimated using mean substitution in order to allow for the calculation of a total score for each measure. Further, for participants with missing total scores for a measure, multilevel linear models (MLMs) using maximum likelihood (ML) were leveraged to obtain robust estimates of changes over time. Follow-up data was obtained for 78 participants (87%) at the week 2 follow-up, 70 participants (78%) at the week 4 follow-up, 61 participants at the week 6 follow-up (68%), and 62 participants (69%) at the week 8 follow-up. The majority of participants ( n = 48, 53.33%) provided data at all five time points. Overall, 91% ( n = 82) of participants provided follow-up data for at least one time point and 8 were considered lost to follow-up and dropped from all subsequent analyses. Outcomes Models were tested iteratively beginning with a null model that was then compared to a random intercept only model and finally a random intercept and slope model. If the subsequent model did not significantly account for more variance in outcomes (using a standard likelihood ratio test of deviance statistics), the simpler model was retained. For example, if the model incorporating both random intercepts and slopes did not outperform the random intercept only model, the latter was retained for subsequent analyses and summary statistics. Behavior Problems Total Disruptive Behavior Disorder (DBD) Scores The best fitting model for total behavior problems included a random intercept and random slope with no covariates, ( χ² [2] = 11.08, p = .004). The MLM examining changes in total behavior problems over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -2.95, 95% CI = -3.62 to -2.29; p < .001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 28.85% improvement in total behavior problems (Cohen’s d = 0.57; Fig. 1). ADHD - Inattentive Total Scores The best fitting model for symptoms related to ADHD - Inattentive symptoms was one that included a random intercept and random slope with no covariates ( χ² [2] = 6.06, p = .048). The MLM examining changes in total inattentive scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -1.13, 95% CI = -1.40 to -0.86; p < .001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 27.74% improvement in symptoms of ADHD-I symptoms (Cohen’s d = 0.51). Table 3 Descriptive Statistics for Behavior Problems and Executive Functioning Over Time Measure Baseline M (SD) 2 Weeks M (SD) 4 Weeks M (SD) 6 Weeks M (SD) 8 Weeks M (SD) Total Behavior Problems 40.96 (13.71) 38.00 (12.43) 35.05 (12.13) 32.10 (12.21) 29.14 (13.68) ADHD - I 16.26 (5.91) 15.13 (5.33) 14.01 (5.14) 12.88 (5.11) 11.75 (5.66) ADHD - HI 13.99 (6.05) 12.85 (5.35) 11.71 (5.04) 10.58 (4.91) 9.44 (5.35) ODD 10.71 (5.50) 10.02 (4.89) 9.32 (4.59) 8.63 (4.40) 7.94 (4.71) Total Executive Functioning 84.99 (15.08) 82.34 (14.23) 79.70 (14.24) 77.05 (14.47) 74.41 (16.14) Working Memory 43.19 (9.77) 41.88 (9.08) 40.58 (8.93) 39.27 (8.92) 37.96 (9.83) Inhibition 41.81 (6.64) 40.46 (6.25) 39.10 (6.30) 37.75 (6.48) 36.39 (7.32) Total Impairment 9.01 (4.17) 8.25 (3.66) 7.50 (3.42) 6.74 (3.32) 5.99 (3.61) Notes : ADHD - I = ADHD - Inattentive; ADHD - HI = ADHD - Hyperactive/Impulsive; M = Mean; ODD = Oppositional Defiant Disorder; SD = Standard Deviation ADHD - Hyperactive/Impulsive Total Scores The best fitting model for symptoms related to ADHD - Hyperactive/Impulsive symptoms was one that included a random intercept and random slope with no covariates ( χ² [2] = 7.86, p = .02). The MLM examining changes in total hyperactive/impulsive symptoms over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -1.13, 95% CI = -1.40 to -0.87; p < .001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 32.52% improvement in ADHD-HI symptoms (Cohen’s d = 0.53). ODD Total Scores The best fitting model for symptoms related to ODD symptoms was one that included a random intercept with no covariates ( χ² [1] = 39.24, p < .001). The MLM examining changes in ODD symptoms over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -0.69, 95% CI = -0.90 to -0.48; p < .001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 25.9% improvement in ODD symptoms (Cohen’s d = 0.36). Executive Functioning Total Childhood Executive Functioning Inventory (CHEXI) Scores The best fitting model for total executive functioning problems was one that included a random intercept and random slope with no covariates ( χ² [2] = 12.34, p = .002). The MLM examining changes in total executive function problems over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -2.64, 95% CI = -3.32 to -1.96; p < .001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 12.44% improvement in total executive function problems (Cohen’s d = 0.50; Fig. 2). Working Memory (WM) Total Scores The best fitting model for total working memory problems was one that included a random intercept and random slope with no covariates ( χ² [2] = 8.38, p = .02). The MLM examining changes in total working memory problem scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -1.31, 95% CI = -1.72 to -0.89; p < .001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 12.11% improvement in working memory problems (Cohen’s d = 0.42). Inhibition Total Scores The best fitting model for total inhibition problems was one that included a random intercept and random slope with covariates ( χ² [2] = 11.47, p = .003). Notably, the only significant covariate was whether or not the child was reported by their parent as currently receiving therapy. Unsurprisingly, these children scored substantially higher, on average, on the inhibition problems scale than children not receiving therapy ( B = 5.22, p < .001). This covariate did not change the overall pattern of results so the simple model with random intercept and random slope are reported. The MLM examining changes in total inhibition problem scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study ( B = -1.35, 95% CI = -1.68 to -1.02; p < .001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 12.96% improvement in inhibition problems (Cohen’s d = 0.49). Impairment Total Impairment Scores The best fitting model for overall impairment was one that included only a random intercept ( χ² [1] = 59.65, p < .001). The MLM examining changes in total impairment scores over 8 weeks revealed a significant main effect for time such that impairment decreased over the course of the study ( B = -0.76, 95% CI = -0.94 to -0.57; p < .001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 33.53% reduction in impairment (Cohen’s d = 0.43; Fig. 3). Usability Because only 62 parents completed the questionnaires administered at Week 8, responses from only these parents were available for the usability survey (Fig. 4). Overall, parent endorsements on the usability survey indicated the overwhelming majority of parents found the applications easy to use and liked their look and feel (88.7%). Further, nearly all parents enjoyed using them (93.5%) and would recommend them to a friend (90.4%). Additionally, while over two-thirds (67.7%) of parents felt the application kept them accountable, only 37.1% indicated they had no difficulty remembering to use the application. Further, most parents reported that their child enjoyed using the application (85.5%) and would recommend it to a friend of theirs (80.6%). Finally, nearly three-quarters (72.6%) of parents indicated they felt the application reduced arguments between them and their child and nearly 60% felt less stressed after using the application. Discussion Well-documented increases in behavior problems in youth (Danielsen et al., 2024) and increasing parent stress over the last decade have resulted in numerous calls to action to improve access to services (OSG, 2021; 2024). This has resulted in substantial interest in developing both novel therapeutic approaches (Kollins et al., 2020 ; Kofler et al., 2018 ; 2020 ) as well as innovating on extant evidence-based interventions by converting traditional approaches to a digital format (Owens et al., 2022) affording increased scalability and adherence. Despite this, however, adoption remains low (Owens et al., 2022) and substantial skepticism related to some of these interventions (Evans et al., 2021 ) persists, undermining the potential for these approaches to address unmet needs. The current study evaluated a novel approach to improving behavior problems and executive functioning in youth. Specifically, the digital approach used in the current study is adapted from existing analogue interventions that have been shown to work effectively (Barkley, 2020 ; Coelho et al., 2015 ; Reitman et al., 2001 ; van Langen et al., 2021 ). Further, the digital applications used in this study were developed as a pair of applications that are intended to engage multiple stakeholders invested in children’s success (i.e., parents and children) rather than a single stakeholder as has been attempted previously with more novel digital approaches. This innovative eight-week study was intended to examine improvements in behavior problems and executive functioning after using [MASKED FOR REVIEW] as well as characterize parent perceptions of the usability of these applications. Consistent with expectations and past work leveraging similar digital interventions (e.g., DRC-Online; Owens et al., 2022), overall behavior problems improved nearly 30% with 82% of participants demonstrating some level of improvement. Specifically, nearly a 3-point decrease ( B = -2.95) every two weeks was noted resulting in a moderate magnitude improvement in these symptoms. Examination of specific subscales of the DBD-Modified (Pelham et al., 1992 ) indicated that these improvements were largely consistent across problems with inattention (27.74% improvement), hyperactive, impulsive (32.52%), and oppositional and defiant (25.9%) behaviors. This indicates that following eight weeks of using [MASKED FOR REVIEW], parents perceived their children as exhibiting reductions in behaviors such as arguing with adults, being easily distracted, fidgeting, blaming others, and refusing to comply among others. Importantly, while the study did not specifically recruit for children elevated in behavior problems, average scores at baseline indicated that the sample had substantially elevated DBD scores that averaged nearly twice those observed in typically developing children (Fosco et al., 2013). This is unsurprising given that only a quarter of the sample was described by their parents as having no diagnosis. Collectively, this evidence indicates that among children with elevated symptoms of disruptive behavior problems, parents using these applications for eight weeks perceive significant, small to moderate magnitude improvement across these behaviors. A similar pattern was observed for executive functioning such that working memory (12.11%) and inhibition (12.96%) both improved following eight weeks of use. The magnitude of these changes (Cohen’s d = 0.42 to 0.49 ) was somewhat smaller than what was observed for overt behavior problems. Like baseline scores on the DBD-Modified, baseline scores on the CHEXI in the current sample were substantially higher relative to typically developing children and approximated those in a sample of children diagnosed with ADHD (Catale et al., 2015 ) consistent with the demographic nature of the current sample. Overall, these results indicate that parents reported substantial improvements in areas such as remembering to complete tasks, motivation, follow through, and inhibiting themselves in familial or social situations. This is unsurprising given a variety of features of the applications are designed to facilitate the development of better real-world executive functioning (e.g., practicing skills repeatedly) or afford compensatory strategies to circumvent underlying executive dysfunction (e.g., visual cues on the task board). With respect to impairment, parents were asked to provide information regarding how much their child’s problems have interfered across a variety of domains including home, socially, academically, and with daily expectations. Similar to behavior problems and executive functioning, a moderate magnitude improvement (Cohen’s d = 0.43) was observed for impairment scores over the course of the study. This indicates that parents reported not only improvements in traditional neurocognitive and behavioral challenges associated with disruptive behavior but also perceived their children as functioning better after using. Collectively, while parents reported improvements in behavioral and executive functioning as well as impairment over the course of the study, digital applications like this are only particularly useful if parents and children also perceive them as usable. To this end, a final aim of the current study was to evaluate multiple aspects of usability including ease of use, preference for the look and the feel of the application, and likelihood that the application would be recommended by parents. Further, we also asked additional questions regarding the parent’s perceptions of how the applications impacted traditional concerns reported by parents of children with disruptive behavior problems such as stress and arguments with their child. Notably, the majority of parents agreed the applications had a nice look and feel, were easy to use, and were enjoyable to use. Additionally, two-thirds of parents thought the applications helped them stay accountable and an even greater proportion (nearly three-fourths) felt it helped their child stay accountable. This is encouraging in light of the fact that only 37.1% of parents indicated they had no difficulty remembering to use them. This highlights the need to refine features that will help parents and children remember to use the application as this is likely to provide an even greater benefit with respect to accountability as well as improved outcomes. Importantly, 60% of parents felt less stressed after using the applications and 72.6% agreed they reduced arguments between them and their child. Given the importance of the parent-child relationship to implementation of optimal behavioral principles for improving problematic behavior, tools with the potential to reduce stress and arguments in families are likely to contribute meaningfully to the delivery of extant evidence-based interventions such as behavior therapy. Future work incorporating parents who use digital applications such as these in conjunction with treatment providers would shed further light on the extent to which this is a useful tool for improving family dynamics. Further understanding the impact of combining application such as these with other novel digital neurocognitive interventions would significantly advance this literature. Despite our inclusion of a moderately sized sample ( n = 90), relatively high retention across all time points (53.33%), and the use of robust analytic techniques to evaluate changes over time (i.e., MLM), this study was not a randomized controlled trial. As a result, the extent to which the observed improvements in behavior, executive functioning, and impairment are directly attributable to these applications cannot be estimated. Additional limitations included that parents were given minimal directions on how to use the applications so it is unknown whether additional instruction would have enhanced any impact on these outcomes. Though it is expected that using a digital application such as this with a trained professional and/or greater guidance is likely to enhance rather than attenuate the changes observed in the current study. Further, examination of dose effects among those using these applications would significantly advance this literature and provide a better understanding of who is most likely to benefit from this approach. Baseline scores across measures as well as parent-reported diagnostic information indicated that this sample was largely composed of children diagnosed with ADHD and a little over one-third of the sample was taking medication or receiving some form of therapy. Importantly, concomitant treatment was not a significant covariate across the analyses with the exception of one; however, this did not alter the overall pattern of results. This suggests that these applications may confer benefit even in the presence of ongoing treatment. Given that these applications may be useful for any family struggling with routines or behavioral challenges, it would be beneficial to better understand their relative impact in children with and without behavioral disorders. Along similar lines, given that this sample was predominantly White (84.4%) and 62% of parents reported family incomes in excess of $ 75,000, it would be important to better understand the potential impact in more representative samples. This study is the first to evaluate a pair of digital applications for improving behavior problems and executive functions that is designed to engage both parents and children. The current study provides proof-of-concept regarding the potential impact of [MASKED FOR REVIEW] on behavior problems, executive functions, and impairment. Given the high level of need that currently exists to support children and families dealing with behavior problems, innovative solutions like this are critically important. Improving our understanding of how [MASKED FOR REVIEW] works alone or in combination with other interventions will provide additional tools for individuals and providers to leverage and have implications beyond just the home. For example, applications like this may be useful for children struggling academically or when learning any new skill or routine. Ongoing work in this area will facilitate a better understanding of how parents and children engage with digital interventions as well as ultimately lead to iteration and improvement of existing feature sets. The development of scalable tools such as these have the potential to substantially improve child outcomes and help families thrive as well as deliver greater access to families who cannot otherwise attain traditional means of support. Declarations Competing Interests J.R., I.E., K.B., and B.B. are all full time employees of Joon App and own vested stock options in the company. R.H. was an independent statistical consultant with no affiliation with Joon App, Inc. and Assistant Professor who validated all data analyses and interpretations of results and has no conflicts of interest to report consistent with requirements from the commercial IRB who approved this study. Author Contribution All authors reviewed the manuscript. J.R., I.E., K.B., and B.B. conceptualized the study, designed the gamified reward system, and facilitated recruitment and data collection. J.R. was responsible for running all data analysis and writing the manuscript. R.H. served as an independent statistical consultant, verified the accuracy of the analyses, and provided feedback on the manuscript. Data Availability The data that support the findings of this study are available upon request. References American Psychological Association (APA). (2022). 2021 Survey of Health Service Psychologists: Technical report . https://www.apa.org/workforce/ publications/health-service-psychologists-survey/full-technical-report.pdf Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin, 121(1) , 65-94. Barkley, R. A. (2020). Taking charge of ADHD: The complete, authoritative guide for parents. Guilford Publications. Catale, C., Meulemans, T., & Thorell, L.B. (2015). The Childhood Executive Function Inventory: Confirmatory Factor Analyses and Cross-Cultural Clinical Validity in a Sample of 8- to 11-Year Old Children. Journal of Attention Disorders, 19 (6), 489-495. Coelho, L. F., Barbosa, D. L. F., Rizzutti, S., Muszkat, M., Bueno, O. F. A., & Miranda, M. C. (2015). Use of cognitive behavioral therapy and token economy to alleviate dysfunctional behavior in children with attention-deficit hyperactivity disorder. Frontiers in psychiatry, 6 , 167. Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45 , 1304-1312. Craske, M. G., Meuret, A. E., Echiverri-Cohen, A., Rosenfield, D., & Ritz, T. (2023). Positive affect treatment targets reward sensitivity: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 91 (6), 350-366. Danielson, M. L., Claussen, A. H., Bitsko, R. H., Katz, S. M., Newsome, K., Blumberg, S. J., ... & Ghandour, R. (2024). ADHD prevalence among US children and adolescents in 2022: diagnosis, severity, co-occurring disorders, and treatment. Journal of Clinical Child & Adolescent Psychology, 53 (3), 343-360. Danielson, M. L., Holbrook, J. R., Bitsko, R. H., Newsome, K., Charania, S. N., McCord, R. F., ... & Blumberg, S. J. (2022). State-level estimates of the prevalence of parent-reported ADHD diagnosis and treatment among US children and adolescents, 2016 to 2019. Journal of Attention Disorders, 26 (13), 1685-1697. Doulou, A., Pergantis, P., Drigas, A., & Skianis, C. (2025). Managing ADHD Symptoms in Children Through the Use of Various Technology-Driven Serious Games: A Systematic Review. Multimodal Technologies and Interaction, 9(1) , 8. Evans, S. W., Beauchaine, T. P., Chronis-Tuscano, A., Becker, S. P., Chacko, A., Gallagher, R., ... & Youngstrom, E. A. (2021). The efficacy of cognitive videogame training for ADHD and what FDA clearance means for clinicians. Evidence-Based Practice in Child and Adolescent Mental Health, 6(1) , 116-130. Fabiano, G. A., Vujnovic, R. K., Pelham, W. E., Waschbusch, D. A., Massetti, G. M., Pariseau, M. E., et al. (2010). Enhancing the effectiveness of special education programming for children with Attention Deficit Hyperactivity Disorder using a daily report card. School Psychology Review. 39 , 219–239. Feingold, A. (2018). Meta-Analysis with Standardized Effect Sizes from Multilevel and Latent Growth Models. Journal of Consulting and Clinical Psychology, 85 , 262-266. Fosco, W. D., Babinski, D. E., & Waschbusch, D. A. (2023). The Disruptive Behavior Disorders Rating Scale: Updated factor structure, measurement invariance, and national caregiver norms. Journal of Pediatric Psychology, 48 (5), 468-478. Kofler, M. J., Sarver, D. E., Austin, K. E., Schaefer, H. S., Holland, E., Aduen, P. A., ... & Lonigan, C. J. (2018). Can working memory training work for ADHD? Development of central executive training and comparison with behavioral parent training. Journal of Consulting and Clinical Psychology , 86 (12), 964-979. Kofler, M. J., Wells, E. L., Singh, L. J., Soto, E. F., Irwin, L. N., Groves, N. B., ... & Lonigan, C. J. (2020). A randomized controlled trial of central executive training (CET) versus inhibitory control training (ICT) for ADHD. J ournal of Consulting and Clinical Psychology, 88 (8), 738-756. Kollins, S. H., DeLoss, D. J., Cañadas, E., Lutz, J., Findling, R. L., Keefe, R. S., ... & Faraone, S. V. (2020). A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. The Lancet Digital Health , 2(4), e168-e178. Landis, T. D., Hart, K. C., & Graziano, P. A. (2019). Targeting self-regulation and academic functioning among preschoolers with behavior problems: Are there incremental benefits to including cognitive training as part of a classroom curriculum?. Child Neuropsychology, 25 (5), 688-704. Leopold, D. R., Christopher, M. E., Olson, R. K., Petrill, S. A., & Willcutt, E. G. (2019). Invariance of ADHD symptoms across sex and age: A latent analysis of ADHD and impairment ratings from early childhood into adolescence. Journal of Abnormal Child Psychology, 47 , 21-34. Office of the Surgeon General. "Protecting youth mental health: The US surgeon general’s advisory [Internet]." (2021). Office of the Surgeon General, U. S. (2024). Parents Under Pressure: The US Surgeon General's Advisory on the Mental Health & Well-Being of Parents. Oh, S., Choi, J., Han, D. H., & Kim, E. (2024). Effects of game-based digital therapeutics on attention deficit hyperactivity disorder in children and adolescents as assessed by parents or teachers: a systematic review and meta-analysis. European Child & Adolescent Psychiatry, 33 (2), 481-493. Pelham, W.E., Gnagy, E.M., Greenslade, K.E., & Milich, R. (1992). Teacher ratings of DSM-III-R symptoms for the disruptive behavior disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 31 (2), 210-218. Pyle, K., and Fabiano, G. A. (2017). Daily report card intervention and attention deficit hyperactivity disorder: a meta-analysis of single-case studies. Exceptional Children, 83 , 378–395. R Core Team. (2020). R: A language and environment for statistical computing . R Foundation for Statistical Computing. Reitman, D., Hupp, S. D., O’Callaghan, P. M., Gulley, V., & Northup, J. (2001). The influence of a token economy and methylphenidate on attentive and disruptive behavior during sports with ADHD-diagnosed children. Behavior Modification, 25 (2), 305-323. Rodrigo-Yanguas, M., González-Tardón, C., Bella-Fernández, M., & Blasco-Fontecilla, H. (2022). Serious video games: angels or demons in patients with attention-deficit hyperactivity disorder? A quasi-systematic review. Frontiers in Psychiatry, 13 , 798480. van Langen, M. J., van Hulst, B. M., Douma, M., Steffers, M., van de Wiel, N. M., van den Ban, E., ... & de Zeeuw, P. (2021). Which child will benefit from a behavioral intervention for ADHD? A pilot study to predict intervention efficacy from individual reward sensitivity. Journal of Attention Disorders, 25 (12), 1754-1764. Westwood, S. J., Parlatini, V., Rubia, K., Cortese, S., & Sonuga-Barke, E. J. (2023). Computerized cognitive training in attention-deficit/hyperactivity disorder (ADHD): a meta-analysis of randomized controlled trials with blinded and objective outcomes. Molecular Psychiatry, 28 (4), 1402-1414. Wolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., ... & Zurhellen, W. (2019). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 144(4) , 2019-2528. Additional Declarations Competing interest reported. J.R., I.E., K.B., and B.B. are all full time employees of Joon App and own vested stock options in the company. R.H. was an independent statistical consultant with no affiliation with Joon App, Inc. and Assistant Professor who validated all data analyses and interpretations of results and has no conflicts of interest to report consistent with requirements from the commercial IRB who approved this study. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6316490","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445884245,"identity":"05781640-d802-41e2-a38b-54c0caed1942","order_by":0,"name":"Joseph Raiker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxgYwJWHAwMB8wAAmeoBILWwJxGmBAaBqHgPCykCAub35AHNFjYUxv/SZDwUfKu7Im0s3MB74gM9hPccSGM8ckzCT7MvdYDjjzDPDnXMOMBycgU/LjBwDxgY2CRuDM7wbjHnbDjNuuJHAcJgHr5b8D4wN/yRs7M/wPABpsSdCSw4DY2ObhJkBDw8DSEsiYS09xwwONvZJGEucYTMA+uVw8s45Bxvw+sWwvfnhw4ZvdYb9PczPDD5UHLbdLt18+AO+EDNsQEQcGzhiDCSg0YsLyCOxmR9AtODVMApGwSgYBSMQAADp91FGu901mgAAAABJRU5ErkJggg==","orcid":"","institution":"Joon","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Raiker","suffix":""},{"id":445884247,"identity":"36f1618c-a1d7-4386-9cec-b1826e0e0d7f","order_by":1,"name":"Kevin Bunarjo","email":"","orcid":"","institution":"Joon","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Bunarjo","suffix":""},{"id":445884249,"identity":"a3b3fc9c-ef7b-4303-aa62-77c835c145b3","order_by":2,"name":"Isaac Eaves","email":"","orcid":"","institution":"Joon","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Eaves","suffix":""},{"id":445884250,"identity":"4a45a39e-1e05-4817-8663-e95278e9d022","order_by":3,"name":"Brad Brenner","email":"","orcid":"","institution":"Joon","correspondingAuthor":false,"prefix":"","firstName":"Brad","middleName":"","lastName":"Brenner","suffix":""},{"id":445884251,"identity":"f76bf196-8774-4a6c-a218-e995db56f306","order_by":4,"name":"Robert Henry","email":"","orcid":"","institution":"Hope College","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Henry","suffix":""}],"badges":[],"createdAt":"2025-03-27 03:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6316490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6316490/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82133643,"identity":"de672faf-3ac1-4835-a3cf-02b04cbf7d3f","added_by":"auto","created_at":"2025-05-07 06:00:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157141,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6316490/v1/2d3623a783f34bd9c9bc0dd2.png"},{"id":82132464,"identity":"fe032fa7-672e-41b5-bf83-c1f0dfe6a4cd","added_by":"auto","created_at":"2025-05-07 05:43:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156767,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6316490/v1/325e0c74d6b28493dc2ff744.png"},{"id":82132466,"identity":"8531cfb0-3d71-4102-b051-5013c9c5e1e3","added_by":"auto","created_at":"2025-05-07 05:43:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141483,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6316490/v1/8fc25b6cdbd5757072b1b7d2.png"},{"id":82132467,"identity":"915240e6-6f9d-45ed-aeaf-6d368a0cbc2f","added_by":"auto","created_at":"2025-05-07 05:43:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":408416,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6316490/v1/2589b14e9e6564fc5d474491.png"},{"id":82133980,"identity":"111c9c14-8e3e-4368-b9d8-ccd5a5e348f1","added_by":"auto","created_at":"2025-05-07 06:01:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1773901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6316490/v1/d5700b5b-211b-4b65-8b1a-c702ecbc4ad3.pdf"}],"financialInterests":"Competing interest reported. J.R., I.E., K.B., and B.B. are all full time employees of Joon App and own vested stock options in the company. R.H. was an independent statistical consultant with no affiliation with Joon App, Inc. and Assistant Professor who validated all data analyses and interpretations of results and has no conflicts of interest to report consistent with requirements from the commercial IRB who approved this study.","formattedTitle":"Executive Function and Behavior Improvement: An 8-Week Observational Study of a Gamified Reward System for Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBehavioral challenges and executive functioning deficits in children have been investigated for decades given their centrality to disorders such as ADHD and ODD (Barkley, 1997; for a review, see Wilens \u0026amp; Spencer, 2013). To date, only two evidence-based treatments for these disorders have been found to be both efficacious and effective and they include stimulant medication and behavior therapy (Wolraich et al., 2019). Despite a robust evidence base for their use, however, an average of 28.3% of parents of children with ADHD report not using stimulant medications in the last 12 months and less than two-thirds (Median = 61.8%) report receiving some form of behavioral therapy in the last 12 months highlighting significant unmet treatment need (Danielson et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe discrepancy between those in need of services for behavioral challenges and the availability of such services has only intensified over the last several years. For example, the number of children diagnosed with ADHD has risen substantially over the last 6 years with more than 1 million more children receiving a diagnosis in 2022 relative to 2016 (Danielson et al., 2024). Further, workforce surveys such as those distributed by APA reveal extensive wait lists among providers willing to provide such services (APA, 2022). The need for innovative solutions to address growing mental health challenges has never been greater as evidenced by the US surgeon general’s recent proclamation of a youth mental health crisis (OSG, 2021) as well as an even more recent advisory related to the mental health and well-being of parents (OSG, 2024). Notably, this problem extends even beyond those with a diagnosis such as ADHD and ODD and includes even youth without formal diagnoses (e.g., at-risk, typically developing) that might benefit from implementation of evidence-based behavioral strategies before problems begin to emerge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe continued rise - particularly among youth – in mental health challenges has corresponded with increased interest in leveraging emerging technologies to address them. For example, for children with behavior problems, a number of interventions that rely on digital technology and/or serious games have been developed in recent years in hopes of expanding the availability of services as well as addressing underlying hypothesized core deficits such as executive functioning (Doulou et al., 2025; Oh, Choi, Han, \u0026amp; Kim, 2023; Rodrigo-Yanguas, Gonzalez-Tardon, Bella-Fernandez, \u0026amp; Blasco-Fontecilla, 2022; Westwood et al., 2023). For example, EndeavorRX, was the first FDA-cleared prescription video game geared towards families of children with ADHD (Kollins et al., 2020). Similarly, Central Executive Training (CET) was developed shortly thereafter and has demonstrated promise in improving underlying executive functioning deficits as well as behavioral symptoms of ADHD (Kofler et al., 2018; Kofler et al., 2020). Despite this, however, these approaches have garnered substantial skepticism (Evans et al., 2021) or have not been widely adopted by treatment providers to date.\u003c/p\u003e\n\u003cp\u003eDifficulties gaining widespread adoption among providers likely reflects a variety of challenges including lack of reimbursement pathways, knowledge around new and emerging approaches for intervening, as well as overall skepticism related to the adoption of alternate or more novel approaches to the treatment of these disorders. Importantly, many of the technologies described above were developed primarily around hypothesized upstream neurocognitive deficits presumed to underlie behavioral symptoms of disorders such as ADHD and thus relied on entirely different mechanisms of action than those traditionally hypothesized to be responsible for improvements observed with currently available evidence-based treatments such as behavior therapy. For example, the prevailing clinical treatment approaches of stimulant medication and behavioral management training are hypothesized to improve symptoms via neurochemical changes or implementation of operant conditioning procedures such as reward and punishment, respectively. As a result, providers may feel more comfortable recommending or applying traditional approaches rather than transitioning to adopting entirely novel treatments hypothesized to improve other suspected core deficits (e.g., neurocognitive dysfunction) with which they may be less familiar.\u003c/p\u003e\n\u003cp\u003eGiven these challenges, others have attempted instead to develop digital analogues of already established evidence-based interventions for disorders such as ADHD and ODD that may be more familiar to providers and result in increased adoption. For example, Fabiano and Pelham’s well-known daily report card (DRC) intervention was recently integrated into an online portal known as DRCO and evaluated amongst teachers (Owens, Lee, Eackles, Medina, Evans, \u0026amp; Reid, 2022). \u0026nbsp;Briefly, the DRC is a well-established intervention in which specific goals are set for a child (e.g., complete 80% of homework; interrupt fewer than 3 times each lesson). At the end of each day, the proportion of goals achieved are reviewed with the child and this information is provided to the parent or guardian. For successful goal achievement, children earn short- (e.g., selecting a toy from a box) and long-term (e.g., getting to participate in a classroom-wide pizza party) rewards. Expectations regarding successful goal achievement are dynamically adjusted following review of data related to how the child is progressing and goals are revised as the child gains success. This is a well-documented intervention for improving behavior (Fabiano et al., 2010; Pyle \u0026amp; Fabiano, 2017) and has been used in both home and school settings. Owens and colleagues (2019; 2022) recently developed and evaluated an online version of this intervention for use in the school setting. Results revealed that while the majority of teachers (55.56%) were willing to adopt an online version of this intervention, only 20% used it for longer than two months. Notably, significant, large-magnitude (Within-subject effect size = 0.65 to 0.86) improvements were noted across multiple domains including behavior problems and social functioning despite only approximately eight weeks of use (Owens et al., 2019). This study highlights the importance of adapting existing interventions for digital use in schools and points to a significant need for similar studies to be conducted for tools that may be equally, if not even more, useful to parents (e.g., reward charts).\u003c/p\u003e\n\u003cp\u003eWhile emerging technology has substantial potential to fill the gap in delivering services to those most in need, it is important to note that currently most digital interventions (including those reviewed above) are intended to be used on a digital device by only a single stakeholder such as a child playing a videogame individually or a teacher inputting data on a mobile device. Critically, however, behavioral interventions for disorders such as ADHD and ODD rely on not only behavioral changes exhibited by the child (e.g., being more compliant, being more organized) but depend also on changes implemented by others within the child’s immediate environment (e.g., parents or teachers consistently delivering contingencies to increase or decrease behaviors of interest). As a result, digital interventions designed to engage multiple stakeholders (e.g., both parents and children) are likely to confer greater benefit by increasing accountability for everyone involved in a child’s success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn an effort to fill these gaps, commercially available digital applications, [MASKED FOR REVIEW], that reflect analogues of components of existing evidence-based interventions and are intended to be used by \u003cem\u003eboth\u003c/em\u003e parents and children have been developed. These enhancements are expected to ultimately facilitate improvements in children’s behavior and executive functioning. Specifically, [MASKED FOR REVIEW] leverages existing operant conditioning principles (i.e., response-reward contingencies), automation (e.g., notifying parents when tasks are completed), and gamification of the child application with the goal of improving the consistency and efficacy of the delivery of a common behavioral treatment component referred to as contingency management (i.e., reward charts). The parent application allows parents to assign tasks or expectations to children using a task board. Tasks can include any behavioral expectations the parent has for their children and include things such as completing homework, certain household chores, behaving appropriately with peers and siblings, and any other relevant behaviors parents are interested in improving. Each task can be assigned to be completed a single time, daily, weekly, or monthly depending on expectations. Further, each task is assigned a corresponding coin value by the parent. The parent is able to view which tasks the child has completed (while children can self report on this as well) and approve them as they are completed at which point the child earns the corresponding number of coins to use in a companion videogame. The connected child application is the videogame in which children raise a virtual pet the child names, feeds, pets, and allows to explore various elements throughout the game. Coins earned for task completion are used throughout the videogame to redeem desired items such as clothing articles for the child’s pet. The child can view what tasks have been assigned by their parent in the videogame by accessing the task board. Ultimately, the applications are designed such that children’s access to the video game depends primarily upon their success in the ‘real-world.’\u003c/p\u003e\n\u003cp\u003e[MASKED FOR REVIEW] use of a token economy system reflects similar real life response-reward contingencies parents are often encouraged to develop and implement as part of existing evidence-based parenting programs (e.g., sticker charts, reward charts). It is well-established that these approaches result in improvements in behavioral functioning (Barkley, 2020; Coelho et al., 2015; \u0026nbsp;Reitman et al., 2001; van Langen et al., 2021). Additionally, emerging evidence among preschoolers indicates that embedding these types of contingency management programs into more intensive treatment options may also improve executive functioning (Landis, Hart, \u0026amp; Graziano, 2018). Aside from understanding whether use of [MASKED FOR REVIEW] is associated with changes in behavioral and executive functioning, it is also critical to understand parent perceptions and preferences of the applications themselves as negative perceptions regarding the applications are unlikely to result in utilization undermining the potential effectiveness and scalability of these applications.\u003c/p\u003e\n\u003cp\u003eThe current study is the first to examine changes in parent perceptions of their children’s behavioral and executive functioning over eight weeks of use digital applications designed to be used by both parents and children. It is expected that parents will report significant improvements in both behavioral and executive functioning in their children after eight weeks of use. Further, it is expected that reductions in impairment will be reported following eight weeks of use. Finally, we anticipate that users will find [MASKED FOR REVIEW] acceptable, will enjoy using the applications, and perceive [MASKED FOR REVIEW] as helping across a variety of areas (e.g., stress, accountability) relevant to the implementation of behavioral strategies.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study was approved by the Biomedical Research Alliance of New York (BRANY) Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 90 participants. Participants were parents, 18 years of age or older, of children between the ages of 6 and 12 who created an account, signed up for a paid subscription to use the iPhone app, and verified a task at least once within 48 hours (i.e., used the application). Details regarding the demographic composition of this sample is included in Tables 1 and 2 below.\u003c/p\u003e\n\u003cp\u003eOverall, the sample was composed primarily of parents of male children (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 49, 54.40%) and children were approximately 8.92 (\u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 1.76) years, on average. Nearly three-quarters of the children had been previously diagnosed with at least one mental health disorder (\u003cem\u003en\u003c/em\u003e = 67, 74.4%) with the most common being ADHD (\u003cem\u003en\u003c/em\u003e = 17, 18.9%). It is important to note that many of the children with multiple diagnoses also had ADHD. More than one-third of participants were receiving treatment in the form of either medication (\u003cem\u003en\u003c/em\u003e = 35, 38.90%) or therapy (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 34, 37.8%).\u003c/p\u003e\n\u003cp\u003eParents enrolled in the study were primarily White (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 76, 84.4%) and female (\u003cem\u003en\u003c/em\u003e = 85, 94.40%) with an average age of 37.19 (\u003cem\u003eSD\u003c/em\u003e = 6.31). Approximately 7.78% (\u003cem\u003en\u003c/em\u003e = 7) of the parents were Hispanic/Latino. Additionally, most were currently married (\u003cem\u003en\u003c/em\u003e = 66, 73.3%). The majority of parents had attained a Bachelor\u0026rsquo;s Degree or higher (\u003cem\u003en\u003c/em\u003e = 48, 53.32%) and were employed full-time (\u003cem\u003en\u003c/em\u003e = 46, 51.1%) with nearly two-thirds reporting an annual income of $75,000 or higher (\u003cem\u003en\u003c/em\u003e = 56, 62.22%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProcedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParents who downloaded the commercially available app, signed up for a free trial of the application (or paid for the annual subscription), and verified at least one task within the first 48 hours of beginning their subscription were invited to participate in the study. Specifically, they were shown a brief modal outlining the study requirements and associated study compensation. After indicating that they were interested in participating, parents were re-directed to Qualtrics to complete the informed consent and corresponding study measures to determine eligibility. Inclusion criteria included that the parent and child were English-speaking, new users of the application, the parent was 18 years of age or older, the family resided in the US and possessed an iPhone, and that the child was between the ages of 6 and 12. Additionally, if a\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable 1. Child Demographic Characteristics (n = 90)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%) or M (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e8.92 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e40 (44.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e49 (54.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot Reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDepression Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAnxiety Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e6 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eADHD Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e17 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eASD Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eLD Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOther diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e7 (7.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e23 (25.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMultiple diagnoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e32 (35.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eTaking Medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e35 (38.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eReceiving Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e34 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable 2. Parent Demographic Characteristics (n = 90)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%) or M (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e37.19 (6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e85 (94.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e4 (4.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot Reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e6 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e76 (84.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMultiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e4 (4.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNone of the above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e7 (7.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest Level of Education Completed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHigh School Diploma or Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e10 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e31 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e22 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e24 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable 2 (continued). Parent Demographic Characteristics (n = 90)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%) or M (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e16 (17.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e46 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e10 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e6 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e9 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e1 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e5 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e13 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e66 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income (annually)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026lt; $20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e2 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e$20,000 - $34,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e5 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e$35,000 - $49,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e9 (10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e$50,000 - $74,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e13 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e$75,000 - $99,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e18 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e$100,000 - $149,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e20 (22.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026gt; $150,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e18 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e5 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eparent signed up to use the application with more than one child, only one child was eligible to participate in this study.\u003c/p\u003e\n\u003cp\u003eTo facilitate retention, participants were offered a free subscription to the applications for one year for participating in the study. Additionally, participants were offered a $10.00 gift card for each time point at which they completed the questionnaires for a possible total of $60.00 if questionnaires for all time points were completed by a participant. Participants received half of their earned compensation following the mid-point of the study and the final half of their earned compensation following the final time point of the study. Finally, reminders were provided to participants who did not complete the questionnaires. They were given a 3 day window in which to complete these questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIntervention\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll parents who agreed to participate in the study used [MASKED FOR REVIEW], an integrated platform for families that consists of two primary applications. One application is for children and is called the [MASKED FOR REVIEW] and the other application is for parents and is called [MASKED FOR REVIEW]. In the [MASKED FOR REVIEW], children access a video game where they advance through the game using coins they have earned by completing real-life behaviors that are managed and monitored by parents using the parent companion application.\u003c/p\u003e\n\u003cp\u003eThe companion applications were designed based on extant evidence-based principles (e.g., operant conditioning) such as reward-response contingencies that have been shown to be beneficial for improving behavior (Fabiano et al., 2010; Pyle et al., 2017; Reitman et al., 2001). Positive reinforcement, in the form of coins, is rewarded to children after their parent has reviewed what tasks they have completed and approved that these were indeed completed, as desired. Children see these coins in the application interface and can redeem the coins for various items throughout the game (e.g., food for their virtual pet, outfits, etc.). Additionally, parents can create custom real-life rewards (e.g., an allowance, going to bed later, getting to pick a family movie) that can also be redeemed for these virtual coins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen parents assign tasks they can select from pre-determined templates of other tasks parents have assigned to children or create completely new tasks based on the needs of them and their family. Parents can select how many coins are rewarded for each task they assign to their child as well as customize other factors related to the task (e.g., what days it needs to be completed). Children can view these tasks and how many coins they will earn in the video game by clicking on the icon corresponding to the task list. Parents who elected to participate in the study were instructed to use the applications daily for eight weeks. No other instructions regarding how to use the applications were provided to participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe following outcomes were collected at baseline and every two weeks until the end of the eight week study period. This frequency of data collection was selected to ensure sufficient data was collected. To account for missing data, multilevel models (MLMs) were utilized with maximum-likelihood (ML) estimation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eBehavior Problems\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDisruptive Behavior Disorders (DBD) Rating Scale - Modified (Pelham et al., 1992):\u003c/em\u003e The DBD - Modified is a 30-item questionnaire completed by the parent regarding their child that assesses severity of symptoms related to inattention (9 items), hyperactivity/impulsivity (9 items), and oppositional behavior (8 items). Items related to conduct disorder were removed as they were not relevant to this study. Parents were asked to rate each item with respect to how well it described their child on a Likert scale from 0 to 3 (0: not at all; 1: just a little; 2: pretty much; 3: very much). Items were summed across the total scale as well as each subscale (i.e., ADHD-I, ADHD-HI, and ODD) and changes over time across each of these sum scores were evaluated. Higher scores indicated greater levels of inattention, hyperactivity/impulsivity, and oppositional behavior. Internal consistency across all five time points in this sample was excellent and ranged from Cronbach\u0026rsquo;s \u0026alpha; of 0.93 to 0.95.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExecutive Functioning\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChildhood Executive Functioning Inventory (CHEXI; Catale, Meulemans, \u0026amp; Thorell, 2015):\u0026nbsp;\u003c/em\u003eThe CHEXI is a 24-item questionnaire completed by the parent regarding their child that assesses executive functioning (e.g., working memory, inhibition, planning, and regulation). Parents were asked to rate each item with respect to how well it described their child on a Likert scale from 1 to 5 (1: definitely not true; 2: not true; 3: partially true; 4: true; 5: definitely true). Items were summed across the total scale as well as each subscale (i.e., Working Memory, Inhibition; Planning; Regulation) to evaluate changes over time across each of these sum scores. Notably, consistent with the optimal factor structure identified by Catale and colleagues (2015) and given limitations associated with the current sample size, scores across these four subscales were further reduced to reflect either a Working Memory or Inhibition total score. Items related to working memory and planning contributed to the Working Memory Total score whereas items related to inhibition and regulation contributed to the Inhibition Total score. Higher scores indicated greater levels of difficulties with executive functioning, working memory, and inhibition. Internal consistency across all five time points was excellent in this sample and ranged from Cronbach\u0026rsquo;s \u0026alpha; of 0.92 to 0.96.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eImpairment\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImpairment rating scale (IRS):\u003c/em\u003e The IRS is a 7-item questionnaire adapted from Leopold et al. (2019) and completed by the parent regarding their child that assesses impairment across recreational activities, daily responsibilities, educational activities, participation in community activities, interactions with adults (e.g., teachers), home life and family. Parents were asked to rate how much their child\u0026rsquo;s problems have interfered with these areas over the last two weeks on a Likert scale from 0 to 3 (0: not at all; 1: just a little; 2: quite a bit; 3: very much). Scores were totaled across the 7-item IRS to obtain an overall impairment total score. Higher scores indicated greater levels of impairment. \u0026nbsp;Internal consistency across all five time points in this sample was good and ranged from Cronbach\u0026rsquo;s \u0026alpha; of 0.79 to 0.89.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eUsability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUsability survey\u003c/em\u003e: A usability survey was developed for the purposes of this study and contained 12 items completed by the parent regarding their experience and their child\u0026rsquo;s experience with the applications. Parents were asked to rate each item with respect to how much they agreed with it on a Likert scale from 1 to 5 (1: do not agree at all; 2: do not agree; 3: neither agree nor disagree; 4: agree; 5: definitely agree). Proportions of agreement across each item are reported below.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis Plan\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor categorical variables (e.g., endorsements on the usability survey), data were summarized using percentages. For continuous measures, data were summarized with traditional descriptive statistics including mean and standard deviation. For all inferential statistical tests, an alpha of .05 was used and 95% confidence intervals were calculated. Two-tailed tests without correction for experiment-wise error were used.\u003c/p\u003e\n\u003cp\u003eFor each outcome variable, a repeated-measures multilevel model (MLM) was used to capitalize on data collected at each time point as well as overcome limitations associated with missing data. Thus, repeated observations (Level-1) were nested within individual parents (Level-2). MLMs were run in \u003cem\u003eR\u003c/em\u003e (R Core Team, 2020) using the\u003cem\u003e\u0026nbsp;lme4\u003c/em\u003e library. For each outcome variable, models included a random slope for time (baseline, 2 weeks, 4 weeks, 6 weeks, 8 weeks). Models were run with and without relevant Level-2 covariates (i.e., child biological sex, parent race/ethnicity, child age, and concomitant treatment with medications or therapy). The covariance across repeated measures was unstructured. If the pattern between the models with and without covariates was similar, simple model results with no covariates are reported with the mean and 95% confidence intervals to ease interpretation. If the models differ significantly, the same information is presented for the model with covariates with the exception of least-squares means being included instead of raw means.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo obtain an estimate of the magnitude of change from baseline to Week 8, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect sizes were computed using the \u003cem\u003eeffectsize\u003c/em\u003e library from \u003cem\u003eR\u003c/em\u003e. Due to technical limitations, these were estimated from the random intercept only model for each outcome. Specifically, estimated marginal means were obtained for each model and pairwise comparisons were conducted for each pair of timepoints to obtain \u003cem\u003et\u003c/em\u003e-ratios. The \u003cem\u003et\u003c/em\u003e-ratio for the comparison between Week 8 and baseline was then used to derive an estimate of the Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect size over time (Craske et al., 2023; Feingold, 2018). Effect size magnitude was interpreted consistent with existing guidelines (i.e., 0.2 or smaller = small, 0.5 = moderate, 0.8 or larger = large; Cohen, 1990).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMissingness\u003c/h2\u003e \u003cp\u003eData for each participant at each time point was evaluated for substantial portions of missingness. Overall, there was very little missing data across scales with most participants with missing data only missing between 1 and 3 items across all measures. In cases where the amount of missing data was 5% or greater, participants' individual item endorsements across the measures were examined to determine whether there was sufficient information reported by the participant to compute a total scale score using mean substitution while minimizing bias. If more than a third of the individual items for a given scale were missing, the scale was treated as though it had not been completed in subsequent analyses and relevant missing data approaches were applied to estimate the total scale score for that measure (e.g., see information on the use of multilevel linear modeling [MLM] below).\u003c/p\u003e \u003cp\u003eUltimately, this threshold resulted in two partially completed scales being dropped for a single participant at baseline, one partially completed scale being dropped for a single participant at the week 2 follow-up, and one partially completed scale being dropped for two participants at the week 8 follow-up. For all other participants, given the low number of missing items across each scale, missing item endorsements were estimated using mean substitution in order to allow for the calculation of a total score for each measure. Further, for participants with missing total scores for a measure, multilevel linear models (MLMs) using maximum likelihood (ML) were leveraged to obtain robust estimates of changes over time.\u003c/p\u003e \u003cp\u003eFollow-up data was obtained for 78 participants (87%) at the week 2 follow-up, 70 participants (78%) at the week 4 follow-up, 61 participants at the week 6 follow-up (68%), and 62 participants (69%) at the week 8 follow-up. The majority of participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, 53.33%) provided data at all five time points. Overall, 91% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82) of participants provided follow-up data for at least one time point and 8 were considered lost to follow-up and dropped from all subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eModels were tested iteratively beginning with a null model that was then compared to a random intercept only model and finally a random intercept and slope model. If the subsequent model did not significantly account for more variance in outcomes (using a standard likelihood ratio test of deviance statistics), the simpler model was retained. For example, if the model incorporating both random intercepts and slopes did not outperform the random intercept only model, the latter was retained for subsequent analyses and summary statistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBehavior Problems\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eTotal Disruptive Behavior Disorder (DBD) Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for total behavior problems included a random intercept and random slope with no covariates, (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;11.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004). The MLM examining changes in total behavior problems over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -2.95, 95% CI = -3.62 to -2.29; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 28.85% improvement in total behavior problems (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.57; Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eADHD - Inattentive Total Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for symptoms related to ADHD - Inattentive symptoms was one that included a random intercept and random slope with no covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;6.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048). The MLM examining changes in total inattentive scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -1.13, 95% CI = -1.40 to -0.86; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 27.74% improvement in symptoms of ADHD-I symptoms (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for Behavior Problems and Executive Functioning Over Time\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 Weeks\u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 Weeks\u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 Weeks\u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 Weeks\u003c/p\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Behavior Problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.96 (13.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003cp\u003e(12.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.05\u003c/p\u003e \u003cp\u003e(12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.10\u003c/p\u003e \u003cp\u003e(12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.14\u003c/p\u003e \u003cp\u003e(13.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADHD - I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003cp\u003e(5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.13\u003c/p\u003e \u003cp\u003e(5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.01\u003c/p\u003e \u003cp\u003e(5.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003cp\u003e(5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.75\u003c/p\u003e \u003cp\u003e(5.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADHD - HI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.99\u003c/p\u003e \u003cp\u003e(6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003cp\u003e(5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003cp\u003e(5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003cp\u003e(4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003cp\u003e(5.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eODD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003cp\u003e(5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.02\u003c/p\u003e \u003cp\u003e(4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003cp\u003e(4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.63\u003c/p\u003e \u003cp\u003e(4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003cp\u003e(4.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Executive Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.99\u003c/p\u003e \u003cp\u003e(15.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.34\u003c/p\u003e \u003cp\u003e(14.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.70\u003c/p\u003e \u003cp\u003e(14.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.05\u003c/p\u003e \u003cp\u003e(14.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.41\u003c/p\u003e \u003cp\u003e(16.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.19\u003c/p\u003e \u003cp\u003e(9.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.88\u003c/p\u003e \u003cp\u003e(9.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.58\u003c/p\u003e \u003cp\u003e(8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.27\u003c/p\u003e \u003cp\u003e(8.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.96\u003c/p\u003e \u003cp\u003e(9.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.81\u003c/p\u003e \u003cp\u003e(6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.46\u003c/p\u003e \u003cp\u003e(6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.10\u003c/p\u003e \u003cp\u003e(6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.75\u003c/p\u003e \u003cp\u003e(6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.39\u003c/p\u003e \u003cp\u003e(7.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003cp\u003e(4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003cp\u003e(3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003cp\u003e(3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003cp\u003e(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003cp\u003e(3.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e: ADHD - I\u0026thinsp;=\u0026thinsp;ADHD - Inattentive; ADHD - HI\u0026thinsp;=\u0026thinsp;ADHD - Hyperactive/Impulsive; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean; ODD\u0026thinsp;=\u0026thinsp;Oppositional Defiant Disorder; SD\u0026thinsp;=\u0026thinsp;Standard Deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eADHD - Hyperactive/Impulsive Total Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for symptoms related to ADHD - Hyperactive/Impulsive symptoms was one that included a random intercept and random slope with no covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;7.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02). The MLM examining changes in total hyperactive/impulsive symptoms over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -1.13, 95% CI = -1.40 to -0.87; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 32.52% improvement in ADHD-HI symptoms (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eODD Total Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for symptoms related to ODD symptoms was one that included a random intercept with no covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[1]\u0026thinsp;=\u0026thinsp;39.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The MLM examining changes in ODD symptoms over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -0.69, 95% CI = -0.90 to -0.48; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 25.9% improvement in ODD symptoms (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExecutive Functioning\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eTotal Childhood Executive Functioning Inventory (CHEXI) Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for total executive functioning problems was one that included a random intercept and random slope with no covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;12.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002). The MLM examining changes in total executive function problems over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -2.64, 95% CI = -3.32 to -1.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 12.44% improvement in total executive function problems (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50; Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eWorking Memory (WM) Total Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for total working memory problems was one that included a random intercept and random slope with no covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;8.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02). The MLM examining changes in total working memory problem scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -1.31, 95% CI = -1.72 to -0.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 12.11% improvement in working memory problems (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eInhibition Total Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for total inhibition problems was one that included a random intercept and random slope with covariates (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[2]\u0026thinsp;=\u0026thinsp;11.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003). Notably, the only significant covariate was whether or not the child was reported by their parent as currently receiving therapy. Unsurprisingly, these children scored substantially higher, on average, on the inhibition problems scale than children not receiving therapy (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This covariate did not change the overall pattern of results so the simple model with random intercept and random slope are reported. The MLM examining changes in total inhibition problem scores over 8 weeks revealed a significant main effect for time such that these problems decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -1.35, 95% CI = -1.68 to -1.02; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a moderate magnitude 12.96% improvement in inhibition problems (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImpairment\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eTotal Impairment Scores\u003c/h2\u003e \u003cp\u003eThe best fitting model for overall impairment was one that included only a random intercept (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e[1]\u0026thinsp;=\u0026thinsp;59.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The MLM examining changes in total impairment scores over 8 weeks revealed a significant main effect for time such that impairment decreased over the course of the study (\u003cem\u003eB\u003c/em\u003e = -0.76, 95% CI = -0.94 to -0.57; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, from baseline to Week 8, participants demonstrated a small to moderate magnitude 33.53% reduction in impairment (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43; Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eUsability\u003c/h2\u003e \u003cp\u003e Because only 62 parents completed the questionnaires administered at Week 8, responses from only these parents were available for the usability survey (Fig.\u0026nbsp;4). Overall, parent endorsements on the usability survey indicated the overwhelming majority of parents found the applications easy to use and liked their look and feel (88.7%). Further, nearly all parents enjoyed using them (93.5%) and would recommend them to a friend (90.4%).\u003c/p\u003e \u003cp\u003e Additionally, while over two-thirds (67.7%) of parents felt the application kept them accountable, only 37.1% indicated they had no difficulty remembering to use the application. Further, most parents reported that their child enjoyed using the application (85.5%) and would recommend it to a friend of theirs (80.6%). Finally, nearly three-quarters (72.6%) of parents indicated they felt the application reduced arguments between them and their child and nearly 60% felt less stressed after using the application.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWell-documented increases in behavior problems in youth (Danielsen et al., 2024) and increasing parent stress over the last decade have resulted in numerous calls to action to improve access to services (OSG, 2021; 2024). This has resulted in substantial interest in developing both novel therapeutic approaches (Kollins et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kofler et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as well as innovating on extant evidence-based interventions by converting traditional approaches to a digital format (Owens et al., 2022) affording increased scalability and adherence. Despite this,\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ehowever, adoption remains low (Owens et al., 2022) and substantial skepticism related to some of these interventions (Evans et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) persists, undermining the potential for these approaches to address unmet needs.\u003c/p\u003e \u003cp\u003eThe current study evaluated a novel approach to improving behavior problems and executive functioning in youth. Specifically, the digital approach used in the current study is adapted from existing analogue interventions that have been shown to work effectively (Barkley, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Coelho et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reitman et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; van Langen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, the digital applications used in this study were developed as a pair of applications that are intended to engage multiple stakeholders invested in children\u0026rsquo;s success (i.e., parents and children) rather than a single stakeholder as has been attempted previously with more novel digital approaches. This innovative eight-week study was intended to examine improvements in behavior problems and executive functioning after using [MASKED FOR REVIEW] as well as characterize parent perceptions of the usability of these applications.\u003c/p\u003e \u003cp\u003eConsistent with expectations and past work leveraging similar digital interventions (e.g., DRC-Online; Owens et al., 2022), overall behavior problems improved nearly 30% with 82% of participants demonstrating some level of improvement. Specifically, nearly a 3-point decrease (\u003cem\u003eB\u003c/em\u003e = -2.95) every two weeks was noted resulting in a moderate magnitude improvement in these symptoms. Examination of specific subscales of the DBD-Modified (Pelham et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) indicated that these improvements were largely consistent across problems with inattention (27.74% improvement), hyperactive, impulsive (32.52%), and oppositional and defiant (25.9%) behaviors. This indicates that following eight weeks of using [MASKED FOR REVIEW], parents perceived their children as exhibiting reductions in behaviors such as arguing with adults, being easily distracted, fidgeting, blaming others, and refusing to comply among others. Importantly, while the study did not specifically recruit for children elevated in behavior problems, average scores at baseline indicated that the sample had substantially elevated DBD scores that averaged nearly twice those observed in typically developing children (Fosco et al., 2013). This is unsurprising given that only a quarter of the sample was described by their parents as having no diagnosis. Collectively, this evidence indicates that among children with elevated symptoms of disruptive behavior problems, parents using these applications for eight weeks perceive significant, small to moderate magnitude improvement across these behaviors.\u003c/p\u003e \u003cp\u003eA similar pattern was observed for executive functioning such that working memory (12.11%) and inhibition (12.96%) both improved following eight weeks of use. The magnitude of these changes (Cohen\u0026rsquo;s \u003cem\u003ed\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.42 to 0.49\u003cem\u003e)\u003c/em\u003e was somewhat smaller than what was observed for overt behavior problems. Like baseline scores on the DBD-Modified, baseline scores on the CHEXI in the current sample were substantially higher relative to typically developing children and approximated those in a sample of children diagnosed with ADHD (Catale et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) consistent with the demographic nature of the current sample. Overall, these results indicate that parents reported substantial improvements in areas such as remembering to complete tasks, motivation, follow through, and inhibiting themselves in familial or social situations. This is unsurprising given a variety of features of the applications are designed to facilitate the development of better real-world executive functioning (e.g., practicing skills repeatedly) or afford compensatory strategies to circumvent underlying executive dysfunction (e.g., visual cues on the task board).\u003c/p\u003e \u003cp\u003e With respect to impairment, parents were asked to provide information regarding how much their child\u0026rsquo;s problems have interfered across a variety of domains including home, socially, academically, and with daily expectations. Similar to behavior problems and executive functioning, a moderate magnitude improvement (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43) was observed for impairment scores over the course of the study. This indicates that parents reported not only improvements in traditional neurocognitive and behavioral challenges associated with disruptive behavior but also perceived their children as functioning better after using.\u003c/p\u003e \u003cp\u003eCollectively, while parents reported improvements in behavioral and executive functioning as well as impairment over the course of the study, digital applications like this are only particularly useful if parents and children also perceive them as usable. To this end, a final aim of the current study was to evaluate multiple aspects of usability including ease of use, preference for the look and the feel of the application, and likelihood that the application would be recommended by parents. Further, we also asked additional questions regarding the parent\u0026rsquo;s perceptions of how the applications impacted traditional concerns reported by parents of children with disruptive behavior problems such as stress and arguments with their child. Notably, the majority of parents agreed the applications had a nice look and feel, were easy to use, and were enjoyable to use. Additionally, two-thirds of parents thought the applications helped them stay accountable and an even greater proportion (nearly three-fourths) felt it helped their child stay accountable. This is encouraging in light of the fact that only 37.1% of parents indicated they had no difficulty remembering to use them. This highlights the need to refine features that will help parents and children remember to use the application as this is likely to provide an even greater benefit with respect to accountability as well as improved outcomes.\u003c/p\u003e \u003cp\u003e Importantly, 60% of parents felt less stressed after using the applications and 72.6% agreed they reduced arguments between them and their child. Given the importance of the parent-child relationship to implementation of optimal behavioral principles for improving problematic behavior, tools with the potential to reduce stress and arguments in families are likely to contribute meaningfully to the delivery of extant evidence-based interventions such as behavior therapy. Future work incorporating parents who use digital applications such as these in conjunction with treatment providers would shed further light on the extent to which this is a useful tool for improving family dynamics. Further understanding the impact of combining application such as these with other novel digital neurocognitive interventions would significantly advance this literature.\u003c/p\u003e \u003cp\u003eDespite our inclusion of a moderately sized sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;90), relatively high retention across all time points (53.33%), and the use of robust analytic techniques to evaluate changes over time (i.e., MLM), this study was not a randomized controlled trial. As a result, the extent to which the observed improvements in behavior, executive functioning, and impairment are directly attributable to these applications cannot be estimated. Additional limitations included that parents were given minimal directions on how to use the applications so it is unknown whether additional instruction would have enhanced any impact on these outcomes. Though it is expected that using a digital application such as this with a trained professional and/or greater guidance is likely to enhance rather than attenuate the changes observed in the current study. Further, examination of dose effects among those using these applications would significantly advance this literature and provide a better understanding of who is most likely to benefit from this approach.\u003c/p\u003e \u003cp\u003eBaseline scores across measures as well as parent-reported diagnostic information indicated that this sample was largely composed of children diagnosed with ADHD and a little over one-third of the sample was taking medication or receiving some form of therapy. Importantly, concomitant treatment was not a significant covariate across the analyses with the exception of one; however, this did not alter the overall pattern of results. This suggests that these applications may confer benefit even in the presence of ongoing treatment. Given that these applications may be useful for any family struggling with routines or behavioral challenges, it would be beneficial to better understand their relative impact in children with and without behavioral disorders. Along similar lines, given that this sample was predominantly White (84.4%) and 62% of parents reported family incomes in excess of \u003cspan\u003e$\u003c/span\u003e75,000, it would be important to better understand the potential impact in more representative samples.\u003c/p\u003e \u003cp\u003eThis study is the first to evaluate a pair of digital applications for improving behavior problems and executive functions that is designed to engage both parents and children. The current study provides proof-of-concept regarding the potential impact of [MASKED FOR REVIEW] on behavior problems, executive functions, and impairment. Given the high level of need that currently exists to support children and families dealing with behavior problems, innovative solutions like this are critically important. Improving our understanding of how [MASKED FOR REVIEW] works alone or in combination with other interventions will provide additional tools for individuals and providers to leverage and have implications beyond just the home. For example, applications like this may be useful for children struggling academically or when learning any new skill or routine. Ongoing work in this area will facilitate a better understanding of how parents and children engage with digital interventions as well as ultimately lead to iteration and improvement of existing feature sets. The development of scalable tools such as these have the potential to substantially improve child outcomes and help families thrive as well as deliver greater access to families who cannot otherwise attain traditional means of support.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.R., I.E., K.B., and B.B. are all full time employees of Joon App and own vested stock options in the company. R.H. was an independent statistical consultant with no affiliation with Joon App, Inc. and Assistant Professor who validated all data analyses and interpretations of results and has no conflicts of interest to report consistent with requirements from the commercial IRB who approved this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript. J.R., I.E., K.B., and B.B. conceptualized the study, designed the gamified reward system, and facilitated recruitment and data collection. J.R. was responsible for running all data analysis and writing the manuscript. R.H. served as an independent statistical consultant, verified the accuracy of the analyses, and provided feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmerican Psychological Association (APA). (2022). \u003cem\u003e2021 Survey of Health Service Psychologists: Technical report\u003c/em\u003e. https://www.apa.org/workforce/ publications/health-service-psychologists-survey/full-technical-report.pdf\u003c/li\u003e\n \u003cli\u003eBarkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. \u003cem\u003ePsychological Bulletin, 121(1)\u003c/em\u003e, 65-94.\u003c/li\u003e\n \u003cli\u003eBarkley, R. A. (2020). Taking charge of ADHD: The complete, authoritative guide for parents. Guilford Publications.\u003c/li\u003e\n \u003cli\u003eCatale, C., Meulemans, T., \u0026amp; Thorell, L.B. (2015). The Childhood Executive Function Inventory: Confirmatory Factor Analyses and Cross-Cultural Clinical Validity in a Sample of 8- to 11-Year Old Children. \u003cem\u003eJournal of Attention Disorders, 19\u003c/em\u003e(6), 489-495.\u003c/li\u003e\n \u003cli\u003eCoelho, L. F., Barbosa, D. L. F., Rizzutti, S., Muszkat, M., Bueno, O. F. A., \u0026amp; Miranda, M. C. (2015). Use of cognitive behavioral therapy and token economy to alleviate dysfunctional behavior in children with attention-deficit hyperactivity disorder. \u003cem\u003eFrontiers in psychiatry, 6\u003c/em\u003e, 167.\u003c/li\u003e\n \u003cli\u003eCohen, J. (1990). Things I have learned (so far). \u003cem\u003eAmerican Psychologist, 45\u003c/em\u003e, 1304-1312.\u003c/li\u003e\n \u003cli\u003eCraske, M. G., Meuret, A. E., Echiverri-Cohen, A., Rosenfield, D., \u0026amp; Ritz, T. (2023). Positive affect treatment targets reward sensitivity: A randomized controlled trial. \u003cem\u003eJournal of Consulting and Clinical Psychology, 91\u003c/em\u003e(6), 350-366.\u003c/li\u003e\n \u003cli\u003eDanielson, M. L., Claussen, A. H., Bitsko, R. H., Katz, S. M., Newsome, K., Blumberg, S. J., ... \u0026amp; Ghandour, R. (2024). ADHD prevalence among US children and adolescents in 2022: diagnosis, severity, co-occurring disorders, and treatment.\u003cem\u003e\u0026nbsp;Journal of Clinical Child \u0026amp; Adolescent Psychology, 53\u003c/em\u003e(3), 343-360.\u003c/li\u003e\n \u003cli\u003eDanielson, M. L., Holbrook, J. R., Bitsko, R. H., Newsome, K., Charania, S. N., McCord, R. F., ... \u0026amp; Blumberg, S. J. (2022). State-level estimates of the prevalence of parent-reported ADHD diagnosis and treatment among US children and adolescents, 2016 to 2019. \u003cem\u003eJournal of Attention Disorders, 26\u003c/em\u003e(13), 1685-1697.\u003c/li\u003e\n \u003cli\u003eDoulou, A., Pergantis, P., Drigas, A., \u0026amp; Skianis, C. (2025). Managing ADHD Symptoms in Children Through the Use of Various Technology-Driven Serious Games: A Systematic Review. \u003cem\u003eMultimodal Technologies and Interaction, 9(1)\u003c/em\u003e, 8.\u003c/li\u003e\n \u003cli\u003eEvans, S. W., Beauchaine, T. P., Chronis-Tuscano, A., Becker, S. P., Chacko, A., Gallagher, R., ... \u0026amp; Youngstrom, E. A. (2021). The efficacy of cognitive videogame training for ADHD and what FDA clearance means for clinicians. \u003cem\u003eEvidence-Based Practice in Child and Adolescent Mental Health, 6(1)\u003c/em\u003e, 116-130.\u003c/li\u003e\n \u003cli\u003eFabiano, G. A., Vujnovic, R. K., Pelham, W. E., Waschbusch, D. A., Massetti, G. M., Pariseau, M. E., et al. (2010). Enhancing the effectiveness of special education programming for children with Attention Deficit Hyperactivity Disorder using a daily report card.\u003cem\u003e\u0026nbsp;School Psychology Review. 39\u003c/em\u003e, 219\u0026ndash;239.\u003c/li\u003e\n \u003cli\u003eFeingold, A. (2018). Meta-Analysis with Standardized Effect Sizes from Multilevel and Latent Growth Models. \u003cem\u003eJournal of Consulting and Clinical Psychology, 85\u003c/em\u003e, 262-266.\u003c/li\u003e\n \u003cli\u003eFosco, W. D., Babinski, D. E., \u0026amp; Waschbusch, D. A. (2023). The Disruptive Behavior Disorders Rating Scale: Updated factor structure, measurement invariance, and national caregiver norms. \u003cem\u003eJournal of Pediatric Psychology, 48\u003c/em\u003e(5), 468-478.\u003c/li\u003e\n \u003cli\u003eKofler, M. J., Sarver, D. E., Austin, K. E., Schaefer, H. S., Holland, E., Aduen, P. A., ... \u0026amp; Lonigan, C. J. (2018). Can working memory training work for ADHD? Development of central executive training and comparison with behavioral parent training. \u003cem\u003eJournal of Consulting and Clinical Psychology\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(12), 964-979.\u003c/li\u003e\n \u003cli\u003eKofler, M. J., Wells, E. L., Singh, L. J., Soto, E. F., Irwin, L. N., Groves, N. B., ... \u0026amp; Lonigan, C. J. (2020). A randomized controlled trial of central executive training (CET) versus inhibitory control training (ICT) for ADHD. J\u003cem\u003eournal of Consulting and Clinical Psychology, 88\u003c/em\u003e(8), 738-756.\u003c/li\u003e\n \u003cli\u003eKollins, S. H., DeLoss, D. J., Ca\u0026ntilde;adas, E., Lutz, J., Findling, R. L., Keefe, R. S., ... \u0026amp; Faraone, S. V. (2020). A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. \u003cem\u003eThe Lancet Digital Health\u003c/em\u003e, 2(4), e168-e178.\u003c/li\u003e\n \u003cli\u003eLandis, T. D., Hart, K. C., \u0026amp; Graziano, P. A. (2019). Targeting self-regulation and academic functioning among preschoolers with behavior problems: Are there incremental benefits to including cognitive training as part of a classroom curriculum?. \u003cem\u003eChild Neuropsychology, 25\u003c/em\u003e(5), 688-704.\u003c/li\u003e\n \u003cli\u003eLeopold, D. R., Christopher, M. E., Olson, R. K., Petrill, S. A., \u0026amp; Willcutt, E. G. (2019). Invariance of ADHD symptoms across sex and age: A latent analysis of ADHD and impairment ratings from early childhood into adolescence. \u003cem\u003eJournal of Abnormal Child Psychology, 47\u003c/em\u003e, 21-34.\u003c/li\u003e\n \u003cli\u003eOffice of the Surgeon General. \u0026quot;Protecting youth mental health: The US surgeon general\u0026rsquo;s advisory [Internet].\u0026quot; (2021).\u003c/li\u003e\n \u003cli\u003eOffice of the Surgeon General, U. S. (2024). Parents Under Pressure: The US Surgeon General\u0026apos;s Advisory on the Mental Health \u0026amp; Well-Being of Parents.\u003c/li\u003e\n \u003cli\u003eOh, S., Choi, J., Han, D. H., \u0026amp; Kim, E. (2024). Effects of game-based digital therapeutics on attention deficit hyperactivity disorder in children and adolescents as assessed by parents or teachers: a systematic review and meta-analysis. \u003cem\u003eEuropean Child \u0026amp; Adolescent Psychiatry, 33\u003c/em\u003e(2), 481-493.\u003c/li\u003e\n \u003cli\u003ePelham, W.E., Gnagy, E.M., Greenslade, K.E., \u0026amp; Milich, R. (1992). Teacher ratings of DSM-III-R symptoms for the disruptive behavior disorders. \u003cem\u003eJournal of the American Academy of Child \u0026amp; Adolescent Psychiatry, 31\u003c/em\u003e(2), 210-218.\u003c/li\u003e\n \u003cli\u003ePyle, K., and Fabiano, G. A. (2017). Daily report card intervention and attention deficit hyperactivity disorder: a meta-analysis of single-case studies.\u003cem\u003e\u0026nbsp;Exceptional Children, 83\u003c/em\u003e, 378\u0026ndash;395.\u003c/li\u003e\n \u003cli\u003eR Core Team. (2020). \u003cem\u003eR: A language and environment for statistical computing\u003c/em\u003e. R Foundation for Statistical Computing.\u003c/li\u003e\n \u003cli\u003eReitman, D., Hupp, S. D., O\u0026rsquo;Callaghan, P. M., Gulley, V., \u0026amp; Northup, J. (2001). The influence of a token economy and methylphenidate on attentive and disruptive behavior during sports with ADHD-diagnosed children. \u003cem\u003eBehavior Modification, 25\u003c/em\u003e(2), 305-323.\u003c/li\u003e\n \u003cli\u003eRodrigo-Yanguas, M., Gonz\u0026aacute;lez-Tard\u0026oacute;n, C., Bella-Fern\u0026aacute;ndez, M., \u0026amp; Blasco-Fontecilla, H. (2022). Serious video games: angels or demons in patients with attention-deficit hyperactivity disorder? A quasi-systematic review.\u003cem\u003e\u0026nbsp;Frontiers in Psychiatry, 13\u003c/em\u003e, 798480.\u003c/li\u003e\n \u003cli\u003evan Langen, M. J., van Hulst, B. M., Douma, M., Steffers, M., van de Wiel, N. M., van den Ban, E., ... \u0026amp; de Zeeuw, P. (2021). Which child will benefit from a behavioral intervention for ADHD? A pilot study to predict intervention efficacy from individual reward sensitivity. \u003cem\u003eJournal of Attention Disorders, 25\u003c/em\u003e(12), 1754-1764.\u003c/li\u003e\n \u003cli\u003eWestwood, S. J., Parlatini, V., Rubia, K., Cortese, S., \u0026amp; Sonuga-Barke, E. J. (2023). Computerized cognitive training in attention-deficit/hyperactivity disorder (ADHD): a meta-analysis of randomized controlled trials with blinded and objective outcomes. \u003cem\u003eMolecular Psychiatry, 28\u003c/em\u003e(4), 1402-1414.\u003c/li\u003e\n \u003cli\u003eWolraich, M. L., Hagan, J. F., Allan, C., Chan, E., Davison, D., Earls, M., ... \u0026amp; Zurhellen, W. (2019). Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. \u003cem\u003ePediatrics, 144(4)\u003c/em\u003e, 2019-2528.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6316490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6316490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eProvider shortages, lengthy waitlists, and a variety of other challenges associated with implementing traditional evidence-based strategies to reduce parent stress and improve childhood outcomes have resulted in the development of digital solutions intended to overcome many of these challenges. Unfortunately, provider uptake of many of these digital approaches remains low. The current study examines parent perceptions of usability and changes in their child’s behavior, executive functioning, and impairment following eight weeks of use of a digital contingency management system that overcomes limitations of past approaches and integrates a parent application with a child video game. \u003cstrong\u003eMethod: \u003c/strong\u003eNinety children between the ages of 6 and 12 were enrolled in this observational study. Parents were asked to use the digital application for eight weeks and complete the Disruptive Behavior Disorders Rating Scale - Modified (DBD-Modified), Child Executive Function Inventory (CHEXI), and Impairment Rating Scale (IRS) every two weeks. Multilevel models (MLM) were used with repeated observations (Level-1) nested within individual parents (Level-2) to examine changes on these outcomes over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSmall to moderate magnitude improvements in behavior (Cohen’s \u003cem\u003ed\u003c/em\u003e = 0.57), executive functioning (Cohen’s \u003cem\u003ed\u003c/em\u003e = 0.50), and impairment (Cohen’s \u003cem\u003ed\u003c/em\u003e = 0.43) following eight weeks of use were observed. Additionally, most parents found the application easy to use (88.7%), would recommend it to a friend (90.4%) and perceived it as reducing stress (59.7%) and arguments with their child (72.6%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eThis study provides preliminary evidence for the potential benefits of a parent-child digital application leveraging gamification for youth with behavioral and executive functioning challenges.\u003c/p\u003e","manuscriptTitle":"Executive Function and Behavior Improvement: An 8-Week Observational Study of a Gamified Reward System for Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:43:49","doi":"10.21203/rs.3.rs-6316490/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59c85f37-f19c-41a1-bd63-23a8869b6a51","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 05:43:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6316490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6316490","identity":"rs-6316490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}