Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental condition and substantially affects individuals' daily functioning and academic performance, particularly among children and college-aged populations. Current diagnostic methods rely largely on clinicianadministered assessments and self-reports, making them both resource-intensive and costly. While prior research has explored objective data sources, such as EEG and actigraphy for monitoring ADHD symptoms and sleep patterns, these approaches have not fully addressed the need to track symptom progression in real time. In this work, we present a novel ADHDPulse approach that takes a step forward not only to monitor ADHD symptoms but also to capture day-to-day improvements that can support more informed clinical decisionmaking. ADHDPulse integrates the smartphone sensing data, specifically physical activity and phone battery data, with the self-reports collected from 38 college-age students. AD-HDPulse is based on a comprehensive analysis, set up in two scenarios: (1) momentary experiences captured through Ecological Momentary Assessment (EMA), and (2) weekly self-report measures. Our multi-feature regression models accurately predicted the EMA and weekly levels of ADHD symptoms, with $r$ values between the regressed and groundtruth values as high as 0.60. Similarly, our classification models could predict the symptoms improvement with an $F_{1}$ score as high as 0.70. These findings demonstrate that ADHDPulse offers a promising and practical approach for daily ADHD symptom tracking and for providing clinically relevant insights into both short- and long-term symptom improvement, thereby supporting more effective treatment planning.

OBJECTIVE:College students with ADHD often experience academic and life challenges related to executive functioning (EF). Telehealth interventions are generally beneficial for emerging adults, but this claim needs to be tested for ADHD. We examined a telehealth adaptation of a cognitive-behavioral intervention for college students with ADHD that focuses on organizational, time management, and planning (OTMP) skills. PARTICIPANTS:Students at three universities (n = 106; 72.6% female; M age = 22.21; 86.7% White; 91.5% Not Hispanic or Latina/e/o) presented for ADHD treatment. METHOD:Six group sessions and three individual sessions were delivered via video conferencing software. RESULTS:Pre-post differences in self-report measures indicated positive effects, including improvements in ADHD symptoms, impairment, EF, and use of OTMP skills. CONCLUSIONS:A telehealth adaptation of this intervention may be efficacious; however, additional studies of the intervention are needed (e.g., larger, more diverse samples; randomized controlled trials).

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition associated with impairments across educational, occupational, and social domains. Individuals with ADHD are often exposed to misunderstanding and negative evaluation, which can contribute to stigma and psychological distress. Recently, “masking”—efforts to conceal or compensate for ADHD-related characteristics to meet social expectations—has been discussed as a potential behavioral response to stigma, yet empirical research on this phenomenon remains limited. This narrative review synthesizes English-language research on public stigma and self-stigma related to ADHD and examines how stigma may be associated with masking behaviors in ADHD, drawing on conceptual insights from the literature on autism spectrum disorder (ASD) and other related fields. In addition, the review situates these processes within the Japanese cultural context, highlighting how cultural characteristics may intensify stigma and contribute to distinctive patterns of masking. By integrating cross-cultural perspectives and highlighting gaps in the current literature, this review underscores the need for ADHD-specific conceptual frameworks, culturally sensitive research, and longitudinal studies to clarify the mechanisms linking stigma and masking. These insights highlight the need for ADHD-specific and culturally sensitive frameworks to inform future research and intervention development.

Despite a growing body of literature linking mindsets and mental health, little is known about how these emotional mindsets develop. In this two-study direct replication, we investigated the associations between parents' anxiety mindsets (fixed and debilitating), reported behavioral orientations (avoid/control vs. approach/learning), child anxiety, and children's (aged 8–12 years) anxiety mindsets. Parents' fixed, but not debilitating, anxiety mindsets were associated with ( r= .19 Study 1 ; r = .25, Study 2 ) and predicted children’s fixed anxiety mindsets, even after accounting for child anxiety. Parents holding stronger fixed and debilitating anxiety beliefs reported greater engagement in avoid/control behaviors. However, findings were mixed regarding their engagement in approach/learning behaviors. Neither parent-reported behavioral orientation was associated with children's fixed anxiety mindsets. Child anxiety was strongly associated with their fixed anxiety mindsets ( r= .51 Study 1 , r= .61 Study 2 ). Parents’ fixed mindsets and child anxiety matter for children’s fixed anxiety mindsets. Implications for mindset and anxiety interventions are discussed.

ADHD in adults is associated with impairing procrastination and task avoidance. Prior studies using ecological momentary assessment (EMA; Knouse, Ziegler, et al., 2023; Knouse et al., 2025) found that avoidant automatic thoughts (AAT), spontaneously arising thoughts about delaying the starting or ending of an activity, were associated with task avoidance and inattention in the moment. In these studies, people with more severe self-reported ADHD symptoms also experienced more frequent AAT. We replicated and extended these prior studies in a sample of college students diagnosed with ADHD compared to those not diagnosed. 97 undergraduate students (45 diagnosed with ADHD and 52 with no ADHD diagnosis) completed baseline measures and 6 days of EMA up to 5 times per day. The median participant reported AAT at 41% of sampled moments and participants with ADHD reported a higher frequency of AAT compared to those not diagnosed (62% vs. 35% of moments, p < .001). AAT in the moment predicted more intense inattention and task avoidance; however, ADHD did not strengthen these associations. Nonetheless, the more frequent experience of AAT for people with ADHD may be one mechanism leading to more frequent problematic avoidance and a potential target for cognitive-behavioral intervention.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent condition that impacts cognitive and behavioral functioning, posing significant challenges for individuals’ academic and daily lives, particularly among college students. The core symptoms are inattention, hyperactivity and impulsivity. Current diagnostic and symptom tracking methods, whether clinician-administered or self-reported, have several limitations, such as recall bias, high costs, and the necessity for manual intervention. This underscores the necessity for an objective, accurate, and cost-effective tool for ADHD diagnosis that requires minimal manual intervention. To address this issue, we propose a novel approach, ADHDSymTracker, which uses Apple HealthKit data to predict ADHD symptoms. We calculated behavioral features using data collected from 38 college-age students including some with ADHD and developed a suite of machine learning models for ADHD symptom prediction. Our results from ADHDSymTracker indicate that most symptoms can be predicted with reasonable accuracy, achieving an F1 score as high as 0.72, rendering it a promising solution for automatic and continuous ADHD monitoring.

Purpose: Avoidant automatic thoughts (AAT) are thoughts that precede or accompany a delay in the starting or ending of a task. In a prior study of college students using Ecological Momentary Assessment (EMA), AATs were frequent daily occurrences and participants with more severe ADHD symptoms at baseline reported more frequent AAT. Importantly, momentary presence of AAT was associated with greater task avoidance and inattentive symptoms. In the current study, we replicated and extended this study in a community sample of adults.Method: We measured AAT in the daily lives of 106 adults using EMA. Data were collected at baseline and up to five times per day for 6 days.Results: Using multilevel modeling, we found that baseline ADHD symptoms predicted more frequent AAT and more severe task avoidance and negative emotion in daily life. Recent presence of AAT was associated with inattention, task avoidance, and slightly elevated negative emotion in the moment. More severe baseline ADHD strengthened the relationship between AAT and both inattention and task avoidance. In exploratory analyses, we found that work tasks and household chores were the most avoided activities and that, instead, people were most likely to be doing other work tasks or engaging in screen time or self-care, respectively.Conclusions: This study replicates and extends our prior findings regarding AAT in daily life and their relationship to ADHD and supports continued research on this construct, which may have clinical utility for intervening in problematic avoidance behaviors.

OBJECTIVE:Cognitive behavioral therapy (CBT) is an efficacious treatment for adult ADHD, yet access and availability concerns limit scalability. Mobile health apps are promising tools for delivering scalable CBT. The current study reports findings from a randomized controlled trial (RCT) of a CBT-informed health app for adults with ADHD. METHODS:A sample of assessed adults with ADHD (N = 154; ages 18-55 years) were recruited to participate and randomized to either 8 weeks of use of the CBT-informed app or a waitlist control condition. Participants in both groups completed measures of ADHD symptoms and functioning at baseline, at 4 weeks, and at 8 weeks. RESULTS:Linear mixed-effects models for repeated measurements revealed significant group x time interactions for inattentive symptoms (η2 = .15), hyperactive-impulsive symptoms (η2 = .05), and ADHD associated quality of life (η2 = .04) in favor of the CBT-informed app relative to participants who knew they were not receiving help; however, these results did not extend to a measure of functional impairment. Changes in organizational, time management, and planning behaviors and ADHD-related cognitions partially mediated the association between group and inattentive symptom changes. ADHD inattentive symptom reductions were positively associated with the total number of app exercises completed. CONCLUSIONS:The confidence in our results is limited by our use of a waitlist control design. However, participants who used the CBT-informed app perceived improvements in inattentive and hyperactive-impulsive symptoms and quality of life relative to participants who knew they were not receiving help.

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition affecting both children and adults alike, characterized primarily by problematic inattention and hyperactivity/impulsivity that causes functional impairment in daily life. The current ADHD diagnosis relies on the physician-administered approach and requires significant manual intervention, which in turn could be more prone to human error and may also suffer from recall bias. The ubiquitous nature of smartphones and their rich set of embedded sensors make them an ideal solution for behavior tracking and diagnosis. In this study, we introduce SmartADHDMonitor, a novel approach for predicting weekly ADHD symptom levels and diagnostic status. This approach utilizes passively collected smartphone app usage data from 12 college-age students on the Android platform. We calculated a comprehensive set of features using the smartphone app usage behavioral data and constructed a family of machine-learning models for predicting weekly levels of ADHD symptoms and ADHD diagnostic status. Our results demonstrate that the app usage data could be used for predicting ADHD diagnostic status fairly accurately with F1 scores as high as 0.88. Our preliminary findings offer a promising research direction into machine learning applications for ADHD diagnosis and monitoring through smartphone sensing data. As one of the first studies in this domain, SmartADHDMonitor offers a novel, technology-driven perspective on addressing the growing need for accessible mental health care solutions, further advancing the smart connected health in the field of ADHD monitoring.

People with attention-deficit/hyperactivity disorder (ADHD) experience higher rates of sleep difficulties coupled with greater circadian preference for eveningness. Emerging evidence suggests that symptoms of cognitive disengagement syndrome (CDS) may be associated with sleep difficulties and eveningness preference independently of ADHD symptoms. However, most studies have been conducted with children, adolescents, or college students. This study examined unique associations between ADHD and CDS symptom dimensions and sleep problems and circadian preference in a non-referred sample of adults. 106 adults (ages 18-75 years; Mage = 38.69 years) completed self-report assessments of ADHD and CDS symptoms, sleep quality and functioning, and circadian preference. ADHD inattentive (ADHD-IN), ADHD hyperactive-impulsive (ADHD-HI), and CDS symptoms evinced differential unique associations in regression analyses. Only ADHD-IN symptoms were uniquely associated with more frequent sleep medication use. Only ADHD-HI symptoms were uniquely associated with shorter sleep duration and greater nighttime sleep disturbance. Only CDS symptoms were uniquely associated with poorer sleep quality, longer sleep onset latency, greater daytime dysfunction, greater global sleep impairment, and greater eveningness preference. Findings support the importance of considering the role of CDS in sleep disturbance and circadian preference in adults with ADHD and point to the need for careful assessment of these dimensions in research and clinical care.

Purpose Research and clinical attention in psychology has focused heavily on negative automatic thoughts and their role in symptoms of psychopathology and maladaptive behavior; however, the role of thoughts that appear to be overly positive in content has received much less attention. Recent work in the cognitive-behavioral treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) has identified overly positive thoughts that may be associated with avoidance and functional impairment. Method We defined, described, and measured Avoidant Automatic Thoughts (AAT) in the daily lives of 101 undergraduate students using ecological momentary assessment and tested hypotheses about the association of these thoughts with ADHD symptoms and in-the-moment avoidance and negative emotion. Data were collected at baseline and up to three times per day for six days and analyzed using multilevel modeling. Results We found that AAT were frequent daily occurrences for the undergraduates in our sample and that recent presence of AAT was associated with greater task avoidance and inattentive symptoms at the momentary level. AAT were not, however, associated with momentary negative emotion. Participants’ general level of ADHD symptoms predicted greater momentary AAT, task avoidance, negative emotion and negative thoughts and less positive emotion. Conclusions This study introduces AAT as a construct with potential research and clinical utility for understanding, predicting, and intervening in problematic avoidance behaviors that reduce people’s quality of life and prevent them from reaching their meaningful goals.

Research and clinical attention in psychology has focused heavily on negative automatic thoughts and their role in symptoms of psychopathology and maladaptive behavior; however, the role of thoughts that appear to be overly positive in content has received much less attention. Recent work in the cognitive-behavioral treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) has identified overly positive thoughts that may be associated with avoidance and functional impairment. We defined, described, and measured Avoidant Automatic Thoughts (AAT) in the daily lives of 101 undergraduate students using ecological momentary assessment and tested hypotheses about the association of these thoughts with ADHD symptoms and in-the-moment avoidance and negative emotion. Data were collected at baseline and up to three times per day for six days and analyzed using multilevel modeling. We found that AAT were frequent daily occurrences for the undergraduates in our sample and that recent presence of AAT was associated with greater task avoidance and inattentive symptoms at the momentary level. AAT were not, however, associated with momentary negative emotion. Participants’ general level of ADHD symptoms predicted greater momentary AAT, task avoidance, negative emotion and negative thoughts and less positive emotion. This study introduces AAT as a construct with potential research and clinical utility for understanding, predicting, and intervening in problematic avoidance behaviors that reduce people’s quality of life and prevent them from reaching their meaningful goals.

As growth mindset interventions increase in scope and popularity, scientists and policymakers are asking: Are these interventions effective? To answer this question properly, the field needs to understand the meaningful heterogeneity in effects. In the present systematic review and meta-analysis, we focused on two key moderators with adequate data to test: Subsamples expected to benefit most and implementation fidelity. We also specified a process model that can be generative for theory. We included articles published between 2002 (first mindset intervention) through the end of 2020 that reported an effect for a growth mindset intervention, used a randomized design, and featured at least one of the qualifying outcomes. Our search yielded 53 independent samples testing distinct interventions. We reported cumulative effect sizes for multiple outcomes (i.e., mindsets, motivation, behavior, end results), with a focus on three primary end results (i.e., improved academic achievement, mental health, or social functioning). Multilevel metaregression analyses with targeted subsamples and high fidelity for academic achievement yielded, d = 0.14, 95% CI [.06, .22]; for mental health, d = 0.32, 95% CI [.10, .54]. Results highlighted the extensive variation in effects to be expected from future interventions. Namely, 95% prediction intervals for focal effects ranged from -0.08 to 0.35 for academic achievement and from 0.07 to 0.57 for mental health. The literature is too nascent for moderators for social functioning, but average effects are d = 0.36, 95% CI [.03, .68], 95% PI [-.50, 1.22]. We conclude with a discussion of heterogeneity and the limitations of meta-analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adults with ADHD may engage in two distinct but related social-cognitive processes: positive illusory bias (PIB) and self-handicapping (SH). A theoretical basis for these mechanisms in ADHD is provided through the self-worth theory of achievement motivation. These mechanisms may initially serve a self-protective function, but ultimately lead to negative outcomes. However, research in this area is limited, and most of what is known about PIB and SH is not specific to adults with ADHD. We conducted a scoping review of the extant literature of PIB and SH among adults with ADHD. Eight studies were reviewed (six PIB and two SH). Results suggest that adults with ADHD may be more likely to engage in PIB and SH than their non-ADHD peers, and that engaging in these patterns may lead to worsened outcomes over time. The findings are limited by a small number of studies with varying sampling, operational definitions, and measures. Recommendations for research and clinical work are provided, including considering various ways to operationalize PIB and SH, investigating the impact of PIB and SH on assessment reporting style (i.e., under- versus over-reporting), and considering the role of PIB and SH in the treatment of ADHD in adults.

Developed by four professors who also happen to be ADHD experts, this interactive and customizable workbook provides coaching to students with ADHD to make skills like managing time, motivating and organizing oneself, and "adulting" a workable part of everyday college life. Other books for college students with ADHD only describe personal experiences or just give advice, but this workbook promotes learning through interactive exercises and behavioral practice. It will allow you to address issues most relevant to your needs at whatever pace feels right. Modules are designed to be engaging, digestible, and activity-oriented. With practice, you will come away with improved skills that will help you to succeed in college, and to live your best life. This workbook can be used on its own; however, an accompanying Thriving in College guide for therapists uses an approach that mirrors what you will be learning and doing. If you have this workbook and are getting support from a therapist, encourage them to use the therapist guide along with you! Parents can also benefit from information in this workbook, to help their college students along the way and to understand ADHD and how it impacts the college years.