Effort-Related Decision-Making in ADHD

Mitchell, Suzanne H.; Sevigny-Resetco, Deborah J.

doi:10.20900/jpbs.20200027

Cited by 5 publications

(1 citation statement)

References 58 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the long duration of the experimental protocol and the need for commitment to the study procedures may have negatively affected users’ adherence to pilot trials. Moreover, ADHD has been characterized by “a dislike of mental effort” [ 45 ]. Considering the aforementioned limitations, we recruited about twice as many participants as originally intended.…”

Section: Discussionmentioning

confidence: 99%

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

Pandria¹,

Petronikolou²,

Lazaridis³

et al. 2022

JMIR Res Protoc

View full text Add to dashboard Cite

Background Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior. Objective To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management. Methods A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed. Results A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform. Conclusions ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. Trial Registration ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982 International Registered Report Identifier (IRRID) RR1-10.2196/40189

show abstract

Section: Discussionmentioning

confidence: 99%

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

Pandria¹,

Petronikolou²,

Lazaridis³

et al. 2022

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

Validation of a novel effort‐discounting assessment and evaluation of the effort‐delay confound on effort discounting

Peck,

Madden

2024

J Exper Analysis Behavior

View full text Add to dashboard Cite

A vast literature highlights the prevalence of impulsive decision making in maladaptive outcomes. Most research has focused on one form—delay discounting. Less research has focused on effort discounting, possibly because of a lack of a standardized task for assessment. In published effort‐discounting tasks, effort is conceptualized in many ways, making it difficult to compare findings across studies. Additionally, most effort‐discounting tasks do not control for the time inherent in completing the effortful task, which makes it difficult to disentangle effort discounting from delay discounting. The current study evaluated the validity of a novel hypothetical effort‐discounting task. The novel task was used to evaluate the influence of the effort‐delay confound on rates of effort discounting in humans. Participants were randomly assigned to complete a confounded or a controlled version of the novel effort‐discounting task. The effort‐discounting data were well described by hyperbolic and exponential functions. When effort and delay were confounded, effort‐discounting rates were significantly higher than when effort alone influenced discounting. The results suggest that data that are produced by effort‐discounting tasks that do not control the effort‐delay confound should be interpreted cautiously because they are also influenced by delay discounting. Task limitations and future directions are discussed.

show abstract

Neurobiological foundations and clinical relevance of effort-based decision-making

Brassard,

Liu,

Dosanjh

et al. 2024

Brain Imaging and Behavior

View full text Add to dashboard Cite

Effort-Related Decision-Making in ADHD

Cited by 5 publications

References 58 publications

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

Validation of a novel effort‐discounting assessment and evaluation of the effort‐delay confound on effort discounting

Neurobiological foundations and clinical relevance of effort-based decision-making

Contact Info

Product

Resources

About