Objective To provide national norms and percentiles for both research and clinical scoring modalities of the Vanderbilt Attention Deficit/Hyperactivity Disorder (ADHD) Diagnostic Parent Rating Scale (VADPRS) for a representative sample of children ages 5–12 in the United States. Method The five clinical subscales of the VADPRS were completed by 1,570 caregivers of children ages 5–12 in the United States, with children representative of the national population on key demographic variables including race, sex, ethnicity, family income, and family educational level. Descriptive statistics and measures of internal consistency of both dimensional and symptom count scoring were provided for each of the five clinical subscales of the inventory, as well as percentiles and group comparisons for select dimensional scoring subscales based on age and child sex. Results Measures of internal consistency for each subscale using both scoring modalities of the VADPRS ranged from high to acceptable. There were statistically significant differences among the different subscales for both age (ADHD hyperactivity, anxiety/depression) and sex [both presentations of ADHD, oppositional defiant disorder (ODD)] for the total sample. These differences, however, were modest in magnitude and unlikely to be clinically meaningful. Conclusions This study enhances the research and clinical utility of the VADPRS by providing national norms and percentiles for each of its subscales. Differences between age and sex across the sample were statistically significant for two of the subscales (Hyperactivity and Anxiety/Depression) with additional subscales significant for sex alone (Inattentive and ODD), but these differences were not substantial enough to indicate a need for separate cut-offs for screening purposes.
Background Healthcare and human services increasingly rely on teams of individuals to deliver services. Implementation of evidence-based practices and other innovations in these settings requires teams to work together to change processes and behaviors. Accordingly, team functioning may be a key determinant of implementation outcomes. This systematic review will identify and summarize empirical research examining associations between team functioning and implementation outcomes in healthcare and human service settings. Methods We will conduct a comprehensive search of bibliographic databases (e.g., MEDLINE, PsycINFO, CINAHL, ERIC) for articles published from January 2000 or later. We will include peer-reviewed empirical articles and conference abstracts using quantitative, qualitative, or mixed methods. We will include experimental or observational studies that report on the implementation of an innovation in a healthcare or human service setting and examine associations between team functioning and implementation outcomes. Implementation outcomes of interest are acceptability, adoption, appropriateness, cost, feasibility, fidelity, penetration, and sustainability. Two reviewers will independently screen all titles/abstracts, review full-text articles, and extract data from included articles. We will use the Mixed Methods Appraisal Tool to assess methodological quality/bias and conduct a narrative synthesis without meta-analysis. Discussion Understanding how team functioning influences implementation outcomes will contribute to our understanding of team-level barriers and facilitators of change. The results of this systematic review will inform efforts to implement evidence-based practices in team-based service settings. Systematic review registration PROSPERO CRD42020220168
Background Although hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings. Objective We describe the development of a smartwatch app to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning to measure hyperactivity. The goal is to differentiate children with ADHD combined presentation (a combination of inattentive and hyperactive/impulsive presentations) or predominantly hyperactive/impulsive presentation from children with typical levels of activity. Methods In this pilot study, we recruited 30 children, aged 6 to 11 years, to wear a smartwatch with the LemurDx app for 2 days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half of the participants had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n=15), and half were in the healthy control group (n=15). Results The results indicated high usability scores and an overall diagnostic accuracy of 0.89 (sensitivity=0.93; specificity=0.86) when the motion sensor output was paired with the activity labels. Conclusions State-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity.
UNSTRUCTURED Although hyperactivity is a core symptom of ADHD, there are no objective measures that are widely used in clinical settings. We describe the development of a smartwatch application to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning (ML) to measure hyperactivity, with the goal of differentiating children with ADHD combined presentation or predominantly hyperactive/impulsive presentation from children with typical levels of activity. In this pilot study, we recruited 30 children (ages 6-11) to wear the smartwatch with the LemurDx app for two days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half the sample had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n = 15) and half were healthy controls (n = 15). Results indicated high usability scores and an overall diagnostic accuracy of .89 (sensitivity = .93; specificity = .86) when the motion sensor output was paired with the activity labels, suggesting that state-of-the-art sensors and ML may provide a promising avenue for the objective measurement of hyperactivity.
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