Adapting the Posterior Probability of Diagnosis Index to Enhance Evidence-Based Screening: An Application to ADHD in Primary Care

This study adapts the Posterior Probability of Diagnosis (PPOD) Index for use with screening data. The original PPOD Index, designed for use in the context of comprehensive diagnostic assessments, is overconfident when applied to screening data. To correct for this overconfidence, we describe a simple method for adjusting the PPOD Index to improve its calibration when used for screening. Specifically, we compare the adjusted PPOD Index to the original index and Naïve Bayes probability estimates on two dimensions of accuracy, discrimination and calibration, using a clinical sample of children and adolescents (N = 321) whose caregivers completed the Vanderbilt Assessment Scale to screen for Attention-Deficit/Hyperactivity Disorder (ADHD) and who subsequently completed a comprehensive diagnostic assessment. Results indicated that the adjusted PPOD Index, original PPOD Index, and Naïve Bayes probability estimates are comparable using traditional measures of accuracy (sensitivity, specificity, AUC) but the adjusted PPOD Index showed superior calibration. We discuss the importance of calibration for screening and diagnostic support tools when applied to individual patients

How should we evaluate research on counselling and the treatment of depression? A case study on how NICE’s draft 2018 guideline considered what counts as best evidence

Background: Health guidelines are developed to improve patient care by ensuring the most recent and “best available evidence” is used to guide treatment recommendations (NICE Guidance, 2017). NICE’s revised guideline development methodology acknowledges that evidence needed to answer one question (treatment efficacy) may be different from evidence needed to answer another (cost effectiveness, treatment acceptability to patients; NICE, 2014/2017). This review uses counselling in the treatment of depression as a case study, and interrogates the constructs of ‘best’ evidence and ‘best’ guideline methodologies. Method: The review comprises six sections: (1) the implications of diverse definitions of counselling in research; (2) research findings from meta-analyses and randomised controlled trials (RCTs); (3) limitations to trials-based evidence; (4) findings from large routine outcome datasets; (5) the inclusion of qualitative research that emphasises service-user voices; and (6) conclusions and recommendations. Results: Research from meta-analyses and RCTs reviewed in the draft 2018 NICE guideline is limited but positive in relation to the effectiveness of counselling in the treatment for depression. The weight of evidence suggests little, if any, advantage to CBT over counselling once bias and researcher allegiance are taken into account. A growing body of evidence from large NHS datasets also evidences that counselling is both effective and cost-effective when delivered in NHS settings. Conclusion: Recommendations in NICE’s own updated procedures suggest that sole reliance on RCTs and meta-analyses as best methodologies is no longer adequate. There is a need to include large standardised collected datasets from routine practice as well as the voice of patients via high-quality qualitative research

Callous-unemotional traits only versus the multidimensional psychopathy construct as predictors of various antisocial outcomes during early adolescence

New methods for child psychiatric diagnosis and treatment outcome evaluatio

An Exposure-Based Video Game (Dr. Zoo) to Reduce Needle Phobia in Children Aged 3 to 6 Years: Development and Mixed Methods Pilot Study

BACKGROUND: Needle phobia, which affects 19% of children aged 4 to 6 years, prevents many children from receiving necessary or preventive medical treatments. Digital interventions have been made to target needle phobia but currently rely on distraction rather than evidence-based exposure. OBJECTIVE: We designed and evaluated a serious exposure-based mobile game called Dr. Zoo to reduce the fear of needles in children aged 3 to 6 years, where players administered shots to cartoon animals. METHODS: We conducted a mixed methods study with 30 parents (mean age 35.87, SD 4.39 years) and their 36 children (mean age 4.44, SD 1.11 years) who played the game for 5 days leading to a scheduled appointment that included an injection (eg, influenza vaccination). After the study, parents completed exit surveys and participated in semistructured interviews to evaluate ease of use, acceptability, and preliminary effectiveness of the game and to provide insights on their experience with the game to inform future developments. Interview transcripts were analyzed by 3 independent coders following an open coding process and subsequently coded and discussed to reach consensus. RESULTS: Parents rated their child\u27s difficulty in completing the game as very low on average (scale 1-5; mean 1.76, SD 0.82) and were highly likely to recommend Dr. Zoo to other parents (scale 1-5; mean 4.41, SD 0.87), suggesting Dr. Zoo\u27s strong ease of use and high acceptability. In the exit survey, parents rated their child\u27s fear as significantly lower after participating in the study (scale 1-5; mean 3.09, SD 1.17) compared with that before participating (scale 1-5; mean 4.37, SD 0.81; z score=-4.638; P\u3c.001). Furthermore, 74% (26/35) of the parents reported that the game had a positive impact on their child\u27s fear or perception of needles (only 2 parents reported a negative impact). Qualitative analysis of the interview transcripts revealed potentially important features of the game in this positive impact, such as the game\u27s interactive design, as observed in 69% (24/35) of our participants. CONCLUSIONS: The results suggest that an evidence-based serious mobile game can be an easy-to-use, acceptable, and potentially effective intervention for changing young children\u27s fear and perceptions of needles. Leveraging digital interventions may be a potential solution to needle anxiety as a public health concern

Perceived Facilitators of and Barriers to Implementation of a Decision Support Tool for Adolescent Depression and Suicidality Screening: Focus Group and Interview Study

BackgroundScreening Wizard (SW) is a technology-based decision support tool aimed at guiding primary care providers (PCPs) to respond to depression and suicidality screens in adolescents. Separate screens assess adolescents’ and parents’ reports on mental health symptoms, treatment preferences, and potential treatment barriers. A detailed summary is provided to PCPs, also identifying adolescent-parent discrepancies. The goal of SW is to enhance decision-making to increase the utilization of evidence-based treatments. ObjectiveThis qualitative study aims to describe multi-stakeholder perspectives of adolescents, parents, and providers to understand the potential barriers to the implementation of SW. MethodsWe interviewed 11 parents and 11 adolescents and conducted two focus groups with 18 health care providers (PCPs, nurses, therapists, and staff) across 2 pediatric practices. Participants described previous experiences with screening for depression and were shown a mock-up of SW and asked for feedback. Interviews and focus groups were transcribed verbatim, and codebooks were inductively developed based on content. Transcripts were double coded, and disagreements were adjudicated to full agreement. Completed coding was used to produce thematic analyses of the interviews and focus groups. ResultsWe identified five main themes across the interviews and focus groups: parents, adolescents, and pediatric PCPs agree that depression screening should occur in pediatric primary care; there is concern that accurate self-disclosure does not always occur during depression screening; SW is viewed as a tool that could facilitate depression screening and that might encourage more honesty in screening responses; parents, adolescents, and providers do not want SW to replace mental health discussions with providers; and providers want to maintain autonomy in treatment decisions. ConclusionsWe identified that providers, parents, and adolescents are all concerned with current screening practices, mainly regarding inaccurate self-disclosure. They recognized value in SW as a computerized tool that may elicit more honest responses and identify adolescent-parent discrepancies. Surprisingly, providers did not want the SW report to include treatment recommendations, and all groups did not want the SW report to replace conversations with the PCP about depression. Although SW was originally developed as a treatment decision algorithm, this qualitative study has led us to remove this component, and instead, SW focuses on aspects identified as most useful by all groups. We hope that this initial qualitative work will improve the future implementation of SW

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study

BackgroundAlthough hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings. ObjectiveWe 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. MethodsIn 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). ResultsThe 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. ConclusionsState-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity