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Purpose: Young people with cancer are at increased risk of depression, yet evidence-based psychological interventions that are tailored to the specific needs of young people with cancer are scarce, and depression in this group may be particularly challenging to recognize and treat. The aims of this study were to (1) explore the views of health professionals in recognizing and treating low mood in young people with cancer and (2) identify the key components of an effective online treatment package for depression in this population. Methods: Eighteen NHS health professionals with a range of professional backgrounds working directly with young people with cancer were interviewed using a semi-structured interview schedule. Responses were analyzed using thematic analysis. Results: Five themes emerged: (1) one size doesn't fit all—the nature of depression is complex and varied, and symptoms fluctuate greatly in relation to physical health; (2) distress is completely understandable—it is important not to pathologize a normal reaction; (3) a stepping stone intervention—online interventions may promote engagement with face-to-face therapy; (4) connecting with others—the intervention should promote sharing experiences with others to reduce isolation; and (5) ownership and empowerment—lack of independence may be a cause of distress, and young people should have control of the intervention. Conclusion: The nature of depression in young people with cancer is complex and multifaceted. Online guided self-help may be useful when added as a component or first step of a treatment package including face-to-face talking therapies. [ABSTRACT FROM AUTHOR]

Gilles de la Tourette syndrome is a chronic neuropsychiatric disorder that can have a detrimental impact on the health-related quality of life of children with the condition. To date no patient-reported health-related quality of life measures have been developed for children and adolescents in the English language. This study validated the first disease-specific scale for the quantitative assessment of health-related quality of life in 118 children and adolescents with Gilles de la Tourette syndrome (C&A-GTS-QOL) following language adaptation from Italian to English in the United Kingdom. Standard statistical methods were used to test the psychometric properties of the rating scale. Principal component factor analyses led to the identification of six health-related quality of life domains (cognitive, copro-phenomena, psychological, physical, obsessive-compulsive, and activities of daily living), explaining 66.7% of the overall variance. The C&A-GTS-QOL demonstrated satisfactory scaling assumptions and acceptability; validity was supported by interscale correlations (range 0.2-0.7), confirmatory factor analysis, and correlation patterns with other rating scales and clinical variables. [ABSTRACT FROM AUTHOR]

Introduction: Biomarkers of mental effort may help to identify subtle cognitive impairments in the absence of task performance deficits. Here, we aim to detect mental effort on a verbal task, using automated voice analysis and machine learning., Methods: Audio data from the digit span backwards task were recorded and scored with automated speech recognition using the online platform NeuroVocalix TM , yielding usable data from 2,764 healthy adults (1,022 male, 1,742 female; mean age 31.4 years). Acoustic features were aggregated across each trial and normalized within each subject. Cognitive load was dichotomized for each trial by categorizing trials at >0.6 of each participants' maximum span as "high load." Data were divided into training (60%), test (20%), and validate (20%) datasets, each containing different participants. Training and test data were used in model building and hyper-parameter tuning. Five classification models (Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting) were trained to predict cognitive load ("high" vs. "low") based on acoustic features. Analyses were limited to correct responses. The model was evaluated using the validation dataset, across all span lengths and within the subset of trials with a four-digit span. Classifier discriminant power was examined with Receiver Operating Curve (ROC) analysis., Results: Participants reached a mean span of 6.34 out of 8 items (SD = 1.38). The Gradient Boosting classifier provided the best performing model on test data (AUC = 0.98) and showed excellent discriminant power for cognitive load on the validation dataset, across all span lengths (AUC = 0.99), and for four-digit only utterances (AUC = 0.95)., Discussion: A sensitive biomarker of mental effort can be derived from vocal acoustic features in remotely administered verbal cognitive tests. The use-case of this biomarker for improving sensitivity of cognitive tests to subtle pathology now needs to be examined., Competing Interests: The authors are current or former employees of Cambridge Cognition, a neuroscience technology company that has developed the NeuroVocalixTM platform., (Copyright © 2023 Taptiklis, Su, Barnett, Skirrow, Kroll and Cormack.)