Children’s mental health and recreation: Limited evidence for associations with screen use

Aim This study examined the direct and indirect associations between childhood psychopathology symptoms, screen use, media multitasking and participation in non-digital recreation. Methods Psychopathology symptoms, media use, media multitasking, participation in sports, social clubs and reading/games were reported by 520 parents about their 3- to 11-year-old children. The data were analysed using structural equation modelling. Results There were bidirectional negative associations between sports participation and emotional problems (β = −0.16, P < .001 and β = −0.15, P < .001); attention deficit hyperactivity disorder (ADHD) symptoms were associated with reduced reading/games (β = −0.14, P = .004). A bidirectional positive association was found between media use and conduct problems (β = 0.10, P = .015 and β = 0.14, P = .015). Increased media multitasking was indirectly associated with elevated symptoms of ADHD via a reduction in reading/games (β = 0.10, P = .026). However, there was no evidence that screen use mediated the associations between psychopathology symptoms and non-digital recreation. Conclusion Depending on the specific psychological difficulties, children are either less likely to participate in non-digital recreation or are more likely to use screen media or multitask with media. Interventions for children, who experience emotional or behavioural difficulties, are needed to improve participation in non-digital recreation

Mapping the structural organization of the brain in conduct disorder: replication of findings in two independent samples.

BACKGROUND: Neuroimaging methods that allow researchers to investigate structural covariance between brain regions are increasingly being used to study psychiatric disorders. Structural covariance analyses are particularly well suited for studying disorders with putative neurodevelopmental origins as they appear sensitive to changes in the synchronized maturation of different brain regions. We assessed interregional correlations in cortical thickness as a measure of structural covariance, and applied this method to investigate the coordinated development of different brain regions in conduct disorder (CD). We also assessed whether structural covariance measures could differentiate between the childhood-onset (CO-CD) and adolescence-onset (AO-CD) subtypes of CD, which may differ in terms of etiology and adult outcomes. METHODS: We examined interregional correlations in cortical thickness in male youths with CO-CD or AO-CD relative to healthy controls (HCs) in two independent datasets. The age range in the Cambridge sample was 16-21 years (mean: 18.0), whereas the age range of the Southampton sample was 13-18 years (mean: 16.7). We used FreeSurfer to perform segmentations and applied structural covariance methods to the resulting parcellations. RESULTS: In both samples, CO-CD participants displayed a strikingly higher number of significant cross-cortical correlations compared to HC or AO-CD participants, whereas AO-CD participants presented fewer significant correlations than HCs. Group differences in the strength of the interregional correlations were observed in both samples, and each set of results remained significant when controlling for IQ and comorbid attention-deficit/hyperactivity disorder symptoms. CONCLUSIONS: This study provides new evidence for quantitative differences in structural brain organization between the CO-CD and AO-CD subtypes, and supports the hypothesis that both subtypes of CD have neurodevelopmental origins.This research was funded by Wellcome Trust grant 083140 (Fairchild, Goodyer), Medical Research Council project U.1055.02.001.00001.01 (Calder), an Adventure in Research grant from Southampton University (Fairchild), a PhD studentship from Southampton University (Sully), and the Betty Behrens Research Fellowship at Clare Hall, Cambridge University (Passamonti).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/jcpp.1258

The Effectiveness and Cost-Effectiveness of a Universal Digital Parenting Intervention Designed and Implemented During the COVID-19 Pandemic: Evidence From a Rapid-Implementation Randomized Controlled Trial Within a Cohort

Background: Children’s conduct and emotional problems increased during the COVID-19 pandemic. Objective: We tested whether a smartphone parenting support app, Parent Positive, developed specifically for this purpose, reversed these effects in a cost-effective way. Parent Positive includes 3 zones. Parenting Boosters (zone 1) provided content adapted from standard face-to-face parent training programs to tackle 8 specific challenges identified by parents and parenting experts as particularly relevant for parents during the pandemic. The Parenting Exchange (zone 2) was a parent-to-parent and parent-to-expert communication forum. Parenting Resources (zone 3) provided access to existing high-quality web-based resources on a range of additional topics of value to parents (eg, neurodevelopmental problems, diet, and sleep). Methods: Supporting Parents And Kids Through Lockdown Experiences (SPARKLE), a randomized controlled trial, was embedded in the UK-wide COVID-19: Supporting Parents, Adolescents and Children during Epidemics (Co-SPACE) longitudinal study on families’ mental health during the pandemic. Parents of children aged 4 to 10 years were randomized 1:1 to Parent Positive or follow-up as usual (FAU) between May 19, 2021, and July 26, 2021. Parent Positive provided advice on common parenting challenges and evidence-based web-based resources and facilitated parent-to-parent and expert-to-parent support. Child conduct and emotional problems and family well-being were measured before randomization (T1) and at 1 (T2) and 2 (T3) months after randomization. Service use, costs, and adverse events were measured, along with app use and satisfaction. The primary outcome was T2 parent-reported child conduct problems, which were analyzed using linear mixed regression models. Results: A total of 320 participants were randomized to Parent Positive, and 326 were randomized to FAU. The primary outcome analysis included 79.3% (512/646) of the participants (dropout: 84/320, 26% on Parent Positive and 50/326, 15% on FAU). There were no statistically significant intervention effects on conduct problems at either T2 (standardized effect=−0.01) or T3 (secondary outcome; standardized effect=−0.09) and no moderation by baseline conduct problems. Significant intervention-related reductions in emotional problems were observed at T2 and T3 (secondary outcomes; standardized effect=−0.13 in both cases). Parent Positive, relative to FAU, was associated with more parental worries at T3 (standardized effect=0.14). Few intervention-attributable adverse events were reported. Parent Positive was cost-effective once 4 outliers with extremely high health care costs were excluded. Conclusions: Parent Positive reduced child emotional problems and was cost-effective compared with FAU once outliers were removed. Although small when considered against targeted therapeutic interventions, the size of these effects was in line with trials of nontargeted universal mental health interventions. This highlights the public health potential of Parent Positive if implemented at the community level. Nevertheless, caution is required before making such an interpretation, and the findings need to be replicated in large-scale, whole-community studies

Methylphenidate and Sleep Difficulties in Children and Adolescents With ADHD: Results From the 2-Year Naturalistic Pharmacovigilance ADDUCE Study

Objective: Short-term RCTs have demonstrated that MPH-treatment significantly reduces ADHD-symptoms, but is also associated with adverse events, including sleep problems. However, data on long-term effects of MPH on sleep remain limited. Methods: We performed a 2-year naturalistic prospective pharmacovigilance multicentre study. Participants were recruited into three groups: ADHD patients intending to start MPH-treatment (MPH-group), those not intending to use ADHD-medication (no-MPH-group), and a non-ADHD control-group. Sleep problems were assessed with the Children’s-Sleep-Habits-Questionnaire (CSHQ). Results: 1,410 participants were enrolled. Baseline mean CSHQ-total-sleep-scores could be considered clinically significant for the MPH-group and the no-MPH-group, but not for controls. The only group to show a significant increase in any aspect of sleep from baseline to 24-months was the control-group. Comparing the MPH- to the no-MPH-group no differences in total-sleep-score changes were found. Conclusion: Our findings support that sleep-problems are common in ADHD, but don’t suggest significant negative long-term effects of MPH on sleep

The Impact of Methylphenidate on Pubertal Maturation and Bone Age in ADHD Children and Adolescents: Results from the ADHD Drugs Use Chronic Effects (ADDUCE) Project

Objective: The short-term safety of methylphenidate (MPH) has been widely demonstrated; however the long-term safety is less clear. The aim of this study was to investigate the safety of MPH in relation to pubertal maturation and to explore the monitoring of bone age.// Method: Participants from ADDUCE, a two-year observational longitudinal study with three parallel cohorts (MPH group, no-MPH group, and a non-ADHD control group), were compared with respect to Tanner staging. An Italian subsample of medicated-ADHD was further assessed by the monitoring of bone age.// Results: The medicated and unmedicated ADHD groups did not differ in Tanner stages indicating no higher risk of sexual maturational delay in the MPH-treated patients. The medicated subsample monitored for bone age showed a slight acceleration of the bone maturation after 24 months, however their predicted adult height remained stable.// Conclusion: Our results do not suggest safety concerns on long-term treatment with MPH in relation to pubertal maturation and growth

Attention-induced deactivations in very low frequency EEG oscillations: differential localisation according to ADHD symptom status

Background: the default-mode network (DMN) is characterised by coherent very low frequency (VLF) brain oscillations. The cognitive significance of this VLF profile remains unclear, partly because of the temporally constrained nature of the blood oxygen-level dependent (BOLD) signal. Previously we have identified a VLF EEG network of scalp locations that shares many features of the DMN. Here we explore the intracranial sources of VLF EEG and examine their overlap with the DMN in adults with high and low ADHD ratings.Methodology/Principal Findings: DC-EEG was recorded using an equidistant 66 channel electrode montage in 25 adult participants with high- and 25 participants with low-ratings of ADHD symptoms during a rest condition and an attention demanding Eriksen task. VLF EEG power was calculated in the VLF band (0.02 to 0.2 Hz) for the rest and task condition and compared for high and low ADHD participants. sLORETA was used to identify brain sources associated with the attention-induced deactivation of VLF EEG power, and to examine these sources in relation to ADHD symptoms. There was significant deactivation of VLF EEG power between the rest and task condition for the whole sample. Using s-LORETA the sources of this deactivation were localised to medial prefrontal regions, posterior cingulate cortex/precuneus and temporal regions. However, deactivation sources were different for high and low ADHD groups: In the low ADHD group attention-induced VLF EEG deactivation was most significant in medial prefrontal regions while for the high ADHD group this deactivation was predominantly localised to the temporal lobes.Conclusions/Significance: attention-induced VLF EEG deactivations have intracranial sources that appear to overlap with those of the DMN. Furthermore, these seem to be related to ADHD symptom status, with high ADHD adults failing to significantly deactivate medial prefrontal regions while at the same time showing significant attenuation of VLF EEG power in temporal lobe

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-5

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p>nding spectrograms

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-3

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p>inal EEG signal (amplitude in log scale) and of the filtered signal (amplitude in linear scale) showing a dominant peak at 0.1 Hz

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-2

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p> (b) The inverse filter frequency response obtained from the corresponding 6th order MA model

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-0

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p>e 4 conditions forms the normalisation curve(-)