Screen Use and Mental Health Symptoms in Canadian Children and Youth during the COVID-19 Pandemic

Importance: Longitudinal research on specific forms of electronic screen use and mental health symptoms in children and youth during COVID-19 is minimal. Understanding the association may help develop policies and interventions targeting specific screen activities to promote healthful screen use and mental health in children and youth. Objective: To determine whether specific forms of screen use (television [TV] or digital media, video games, electronic learning, and video-chatting time) were associated with symptoms of depression, anxiety, conduct problems, irritability, hyperactivity, and inattention in children and youth during COVID-19. Design, Setting, and Participants: A longitudinal cohort study with repeated measures of exposures and outcomes was conducted in children and youth aged 2 to 18 years in Ontario, Canada, between May 2020 and April 2021 across 4 cohorts of children or youth: 2 community cohorts and 2 clinically referred cohorts. Parents were asked to complete repeated questionnaires about their children\u27s health behaviors and mental health symptoms during COVID-19. Main Outcomes and Measures: The exposure variables were children\u27s daily TV or digital media time, video game time, electronic-learning time, and video-chatting time. The mental health outcomes were parent-reported symptoms of child depression, anxiety, conduct problems and irritability, and hyperactivity/inattention using validated standardized tools. Results: This study included 2026 children with 6648 observations. In younger children (mean [SD] age, 5.9 [2.5] years; 275 male participants [51.7%]), higher TV or digital media time was associated with higher levels of conduct problems (age 2-4 years: β, 0.22 [95% CI, 0.10-0.35]; P \u3c.001; age ≥4 years: β, 0.07 [95% CI, 0.02-0.11]; P =.007) and hyperactivity/inattention (β, 0.07 [95% CI, 0.006-0.14]; P =.04). In older children and youth (mean [SD] age, 11.3 [3.3] years; 844 male participants [56.5%]), higher levels of TV or digital media time were associated with higher levels of depression, anxiety, and inattention; higher levels of video game time were associated with higher levels of depression, irritability, inattention, and hyperactivity. Higher levels of electronic learning time were associated with higher levels of depression and anxiety. Conclusions and Relevance: In this cohort study, higher levels of screen use were associated poor mental health of children and youth during the COVID-19 pandemic. These findings suggest that policy intervention as well as evidence-informed social supports are needed to promote healthful screen use and mental health in children and youth during the pandemic and beyond

New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data

Background Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers. While various methods are available for detecting outlier measurements, a gold standard has yet to be identified, and there is no established method for outlying trajectories. Thus, outlier types and their effects on growth pattern detection still need to be investigated. This work aimed to assess the performance of six methods at detecting different types of outliers, propose two novel methods for outlier trajectory detection and evaluate how outliers affect growth pattern detection. Methods We included 393 healthy infants from The Applied Research Group for Kids (TARGet Kids!) cohort and 1651 children with severe malnutrition from the co-trimoxazole prophylaxis clinical trial. We injected outliers of three types and six intensities and applied four outlier detection methods for measurements (model-based and World Health Organization cut-offs-based) and two for trajectories. We also assessed growth pattern detection before and after outlier injection using time series clustering and latent class mixed models. Error type, intensity, and population affected method performance. Results Model-based outlier detection methods performed best for measurements with precision between 5.72-99.89%, especially for low and moderate error intensities. The clustering-based outlier trajectory method had high precision of 14.93-99.12%. Combining methods improved the detection rate to 21.82% in outlier measurements. Finally, when comparing growth groups with and without outliers, the outliers were shown to alter group membership by 57.9 -79.04%. Conclusions World Health Organization cut-off-based techniques were shown to perform well in few very particular cases (extreme errors of high intensity), while model-based techniques performed well, especially for moderate errors of low intensity. Clustering-based outlier trajectory detection performed exceptionally well across all types and intensities of errors, indicating a potential strategic change in how outliers in growth data are viewed. Finally, the importance of detecting outliers was shown, given its impact on children growth studies, as demonstrated by comparing results of growth group detection

Parental use of routines, setting limits, and child screen use during COVID-19: findings from a large Canadian cohort study

BackgroundAn increase in child screen time has been observed throughout the COVID-19 pandemic. Home environment and parenting practices have been associated with child screen time. The purpose of this study was to examine associations between parental use of routines, limit setting, and child screen time during the (COVID-19) pandemic to inform harm-reducing strategies to limit the potential harms ensued by excessive screen use.MethodsA cohort study was conducted in 700 healthy children (3,628 observations) aged 0–11 years though the TARGet Kids! COVID-19 Study of Children and Families in Toronto, Canada from May 2020-May 2021. The independent variables assessed were parent-reported use of routines and setting limits. Outcomes were parent-reported child daily screen time in minutes and whether the Canadian 24-Hour screen time guideline was met, defined as 0 for <1 years, 60 or less for 1–5 years, and 120 or less for >5 years. Linear and logistic mixed-effects models were fitted using repeated measures of independent variables and outcomes with a priori stratification by developmental stages (<3, 3–4.99, ≥5 years).ResultsA total of 700 children with 3,628 observations were included in this study [mean age = 5.5 (SD = 2.7, max = 11.9) years, female = 47.6%]. Mean change in child screen time before vs. during the pandemic was +51.1 min/day and level of parental use of routines and setting limits remained stable. Lower use of routines was associated with higher child screen time (β = 4.0 min; 95% CI: 0.9, 7.1; p = 0.01) in ages ≥5 years and lower odds of meeting the screen time guideline in ages <3 years and ≥5 years (OR = 0.59; 95% CI: 0.38, 0.88; p = 0.01; OR = 0.76; 95% CI: 0.67, 0.87; p < 0.01). Lower use of limit setting was associated with higher child screen time and lower odds of meeting the screen time guideline in ages ≥5 years (β = 3.8 min; 95% CI: 0.69, 6.48; p < 0.01; OR = 0.86; 95% CI: 0.78, 0.94; p < 0.01).ConclusionsLower parental use of routines and limits during the COVID-19 pandemic were associated with higher screen time and lower odds of meeting the screen time guideline among school-age children. Results may help inform strategies to promote healthy screen use in this age group

Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach

Abstract Background Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals. Results Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination. Conclusions This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample

Repeated salivary daytime cortisol and onset of mood episodes in offspring of bipolar parents

Abstract Background Differences in cortisol secretion may differentiate individuals at high compared to low genetic risk for bipolar disorder (BD) and predict the onset or recurrence of mood episodes. The objectives of this study were to determine if salivary cortisol measures are: (1) different in high-risk offspring of parents with BD (HR) compared to control offspring of unaffected parents (C), (2) stable over time, (3) associated with the development of mood episode onset/recurrence, and (4) influenced by comorbid complications. Methods Fifty-three HR and 22 C completed salivary cortisol sampling annually for up to 4 years in conjunction with semi-structured clinical interviews. The cortisol awakening response (CAR), daytime cortisol [area under the curve (AUC)], and evening cortisol (8:00 p.m.) were calculated. Results There were no differences in baseline CAR, AUC and evening cortisol between HR and C (p = 0.38, p = 0.30 and p = 0.84), respectively. CAR, AUC and evening cortisol were stable over yearly assessments in HR, while in Cs, evening cortisol increased over time (p = 0.008), and CAR and AUC remained stable. In HR, AUC and evening cortisol increased the hazard of a new onset mood disorder/recurrence by 2.7 times (p = 0.01), and 3.5 times (p = 0.01), respectively, but this was no longer significant after accounting for multiple comparisons. Conclusions Salivary cortisol is stable over time within HR offspring. However, between individuals, basal salivary cortisol is highly variable. More research is needed, with larger samples of prospectively studied HR youth using a more reliable method of cortisol measurement, to determine the potential role of cortisol in the development of mood disorders

Dataset for: Exponential decay for binary time-varying covariates in Cox models

Cox models are commonly used in the analysis of time to event data. One advantage of Cox models is the ability to include time-varying covariates, often a binary covariate that codes for the occurrence of an event that affects an individual subject. A common assumption in this case is that the effect of the event on the outcome of interest is constant and permanent for each subject. In this paper we propose a modification to the Cox model to allow the influence of an event to exponentially decay over time. Methods for generating data using the inverse cumulative density function for the proposed model are developed. Likelihood ratio tests and AIC are investigated as methods for comparing the proposed model to the commonly used permanent exposure model. A more general model proposed by Cox and Oakes [1] is also discussed. A simulation study is performed and three different data sets are presented as examples

The Association Between Growth Trajectories and Mental Health in Early- to Mid-childhood

With increasing recognition of mental health’s importance for overall health, public health professionals are seeking to better understand early risk factors for mental illness. A majority of mental health problems emerge during childhood; there is evidence of a particular association between increased childhood growth and poorer mental health. The current study sought to determine the association between growth trajectories during infancy and early childhood (birth to age 5) and mental health (behavioural and emotional difficulties) in early- to mid-childhood (age 3 to 8). The study was conducted among a subset (n=665) of participants from The Applied Research Group for Kids (TARGet Kids!), an ongoing longitudinal cohort study. Five growth trajectories were determined via repeated measures of age- and sex-standardized body mass index (BMI). Mental health was assessed using the Strengths & Difficulties Questionnaire (SDQ) total difficulties, externalizing problems, and internalizing problems scores. The sociodemographic and health characteristics of the sample were described by mental health status (per the SDQ). The sociodemographic and health characteristics of the sample were described by mental health status (per the SDQ). Regression analyses were run to determine the association between growth trajectories and SDQ scores. There was no statistically significant association between increased growth (“rapidly accelerating” trajectory) and SDQ total difficulties (b=1.49[-3.82,6.81],p=0.58), externalizing problems (b=0.31[-3.29,3.91],p=0.86), or internalizing problems (b=1.18[-1.73,4.09],p=0.43). There was a significant association between decelerating growth and increased internalizing problems (b=0.69[0.07,1.31],p=0.03). Current results do not support an association between increased growth and poorer mental health overall in early- to mid-childhood; however, a pattern of decelerating growth may be associated with more internalizing problems. Understanding early risk factors for poor mental health may allow public health researchers to develop targeted interventions and ultimately improve mental health outcomes across the lifespan. Implications and future directions will be discussed

Multi-state models for investigating possible stages leading to bipolar disorder

Abstract Background It has been proposed that bipolar disorder onsets in a predictable progressive sequence of clinical stages. However, there is some debate in regard to a statistical approach to test this hypothesis. The objective of this paper is to investigate two different analysis strategies to determine the best suited model to assess the longitudinal progression of clinical stages in the development of bipolar disorder. Methods Data previously collected on 229 subjects at high risk of developing bipolar disorder were used for the statistical analysis. We investigate two statistical approaches for analyzing the relationship between the proposed stages of bipolar disorder: 1) the early stages are considered as time-varying covariates affecting the hazard of bipolar disorder in a Cox proportional hazards model, 2) the early stages are explicitly modelled as states in a non-parametric multi-state model. Results We found from the Cox model thatthere was evidence that the hazard of bipolar disorder is increased by the onset of major depressive disorder. From the multi-state model, in high-risk offspring the probability of bipolar disorder by age 29 was estimated as 0.2321. Cumulative incidence functions representing the probability of bipolar disorder given major depressive disorder at or before age 18 were estimated using both approaches and found to be similar. Conclusions Both the Cox model and multi-state model are useful approaches to the modelling of antecedent risk syndromes. They lead to similar cumulative incidence functions but otherwise each method offers a different advantage