Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis

Fairclough, Stuart J.; Tyler, Richard; Dainty, J.; Dumuid, Dorothea; Richardson, Cassandra; Shepstone, Lee; Atkin, Andrew J.

doi:10.1080/02640414.2021.1890351

Cited by 31 publications

(42 citation statements)

References 73 publications

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“…These findings align with the 24-h movement guidelines [ 9 , 37 , 57 ], which recommend a daily balance of time spent in various types of movement behaviours for optimal health. Consistent with the findings of Fairclough et al [ 14 ], we found that the movement behaviour composition was associated with internalising (emotional and peer/relationship problems) but not externalising problems (conduct problems and hyperactivity). However, contrary to Fairclough et al’s study [ 14 ], there was no association between movement behaviour composition and prosocial behaviour in the current study.…”

Section: Discussionsupporting

confidence: 92%

“…Our cross-sectional findings confirm and extend previous CoDA research [ 14 , 15 ] by showing that the association between daily movement behaviour composition and psychosocial health outcomes is independent of children’s recreational screen use levels. These findings align with the 24-h movement guidelines [ 9 , 37 , 57 ], which recommend a daily balance of time spent in various types of movement behaviours for optimal health.…”

Section: Discussionsupporting

confidence: 89%

“…A sleep detection algorithm (based on the distribution of change in arm angle) developed by van Hees and colleagues [ 30 ] was applied to identify the sleep period time window for the estimation of sleep duration (including awakening periods). This algorithm has been applied previously in wrist-worn accelerometry studies involving children [ 14 , 31 , 32 ]. Waking time data were further categorised as ST (ENMO < 52 m g ), LPA (ENMO 52–191 m g ) or MVPA (ENMO ≥ 192 m g ) using validated acceleration intensity thresholds [ 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…This can be done using compositional data analysis (CoDA), a statistical approach designed to handle compositional data that convey relative information (i.e., only the ratios between the parts of a composition are informative), therefore making it suitable for analysis of time-use data [ 11 , 12 , 13 ]. To the best of our knowledge, only two cross-sectional studies have examined the association between the full 24-h movement behaviour composition (i.e., time spent in sleep, sedentary time (ST), light-intensity PA (LPA) and moderate- to vigorous-intensity PA (MVPA)) and psychosocial health outcomes in school-aged children and adolescents using a CoDA approach [ 14 , 15 ]. The findings from these studies suggest that overall movement behaviour composition is important for emotional and behavioural development.…”

Section: Introductionmentioning

confidence: 99%

“…However, it remains largely unknown whether the effects of screen use are explained by the amount of time spent on screens while sitting (i.e., being sedentary) or the content/context of the screen viewing [ 18 , 20 ]. While it has been proposed that the negative effects of ST may be specific to screen-based activities [ 20 ], there remains a lack of evidence justifying whether overall ST (i.e., time spent in all sedentary activities) or specific types of sedentary behaviour are more strongly related to psychosocial health indicators [ 14 ]. Most of the published studies on the psychosocial health effects of screen time did not include newer forms of screen technologies (e.g., tablets and smartphones) [ 16 ], nor did they account for the confounding influence of time spent in other movement behaviours (i.e., PA and sleep) [ 16 , 17 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

Chong

Parrish

Cliff

et al. 2021

IJERPH

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It remains unclear whether the time-use composition of 24-h movement behaviours (sleep, sedentary time (ST), physical activity (PA)) and recreational screen use are independently associated with psychosocial health. This study examined the cross-sectional and longitudinal associations between 24-h movement behaviour composition, recreational screen use and psychosocial health outcomes in children. Measures completed at baseline (n = 127; 11.7 years) and follow-up (n = 88; 12.8 years) included accelerometer-based 24-h movement behaviours, self-reported recreational screen use and psychosocial health (Strengths and Difficulties Questionnaire, Kessler’s Psychological Distress Scale). Linear mixed models were used to examine the cross-sectional and longitudinal associations between the 24-h movement behaviour composition and recreational screen use levels with psychosocial health outcomes. Overall, the movement behaviour composition (p < 0.05) and recreational screen use levels (p < 0.01) were both cross-sectionally but not longitudinally associated with psychosocial health outcomes. Relative to other behaviours, sleep was negatively associated, while light-intensity PA was positively associated with internalising problems and total difficulties scores. ST was positively associated with internalising problems. High levels of recreational screen use (>2 h/day) were associated with greater externalising problems, total difficulties scores and psychological distress. These findings reinforce the importance of achieving a balance between different types of movement behaviours over a 24-h period for psychosocial health.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

Chong

Parrish

Cliff

et al. 2021

IJERPH

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Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

Kumar¹,

Routray

Prabu

et al. 2022

Cluster Comput

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Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8–10% of better system performance than other existing techniques.

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The Overflow Effects of Movement Behaviour Change Interventions for Children and Adolescents: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Feng,

Huang,

Zheng

et al. 2024

Sports Med

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Background Considering the finite time within a 24-h day, the distribution of time spent on movement behaviours has been found to be associated with health outcomes. Objectives This systematic review and meta-analysis aimed to summarise and evaluate the overflow effects of interventions targeting a single behaviour (physical activity, sedentary behaviour/screen time, or sleep) on other non-targeted behaviours among children and adolescents. Methods Six databases (MEDLINE [Ovid], PsycINFO [ProQuest], EMBASE [Ovid], PubMed, Web of Science and SPORTDiscus [EBSCO]) were searched for relevant studies published before 13 May, 2024. Randomised controlled trials and clustered randomised controlled trials that targeted a single behaviour and also assessed the effects on non-targeted behaviours, comprised of healthy children under the age of 18 years, were included. Movement behaviours can be measured either objectively or subjectively. The revised Cochrane risk-of-bias tool for randomised trials was adopted to evaluate the risk of bias. Results A total of 102 studies with 45,998 participants from 21 countries were identified, and 60 of them with 26,183 participants were incorporated into the meta-analysis. The meta-analysis demonstrated that physical activity interventions led to a reduction in the proportion of each day spent in sedentary behaviour (mean difference = − 0.95% of wear time, 95% confidence interval − 1.44, − 0.45, I2 = 39%). Sedentary behaviour interventions resulted in increased standing time (mean difference = 3.87%, 95% confidence interval 1.99, 5.75, I2 = 0%). Interventions targeting screen time did not yield changes in physical activity or sleep. The findings on the effectiveness of sleep interventions on non-targeted behaviours and of physical activity interventions on sleep were inconclusive. Conclusions Overall, the findings suggested that interventions aimed at increasing physical activity or reducing sedentary behaviour had overflow effects on non-targeted behaviours, but the effect sizes were small. Additional evidence is needed to reach definitive conclusions regarding the impact of behaviour change interventions on sleep and of the overflow effects of sleep interventions.

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Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis

Cited by 31 publications

References 73 publications

Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

The Overflow Effects of Movement Behaviour Change Interventions for Children and Adolescents: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

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