Children's perceptions of factors influencing their physical activity: a focus group study on primary school children

Purpose

Establishing healthy lifestyle behaviours in primary school children is important, as these behaviours are likely to track into adulthood. This study aimed to explore the factors influencing physical activity (PA) in primary school children through their perspectives.

Approach

Eleven focus group discussions and one interview were conducted with 52 children (n = 29 girls) aged 9–12 years from two primary schools in Singapore. Data analyses were conducted using thematic analysis, deductively following the socio-ecological model (SEM) and inductively for themes at each SEM level.

Results

At individual level, children’s perceived enjoyment, health benefits and expectation of rewards motivated them to engage in PA, while time constraints and their apathy towards PA hindered PA engagement. Children’s PA occasions at home were reported to be influenced by parental permission, priorities and availability, and the availability of preferred peers. Physical environmental factors such as opportunities for PA in school, access to facilities for PA and weather influenced children’s time spent on PA and the types of activities they engaged in.

Conclusion

This study summarized some factors that children have reported to influence their PA behaviour. These findings could help inform future interventions aimed at promoting PA among primary school children in Singapore.

Understanding children's perspectives of the influences on their dietary behaviours

OBJECTIVE

This study aimed to examine the intrapersonal, interpersonal, environmental and macrosystem influences on dietary behaviours among primary school children in Singapore.

DESIGN

A qualitative interpretive approach was used in this study. Focus group discussions guided by the socio-ecological model (sem), of which transcripts were analysed deductively using the sem and inductively using thematic analysis to identify themes at each sem level.

SETTING

Two co-educational public primary schools in Singapore.

PARTICIPANTS

A total of 48 children (n 26 girls) took part in the semi-structured focus group discussions. Their mean age was 10·8 years (sd = 0·9, range 9-12 years), and the majority of the children were Chinese (n 36), along with some Indians (n 8) and Malays (n 4).

RESULTS

Children's knowledge of healthy eating did not necessarily translate into healthy dietary practices and concern for health was a low priority. Instead, food and taste preferences were pivotal influences in their food choices. Parents had a large influence on children with regards to their accessibility to food, their attitudes and values towards food. Parental food restriction led to some children eating in secrecy. Peer influence was not frequently reported by children. Competitions in school incentivised children to consume fruits and vegetables, but reinforcements from teachers were inconsistent. The proximity of fast-food chains in the neighbourhood provided children easy access to less healthy foods. Health advertisements on social media rather than posters worked better in drawing children's attention.

CONCLUSIONS

Findings highlighted important factors that should be considered in future nutrition interventions targeting children.

The Utility of Amino Acid Metabolites in the Diagnosis of Major Depressive Disorder and Correlations with Depression Severity

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a high disease burden. There are currently no validated biomarkers for the diagnosis and treatment of MDD. This study assessed serum amino acid metabolite changes between MDD patients and healthy controls (HCs) and their association with disease severity and diagnostic utility. In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. For amino acid profiling, serum samples were analysed and quantified by liquid chromatography-mass spectrometry (LC-MS). Receiver-operating characteristic (ROC) curves were used to classify putative candidate biomarkers. MDD patients had significantly higher serum levels of glutamic acid, aspartic acid and glycine but lower levels of 3-Hydroxykynurenine; glutamic acid and phenylalanine levels also correlated with depression severity. Combining these four metabolites allowed for accurate discrimination of MDD patients and HCs, with 65.7% of depressed patients and 62.9% of HCs correctly classified. Glutamic acid, aspartic acid, glycine and 3-Hydroxykynurenine may serve as potential diagnostic biomarkers, whereas glutamic acid and phenylalanine may be markers for depression severity. To elucidate the association between these indicators and clinical features, it is necessary to conduct additional studies with larger sample sizes that involve a spectrum of depressive symptomatology.

The diagnostic utility of miRNA and elucidation of pathological mechanisms in major depressive disorder

AIMS

Our study aims to explore how miRNAs can elucidate the molecular mechanisms of major depressive disorder (MDD) by comparing the miRNA levels in the blood serum of patients with depression and healthy individuals. It also explores the potential of miRNAs to differentiate between depressed patients and healthy controls.

METHODS

60 healthy controls (n = 45 females) were matched to 60 depressed patients (n = 10 unmedicated) for age (±7), sex, ethnicity, and years of education. Depression severity was measured using the Hamilton Depression Rating Scale, and venous blood was collected using PAXgene Blood RNA tubes for miRNA profiling. To further identify the depression-related biological pathways that are influenced by differentially expressed miRNAs, networks were constructed using QIAGEN Ingenuity Pathway Analysis. Receiver operating characteristic (ROC) analyses were also conducted to examine the discriminative ability of miRNAs to distinguish between depressed and healthy individuals.

RESULTS

Six miRNAs (miR-542-3p, miR-181b-3p, miR-190a-5p, miR-33a-3p, miR-3690 and miR-6895-3p) showed to be considerably down-regulated in unmedicated depressed patients relative to healthy controls. miR-542-3p, in particular, also has experimentally verified mRNA targets that are predicted to be associated with MDD. ROC analyses found that a panel combining miR-542-3p, miR-181b-3p and miR-3690 produced an area under the curve value of 0.67 in distinguishing between depressed and healthy individuals.

CONCLUSIONS

miRNAs - most notably, miR-542-3p, miR-181b-3p and miR-3690 - may be biomarkers with targets that are implicated in the pathophysiology of depression. They could also be used to distinguish between depressed and healthy individuals with reasonable accuracy.

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy

BACKGROUND

Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

AIM

Establish an objective measurement and evaluation of MDD.

METHODS

Functional near-infrared spectroscopy (fNIRS) was used to investigate the brain activity of MDD patients and healthy controls (HCs). Leveraging a sizeable fNIRS dataset of 263 HCs and 251 patients with MDD, including mild to moderate MDD (mMDD; n = 139) and severe MDD (sMDD; n = 77), we developed an interpretable deep learning model for screening MDD and staging its severity.

RESULTS

The proposed deep learning model achieved an accuracy of 80.9% in diagnostic classification and 78.6% in severity staging for MDD. We discerned five channels with the most significant contribution to MDD identification through Shapley additive explanations (SHAP), located in the right medial prefrontal cortex, right dorsolateral prefrontal cortex, right superior temporal gyrus, and left posterior superior frontal cortex. The findings corresponded closely to the features of haemoglobin responses between HCs and individuals with MDD, as we obtained a good discriminative ability for MDD using cortical channels that are related to the disorder, namely the frontal and temporal cortical channels with areas under the curve of 0.78 and 0.81, respectively.

CONCLUSION

Our study demonstrated the potential of integrating the fNIRS system with artificial intelligence algorithms to classify and stage MDD in clinical settings using a large dataset. This approach can potentially enhance MDD assessment and provide insights for clinical diagnosis and intervention.

Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA

While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders' group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement-The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder

Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. The task change of total haemoglobin (HbT), defined as the difference between pre-task and post-task average HbT concentrations, in the dorsolateral prefrontal cortex (dlPFC) is significantly correlated with treatment response (p < 0.005). Leveraging a Naïve Bayes model, inner cross-validation performance (bAcc = 70% [SD = 4], AUC = 0.77 [SD = 0.04]) and outer cross-validation results (bAcc = 73% [SD = 3], AUC = 0.77 [SD = 0.02]) were yielded for predicting response using solely fNIRS data. The bimodal model combining fNIRS and clinical data showed inferior performance in outer cross-validation (bAcc = 68%, AUC = 0.70) compared to the fNIRS-only model. Collectively, fNIRS holds potential as a scalable neuroimaging modality for predicting treatment response in MDD.