Self-Rated Depressive Symptoms in Children and Youth with and without Cerebral Palsy: A Pilot Study

Asano, Daiki; Takeda, Masaki; Nobusako, Satoshi; Morioka, Shu

doi:10.3390/bs10110167

Cited by 8 publications

(10 citation statements)

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“…The parent version of the Strengths and Difficulties Questionnaire was used to assess students’ psychological attributes [ 30 ]. The scale was composed of 25 items covering negative and positive attributes; each item was answered with either “not true=0,” “somewhat true=1,” and “certainly true=2.” The scale included emotional symptoms, behavior problems, hyperactivity or inattention, peer relationship problems, and pro-social behavior.…”

Section: Methodsmentioning

confidence: 99%

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Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

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Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

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Section: Methodsmentioning

confidence: 99%

“…The parent version of the Strengths and Difficulties Questionnaire was used to assess students' psychological attributes [30]. The scale was composed of 25 items covering negative and positive attributes; each item was answered with either "not true=0," "somewhat true=1," and "certainly true=2."…”

Section: Psychological Attributesmentioning

confidence: 99%

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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“…Other factors that could be considered in the future to explain the variation include measuring if AYA with CP have meaningful participation at work and in recreation activities, social isolation, and stress associated with school, work, peer relationships, managing finances, and making health care decisions [ 54 ]. A decline in social participation can also contribute to a decline in mental health [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Correlates of Mental Health in Adolescents and Young Adults with Cerebral Palsy: A Cross-Sectional Analysis of the MyStory Project

Gorter

Ferro²,

González³

et al. 2022

JCM

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Background: It is important to gain a better understanding of mental health issues in adolescents and young adults (AYA) with cerebral palsy (CP). In this cross-sectional study, we explore if demographics, social and clinical questionnaire scores, and cortisol levels in hair samples from AYA with CP are associated with higher scores on anxiety and/or depression questionnaires. Methods: Data from a community-based sample of 63 AYA with CP (30 females; ages 16 to 30 (median age of 25)) were analyzed. Forty-one (65%) participants (20 females) provided a hair sample. Outcomes were assessed using bivariate linear regression analyses and hierarchical regression analyses. Results: Clinical depressive and anxiety symptoms were present in 33% and 31% of participants, respectively. Family functioning, B = 9.62 (95%CI: 5.49–13.74), fatigue, B = 0.15 (95%CI: 0.05–0.25), and pain, B = 1.53 (95%CI: 0.48–2.58) were statistically significant predictors of depressive symptoms. Fatigue, B = 0.24 (95%CI: 0.12–0.35) and pain, B = 1.63 (95%CI: 0.33–2.94) were statistically significant predictors of anxiety. Cortisol levels from hair samples were not found to be associated with depressive symptoms or anxiety. Conclusions: A high prevalence of mental health problems and co-occurring physical problems was found in AYA with CP. Integrating mental support into regular care for AYA with CP is recommended.

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“…Previous research has shown that the restriction of various daily and outdoor activities can lead to mental health problems, such as depressive symptoms, in children with CP [ 22 ]. Particularly, decline of activities and enjoyment in their daily life was identified as a contributing factor to greater severity of depressive symptoms [ 23 ]. Therefore, it is necessary to pay attention to changes in mental health and behavior and take appropriate action as soon as signs are detected in such children.…”

Section: Discussionmentioning

confidence: 99%

Decline in Motor Function during the COVID-19 Pandemic Restrictions and Its Recovery in a Child with Cerebral Palsy: A Case Report

Asano¹,

Kikuchi²,

Yamakawa³

et al. 2021

Children

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Children with cerebral palsy (CP) experience various restrictions owing to their underdeveloped mobility. Home confinement due to the coronavirus disease 2019 pandemic may further increase these restrictions. We report the case of a 7-year-old boy with CP (Gross Motor Function Classification System level IV) whose motor function declined during the period when physical therapy was discontinued due to lockdown, approximately four months. At the end of the home confinement, the patient’s ability to maintain a sitting posture and weight-bearing capacity of the lower extremities decreased. His Gross Motor Function Measure total score of 88 also decreased from 34.5% to 31.9%. After resuming physical therapy, the patient recovered the function status seen before the discontinuation of physical therapy, but this took almost twice as long as the confinement period. We reaffirm that frequent physical therapy is crucial for maintaining motor function in non-ambulatory children with CP. As a countermeasure for the future, urgent efforts are needed for the development of telerehabilitation.

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Self-Rated Depressive Symptoms in Children and Youth with and without Cerebral Palsy: A Pilot Study

Cited by 8 publications

References 50 publications

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Correlates of Mental Health in Adolescents and Young Adults with Cerebral Palsy: A Cross-Sectional Analysis of the MyStory Project

Decline in Motor Function during the COVID-19 Pandemic Restrictions and Its Recovery in a Child with Cerebral Palsy: A Case Report

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