scholarly journals Beta Inflated Regression Models on the Physical and Mental Health of Nigerian Stroke Survivors

2021 ◽  
Vol 7 (3) ◽  
pp. 302-312
Author(s):  
KS Oritogun ◽  
OO Oyewole
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Blood Pressure ◽  
Health Risk ◽  
Physical Health ◽  
Regression Models ◽  
Outcome Variable ◽  
Stroke Survivors ◽  
Physical And Mental Health ◽  
Health Risk Factors

Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions. Therefore, the Beta Regression models family for data between zero and one will be appropriate. Objectives: To identify a suitable model and the likely risk factors of physical and mental health of stroke survivors. Method: Secondary data of stroke survivors from two tertiary health Institutions in Ogun State, Nigeria, were analysed. Inflated Beta (BEINF) and Inflated-at-one-Beta (BEINF1) models were compared using Deviance (DEV), Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) for model selection. The model with minimum DEV, AIC and BIC was considered to be better. Results: The deviance (-86.0604,), AIC (-46.0604) and BIC (6.4391) values of the BEINF1 model for physical health and the deviance (-20.1217), AIC (19.8783) and BIC (72.3778) values of BEINF1 model for mental health were smaller than BEINF models. Therefore, BEINF1 was the better model to identify the health risk factors of stroke survivors. Age, marital status, diastolic blood pressure, disability duration and systolic blood pressure had a significant association with physical health, while BMI had a significant positive association with mental health.  Conclusion: The beta-inflated-at-one (BEINF1) model is suitable for identifying health risk factors of stroke survivors when the outcome variable is a proportion. Both demographic and clinical characteristics were significantly associated with the health of stroke survivors. This study would assist researchers in knowing the appropriate model for analysing proportion or percentage response variables.

scholarly journals Prediction Algorithm of Young Students’ Physical Health Risk Factors Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xianping Yin
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Physical Health ◽  
Human Body ◽  
Vascular System ◽  
Overweight And Obesity ◽  
Prediction Algorithm ◽  
Fundus Images ◽  
Physical And Mental Health ◽  
Health Risk Factors

Young people’s physical and mental health is the foundation of society’s overall development and the key to improving people’s health quality. Middle school students’ physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students caused by adolescent health risk behavior factors. Researchers have been concerned about the retinal fundus vascular system, which is the only internal vascular system that can be observed in a noninvasive state of the human body. Fundus images contain a wealth of disease-related information. Fundus images have been widely used in the field of medical auxiliary diagnosis because many important systemic diseases of the human body cause specific reactions in the fundus. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. In light of the retinal arteriovenous division of discontinuous challenges, the topological structure of the constraint system along with overcoming the network and topology restrictions is monitored. Finally, simulation experiments were conducted on two publicly available datasets. The findings show that the proposed method is reliable, effective, and accurate in predicting physical health risk factors in adolescent students.

scholarly journals Timed Up and Go (TUG) Test

2016 ◽  
Vol 8 (1) ◽  
pp. 9-13 ◽  
Author(s):  
Breelan M. Kear ◽  
Thomas P. Guck ◽  
Amy L. McGaha
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Primary Care ◽  
Body Mass Index ◽  
Socioeconomic Status ◽  
Health Risk ◽  
Body Mass ◽  
Physical And Mental Health ◽  
Health Risk Factors ◽  
Timed Up And Go

Purpose: The Timed Up and Go (TUG) test is a reliable, cost-effective, safe, and time-efficient way to evaluate overall functional mobility. However, the TUG does not have normative reference values (NRV) for individuals younger than 60 years. The purpose of this study was to establish NRV for the TUG for individuals aged between 20 and 59 years and to examine the relationship between the TUG and demographic, physical, and mental health risk factors. Methods: Two hundred participants, 50 per decade (ages 20-29, 30-39, 40-49, 50-59 years) were selected at their primary care visit, and timed as they performed the TUG by standing up out of a chair, walking 3 m, turning around, walking back to the chair, and sitting down. Information regarding the risk factors socioeconomic status, body mass index, an index of multimorbidities, perceptions of overall physical and mental health was obtained and used as predictors of TUG time independent of age. Results: TUG times were significantly different among the decades ( F = 6.579, P = .001) with slower times occurring with the 50-year-old decade compared with the 20s ( P = .001), 30s ( P = .001), and 40s ( P = .020). Slower TUG times were associated with lower SES, higher body mass index, more medical comorbidities, and worse perceived physical and mental health. Regression results indicated that perceived physical and mental health accounted for unique variance in the prediction of TUG time beyond age, gender, and socioeconomic status. Conclusions: This study provided TUG NRV for adults in their 20s, 30s, 40s, and 50s. The TUG may have utility for primary care providers as they assess and monitor physical activity in younger adults, especially those with physical and mental health risk factors.

Cannabis use among early adolescents and transdiagnostic mental health risk factors

2021 ◽  
pp. 135910452199463
Author(s):  
Sara Moreno-Mansilla ◽  
Jorge J Ricarte ◽  
David J Hallford
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Health Risk ◽  
Suicide Attempts ◽  
Intolerance Of Uncertainty ◽  
Cannabis Use ◽  
Health Risk Factors ◽  
Symptoms Of Depression ◽  
Modifiable Factors ◽  
Wide Range

Introduction: Cannabis is the most widely used psychoactive substance among adolescents worldwide, and the age at which consumption begins to decrease. Cannabis use in adolescents is associated with a wide range of adverse consequences in adulthood including increased vulnerability to psychosis and other mental disorders, as well as suicidal ideation and attempt. The aim of this study is to extend understanding of the link between cannabis use and mental illness by examining whether cannabis use at early ages predicts transdiagnostic variables that are precursors to severe clinical diagnoses. Methods: A descriptive cross-sectional study was conducted. The sample was made up of 605 adolescents from 7th to 9th grades, with a mean age of 13.2 years ( SD = 1.0, 47% girls). The variables evaluated were: anomalous perception of reality, intolerance of uncertainty, rumination, suicide attempt, hopelessness, and symptoms of depression and anxiety. The administration of the questionnaires was carried out in groups of 20 participants under the supervision of a researcher in a unique session of 1 hour. Results: Adolescent cannabis users scored higher on all variables assessed: anomalous perception of reality (Cohen’s d = .60), rumination ( d = .48), intolerance of uncertainty ( d = .11), suicidal attempt (affirmative answer: 25.9% of users vs 7.7% of non-users), hopelessness ( d = .85), symptoms of depression ( d = .80), and anxiety ( d = .39). A binary logistic regression showed that the only variable uniquely related to cannabis use was hopelessness (Wald = 4.560, OR: 1.159, p = .033). Conclusions: Among some mental health risk factors, hopelessness appears uniquely related to cannabis use. Adolescents may use cannabis as a coping strategy for negative thoughts and emotions, or it may be a consequence of cannabis use. Future prevention programs should focus on preventing/treating modifiable factors such as hopelessness, and delaying cannabis use in specific subgroups of adolescents who experience pathologies such as depression or suicide attempts.

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