scholarly journals Analysis of physical health risk dynamic evaluation system based on sports network technology

2021 ◽  
Author(s):  
Lianzhen Chen ◽  
Hua Zhu
Keyword(s):  
Health Risk ◽  
Physical Health ◽  
Evaluation System ◽  
Network Technology ◽  
Dynamic Evaluation

scholarly journals Exploring the Acceptance of Entomophagy: A Survey of Italian Consumers

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 123
Author(s):  
Roberta Moruzzo ◽  
Simone Mancini ◽  
Fabio Boncinelli ◽  
Francesco Riccioli
Keyword(s):  
Health Risk ◽  
Physical Health ◽  
Food Industry ◽  
Consumer Preferences ◽  
Food Product ◽  
Food Sources ◽  
Food Neophobia ◽  
Significant Interest ◽  
Set Up ◽  
Specific Scale

Insect-based food is not common in Europe, because most people do not consider insects to be edible, but rather a threat and a health risk. Fear and refusal to eat a new food product introduced into a culture is called food neophobia, which results in a hesitation to trying and experimenting with new foods. Although there is significant interest in this novel sector, there is a lack of research on the link between rejection, the level of food neophobia, and consumer behavior related to the introduction of insects into the diet. In this study, through 420 questionnaires, a specific experimental scale of insects was introduced which, together with a neophobia scale, analyzed the probability and the intention of respondents to consume insects. Another issue tested has been their intention to eat food containing insects. We observed that the analyses of the two scales produced different results, confirming the need for a specific scale to measure “insect phobia”. This is important, since knowledge about consumer preferences for and barriers to using insects as human food sources is limited but necessary in order to set up commercialization strategies. The development of insect-based food offers physical health benefits and also improves the sustainability of the food industry.

Physical Health Risk Behaviours in Young People with Mental Illness

2015 ◽  
Vol 36 (10) ◽  
pp. 781-790 ◽  
Author(s):  
Andrea McCloughen ◽  
Kim Foster ◽  
Nikka Marabong ◽  
David Miu ◽  
Judith Fethney
Keyword(s):  
Mental Illness ◽  
Young People ◽  
Health Risk ◽  
Physical Health ◽  
Risk Behaviours ◽  
People With Mental Illness ◽  
Health Risk Behaviours

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 Health risk behaviours among people with severe mental ill health during the COVID-19 pandemic: Analysis of linked cohort data

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258349
Author(s):  
Emily Peckham ◽  
Victoria Allgar ◽  
Suzanne Crosland ◽  
Paul Heron ◽  
Gordon Johnston ◽  
...  
Keyword(s):  
Health Risk ◽  
Physical Health ◽  
Health Inequalities ◽  
Dietary Factors ◽  
Drinking Patterns ◽  
Risk Behaviours ◽  
Health Risk Behaviours ◽  
Severe Mental Ill Health ◽  
Lack Of Exercise ◽  
Data Linkages

Background People with severe mental ill health (SMI) experience a mortality gap of 15–20 years. COVID-19 has amplified population health inequalities, and there is concern that people with SMI will be disproportionately affected. Understanding how health risk behaviours have changed during the pandemic is important when developing strategies to mitigate future increases in health inequalities. Methods We sampled from an existing cohort of people with SMI. Researchers contacted participants by phone or post to invite them to take part in a survey about how the pandemic had affected them. We asked people about their health risk behaviours and how these had changed during the pandemic. We created an index of changed behaviours, comprising dietary factors, smoking, lack of exercise, and drinking patterns. By creating data linkages, we compared their responses during pandemic restrictions to responses they gave prior to the pandemic. Outcomes 367 people provided health risk data. The mean age of the participants was 50.5 (range = 20 to 86, SD ± 15.69) with 51.0% male and 77.4% white British. 47.5% of participants reported taking less physical activity during the pandemic and of those who smoke 54.5% reported smoking more heavily. Self-reported deterioration in physical health was significantly associated with an increase in health risk behaviours (adjusted OR for physical health 1.59, 95%CI 1.22–2.07; adjusted OR for Age 0.99, 95%CI 0.98–1.00). Interpretation COVID-19 is likely to amplify health inequalities for people with SMI. Health services should target health risk behaviours for people with SMI to mitigate the immediate and long lasting impacts of the COVID-19 pandemic.

scholarly journals Executive Function Evaluation System Based on Wiping Desk Behavior in Virtual Space

2020 ◽  
Vol 9 (4) ◽  
pp. 149-159
Author(s):  
Sho Ooi ◽  
◽  
Akihito Mihara ◽  
Mutsuo Sano ◽  
Hajime Tabuchi ◽  
...  
Keyword(s):  
Executive Function ◽  
Brain Injury ◽  
Real Time ◽  
Evaluation System ◽  
Brain Injuries ◽  
Acquired Brain Injury ◽  
Virtual Space ◽  
Dynamic Evaluation ◽  
Time Dynamic ◽  
Cleaning Behavior

In this study, we report the development of a virtual reality (VR) system that evaluates executive function based on cleaning behaviors in real-time. It is known that patients with acquired brain injuries present adverse symptoms such as attention, memory, and functional disorders, as well as aphasia. Current methods to evaluate acquired brain injuries include tests such as the behavioral assessment of the dysexecutive syndrome (BADS) and digital clinical assessment for attention (D-CAT) tests; however, these tests require special/specific toolkits, and the methods are rigorous when applied for real-time dynamic evaluation. Moreover, patients are often burdened by the need to undergo paper tests as required by the evaluation methods. In this context, we propose and verify the efficacy of a method that offers real-time dynamic evaluation of acquired brain injury based on daily-living activities such as cooking, cleaning, and shopping. In this study, we focus on executive function relating to acquired brain injury, and we propose a real-time dynamic evaluation method for executive functioning using VR, which enables the automatic evaluation of subjects’ table-cleaning behavior. Our results indicate that our system can automatically assess subjects’ table cleaning behavior based on BADS test, while affording a cleaning behavior maximum average accuracy of 75.5%.

scholarly journals Health risk behaviours among people with severe mental ill health during the COVID-19 pandemic: analysis of linked cohort data

2021 ◽  
Author(s):  
Emily Peckham ◽  
Victoria Allgar ◽  
Suzanne Crosland ◽  
Paul Heron ◽  
Gordon Johnston ◽  
...  
Keyword(s):  
Health Risk ◽  
Physical Health ◽  
Health Inequalities ◽  
Dietary Factors ◽  
Drinking Patterns ◽  
Risk Behaviours ◽  
Younger Age ◽  
Health Risk Behaviours ◽  
Severe Mental Ill Health ◽  
Data Linkages

Background People with severe mental ill health (SMI) experience a mortality gap of 15-20 years. COVID-19 has amplified population health inequalities, and there is concern that people with SMI will be disproportionately affected. Understanding how health risk behaviours have changed during the pandemic is important when developing strategies to mitigate future increases in health inequalities. Methods We sampled from an existing cohort of people with SMI. Researchers contacted participants by phone or post to invite them to take part in a survey about how the pandemic had affected them. We asked people about their health risk behaviours and how these had changed during the pandemic. We created an index of changed behaviours, comprising dietary factors, smoking, lack of exercise, and drinking patterns. By creating data linkages, we compared their responses during pandemic restrictions to responses they gave prior to the pandemic. Outcomes 367 people provided health risk data. 47.5% of participants reported taking less physical activity during the pandemic and of those who smoke 54.5% reported smoking more heavily. Self-reported deterioration in physical health and younger age were significantly associated with an increase in health risk behaviours (adjusted OR for physical health 1.59, 95%CI 1.22-2.07; adjusted OR for Age 0.99, 95%CI 0.98-1.00). Interpretation COVID-19 is likely to amplify health inequalities for people with SMI. Health services should target health risk behaviours for people with SMI to mitigate the immediate and long lasting impacts of the COVID-19 pandemic.

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.

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