Education, sense of mastery and mental health: results from a nation wide health monitoring study in Norway

AbstractDeveloping effective models of identifying and managing physical ill health amongst mental health service users has become an increasing concern for psychiatric service providers. This article sets out the general professional and Irish statutory obligations to provide physical health monitoring services for individuals with serious mental illness. Review and summary statements are provided in relation to the currently available guidelines on physical health monitoring.

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The importance of mental health monitoring during transfer to adult care settings as examined among paediatric transplant recipients

Journal of Paediatrics and Child Health ◽

10.1111/jpc.12765 ◽

2014 ◽

Vol 51 (2) ◽

pp. 220-222 ◽

Cited By ~ 9

Author(s):

Rachel A Annunziato ◽

Nicole Arrato ◽

Melissa Rubes ◽

Ronen Arnon

Keyword(s):

Mental Health ◽

Health Monitoring ◽

Transplant Recipients ◽

Adult Care

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Deep Learning-based Mental Health Monitoring Scheme for College Students Using Convolutional Neural Network

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021400145 ◽

2021 ◽

Vol 30 (06n08) ◽

Author(s):

Chao Du ◽

Chang Liu ◽

P. Balamurugan ◽

P. Selvaraj

Keyword(s):

Neural Network ◽

Mental Health ◽

College Students ◽

Deep Learning ◽

Convolutional Neural Network ◽

Health Monitoring ◽

Simulation Analysis ◽

Personality Development ◽

Monitoring Scheme ◽

Depression Level

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models.

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Automatical Emotion Recognition Based on Daily Gait

Analyzing Human Behavior in Cyberspace - Advances in Human and Social Aspects of Technology ◽

10.4018/978-1-5225-7128-5.ch004 ◽

2019 ◽

pp. 61-74

Author(s):

Xiaoqian Liu ◽

Tingshao Zhu

Keyword(s):

Mental Health ◽

Emotion Recognition ◽

Health Monitoring ◽

Wearable Devices ◽

Psychology Research ◽

Acceleration Sensor ◽

Emotion Identification ◽

Technology Application ◽

Sensing Technology ◽

Acceleration Data

In this chapter, a kind of emotion recognition method based on gait using a customized smart bracelet with a built-in acceleration sensor was introduced in detail. The results showed that the classification accuracies of angry-neutral, happy-neutral, angry-happy, and angry-happy-neutral using the acceleration data of wrist are 91.3%, 88.5%, 88.5%, and 81.2%, respectively. Besides, the wearable devices and motion-sensing technology application in psychology research have been further discussed, and non-contact emotion identification and mental health monitoring based on offline behaviors were reviewed summarily.

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Nutrient profiling and adherence to components of the UK national dietary guidelines association with metabolic risk factors for CVD and diabetes: Airwave Health Monitoring Study

British Journal Of Nutrition ◽

10.1017/s0007114518000016 ◽

2018 ◽

Vol 119 (6) ◽

pp. 695-705 ◽

Cited By ~ 7

Author(s):

Rebeca Eriksen ◽

Rachel Gibson ◽

Kathryn Lamb ◽

Yvonne McMeel ◽

Anne-Claire Vergnaud ◽

...

Keyword(s):

Risk Factors ◽

Waist Circumference ◽

Diet Quality ◽

Health Monitoring ◽

Quality Index ◽

Cardiometabolic Risk ◽

Epidemiological Studies ◽

Metabolic Risk ◽

Point Increase ◽

Monitoring Study

AbstractCVD is the leading cause of death worldwide. Diet is a key modifiable component in the development of CVD. No official UK diet quality index exists for use in UK nutritional epidemiological studies. The aims of this study are to: (i) develop a diet quality index based on components of UK dietary reference values (DRV) and (ii) determine the association between the index, the existing UK nutrient profile (NP) model and a comprehensive range of cardiometabolic risk markers among a British adult population. A cross-sectional analysis was conducted using data from the Airwave Health Monitoring Study (n 5848). Dietary intake was measured by 7-d food diary and metabolic risk using waist circumference, BMI, blood lipid profile, glycated Hb (HbA1c) and blood pressure measurements. Diet quality was assessed using the novel DRV index and NP model. Associations between diet and cardiometabolic risk were analysed via multivariate linear models and logistic regression. A two-point increase in NP score was associated with total cholesterol (β −0·33 mmol/l, P<0·0001) and HbA1c (β −0·01 %, P<0·0001). A two-point increase in DRV score was associated with waist circumference (β −0·56 cm, P<0·0001), BMI (β −0·15 kg/m2, P<0·0001), total cholesterol (β −0·06 mmol/l, P<0·0001) and HbA1c (β −0·02 %, P=0·002). A one-point increase in DRV score was associated with type 2 diabetes (T2D) (OR 0·94, P=0·01) and obesity (OR 0·95, P<0·0001). The DRV index is associated with overall diet quality and risk factors for CVD and T2D, supporting its application in nutritional epidemiological studies investigating CVD risk in a UK population.

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