Wearables, Artificial intelligence, and the Future of Healthcare

2020 ◽  
pp. 104-129 ◽  
Author(s):  
Omar F. El-Gayar ◽  
Loknath Sai Ambati ◽  
Nevine Nawar
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Eating Habits ◽  
Wearable Devices ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Health And Wellbeing ◽  
Current State ◽  
Unhealthy Eating ◽  
Underlying Risk

Common underlying risk factors for chronic diseases include physical inactivity accompanying modern sedentary lifestyle, unhealthy eating habits, and tobacco use. Interestingly, these prominent risk factors fall under what is referred to as modifiable behavioral risk factors, emphasizing the importance of self-care to improve wellness and prevent the onset of many debilitating conditions. In that regard, advances in wearable devices capable of pervasively collecting data about oneself coupled with the analytic capability provided by artificial intelligence and machine learning can potentially upend how we care for ourselves. This chapter aims to assess the current state and future implications of using big data and artificial intelligence in wearables for health and wellbeing. The results of the systematic review capture key developments and emphasize the potential for leveraging AI and wearables for inducing a paradigm shift in improving health and wellbeing.

scholarly journals Prevalence and characteristics of risk factors for non-communicable diseases in semi-urban communities

2020 ◽  
Vol 34 (4) ◽  
pp. 295-303 ◽  
Author(s):  
Juntima Nawamawat ◽  
Wipa Prasittichok ◽  
Thansinee Prompradit ◽  
Suwapich Chatchawanteerapong ◽  
Vipaporn Sittisart
Keyword(s):  
Risk Factors ◽  
Alcohol Consumption ◽  
Disease Prevention ◽  
Prevalence Rate ◽  
Eating Habits ◽  
Communicable Diseases ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Non Communicable Diseases ◽  
Content Type

PurposeThe purpose of this research aimed to identify the risk factors for non-communicable diseases (NCDs) and determine their prevalence and characteristics in a semi-urban community in Thailand.Design/methodology/approachThe survey was designed to determine the type and prevalence of risk factors for NCDs among populations in semi-urban areas in the Takianleurn subdistrict of Nakhonsawan, Thailand. A stratified random sampling design was used to select 352 subjects, aged over 15 years and living in this region. Data were collected by questionnaire and analyzed to show frequency, percentage, mean, standard deviation, chi-squared, prevalence rate and prevalence rate ratio with significance indicated by p-value < 0.05 and confidence interval 95 percent.Research limitations/implicationsThe implications for the future study are as follows: (1) a comparative study between rural and urban or rural and semi-urban or urban and semi-urban should be studied to understand how risk factors cause NCDs and (2) Participatory action research should be introduced to assess the effectiveness of the decrease in NCDs risk factors management in the community.Practical implications(1) To scale up public health interventions measures to promote and prevent NCDs should be focused on behavioral risk factors of NCDs such as eating habits, physical activity, smoking and alcohol consumption. (2) Health promotion and disease prevention for decrease in NCDs should consist of reducing alcohol consumption and enhancing healthy eating habits and (3) To manage unmodified risk factors such as age, gender, educational level, etc. should be focused on surveillance and physical health examination yearly.FindingsThe results revealed a prevalence of NCDs of 14.8 percent. The main unmodifiable risk factors affecting NCD prevalence were gender, age, low level of education and poverty; behavioral risk factors included not eating enough fruit and vegetables, high alcohol consumption, a high-fat fast-food diet and smoking.Originality/valueThe prevention of NCDs requires more focus on changing the eating behavior of high-risk groups and providing easily accessible health care information and services. The entire family should be involved in the process of maintaining good health and disease prevention for all family members.

scholarly journals Psychological and Behavioral Predictors of Vaccine Efficacy: Considerations for COVID-19

2021 ◽  
pp. 174569162198924
Author(s):  
Annelise A. Madison ◽  
M. Rosie Shrout ◽  
Megan E. Renna ◽  
Janice K. Kiecolt-Glaser
Keyword(s):  
Risk Factors ◽  
Side Effects ◽  
Vaccine Efficacy ◽  
Psychological Factors ◽  
Behavioral Interventions ◽  
Vulnerable Groups ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Vaccine Response ◽  
The Impact

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidates are being evaluated, with the goal of conferring immunity on the highest percentage of people who receive the vaccine as possible. It is noteworthy that vaccine efficacy depends not only on the vaccine but also on characteristics of the vaccinated. Over the past 30 years, a series of studies has documented the impact of psychological factors on the immune system’s vaccine response. Robust evidence has demonstrated that stress, depression, loneliness, and poor health behaviors can impair the immune system’s response to vaccines, and this effect may be greatest in vulnerable groups such as the elderly. Psychological factors are also implicated in the prevalence and severity of vaccine-related side effects. These findings have generalized across many vaccine types and therefore may be relevant to the SARS-CoV-2 vaccine. In this review, we discuss these psychological and behavioral risk factors for poor vaccine responses, their relevance to the COVID-19 pandemic, as well as targeted psychological and behavioral interventions to boost vaccine efficacy and reduce side effects. Recent data suggest these psychological and behavioral risk factors are highly prevalent during the COVID-19 pandemic, but intervention research suggests that psychological and behavioral interventions can increase vaccine efficacy.

scholarly journals Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach

2021 ◽  
Vol 18 (2) ◽  
pp. 612
Author(s):  
Nam Jeong Jeong ◽  
Eunil Park ◽  
Angel P. del Pobil
Keyword(s):  
Risk Factors ◽  
Environmental Factors ◽  
South Korea ◽  
Communicable Diseases ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Non Communicable Diseases ◽  
Path Analyses ◽  
Social Environmental ◽  
Social Environmental Factors

Non-communicable diseases (NCDs) are one of the major health threats in the world. Thus, identifying the factors that influence NCDs is crucial to monitor and manage diseases. This study investigates the effects of social-environmental and behavioral risk factors on NCDs as well as the effects of social-environmental factors on behavioral risk factors using an integrated research model. This study used a dataset from the 2017 Korea National Health and Nutrition Examination Survey. After filtering incomplete responses, 5462 valid responses remained. Items including one’s social-environmental factors (household income, education level, and region), behavioral factors (alcohol use, tobacco use, and physical activity), and NCDs histories were used for analyses. To develop a comprehensive index of each factor that allows comparison between different concepts, the researchers assigned scores to indicators of the factors and calculated a ratio of the scores. A series of path analyses were conducted to determine the extent of relationships among NCDs and risk factors. The results showed that social-environmental factors have notable effects on stroke, myocardial infarction, angina, diabetes, and gastric, liver, colon, lung, and thyroid cancers. The results indicate that the effects of social-environmental and behavioral risk factors on NCDs vary across the different types of diseases. The effects of social-environmental factors and behavioral risk factors significantly affected NCDs. However, the effect of social-environmental factors on behavioral risk factors was not supported. Furthermore, social-environmental factors and behavioral risk factors affect NCDs in a similar way. However, the effects of behavioral risk factors were smaller than those of social-environmental factors. The current research suggests taking a comprehensive view of risk factors to further understand the antecedents of NCDs in South Korea.

Abstract WP182: Trends in Acute Ischemic Stroke Hospitalizations and Risk Factors Among Young Adults: 12 Years of Nationally Representative Data

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Urvish K Patel ◽  
Priti Poojary ◽  
Vishal Jani ◽  
Mandip S Dhamoon
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Young Adults ◽  
Ischemic Stroke ◽  
Length Of Stay ◽  
Acute Ischemic Stroke ◽  
Stroke Risk ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Stroke Risk Factors

Background: There is limited recent population-based data of trends in acute ischemic stroke (AIS) hospitalization rates among young adults (YA). Rising prevalence of stroke risk factors may increase stroke rates in YA. We hypothesized that 1) stroke hospitalizations and mortality among YA are increasing over time (2000-2011), 2) besides traditional stroke risk factors, non-traditional factors are associated with stroke in YA, 3) stroke hospitalization among YA is associated with higher mortality, length of stay (LOS), and cost. Methods: In the Nationwide Inpatient Sample database (years 2000-2011), adult hospitalizations for AIS and concurrent diagnoses were identified by ICD-9-CM codes; the analytic cohort constituted all AIS hospitalizations. We performed weighted analysis using chi-square, t-test, and Jonckheere trend test. Multivariable survey regression models evaluated interactions between age group (18-45 vs. >45 years) and traditional and non-traditional risk factors, with outcomes including mortality, LOS, and cost. Models were adjusted for race, sex, Charlson’s Comorbidity Index, primary payer, location and teaching status of hospital, and admission day. Results: Among 5220960 AIS hospitalizations, 231858 (4.4%) were YA. On trend analysis, proportion of YA amongst AIS increased from 3.6% in 2000 to 4.7% in 2011 (p<0.0001) but mortality in YA decreased from 3.7% in 2000 to 2.6% in 2011, compared to 7.1% in 2000 to 4.6% in 2011 (p<0.0001) among older adults. Non-traditional, especially behavioral, risk factors were more common among YA, and LOS and cost were higher (Table). Conclusion: There was a trend for higher proportion of YA among AIS hospitalizations, though there was a decreasing mortality trend over 10 years. Behavioral risk factors were more common among YA, and there was an increased length of stay and cost. AIS in YA may require different preventive approaches compared to AIS among older adults.

Monitoring the dynamics of behavioral risk factors in patients with angina pectoris and comorbid condition

Therapy ◽  
2021 ◽  
Vol 6_2021 ◽  
pp. 51-55
Author(s):  
Didigova R.T. Didigova ◽  
Evloeva D.A. Evloeva ◽  
Ugurchieva Z.O. Ugurchieva ◽  
Ugurchieva P.O. Ugurchieva ◽  
Malsagova I.Ya. Malsagova ◽  
...  
Keyword(s):  
Risk Factors ◽  
Angina Pectoris ◽  
Comorbid Condition ◽  
Behavioral Risk Factors ◽  
Behavioral Risk

Comprehensive Assessment of Behavioral Risk Factors within the Health Risk Management System for Oil Refinery Operators

2021 ◽  
pp. 16-21
Author(s):  
NI Latyshevskaya ◽  
VV Mirochnik ◽  
LA Davydenko ◽  
AI Kireeva ◽  
AV Belyaeva
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Health Risk ◽  
Oil Refinery ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Age Group ◽  
Volgograd Region ◽  
Comprehensive Health ◽  
Health Risk Management

Summary. Introduction: Comprehensive risk management considering behavioral risk factors is a possible way to minimize adverse health effects of occupational factors. The purpose of the study was assess behavioral risk factors and to develop appropriate measures for preventing occupational diseases in oil refinery operators. Materials and methods: The observation groups included crude oil treatment operators of Ritek LLC in the Volgograd Region located in the subarid climatic zone. The first group consisted of 100 workers under the age of 35 while the second group consisted of 106 workers aged 36-60. Previously published studies were used to substantiate priority occupational risk factors for the operators. To assess lifestyle habits, we conducted a questionnaire-based survey and analyzed data in terms of their statistical significance and real controllability using a multidimensional confirmatory factor analysis. Results: We established that the priority occupational health risks of operators in the climatic conditions of the Volgograd Region included labor severity and intensity (3.1) and hot environment (3.2) posing a high occupational risk of disrupting the thermal state (overheating) of workers. We also identified typical behavioral risk factors, the prevalence and quantitative burden of which was age-specific. In the younger age group, bad habits and poor healthcare activity (reluctance to seek medical advice) generated the highest burdens (943 conditional units each) while in the older age group, major burdens were generated by bad habits and malnutrition (849 and 501 units, respectively). The developed mathematical model proved that a comprehensive health risk management for workers exposed to occupational hazards is feasible by correcting certain behavioral risk factors: a 10 % and 50 % decrease in the burden of bad habits and poor healthcare activity led to a 1.1 and 1.5-fold decrease in the extent of health risk, respectively. Conclusion: The study revealed the most significant behavioral risk factors affecting health of oil refinery operators and substantiated options of the most optimal interaction between the elements of the system reducing the overall risk to human health. Comprehensive health risk management based on optimal interaction of system elements (both occupational and behavioral risk factors) reduces health risks for oil refinery operators.

Behavioral Risk Factors and Latino Body Mass Index: A Cross-Sectional Study in Missouri

2012 ◽  
Vol 23 (4) ◽  
pp. 1750-1767 ◽  
Author(s):  
J.S. Onésimo Sandoval ◽  
Jenine K. Harris ◽  
Joel P. Jennings ◽  
Leslie Hinyard ◽  
Gina Banks
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Body Mass ◽  
Cross Sectional Study ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Sectional Study ◽  
Cross Sectional
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