scholarly journals Personalized Healthcare for Dementia

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 128
Author(s):  
Seunghyeon Lee ◽  
Eun-Jeong Cho ◽  
Hyo-Bum Kwak
Keyword(s):  
Engineering Design ◽  
Cognitive Abilities ◽  
Physical Inactivity ◽  
Data Science ◽  
Wearable Devices ◽  
Genetic Profile ◽  
Personalized Healthcare ◽  
Diagnostic Approaches ◽  
Unhealthy Diet ◽  
Digital Biomarkers

Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.

Socio-demographic association of non communicable diseases’ risk factors in a representative population of school children: a cross-sectional study in Sousse (Tunisia)

2016 ◽  
Vol 29 (5) ◽  
Author(s):  
Meriam El Ghardallou ◽  
Jihene Maatoug ◽  
Imed Harrabi ◽  
Sihem Ben Fredj ◽  
Sahli Jihene ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
School Children ◽  
Physical Inactivity ◽  
Cross Sectional Study ◽  
Demographic Characteristics ◽  
Excess Weight ◽  
Sectional Study ◽  
Cross Sectional ◽  
Unhealthy Diet

Abstract Introduction: A better understanding of socio-demographic characteristics of subgroups, which have a high risk to develop chronic diseases, is essential to develop more efficient interventional programs especially for youth. This study aimed to determine the association between clusters of non communicable diseases (NCDs’) risk factors and the socio-demographic characteristics among a sample of Tunisian school children. Materials and methods: We conducted, in 2013/2014, a cross-sectional study among a proportional and stratified school children sample, selected in 17 elementary public schools in Sousse (Tunisia). A cluster analysis was used to identify different NCDs risk factors clusters, based on tobacco use, physical inactivity, unhealthy diet, and excess weight. Subsequent χ2-tests were used to identify differences between the NCDs risk factors clusters in regards to socio-demographic characteristics. Results: Four clusters of NCDs risk factors were found: 1) Cluster 1: physical inactivity behavior with normal weight, 2) Cluster 2: physical inactivity behavior associated to excess weight, 3) Cluster 3: unhealthy diet associated to excess weight and low practice of physical activity, and 4) Cluster 4: smoking behavior with physical activity behavior. The pattern of cluster membership differed across sex (<10–3), school level, and socioeconomic level (<10–3) but there was no significant difference between clusters for mother’s education levels and household tenure. Conclusion: This study can have important implications for health policy and practice. Indeed, it found that many subjects have simultaneous multiple NCDs risk factors which leads to identify groups at risk and implement integrated intervention program.

scholarly journals The Preparation of Stewards with the Mastery Rubric for Stewardship: Re-Envisioning the Formation of Scholars and Practitioners

2019 ◽  
Vol 9 (4) ◽  
pp. 292
Author(s):  
Christopher M. Rios ◽  
Chris M. Golde ◽  
Rochelle E. Tractenberg
Keyword(s):  
Cognitive Abilities ◽  
Data Science ◽  
Doctoral Education ◽  
Work Force ◽  
Performance Level ◽  
Developmental Trajectory ◽  
Doctoral Training ◽  
Original Definition ◽  
Practice Standards ◽  
Directed Development

A steward of the discipline was originally defined as “someone who will creatively generate new knowledge, critically conserve valuable and useful ideas, and responsibly transform those understandings through writing, teaching, and application”. This construct was articulated to support and strengthen doctoral education. The purpose of this paper is to expand the construct of stewardship so that it can be applied to both scholars and non-academic practitioners, and can be initiated earlier than doctoral education. To accomplish and justify this, we describe a general developmental trajectory supporting cross-curriculum teaching for stewardship of a discipline as well as of a profession. We argue that the most important features of stewardship, comprising the public trust for the future of their discipline or profession, are obtainable by all practitioners, and are not limited to those who have completed doctoral training. The developmental trajectory is defined using the Mastery Rubric construct, which requires articulating the knowledge, skills, and abilities (KSAs) to be targeted with a curriculum; recognizable stages of performance of these KSAs; and performance level descriptors of each KSA at each stage. Concrete KSAs of stewardship that can be taught and practiced throughout the career (professional or scholarly) were derived directly from the original definition. We used the European guild structure’s stages of Novice, Apprentice, Journeyman, and Master for the trajectory, and through a consensus-based standard setting exercise, created performance level descriptors featuring development of Bloom’s taxonometric cognitive abilities (see Appendix A) for each KSA. Together, these create the Mastery Rubric for Stewardship (MR-S). The MR-S articulates how stewardly behavior can be cultivated and documented for individuals in any disciplinary curriculum, whether research-intensive (preparing “scholars”) or professional (preparing members of a profession or more generally for the work force). We qualitatively assess the validity of the MR-S by examining its applicability to, and concordance with professional practice standards in three diverse disciplinary examples: (1) History; (2) Statistics and Data Science; and (3) Neurosciences. These domains differ dramatically in terms of content and methodologies, but students in each discipline could either continue on to doctoral training and scholarship, or utilize doctoral or pre-doctoral training in other professions. The MR-S is highly aligned with the practice standards of all three of these domains, suggesting that stewardship can be meaningfully cultivated and utilized by those working in or outside of academia, supporting the initiation of stewardship prior to doctoral training and for all students, not only those who will earn PhDs or be scholars first and foremost. The MR-S can be used for curriculum development or revision in order to purposefully promote stewardship at all levels of higher education and beyond. The MR-S renders features of professional stewardship accessible to all practitioners, enabling formal and informal, as well as self-directed, development and refinement of a professional identity.

Epidemiology of atherosclerotic cardiovascular disease: scope of the problem and its determinants

2015 ◽  
Author(s):  
Martin O’Flaherty ◽  
Susanna Sans-Menendez ◽  
Simon Capewell ◽  
Torben Jørgensen
Keyword(s):  
Cardiovascular Disease ◽  
Physical Inactivity ◽  
Saturated Fat ◽  
Population Level ◽  
Epidemiological Studies ◽  
Population Based ◽  
Atherosclerotic Cardiovascular Disease ◽  
Huge Number ◽  
Unhealthy Diet ◽  
The Life Course

The epidemic of cardiovascular disease (CVD) in the twentieth century prompted many population-based surveys. Now, a huge number of epidemiological studies provide a clear picture of the risk for CVD. Approximately 80% of CVD can be explained by smoking, high blood pressure, and deterioration of lipid and glucose metabolism, the two latter mediated through an unhealthy diet (high intake of salt, saturated fat, and refined sugar) and physical inactivity. A causal web for CVD shows that the influence is seen throughout the life course, and that ‘upstream‘ factors like socioeconomic status, health policies, and industrial influences all have a powerful impact on the more downstream parameters like lifestyle and biomarkers. This emphasizes that population-level interventions represent the most effective options for future strategies for the prevention of CVD.

Shifting Gears: Physical inactivity, unhealthy diet choices and chronic disease risks in Australian truck drivers

2014 ◽  
Vol 18 ◽  
pp. e98 ◽  
Author(s):  
N. Gilson ◽  
T. Pavey ◽  
C. Vandelanotte ◽  
M. Duncan ◽  
O. Wright ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Physical Inactivity ◽  
Truck Drivers ◽  
Disease Risks ◽  
Unhealthy Diet

scholarly journals Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

2021 ◽  
Vol 3 ◽  
Author(s):  
Julio Vega ◽  
Meng Li ◽  
Kwesi Aguillera ◽  
Nikunj Goel ◽  
Echhit Joshi ◽  
...  
Keyword(s):  
Open Source ◽  
Data Streams ◽  
Data Science ◽  
Wearable Devices ◽  
Mobile Sensors ◽  
Sensor Data ◽  
Process Data ◽  
Analysis Pipeline ◽  
Ground Truth Data ◽  
Reproducible Analysis

Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.

scholarly journals Public knowledge of cardiovascular disease and its risk factors in Tangail, Bangladesh: a cross-sectional survey

2019 ◽  
Vol 6 (5) ◽  
pp. 1838
Author(s):  
Masuder Rahman ◽  
Sakila Akter ◽  
Fatama Tous Zohora ◽  
Abu Zaffar Shibly
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Attack ◽  
Physical Inactivity ◽  
Public Health Problem ◽  
Major Public Health Problem ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Unhealthy Diet ◽  
Common Risk Factors

Background: Cardiovascular Disease (CVD) is a major public health problem throughout the world. In Bangladesh, the reliable data concerning various aspects of CVD is inadequate at present due to lack of national population-based surveys or central administrative health data. Given the rising incidence of CVDs in Bangladesh, an improved understanding of the CVD, symptoms and risk factors is needed. Hence, this study was performed to assess the level of knowledge towards CVD types, warning symptoms of heart attack or stroke, and CVD risk factors.Methods: A descriptive cross-sectional survey was conducted from May 2018 to June 2018 using standard questionnaire on a sample of 350 randomly selected Bangladeshi individuals. All the data of the study were input in SPSS (Statistical Package for the Social Sciences) version 20.0 software from IBM for windows and the gathered data thus analyzed using SPSS & Microsoft Excel.Results: The respondents’ knowledge about types of CVD, symptom heart attack, symptom of stroke and the risk factors of CVD are 38.9%, 67.7%, 35.7%, and 92.9% respectively. The most common risk factors of CVD found to be known by around than two-third of respondents were unhealthy diet (66.9%), physical inactivity (64.3), obesity (61.4%), and smoking (58.6%).Conclusions: The respondents’ knowledge about types of CVD, symptom heart attack, symptom of stroke and the risk factors of CVD are 38.9%, 67.7%, 35.7%, and 92.9% respectively. The most common risk factors of CVD found to be known by around than two-third of respondents were unhealthy diet (66.9%), physical inactivity (64.3), obesity (61.4%), and smoking (58.6%).

scholarly journals Lifestyle behaviors changes during the COVID-19 pandemic quarantine among 6,881 Brazilian adults with depression and 35,143 without depression

2020 ◽  
Vol 25 (suppl 2) ◽  
pp. 4151-4156
Author(s):  
André Oliveira Werneck ◽  
Danilo Rodrigues da Silva ◽  
Deborah Carvalho Malta ◽  
Paulo Roberto Borges de Souza-Júnior ◽  
Luiz Otávio Azevedo ◽  
...  
Keyword(s):  
Physical Inactivity ◽  
Fruits And Vegetables ◽  
Vegetable Consumption ◽  
Low Frequency ◽  
Lifestyle Behaviors ◽  
Tv Viewing ◽  
Previous Diagnosis ◽  
Consumption Data ◽  
Unhealthy Diet ◽  
Elevated Frequency

Abstract Our aim was to analyze the association between previously diagnosed lifetime depression and changes in physical activity (PA), TV-viewing, consumption of fruits and vegetables as well as frequency of ultra-processed food (UPF) consumption. Data of 41,923 Brazilian adults (6,881 with depression and 35,042 without depression) were used. Participants reported PA (≥ 150 min/week), TV-viewing (≥ 4 h/day), frequency of eating fruits or vegetables (≤ 4 days/week) and UPF (≥ 5 days/week). For incidence indicators, we only considered participants without the risk behavior before the quarantine. People without and with depression presented, respectively, incidence of physical inactivity [70.1% (95%CI: 67.4-72.8) vs 76.3 (70.3-81.5)], high TV-viewing [31.2 (29.6-32.8) vs 33.9 (30.5-37.4)], low frequency of fruit or vegetable consumption [28.3 (25.8-31.0) vs 31.5 (26.1-37.5)] and elevated frequency of UPF consumption [9.7 (8.9-10.7) vs 15.2 (13.0-17.7)]. Participants with depression were more likely to present elevated frequency of UPF consumption incidence [OR:1.49 (95%CI:1.21-1.83)]. Thus, participants with previous diagnosis of depression were at risk for incidence of unhealthy diet behaviors.

scholarly journals Socioeconomic status, social capital, health risk behaviors, and health-related quality of life among Chinese older adults

2020 ◽  
Author(s):  
Ying Yang ◽  
Shizhen Wang ◽  
Lei Chen ◽  
Mi Luo ◽  
Lina Xue ◽  
...  
Keyword(s):  
Older Adults ◽  
Social Capital ◽  
Health Risk ◽  
Risk Behaviors ◽  
Physical Inactivity ◽  
Health Risk Behaviors ◽  
Chinese Older Adults ◽  
Unhealthy Diet ◽  
Overweight Or Obesity

Abstract Background: The present study described the occurrence of health risk behaviors among Chinese older adults, and developed a structural equation model (SEM) to assess the associations between socioeconomic status (SES), social capital, health risk behaviors, and health-related quality of life (HRQoL). Methods: We conducted this cross-sectional study in Hubei, Jiangxi, Guangdong, and Fujian provinces, etc., China between January and March 2018. Demographic characteristics (age, gender, marital status, place of residence), SES (education level, family income), and health risk behaviors (smoking, alcohol consumption, physical inactivity, unhealthy diet, overweight or obesity, and sleep insufficient or excessive) were investigated. Social capital and HRQoL were assessed by the 16-item Personal Social Capital Scale (PSCS-16) and WHOQOL-Old, respectively. Structural equation modeling was applied to assess the associations between variables. Results: A total of 5439 older adults were included in this study. The prevalence of smoking, alcohol consumption, physical inactivity, unhealthy diet, overweight or obesity, and sleep insufficient or excessive were 34.7%, 34.4%, 64.3%, 45.0%, 26.6%, and 40.1%, respectively. 75% of the participants reported ≥2 health risk behaviors. Elderly individuals with more co-occurrence number of health risk behaviors demonstrated significant poor HRQoL ( F = 52.99, p <0.01). Smoking, physical inactivity, and unhealthy diet exhibited significant negative associations with HRQoL. Social capital, SES, as well as overweight or obesity, and sleep insufficient or excessive showed positive associations with HRQoL. Higher level of social capital positively associated with the occurrence of alcohol consumption, sleep insufficient or excessive, and negatively associated with physical inactivity, unhealthy diet, and overweight or obesity. Conclusions: Chinese older adults demonstrated high prevalence of health risk behaviors, as well as the proportion of their co-occurrences. Socioeconomic status, social capital, and health risk behaviors were important predictors of the elderly’s quality of life. Increasing elderly’s social capital, so as to prevent and control the occurrence of health risk behaviors, which might be an effective approach to improve the elderly’s health.

scholarly journals Identification of Recurrent Genetic Patterns From Targeted Sequencing Panels With Advanced Data Science: A Case-Study On Sporadic And Genetic Neurodegenerative Diseases

2021 ◽  
Author(s):  
Martina Tarozzi ◽  
Anna Bartoletti-Stella ◽  
Daniele Dall'Olio ◽  
Tommaso Matteuzzi ◽  
Simone Baiardi ◽  
...  
Keyword(s):  
Genetic Information ◽  
Binary Data ◽  
Data Science ◽  
Disease Risk ◽  
Genetic Profile ◽  
Predictive Tool ◽  
Target Sequencing ◽  
Network Analyses ◽  
Starting Point ◽  
Genetic Patterns

Abstract BACKGROUND: Targeted Next Generation Sequencing is a common and powerful approach used in both clinical and research settings. However, at present, a large fraction of the acquired genetic information is not used since pathogenicity cannot be assessed for most variants. Further complicating this scenario is the increasingly frequent description of a poli/oligogenic pattern of inheritance showing the contribution of multiple variants in increasing disease risk. We present an approach in which the entire genetic information provided by target sequencing is transformed into binary data on which we performed statistical, machine learning, and network analyses to extract all valuable information from the entire genetic profile. To test this approach and unbiasedly explore the presence of recurrent genetic patterns, we studied a cohort of 112 patients affected either by genetic Creutzfeldt-Jakob (CJD) disease caused by two mutations in the PRNP gene (p.E200K and p.V210I) with different penetrance or by sporadic Alzheimer disease (sAD).RESULTS: Unsupervised methods can identify functionally relevant sources of variation in the data, like haplogroups and polymorphisms that do not follow Hardy-Weinberg equilibrium, such as the NOTCH3 rs11670823 (c.3837+21T>A). Supervised classifiers can recognize clinical phenotypes with high accuracy based on the mutational profile of patients. In addition, we found a similar alteration of allele frequencies compared the European population in sporadic patients and in V210I-CJD, a poorly penetrant PRNP mutation, and sAD, suggesting shared oligogenic patterns in different types of dementia. Pathway enrichment and protein-protein interaction network revealed different altered pathways between the two PRNP mutations.CONCLUSIONS: We propose this workflow as a possible approach to gain deeper insights into the genetic information derived from target sequencing, to identify recurrent genetic patterns and improve the understanding of complex diseases. This work could also represent a possible starting point of a predictive tool for personalized medicine and advanced diagnostic applications.

Sign in / Sign up

Export Citation Format

Share Document