A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.

The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lays the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.

Role of sex and gender-related variables in development of metabolic syndrome: A prospective cohort study.

INTRODUCTION: The burden of metabolic syndrome (MetS) and its components has been increasing mainly amongst male individuals. Nevertheless, clinical outcomes related to MetS (i.e., cardiovascular diseases), are worse among female individuals. Whether these sex differences in the components and sequalae of MetS are influenced by gender (i.e., psycho-socio-cultural factors)) is a matter of debate.  Therefore, the purpose of this study was to determine the association between gender-related factors and the development of MetS, and to assess if the magnitude of the associations vary by sex. METHOD: Data from the Colaus/PsyColaus study, a prospective population-based cohort of 6,734 middle-aged participants in Lausanne (Switzerland) (2003-2006) were used. The primary endpoint was the development of MetS as defined by the Adult Treatment Panel III of the National Cholesterol Education Program. Multivariable models were estimated using logistic regression to assess the association between gender-related factors and the development of MetS. Two-way interactions between sex,  age and gender-related factors were also tested. RESULTS: Among 5,195 participants without MetS (mean age=51.3 ± 10.6, 56.1 % females), 27.9 % developed MetS during a mean follow-up of 10.9 years. Female sex (OR:0.48, 95 %CI:0.41-0.55) was associated with decreased risk of developing MetS. Conversely, older age, educational attainment less than university, and low income were associated with an increased risk of developing MetS. Statistically significant interaction between sex and strata of age, education, income, smoking, and employment were identified showing that the reduced risk of MetS in female individuals was attenuated in the lowest education, income, and advanced age strata. However, females who smoke and reported being employed demonstrated a decreased risk of MetS compared to males. Conversely smoking and unemployment were significant risk factors for MetS development among male adults. CONCLUSIONS: Gender-related factors such as income level and educational attainment play a greater role in the development of MetS in female than individuals. These factors represent novel modifiable targets for implementation of sex- and gender-specific strategies to achieve health equity for all people.

Sex and gender aspects in diabetes mellitus: Focus on access to health care and cardiovascular outcomes.

Aims: The aim of this study was to elucidate whether sex and gender factors influence access to health care and/or are associated with cardiovascular (CV) outcomes of individuals with diabetes mellitus (DM) across different countries. Methods: Using data from the Canadian Community Health Survey (8.4% of respondent reporting DM) and the European Health Interview Survey (7.3% of respondents reporting DM), were analyzed. Self-reported sex and a composite measure of socio-cultural gender was constructed (range: 0-1; higher score represent participants who reported more characteristics traditionally ascribed to women). For the purposes of analyses the Gender Inequality Index (GII) was used as a country level measure of institutionalized gender. Results: Canadian females with DM were more likely to undergo HbA1c monitoring compared to males (OR = 1.26, 95% CI: 1.01-1.58), while conversely in the European cohort females with DM were less likely to have their blood sugar measured compared to males (OR = 0.88, 95% CI: 0.79-0.99). A higher gender score in both cohorts was associated with less frequent diabetes monitoring. Additionally, independent of sex, higher gender scores were associated with higher prevalence of self-reported heart disease, stroke, and hospitalization in all countries albeit European countries with medium-high GII, conferred a higher risk of all outcomes and hospitalization rates than low GII countries. Conclusion: Regardless of sex, individuals with DM who reported characteristics typically ascribed to women and those living in countries with greater gender inequity for women exhibited poorer diabetes care and greater risk of CV outcomes and hospitalizations.

JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution.

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.

Sex, rurality and socioeconomical status in Spanish centennial population (2017).

World's population is exponentially aging as people reaching 100 years old has increased. The number of areas with the highest centennial population rates (Blue Zones), are significantly higher. Are there any determinant factors that favor this situation in Spain? The goal of this study was to determine the possible influence of sex, rurality and socioeconomic factors (Gross Domestic Product (GDP)) on the prevalence of the centennial population of the Spanish society. The Spanish register of inhabitants was published in 2017 by the National Statistics Institute. The analysis was carried out both by Autonomous Communities and by provinces in phases: a first descriptive analysis, followed by an inferential analysis, based on statistical tests (independent T- Student test, Pearson correlation and ANOVA). There were significant interactions between: i) sex and longevity (in favor of the female population); ii) female and rural housing and iii) female, GDP and urban areas. Feminization was proven in the longevity revolution, but, in general, GDP per Capita was not a significant survival factor on its own. This study was the first step of further analysis related to extreme longevity in Spain, which will include other dependent variables such as state of health and well-being as well as social factors.

Determinants of perceived health and unmet healthcare needs in universal healthcare systems with high gender equality.

BACKGROUND: Patient attitudes about health and healthcare have emerged as important outcomes to assess in clinical studies. Gender is increasingly recognized as an intersectional social construct that may influence health. Our objective was to determine potential sex differences in self-reported overall health and access to healthcare and whether those differences are influenced by individual social factors in two relatively similar countries. METHODS: Two public health surveys from countries with high gender equality (measured by UN GII) and universal healthcare systems, Canada (CCHS2014, n = 57,041) and Austria (AT-HIS2014, n = 15,212), were analysed. Perceived health was assessed on a scale of 1 (very bad) to 4 (very good) and perceived unmet healthcare needs was reported as a dichotomous variable (yes/no). Interactions between sex and social determinants (i.e. employment, education level, immigration and marital status) on outcomes were analysed. RESULTS: Individuals in both countries reported high perceived health (Scoring > 2, 85.0% in Canada, 79.9% in Austria) and a low percentage reported unmet healthcare needs (4.6% in Canada, 10.7% in Austria). In both countries, sex and several social factors were associated with high perceived health, and a sex-by-marital status interaction was observed, with a greater negative impact of divorce for men. Female sex was positively associated with unmet care needs in both countries, and sex-by-social factors interactions were only detected in Canada. CONCLUSIONS: The intersection of sex and social factors in influencing patient-relevant outcomes varies even among countries with similar healthcare and high gender equality.

Methods for prospectively incorporating gender into health sciences research.

Numerous studies have demonstrated that sex (a biological variable) and gender (a psychosocial construct) impact health and have discussed the mechanisms that may explain these relationships. Funding agencies have called for all health researchers to incorporate sex and gender into their studies; however, the way forward has been unclear to many, particularly due to the varied definition of gender. We argue that just as there is no standardized definition of gender, there can be no standardized measurement thereof. However, numerous measurable gender-related variables may influence individual or population-level health through various pathways. The initial question should guide the selection of specific gender-related variables based on their relevance to the study, to prospectively incorporate gender into research. We outline various methods to provide clarification on how to incorporate gender into the design of prospective clinical and epidemiological studies as well as methods for statistical analysis.

A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation.

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs.