Academic literature on the topic 'Applications in health'

This article aims to provide an overview of the emerging field of health literacy. It begins with a definition of health literacy and brief information on the current prevalence of low literacy levels, followed by three examples of how health literacy can be relevant to health psychology. The article then leads onto our recommendations for future research directions.

Background: This thesis aims to investigate if the use of mobile health (mHealth) applications have positive effects on health.   Objectives: This research are to provide an insight of how people are using mHealth applications but also provide an insight of the target group. Additionally, the thesis will provide an analysis of the proposed Structural Equation Model (SEM) to understand the influencing factors (constructs) for health improvement.    Method: In this research a Structural Equation Model (SEM) approach was used in which a questionnaire with closed ended questions related to each construct were provided to collect the data. The data were analyzed by the computer program IBM SPSS 25 and the SEM was made by the IBM SPSS AMOS 25.   Results: The proposed theoretical SEM model showed validity and the proposed hypotheses 1 and 2 were significant for this model. The corresponding contributed construct to improve health, were “Healthcare Service” and “mHealth App Behavior”. “New Technology” did not contribute to improvement of health directly, but it correlated strongly to “Healthcare Service” but also to “mHealth App Behavior”.   Conclusions: The target group was defined as a group of early adopters who used mobile health applications and more specifically, they used fitness apps to enhance health. People in the target group were high educated and had occupations which corresponded well with their education. Additionally, this group used their knowledge by reading and understanding health information when they needed healthcare service to improve health.  Keywords: Adoption, Health, mHealth, New Technology

As mobile devices become more prevalent in healthcare scenarios, it is becoming necessary to develop infrastructure to allow these devices to securely participate in emergency healthcare response scenarios. In this work, the scenario of an emergency response team requesting access to the health care records of a patient is analyzed. As these records may not be immediately available to the requesting party, several parties may need to be contacted to ensure to validate the identity of the requester and deliver the records. Existing works exploring this topic are briefly analyzed. Using recommendations found in these works as well as standard technologies such as SSL, X.509, and AES, a proposed protocol for such a scenario is presented. The construction of a prototype using Android as the mobile phone OS and Tomcat as the Java HTTP Servlet container and web server is discussed, focusing on the implementation decisions as well as the difficulties encountered during development. Finally, weaknesses of the proposed protocol that were realized during prototype implementation are discussed and future improvements are proposed.

Consumers with chronic conditions have access to a plethora of mobile applications (‘apps’) to manage their health. Qualitative research confirmed consumers’ willingness to engage with this technology. A checklist was designed to assess the quality and usability of health apps, and trialled 11 asthma and 17 hypertension apps, identifying variable yet generally substandard quality. A concept map incorporating blockchain technology subsequently proposed how improved functionality and connectivity can enhance use of consumers’ self-monitored health data.

Abstract Data mining is a relatively new field of research whose major objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make timely and accurate decisions. Two medical databases are considered, one for describing the various tools and the other as the case study. The first database is related to breast cancer and the second is related to the minimum data set for mental health (MDS-MH). The breast cancer database consists of 10 attributes and the MDS-MH dataset consists of 455 attributes. As there are a number of data mining algorithms and tools available we consider only a few tools to evaluate on these applications and develop classification rules that can be used in prediction. Our results indicate that for the major case study, namely the mental health problem, over 70 to 80% accurate results are possible. A further extension of this work is to make available classification rules in mobile devices such as PDAs. Patient information is directly inputted onto the PDA and the classification of these inputted values takes place based on the rules stored on the PDA to provide real time assistance to practitioners.

Aquesta tesi està formada per dues parts relacionades amb l'estudi de la salut d'una regió geogràfica dividida en un conjunt de zones (àrees petites). La primera part es basa en un estudi amb informació de salut agregada per cadascuna de les àrees que formen la regió d'estudi. En concret, es tracta d'una aplicació de mapes de salut (disease mapping), que utilitza mètodes Bayesians empírics per generar un Atles de mortalitat en àrees petites de Catalunya en el període 1984-1998. La segona part utilitza una nova perspectiva basada en la integració de les dades agregades i individuals de salut per cadascuna de les zones que formen la regió d'estudi, mitjançant equacions d'estimació (estimating equations). Aquesta nova perspectiva és una extensió de la regressió geogràfica.
L'elaboració de la primera part d'aquesta tesi està justificada per diferents raons. En primer lloc, els atles de salut i en general els mapes d'indicadors de salut, ens han mostrat la seva gran utilitat per identificar les localitzacions geogràfiques de les malalties, formular hipòtesis sobre les causes de la malaltia i monitoritzar intervencions en salut pública. En segon lloc, els atles de mortalitat en àrees petites presenten la distribució del risc relatiu per les causes de mortalitat més importants utilitzant mapes amb un alt nivell de resolució geogràfica.
El primer objectiu d'aquesta tesi va ser construir un atles de mortalitat en 289 àrees petites (municipis o municipis agregats) de la Comunitat Autònoma de Catalunya i 66 àrees bàsiques de salut de la ciutat de Barcelona (l'àrea petita analitzada amb una major població) per al període 1984-1998. Per obtenir els indicadors de salut en àrees petites s'han utilitzat mètodes Bayesians. Aquests mapes presenten, en un format de doble pàgina, els riscs relatius ajustats per edat, les àrees significatives d'alt i baix risc, el risc relatiu de la ciutat de Barcelona respecte a Catalunya i internament respecte a Barcelona, el risc relatiu per grups d'edat (0-64 i 65) i addicionalment l'evolució temporal del risc relatiu en cada àrea resumida en un únic mapa. En concret, per estudiar l'evolució del risc relatiu de mortalitat s'inclou: 1) l'evolució del risc relatiu en el període d'estudi de cada àrea comparada amb la tendència global de Catalunya i 2) l'evolució absoluta del risc relatiu a cada àrea. Segons el nostre coneixement, aquesta és la primera vegada que aquests dos tipus d'informació es combinen en un únic mapa. A més, aquest és el primer Atles que presenta informació sobre la distribució geogràfica de zones que formen àrees petites de gran població, com ciutats d'un país, i inclou l'esperança de vida obtinguda amb mètodes Bayesians empírics.
La segona part d'aquesta tesi és útil per estudis epidemiològics on s'inclouen variables d'exposició i confusió que poden tenir diferents fonts de variabilitat (variabilitat dins les poblacions i entre les poblacions). Específicament, els anàlisis individuals que valoren la relació entre la malaltia i l'exposició dins d'una població són útils quan l'exposició presenta variabilitat dins la població. Quan aquesta variabilitat és limitada, la força dels anàlisis individuals es debilita. En aquesta situació, un anàlisis de dades agregades de la malaltia entre poblacions, amb una mostra de dades individuals d'exposició, pot ser eficaç en l'estimació de l'efecte d'exposició si aquest presenta gran variabilitat entre poblacions. No obstant, encara que es pugui conèixer quina de les dues variacions domina en la variable d'exposició, es poden considerar conjuntament variables d'exposició i/o confusió amb diferents tipus de variació. El segon objectiu d'aquesta tesi va ser considerar una nova perspectiva, combinació dels anàlisis de dades individuals i agregades, basat en equacions d'estimació (perspectiva population-based estimating equation (PBEE)). En funció de la variabilitat que domina en la exposició, la anàlisis proposada pren força de la perspectiva basada en dades individuals i agrades de salut, per estimar els efectes d'exposició. Es van realitzar estudis de simulació en diferents escenaris per a mostrar el poder de la perspectiva proposada en l'estimació dels efectes d'exposició d'interès.
Finalment, esperem que els mètodes i els diferents aspectes utilitzats en aquesta tesi puguin ser d'utilitat per a aquells investigadors que vulguin millorar l'estudi de la salut a l'espai i temps.
Esta tesis esta formada por dos partes relacionadas con el estudio de la salud en una región geográfica dividida en un conjunto de zonas (áreas pequeñas). La primera parte considera un estudio con información de salud agregada para cada una de las áreas que forman la región analizada. En concreto, se trata de una aplicación de mapas de salud (disease mapping), consistente en el uso de métodos Bayesianos empíricos para generar un Atlas de mortalidad en áreas pequeñas de Cataluña en el periodo 1984-1998. La segunda parte considera un nuevo enfoque que realiza una integración de los datos agregados e individuales de salud para cada una de las zonas que forman la región en estudio, mediante ecuaciones de estimación (estimating equations). Se considera que este nuevo enfoque es una extensión de la regresión geográfica.
La elaboración de la primera parte de esta tesis esta justificada por diferentes razones. Primero, los atlas de salud y en general los mapas de indicadores de salud, han mostrado su gran utilidad para identificar localizaciones geográficas de las enfermedades, formular hipótesis sobre las causas de la enfermedad y monitorizar intervenciones en salud pública. En segundo lugar, los atlas de mortalidad en áreas pequeñas presentan la distribución del riesgo relativo para las causas de mortalidad más importantes usando mapas con un alto nivel de resolución geográfica.
El primer objetivo de esta tesis fue construir un atlas de mortalidad en 289 áreas pequeñas (municipios o municipios agregados) de la Comunidad Autónoma de Cataluña y 66 áreas básicas de salud de la ciudad de Barcelona (el área pequeña analizada con mayor población) para el periodo 1984-1998. Para obtener los indicadores de salud en las áreas pequeñas se han aplicado métodos Bayesianos. Estos mapas presentan, en un formato de página doble, los riesgos relativos ajustados por edad, las áreas significativas de alto y bajo riesgo, el riesgo relativo de la ciudad de Barcelona con respecto a Cataluña e internamente con respecto a Barcelona, el riesgo relativo por grupos de edad (0-64 y 65) y adicionalmente la evolución temporal del riesgo relativo en cada área resumida en un único mapa. En concreto, para estudiar la evolución del riesgo relativo de mortalidad se incluye: 1) la evolución del riesgo relativo en el periodo de estudio de cada área comparada con la tendencia global de Cataluña y 2) la evolución absoluta del riesgo relativo en cada área. Según nuestro conocimiento, esta es la primera vez que ambos tipos de información se combinan en un único mapa. Además, este es el primer Atlas que presenta información sobre la distribución geográfica de zonas que forman áreas pequeñas de gran población, como ciudades de un país, e incluye la esperanza de vida obtenida mediante métodos Bayesianos empíricos.
La segunda parte de esta tesis es útil en estudios epidemiológicos donde se incluyen variables de exposición y confusión que pueden tener diferentes fuentes de variabilidad (variabilidad dentro de las poblaciones y entre poblaciones). Específicamente, los análisis individuales que valoran la relación entre enfermedad y exposición dentro de una población son útiles cuando la exposición presenta variabilidad dentro de la población. Cuando dicha variabilidad es limitada el poder de los análisis individuales se reduce. En esta situación, un análisis de datos agregados de enfermedad entre poblaciones, con una muestra de datos individuales de exposición, puede ser eficaz en la estimación del efecto de exposición si este presenta gran variabilidad entre poblaciones. No obstante, aunque se pueda conocer cual de las dos variaciones domina en la variable de exposición, se pueden considerar conjuntamente variables de exposición y/o confusión con diferentes tipos de variación. El segundo objetivo de esta tesis fue considerar un nuevo enfoque, combinación de los análisis de datos individuales y agregados, basado en ecuaciones de estimación (enfoque population-based estimating equation (PBEE)). Dependiendo de la variabilidad que domina en dicha exposición, el análisis propuesto toma fuerza de los enfoques basados en datos individuales y agregados de salud, para estimar los efectos de exposición. Estudios de simulación bajo diferentes escenarios fueron realizados para mostrar el poder del enfoque propuesto en la estimación de los efectos de exposición de interés.
Finalmente, esperamos que los métodos y diferentes aspectos empleados en esta tesis puedan ser de utilidad para aquellos investigadores que quieran mejorar el estudio de la salud en el espacio y en el tiempo.
This thesis consists of two related parts based on the study of health in a geographical region divided in a set of zones (small areas). The first part considers studies based on health information aggregated for each area into which the region under study has been divided. Specifically, it is a disease mapping application, based on generation of an Atlas of mortality in small areas of Catalonia over the period 1984-1998, using empirical Bayes methods. The second part considers an innovative approach, based on an integration of aggregated and individual health data in each of the zones of the region under study, using an estimating equation approach. Specifically, we consider this new approach as an extension of geographical regression.
The elaboration of the first part of this thesis is justified for different reasons. First, health atlases and the mapping of health indicators in general, has demonstrated its great utility in identifying geographical localizations of health problems, in formulation of hypotheses about disease causes, and in monitoring public health interventions. Second, most atlases of mortality at the small area level present patterns of relative mortality risk for the most important causes of death using maps with a high level of geographical resolution. The first goal of this thesis was to construct a mortality Atlas involving a decomposition of the Autonomous Community of Catalonia into 289 small areas (municipalities or aggregates thereof) and 66 primary health areas of Barcelona city (being a small area but with a large population) for the period 1984-1998. For Catalonia as a whole, these maps presented, using a double-page format, the age adjusted relative risk, significantly high and low relative risk areas, relative risk in Barcelona City with respect to Catalonia and internally with respect to Barcelona, relative risk by age group (0-64 and 65) and additionally the relative risk evolution over time in each area summarized in an single map, using spatial and temporal information modeled through Bayesian methods. Specifically, the atlas uses a strategy to include both: 1) relative risk evolution throughout the study period of each area compared to the average trend for all Catalonia and 2) the absolute relative risk evolution of each area. To our knowledge, this is the first time that both types of information have been combined in a single map. In addition, this is the first Atlas that presents information about geographical patterns in zones within small areas having a large population such as the cities of a country and includes life expectancy obtained with an empirical Bayes approach.
The second part of this thesis can be useful in epidemiological studies where we include exposure and confounding variables that may have different sources of within and between-population variability. Specifically, analyses of individual disease-exposure data within a population are useful when exposure of interest varies sufficiently within the population. When the within-population variance of exposure is limited power of the individual-data analysis within a population is reduced. In such situations, aggregated-data analyses of disease data across populations, with a sample of individual exposure data from populations, can be powerful in estimating the exposure effect if between-population variation of exposure is large. However, although we may have knowledge of which variations dominate in each variable, exposure and/or confounding variables with different types of variation can be considered jointly. The second goal of this thesis was to consider a new analytical framework that is a combination of the individual- and aggregated-data analyses, based on an estimating equation approach ("population-based estimating equation" (PBEE) approach). The proposed analysis utilizes strengths from individual and aggregated health data approaches in the estimation of the exposure effect of interest, depending on which of the exposure variations (within- vs. between-population) dominates. Simulation studies under different scenarios were performed to show the strengths of the proposed approach in the estimation of the exposure effects of interest.
Finally, we hope that some of the methods and topics employed may be of use to researchers who want to improve the study of health in space and time.

Background Genetic epidemiology is at the interface of translational and basic research and the pace of progress has been unprecedented, with findings representing some of the most robust available in the Scientific literature. However, how we can translate this high- fidelity genomic information into improvements in health of the population? Two dis¬tinct translational opportunities include personalized medicine (pharmacogenetics) and using Mendelian randomization to investigate disease aetiology to inform public health policy and develop new therapies. Objectives In this PhD thesis, I investigated the evidence base underlying the well-publicized use of the pharmacogenetic biomarker CYP2C19 genotype to predict the response to clopi¬dogrel, a widely prescribed antiplatelet drug. Second, I used Mendelian randomization to investigate the role of an endogenous biomarker, secretory phospholipase A2-IIA (sPLA2-IIA), thought to be a pro-atherogenic enzyme and a potential drug target for the prevention of cardiovascular disease (CVD). Third, I used Mendelian randomization to investigate the relationship between alcohol, an exogenous exposure, and cardiovas¬cular traits and disease events. Results CYP2C19 and cardiovascular disease I identified 32 studies of 42,016 patients reporting 3545 CVD events. Only 6 studies were set within randomized trials (“effect-modification” design) and the remaining 26 reported individuals exposed to clopidogrel (“treatment-only” design). In treatment- only studies, possession of one or more *2-*8 CYP2C19 alleles was associated with lower cytochrome P450 C19 (CYP2C19) enzyme activity and a higher risk of CVD events (RR 1.18; 95%CI:1.09, 1.28), however, there was strong evidence of small-study bias (Harbord test P=0.001) and, when restricted to large studies (≥200 events), the association of CYP2C19 *2-*8 carrier status with CVD was null (RR 0.97; 95%CI: 0.86, 1.09). In the effect-modification studies, CYP2C19 genotype did not modify the effect of clopidogrel on CVD end-points. These findings cast doubt on whether information on CYP2C19 genotype would be helpful to guide selection of the dose of clopidogrel or use of an alternative antiplatelet agent. The role of secretory phospholipase A2-IIA (sPLA2-IIA) in CVD I used Mendelian randomization to make causal inference on the role of sPLA2-IIA in CVD. I identified a single nucleotide polymorphism (SNP) in PLA2G2A (rs11573 156) that was specific for and had a very strong impact on circulating levels of the sPLA2-IIA isoform. Using data from 36 studies and over 100,000 participants, instrumental variable analysis found no association between sPLA2-IIA with incident, prevalent or recurrent CVD events. These findings suggest sPLA2-IIA is not a valid therapeutic target for CVD prevention, which was in keeping with a phase III randomized clinical trial that was halted for futility in 2012 (during this thesis). Alcohol and CVD I used a SNP in ADH1B to investigate the relationship between alcohol and coronary heart disease (CHD) in >260,000 participants. The genetic variant (ADH1B rs1229984 A-allele) showed very strong association with reduced alcohol consumption when evalu¬ated as volume of alcohol consumed, binge drinking and abstaining from alcohol. The A-allele of rs1229984 showed associations with SBP, CRP, IL-6, BMI and waist circum¬ference that were all directionally concordant with a reduced risk of CHD. Indeed, when the clinical outcome CHD was investigated, individuals carrying the A-allele (who con¬sumed less alcohol than non-carriers) had a reduced risk of CHD at all levels of alcohol consumption. No evidence of a cardioprotective association of alcohol with CHD was identified. Conclusions My investigation into use of CYP2C19 genotype as a pharmacogenetic biomarker for clopidogrel response did not identify evidence to support its clinical use and limitations were identified that could apply to other pharmacogenetic tests. Use of Mendelian randomization revealed no evidence to support a causal role of sPLA2- IIA in CVD, which paralleled findings from a phase III randomized clinical trial, and provides support for the use of Mendelian randomization studies more widely to in¬form drug development. Finally, using the ADH1B gene to interrogate the relationship of alcohol yielded findings that argue against a cardioprotective effect of alcohol con¬sumption. These findings should encourage rethinking of public health advice about the cardiovascular benefits of moderate levels of alcohol consumption.

There are such diseases that people had to live with all their lives. Such simple actions as talking on the phone, going downstairs or even determining the expiration date of milk can become difficult for people with disabilities. We present a selection of mobile applications that make life of people with disabilities easier

Developing native applications for different platforms with different resolutions and screen sizes is both time consuming and costly. If developers were able to develop one web based application which can be used on multiple platforms, yet retain the same level of security as a native application, they would be able to reduce both development time and costs. In this thesis we will investigate the possibilities of achieving a level of security in a web-based application that can equal that of a native application, as well as how to develop an application that uses the Mina Vårdkontakter (My Healthcare Contacts) framework.

This dissertation deals with three problems in health care. In the first, we consider the incentives to change prices and capital levels at hospitals, using optimal control under the assumption that private payers charge higher prices if patients consume more hospital services. The main results are that even with fixed technology, investment and prices exhibit explosive growth, and that prices and capital stock grow in proportion to one another. In the second chapter, we study the flow of nosocomial infections in an intensive care unit. We use data from Cook County Hospital, along with numerous results from the literature, to construct a discrete event simulation. This model highlights emergent properties from treating the flow of patients and pathogens in one interconnected system, and sheds light on how nosocomial infections relate to hospital costs. We find that the system is not decomposable to individual systems, exhibiting behavior that would be difficult to explain in isolation. In the third chapter, we analyze a proposed change in diversion policies at hospitals, in order to increase the number of patients served, without an increase in resources. Overcrowding in hospital emergency departments is caused in part by the inability to send patients to main hospital wards, due to limited capacity. When a hospital is completely full, the hospital often goes on ambulance diversion, until some spare capacity has opened up. Diversion is costly, and often leads to waves of diversions in systems of hospitals, a situation that is regarded as highly problematic in public health. We construct and analyze a continuous-time Markov chain model for one hospital. The intuition behind the model is that load-balancing between various hospitals in a metro area may hinder full congestion. We find that a more flexible contract may benefit all parties, through the partial diversion of federally insured patients, when a hospital is very close to full. Discrete event simulation models are run to assess the effect, using data from DeKalb Medical Center, and also to show that in a two-hospital system, more federally insured patients are served using this mechanism.

Background. Mental health mobile applications (apps) have the potential to expand the provision of mental health and wellness services to traditionally underserved populations. There is a lack of guidance on how to choose wisely from the thousands of mental health apps without clear evidence of safety, efficacy, and consumer protections. Purpose. This Technical Brief proposes a framework to assess mental health mobile applications with the aim to facilitate selection of apps. The results of applying the framework will yield summary statements on the strengths and limitations of the apps and are intended for use by providers and patients/caregivers. Methods. We reviewed systematic reviews of mental health apps and reviewed published and gray literature on mental health app frameworks, and we conducted four Key Informant group discussions to identify gaps in existing mental health frameworks and key framework criteria. These reviews and discussions informed the development of a draft framework to assess mental health apps. Iterative testing and refinement of the framework was done in seven successive rounds through double application of the framework to a total of 45 apps. Items in the framework with an interrater reliability under 90 percent were discussed among the evaluation team for revisions of the framework or guidance. Findings. Our review of the existing frameworks identified gaps in the assessment of risks that users may face from apps, such as privacy and security disclosures and regulatory safeguards to protect the users. Key Informant discussions identified priority criteria to include in the framework, including safety and efficacy of mental health apps. We developed the Framework to Assist Stakeholders in Technology Evaluation for Recovery (FASTER) to Mental Health and Wellness and it comprises three sections: Section 1. Risks and Mitigation Strategies, assesses the integrity and risk profile of the app; Section 2. Function, focuses on descriptive aspects related to accessibility, costs, organizational credibility, evidence and clinical foundation, privacy/security, usability, functions for remote monitoring of the user, access to crisis services, and artificial intelligence (AI); and Section 3. Mental Health App Features, focuses on specific mental health app features, such as journaling and mood tracking. Conclusion. FASTER may be used to help appraise and select mental health mobile apps. Future application, testing, and refinements may be required to determine the framework’s suitability and reliability across multiple mental health conditions, as well as to account for the rapidly expanding applications of AI, gamification, and other new technology approaches.

Ammonia has been previously trialed as an automotive fuel; however, it was hardly competitive with fossil fuels in terms of cost, energy density, and practicality. However, due to climate change, those practical and cost-related parameters have finally become secondary deciding factors in fuel selection. Ammonia is safer than most fuels and it offers superior energy densities compared to compressed or liquefied hydrogen. It is believed that ammonia might be an ultimate clean fuel choice and an extension to the emerging hydrogen economy. Unsettled Economic, Environmental, and Health Issues of Ammonia for Automotive Applications examines the major unsettled issues of using ammonia as a clean automotive fuel alternative, including the lack of regulations and standards for automotive applications, technology readiness, safety perception, and presently limited supply. While ammonia as a fuel is still in its infancy, identifying and addressing these challenges early could enable a safe and smooth transition.