Anxiety Detection Leveraging Mobile Passive Sensing

Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features

Introduction of e-mental health in national health systems - a health professionals' perspective

Objectives Evidence exists that e-mental health applications for maternal depression could assist in diagnosing such conditions in an early stage. This study explores the intention of health professionals to use and recommend e-mental health applications and how they think these applications should be integrated in the national health system. Methods We applied an exploratory sequential mixed-method research design. First, we collect and analyze responses from 131 health professionals in the field of pregnancy and maternal care. Based on these findings, we conduct semi-structured interviews with 16 experts to expand on the initial results. Results Our study reveals that health professionals would in general intend to recommend and use e-mental health applications. However, their attitude towards e-mental health applications varies with respect to the coverage of the mental health process. Conclusion The results are of relevance for research and practice. Two scenarios are described that show how health professionals perceive an introduction of e-mental health to be useful

Raskauspäiväkirjan käyttö KESÄLATU-tutkimuspopulaatiossa

Raskaus on ainutlaatuinen elämänkokemus koko perheelle. Neuvolan määräaikaisterveystarkastuksista huolimatta, tuleville vanhemmille saattaa jäädä kysymyksiä, joihin he toivovat vastausta nopeasti olinpaikasta riippumatta. Fertiili-ikäisten naisten keskuudessa mobiililaitteet ovat päivittäisessä käytössä, ja raskaussovellusten käyttö onkin lisääntynyt merkittävästi viime vuosikymmenen aikana. Sovellusten sisältämien terveystietojen luotettavuus on kuitenkin havaittu puutteelliseksi. Tutkimuksen tarkoituksena on tarkastella terveydenhuoltoalan moniammatillisen työryhmän kehittämän Vauva mielessä -raskauspäiväkirjan käyttöä KESÄLATU-tutkimuspopulaatiossa sekä siihen vaikuttavia taustatekijöitä. Lisäksi tarkoituksena on selvittää raskauspäiväkirjan käytön vaikutusta odottavan äidin mielialaan tai ahdistuksen määrään. Tutkimus on osa Keski-Satakunnan terveydenhuollon kuntayhtymän äitiys- ja lastenneuvolatutkimusta (KESÄLATU). Tutkimusaineisto on kerätty vuosina 2016-2019 äitiysneuvolan ensikäynnille tulleista raskaana olevista, heidän puolisoistaan ja heille syntyvistä lapsista. Tavallisen neuvolaseurannan lisäksi tutkimukseen osallistuvat vanhemmat täyttivät kyselylomakkeita kolme kertaa raskauden aikana sekä synnytyksen jälkeen. Tutkimuksessa tarkastelun kohteena ovat alkuraskaudessa täytetyt esitietolomakkeet, odottavien äitien jokaisessa raskauskolmanneksessa täyttämät EPDS- ja PASS-lomakkeet sekä vanhemmilta ensimmäisessä jälkitarkastuslomakkeessa kartoitettu Vauva mielessä -raskauspäiväkirjan käyttö. Tutkimukseen osallistuneista äideistä 21 (15,3 %) oli käyttänyt jotain raskauspäiväkirjaa. Heistä 14 (66,7 %) ja puolisoista 3 (3,8 %) käytti Vauva mielessä -raskauspäiväkirjaa. Raskauspäiväkirjan käytöllä ja taustatekijöillä ei havaittu tilastollisesti merkitsevää yhteyttä. Raskauspäiväkirjalla oli positiivinen yhteys odottavan äidin mielialaan alkuraskaudessa ja mielialan havaittiin olevan raskauspäiväkirjaa käyttäneillä äideillä parempi koko raskausajan. Raskauspäiväkirjan käytöstä riippumatta odottavien äitien mieliala koheni ja ahdistuksen määrä väheni raskauden edetessä molemmissa ryhmissä. Yhteenvetona voidaan todeta, että raskauspäiväkirjojen ja -sovellusten avulla mahdollisesti pystytään vaikuttamaan odottaviin äiteihin ja koko perheeseen. Useat tutkittavista eivät olleet tietoisia Vauva mielessä -raskauspäiväkirjasta, joten tiedotusta tulisi lisätä jo ennen raskautta ja viimeistään äitiysneuvolakäynneillä. Tulevaisuudessa raskauspäiväkirjan muuttaminen sovellusmuotoon voisi lisätä helppokäyttöisyyttä ja tulevien vanhempien tavoittamista

2021- The Twenty-fifth Annual Symposium of Student Scholars

The full program book from the Twenty-fifth Annual Symposium of Student Scholars, held on April 29, 2021. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1023/thumbnail.jp

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Maternity care for refugees and asylum seekers in the Netherlands

Aangezien de wereldwijde vluchtelingen populatie blijft groeien, is het belangrijk dat de geboortezorg in gastlanden is afgestemd op de specifieke behoeften van vrouwen met een vluchtachtergrond. Ons onderzoek laat zien dat asielzoekers een verhoogd risico lopen op ongunstige zwangerschapsuitkomsten in vergelijking met de lokale Nederlandse bevolking. Zo hebben asielzoekers in Ter Apel bijvoorbeeld een 7 keer hogere kans dat hun kindje sterft rondom de geboorte. Veel verschillende factoren compliceren het leveren van goede zwangerschapszorg voor asielzoekers en statushouders. Voorbeelden hiervan zijn overplaatsingen, taalbarrières, bijkomende psychische problematiek en moeite met het navigeren van zorg. Twee veelbelovende initiatieven voor het verbeteren van de zwangerschapszorg voor deze populaties zijn prenatale groepszorg en het invoeren van een mentale gezondheidsscreening. Echter is er meer nodig om de zwangerschapsuitkomsten voor vluchtelingen in Nederland te verbeteren. Om dit te bereiken zijn ingrijpende aanpassingen aan zowel het vluchtelingen- als het verloskundige zorgsysteem in Nederland noodzakelijk. Om in het huidige Nederland deze ambitieuze doelen te bereiken, is er een nieuwe visie nodig binnen onze samenleving. Deze visie moet onze morele verantwoordelijkheid erkennen en de positieve kansen van een inclusief migratiebeleid omarmen. In een samenleving die geconfronteerd wordt met polarisatie, ligt immers de toekomstige uitdaging in het effectief beheren van migratie, zodat het zowel de migranten als de bevolking van het gastland ten goede komt