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EVOTION – Big Data Supporting Public Hearing Health Policies
... Objective Audiometry Based on Middle Ear Parameter Formulas: A New Technique for Research and Differential Diagnosis of Hearing was investigated by Naida [2]. EVOTION -Big Data Supports Public Hearing Health Policy researched by Christensen and Pontoppidan [3]. The application of IOS-based Ear Scales for Clinical Audiology and Otology was studied by Liao [4]. ...
The ear is an organ that is able to detect or recognize sound and also has a lot to play in the balance and position of the body. The ears are organs that are very vulnerable to noise. There are two common causes of hearing loss, namely decreased hearing conduction (hearing loss) and nerve hearing (sensorineural hearing loss). To prevent deafness, hearing control is necessary. Generally to test hearing function is done regularly by the ENT doctor at the hospital. This if done many times is deemed ineffective because it is time consuming and requires relatively expensive costs, therefore an early diagnosis of portable hearing loss is designed that is expected to be able to test independently independently over and over again. This tool is equipped with SD Card data storage, where the results of the data can be consulted by a doctor for further diagnosis. This tool uses an arduino uno R3 control, the frequency generator uses IC XR2206. The highest error is at the frequency of 8000 Hz which is 0.52%, but overall all systems on the device are functioning properly and the error is still within tolerance of 10%. From the results of these data, this tool can be recommended for early diagnosis of hearing function.
... how sound characteristics varied) over the same time period; this is illustrated in Fig. 2. Our findings showed a positive correlation between HA usage and overall sound level and diversity and a negative correlation between HA usage and overall signal-to-noise ratio [34]. We also presented preliminary findings suggesting how the EVOTION HA data can be used to predict temporary threshold shifts and noise-induced hearing loss for individuals and the general public [35,36]. The EVOTION platform demonstration (item 3) included the user interface of the dashboard, how to perform queries in the EDR, use of analytic tools (including the creation of tasks, workflows and policies) and results visualisation [38]. ...
Background
Hearing loss (HL) affects 466 million people of all ages worldwide, with a rapidly increasing prevalence, and therefore requires appropriate public health policies. Multi-disciplinary approaches that make use of eHealth services can build the evidence to influence public policy. The European Union-funded project EVOTION developed a platform that is fed with real-time data from hearing aids, a smartphone, and additional clinical data and makes public health policy recommendations based on hypothetical public health policy-making models, a big data engine and decision support system. The present study aimed to evaluate this platform as a new tool to support policy-making for HL.
Methods
A total of 23 key stakeholders in the United Kingdom, Croatia, Bulgaria and Poland evaluated the platform according to the Strengths, Weaknesses, Opportunities and Threats methodology.
Results
There was consensus that the platform, with its advanced technology as well as the amount and variety of data that it can collect, has huge potential to inform commissioning decisions, public health regulations and affect healthcare as a whole. To achieve this, several limitations and external risks need to be addressed and mitigated. Differences between countries highlighted that the EVOTION tool should be used and managed according to local constraints to maximise success.
Conclusion
Overall, the EVOTION platform can equip HL policy-makers with a novel data-driven tool that can support public health policy-making for HL in the future.
Purpose
The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies.
Method
The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes.
Results
The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care.
Conclusion
In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.
Introduction
The holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect ‘big data’, including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors. The main objective is to enable the validation of the EVOTION platform as a public health policy-making tool for HL.
Methods and analysis
This will be a big data international multicentre study consisting of retrospective and prospective data collection. Existing data from approximately 35 000 HA users will be extracted from clinical repositories in the UK and Denmark. For the prospective data collection, 1260 HA candidates will be recruited across four clinics in the UK and Greece. Participants will complete a battery of audiological and other assessments (measures of patient-reported HA benefit, mood, cognition, quality of life). Patients will be offered smart HAs and a mobile phone application and a subset will also be given wearable biosensors, to enable the collection of dynamic real-life HA usage data. Big data analytics will be used to detect correlations between contextualised HA usage and effectiveness, and different factors and comorbidities affecting HL, with a view to informing public health decision-making.
Ethics and dissemination
Ethical approval was received from the London South East Research Ethics Committee (17/LO/0789), the Hippokrateion Hospital Ethics Committee (1847) and the Athens Medical Center’s Ethics Committee (KM140670). Results will be disseminated through national and international events in Greece and the UK, scientific journals, newsletters, magazines and social media. Target audiences include HA users, clinicians, policy-makers and the general public.
Trial registration number
NCT03316287 ; Pre-results.
The current paper summarises the research investigating associations between physiological data and hearing performance. An overview of state-of-the-art research and literature is given as well as promising directions for associations between physiological data and data regarding hearing loss and hearing performance. The physiological parameters included in this paper are: electrodermal activity, heart rate variability, blood pressure, blood oxygenation and respiratory rate. Furthermore, the environmental and behavioural measurements of physical activity and body mass index, alcohol consumption and smoking have been included. So far, only electrodermal activity and heart rate variability are physiological signals simultaneously associated with hearing loss or hearing performance. Initial findings suggest blood pressure and respiratory rate to be the most promising physiological measures that relate to hearing loss and hearing performance.
The current paper summarises the research investigating associations between physiological data and hearing performance. An overview of state-of-the-art research and literature is given as well as promising directions for associations between physiological data and data regarding hearing loss and hearing performance. The physiological parameters included in this paper are: electrodermal activity, heart rate variability, blood pressure, blood oxygenation and respiratory rate. Furthermore, the environmental and behavioural measurements of physical activity and body mass index, alcohol consumption and smoking have been included. So far, only electrodermal activity and heart rate variability are physiological signals simultaneously associated with hearing loss or hearing performance. Initial findings suggest blood pressure and respiratory rate to be the most promising physiological measures that relate to hearing loss and hearing performance.
The vast amount of data, which arise in healthcare applications makes traditional data processing technology inadequate and requires the use of fast emerging big data technologies to cope with key challenges, including data heterogeneity, pace of acquisition, size, privacy and security. Addressing these challenges requires a shift from traditional data analysis systems and techniques to big data management and processing platforms as well as big data analytics centric architectures. In this paper, we introduce such an architecture. The architecture has been developed to support the acquisition and analysis of big data sets regarding hearing loss and the provision of related healthcare services for the purpose of informing public health policy making. The paper provides an overview of the system and presents the outcomes of an initial evaluation of its performance.
Smart cities make use of a variety of technologies, protocols, and devices to support and improve the quality of everyday activities of their inhabitants. An important aspect for the development of smart cities are innovative public policies, represented by requirements, actions, and plans aimed at reaching a specific goal for improving the society's welfare. With the advent of Big Data, the definition of such policies could be improved and reach an unprecedented effectiveness on several dimensions, e.g., social or economic. On the other hand, however, the safeguard of the privacy of its citizens is part of the quality of life of a smart city. In this paper, we focus on balancing quality of life and privacy protection in smart cities by providing a new Big Data-assisted public policy making process implementing privacy-by-design. The proposed approach is based on a Big Data Analytics as a Service approach, which is driven by a Privacy Compliance Assessment derived from the European Union's GDPR, and discussed in the context of a public health policy making process.
As Decision Support Systems start to play a significant role in decision making, especially in the field of public-health policy making, we present an initial attempt to formulate such a system in the concept of public health policy making for hearing loss related problems. Justification for the system's conceptual architecture and its key functionalities are presented. The introduction of the EVOTION DSS sets a key innovation and a basis for paradigm shift in policymaking, by incorporating relevant models, big data analytics and generic demographic data. Expected outcomes for this joint effort are discussed from a public-health point of view.
Predicting Impact of Loud Incidents on Individual Hearing for Public Health Policy in the Framework of EVOTION
- Adam Dudarewicz
- Malgorzata Pawlaczyk-Łuszczyńska
Dudarewicz, Adam, Małgorzata Pawlaczyk-Łuszczyńska,
Panagiotis Katrakazas, Niels Henrik Pontoppidan, and
Dimitrios Koutsouris. "Predicting Impact of Loud
Incidents on Individual Hearing for Public Health
Policy in the Framework of EVOTION." Heraclion,
Greece: 2018, 2018.
Decision Modelling in Public Health Policy-Making: EVOTION and Hearing Loss
- Lyubov Trenkova
- George Spanoudakis
- Ariane Laplante-Lévesque
- Andrew Smith
- Josip Milas
- Dario Brdarić
Trenkova, Lyubov, George Spanoudakis, Ariane Laplante-Lévesque, Andrew Smith, Josip Milas, and Dario
Brdarić. "Decision Modelling in Public Health Policy-Making: EVOTION and Hearing Loss." In Data for
Policy 2017. London, 2017.
Decision Modelling in Public Health Policy-Making: EVOTION and Hearing Loss
- trenkova