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Smart Cities
Special Issues
Systems, Applications and Services for Smart Health
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Systems, Applications and Services for Smart Health

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Health".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 16137

Special Issue Editors

Dr. Sten Hanke

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Guest Editor
eHealth Institute, FH JOANNEUM University of Applied Sciences, 8020 Graz, Austria
Interests: health informatics; personal health; eHealth; mHealth
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christopher Nugent

E-Mail Website
Guest Editor
School of Computing, Ulster University, 2-24 York Street, Belfast BT15 1AP, UK
Interests: ambient assisted living; smart homes; smart environments; activity recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Hossein Zoualfaghari

E-Mail Website
Guest Editor
Applied Research, BT Technology, UK
Interests: Internet of Things; Healthcare Informatics

Special Issue Information

Dear Colleagues,

Changing infrastructure and new ICT developments are leading to new health services and applications that have the potential to disrupt the way with which we will perceive health care in the near future. Mobile Health solutions and medical Internet of Things (mIoT) devices, together with clinical wearables, personalized health coaching, mobile Apps, fitness trackers, digital diagnostics, and mobile health monitoring, will provide increased levels of data about our health and lifestyle in a continuous manner.

Sensory and quantitative measurements with objective data provide context-rich continuous longitudinal data. Due to the invention of an increasing number of medical-certified mobile devices, health data measurements are shifting from infrequent to frequent information retrieval and providing additional information for medical technology-reported outcomes. It can be foreseen that frequent health measurements (from smart devices) will also lead to a demand for more frequent health service consultations. Extracted digital biomarkers will feed prediction models and will subsequently lead to person-centered and personalized care models.

Prediction models can simulate the short-term and long-term effects of established behavioral interventions (e.g., improvements in dietary behavior, physical activity) in addition to simulating potential new interventions of interest (e.g., the improvement of sleep and smoking cessation). Based on the simulation results of these interventions, current care pathways can be adapted and personalized to specific patient contexts and phenotypic characteristics. As a direct result, instead of curing diseases, new health services can help to maintain health and a healthy lifestyle within a healthy environment.

Dr. Sten Hanke
Prof. Dr. Christopher Nugent
Dr. Mohammad Hossein Zoualfaghari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Personalized health
  • Internet of Things
  • Behavior monitoring
  • eCoaching
  • Predictive models

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Published Papers (4 papers)

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Research

15 pages, 1320 KiB  
Article
AI-Based Predictive Modelling of the Onset and Progression of Dementia
by Sten Hanke, Francesca Mangialasche, Markus Bödenler, Bernhard Neumayer, Tiia Ngandu, Patrizia Mecocci, Helena Untersteiner and Elisabeth Stögmann
Smart Cities 2022, 5(2), 700-714; https://doi.org/10.3390/smartcities5020036 - 20 May 2022
Cited by 8 | Viewed by 4425
Abstract
Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be [...] Read more.
Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres. Full article
(This article belongs to the Special Issue Systems, Applications and Services for Smart Health)
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21 pages, 654 KiB  
Article
Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach
by Daniel Reichenpfader and Sten Hanke
Smart Cities 2021, 4(4), 1316-1336; https://doi.org/10.3390/smartcities4040070 - 12 Oct 2021
Cited by 2 | Viewed by 3593
Abstract
Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. [...] Read more.
Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed. Full article
(This article belongs to the Special Issue Systems, Applications and Services for Smart Health)
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9 pages, 1369 KiB  
Article
Dynamic Restaurants Quality Mapping Using Online User Reviews
by Didier Grimaldi, Carly Collins and Sebastian Garcia Acosta
Smart Cities 2021, 4(3), 1104-1112; https://doi.org/10.3390/smartcities4030058 - 2 Aug 2021
Cited by 6 | Viewed by 3452
Abstract
Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit [...] Read more.
Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises. Full article
(This article belongs to the Special Issue Systems, Applications and Services for Smart Health)
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14 pages, 2264 KiB  
Article
Food Desires, Negative Emotions and Behaviour Change Techniques: A Computational Analysis
by Nimat Ullah, Michel Klein and Jan Treur
Smart Cities 2021, 4(2), 938-951; https://doi.org/10.3390/smartcities4020048 - 15 Jun 2021
Cited by 1 | Viewed by 2844
Abstract
Behaviour change techniques are considered effective means for changing behaviour, and with an increase in their use the interest in their exact working principles has also expanded. This information is required to make informed choices about when to apply which technique. Computational models [...] Read more.
Behaviour change techniques are considered effective means for changing behaviour, and with an increase in their use the interest in their exact working principles has also expanded. This information is required to make informed choices about when to apply which technique. Computational models that describe human behaviour can be helpful for this. In this paper a few behaviour change techniques have been connected with a computational model of emotion and desire regulation. Simulations have been performed to illustrate the effect of the techniques. The results demonstrate the working mechanisms and feasibility of the techniques used in the model. Full article
(This article belongs to the Special Issue Systems, Applications and Services for Smart Health)
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