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mHealth Platform and Sensors
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mHealth Platform and Sensors

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 19478

Special Issue Editors

Dr. Maria Teresa Arredondo Waldmeyer

E-Mail Website
Guest Editor
LifeSTech Group, Tecnología Fotónica y Bioingeniería Dep, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: personalized health; ubiquitous computing; mobile healthcare; integrated care
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Fernanda Cabrera-Umpiérrez

E-Mail Website
Guest Editor
LifeSTech Group, Tecnología Fotónica y Bioingeniería Dep, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: ehealth; einclusion; human computer interaction; accessibility
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Fico

E-Mail Website
Guest Editor
Assistant Professor of Biomedical Engineering, Universidad Politécnica de Madrid, ETSI Telecomunicación, Avenida Complutense, 30, Ciudad Universitaria, 28040 Madrid, Spain
Interests: artificial intelligence and digital health; artificial intelligence and clinical decision support systems; artificial intelligence and health knowledge management; artificial intelligence and health technology assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past decades, mortality rates have fallen significantly around the world, leading to considerable changes in the age distribution of societies. Parallel to the ageing phenomenon, there is systematic evidence that frailty syndrome, associated with physical, functional, and cognitive decline; mental diseases like dementia and Alzheimer’s; as well as social isolation, are health ailments that trigger more dependencies and comorbidities. Long-term care and health care costs are expected to contribute the most to the rise in age-related spending and thus will have wide-ranging implications regarding the sustainability of the current social and health systems models. While a considerable proportion of this expenditure relates to direct drugs costs and new health technologies, the lack of integration between high-cost hospital care and community care with appropriate digital tools adds to this burden.

This has led many governments to search and invest in sustainable solutions that help to slow down the progression of these conditions, allowing older people to stay longer in smart living environments that evolve along with them as they age. Other initiatives include progress towards the standardization and interoperability of eHealth solutions in support of health system reforms, the adoption of new standards for digital health and care, the strengthening of the digital infrastructure for the cross border exchange of health data, and new investment in the large-scale implementation of digital health and social care programmes by national and regional authorities.

This Special Issue aims to explore approaches for mHealth platforms, frameworks, and sensors for medical and consumer applications to be adopted in this vast emerging scenario. Contributions that address but are not restricted to the following topics are welcome:

  • New models of digital services design, procurement, and delivery;
  • Novel platforms for smart living healthy environments;
  • New technologies as enablers of new healthcare paradigms;
  • New accessible and intuitive user led-adaptable services to prolong and support autonomy and continuous care;
  • An in-depth understanding of end users and the way data is produced and used by citizen and healthcare systems;
  • Maintaining cybersecurity, as connected medical devices present additional risks for data security;
  • Interoperability, governance, accountability, and tracking;
  • Quality of data: there is often a focus on combining clinical data, but the quality of combined datasets could be enhanced by integrating all types of data, including social information and climate data;
  • Sources of data: information from personal spaces is often perceived as ‘dirty data’, since it may be incomplete or seen as biased or less reliable;
  • Use of data: current diagnostic techniques rely on the reporting of symptoms and syndromes. Data usage and impact need to be fed back to close the loop;
  • Evidence and standards.

Submitted papers should present novel contributions and innovative applications. Relevant topical reviews are also welcome.

Dr. Maria Teresa Arredondo Waldmeyer
Dr. Maria Fernanda Cabrera-Umpiérrez
Dr. Giuseppe Fico
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. Sensors 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 2600 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

  • Smart living environments and platforms;
  • Internet of Things for health;
  • Wearable sensors;
  • User monitoring;
  • Mobile health applications;
  • Decision support systems;
  • Medical devices;
  • Health technology assessment.

Published Papers (3 papers)

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Review

19 pages, 849 KiB  
Review
A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)
by Vibhuti Gupta, Thomas M. Braun, Mosharaf Chowdhury, Muneesh Tewari and Sung Won Choi
Sensors 2020, 20(21), 6100; https://doi.org/10.3390/s20216100 - 27 Oct 2020
Cited by 21 | Viewed by 5642
Abstract
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic [...] Read more.
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required. Full article
(This article belongs to the Special Issue mHealth Platform and Sensors)
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23 pages, 1558 KiB  
Review
Playability and Player Experience in Digital Games for Elderly: A Systematic Literature Review
by Antonio Rienzo and Claudio Cubillos
Sensors 2020, 20(14), 3958; https://doi.org/10.3390/s20143958 - 16 Jul 2020
Cited by 23 | Viewed by 5571
Abstract
A higher number of people increasingly uses digital games. This growing interest in games, with different objectives, justifies the investigation of some aspects and concepts involved, such as product quality (game), usability, playability, and user or player experience, topics investigated by the multidisciplinary [...] Read more.
A higher number of people increasingly uses digital games. This growing interest in games, with different objectives, justifies the investigation of some aspects and concepts involved, such as product quality (game), usability, playability, and user or player experience, topics investigated by the multidisciplinary area called Human–Computer Interaction (HCI). Although the majority of users of these games are children and young people, an increasing number of older adults join technology and use different types of digital games. Several studies establish the increase in learning, socialization and exercise promotion, and cognitive and psychomotor skills improvement, all within the context of active and healthy aging. The objective of this work is to carry out a systematic literature review investigating the player experience of the elderly in digital games. The work allowed answering five research questions that were formulated. The evolution and maturity level of the research area are studied together with the research methods used. The factors that motivate adults to play were also analyzed; what are the recommended technical characteristics for games and some tools and metrics with which games are evaluated for older adults? Research gaps were detected in the area; there are not many specific studies on playability and player experience applied to the older adult, nor are there proven tools and metrics to evaluate them. Particular techniques for assessing and designing games focused on older adults are lacking, and quantitative studies that better identify the factors that affect the playability and experience of older adults in digital games. Full article
(This article belongs to the Special Issue mHealth Platform and Sensors)
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21 pages, 582 KiB  
Review
Smartphone Apps in the Context of Tinnitus: Systematic Review
by Muntazir Mehdi, Constanze Riha, Patrick Neff, Albi Dode, Rüdiger Pryss, Winfried Schlee, Manfred Reichert and Franz J. Hauck
Sensors 2020, 20(6), 1725; https://doi.org/10.3390/s20061725 - 19 Mar 2020
Cited by 18 | Viewed by 7638
Abstract
Smartphones containing sophisticated high-end hardware and offering high computational capabilities at extremely manageable costs have become mainstream and an integral part of users’ lives. Widespread adoption of smartphone devices has encouraged the development of many smartphone applications, resulting in a well-established ecosystem, which [...] Read more.
Smartphones containing sophisticated high-end hardware and offering high computational capabilities at extremely manageable costs have become mainstream and an integral part of users’ lives. Widespread adoption of smartphone devices has encouraged the development of many smartphone applications, resulting in a well-established ecosystem, which is easily discoverable and accessible via respective marketplaces of differing mobile platforms. These smartphone applications are no longer exclusively limited to entertainment purposes but are increasingly established in the scientific and medical field. In the context of tinnitus, the ringing in the ear, these smartphone apps range from relief, management, self-help, all the way to interfacing external sensors to better understand the phenomenon. In this paper, we aim to bring forth the smartphone applications in and around tinnitus. Based on the PRISMA guidelines, we systematically analyze and investigate the current state of smartphone apps, that are directly applied in the context of tinnitus. In particular, we explore Google Scholar, CiteSeerX, Microsoft Academics, Semantic Scholar for the identification of scientific contributions. Additionally, we search and explore Google’s Play and Apple’s App Stores to identify relevant smartphone apps and their respective properties. This review work gives (1) an up-to-date overview of existing apps, and (2) lists and discusses scientific literature pertaining to the smartphone apps used within the context of tinnitus. Full article
(This article belongs to the Special Issue mHealth Platform and Sensors)
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