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Digital Healthcare IoT and Sensing Platforms

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 May 2026) | Viewed by 954

Editor


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Guest Editor
Department of Microelectronics and Electronic Systems, Universitat Autònoma de Barcelona, Barcelona, Spain
Interests: medical Internet of Things (MIoT) platforms; wearable technology for health monitoring; sensor platforms for healthcare; biometric security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of Internet of Things (IoT) platforms that conet users to the cloud services, smartphone apps and health devices and wearables, integrating heterogeneous technologies and a diversity providers together with an increase in the use of AI at different levels, has revolutionized healthcare, driving innovation in digital health platforms and monitoring systems.

This Special Issue focuses on the design, development, and application of IoT platforms and smart sensing systems in digital healthcare, exploring how they can enhance patient monitoring, diagnostics, treatment, and overall healthcare prvisioning. The integration of IoT with wearable or portable devices, smartphones and data analytics provides continuous, real-time health monitoring with the patient in the loop thanks to new user interfaces, promoting personalized care, early detection of medical events, and improve the management of chronic diseases.

This issue invites contributions that address key technological, clinical, and social challenges in implementing platform-based healthcare solutions. Topics of interest include, but are not limited to:

  • Wearable Sensors and Medical Devices: Advances in biosensors, smart wearables, and implantable devices that enable seamless data collection and health monitoring and control.
  • Remote Patient Monitoring: IoT frameworks for remote health supervision, telemedicine platforms, and the role of sensing technologies in chronic disease management.
  • Healthcare Data Analytics: AI-driven approaches for processing and analyzing large-scale health data collected via IoT devices.
  • Smart Health Systems: Integration of IoT in hospital infrastructure, smart healthcare facilities, and interoperable health systems for improved patient outcomes.
  • Security and Privacy: Addressing the cybersecurity risks and data privacy concerns associated with digital healthcare platforms.
  • Technoeconomic and value capture of platform solutions in Healthcare: Quantitative research on the impact of healthcare platforms.

This issue seeks original research, review articles, and case studies that highlight breakthroughs and practical applications of IoT and IoMT platforms and sensing technologies in healthcare, with an emphasis on improving efficiency, accessibility, and patient-centric care in the digital health landscape.

Dr. Jordi Carrabina
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • Internet of Medical Things (IoMT)
  • digital healthcare platforms
  • wearable devices
  • smartphone health APPs
  • AI on healthcare platforms
  • technoeconomic analysis
  • remote patient monitoring

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Published Papers (1 paper)

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Research

16 pages, 14070 KB  
Article
A Modular Digital Health Architecture for Longitudinal Menstrual Cycle Monitoring: System Design and Formative Usability Evaluation
by Tomasz Bolesław Cedro, Grzegorz Południewski and Wojciech Michał Glinkowski
Appl. Sci. 2026, 16(13), 6469; https://doi.org/10.3390/app16136469 - 29 Jun 2026
Viewed by 132
Abstract
Background: Longitudinal menstrual cycle monitoring requires digital health systems capable of handling individual variability, irregular sampling, and incomplete real-world observations. Most consumer-focused menstrual-tracking applications depend on simplified calendar-based logic, offering limited support for transparent longitudinal data handling, interoperability, and the management of irregular [...] Read more.
Background: Longitudinal menstrual cycle monitoring requires digital health systems capable of handling individual variability, irregular sampling, and incomplete real-world observations. Most consumer-focused menstrual-tracking applications depend on simplified calendar-based logic, offering limited support for transparent longitudinal data handling, interoperability, and the management of irregular real-world observations. Objective: This study presents the design, implementation, and formative evaluation of a non-clinical digital health infrastructure for longitudinal menstrual cycle monitoring, with an emphasis on modular system architecture, longitudinal data processing, and user-perceived usability. Methods: A modular digital health system was developed in accordance with separation-of-concerns and privacy-by-design principles, combining a backend analytical infrastructure with a mobile application interface. The architecture was designed to support longitudinal data acquisition, variability-aware processing, and extensibility while remaining independent of proprietary analytical services. System evaluation included technical and functional verification, formative usability assessment, and quality evaluation using the user version of the Mobile App Rating Scale (uMARS). Results: In the uMARS evaluation (N = 63), the mean total score across core domains was 3.11 ± 0.76. Information quality (3.44 ± 0.85) and functionality (3.27 ± 0.88) received the highest ratings, whereas engagement (2.83 ± 0.84) received the lowest, consistent with the system’s prototype character. Internal consistency was high (Cronbach’s α = 0.91), and sensitivity analysis restricted to female participants yielded results comparable to those of the full sample. Conclusions: The proposed system demonstrates the technical and functional feasibility of a modular digital health architecture for longitudinal menstrual cycle monitoring under heterogeneous real-world data conditions. The findings support the use of variability-aware and extensible monitoring infrastructures as a foundation for future applied research and iterative development of women’s digital health systems without making diagnostic or predictive clinical claims. Full article
(This article belongs to the Special Issue Digital Healthcare IoT and Sensing Platforms)
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