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AI Technologies for eHealth and mHealth, 2nd Edition
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AI Technologies for eHealth and mHealth, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 689

Special Issue Editors

Dr. István Vassányi

E-Mail Website
Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprem, Hungary
Interests: health informatics; data modeling; data analysis; expert systems
Special Issues, Collections and Topics in MDPI journals
Dr. István Kósa

E-Mail Website
Guest Editor
Department of Preventive Medicine, University of Szeged, 6700 Szeged, Hungary
Interests: medical informatics; m-health; telemedicine
Special Issues, Collections and Topics in MDPI journals
Dr. László Balkányi

E-Mail Website
Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprem, Hungary
Interests: medical knowledge management; coding systems

Special Issue Information

Dear Colleagues,

The ongoing aging of modern societies will lead to a growing portion of society relying on the financial support of a shrinking workforce. At the same time, the sustainability of public health services is expected to be further affected by the application of new sophisticated and expensive imaging and diagnostic tools and processes developed for the care of widespread chronic diseases such as diabetes, cancer, and cardiovascular and neurological diseases. These trends will inevitably necessitate changes to the current healthcare system, even in the short run, and the only realistic answer to this challenge is preventive self-management supported by ambient assisted living devices, the IoT, and modern information technology that relies on artificial intelligence. It is the objective of this Special Issue to offer a cross-section of the current research related to all applications of AI in the eHealth and mHealth domains, with an emphasis on the following fields:

  • Machine learning methods used for feature extraction, diagnostics, and personalized treatment recommendations. Within this field, special attention should be given to personalized and mobile lifestyle counseling for chronic disease prevention and management.
  • Medical expert systems using traditional rule-based or case-based reasoning, or evolutionary/swarm intelligence algorithms, assisting medical professionals in research or daily care.
  • Natural language processing methods for analyzing current and legacy textual records and implementing intelligent chat support for patients.

This Special Issue welcomes original research articles and review papers. Case studies and reports on controlled clinical trials are especially welcome.

Dr. István Vassányi
Dr. István Kósa
Dr. László Balkányi
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. 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

  • eHealth
  • ambient assisted living
  • personalized care
  • machine learning
  • medical expert systems

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Published Papers (2 papers)

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Research

15 pages, 855 KB  
Article
Integrating Fitbit Wearables and Self-Reported Surveys for Machine Learning-Based State–Trait Anxiety Prediction
by Archana Velu, Jayroop Ramesh, Abdullah Ahmed, Sandipan Ganguly, Raafat Aburukba, Assim Sagahyroon and Fadi Aloul
Appl. Sci. 2025, 15(19), 10519; https://doi.org/10.3390/app151910519 (registering DOI) - 28 Sep 2025
Abstract
Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait [...] Read more.
Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO2Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth, 2nd Edition)
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13 pages, 7931 KB  
Article
Machine Learning Prediction of Agitation in Dementia Patients Using Sleep and Physiological Data
by Keshav Ramesh, Anna Yakoub, Youssef Ghoneim, Rehab Al Korabi, Jayroop Ramesh, Assim Sagahyroon and Fadi Aloul
Appl. Sci. 2025, 15(18), 9908; https://doi.org/10.3390/app15189908 - 10 Sep 2025
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Abstract
Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden [...] Read more.
Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes. Accurately predicting and promptly responding to agitation events is thus critical for enhancing the safety and well-being of PLwD. Leveraging artificial intelligence, tools can be used to monitor behavioral patterns and alert healthcare providers about potential agitation to facilitate timely and effective interventions. Despite the link between poor sleep quality and the likelihood of agitation, there remains a gap in utilizing sleep parameters for predictive analytics in this domain. This study explores the potential of integrating sleep and associated physiological data to predict the risk of agitation in dementia patients the next day, leveraging the Technology Integrated Health Management (TIHM) dataset. Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models. The findings underscore the value of incorporating sleep data into automated models and advocate for continued efforts to develop long-term agitation prediction methods. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth, 2nd Edition)
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