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AI-Driven Health and Wellbeing: Self-Monitoring, Early Detection, and Multi-Criteria Personalized Decision Support

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

Deadline for manuscript submissions: 20 June 2026 | Viewed by 2118

Special Issue Editor

Prof. Dr. Stanislav Dadelo

E-Mail Website
Guest Editor
Department of Entertainment Industry, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
Interests: artificial intelligence; healthcare; public health; healthy lifestyle; psychomotor competencies; education; security; MCDM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue examines how artificial intelligence can support health by integrating self-monitoring data, self-testing insights, predictive modeling, and personalized lifestyle guidance and healthcare. As wearable devices, mobile apps, and digital self-assessment tools have become common; individuals have been increasingbly able to access physiological and behavioral information, yet turning these data streams into clear and meaningful recommendations remains a challenge.

We invite research regarding the use of AI techniques in trend analysis, risk prediction, and data analysis for self-testing and self-monitoring, as well as regarding adaptations to guidance over time. Further, we highlight how MCDM frameworks help ensure that suggestions are clear, sensitive to context, and aligned with people's priorities and everyday situations.

Reserachers in public health, computer science, behavioral science, digital health, sports and movement science, healthcare, and decision sciences are all welcome to contribute. Topics include, but are not limited to, the following:

  • AI-based interpretation of self-monitoring and self-testing data;
  • Predictive and early risk detection models;
  • Data selection and feature engineering in personalized health systems;
  • AI and MCDM decision support for lifestyle recommendations;
  • Wearable and movement analytics in daily settings;
  • Personalized guidance for exercise, nutrition, recovery, and stress management;
  • Ethical and practical issues in implementing AI-supported wellbeing.

This Special Issue highlights technologically advanced yet human-centered approaches that enable health guidance to be adaptive, clear, and personally meaningful.

Prof. Dr. Stanislav Dadelo
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-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

  • digital health analytics
  • self-assessment data
  • wearable sensor data
  • behavioral and physiological indicators
  • feature selection and data fusion
  • adaptive decision-support systems
  • hybrid evaluation models
  • explainable modeling
  • personalized lifestyle adaptation
  • proactive wellness interventions

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Published Papers (3 papers)

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Research

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24 pages, 476 KB  
Article
From Prediction to Decision: The Decision Integration Deficit Index (DIDI) and Structural Imbalance in AI-Driven Digital Health Systems
by Stanislav Dadelo
Appl. Sci. 2026, 16(7), 3380; https://doi.org/10.3390/app16073380 - 31 Mar 2026
Viewed by 353
Abstract
Artificial intelligence (AI) has significantly advanced predictive capabilities in digital health systems; however, the structural integration of these predictions into formal decision-making processes remains insufficiently addressed. This study introduces the Decision Integration Deficit Index (DIDI), a structural diagnostic metric designed to assess the [...] Read more.
Artificial intelligence (AI) has significantly advanced predictive capabilities in digital health systems; however, the structural integration of these predictions into formal decision-making processes remains insufficiently addressed. This study introduces the Decision Integration Deficit Index (DIDI), a structural diagnostic metric designed to assess the alignment between inference- and decision-oriented components in AI-driven health system architectures. A domain × domain integration matrix represents structurally possible and empirically observed relationships between system components, enabling the formal assessment of integration patterns. The framework suggests that apparent balance at an aggregated level may conceal substantial structural asymmetries, particularly in the limited integration of modelling outputs into formal evaluation and decision-support mechanisms. The results suggest that the analyzed corpus reflects structurally incomplete architectures, characterized by an imbalance in decision integration across domains. In contrast to performance-based evaluation metrics, the DIDI provides a system-level diagnostic perspective that identifies missing or weakly specified integration pathways within decision-process architectures. This study contributes to digital health and decision-support research by introducing a reproducible structural assessment framework that enables evaluation of decision-process completeness and supports the development of more coherent, transparent, and accountable AI-driven decision-support systems. Full article
(This article belongs to the Special Issue AI-Driven Health and Wellbeing: Self-Monitoring, Early Detection, and Multi-Criteria Personalized Decision Support)
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Review

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18 pages, 340 KB  
Review
AI-Driven Inpatient Fall Prevention Using Continuous Monitoring: From Early Detection to Workflow-Integrated Decision Support: A Scoping Review
by Kazumi Kubota, Satoko Tsuda and Anna Kubota
Appl. Sci. 2026, 16(7), 3383; https://doi.org/10.3390/app16073383 - 31 Mar 2026
Cited by 1 | Viewed by 795
Abstract
Inpatient falls often occur at the bedside during unsupervised bed egress or bed exit attempts. Many artificial intelligence methods predict fall risk, but clinical value depends on workflow-ready decision support. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic [...] Read more.
Inpatient falls often occur at the bedside during unsupervised bed egress or bed exit attempts. Many artificial intelligence methods predict fall risk, but clinical value depends on workflow-ready decision support. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), mapped AI-driven inpatient fall prevention systems using continuous monitoring data that generate explicit action triggers. We searched PubMed, Scopus, and Web of Science Core Collection for English-language studies published between 2016 and 2026 on 15 February 2026. Of 200 records identified, 32 duplicates were removed, and 168 records were screened. Eighty-three full-text reports were assessed for eligibility. Thirty-eight studies were included in the Tier 1 synthesis as action-trigger decision support systems, and 20 were classified as Tier 2 prediction or detection only to characterize evidence gaps. Tier 1 systems clustered into room-based monitoring with direct nurse alerting, wearable or batteryless sensing for bed egress and bed or chair exit alarms, and bed-centered early warning. Reporting was often incomplete for implementation-critical metrics such as alert burden, false alarms, response times, alert routing, and downstream actions. We propose a minimum operational reporting set to support clearer evaluation and comparison of AI-enabled inpatient fall-prevention systems in real-world ward settings. Full article
(This article belongs to the Special Issue AI-Driven Health and Wellbeing: Self-Monitoring, Early Detection, and Multi-Criteria Personalized Decision Support)
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18 pages, 375 KB  
Review
AI-Driven and Algorithm-Supported Decision Support Using Continuous, Remote, and Self-Monitoring Patient Data for Early Deterioration Detection and Escalation: A Scoping Review
by Kazumi Kubota and Anna Kubota
Appl. Sci. 2026, 16(7), 3131; https://doi.org/10.3390/app16073131 - 24 Mar 2026
Viewed by 608
Abstract
Continuous ward monitoring, remote patient monitoring, and self-monitoring can generate high-frequency physiological data streams, yet clinical benefit depends on whether signals lead to timely escalation without excessive non-actionable alerts and workflow burden. This scoping review, reported in accordance with the Preferred Reporting Items [...] Read more.
Continuous ward monitoring, remote patient monitoring, and self-monitoring can generate high-frequency physiological data streams, yet clinical benefit depends on whether signals lead to timely escalation without excessive non-actionable alerts and workflow burden. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), mapped AI-driven and algorithm-supported decision support approaches using continuous, remote, or self-monitoring patient data for early deterioration detection or prediction and escalation support, with emphasis on nursing relevance, workflow integration, alert burden, and implementation outcomes. PubMed (MEDLINE), Ovid MEDLINE, Web of Science Core Collection, and Scopus were searched on 14 February 2026. The search identified 47 records; 12 duplicates were removed; 35 records were screened; 28 were excluded; and 7 full-text reports were included. The included evidence comprised two original studies, two protocol/design papers, and three reviews. Within these included sources, decision support was commonly described as linking monitoring inputs to interpretive outputs, such as tiered alerts or risk predictions, and then to escalation-related actions or response pathways. Because the evidence base was small and heterogeneous, the review should be interpreted as exploratory evidence mapping rather than as a basis for broad generalization. Within the included studies, key reporting gaps included inconsistent description of escalation endpoints, limited standardized reporting of alert burden and acknowledgment patterns, incomplete workflow descriptions in some remote monitoring evidence, and limited attention to maintenance risks such as dataset shift. Full article
(This article belongs to the Special Issue AI-Driven Health and Wellbeing: Self-Monitoring, Early Detection, and Multi-Criteria Personalized Decision Support)
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