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Chronic Disease Management and Prevention Using Smart Technologies
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Chronic Disease Management and Prevention Using Smart Technologies

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1341

Editors

Dr. Mirela Frandes

E-Mail Website
Guest Editor
Biostatistics and Medical Informatics, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
Interests: biostatistics; medical informatics; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Adriana Gherbon

E-Mail Website
Guest Editor
Department of Internal Medicine II, Diabetes, Metabolic Diseases and Systemic Rheumatology, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
Interests: diabetes management; internal medicine; metabolic disorders
Dr. Paula Barata

E-Mail Website
Guest Editor
Faculty of Medicine, Department of Physiology, "Vasile Goldiș" Western University, 310025 Arad, Romania
Interests: pulmonology; COPD; respiratory rehabilitation

Special Issue Information

Dear Colleagues,

Chronic diseases are conditions with long-term evolution, and their symptoms improve with treatment. These conditions can have multiple causes that may involve several risk factors, such as smoking, genetic factors, an unhealthy diet, or a sedentary lifestyle. As these diseases do not go away and are never cured, they must be carefully monitored by both the patient and their doctor. In the absence of adequate treatment, disease evolution tends toward aggravation and even death due to the complications that may occur (e.g., myocardial infarction or stroke). Primary chronic diseases include ischemic cardiovascular diseases, diabetes, COPD, bronchial asthma, lung cancer, colorectal cancer, arthritis, depression, Parkinson's disease, and chronic kidney disease. Preventive measures, early detection, and appropriate treatment strategies are essential and differ from patient to patient, depending on the symptomatology and severity of the disease.

Chronic disease prevention measures include stopping smoking, avoiding a sedentary lifestyle, engaging in daily physical activity, reducing the hours spent in front of a screen (resting one’s eyes for at least 10 minutes per hour of computer work), and adopting a healthy meal schedule. In other words, to prevent chronic diseases, significant lifestyle changes are necessary, such as smoking cessation, regular physical activity, and a balanced, healthy diet.

There is ongoing research focusing on using innovative and smart technologies in the prevention and management of chronic diseases. We invite you to contribute research or review articles on the use of eHealth, mobile devices, and mobile applications in chronic disease management and prevention.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Self-managing medical applications in chronic disease management and prevention;
  • Artificial-intelligence-based solutions in chronic disease management and prevention;
  • Remote patient-monitoring devices in chronic disease management and prevention;
  • Wearables in chronic disease monitoring and patient engagement;
  • Personal health record applications;
  • Telemedicine in chronic disease management and prevention;
  • Patient portals;
  • Smart medication adherence solutions;
  • Continuous biometric monitoring in chronic disease management and prevention;
  • Advanced sensor technologies in chronic disease management and prevention.

We look forward to receiving your contributions.

Dr. Mirela Frandes
Dr. Adriana Gherbon
Dr. Paula Barata
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 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. Healthcare 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 2700 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

  • chronic disease management
  • chronic disease prevention
  • smart technologies
  • artificial intelligence-based solutions
  • remote patient monitoring devices
  • wearables
  • continuous biometric monitoring
  • advanced sensor technologies

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Published Papers (1 paper)

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Research

17 pages, 939 KB  
Article
Digital Engagement in Diabetes Care: A Multi-Domain Analysis of Psychosocial and Clinical Determinants
by Mirela Frandes, Adriana Gherbon, Bogdan Timar and Cǎlin Muntean
Healthcare 2026, 14(6), 800; https://doi.org/10.3390/healthcare14060800 - 21 Mar 2026
Viewed by 526
Abstract
Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with [...] Read more.
Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with the use of digital health technologies is crucial for developing targeted implementation strategies. Objectives: The aim of this study was to assess the use of digital health technologies among adults with diabetes and to explore their relationship with psychosocial factors—especially technology acceptance and self-efficacy—as well as certain clinical characteristics, including diabetes-related stress, age, and disease duration. Methods: We conducted a cross-sectional study involving 304 adults with diabetes. Digital engagement was measured using the Digital Adherence and Use Questionnaire (DAUQ), a 7-item self-report instrument (Cronbach’s α = 0.89), from which a composite Digital Engagement Score was calculated (range 1–5) to indicate the level of technology-related self-management behaviors. Participants were descriptively categorized into low- and high-engagement groups. Engagement patterns were also analyzed by diabetes type to understand structural differences in technology exposure. Relationships between psychosocial variables and the outcome were examined using correlation analyses. Since engagement among participants with type 1 diabetes (T1D) showed limited variability, multivariable regression analyses were performed on participants with type 2 diabetes (T2D) using beta regression, with linear regression as a sensitivity analysis. An exploratory beta regression was also conducted for T1D. Results: Overall, 35.5% of participants were classified as having high digital engagement. High engagement was observed in more than 90% of participants with T1D, compared to 4.1% of those with T2D. Median engagement scores differed significantly between low- and high-engagement groups (median [Q1–Q3]: 1.71 [1.71–2.39] vs. 3.86 [3.86–4.43]). Highly engaged participants reported much higher levels of openness to technology (median [Q1–Q3]: 5.00 [1.00–5.00] vs. 1.00 [1.00–1.00], p < 0.001) and self-efficacy (median [Q1–Q3]: 3.00 [3.00–3.00] vs. 5.00 [5.00–5.00], p < 0.001). In T1D, multivariable beta regression analyses showed that age was independently associated with digital engagement, with each 10-year increase corresponding to a decrease in engagement (β = −0.147, 95% CI −0.219 to −0.075, p < 0.001). Diabetes duration and psychosocial variables were not independently associated with engagement in the multivariable model. In contrast, among participants with T2D, insulin treatment emerged as the strongest independent predictor of engagement (β = 0.996, 95% CI 0.859–1.134, p < 0.001), and diabetes-related stress emerged as an independent predictor of engagement (β = 0.069, 95% CI 0.006–0.132, p = 0.033). Technology acceptance was positively associated with engagement (β = 0.694, 95% CI 0.350–1.037, p < 0.001), whereas higher self-efficacy was independently associated with lower engagement intensity (β = −0.366, 95% CI −0.608 to −0.124, p = 0.003). Age and diabetes duration were not independently associated with engagement after adjustment. Conclusions: Digital engagement appears to function as a structurally embedded component of self-management in T1D, with limited variability and largely independent of psychosocial modulation. In T2D, engagement is predominantly driven by treatment characteristics (insulin treatment), psychosocial dynamics (stress, technology acceptance), with higher self-efficacy associated with reduced reliance on digital tools. These findings suggest distinct behavioral mechanisms underlying digital health utilization across diabetes types and support the need for tailored implementation strategies. Full article
(This article belongs to the Special Issue Chronic Disease Management and Prevention Using Smart Technologies)
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