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Science and Health Technology for Health Promotion: With a Focus on Applied Health Research, Second Edition

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 800

Special Issue Editors

Dr. Marius Baranauskas

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Guest Editor
Faculty of Biomedical Sciences, Panevezys College, State Higher Education Institution, 35200 Panevėžys, Lithuania
Interests: applied health research; health technology; foods; public health; applied epidemiology; biomedicine; environmental health; athletes; nutrition; exercise physiology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rimantas Stukas

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Guest Editor
Department of Public Health, Institute of Health Sciences, Faculty of Medicine, Vilnius University, 701513 Vilnius, Lithuania
Interests: applied public health; nutrition and food safety; lifestyle studies of various population groups
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building upon the resounding success of the first edition of our Special Issue "Science and Health Technology for Health Promotion: With a Focus on Applied Health Research" in 2024, we are pleased to announce the launch of its second edition. Our goal is to maintain our commitment to publishing cutting-edge scientific research that mirrors the current state of the art in the realm of applied health science.

Applied health science focuses on improving individual and community health through a variety of applied methods, with an emphasis on preventive measures and health promotion. Interdisciplinary collaboration is essential to applied health research and to the overall health and wellbeing of the populations served. This Special Issue focuses on how professionals via the implementation of health technology can help individuals achieve optimal health and fitness while leading more balanced and meaningful lives. Health technology is defined as the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve patients’ quality of lives. This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. Additionally, this Special Issue is designed to foster new knowledge about human health as well as support physical and mental health and to prevent disease. Scientists are invited to provide academic works (reviews, findings from observational and experimental studies, and population-based and clinically orientated health research) related to global health challenges across a range of conditions. Future manuscripts can also include studies of biology, anatomy, physiology, nutrition, fitness assessment, exercise prescription, and the prevention and control of disease. Manuscripts should reflect original research with well-articulated research aims/questions, precise methodologies (including clearly identified outcomes, a description of the sample population, recruitment, implementation process, measurement instruments, analytic plan, and special software), and elaborated discussions of the findings, policies, practices, and research implications.

Dr. Marius Baranauskas
Prof. Dr. Rimantas Stukas
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

  • health promotion
  • health technology
  • healthy life
  • lifestyle factors
  • public health
  • rural health
  • women’s health
  • men’s health
  • chronic disease
  • primary care
  • musculoskeletal health
  • exercise physiology
  • physical activity
  • nutrition and dietetics
  • cardiac rehabilitation
  • recreational therapy
  • applied nursing and health

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 631 KiB  
Article
Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
by Tomohito Kuroda, Shogo Okamoto and Yasuhiro Akiyama
Appl. Sci. 2025, 15(11), 5825; https://doi.org/10.3390/app15115825 - 22 May 2025
Abstract
Human gait motions differ depending on the age of the person. Previous studies have estimated age categories of walkers or have used age analysis for security or commercial surveillance purposes using images. However, few studies have estimated age from gait parameters alone. We [...] Read more.
Human gait motions differ depending on the age of the person. Previous studies have estimated age categories of walkers or have used age analysis for security or commercial surveillance purposes using images. However, few studies have estimated age from gait parameters alone. We estimated the age of people using kernel regression analysis based on their height, weight, and representative gait parameters, i.e., walking features that are interpretable with relative ease. Samples were obtained from 75 Japanese women aged 20–70 in a database. Through a variable selection based on sensitivity analysis, the established model estimated the ages of the women with a correlation coefficient of 0.78 with their actual ages, and the mean absolute error was 9.99 years. The sensitive variables included the minimum foot clearance, body weight, walking velocity, step width, and stride length. Estimation errors were significantly greater for elderly adults than for young people. Specifically, the mean absolute error for people in their 20s was 7.4 years, whereas that for those over 60 was 13.1 years. The proposed method uses gait parameters that can be measured with wearable devices, such as inertial measurement units; therefore, it offers an accessible approach to estimating a walker’s age with moderate certainty and promoting healthcare awareness in daily life. Full article
(This article belongs to the Special Issue Science and Health Technology for Health Promotion: With a Focus on Applied Health Research, Second Edition)
13 pages, 962 KiB  
Article
The Concordance Between the Clínica Universidad de Navarra Body Adiposity Estimator and a Bioelectrical Impedance Analysis in Assessing the Body Fat of Athletes
by Marius Baranauskas, Ingrida Kupčiūnaitė, Jurgita Lieponienė and Rimantas Stukas
Appl. Sci. 2025, 15(4), 2197; https://doi.org/10.3390/app15042197 - 19 Feb 2025
Viewed by 590
Abstract
An equation-derived body fat estimator, namely the Clínica Universidad de Navarra Body Adiposity Estimator (CUN-BAE), was established to assess the body fat percentage in adults. However, its efficiency compared to that of the bioelectrical impedance analysis (BIA) approach remains under-researched. This study aimed [...] Read more.
An equation-derived body fat estimator, namely the Clínica Universidad de Navarra Body Adiposity Estimator (CUN-BAE), was established to assess the body fat percentage in adults. However, its efficiency compared to that of the bioelectrical impedance analysis (BIA) approach remains under-researched. This study aimed to assess the agreement between the body fat percentages measured using a BIA and estimated using the CUN-BAE in a sample of Lithuanian professional athletes. A single cross-sectional study was conducted using the BIA technique to measure and the CUN-BAE equation to calculate the body fat percentages of 323 study participants. The Bland–Altman plot system was applied to comparing both the body fat percentages estimated using the CUN-BAE equation and those obtained via the BIA approach. The average values of the body fat percentages found in the total sample of elite athletes and estimated using the BIA and CUN-BAE equaled 18.4 ± 5.3% and 18.7 ± 6.6%, respectively (ICC: 0.91; 95% confidence interval (CI): 0.88; 0.93). This study found that the CUN-BAE method overestimated the BIA’s calculation of the body fat percentages by 2.7% on average. Meanwhile, the comparison of adiposity in the athletes using the CUN-BAE equation and the BIA methods demonstrated a similar, although not identical, accuracy. The BIA method cannot be replaced by the CUN-BAE equation in routine sports medicine practice due to moderately sized limits of agreement (95% CI: −6.5; 7.1), even when the access to body fat measurement devices is limited. From a public health perspective, the outcomes derived from the CUN-BAE equation can possibly be extrapolated to females and to individuals competing in strength–power sports, as well as to populations of adults. Full article
(This article belongs to the Special Issue Science and Health Technology for Health Promotion: With a Focus on Applied Health Research, Second Edition)
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