Special Issue "Applied Bayesian Data Analysis in Exercise and Health Research"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Jorge del Rosario Fernández Santos
E-Mail Website
Guest Editor
1. Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, 11009 Cádiz, Spain
2. GALENO Research Group and Department of Physical Education, Faculty of Education Sciences, University of Cádiz, 11519 Cádiz, Spain
Interests: Bayesian data analysis; applied statistics; exercise and health
Prof. Dr. Jesús Gustavo Ponce González
E-Mail Website
Co-Guest Editor
1. MOVE-IT Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cádiz, Cádiz, Spain
2. Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, Spain
Interests: exercise training; sports; physical fitness; performance; exercise physiology; health; obesity and comorbidities; metabolism; nutrition and endocrine system
Special Issues and Collections in MDPI journals
Prof. Dr. Cristina Casals
E-Mail Website
Co-Guest Editor
1. MOVE-IT Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cádiz, Cádiz, Spain
2. Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Cádiz, Spain
Interests: sport and exercise physiology; physical exercise; combat sports; cardiorespiratory fitness; athlete performance; nutritional assessment; gut microbiota for health and performance
Special Issues and Collections in MDPI journals
Prof. Dr. Jose Luis Gonzalez Montesinos
E-Mail Website
Assistant Guest Editor
Department of Physical Education, Faculty of Education Sciences, University of Cádiz, Cádiz, Spain
Interests: biomechanics; respiratory muscle training; patents

Special Issue Information

Dear Colleagues,

Bayesian data analysis is already a well-established method of statistical inference in many different fields of science such as psychology, ecology, economy or medicine. The increasing power of computers and the development of multiple programming languages specially designed to specify statistical models have allowed researchers to use Bayesian methods to analyze their data. Briefly, this methodology uses Bayes’ theorem to compute and update probabilities after observing new data.

There are several benefits that can be obtained using this method of statistical inference, highlighting among them the use of prior information within the model when estimating parameters of interest. However, performing statistical inference to draw conclusions from the data is complicated in general and using Bayesian methods in particular. Several steps must be carried out to ensure that the results obtained are correct and their interpretation in adequate.

Therefore, this Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) will accept manuscripts on exercise and health that apply a proper Bayesian workflow analysis, specifying correctly prior distributions, the statistical model fitted, and model and predictive checking regardless of the study design (e.g., longitudinal, randomized control trials or meta-analysis). We believe that all exercise and health scientists can benefit from having a Special Issue where proper Bayesian data analysis is performed.

Prof. Dr. Jorge del Rosario Fernández Santos
Prof. Dr. Jose Luis Gonzalez Montesinos
Prof. Dr. Jesús Gustavo Ponce González
Prof. Dr. Cristina Casals
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 papers will be 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. International Journal of Environmental Research and Public Health 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 2300 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

  • Bayesian statistics
  • statistical inference
  • data analysis
  • exercise science
  • health research

Published Papers (1 paper)

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Research

Article
Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys
Int. J. Environ. Res. Public Health 2021, 18(14), 7477; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147477 - 13 Jul 2021
Viewed by 561
Abstract
Background: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population. Objective: We developed a multilevel regression and [...] Read more.
Background: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population. Objective: We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018). Methods: We used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested. Results: MRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates. Conclusion: The MRP is a promising strategy to predict subpopulations’ physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
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