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Applied Bayesian Data Analysis in Exercise and Health Research
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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: closed (31 December 2022) | Viewed by 12191

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. ExPhy Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cadiz, 11519 Cadiz, Spain
2. Unidad de Investigación, Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, 11009 Cadiz, Spain
Interests: exercise training; HIIT; sports; physical fitness; performance; exercise physiology; health; obesity and comorbidities; type 2 diabetes; metabolism; nutrition; appetite and endocrine system
Special Issues, Collections and Topics 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, Collections and Topics 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 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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

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Published Papers (6 papers)

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Research

12 pages, 360 KiB  
Article
Bayesian Analysis of the HR–VO2 Relationship during Cycling and Running in Males and Females
by Pat R. Vehrs, Nicole D. Tafuna’i and Gilbert W. Fellingham
Int. J. Environ. Res. Public Health 2022, 19(24), 16914; https://doi.org/10.3390/ijerph192416914 - 16 Dec 2022
Cited by 1 | Viewed by 1917
Abstract
Professional organizations advise prescribing intensity of aerobic exercise using heart rate reserve (%HRR) which is presumed to have a 1:1 relationship with either maximal oxygen uptake (%VO2max) or %VO2 reserve (%VO2R). Even though running and cycling are popular [...] Read more.
Professional organizations advise prescribing intensity of aerobic exercise using heart rate reserve (%HRR) which is presumed to have a 1:1 relationship with either maximal oxygen uptake (%VO2max) or %VO2 reserve (%VO2R). Even though running and cycling are popular modes of training, these relationships have not been investigated in a group of males and females during both running and cycling. This study evaluated the %HRR-%VO2max and %HRR–%VO2R relationships in 41 college-aged males (n = 21) and females (n = 20) during treadmill running and cycling. Heart rate (HR) and VO2 data were collected at rest and during maximal exercise tests on a treadmill and cycle ergometer. The HR and VO2 data were analyzed using a Bayesian approach. Both the %HRR-%VO2max and %HRR–%VO2R relationships did not coincide with the line of identity in males and females in both treadmill running and cycling. %HRR was closer to %VO2max than to %VO2R. There were no significant differences in the intercepts of the %HRR–%VO2max and %HRR–%VO2R relationships between males and females during running or cycling, or between running and cycling in males or females. The credible intervals of the intercepts and slopes suggest interindividual variability in the HR–VO2 relationship that would yield significant error in the prescription of intensity of aerobic exercise for an individual. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
17 pages, 3303 KiB  
Article
A Multilevel Analysis of the Associated and Determining Factors of TB among Adults in South Africa: Results from National Income Dynamics Surveys 2008 to 2017
by Hilda Dhlakama, Siaka Lougue, Henry Godwell Mwambi and Ropo Ebenezer Ogunsakin
Int. J. Environ. Res. Public Health 2022, 19(17), 10611; https://doi.org/10.3390/ijerph191710611 - 25 Aug 2022
Cited by 1 | Viewed by 1639
Abstract
TB is preventable and treatable but remains the leading cause of death in South Africa. The deaths due to TB have declined, but in 2017, around 322,000 new cases were reported in the country. The need to eradicate the disease through research is [...] Read more.
TB is preventable and treatable but remains the leading cause of death in South Africa. The deaths due to TB have declined, but in 2017, around 322,000 new cases were reported in the country. The need to eradicate the disease through research is increasing. This study used population-based National Income Dynamics Survey data (Wave 1 to Wave 5) from 2008 to 2017. By determining the simultaneous multilevel and individual-level predictors of TB, this research examined the factors associated with TB-diagnosed individuals and to what extent the factors vary across such individuals belonging to the same province in South Africa for the five waves. Multilevel logistic regression models were fitted using frequentist and Bayesian techniques, and the results were presented as odds ratios with statistical significance set at p < 0.05. The results obtained from the two approaches were compared and discussed. Findings reveal that the TB factors that prevailed consistently from wave 1 to wave 5 were marital status, age, gender, education, smoking, suffering from other diseases, and consultation with a health practitioner. Also, over the years, the single males aged 30–44 years suffering from other diseases with no education were highly associated with TB between 2008 and 2017. The methodological findings were that the frequentist and Bayesian models resulted in the same TB factors. Both models showed that some form of variation in TB infections is due to the different provinces these individuals belonged. Variation in TB patients within the same province over the waves was minimal. We conclude that demographic and behavioural factors also drive TB infections in South Africa. This research supports the existing findings that controlling the social determinants of health will help eradicate TB. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
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17 pages, 2448 KiB  
Article
A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic
by Peter Congdon
Int. J. Environ. Res. Public Health 2022, 19(11), 6669; https://doi.org/10.3390/ijerph19116669 - 30 May 2022
Cited by 2 | Viewed by 1616
Abstract
Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that [...] Read more.
Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
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9 pages, 1436 KiB  
Article
Potential Energy as an Alternative for Assessing Lower Limb Peak Power in Children: A Bayesian Hierarchical Analysis
by Jorge R. Fernandez-Santos, Jose V. Gutierrez-Manzanedo, Pelayo Arroyo-Garcia, Jose Izquierdo-Jurado and Jose L. Gonzalez-Montesinos
Int. J. Environ. Res. Public Health 2022, 19(10), 6300; https://doi.org/10.3390/ijerph19106300 - 22 May 2022
Viewed by 1439
Abstract
The aim of this study was to analyze the use of potential energy (PE) as an alternative method to assess peak power of the lower limbs (PP) in children. 815 Spanish children (416 girls; 6–11 years old; Body Mass Index groups (n): underweight [...] Read more.
The aim of this study was to analyze the use of potential energy (PE) as an alternative method to assess peak power of the lower limbs (PP) in children. 815 Spanish children (416 girls; 6–11 years old; Body Mass Index groups (n): underweight = 40, normal weight = 431, overweight = 216, obese = 128) were involved in this study. All participants performed a Countermovement Jump (CMJ) test. PP was calculated using Duncan (PPDUNCAN), Gomez-Bruton (PPGOMEZ) and PECMJ formulas. A model with PECMJ as the predictor variable showed a higher predictive accuracy with PPDUNCAN and PPGOMEZ than CMJ height (R2 = 0.99 and 0.97, respectively; ELPDdiff = 1037.0 and 646.7, respectively). Moreover, PECMJ showed a higher linear association with PPDUNCAN and PPGOMEZ across BMI groups than CMJ height (βPECMJ range from 0.67 to 0.77 predicting PPDUNCAN; and from 0.90 to 1.13 predicting PPGOMEZ). Our results provide further support for proposing PECMJ as an index to measure PP of the lower limbs, taking into account the children’s weight and not only the height of the jump. Therefore, we suggest the use of PECMJ in physical education classes as a valid method for estimating PP among children when laboratory methods are not feasible. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
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16 pages, 859 KiB  
Article
Estimation of Behavior Change Stage from Walking Information and Improvement of Walking Volume by Message Intervention
by Tomoya Yuasa, Fumiko Harada and Hiromitsu Shimakawa
Int. J. Environ. Res. Public Health 2022, 19(3), 1668; https://doi.org/10.3390/ijerph19031668 - 1 Feb 2022
Cited by 2 | Viewed by 1916
Abstract
Lifestyle-related diseases are a major problem all over the world although exercising can prevent them. Therefore, it is necessary to encourage users to exercise regularly and to support their exercises. The purpose of this study is to investigate whether the estimation of behavior [...] Read more.
Lifestyle-related diseases are a major problem all over the world although exercising can prevent them. Therefore, it is necessary to encourage users to exercise regularly and to support their exercises. The purpose of this study is to investigate whether the estimation of behavior change stages can be predicted from the gait information obtained from wearable devices, and whether message interventions created based on the behavior change stages are effective in improving the amount of walking. As for the estimation of the behavior change stages, we investigated whether the behavior change stages could be correctly estimated compared with the ones obtained from the questionnaire. As for the effect of the message, we compared the period of no intervention with that of intervention to examine whether there was any change in the amount of walking. As a result of the experiment, we could not properly estimate the behavior change stage of users, but we found that the message intervention improved the amount of walking for many subjects. This suggests that further research is needed to estimate the stage of behavior change. However, message intervention is confirmed as an effective means to improve walking volume. Full article
(This article belongs to the Special Issue Applied Bayesian Data Analysis in Exercise and Health Research)
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16 pages, 2428 KiB  
Article
Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys
by Marina Christofoletti, Tânia R. B. Benedetti, Felipe G. Mendes and Humberto M. Carvalho
Int. J. Environ. Res. Public Health 2021, 18(14), 7477; https://doi.org/10.3390/ijerph18147477 - 13 Jul 2021
Cited by 2 | Viewed by 2429
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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