Physical Activity and Type 2 Diabetes Mellitus

Data from the World Health Organization shows that the number of people with type 2 diabetes mellitus (T2DM) skyrocketed from 108 million in 1980 to 422 million in 2014. Not surprinsingly considering our current lifestyle, the prevalence has been sharply rising: there was a 3% increase in diabetes mortality rates by age between 2000 and 2019. Diabetes is a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation. Thus, it is considered one of the greatest public health concerns of the 21th century.

Physical activity has been demonstrated as a critical part of the treatment of this chronic condition. However, there is no consistent evidence about which and how much physical activity these people should engage to improve their global health. Therefore, this repository tries to clarify this information, providing the required data to reproduce the results presented in the scientific works that arose from this research question. We firstly focused on one of the most interesting outcomes in diabetes: the glycosylated hemoglobin (HbA1c). Next, we explored the effectiveness of physical activity in a health condition that is strongly associated with T2DM: hypertension.

Glycosylated hemoglobin

In this repository you will find the Glycosylated Hemoglobin (HbA1c) folder that contains all the documentation needed to reproduce the results of the manuscript entitled Optimal Dose and Type of Physical Activity to Improve Glycemic Control in People Diagnosed With Type 2 Diabetes: A Systematic Review and Meta-Analysis published in Diabetes Care (link: https://diabetesjournals.org/care/article/47/2/295/154149/Optimal-Dose-and-Type-of-Physical-Activity-to).

For a better understanding of the dataset that you have to import in R (read the intructions below), we state a short description of the variables you will find within the dataset.

Download the Excel file

First, the dataset in Excel format must be downloaded. It has all the required data to conduct the posterior analyses presented in the R code file. For a better understanding of the data, here we explain the variables meaning.

Variables' explanation

• studyID is the included studies in this meta-analysis. Each row (i.e., observation) corresponds to each study-arm.
• N is the total sample of the study.
• age is the average age of each study.
• sex_male is the number of males within the study sample.
• supervised corresponds to whether or not the intervention was supervised by a physical activity/ fitness/ medical/ physiotherapy professional.
• baseline_glyc (if reported) is the average baseline level of glycemia that participants begin the intervention.
• ìllness_duration is the duration between the diagnosis of diabetes and the intervention beginning.
• BMI is the average body mass index (BMI) of the study sample.
• agent is the type of physical activity performed on each study.
• outcome corresponds to our outcome of interest.
• pren is the study sample at baseline.
• premean is the mean value of glycemia level at baseline.
• presd is the standard deviation of the mean glycemia value at baseline.
• postn is the study sample at post-intervention time point.
• postmean is the mean value of glycemia level at post-intervention time point.
• postsd is the standard deviation of the mean glycemia value at post-intervention time point.
• y represents the mean change from baseline within the study arm.
• se is the standard error of the mean change from baseline.
• diff is the mean difference between study-arms at post-intervention time point.
• pooled_var ia the pooled variance within a study.
• se_diff corresponds to the standard error of the mean difference (when diff = NA, then the standard error of the mean change and the mean difference were identical).
• outcome_group is the outcome of interest.
• lower_is_bettercorresponds to the direction of the outcome: the lower the value, the greater the improvement.
• Notesis the variable where we specify the intervention parameters.
• trt is the most specific level showing what studies had performed.
• duration_weeks is the duration in weeks of the interventions.
• sessions represents the frequency of the intervention (i.e., number of sessions per week).
• time_session is the minutes an intervention session lasted.
• code_compendium is the code that belongs the classification made by the international and validated Compendium of Physical Activities.
• dose_calculation corresponds to the METs-min/day (i.e., daily dose of physical activity) of each study-arm.
• weekly_dose is the daily dose multiplied by the intervention frequency, obtaining the weekly dose of physical activity (i.e., mETs-min/week).
• dose_by_50 is the approximation of weekly dose by 50 METs-min/week increments (that is to favour the data exploratory analysis).
• dose_by_100 is the approximation of weekly dose by 100 METs-min/week increments.

File path inspection

Friendly reminder: you have to fill the gap of the file path within the code when the dataset is imported in our R environment.

Hypertension

Additionally, you will also find in this repository the Hypertension folder, which contains the documentation needed to reproduce the analyses conducted in title of the manuscript that is not finished yet. To ease the data import, we export directly the clean dataset from our R environment, and save it in a .Rda doc, which can be directly read in R. So, you just have to download the SBP_data and DBP_data from the aforementioned repository, and run the code in your console.