This is a cross-sectional analysis of physical activity and supplements use in Kazakhstan university students in a representative sample of al-Farabi Kazakh National University in Almaty. The study was approved by a Committee on Bioethics of the School of Public Health et al.-Farabi Kazakh National University, and each participant provided a written informed consent to participate. We invited students to participate through the internal university media and during presentation in classes. All volunteering students signed an informed consent to participate. They were offered a 45-item self-administered questionnaire in Russian in their off-class time. Al-Farabi Kazakh National University is a leading and the largest higher education facility in the country, offering tuition in a wide range of sciences, including School of Public Health, School of Chemistry, School of Physics, School of Biology, School of Biotechnology, School of Political Science, School of Mathematics, and few other. Both undergraduate (years 1–4) and graduate students (years 5–6) were enrolled. From a total of approx. 14,000 students, 889 agreed to participate and provided their filled questionnaires.
We collected data on demographics (age, sex, year of study) with living conditions questions (residence at the dorm, parent, or rent); cumulative monthly income in tenge (local currency, one USD is approximately 325 tenge); questions on tobacco products and alcohol use, including waterpipe and smokeless tobacco use. Self-reported smoking status was classified into never-, ever- and daily smokers, also asking for a number of smoked cigarettes a day and smoking duration. In an alcohol section, we clarified preferred beverage.
We then asked about adherence to daily routine (waking and going to bed at one time daily; trying to do that but not always good with that; often different time of sleep; and failure to fulfill sleeping recommendations and staying awake at night). Sleep duration was also categorized into (8 h or more a day; 6–8 h a day; and less than 6 h); with an adjacent question on the level of daytime sleepiness. Physical activity was classified using series of questions adapted from Health-Promoting Lifestyle Profile II, adult version. We asked if a student walked 6 km or 10,000 steps a day including weekends; was engaged in any leisure physical activity (recreational physical activity (RPA)) at least 3 times a week for at least 40 min. This question stratified students into those involved in RPA and those who are not. Those involved in RPA should have attributed them to one of the leading activity from the list offered: (1) walking, jogging or track and field; (2) cycling; (3) swimming; (4) volleyball, basketball, soccer or other games using a ball; (5) Step aerobics, fitness or dancing; (6) yoga; (7) gym, weight- or powerlifting; (8) martial arts (combat sport); and (9) all other.
We also addressed the motivation for RPA in students asking them to choose one of the most relevant options: (1) RPA is part of my healthy lifestyle; (2) losing or maintaining lower weight; (3) gaining weight or muscle mass; (4) RPA improves my mood; (5) I am coping with stress; (6) I am moving towards professional career in sports; and (7) I am trying to make new friends. Supplement users were identified whether they answered yes to the question “Are you currently using any of the sport supplements (nutrients used to attain better results in RPA)?”. Those who did, were directed to the next question asking to choose one of the most often used supplements of the list offered: (1) vitamins or multivitamins; (2) creatine, energy drink or pre-workout mixture; (3) protein; (4) carbohydrate and protein mix or gainer; (5) amino acids, including branched-chain amino acids (BCAA); (6) fat burners; (6) carnitine or arginine; and (7) Tribulus terrestis extract.
In addition, we asked whether a student used any electronic fitness tracker for the recreational physical activity. The next six questions were adapted from Health-Promoting Lifestyle Profile II to ascertain nutrition habits of the university students. We asked whether responders (1) choose a diet low in fat, saturated fat, and cholesterol; (2) limit use of sugars and food containing sugar (sweets); (3) eat 2–4 servings of fruit each day; (4) eat 3–5 servings of vegetables each day; (5) eat 2–3 servings of milk, yogurt or cheese each day; (6) eat breakfast daily.
The primary outcome in this analysis was supplement use, treated as a binary variable. Secondary outcomes were cigarette and waterpipe smoking, electronic cigarette, smokeless tobacco use, alcohol use, use of fitness tracker, different attributes of sleep; and nutrition habits (eating pattern). For each continuous variable, we report mean with its standard deviation, whether the data were normally distributed, otherwise – median with its interquartile range (IQR). We tested difference in variance between two groups using Mann-Whitney U-test, but for more groups we report p-values of F-statistic from ANOVA. Binary variables were compared using contingency tables with Fischer test.
After descriptive statistics in univariate analyses, we tested selected demographic and lifestyle attributes and variables against supplement use, expressed as a binary variable. These crude analyses are reported with significance values of selected predictors. Significant variables (p < 0.05) from such analysis were then considered predictors in multivariate regression models, in which their association with supplement use first in crude and then in adjusted models. We set up two types of adjusted regression models, including (1) adjusted for basic significant demographic confounders, such as sex, age and income; (2) adjusted for sex, age, income and all other predictors for a specific model, which predicted supplement use in the univariate analysis with p < 0.05. From these analyses, we report odds ratios (OR) with their corresponding 95% confidence intervals (CI). All tests were performed in NCSS 12 (Utah, USA).