Infertility is a disease globally affecting 20–30% of the reproductive age female population. However, in up to 50% on recorded cases, problems with infertility are ascribed to men; therefore, it is important to popularize healthy eating also in this group. During the last decade, it has been observed that society’s lifestyle changed drastically: reduced energy expenditure in physical activity per day, increased consumption of hypercaloric and high-glycemic-index foods with high content of trans fats, and reduced consumption of dietary fiber, which negatively affects fertility. Increasing evidence points to a link between diet and fertility. It is becoming clear that well-planned nutrition can also contribute to the effectiveness of ART. The low-GI plant-based diet appears to have a positive effect, especially when it is based on Mediterranean dietary patterns: rich in antioxidants, vegetable protein, fiber, MUFA fatty acids, omega-3, vitamins, and minerals. Importantly, this diet has been shown to protect against chronic diseases associated with oxidative stress, which also translates into pregnancy success. As lifestyle and nutrition seem to be important factors affecting fertility, it is worth expanding knowledge in this regard among couples trying to conceive a child.

Cardiopulmonary exercise testing (CPET) is the method of choice to assess aerobic fitness. Previous research was ambiguous as to whether treadmill (TE) and cycle ergometry (CE) results are transferrable or different between testing modalities in triathletes. The aim of this paper was to investigate the differences in HR and VO₂ at maximum exertion between TE and CE, at anaerobic threshold (AT) and respiratory compensation point (RCP) and evaluate their association with body fat (BF), fat-free mass (FFM) and body mass index (BMI). In total, 143 adult (n = 18 female), Caucasian triathletes had both Tr and CE CPET performed. The male group was divided into 40 years (n = 45). Females were aged between 18 and 46 years. Body composition was measured with bioelectrical impedance before tests. Differences were evaluated using paired t-tests, and associations were evaluated in males using multiple linear regression (MLR). Significant differences were found in VO₂ and HR at maximum exertion, at AT and at RCP between CE and TE testing, in both males and females. VO_2AT was 38.8 (±4.6) mL/kg/min in TE vs. 32.8 (±5.4) in CE in males and 36.0 (±3.6) vs. 32.1 (±3.8) in females (p < 0.001). HR_AT was 149 (±10) bpm in TE vs. 136 (±11) in CE in males and 156 (±7) vs. 146 (±11) in females (p < 0.001). VO₂max was 52 (±6) mL/kg/min vs. 49 (±7) in CE in males and 45.3 (±4.9) in Tr vs. 43.9 (±5.2) in females (p < 0.001). HRmax was 183 (±10) bpm in TE vs. 177 (±10) in CE in males and 183 (±9) vs. 179 (±10) in females (p < 0.001). MLR showed that BMI, BF and FFM are significantly associated with differences in HR and VO₂ at maximum, AT and RCP in males aged >40. Both tests should be used independently to achieve optimal fitness assessments and further training planning.

Maximal heart rate (HRmax) is associated mostly with age, but age alone explains the variance in HRmax to a limited degree and may not be adequate to predict HRmax in certain groups. The present study was carried out on 3374 healthy Caucasian, Polish men and women, clients of a sports clinic, mostly sportspeople, with a mean age of 36.57 years, body mass 74.54 kg, maximum oxygen uptake (VO₂max, ml^∗kg^–1∗min^–1) 50.07. Cardiopulmonary exercise tests (CPET) were carried out on treadmills or cycle ergometers to evaluate HRmax and VO₂max. Linear, multiple linear, stepwise, Ridge and LASSO regression modeling were applied to establish the relationship between HRmax, age, fitness level, VO₂max, body mass, age, testing modality and body mass index (BMI). Mean HRmax predictions calculated with 5 previously published formulae were evaluated in subgroups created according to all variables. HRmax was univariately explained by a 202.5–0.53^∗age formula (R² = 19.18). The weak relationship may be explained by the similar age with small standard deviation (SD). Multiple linear regression, stepwise and LASSO yielded an R² of 0.224, while Ridge yielded R² 0.20. Previously published formulae were less precise in the more outlying groups of the studied population, overestimating HRmax in older age groups and underestimating in younger. The 202.5–0.53^∗age formula developed in the present study was the best in the studied population, yielding lowest mean errors in most groups, suggesting it could be used in more active individuals. Tanaka’s formula offers the second best overall prediction, while the 220-age formula yields remarkably high mean errors of up to 9 bpm. In conclusion, adding the studied variables in multiple regression models improves the accuracy of prediction only slightly over age alone and is unlikely to be useful in clinical practice.

The COVID-19 pandemic and imposed restrictions were strong stress factors for young people, especially students. Increased alcohol consumption, smoking cigarettes, usage of heated tobacco products, and other stimulants are common methods of coping with anxiety. However, they can have serious negative health effects. A survey consisting of 12 questions related to mental health and psychoactive substance taking habits was distributed among Polish students between 22 February 2021 and 3 April 2021. A total of 1323 participants met all inclusion criteria (n_females = 1021, n_males = 297, n_{other gender} = 5). The mean age was 22 years old (±4.17); 47.62% were medical university students. A total of 71.92% reported negative impact, 8.25% did not notice changes, and 12.58% declared a positive pandemic impact on their mental health. A total of 12.58% declared an increase, 70.22% did not see any differences, and 17.20% reported a decrease in their psychoactive substance usage tendency due to the pandemic. Worse perceived psychologic well-being was correlated with a higher tendency to use tobacco (p < 0.001) and alcohol (p < 0.001), and not with marijuana and products containing tetrahydrocannabinol (p = 0.136), and hard drugs (p = 0.799). The majority of participants declared a negative pandemic impact on mental health and did not report significant changes in psychoactive substance taking habits. Medical personnel should be aware of the current situation and apply for proper prevention and treatment programs.

The COVID-19 pandemic has changed the way many people live. To assess its impact on sleep quality and quantity, blue light exposure, and the mental health of Polish university students, a cross-sectional survey was conducted. Almost half of the participants were medical students (47.62%; n = 630). The majority of students were suffering from insomnia (58.13%, n = 769). Almost every third student was sleeping less than 7 h a day (30.39%, n = 402). Our study showed that a short sleep duration correlates with poorer mental health outcomes. Respondents who declared sadness and depression were more likely to suffer from insomnia (OR = 5.6997, 95% CI: 4.3641–7.4441). Difficulty with tasks was also more likely to co-occur with insomnia (OR = 5.4723, 95% CI: 4.3007 to 6.9630). The results of this study showed that the COVID-19 pandemic contributed to the deterioration of sleep quality and quantity as well as the psychological well-being of Polish students. It is important to take steps to promote proper sleeping habits to alleviate the risk of mental health disorders in this group of people.

In recent years, numerous prognostic models have been developed to predict VO2max. Nevertheless, their accuracy in endurance athletes (EA) stays mostly unvalidated. This study aimed to compare predicted VO2max (pVO2max) with directly measured VO2max by assessing the transferability of the currently available prediction models based on their R², calibration-in-the-large, and calibration slope. 5,260 healthy adult EA underwent a maximal exertion cardiopulmonary exercise test (CPET) (84.76% male; age 34.6±9.5 yrs.; VO2max 52.97±7.39 mL·min^-1·kg^-1, BMI 23.59±2.73 kg·m^-2). 13 models have been selected to establish pVO2max. Participants were classified into four endurance subgroups (high-, recreational-, low- trained, and “transition”) and four age subgroups (18–30, 31–45, 46–60, and ≥61 yrs.). Validation was performed according to TRIPOD guidelines. pVO2max was low-to-moderately associated with direct CPET measurements (p>0.05). Models with the highest accuracy were for males on a cycle ergometer (CE) (Kokkinos R² = 0.64), females on CE (Kokkinos R² = 0.65), males on a treadmill (TE) (Wasserman R² = 0.26), females on TE (Wasserman R² = 0.30). However, selected models underestimated pVO2max for younger and higher trained EA and overestimated for older and lower trained EA. All equations demonstrated merely moderate accuracy and should only be used as a supplemental method for physicians to estimate CRF in EA. It is necessary to derive new models on EA populations to include routinely in clinical practice and sports diagnostic.