Background. The aim of this study was to evaluate the effect of a six-month home-based resistance-training program on muscle health and physical performance in healthy older subjects during the unique condition of home confinement caused by the COVID-19 pandemic. Methods. This was a randomized-controlled study that enrolled older participants that were allocated to either an experimental group performing the six-months exercise prescription (EXE) or a control group (CON). At the beginning (PRE), and after 6 months (POST), participants were assessed for muscle strength, balance, gait assessment and body composition by dual energy X-ray absorptiometry and magnetic resonance imaging. Normality distribution of data was checked with the D’Agostino and Pearson test and changes between PRE and POST were assessed by paired Student’s t-test while percentage and absolute changes between groups at POST were tested by unpaired t-test. Results. Nine participants were included for the final analysis: EXE, n = 5 (age: 66 ± 4; BMI: 27.5 ± 3.7) and CON, n = 4 (age: 71 ± 9; BMI: 24.2 ± 4.1). Significant PRE-to-POST changes were observed in the EXE group only in the chair-stand test (+19.8%, p = 0.048 and ES:1.0, moderate) and in total fat mass (+5.0%, p = 0.035 and ES:1.4, large) with no between-group differences. Moreover, EXE had significantly higher absolute thigh CSA values than CON at POST (14.138 ± 2977 vs. 9039 ± 1015, p = 0.0178, ES = 1.7). No other within- and between-group differences were detected. Conclusions. The home-based resistance-training program during the lockdown period, caused by the COVID-19 outbreak, determined only within-group improvement in lower limb muscle strength but not in muscle mass and composition in older subjects. Home confinement may partially explain the increase in total body fat due to a reduced daily PA regime and altered diet pattern.

Background: The coronavirus (COVID-19) pandemic led governments to adopt strict containment measures to avoid spreading the virus. These essential measures led to home confinement that influenced both the physical and mental health of populations. Physical activity plays a key role in preventing chronic diseases and promoting protective psychological factors. In the context of a lockdown, understanding the motives that guide people to enact physical activity is an important issue for public health. The present study aimed to evaluate the relation between autonomous motivation and physical activity, considering the role of behavioral intention and anxiety in a longitudinal moderated mediation model. Methods: Italian participants (N = 86; mean_age = 29.74, standard deviation = 9.74; female = 53.5%) completed a booklet composed of different questionnaires (motivation, intention, anxiety, and physical activity) 3 weeks apart. Results: The hypothesized model is supported by the evidence; both autonomous motivation and intention are direct predictors of physical activity. The results also show that the direct effect of autonomous motivation on physical activity is stronger in participants with low anxiety, while high levels of anxiety are a significant moderator of the intention–behavior relation. Conclusions: In conclusion, a multidisciplinary approach should be promoting methods and infrastructures to permit people to adhere to physical activity, as a front line against any health emergency.

The objective of the study was to assess chronotype, sleep hygiene, and sleep characteristics of Italian athletes in order to identify differences among sport disciplines (team- vs individual-sports), level of competition (Elite vs Non-Elite) and sexes. Moreover, correlations between chronotype, sleep hygiene and sleep quality were evaluated. Two hundred eighty Italian athletes (Females:113; Males:167) participated in the study. They completed the Pittsburgh Sleep Quality Index (PSQI), the Sleep Hygiene Index (SHI) and the MorningnessEveningness Questionnaire (MEQ). Among the athletes, 66.4% were intermediate-type, 21.8% were morning-type and 11.8% were evening-type. 55.4% of the sample was classified as good sleeper (PSQI<5), while 16.1% had poor sleep hygiene (SHI≥35). Females had higher total sleep time (p=0.022) than males. Individual-sports showed more morning-oriented score (p=0.001) and lower PSQI (p=0.006) and SHI (p<0.001) scores, with earlier timing of sleep (bedtime:p<0.001; wake-up time:p<0.001) compared to teamsports. Elite athletes exhibited greater sleep efficiency (p=0.009), and lower sleep latency (p=0.018) and wake after sleep onset (p=0.043) than Non-Elite athletes. Additionally, significant correlations were found between PSQI and SHI (r=0.43, p<0.001), and between MEQ and SHI (r=−0.35, p<0.001). This study yields crucial insights into the sleep behaviors of Italian athletes, revealing that nearly half of the sample were bad sleepers.

In recent years, cutting-edge technologies have been increasingly integrated into sports to assess physical and cognitive capacities. Virtual Reality (VR) technology has emerged as a powerful tool, offering an immersive and controlled scenario for examining cognitive functions. In light of the growing adoption of VR-systems, this study aimed to investigate the differences in visual attention and response time (RT) between athletes and non-athletes utilizing a VR-system and evaluate the discriminatory power of VR assessments towards the two groups. Sixty-one participants (Age: 22 ± 1.8years; athletes = 33, non-athletes = 28) underwent two visual attention evaluations through the Multiple Object Tracking (MOT) paradigm and three RT evaluations, all within a fully immersive VR environment. The visual attention assessments included the “MOT Assessment” (MOT), which did not have a primary target to select, and the “MOT, Primary Target Onset” (MOT-PT), which required selecting a primary target. The three RT evaluations included Continuous RT (RT-C), RT with Inter-Time between stimuli (RT-I), and Go No-Go RT (RT-GNG). In all RT assessments, participants aimed to touch the target that turned green in the shortest time as possible. In the RT-GNG test, participants responded to green targets by touching them with the hand controllers and refrained from touching red targets. No differences were found between athletes and non-athletes in visual attention tasks. However, athletes outperformed non-athletes in RT assessments (RT-C: 504.8 ± 45.9ms vs. 549.1 ± 45.6ms; p < 0.001. RT-I: 481.1 ± 44.9ms vs. 534.2 ± 58.6ms; p < 0.001. RT-GNG: 502.9 ± 38.8ms vs. 555.6 ± 57.8ms; p < 0.001). ROC curve analysis demonstrated moderate accuracy in differentiating athletes from non-athletes in RT assessments (RT-C: AUC = 0.75, p < 0.001; RT-I: AUC = 0.75, p < 0.001; RT-GNG: AUC = 0.80, p < 0.001). These findings underscore the significant role of RT in distinguishing athletes from non-athletes and highlight the discriminative potential of VR-systems as valuable tools in sports evaluation. Including RT assessments into traditional training regimens could offer new insights for evaluating athletic performance.

The aim of this cross-sectional study was to investigate the association between lower limb strength, muscle mass and composition, and balance ability in elders. Thirthy-four older participants (Age: 65.6 ± 4.73 years; male = 10 and female = 24) were assessed for muscle strength (maximal isometric strength of knee extensors and one repetition maximum by leg press, the one repetition maximum [1RM]), balance and gait capacity (Mini-BESTest), body composition by whole-body dual energy x-ray absorptiometry (obtaining Appendicular Skeletal Muscle Mass Index, ASMMI), and magnetic resonance imaging of thigh to evaluate Intermuscular Adipose Tissue (IMAT) and muscle Cross Sectional Area (CSA). Positive correlations between 1RM and ASMMI (r_s = 0.64, P < .0001) and thigh CSA (r_s = 0.52, P = .0017), but not with thigh IMAT, were found. In addition, significant correlations between knee extensors strength and ASMMI (r_s = 0.48, P = .004) and thigh CSA (r_s = 0.49, P = .0033) and IMAT (r_s = -0.35, P = .043) were observed, whereas no significant correlations between the Mini-BESTest with ASMMI, thigh CSA, and IMAT were observed. Lower limb strength positively correlated with appendicular muscle mass. Further, the maximal isometric strength of knee extensors negatively correlated with thigh IMAT in elderly patients, whereas the dynamic balance ability did not correlate with any of the morphological variables of the muscle (i.e., ASMMI, CSA, and IMAT). A reduced muscle size and strength could affect movement and reduce physical function in older patients. Improving the composition and size of muscle in elder subjects could reduce frailty and risk of falls.

Purpose: Few data are available on sleep characteristics of elite track-and-field athletes. Our study aimed to assess (1) differences in sleep between sexes and among different track-and-field disciplines, (2) the effect of individualized sleep-hygiene strategies on athletes’ sleep parameters, and (3) daytime nap characteristics in track-and-field athletes. Methods: Sleep characteristics of 16 elite Olympic-level track-and-field athletes (male: n = 8; female: n = 8) were assessed during the preseason period, at baseline (T0), and during the in-season period, after the adoption of individualized sleep-hygiene strategies (T1). Sleep parameters were objectively monitored by actigraphy for a minimum of 10 days, for each athlete, at both T0 and T1. A total of 702 nights were analyzed (T0 = 425; T1 = 277). Results: Female athletes displayed better sleep efficiency (88.69 [87.69–89.68] vs 91.72 [90.99–92.45]; P = .003, effect size [ES]: 0.44), lower sleep latency (18.99 [15.97–22.00] vs 6.99 [5.65–8.32]; P < .001, ES: 0.65), higher total sleep time (07:03 [06:56–07:11] vs 07:18 [07:10–07:26]; P = .030, ES: 0.26), earlier bedtime (00:24 [00:16–00:32] vs 00:13 [00:04–00:22]; P = .027, ES: 0.18), and lower nap frequency (P < .001) than male athletes. Long-distance runners had earlier bedtime (00:10 [00:03–00:38] vs 00:36 [00:26–00:46]; P < .001, ES: 0.41) and wake-up time (07:41 [07:36–07:46] vs 08:18 [08:07–08:30]; P < .001, ES: 0.61), higher nap frequency, but lower sleep efficiency (88.79 [87.80–89.77] vs 91.67 [90.95–92.38]; P = .013, ES: 0.44), and longer sleep latency (18.89 [15.94–21.84] vs 6.69 [5.33–8.06]; P < .001, ES: 0.67) than athletes of short-term disciplines. Furthermore, sleep-hygiene strategies had a positive impact on athletes’ total sleep time (429.2 [423.5–434.8] vs 451.4 [444.2–458.6]; P < .001, ES: 0.37) and sleep latency (14.33 [12.34–16.32] vs 10.67 [8.66–12.68]; P = .017, ES: 0.19). Conclusions: Sleep quality and quantity were suboptimal at baseline in Olympic-level track-and-field athletes. Large differences were observed in sleep characteristics between sexes and among different track-and-field disciplines. Given the positive effect of individualized sleep-hygiene strategies on athlete’s sleep, coaches should implement sleep education sessions in the daily routine of top-level athletes.