Activity

In contrast, non-nature-based PA was a stronger predictor of introjected regulation compared to nature-based PA, which was negatively associated with psychological well-being. Overall, these findings suggest that (1) weekly PA was associated with increased psychological well-being during the lockdown, and (2) nature-based PA may foster psychological well-being via effects on motivation. The implications for continued participation in PA will be discussed.Humans tend to select motor planning with a high reward and low success compared with motor planning, which has a small reward and high success rate. Previous studies have shown such a risk-seeking property in motor decision tasks. However, it is unclear how to facilitate a shift from risk-seeking to optimal motor planning that maximizes the expected reward. Here, we investigate the effect of interacting with virtual partners/opponents on motor plans since interpersonal interaction has a powerful influence on human perception, action, and cognition. This study compared three types of interactions (competition, cooperation, and observation) and two types of virtual partners/opponents (those engaged in optimal motor planning and those engaged in risk-averse motor planning). As reported in previous studies, the participants took a risky aim point when they performed a motor decision task alone. However, we found that the participant’s aim point was significantly modulated when they performed the same task while competing with a risk-averse opponent (p = 0.018) and that there was no significant difference from the optimal aim point (p = 0.63). No significant modulation in the aim points was observed during the cooperation and observation tasks. These results highlight the importance of competition for modulating suboptimal decision-making and optimizing motor performance.Objectives This study aimed to assess how menstrual cycle phase and extended menstrual cycle length influence the incidence of injuries in international footballers. Methods Over a 4-year period, injuries from England international footballers at training camps or matches were recorded, alongside self-reported information on menstrual cycle characteristics at the point of injury. (R)-2-Hydroxyglutarate Injuries in eumenorrheic players were categorized into early follicular, late follicular, or luteal phase. Frequencies were also compared between injuries recorded during the typical cycle and those that occurred after the cycle would be expected to have finished. Injury incidence rates (per 1,000 person days) and injury incidence rate ratios were calculated for each phase for all injuries and injuries stratified by type. Results One hundred fifty-six injuries from 113 players were eligible for analysis. Injury incidence rates per 1,000 person-days were 31.9 in the follicular, 46.8 in the late follicular, and 35.4 in the luteal phase, resulting in injury incidence rate ratios of 1.47 (Late follicularFollicular), 1.11 (LutealFollicular), and 0.76 (LutealLate follicular). Injury incident rate ratios showed that muscle and tendon injury rates were 88% greater in the late follicular phase compared to the follicular phase, with muscle rupture/tear/strain/cramps and tendon injuries/ruptures occurring over twice as often during the late follicular phase compared to other phases 20% of injuries were reported as occurring when athletes were “overdue” menses. Conclusion Muscle and tendon injuries occurred almost twice as often in the late follicular phase compared to the early follicular or luteal phase. Injury risk may be elevated in typically eumenorrheic women in the days after their next menstruation was expected to start.Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC) 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC 0.98) and OURA (6.84%; CCC 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.