The role of different acoustic environmental stimuli on manual dexterity.

Music has been reported to facilitate motor performance. However, there is no data on the effects of different acoustic environmental stimuli on manual dexterity. The present observational study aimed at investigating the effects of background music and noise on a manual dexterity task in young, middle-aged and elderly subjects. Sixty healthy, right-handed subjects aged between 18 and 80 years were enrolled. Twenty young (mean age: 22±2 years), 20 middle-aged (mean age: 55±8 years) and 20 elderly (mean age: 72±5 years) subjects performed the Nine Hole Peg Test (NHPT) in four different acoustic environments: silence (noise < 20dBA), classical music at 60dBA, rock music at 70 dBA, and a noise stimulus at 80dBA. Performance was recorded using an optical motion capture system and retro-reflective markers (SMART DX, 400, BTS). Outcome measures included the total test time and peg-grasp, peg-transfer, peg-in-hole, hand-return, and removing phases times. Normalized jerk, mean and peak of velocity during transfer and return phases were also computed. No differences were found for NHPT phases and total times, normalized jerk, peak of velocity and mean velocity between four acoustic conditions (p>0.05). Between-group differences were found for NHPT total time, where young subjects revealed better performance than elderly (p˂0.001) and middle-aged (p˂0.001) groups. Music and noise stimuli in the considered range of intensity had no influence on the execution of a manual dexterity task in young, middle-aged and elderly subjects. These findings may have implications for working, sportive and rehabilitative activities.

Long-COVID autonomic syndrome in working age and work ability impairment.

Long-COVID19 has been recently associated with long-sick leave and unemployment. The autonomic nervous system functioning may be also affected by SARS-CoV-2, leading to a chronic autonomic syndrome. This latter remains widely unrecognized in clinical practice. In the present study, we assessed the occurrence of Long-COVID19 Autonomic Syndrome in a group of active workers as well as the relationships between their autonomic dysfunction and work ability. This prospective observational study was conducted during the 2nd wave of the pandemic in Italy. Forty-five patients (53.6 ± 8.4 years; 32 M) hospitalized for COVID19, were consecutively enrolled at the time of their hospital discharge (T0) and followed-up for 6 months. Autonomic symptoms and work ability were assessed by COMPASS31 and Work Ability Index questionnaires at T0, one (T1), three and six (T6) months after hospital discharge and compared to those retrospectively collected for a period preceding SARS-CoV-2 infection. Clinical examination and standing test were also performed at T1 and T6. One in three working-age people developed a new autonomic syndrome that was still evident 6 months after the acute infection resolution. This was associated with a significant reduction in the work ability. Recognition of Long-COVID19 Autonomic Syndrome may promote early intervention to facilitate return to work and prevent unemployment.

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.