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Cognitive impairment (CI) and fatigue are common in people with multiple sclerosis (MS), with well‐known profound effects on quality of life. Sleep disorders, including obstructive sleep apnoea (OSA), are also common in MS patients. The presence of CI has previously been shown to strongly correlate with OSA diagnosed using polysomnography in MS. Treatment of OSA has not previously been investigated as a potential modality to improve cognition in MS patients. Therefore, we sought to investigate the potential effects of OSA treatment on both cognitive function and fatigue in MS patients. Twenty‐three participants with MS reporting significant fatigue were enrolled. CI was assessed by the Brief International Cognitive Assessment in MS and the 3‐second Paced Auditory Serial Addition Test. All participants underwent overnight polysomnography to assess for possible OSA. Cognitive and fatigue measures were repeated in those subsequently treated for OSA and in a comparative untreated sample. Seven participants (30%) had a diagnosis of OSA based on an apnoea–hypopnea index greater than 5 per hour, with no correlation between the presence of CI and OSA. Verbal learning at follow‐up assessment was seen to improve significantly in those treated for OSA, compared with those who were not treated for a sleep disorder. This small study demonstrates the potential for OSA treatment to improve verbal learning in people with MS, larger studies are indicated to further investigate the potential for cognitive and fatigue improvement in people with MS through treatment of comorbid OSA. [ABSTRACT FROM AUTHOR]

Used the official records of all games played in the Western Hockey League (N=432) during a season for a Principal Components analysis of 19 aggressive penalties. Results suggested that inter-personal aggression in the sport is multiply determined and can arise for eight different sets of conditions or antecedents. (Author/LLL)

Summary: The high prevalence of obstructive sleep apnea has led to increasing interest in ambulatory diagnosis. The SleepMinder™ (SM) is a novel non‐contact device that employs radiofrequency wave technology to assess the breathing pattern, and thereby estimate obstructive sleep apnea severity. We assessed the performance of SleepMinder™ in the home diagnosis of obstructive sleep apnea. One‐hundred and twenty‐two subjects were prospectively recruited in two protocols, one from an unselected sleep clinic cohort (n = 67, mean age 51 years) and a second from a hypertension clinic cohort (n = 55, mean age 58 years). All underwent 7 consecutive nights of home monitoring (SMHOME) with the SleepMinder™ as well as inpatient‐attended polysomnography in the sleep clinic cohort or cardiorespiratory polygraphy in the hypertension clinic cohort with simultaneous SleepMinder™ recordings (SMLAB). In the sleep clinic cohort, median SMHOME apnea–hypopnea index correlated significantly with polysomnography apnea–hypopnea index (r =.68; p <.001), and in the hypertension clinic cohort with polygraphy apnea–hypopnea index (r =.7; p <.001). The median SMHOME performance against polysomnography in the sleep clinic cohort showed a sensitivity and specificity of 72% and 94% for apnea–hypopnea index ≥ 15. Device performance was inferior in females. In the hypertension clinic cohort, SMHOME showed a 50% sensitivity and 72% specificity for apnea–hypopnea index ≥ 15. SleepMinder™ classified 92% of cases correctly or within one severity class of the polygraphy classification. Night‐to‐night variability in home testing was relatively high, especially at lower apnea–hypopnea index levels. We conclude that the SleepMinder™ device provides a useful ambulatory screening tool, especially in a population suspected of obstructive sleep apnea, and is most accurate in moderate–severe obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m 2 ; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.