Does exercise modality and posture influence cerebrovascular and cardiovascular systems similarly?

Cerebral hemodynamics have been quantified during exercise via transcranial Doppler ultrasound, as it has high-sensitivity to movement artifacts and displays temporal superiority. Currently, limited research exists regarding how different exercise modalities and postural changes impact the cerebrovasculature across the cardiac cycle. Ten participants (4 females and 6 males) ages 20-29 completed three exercise tests (treadmill, supine, and upright cycling) to volitional fatigue. Physiological data collected included middle cerebral artery velocity (MCAv), blood pressure (BP), heart rate, and respiratory parameters. Normalized data were analyzed for variance and effect sizes were calculated to examine differences between physiological measures across the three exercise modalities. Systolic MCAv was greater during treadmill compared to supine and upright cycling (p < 0.001, (large) effect size), and greater during upright versus supine cycling (p < 0.017, (large)). Diastolic MCAv was lower during treadmill versus cycling exercise only at 60% maximal effort (p < 0.005, (moderate)) and no differences were observed between upright and supine cycling. No main effect was found for mean and diastolic BP (p > 0.05, (negligible)). Systolic BP was lower during treadmill versus supine cycling at 40% and 60% intensity (p < 0.05, (moderate-large)) and greater during supine versus upright at only 60% intensity (p < 0.003, (moderate)). The above differences were not explained by partial pressure of end-tidal carbon dioxide levels (main effect: p = 0.432). The current study demonstrates the cerebrovascular and cardiovascular systems respond heterogeneously to different exercise modalities and aspects of the cardiac cycle. As physiological data were largely similar between tests, differences associated with posture and modality are likely contributors.

Erratum: Author Correction: Deep learning for automated sleep staging using instantaneous heart rate.

[This corrects the article DOI: 10.1038/s41746-020-0291-x.].

Deep learning for automated sleep staging using instantaneous heart rate.

Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.