Moderate exercise training modulates cytokine profile and sleep in elderly people.

Aging causes several physiological alterations, including alterations in sleep. It is possible that difficulty sleeping can be exacerbated by increased inflammation in older individuals. Moderate exercise training may be a modality of non-pharmacological treatment for sleep disorders and inflammation. We aimed to assess the effects of moderate exercise training on sleep in elderly people as well as their cytokine profiles. Additionally, we examined the effect of exercise training on quality of life parameters using a SF-36 questionnaire. Twenty-two male, sedentary, healthy, elderly volunteers performed moderate training for 60 min/day, 3 days/week for 24 week at a work rate equivalent to their ventilatory aerobic threshold. The environment was kept at a temperature of 23 ± 2°C, with a humidity of 60 ± 5%. Blood and polysomnograph were collected twice: at baseline (1 week before training began) and after 6 months of training. Training increased aerobic capacity parameters (p<0.0001), decreased REM latency (p<0.02), and decreased time awake (p<0.05). After training, the levels of IL-6 (p<0.0001) and TNF-α (p<0.0001) and the ratio of TNF-α/IL-10 (p<0.0001) were decreased, whereas IL-10 levels were increased after training (p<0.001). Furthermore, exercise training was shown to improve quality of life parameters. Our results suggest that 6 months of training can improve sleep in the elderly and is related to the anti-inflammatory effect of moderate training, which modifies cytokine profiles.

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals.

OBJECTIVE: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. APPROACH: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DF BSPM (HDF BSPM ) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ≤1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with [Formula: see text] Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). MAIN RESULTS: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. SIGNIFICANCE: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.