A Single Session of Heart Rate Variability Biofeedback Produced Greater Increases in Heart Rate Variability Than Autogenic Training.

Heart rate variability biofeedback (HRV-BF) has been confirmed to increase heart rate variability (HRV) and cardiac outflows by baroreflex in healthy populations and clinical patients. Autogenic training (AT) is common used in the psychological intervention. This study integrates a single-session of HRV-BF and AT into a high-technology mobile application (app), and examines the effects on HRV indices, breathing rates, and subjective relaxation scores. Healthy college students were recruited and assigned to the single-session HRV-BF group or AT group. Participants in the HRV-BF group received HRV-BF combined with paced breathing training, which gradually stepped down their breathing rates from 12, to 8, to 6 breaths/per min; and received feedback of HRV indices from the app. Participants in the AT group received autogenic training and feedback of heart rate from the app. A chest belt Zephyr BioHarness was connected through Bluetooth to a Zenfone5 mobile phone, it collected the signals of interbeat intervals and breathing rates at pre-training, mid-training, and post-training stages. The Kubios HRV software was used to analyze HRV indices. The results reveal higher HRV indices and lower breathing rates during mid-training and post-training in the HRV-BF group compared to the AT group. There were higher high-frequency of HRV at post-training than pre-training in the AT group. Participants of both groups increased their subjective relaxation scores after training. The HRV-BF protocol increased cardiac outflows by baroreflex and decreased breathing rates, and the AT protocol increased high-frequency of HRV. These high-technology wearable devices combined with psychological interventions will apply to various populations in the future.

A Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype data.

This paper uses a Bayesian formulation of a clustering procedure to identify gene-gene interactions under case-control studies, called the Algorithm via Bayesian Clustering to Detect Epistasis (ABCDE). The ABCDE uses Dirichlet process mixtures to model SNP marker partitions, and uses the Gibbs weighted Chinese restaurant sampling to simulate posterior distributions of these partitions. Unlike the representative Bayesian epistasis detection algorithm BEAM, which partitions markers into three groups, the ABCDE can be evaluated at any given partition, regardless of the number of groups. This study also develops permutation tests to validate the disease association for SNP subsets identified by the ABCDE, which can yield results that are more robust to model specification and prior assumptions. This study examines the performance of the ABCDE and compares it with the BEAM using various simulated data and a schizophrenia SNP dataset.

Patterns of cognitive change in elderly patients during and 6 months after hospitalisation: a prospective cohort study.

BACKGROUND: The extent and patterns of cognitive change regularly occurring in elderly patients who experience prolonged hospitalisation have not been well examined. OBJECTIVE: To describe patterns of cognitive change during and 6 months after hospitalisation and to identify prognostic factors associated with different patterns of changes. DESIGN: A prospective cohort study. SETTING: Five med-surgical units at a tertiary hospital in Taipei, Taiwan. PARTICIPANTS: Patients ≥65 years old without preexisting profound cognitive impairment (Mini-Mental State Examination score ≥20) and with an expected hospital length of stay >5 days were drawn from consecutive admissions. Of 351 patients, 82.9% (138 women, 153 men, mean age=71.6 years) completed all four scheduled assessments. METHODS: Cognition was measured by the Mini-Mental State Examination at 4 times: admission, discharge, and 3 and 6 months post-discharge. Possible prognostic factors at admission included demographics, comorbidities, number of medications, serum haemoglobin, length of hospital stay, and surgery. RESULTS: Four cognitive-change patterns with a high prevalence of decline were identified by cluster analysis. The worsening then improve group (n=47) had a deep V-shape with a mean fluctuation of 3.9 points on the Mini-Mental State Examination, and the low continuous group (n=83) had little change. Both the start high and decline (n=66) and start low and decline (n=95) groups showed persistent and accelerated declines, with baseline cognitive scores of 29.1 and 25.5 points, respectively. Predictor variables at admission for different patterns of cognitive change were age, total education (years), cardiovascular comorbidities, number of medications, functional and nutritional status, depressive symptoms, surgical treatment, and haemoglobin level <12 g/dL. CONCLUSIONS: Cognitive decline during and after hospitalisation shows four heterogeneous patterns of change. Different patterns of change were predicted by age, education, cardiovascular comorbidities, number of medications, functional and nutritional status, depressive symptoms, surgical treatment, and haemoglobin level <12 g/dL, most of which are potentially modifiable factors.