METHODS: Audio-guided DB with natural sounds to guide the DB was developed. Meanwhile, audio-based Go/No-Go paradigm with Arduino was built to measure the attention level. Thirty-two healthy young adults (n=32) were recruited. Psychological questionnaires (Rosenberg's Self-Esteem Scale (RSES), Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Perceived Stress Scale (PSS)), objective measurements with tidal volume and attention level with auditory Go/No-Go task were conducted before and after 5 min of DB.
RESULTS: Results showed a significant increment in tidal volume and task reaction time from baseline (p=0.003 and p=0.033, respectively). Significant correlations were acquired between (1) task accuracy with commission error (r=-0.905), (2) CAMS-R with task accuracy (r=-0.425), commission error (r=0.53), omission error (r=0.395) and PSS (r=-0.477), and (3) RSES with task reaction time (r=-0.47), task accuracy (r=-0.362), PSS (r=-0.552) and CAMS-R (r=0.591).
CONCLUSIONS: This pilot study suggests a link between it and young adults' wellbeing and proposes auditory Go/No-Go task for assessing attention across various groups while maintaining physical and mental wellness.
Aims: The aim of this study was to examine fatigue in a cohort of stroke survivors in the chronic phase of stroke, compared with matched controls, and to explore associations between the pro-inflammatory cytokine interleukin-6, high-sensitivity C-reactive Protein and fatigue.
Methods: We performed an exploratory cross-sectional study of 70 people in the chronic phase of stroke recovery, and 70 age matched controls. Fatigue was assessed using the Fatigue Assessment Scale. Interleukin-6 was measured in serum using a commercially available enzyme immunoassay kit. Both outcome measures were assessed contemporaneously.
Results: Clinically significant fatigue, defined as a score ≥24 on the Fatigue Assessment Scale, was reported by 60% of stroke survivors, and 15.7% of controls. The odds of experiencing clinically significant fatigue was 8.04 times higher among stroke survivors compared to control participants (odds ratio 8.045; 95% CI: 3.608, 17.939; P
METHODS: Hair cortisol concentration was measured in 307 autistic children and 282 non-autistic controls aged between 2 and 17 years recruited from four Australian states who participated in providing hair samples and demographic data to the Australian Autism Biobank. Independent samples t-test or one-way analysis of variance (ANOVA) were conducted to determine significant differences in the mean hair cortisol concentration (pg/mg) between potential covariates. Primary analysis included multivariable regression modelling of the collapsed sample to identify variables that were significantly associated with hair cortisol concentration after controlling for covariates. We also accounted for the potential interaction of multiple biological (e.g., age, sex, BMI) and psychosocial characteristics at the level of the child, the mother and the father, and the family unit.
RESULTS: Our findings suggest that the diagnosis of autism was not a significant predictor of chronic stress, as measured by hair cortisol concentration. However, findings of the multivariable regression analysis showed that key factors such as area of residence (Queensland vs Victorian state of residence) and decrease in child's age were significantly associated with higher hair cortisol concentration whereas lower family income was significantly associated with higher hair cortisol concentration.
CONCLUSION: To our knowledge, this is the first study to show that socioeconomic factors such as family annual income affect hair cortisol status in autistic children, indicating that the psychosocial environment may be a potential mediator for chronic stress in autistic children just as it has been demonstrated in non-autistic children.