Men’s strategies for preserving emotional well-being in advanced prostate cancer: an interpretative phenomenological analysis

Objective This study explores men with advanced prostate cancers’ own practices for promoting and maintaining emotional well-being using Interpretative Phenomenological Analysis. Design Five men with advanced prostate cancer participated in face-to-face, semi-structured, in-depth interviews. Results Within rich narratives of lost and regained well-being, two super-ordinate themes emerged – ‘living with an imminent and uncertain death’ and ‘holding on to life.’ Well-being was threatened by reduced sense of the future, isolation and uncertainty. Yet, the men pursued well-being by managing their emotions, striving for the future whilst enjoying life in the present, taking care of their families and renegotiating purpose. Running through participant’s accounts was a preference for taking action and problem-solving. Sense of purpose, social connectedness and life-engagement were revealed as concepts central to improving well-being, indicating areas which practitioners could explore with men to help them re-establish personal goals and life-purpose. Conclusions The findings also add weight to the evidence base for the potential value of psychological interventions such as cognitive behaviour therapy and mindfulness in men with prostate cancer.

Post awakening salivary cortisol secretion and trait well-being: The importance of sample timing accuracy

Indices of post awakening cortisol secretion (PACS), include the rise in cortisol(cortisol awakening response: CAR) and overall cortisol concentrations (e.g. area under the curve with reference to ground: AUCg) in the first 30—45 min. Both are commonly investigated in relation to psychosocial variables. Although sampling within the domestic setting is ecologically valid, participant non-adherence to the required timing protocol results in erroneous measurement of PACS and this may explain discrepancies in the literature linking these measures to trait well-being (TWB). We have previously shown that delays of little over 5 min(between awakening and the start of sampling) to result in erroneous CAR estimates. In this study, we report for the first time on the negative impact of sample timing inaccuracy (verified by electronic-monitoring) on the efficacy to detect significant relationships between PACS and TWB when measured in the domestic setting.Healthy females (N = 49, 20.5 ± 2.8 years) selected for differences in TWB collected saliva samples (S1—4) on 4 days at 0, 15, 30, 45 min post awakening, to determine PACS. Adherence to the sampling protocol was objectively monitored using a combination of electronic estimates of awakening (actigraphy) and sampling times (track caps).Relationships between PACS and TWB were found to depend on sample timing accuracy. Lower TWB was associated with higher post awakening cortisol AUCg in proportion to the mean sample timing accuracy (p < .005). There was no association between TWB and the CAR even taking into account sample timing accuracy. These results highlight the importance of careful electronic monitoring of participant adherence for measurement of PACS in the domestic setting. Mean sample timing inaccuracy, mainly associated with delays of >5 min between awakening and collection of sample 1 (median = 8 min delay), negatively impacts on the sensitivity of analysis to detect associations between PACS and TWB.

Brief response to Ashton and colleagues regarding Fractionating Human Intelligence

Ashton and colleagues concede in their response (Ashton, Lee, & Visser, in this issue), that neuroimaging methods provide a relatively unambiguous measure of the levels to which cognitive tasks co-recruit dif- ferent functional brain networks (task mixing). It is also evident from their response that they now accept that task mixing differs from the blended models of the classic literature. However, they still have not grasped how the neuroimaging data can help to constrain models of the neural basis of higher order ‘g’. Specifically, they claim that our analyses are invalid as we assume that functional networks have uncorrelated capacities. They use the simple analogy of a set of exercises that recruit multiple muscle groups to varying extents and highlight the fact that individual differences in strength may correlate across muscle groups. Contrary to their claim, we did not assume in the original article (Hampshire, High- field, Parkin, & Owen, 2012) that functional networks had uncorrelated capacities; instead, the analyses were specifically designed to estimate the scale of those correlations, which we referred to as spatially ‘diffuse’ factors

Fractionating Human Intelligence

What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.