Older Patients' Recall of Online Cancer Information: Do Ability and Motivation Matter More than Chronological Age?

This study proposes and tests a model to provide a more comprehensive understanding of the contribution of chronological age versus age-related ability and motivation factors in explaining recall of online cancer information among older patients (n = 197). Results revealed that recall is not a matter of chronological age per se, but rather a matter of ability and motivation. Age-related ability and motivation factors explained 37.9% of the variance in recall. Health literacy, involvement with the webpage, and satisfaction with the emotional support were positively associated with recall. Furthermore, recall was negatively related to frailty, anger, future time perspective, and perceived cognitive load. The findings pose relevant opportunities for tailoring interventions to improve online information provision for older cancer patient

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

Introduction: Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. Methods: Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. Results: NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89). Conclusions: Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer