RESUMO
BACKGROUND: Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. OBJECTIVE: This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. METHODS: We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. RESULTS: We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. CONCLUSIONS: The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.
RESUMO
Enterotoxigenic Bacteroides fragilis (ETBF) is a commensal bacterium of great importance to human health due to its ability to induce colitis and cause colon tumor formation in mice through the production of B. fragilis toxin (BFT). The formation of tumors is dependent on a pro-inflammatory signaling cascade, which begins with the disruption of epithelial barrier integrity through cleavage of E-cadherin. Here, we show that BFT increases levels of glucosylceramide, a vital intestinal sphingolipid, both in mice and in colon organoids (colonoids) generated from the distal colons of mice. When colonoids are treated with BFT in the presence of an inhibitor of glucosylceramide synthase (GCS), the enzyme responsible for generating glucosylceramide, colonoids become highly permeable, lose structural integrity, and eventually burst, releasing their contents into the extracellular matrix. By increasing glucosylceramide levels in colonoids via an inhibitor of glucocerebrosidase (GBA, the enzyme that degrades glucosylceramide), colonoid permeability was reduced, and bursting was significantly decreased. In the presence of BFT, pharmacological inhibition of GCS caused levels of tight junction protein 1 (TJP1) to decrease. However, when GBA was inhibited, TJP1 levels remained stable, suggesting that BFT-induced production of glucosylceramide helps to stabilize tight junctions. Taken together, our data demonstrate a glucosylceramide-dependent mechanism by which the colon epithelium responds to BFT.
