Understanding Individual Differences for Tailored Smoking Cessation Apps

Finding ways to help people quit smoking is a high priority in health behavior change research. Recent HCI studies involving technologies using specific quitting techniques such as social support and SMS messaging to help people quit have reported some success. Early studies using computer generated print material report significant success of tailored versus non-tailored material, however, there is limited understanding on what aspects of digitally delivered quitting assistance should be tailored and how. To address this, we have conducted an empirical investigation with smokers to identify perceived importance of different types of help when quitting and the potential role of technology in providing such help. We found that people are highly individual in their approach to quitting and the kind of help they regard as relevant to their situation. Our contribution is a collection of empirically derived themes for tailoring smoking cessation apps to individual quitting needs. Author Keywords Smoking cessation; tailoring; individual differences; healt

Factors associated with commencing smoking in 12-year-old students in Catalonia (Spain): a cross-sectional population-based study

<p>Abstract</p> <p>Background</p> <p>Over the last decade notable progress has been made in developed countries on monitoring smoking although experimenting with cigarettes and smoking in young people remains a serious public health problem. This paper reports a cross-sectional study at the beginning of the 3-year follow-up community study TA_BES. The aim was to study the prevalence of smoking in addition to determining predictive factors for when smoking commences in a representative population of 12-year-old first year compulsory secondary education students.</p> <p>Methods</p> <p>Twenty-nine secondary schools (N = 29) from an area of Catalonia participated in the study. In these schools 2245 students answered a questionnaire to study the attitudes, behaviors, and tobacco consumption in the subject's surrounding circle and family in relation to smoking; carbon monoxide measurements were taken by means of co-oximetry on 2 different occasions. A smoker was defined as a student who had smoked daily or occasionally in the last 30 days. For non-smokers the criteria of not considering was set up for those who answered that in the future they would not be smokers and considering those who answered that they did not rule out becoming a smoker in the future.</p> <p>Results</p> <p>Among the total 2245 students included in the analysis 157(7%) were classified as smokers. Among non-smokers we differentiated between those not considering smoking 1757 (78.3%) and those considering smoking 288 (12.8%).</p> <p>Age is among the factors related to commencing smoking. The risk of becoming a smoker increases 2.27 times/year. The influence of the group of friends with a very high risk for boys OR 149.5 and lower, albeit high, in girls OR 38.1. Tobacco consumption of parents produces different effects in young people. A smoking father does not produce alterations in the smoking behavior of young people. However having a smoking mother or former smoking is a risk factor for boys and a protective factor for girls.</p> <p>We detected a gradual risk of becoming a smoker by means of the co-oximetry test. A boy/girl with a test between 6 p.p.m and 10 p.p.m increased the probability of smoking by 2.29 and co-oximetry values > 10 p.p.m multiplied the risk 4 times over.</p> <p>Conclusions</p> <p>Results indicate that the age of commencing smoking is maintained in spite of prevalence having decreased in the last few years. The risk factors identified should be used to involve families and the educational community by offering them tobacco weaning programmes.</p

What Drives Fitness Apps Usage? An Empirical Evaluation

Part 3: Creating Value through ApplicationsInternational audienceThe increased health problems associated with lack of physical activity is of great concern around the world. Mobile phone based fitness applications appear to be a cost effective promising solution for this problem. The aim of this study is to develop a research model that can broaden understanding of the factors that influence the user acceptance of mobile fitness apps. Drawing from Unified Theory of Acceptance and Use of Technology (UTAUT) and Elaboration Likelihood Model (ELM), we conceptualize the antecedents and moderating factors of fitness app use. We validate our model using field survey. Implications for research and practice are discussed

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation