Social Media on Prescription for Colorectal Cancer Patients
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer,Colorectal Neoplasms
PROVIDER: 2283132 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:The overall aim with this study is to gain a deeper understanding of patient perceptions regarding empowerment in relation to a health platform.
| 2282894 | ecrin-mdr-crc
Project description:Using social media for health promotion is an innovative and emerging approach but remains relatively unexplored in cancer screening. Uptake of colorectal cancer (CRC) screening remains low and standard methods of reaching out are expensive with limited impact. The objective of this study is to conduct a cluster randomized controlled trial (RCT) to test the effectiveness of social media messages for CRC screening on screening intention (primary outcome). The results of this trial will be of interest to Cancer Care Ontario and are likely to be taken up by other screening programs looking for innovative and novel ways to increase screening participation. The study results will be easily translatable identifying the most compelling CRC screening messages while the approach can easily be translated to other cancer disease sites with screening programs.
| 2332186 | ecrin-mdr-crc
Project description:Interventions are urgently needed to reduce prescription opioid misuse risk factors, including anxiety and concomitant use of sedatives. However, only a limited number of randomized controlled opioid intervention trials have been conducted. We sought to determine whether an online behavior change/support community, compared to a control Facebook group, could reduce anxiety and opioid misuse among chronic pain patients. 51 high-risk non-cancer chronic pain patients were randomly assigned to either a Harnessing Online Peer Education (HOPE) peer-led online behavior change intervention or a control group (no peer leaders) on Facebook for 12 weeks. Inclusion criteria were: 18 years or older, a UCLA Health System patient, prescribed an opioid for non-cancer chronic pain between 3 and 12 months ago, and a score of ?9 on the Current Opioid Misuse Measure (COMM) and/or concomitant use of benzodiazepines. Participation in the online community was voluntary. Patients completed baseline and follow-up assessments on Generalized Anxiety Disorder screener (GAD-7), COMM, and frequency of social media discussions about pain and opioid use. Compared to control group participants, intervention participants showed a baseline-to-follow-up decrease in anxiety, and more frequently used social media to discuss pain, prescription opioid use, coping strategies, places to seek help, and alternative therapies for pain. Both groups showed a baseline to follow-up decrease in COMM score. Preliminary results support the use an online community interventions as a low-cost tool to decrease risk for prescription opioid misuse and its complications.
| S-EPMC7062763 | biostudies-literature
Project description:BackgroundThe misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month.MethodsWe wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH).ResultsTweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage.ConclusionsMentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.
| S-EPMC5711756 | biostudies-literature
Project description:BackgroundPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.MethodsWe experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.ResultsOur proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.ConclusionsBERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.
| S-EPMC7835447 | biostudies-literature
Project description:Hungarian Tokaji "aszu" and "furmint" wines were analyzed by LC-MS/MS in order to identify the molecular composition of the wines and to found compounds with potential health benefits.
2020-06-17 | MSV000085599 | MassIVE
Project description:Traditional Chinese medicines (TCM), usually composed of a mixture of components, may simultaneously target multiple genes/pathways and thus achieve superior efficacy for complex diseases such as cancer. To identify novel mechanisms of action and potential health benefits for a TCM formula Si-Wu-Tang (SWT) widely used for women’s health, we obtained the DNA microarray expression profiles for SWT, its active component ferulic acid, and estradiol in human breast cancer cell line MCF-7 and analyzed the gene expression signatures associated with each treatment using the “Connectivity Map” (cMAP). This study indicates that DNA microarray profiling analysis and cMAP data mining provide a powerful approach to discover unknown mechanisms of actions and identify potential new health benefits for TCM.
2011-05-18 | GSE23610 | GEO
Project description:Desert farming benefits from microbial potential in arid soils and promotes diversity and plant health
| PRJEB12644 | ENA
Project description:Hungarian Tokaji "aszu" and "furmint" wines were analyzed by LC-MS/MS in order to identify the molecular composition of the wines and to found compounds with potential health benefits.
2020-06-17 | MSV000085599 | GNPS
Project description:Traditional Chinese medicines (TCM), usually composed of a mixture of components, may simultaneously target multiple genes/pathways and thus achieve superior efficacy for complex diseases such as cancer. To identify novel mechanisms of action and potential health benefits for a TCM formula Si-Wu-Tang (SWT) widely used for womenâs health, we obtained the DNA microarray expression profiles for SWT, its active component ferulic acid, and estradiol in human breast cancer cell line MCF-7 and analyzed the gene expression signatures associated with each treatment using the âConnectivity Mapâ (cMAP). This study indicates that DNA microarray profiling analysis and cMAP data mining provide a powerful approach to discover unknown mechanisms of actions and identify potential new health benefits for TCM. We profiled the gene expression of MCF-7 cell lines to SWT, its active component FA, as well as estradiol using Affymetrix human genome U133 plus 2.0 arrays. The data set includes profiles for 24 samples, divided into eight groups of treatment: 0.001% DMSO used as the vehicle control (C), 0.1 µM estradiol (EM), FA at three concentrations (0.1, 1, and 10 µM) (FL, FM and FH) and SWT at three concentrations (0.0256, 0.256, and 2.56 mg/ml) (SL, SM and SH). For each treatment group, 3 biological replicates were included, resulting in 24 (8 groups à 3 replicates/group) RNA samples.
2011-05-18 | E-GEOD-23610 | biostudies-arrayexpress