Describing the Digital Footprints or "Sociomes" of Asthma for Stakeholder Groups on Twitter.
Ontology highlight
ABSTRACT:
SUBMITTER: Carroll CL
PROVIDER: S-EPMC8043287 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Population-level national networks on social media are precious and essential for network science and behavioural science. This study collected a population-level Twitter network, based on both language and geolocation tags. We proposed a set of validation approaches to evaluate the validity of our datasets. Finally, we re-examined classical network and communication propositions (e.g., 80/20 rule, six degrees of separation) on the national network. Our dataset and strategy would flourish the data collection pool of population-level social networks and further develop the research of network analysis in digital media environment.
| S-EPMC9671467 | biostudies-literature
Project description:BackgroundAsthma comorbidity has been correlated with poor asthma control, increased health services use, and decreased quality of life. Managing it improves these outcomes. Little is known about the amount of different types of comorbidity associated with asthma and how they vary by age.Methodology/principal findingsThe authors conducted a population study using health administrative data on all individuals living in Ontario, Canada (population 12 million). Types of asthma comorbidity were quantified by comparing physician health care claims between individuals with and without asthma in each of 14 major disease categories; results were adjusted for demographic factors and other comorbidity and stratified by age. Compared to those without asthma, individuals with asthma had higher rates of comorbidity in most major disease categories. Most notably, they had about fifty percent or more physician health care claims for respiratory disease (other than asthma) in all age groups; psychiatric disorders in individuals age four and under and age 18 to 44; perinatal disorders in individuals 17 years and under, and metabolic and immunity, and hematologic disorders in children four years and under.Conclusion/significanceAsthma appears to be associated with significant rates of various types of comorbidity that vary according to age. These results can be used to develop strategies to recognize and address asthma comorbidity to improve the overall health of individuals with asthma.
| S-EPMC3346768 | biostudies-literature
Project description:Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes-the leading cause of infant mortality-could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms-feature-engineered and deep learning-based classifiers-that automatically distinguish tweets referring to the user's pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the "defect" class and 0.51 for the "possible defect" class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.
| S-EPMC6773753 | biostudies-literature
Project description:BACKGROUND:There are various complex reasons that influence sustainable adoption of innovations in health care systems. Low adoption can be caused by a lack of support from one or more stakeholders because their needs and expectations are not always considered or aligned. OBJECTIVE:This study aimed to identify stakeholders' perceptions of barriers and facilitators toward the sustainable adoption of digital health innovations. METHODS:A stakeholder workshop was attended by 12 participants with a range of backgrounds on August 25, 2017, including people representing the views from patients, carers, local hospitals, pharmacy retailers, health insurers, health services researchers, engineers, and technology and pharmaceutical companies in Switzerland. On the basis of adoption of innovation frameworks, we asked participants to interview each other about 3 factors influencing the adoption of digitally delivered health interventions: (1) Facilitators and barriers in the external system, (2) Needs and expectations of stakeholders, and (3) Safety, quality, and usability of innovations. The worksheets and videos generated from the workshop were qualitatively analyzed and summarized. RESULTS:Facilitators for adoption mentioned were high levels of income and education, and digital health is a high priority to stakeholders. Main common interests of different stakeholders were patient satisfaction and job protection. Health care spending was a misaligned interest: although some stakeholders were keen on spending more to obtain or provide the highest quality of care, others were focused on reducing health care spending to provide cost-effective services. Switzerland's diversity and complexity, in terms of its organization with 26 cantons (administrative divisions), were barriers as these made it harder to ensure interoperability of interventions. A culture of innovation was considered a push factor, but adoption was inhibited by persistent paper-based systems, a fear of change, and unwillingness to share data. The sustainability of interventions can be promoted by making them patient-centered, meaning that patients should be involved throughout their development. CONCLUSIONS:Promoting sustainable adoption of digital health remains challenging despite various push factors being in place. Barriers related to fragmentation, patient-centeredness, data security, privacy, trust, and job security need to be addressed. A strength is that people from a wide range of backgrounds attended the workshop. A limitation is that the findings are focused on the macro level. In-depth case studies of specific issues need to be conducted in different settings.
| S-EPMC6452285 | biostudies-other
Project description:BACKGROUND:Although effective return-to-work (RTW) interventions are not widely available for individuals with common mental disorders on sick leave, there is potential for transforming such interventions into a digital solution in an effort to make them more widely available. However, little is currently known about the viewpoints of different stakeholder groups, which are critical for successful development and implementation of a digital RTW intervention in health care services. OBJECTIVE:The aim of this study was to examine stakeholder groups' perspectives on the role and legitimacy of a digital RTW solution called mWorks for individuals with common mental disorders who are on sick leave. METHODS:A purposeful snowball sampling method was utilized to recruit respondents. Semistructured individual and focus group interviews were conducted for stakeholder groups of service users, RTW professionals, and influential managers regarding their experiences, needs, and preferences for mWorks. Content analysis generated themes and categories that constituted the main findings. RESULTS:The legitimacy of a digital RTW solution was high among all stakeholder groups since such a tool was perceived to enable service users to take control over their RTW process. This was mainly a product of accessible support and promotion of service user decision making, which had the potential to empower service users. All respondents stressed the importance of fostering a positive user experience with usability and emphasis on service user resources and strengths, as opposed to various limitations and shortcomings. Stakeholder groups highlighted critical content to facilitate RTW, such as the need to clarify a back-to-work plan, accompanied by an accessible RTW network and strategies for handling mental health problems. Implementation challenges primarily involved influential managers' concern of legislation incompatibility with innovative technology, and RTW professionals' concern of the possibility that digital solutions may replace them to a certain extent. CONCLUSIONS:This formative research emphasizes the importance of shifting power from RTW professionals to service users. mWorks can play a role in mediating service user control over the RTW process, and thereby increase their empowerment. A digital RTW solution may facilitate the circumvention of implementation barriers associated with introducing evidence-based RTW interventions in a traditional RTW context.
| S-EPMC7527904 | biostudies-literature
Project description:To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.
| S-EPMC6261568 | biostudies-literature
Project description:Background:The integration of digital treatments into national mental health services is on the agenda in the European Union. The E-COMPARED consortium conducted a survey aimed at exploring stakeholders' knowledge, acceptance and expectations of digital treatments for depression, and at identifying factors that might influence their opinions when considering the implementation of these approaches. Method:An online survey was conducted in eight European countries: France, Germany, Netherlands, Poland, Spain, Sweden, Switzerland and The United Kingdom. Organisations representing government bodies, care providers, service-users, funding/insurance bodies, technical developers and researchers were invited to participate in the survey. The participating countries and organisations reflect the diversity in health care infrastructures and e-health implementation across Europe. Results:A total of 764 organisations were invited to the survey during the period March-June 2014, with 175 of these organisations participating in our survey. The participating stakeholders reported moderate knowledge of digital treatments and considered cost-effectiveness to be the primary incentive for integration into care services. Low feasibility of delivery within existing care services was considered to be a primary barrier. Digital treatments were regarded more suitable for milder forms of depression. Stakeholders showed greater acceptability towards blended treatment (the integration of face-to-face and internet sessions within the same treatment protocol) compared to standalone internet treatments. Organisations in countries with developed e-health solutions reported greater knowledge and acceptability of digital treatments. Conclusion:Mental health stakeholders in Europe are aware of the potential benefits of digital interventions. However, there are variations between countries and stakeholders in terms of level of knowledge about such interventions and their feasibility within routine care services. The high acceptance of blended treatments is an interesting finding that indicates a gradual integration of technology into clinical practice may fit the attitudes and needs of stakeholders. The potential of the blended treatment approach, in terms of enhancing acceptance of digital treatment while retaining the benefit of cost-effectiveness in delivery, should be further explored. Funding:The E-COMPARED project has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603098.
| S-EPMC6096292 | biostudies-literature
Project description:In this study, we explore the recent setup of a digital vaccination record in Austria. Working from a social-scientific perspective, we find that the introduction of the electronic vaccination pass was substantially accelerated by the COVID-19 pandemic. Our interviews with key stakeholders (n = 16) indicated that three main factors drove this acceleration. The pandemic (1) sidelined historical conflicts regarding data ownership and invoked a shared sense of the value of data, (2) accentuated the need for enhanced administrative efficiency in an institutionally fragmented system, and (3) helped invoke the national vaccination registry as an indispensable infrastructure for public health governance with the potential to innovate its healthcare system in the long term.
| S-EPMC8706289 | biostudies-literature
Project description:Reconstructing hominin paleoecology is critical for understanding our ancestors' diets, social organizations and interactions with other animals. Most paleoecological models lack fine-scale resolution due to fossil hominin scarcity and the time-averaged accumulation of faunal assemblages. Here we present data from 481 fossil tracks from northwestern Kenya, including 97 hominin footprints attributed to Homo erectus. These tracks are found in multiple sedimentary layers spanning approximately 20 thousand years. Taphonomic experiments show that each of these trackways represents minutes to no more than a few days in the lives of the individuals moving across these paleolandscapes. The geology and associated vertebrate fauna place these tracks in a deltaic setting, near a lakeshore bordered by open grasslands. Hominin footprints are disproportionately abundant in this lake margin environment, relative to hominin skeletal fossil frequency in the same deposits. Accounting for preservation bias, this abundance of hominin footprints indicates repeated use of lakeshore habitats by Homo erectus. Clusters of very large prints moving in the same direction further suggest these hominins traversed this lakeshore in multi-male groups. Such reliance on near water environments, and possibly aquatic-linked foods, may have influenced hominin foraging behavior and migratory routes across and out of Africa.
| S-EPMC4873780 | biostudies-literature
Project description:Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.
| S-EPMC5574558 | biostudies-literature