Dataset Information

Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial.

ABSTRACT:

Background

After the US Surgeon General declared youth electronic cigarette (e-cigarette) use an epidemic in 2018, the number of youth e-cigarette users continued to surge, growing from 3.8 million in 2018 to over 5 million 2019. Youth who use e-cigarettes are at a substantially higher risk of transitioning to traditional cigarettes, becoming regular cigarette smokers, and increasing their risk of developing tobacco-related cancer. A majority of youth are misinformed about e-cigarettes, often believing they are not harmful or contain no nicotine. Middle school students using e-cigarettes have been affected by its normalization leading to influence by their peers. However, social and group dynamics can be leveraged for a school-based peer-led intervention to identify and recruit student leaders to be anti-e-cigarette champions to prevent e-cigarette initiation. This study outlines a project to use social network analysis to identify student opinion-leaders in schools and train them to conduct anti-e-cigarette programming to their peers.

Methods

In the 2019-2020 academic school year, 6th grade students from nine schools in the Pittsburgh area were recruited. A randomized controlled trial (RCT) was conducted with three arms-expert, elected peer-leader, and random peer-leader-for e-cigarette programming. Sixth grade students in each school completed a network survey that assessed the friendship networks in each class. Students also completed pre-intervention and post-intervention surveys about their intention-to-use, knowledge, and attitudes towards e-cigarettes. Within each peer-led arm, social network analysis was conducted to identify peer-nominated opinion leaders. An e-cigarette prevention program was administered by (1) an adult content-expert, (2) a peer-nominated opinion leader to assigned students, or (3) a peer-nominated opinion leader to random students.

Discussion

This study is the first to evaluate the feasibility of leveraging social network analysis to identify 6th grade opinion leaders to lead a school-based e-cigarette intervention.

Trial registration

ClinicalTrials.gov NCT04083469 . Registered on September 10, 2019.

SUBMITTER: Chu KH

PROVIDER: S-EPMC7789399 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial.

Chu Kar-Hai KH Matheny Sara S Furek Alexa A Sidani Jaime J Radio Susan S Miller Elizabeth E Valente Thomas T Robertson Linda L

Trials 20210106 1

<h4>Background</h4>After the US Surgeon General declared youth electronic cigarette (e-cigarette) use an epidemic in 2018, the number of youth e-cigarette users continued to surge, growing from 3.8 million in 2018 to over 5 million 2019. Youth who use e-cigarettes are at a substantially higher risk of transitioning to traditional cigarettes, becoming regular cigarette smokers, and increasing their risk of developing tobacco-related cancer. A majority of youth are misinformed about e-cigarettes, ...[more]

PMID: 33407805

Similar Datasets

Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Project description:BackgroundDupuytren's contracture is a fibro-proliferative disease of the hands affecting over 2 million UK adults, particularly the white, male population. Surgery is the traditional treatment; however, recent studies have indicated that an alternative to surgery-collagenase clostridium histolyticum (collagenase)-is better than a placebo in the treatment of Dupuytren's contracture. There is however no robust randomised controlled trial that provides a definitive answer on the clinical effectiveness of collagenase compared with limited fasciectomy surgery. Dupuytren's intervention surgery vs collagenase trial (DISC) trial was therefore designed to fill this evidence gap.Methods/designThe DISC trial is a multi-centre pragmatic two-arm parallel-group, randomised controlled trial. Participants will be assigned 1:1 to receive either collagenase injection or surgery (limited fasciectomy). We aim to recruit 710 adult participants with Dupuytren's contracture. Potential participants will be identified in primary and secondary care, screened by a delegated clinician and if eligible and consenting, baseline data will be collected and randomisation completed. The primary outcome will be the self-reported patient evaluation measure assessed 1 year after treatment. Secondary outcome measures include the Unité Rhumatologique des Affections de la Main Scale, the Michigan Hand Questionnaire, EQ-5D-5L, resource use, further procedures, complications, recurrence, total active movement and extension deficit, and time to return to function. Given the limited evidence comparing recurrence rates following collagenase injection and limited fasciectomy, and the importance of a return to function as soon as possible for patients, the associated measures for each will be prioritised to allow treatment effectiveness in the context of these key elements to be assessed. An economic evaluation will assess the cost-effectiveness of treatments, and a qualitative sub-study will assess participants' experiences and preferences of the treatments.DiscussionThe DISC trial is the first randomised controlled trial, to our knowledge, to investigate the clinical and cost-effectiveness of collagenase compared to limited fasciectomy surgery for patients with Dupuytren's contracture.Trial registrationClinical.Trials.gov ISRCTN18254597 . Registered on April 11, 2017.

Project description:IntroductionPersonal digital devices that provide health information, such as the Apple Watch, have developed an increasing array of cardiopulmonary tracking features which have received regulatory clearance and are directly marketed to consumers. Despite their widespread and increasing use, data about the impact of personal digital device use on patient-reported outcomes and healthcare utilisation are sparse. Among a population of patients with atrial fibrillation and/or atrial flutter undergoing cardioversion, our primary aim is to determine the impact of the heart rate measurement, irregular rhythm notification, and ECG features of the Apple Watch on quality of life and healthcare utilisation.Methods and analysisWe are conducting a prospective, open-label multicentre pragmatic randomised clinical trial, leveraging a unique patient-centred health data sharing platform for enrolment and follow-up. A total of 150 patients undergoing cardioversion for atrial fibrillation or atrial flutter will be randomised 1:1 to receive the Apple Watch Series 6 or Withings Move at the time of cardioversion. The primary outcome is the difference in the Atrial Fibrillation Effect on QualiTy-of-life global score at 6 months postcardioversion. Secondary outcomes include inpatient and outpatient healthcare utilisation. Additional secondary outcomes include a comparison of the Apple Watch ECG and pulse oximeter features with gold-standard data obtained in routine clinical care settings.Ethics and disseminationThe Institutional Review Boards at Yale University, Mayo Clinic, and Duke University Health System have approved the trial protocol. This trial will provide important data to policymakers, clinicians and patients about the impact of the heart rate, irregular rhythm notification, and ECG features of widely used personal digital devices on patient quality of life and healthcare utilisation. Findings will be disseminated to study participants, at professional society meetings and in peer-reviewed journals.Trial registration numberNCT04468321.

Dataset Information

Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial.

Background

Methods

Discussion

Trial registration

Publications

Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets