New Frontiers in Health Literacy: Using ChatGPT to Simplify Health Information for People in the Community.

BACKGROUND: Most health information does not meet the health literacy needs of our communities. Writing health information in plain language is time-consuming but the release of tools like ChatGPT may make it easier to produce reliable plain language health information. OBJECTIVE: To investigate the capacity for ChatGPT to produce plain language versions of health texts. DESIGN: Observational study of 26 health texts from reputable websites. METHODS: ChatGPT was prompted to 'rewrite the text for people with low literacy'. Researchers captured three revised versions of each original text. MAIN MEASURES: Objective health literacy assessment, including Simple Measure of Gobbledygook (SMOG), proportion of the text that contains complex language (%), number of instances of passive voice and subjective ratings of key messages retained (%). KEY RESULTS: On average, original texts were written at grade 12.8 (SD = 2.2) and revised to grade 11.0 (SD = 1.2), p < 0.001. Original texts were on average 22.8% complex (SD = 7.5%) compared to 14.4% (SD = 5.6%) in revised texts, p < 0.001. Original texts had on average 4.7 instances (SD = 3.2) of passive text compared to 1.7 (SD = 1.2) in revised texts, p < 0.001. On average 80% of key messages were retained (SD = 15.0). The more complex original texts showed more improvements than less complex original texts. For example, when original texts were ≥ grade 13, revised versions improved by an average 3.3 grades (SD = 2.2), p < 0.001. Simpler original texts (< grade 11) improved by an average 0.5 grades (SD = 1.4), p < 0.001. CONCLUSIONS: This study used multiple objective assessments of health literacy to demonstrate that ChatGPT can simplify health information while retaining most key messages. However, the revised texts typically did not meet health literacy targets for grade reading score, and improvements were marginal for texts that were already relatively simple.

Conflict of interest policies at Australian medical schools.

BACKGROUND: Pharmaceutical industry exposure is widespread during medical training and may affect education and clinical decision-making. Medical faculties' conflict of interest (COI) policies help to limit this exposure and protect students against commercial influence. AIMS: Our aim was to investigate the prevalence, content and strength of COI policies at Australian medical schools and changes since a previous assessment conducted in 2009. METHODS: We identified policies by searching medical school and host university websites in January 2021, and contacted deans to identify any missed policies. We applied a modified version of a scorecard developed in previous studies to examine the content of COI policies. All data were coded in duplicate. COI policies were rated on a scale from 0 (no policy) to 2 (strong policy) across 11 items per medical school. Oversight mechanisms and sanctions were also assessed, and current policies were compared with the 2009 study. RESULTS: Of 155 potentially relevant policies, 153 were university-wide and two were specific to medical schools. No policies covered sales representatives, on-site sponsored education or free samples. Oversight of consultancies had improved substantially, with 76% of schools requiring preapproval. Disclosure policies, while usually present, were weak, with no public disclosure required. CONCLUSION: We found little indication that Australian medical students are protected from commercial influence on medical education, and there has been limited COI policy development within the past decade. More attention is needed to ensure the independence of medical education in Australia.

Natural language processing for clinical notes in dentistry: A systematic review.

OBJECTIVE: To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry. MATERIALS AND METHODS: A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results. RESULTS: Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time. CONCLUSIONS: Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems. PROTOCOL REGISTRATION: PROSPERO CRD42021227823.

Experiences and influences of online health information-seeking about statin use in patients with high cardiovascular risk: a qualitative study.

OBJECTIVES: Online health information (OHI) has been shown to influence patients' health decisions and behaviours. OHI about statins has created confusion among healthcare professionals and the public. This study explored the views and experiences of patients with high cardiovascular risk on OHI-seeking about statins and how OHI influenced their decision. DESIGN: This was a qualitative study using semi-structured in-depth interviews. An interpretive description approach with thematic analysis was used for data analysis. SETTING: An urban primary care clinic in Kuala Lumpur, Malaysia. PARTICIPANTS: Patients aged 18 years and above who had high cardiovascular risk and sought OHI on statins were recruited. RESULTS: A total of 20 participants were interviewed. The age of the participants ranged from 38 to 74 years. Twelve (60%) participants took statins for primary cardiovascular disease prevention. The duration of statin use ranged from 2 weeks to 30 years. Six themes emerged from the data analysis: (i) seeking OHI throughout the disease trajectory, (ii) active and passive approaches to seeking OHI, (iii) types of OHI, (iv) views about statin-related OHI, (v) influence of OHI on patients' health decisions, and (vi) patient-doctor communication about OHI. CONCLUSION: This study highlights the changing information needs throughout patient journeys, suggesting the opportunity to provide needs-oriented OHI to patients. Unintentional passive exposure to OHI appears to have an influence on patients' adherence to statins. The quality of patient-doctor communication in relation to OHI-seeking behaviour remains a critical factor in patient decision-making.

Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.

BACKGROUND: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. OBJECTIVE: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. METHODS: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. RESULTS: There were fewer neutral mask-related tweets in 2020 (ß=-3.94 percentage points, 95% CI -4.68 to -3.21; P<.001) and 2021 (ß=-8.74, 95% CI -9.31 to -8.17; P<.001). Following the April 3 recommendation (ß=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (ß=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (ß=-.004, 95% CI -.004 to -.003; P<.001) and May 13 (ß=-.001, 95% CI -.002 to 0; P=.008). CONCLUSIONS: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly.

Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT.

BACKGROUND: The abundance of biomedical text data coupled with advances in natural language processing (NLP) is resulting in novel biomedical NLP (BioNLP) applications. These NLP applications, or tasks, are reliant on the availability of domain-specific language models (LMs) that are trained on a massive amount of data. Most of the existing domain-specific LMs adopted bidirectional encoder representations from transformers (BERT) architecture which has limitations, and their generalizability is unproven as there is an absence of baseline results among common BioNLP tasks. RESULTS: We present 8 variants of BioALBERT, a domain-specific adaptation of a lite bidirectional encoder representations from transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine-tuned for 6 different tasks across 20 benchmark datasets. Experiments show that a large variant of BioALBERT trained on PubMed outperforms the state-of-the-art on named-entity recognition (+ 11.09% BLURB score improvement), relation extraction (+ 0.80% BLURB score), sentence similarity (+ 1.05% BLURB score), document classification (+ 0.62% F1-score), and question answering (+ 2.83% BLURB score). It represents a new state-of-the-art in 5 out of 6 benchmark BioNLP tasks. CONCLUSIONS: The large variant of BioALBERT trained on PubMed achieved a higher BLURB score than previous state-of-the-art models on 5 of the 6 benchmark BioNLP tasks. Depending on the task, 5 different variants of BioALBERT outperformed previous state-of-the-art models on 17 of the 20 benchmark datasets, showing that our model is robust and generalizable in the common BioNLP tasks. We have made BioALBERT freely available which will help the BioNLP community avoid computational cost of training and establish a new set of baselines for future efforts across a broad range of BioNLP tasks.

Association Between Conflicts of Interest and Authors' Positions on Harms of Varenicline: a Cross-Sectional Analysis.

BACKGROUND: Few studies have investigated the relationship between industry funding/conflicts of interest and authors' positions in opinion pieces on drug safety. Harmful effects of varenicline, a treatment for smoking cessation, have been highly contested. OBJECTIVE: To examine the association between pharmaceutical industry funding/authors' financial conflicts of interest and position on varenicline in opinion articles, especially in relation to the minimization of harms; to assess whether opinion pieces on drug safety issues written by authors with conflicts of interest are more frequently cited in the news or social media. DESIGN: Cross-sectional analysis. PARTICIPANTS: English language opinion pieces and narrative reviews about varenicline published between May 2006 and February 2019. MAIN MEASURES: Odds ratios and 95% confidence intervals; the Mann-Whitney two-sample statistic was used to test for differences in Altmetric scores, a measure of media attention. KEY RESULTS: Of the 221 included articles, 30.3% (67) disclosed the funding source and 62.9% (139) disclosed authors' conflicts of interest. Authors of opinion pieces on varenicline who reported financial ties to the pharmaceutical industry (as a conflict of interest or funding source) were more likely to minimise the cardiovascular and psychiatric risk of varenicline compared to those without conflicts of interest or industry funding (OR: 4.00; 95% CI: 1.32 to 12.16 for cardiovascular risk; OR: 8.51; 95% CI: 3.79 to 19.11 for psychiatric risk). These associations persisted in sensitivity analyses. No statistically significant difference in Altmetric score was found between articles with (mean 15.83, median 3) and without (mean 11.90, median 1) conflicts of interest, indicating similar media attention (p-value=0.11). CONCLUSIONS: We found that authors with financial ties to drug companies were more likely to publish opinion pieces that minimised harms of varenicline. These results raise questions about journals' editorial policies to accept reviews of treatments from authors with financial relationships with manufacturers.

Factors influencing healthcare seeking in patients with dengue: Systematic review.

OBJECTIVE: Delays in seeking healthcare for dengue are associated with poor health outcomes. Despite this, the factors influencing such delays remain unclear, rendering interventions to improve healthcare seeking for dengue ineffective. This systematic review aimed to synthesise the factors influencing healthcare seeking of patients with dengue and form a comprehensive framework. METHODS: This review included both qualitative and quantitative studies. Studies were obtained by searching five databases, contacting field experts and performing backward reference searches. The best-fit meta-synthesis approach was used during data synthesis, where extracted data were fitted into the social-ecological model. Sub-analyses were conducted to identify the commonly reported factors and their level of statistical significance. RESULTS: Twenty studies were selected for meta-synthesis. Eighteen factors influencing healthcare seeking in dengue were identified and categorised under four domains: individual (11 factors), interpersonal (one factor), organisational (four factors) and community (two factors). The most reported factors were knowledge of dengue, access to healthcare, quality of health service and resource availability. Overall, more barriers to dengue health seeking than facilitators were found. History of dengue infection and having knowledge of dengue were found to be ambiguous as they both facilitated and hindered dengue healthcare seeking. Contrary to common belief, women were less likely to seek help for dengue than men. CONCLUSIONS: The factors affecting dengue healthcare-seeking behaviour are diverse, can be ambiguous and are found across multiple social-ecological levels. Understanding these complexities is essential for the development of effective interventions to improve dengue healthcare-seeking behaviour.

Reporting of clinical trial safety results in ClinicalTrials.gov for FDA-approved drugs: A cross-sectional analysis.

BACKGROUND: Adverse events identified during clinical trials can be important early indicators of drug safety, but complete and timely data on safety results have historically been difficult to access. The aim was to compare the availability, completeness, and concordance of safety results reported in ClinicalTrials.gov and peer-reviewed publications. METHODS: We analyzed clinical trials used in the Food and Drug Administration safety assessment of new drugs approved between 1 July 2018 and 30 June 2019. The key safety outcomes examined were all-cause mortality, serious adverse events, adverse events, and withdrawals due to adverse events. Availability of safety results was measured by the presence and timing of a record of trial-level results in ClinicalTrials.gov and a corresponding peer-reviewed publication. For the subset of trials with available results, completeness was defined as the reporting of safety results for all participants and compared between ClinicalTrials.gov and publications. To assess concordance, we compared the numeric results for safety outcomes reported in ClinicalTrials.gov and publications to results in Food and Drug Administration trial reports. RESULTS: Among 156 trials studying 52 drugs, 91 (58.3%) trials reported safety results in ClinicalTrials.gov and 106 (67.9%) in peer-reviewed publications (risk difference = -9.6%, 95% confidence interval = -20.3 to 1.0). All-cause mortality was reported sooner in published articles compared with ClinicalTrials.gov (log-rank test, p = 0.01). There was no difference in time to reporting for serious adverse events (p = 0.05), adverse events (p = 0.09), or withdrawals due to adverse events (p = 0.20). Complete reporting of all-cause mortality was similar in ClinicalTrials.gov and publications (74.7% vs 78.3%, respectively; risk difference = -3.6%, 95% confidence interval = -15.5 to 8.3) and higher in ClinicalTrials.gov for serious adverse events (100% vs 79.2%; risk difference = 20.8%, 95% confidence interval = 13.0 to 28.5) and adverse events (100% vs 86.8%; risk difference = 13.2%, 95% confidence interval = 6.8 to 19.7). Withdrawals due to adverse events were less often completely reported in ClinicalTrials.gov (62.6% vs 92.5%; risk difference = -29.8%, 95% confidence interval = -40.1 to -18.7). No difference was found in concordance of results between ClinicalTrials.gov and publications for all-cause mortality, serious adverse events, or withdrawals due to adverse events. CONCLUSION: Safety results were available in ClinicalTrials.gov at a similar rate as in peer-reviewed publications, with more complete reporting of certain safety outcomes in ClinicalTrials.gov. Future efforts should consider adverse event reporting in ClinicalTrials.gov as an accessible data source for post-marketing surveillance and other evidence synthesis tasks.

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact.

When seeking to inform and improve prevention efforts and policy, it is important to be able to robustly synthesize all available evidence. But evidence sources are often large and heterogeneous, so understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor), and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure the following: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as using a data-driven approach for crafting titles, abstracts, and keywords or by creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

The automation of relevant trial registration screening for systematic review updates: an evaluation study on a large dataset of ClinicalTrials.gov registrations.

BACKGROUND: Clinical trial registries can be used as sources of clinical evidence for systematic review synthesis and updating. Our aim was to evaluate methods for identifying clinical trial registrations that should be screened for inclusion in updates of published systematic reviews. METHODS: A set of 4644 clinical trial registrations (ClinicalTrials.gov) included in 1089 systematic reviews (PubMed) were used to evaluate two methods (document similarity and hierarchical clustering) and representations (L2-normalised TF-IDF, Latent Dirichlet Allocation, and Doc2Vec) for ranking 163,501 completed clinical trials by relevance. Clinical trial registrations were ranked for each systematic review using seeding clinical trials, simulating how new relevant clinical trials could be automatically identified for an update. Performance was measured by the number of clinical trials that need to be screened to identify all relevant clinical trials. RESULTS: Using the document similarity method with TF-IDF feature representation and Euclidean distance metric, all relevant clinical trials for half of the systematic reviews were identified after screening 99 trials (IQR 19 to 491). The best-performing hierarchical clustering was using Ward agglomerative clustering (with TF-IDF representation and Euclidean distance) and needed to screen 501 clinical trials (IQR 43 to 4363) to achieve the same result. CONCLUSION: An evaluation using a large set of mined links between published systematic reviews and clinical trial registrations showed that document similarity outperformed hierarchical clustering for identifying relevant clinical trials to include in systematic review updates.

mHealth adoption among primary care physicians in Malaysia and its associated factors: a cross-sectional study.

BACKGROUND: mHealth apps potentially improve health care delivery and patient outcomes, but the uptake of mHealth in primary care is challenging, especially in low-middle-income countries. OBJECTIVE: To measure factors associated with mHealth adoption among primary care physicians (PCPs) in Malaysia. METHODS: A cross-sectional study using a self-administered questionnaire was conducted among PCPs. The usage of mHealth apps by the PCPs has divided into the use of mHealth apps to support PCPs' clinical work and recommendation of mHealth apps for patient's use. Factors associated with mHealth adoption were analysed using multivariable logistic regression. RESULTS: Among 217 PCPs in the study, 77.0% used mHealth apps frequently for medical references, 78.3% medical calculation and 30.9% interacting with electronic health records (EHRs). Only 22.1% of PCPs frequently recommended mHealth apps to patients for tracking health information, 22.1% patient education and 14.3% use as a medical device. Performance expectancy and facilitating conditions were associated with mHealth use for medical references. Family medicine trainees, working in a government practice and performance expectancy were the facilitators for the use of mHealth apps for medical calculation. Internet connectivity, performance expectancy and use by colleagues were associated with the use of mHealth with EHR. Performance expectancy was associated with mHealth apps' recommendation to patients to track health information and provide patient education. CONCLUSIONS: PCPs often used mHealth apps to support their clinical work but seldom recommended mHealth apps to their patients. Training for PCPs is needed on the appraisal and knowledge of the mHealth apps for patient use.

Why do people start or stop using e-cigarettes in Australia? A qualitative interview-based study.

ISSUE ADDRESSED: E-cigarette awareness and use has increased globally, but little is known about the social factors that influence uptake in Australia. We explored the reasons why people started, continued and stopped using e-cigarettes in Australia. METHODS: This was a qualitative study comprising 14 semi-structured interviews with past and current e-cigarette users in Australia. Interview transcripts were analysed thematically to explore reasons why people started, continued or stopped using e-cigarettes. RESULTS: The following three themes emerged from interviews: "social" reasons, including issues of peer influence and social norms; "health" reasons, with aspects related to the reduction in use of cigarettes and health effects of using e-cigarettes; and "access and other personal" reasons, including the cost of e-cigarettes, the ability to access e-cigarettes and participants' experiences with e-cigarettes. CONCLUSIONS: The study revealed that social norms and peer influence were reasons why people started and stopped using e-cigarettes. Smokers often cited health reasons for starting, while non-smokers cited social reasons for both starting and stopping. SO WHAT?: The social and non-social reasons for why people say they start or stop using e-cigarettes appear to vary between smokers and non-smokers. Future studies may benefit from measuring these factors, differentiating between smokers and non-smokers, and considering these factors as part of interventions for limiting e-cigarette uptake among non-smokers.

Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019.

Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017-December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168-4435), of which 27 (IQR = 6-169) were vaccine critical, and 0 (IQR = 0-12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.

A systematic review of studies that measure parental vaccine attitudes and beliefs in childhood vaccination.

BACKGROUND: Acceptance of vaccines is an important predictor of vaccine uptake. This has public health implications as those who are not vaccinated are at a higher risk of infection from vaccine preventable diseases. We aimed to examine how parental attitudes and beliefs towards childhood vaccination were measured in questionnaires through a systematic review of the literature. METHODS: We systematically reviewed the literature to identify primary research studies using tools to measure vaccine attitudes and beliefs, published between January 2012 and May 2018. Studies were included if they involved a quantitative survey of the attitudes and beliefs of parents about vaccinations recommended for children. We undertook a synthesis of the results with a focus on evaluating the tools used to measure hesitancy. RESULTS: A total of 116 studies met the inclusion criteria, 99 used a cross sectional study design, 5 used a case control study design, 4 used a pre-post study design and 8 used mixed methods study designs. Sample sizes of included studies ranged from 49 to 12,259. The most commonly used tool was the Parent Attitudes about Childhood Vaccines (PACV) Survey (n = 7). The most common theoretical framework used was the Health Belief Model (n = 25). Questions eliciting vaccination attitudes and beliefs varied widely. CONCLUSIONS: There was heterogeneity in the types of questionnaires used in studies investigating attitudes and beliefs about vaccination in parents. Methods to measure parental attitudes and beliefs about vaccination could be improved with validated and standardised yet flexible instruments. The use of a standard set of questions should be encouraged in this area of study.

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

BACKGROUND: Bluetooth low energy (BLE) beacons have been used to track the locations of individuals in indoor environments for clinical applications such as workflow analysis and infectious disease modelling. Most current approaches use the received signal strength indicator (RSSI) to track locations. When using the RSSI to track indoor locations, devices need to be calibrated to account for complex interference patterns, which is a laborious process. Our aim was to investigate an alternative method for indoor location tracking of a moving user using BLE beacons in dynamic indoor environments. METHODS AND MATERIALS: We developed a new method based on the received number of signals indicator (RNSI) and compared it to a standard RSSI-based method for predicting a user's location. Experiments were performed in an office environment and a tertiary hospital. Both RNSI and RSSI were compared at various distances from BLE beacons. In moving user experiments, a user wearing a beacon walked from one location to another based on a pre-defined route. Performance in predicting user locations was measured based on accuracy. RESULTS: RNSI values decreased substantially with distance from the BLE beacon than RSSI values. Moving user experiments in the office environment demonstrated that the RNSI-based method produced higher accuracy (80.0%) than the RSSI-based method (76.2%). In the hospital, where the environment may introduce signal quality problems due to increased signal interference, the RNSI-based method still outperformed (83.3%) the RSSI-based method (51.9%). CONCLUSIONS: Our results suggest that the RNSI-based method could be useful to track the locations of a moving user without involving complex calibration, especially when deploying within a new environment. RNSI has the potential to be used together with other methods in more robust indoor positioning systems.

How organisations promoting vaccination respond to misinformation on social media: a qualitative investigation.

BACKGROUND: Vaccination misinformation is associated with serious public health consequences, such as a decrease in vaccination rates and a risk of disease outbreaks. Although social media offers organisations promoting vaccination unparalleled opportunities to promote evidence and counterbalance misinformation, we know relatively little about their internal workings. The aim of this paper is to explore the strategies, perspectives and experiences of communicators working within such organisations as they promote vaccination and respond to misinformation on social media. METHODS: Using qualitative methods, we purposively sampled 21 participants responsible for routine social media activity and strategy from Australian organisations actively promoting vaccination on social media, including government health departments, local health services, advocacy groups, professional associations and technical/scientific organisations. We conducted semi-structured, in-depth interviews to explore their perspectives and practices. Applying Risk Communication principles as a lens, we used Framework Analysis to explore the data both inductively and deductively. RESULTS: Organisations promoting vaccination face multiple challenges on social media, including misinformation, anti-science sentiment, a complex vaccination narrative and anti-vaccine activists. They developed a range of sophisticated strategies in response, including communicating with openness in an evidence-informed way; creating safe spaces to encourage audience dialogue; fostering community partnerships; and countering misinformation with care. CONCLUSIONS: We recommend that communicators consider directly countering misinformation because of the potential influence on their silent audience, i.e. those observing but not openly commenting, liking or sharing posts. Refutations should be straightforward, succinct and avoid emphasizing misinformation. Communicators should consider pairing scientific evidence with stories that speak to audience beliefs and values. Finally, organisations could enhance vaccine promotion and their own credibility on social media by forming strong links with organisations sharing similar values and goals.

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations.

BACKGROUND: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. OBJECTIVE: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. METHODS: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. RESULTS: In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. CONCLUSIONS: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.

Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study.

BACKGROUND: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. OBJECTIVE: The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts. METHODS: Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist. RESULTS: The best-performing classifiers were able to distinguish between low, medium, and high credibility with an accuracy of 78% and labeled low-credibility Web pages with a precision of over 96%. Across the set of unique Web pages, 11.86% (16,961 of 143,003) were estimated as low credibility and they generated 9.34% (1.64 billion of 17.6 billion) of potential exposures. The 100 most popular links to low credibility Web pages were each potentially seen by an estimated 2 million to 80 million Twitter users globally. CONCLUSIONS: The results indicate that although a small minority of low-credibility Web pages reach a large audience, low-credibility Web pages tend to reach fewer users than other Web pages overall and are more commonly shared within certain subpopulations. An automatic credibility appraisal tool may be useful for finding communities of users at higher risk of exposure to low-credibility vaccine communications.

Registration of published randomized trials: a systematic review and meta-analysis.

BACKGROUND: Prospective trial registration is a powerful tool to prevent reporting bias. We aimed to determine the extent to which published randomized controlled trials (RCTs) were registered and registered prospectively. METHODS: We searched MEDLINE and EMBASE from January 2005 to October 2017; we also screened all articles cited by or citing included and excluded studies, and the reference lists of related reviews. We included studies that examined published RCTs and evaluated their registration status, regardless of medical specialty or language. We excluded studies that assessed RCT registration status only through mention of registration in the published RCT, without searching registries or contacting the trial investigators. Two independent reviewers blinded to the other's work performed the selection. Following PRISMA guidelines, two investigators independently extracted data, with discrepancies resolved by consensus. We calculated pooled proportions and 95% confidence intervals using random-effects models. RESULTS: We analyzed 40 studies examining 8773 RCTs across a wide range of clinical specialties. The pooled proportion of registered RCTs was 53% (95% confidence interval 44% to 58%), with considerable between-study heterogeneity. A subset of 24 studies reported data on prospective registration across 5529 RCTs. The pooled proportion of prospectively registered RCTs was 20% (95% confidence interval 15% to 25%). Subgroup analyses showed that registration was higher for industry-supported and larger RCTs. A meta-regression analysis across 19 studies (5144 RCTs) showed that the proportion of registered trials significantly increased over time, with a mean proportion increase of 27%, from 25 to 52%, between 2005 and 2015. CONCLUSIONS: The prevalence of trial registration has increased over time, but only one in five published RCTs is prospectively registered, undermining the validity and integrity of biomedical research.