Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines.

OBJECTIVE: To use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. METHOD: We implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. RESULTS: Precision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. CONCLUSION: Algorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

Meta-analysis in a digitalized world: A step-by-step primer.

In recent years, much research and many data sources have become digital. Some advantages of digital or Internet-based research, compared to traditional lab research (e.g., comprehensive data collection and storage, availability of data) are ideal for an improved meta-analyses approach.In the meantime, in meta-analyses research, different types of meta-analyses have been developed to provide research syntheses with accurate quantitative estimations. Due to its rich and unique palette of corrections, we recommend to using the Schmidt and Hunter approach for meta-analyses in a digitalized world. Our primer shows in a step-by-step fashion how to conduct a high quality meta-analysis considering digital data and highlights the most obvious pitfalls (e.g., using only a bare-bones meta-analysis, no data comparison) not only in aggregation of the data, but also in the literature search and coding procedure which are essential steps in any meta-analysis. Thus, this primer of meta-analyses is especially suited for a situation where much of future research is headed to: digital research. To map Internet-based research and to reveal any research gap, we further synthesize meta-analyses on Internet-based research (15 articles containing 24 different meta-analyses, on 745 studies, with 1,601 effect sizes), resulting in the first mega meta-analysis of the field. We found a lack of individual participant data (e.g., age and nationality). Hence, we provide a primer for high-quality meta-analyses and mega meta-analyses that applies to much of coming research and also basic hands-on knowledge to conduct or judge the quality of a meta-analyses in a digitalized world.

The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP).

Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.

Participatory video from a distance: co-producing knowledge during the COVID-19 pandemic using smartphones.

In this paper, I outline an innovative remote participatory video (PV) methodology that makes use of participants' smartphones. It was developed as an alternative to co-production research and can be employed when face-to-face contact is impossible or undesirable. Because of the COVID-19 pandemic, face-to-face research interactions have been disrupted or become impossible. Yet it is vital to reach those who are most affected by emergencies and to include their voices. The research reported here was a collaboration between women in Medellín, Colombia, and a team of filmmakers and researchers. We developed an innovative remote PV methodology using participants' smartphones, researching how women from poorer neighbourhoods were affected by the pandemic in their everyday lives. Here, I reflect on the strengths and weaknesses of the remote PV methodology, arguing that it offers new avenues for participants to take control of the filming and editing process, and builds technical skills and capacities that have value beyond the timeframe of the project. I conclude that the remote PV method has great potential as a stand-alone method, moving the landscape of co-production research away from a requirement for geographical co-presence and potentially shifting power and ownership towards local co-researchers and participants.

A digital self-report survey of mood for bipolar disorder.

OBJECTIVES: Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity. METHODS: We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure. RESULTS: We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R2  = 0.47) and SIGH-D (R2  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores. CONCLUSIONS: While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity.

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development.

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor's own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.

"I didn't Notice that You Were Watching Me": Exploring a User Acceptance Study to Conduct Cultural Domain Analysis Online During the COVID-19 Pandemic.

This article explores the implementation process of a User Acceptance Study to evaluate the feasibility of conducting cultural domain analysis (CDA) online during the COVID-19 pandemic. We conducted 19 cultural domain analysis sessions involving three techniques: free listing, pile sorts, and rank ordering. A diverse set of participants were recruited to help assess requirements and needs that researching online involves. We found that conducting CDA online is a feasible research method that offers benefits such as generating large amounts of data, making participants feel comfortable joining sessions from a safe space, providing anonymity, reducing research costs such as time and travel, and eliciting large numbers of responses. We also identified several factors for consideration when implementing CDA online and provide recommendations for improvement, including the aesthetics of the digital software employed, user accessibility and digital literacy, participants' environments, Internet connection, and online-specific ethical issues.

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform.

BACKGROUND: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. OBJECTIVE: This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. METHODS: The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. RESULTS: The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs. CONCLUSIONS: Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49204.

When Is a Change Significant? The Update Problem of Apps in Medical and Behavioral Research.

Digital applications (apps) are commonly used across the research ecosystem. While apps are frequently updated in the course of clinical and behavioral research, there is limited guidance as to when an app update should trigger action related to human research participant protections and who should be responsible for monitoring and reviewing these updates. We term this the "update problem" and argue that, while it is the principal investigator's duty to track all relevant updates, the level of involvement and re-review by the institutional review board (IRB) of an approved research protocol should vary depending on whether the update may be classified as minor, not minor, or significant. Minor updates require at most annual notification of the IRB, updates that are not minor require prompt notification of the IRB, and significant updates may require full board re-review or another response. We also suggest how these policies might be implemented.

Lessons for a digital future from the school of the pandemic: From distance learning to virtual reality.

The unexpected onset of the pandemic emergency placed so-called Distance Learning (DL) at the center of the academic world, affecting students and teachers across all formative steps. The DL experience has opened up the way for many queries in terms of research on the front of education, besides showcasing instances of innovation within the schooling institution, both increasingly urgent and no longer deferrable. The collective shock that started in March of 2020 was an opportunity to incentivize a leap in evolution, heavily digital in nature, within the educational system; howbeit, the generation of digital natives were already, prior to the onset of COVID-19, waiting to sense greater openness in the Italian school system toward newer technologies, in addition to less standardized, more innovative, creative and hybrid didactic formulas. In the presented study-a web survey launched in the spring of 2021-a large sample of students were invited to retrace their experience with DL, and express their relating assessments and reviews. Conducting the entirety of the study remotely turned out to be a winning data collection technique given a situation, comparable to the one experienced globally, in which face-to-face meetings had become impossible. Through in-depth analysis of the different contexts-social, cultural, technological, spatial, relational-in which the DL experience took hold, this contribution holds the purpose of illustrating the main DL adaptation profiles of the sample reached, valorizing the perceptual dimension, through the systematic comparison of online and in-person didactics. Analysis of the identified forms of adaptation created an opportunity to reconstruct the image of school that the interviewees held, how much they valued it, the trust they placed therein, the developments they predicted and desired for the institution. Focusing-responsibly, and taking stock of the possible ethical implications-on the future opportunities held by technological progress, in itself boosted by the pandemic, are located within a wider experimentation of VR-equipped classrooms, in a multidisciplinary perspective, offering a concrete solution to the needs of both students and teachers.

Using passive extraction of real-world data from eConsent, electronic patient reported outcomes (ePRO) and electronic health record (EHR) data loaded to an electronic data capture (EDC) system for a multi-center, prospective, observational study in diabetic patients.

As clinical trial complexity has increased over the past decade, using electronic methods to simplify recruitment and data management have been investigated. In this study, the Optum Digital Research Network (DRN) has demonstrated the use of electronic source (eSource) data to ease subject identification, recruitment burden, and used data extracted from electronic health records (EHR) to load to an electronic data capture (EDC) system. This study utilized electronic Informed Consent, electronic patient reported outcomes (SF-12) and included three sites using 3 different EHR systems. Patients with type 2 diabetes with an HbA1c ≥ 7.0% treated with metformin monotherapy were recruited. Endpoints consisted of changes in HbA1c, medications, and quality of life measures over 12-weeks of study participation using data from the subjects' eSources listed above. The study began in June of 2020 and the last patient last visit occurred in January of 2021. Forty-eight participants were consented and enrolled. HbA1c was repeated for 33 and ePRO was obtained from all subjects at baseline and 28 at 12-week follow-up. Using eSource data eliminated transcription errors. Medication changes, healthcare encounters and lab results were identified when they occurred in standard clinical practice from the EHR systems. Minimal data transformation and normalization was required. Data for this observational trial where clinical outcomes are available using lab results, diagnoses, and encounters may be achieved via direct access to eSources. This methodology was successful and could be expanded for larger trials and will significantly reduce staff effort and exemplified clinical research as a care option.

Liar! Liar! Identifying eligibility fraud by applicants in digital health research.

Online studies enable researchers to recruit large, diverse samples, but the nature of these studies provides an opportunity for applicants to misrepresent themselves to increase the likelihood of meeting eligibility criteria for a trial, particularly those that provide financial incentives. This study describes rates of fraudulent applications to an online intervention trial of an Internet intervention for insomnia among older adults (ages ≥55). Applicants were recruited using traditional (e.g., flyers, health providers), online (e.g., Craigslist, Internet searches), and social media (e.g., Facebook) recruitment methods. Applicants first submitted an interest form that included identifying information (name, date of birth, address). This data was then queried against a national database (TransUnion's TLOxp) to determine the application's verification status. Applications were determined to be verified (i.e., information from interest form matched TLOxp report), potentially fraudulent (i.e., potential discrepancy in provided information on interest form versus TLOxp report), or fraudulent (i.e., confirmed discrepancy). Of 1766 total interest forms received, 125 (7.08%) were determined to be fraudulent. Enrollment attempts that were fraudulent were detected among 12.22% of applicants who reported learning of the study through online, 7.04% through social media, 4.58% through traditional, and 4.27% through other methods. Researchers conducting online trials should take precautions, as applicants may provide fraudulent information to gain access to their studies. Reviewing all applications and verifying the identities and eligibility of participants is critical to the integrity of online research trials.

'Go fish': Conceptualising the challenges of engaging national web archives for digital research.

Our work considers the sociotechnical and organisational constraints of web archiving in order to understand how these factors and contingencies influence research engagement with national web collections. In this article, we compare and contrast our experiences of undertaking web archival research at two national web archives: the UK Web Archive located at the British Library and the Netarchive at the Royal Danish Library. Based on personal interactions with the collections, interviews with library staff and observations of web archiving activities, we invoke three conceptual devices (orientating, auditing and constructing) to describe common research practices and associated challenges in the context of each national web archive. Through this framework we centre the early stages of the research process that are often only given cursory attention in methodological descriptions of web archival research, to discuss the epistemological entanglements of researcher practices, instruments, tools and methods that create the conditions of possibility for new knowledge and scholarship in this space. In this analysis, we highlight the significant time and energy required on the part of researchers to begin using national web archives, as well as the value of engaging with the curatorial infrastructure that enables web archiving in practice. Focusing an analysis on these research infrastructures facilitates a discussion of how these web archival interfaces both enable and foreclose on particular forms of researcher engagement with the past Web and in turn contributes to critical ongoing debates surrounding the opportunities and constraints of digital sources, methodologies and claims within the Digital Humanities.

Obesity-Related Communication in Digital Chinese News From Mainland China, Hong Kong, and Taiwan: Automated Content Analysis.

BACKGROUND: The fact that the number of individuals with obesity has increased worldwide calls into question media efforts for informing the public. This study attempts to determine the ways in which the mainstream digital news covers the etiology of obesity and diseases associated with the burden of obesity. OBJECTIVE: The dual objectives of this study are to obtain an understanding of what the news reports on obesity and to explore meaning in data by extending the preconceived grounded theory. METHODS: The 10 years of news text from 2010 to 2019 compared the development of obesity-related coverage and its potential impact on its perception in Mainland China, Hong Kong, and Taiwan. Digital news stories on obesity along with affliction and inferences in 9 Chinese mainstream newspapers were sampled. An automatic content analysis tool, DiVoMiner was proposed. This computer-aided platform is designed to organize and filter large sets of data on the basis of the patterns of word occurrence and term discovery. Another programming language, Python 3, was used to explore connections and patterns created by the aggregated interactions. RESULTS: A total of 30,968 news stories were identified with increasing attention since 2016. The highest intensity of newspaper coverage of obesity communication was observed in Taiwan. Overall, a stronger focus on 2 shared causative attributes of obesity is on stress (n=4483, 33.0%) and tobacco use (n=3148, 23.2%). The burdens of obesity and cardiovascular diseases are implied to be the most, despite the aggregated interaction of edge centrality showing the highest link between the "cancer" and obesity. This study goes beyond traditional journalism studies by extending the framework of computational and customizable web-based text analysis. This could set a norm for researchers and practitioners who work on data projects largely for an innovative attempt. CONCLUSIONS: Similar to previous studies, the discourse between the obesity epidemic and personal afflictions is the most emphasized approach. Our study also indicates that the inclination of blaming personal attributes for health afflictions potentially limits social and governmental responsibility for addressing this issue.

Digital Epidemiologic Research on Multilevel Risks for HIV Acquisition and Other Health Outcomes Among Transgender Women in Eastern and Southern United States: Protocol for an Online Cohort.

BACKGROUND: The HIV epidemic disproportionately impacts transgender women in the United States. Cohort studies identify unique risks for affected populations, but use of facility-based methods may bias findings towards individuals living in research catchment areas, more engaged in health services, or, in the case of transgender populations, those who are open about their transgender identity. Digital clinical trials and other online research methods are increasingly common, providing opportunity to reach those not commonly engaged in research. Simultaneously, there is a need to understand potential biases associated with digital research, how these methods perform, and whether they are accepted across populations. OBJECTIVE: This study aims to assess the feasibility of developing and implementing an online cohort of transgender women to assess risks for HIV acquisition and other health experiences. Further, this study aims to evaluate how an online cohort compares to a site-based, technology-enhanced cohort for epidemiologic research. The overarching goal is to estimate incidence of HIV and other health outcomes among transgender women in eastern and southern United States. METHODS: This substudy is part of a larger multisite prospective cohort (LITE) conducted among transgender women, which also includes a site-based, technology-enhanced cohort in 6 eastern and southern US cities. The online cohort was launched to enroll and follow participants across 72 cities in the same region and with similar demographic characteristics as the site-based cohort. Participants are followed for 24 months. Adult transgender women are recruited via convenience sampling (eg, peer referrals, social media, and dating apps). Participants reporting negative or unknown HIV status are enrolled in a baseline study visit, complete a sociobehavioral survey, and provide oral fluid specimens to test for HIV. Participants not living with HIV (lab-confirmed) at baseline are offered enrollment into the cohort; follow-up assessments occur every 6 months. RESULTS: Enrollment into the online cohort launched in January 2019. Active recruitment stopped in May 2019, and enrollment officially closed in August 2020. A total of 580 participants enrolled into and are followed in the cohort. A recruitment-enrollment cascade was observed across screening, consent, and completion of study activities. Implementation experiences with HIV test kits highlight the need for heavy staff engagement to support participant engagement, visit completion, and retention, even with automated digital procedures. CONCLUSIONS: This study is responsive to increasing research interest in digital observational and intervention research, particularly for populations who are most affected by the HIV epidemic and for those who may otherwise not participate in person. The progression across stages of the recruitment-enrollment cascade provides useful insight for implementation of cohort studies in the online environment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29152.

Shouting at each other into the void: A linguistic network analysis of vaccine hesitance and support in online discourse regarding California law SB277.

In 2015, California passed Senate Bill 277 and became the third state in the United States to ban all nonmedical exemptions from school immunization requirements, effectively prohibiting religious and personal belief exemptions. This attracted grassroots opposition and considerable debate among vaccine hesitant factions online. This mixed-methods study used quantitative linguistic analysis, semantic network analysis, and content analysis techniques to examine 2424 online documents drawn from newspapers, blogs, health websites, government information pages, web forums, personal websites, Facebook groups, among others. The study examined which words and phrases were used most frequently by vaccine skeptics, vaccine defenders, and more neutral media accounts to illuminate how groups with different attitudes towards vaccination discuss and disseminate information about vaccines and vaccine policy online. We proposed an innovative methodology for examining online discourse surrounding vaccine hesitance, as well as for studying the online dissemination of misinformation about vaccines. Our findings highlighted discrepancies in the narratives between what vaccine supporters believe causes vaccine skepticism and the issues that vaccine skeptics actually discuss within their own digital spaces. For example, in these exchanges, the importance of parental rights overshadowed that of children's rights; supporters of vaccines brought up autism in more distinct documents than skeptics do; distrust of government regulators and researchers seemed to unite vaccine skeptics and defenders; and politicians, doctors, and even celebrities often served as proxies in heated exchanges about factual evidence, believability, and the importance of expertise in public discourse.

Drug Information Association (DIA) 2020 Virtual Global Annual Meeting (June 14-18, 2020).

At the 56th Global Annual Meeting of the Drug Information Association (DIA), attendees met virtually during the height of the global COVID-19 pandemic for "rapid cross-stakeholder, cross-border collaboration" to support health worldwide. Sessions included presenters and speakers from regulatory, patient advocacy and academia sectors, with patients at the forefront of those discussions. This report covers various presentations and panel discussions from the 4-day meeting that focus on COVID-19, innovative trial designs spurred by a need to adapt amid a pandemic, digital health, novel products inspiring new regulatory standards, clinical trials, data collection and management, the need for more and better data and the ever-increasing importance of the patient perspective.

Comprehensive scoping review of health research using social media data.

INTRODUCTION: The rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it. METHODS: A version of Arksey and O'Malley's five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date. ETHICS AND DISSEMINATION: The review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.

Three dimensional digital study and clinical significance of thoracic joint angle in children aged from 10 to 12 years / 局解手术学杂志

Objective To explore the morphological characteristics and change rule of thoracic joint angles in children aged from 10 to 12 years through thinner CT scanning and 3D reconstruction,and to provide theoretical basis for early diagnosis,treatment and prevention.Methods Totally 30 normal cases aged from 10 to 12 years were admitted into this study.There was no bone destruction,deformity,fractures,tumors and spine surgery involved.DICOM 3.0 data of multi-slice spiral CT (0.625 ～ 1.25 mm),ranging from T1 to T12,were used for 3D reconstruction,measurement and statistical analysis.Results The difference between left and right sagittal section angle of zygopophysis was less than 10°.There was no significant differences between T1,T11,and T12for sagittal section angle of upper zygopophysis (P ＞ 0.05).So was it between T3,T4,T9,T10 and T11 for sagittal section angle of lower zygopophysis (P ＞ 0.05).While there were significant differences between others(P ＜ 0.05).The was no significant difference between left and right coronal plane angle of zygopophysis (P ＞ 0.05).Coronal plane angle of lower and upper zygopophysis tended to be ‘ spike-like’,and the maximum points were at T7 to T9.For horizontal plane angle,left and right upper zygopophysis made significant differences between T2,T4,T8,T10 and T12 only,so did T7 lower zygopophysis (P ＜ 0.05).Horizontal angle of upper zygopophysis tended to be stable in the upper thoracic both in the left and right side,while a decreasing trend was shown in lower thoracic.Horizontal angle of lower zygopophysis showed a decreasing trend generally except individual vertebrae.Both upper and lower zygopophysis showed negative angle at T11 and T12 levels.Conclusion Thoracic joint angles(coronal,sagittal and horizontal angle) in children aged from 10 to 12 years can directly reflect the developmental regularity with growth,and it verified the tendency that horizontal facet joints of the cervical spine gradually changes to coronal facet joints thoracic spine and then changes to sagittal facet joints of lumbar spine.And the left and right side facet joints are basically symmetrical with the angle difference less than 10°.