3D surface defect map for assessing buccolingual profile of single tooth gaps following alveolar ridge preservation.

AIM: A new, non-invasive approach suggests using single intraoral optical scanning to analyze the ridge profile of single-tooth gaps following alveolar ridge preservation in the absence of a baseline scan. This method involves creating a three-dimensional (3D) surface map to identify and assess contour changes and ridge profiles based on the adjacent teeth. MATERIALS AND METHODS: The present study was designed as a cross-sectional pilot analysis on a convenience sample of patients undergoing alveolar ridge preservation. Intraoral optical scans were taken on 23 patients, capturing data from 30 edentulous sites. The digital models were then imported into an image analysis software for a 3D surface defect map analysis performed by one examiner. This analysis characterized the buccolingual profile of the single tooth gap relative to the adjacent teeth. 10 linear divergence points, spaced 0.5 mm apart in a corona-apical direction, were identified at the midfacial aspect of the sites. Based on these points the sites were plotted and grouped in three different buccolingual profiles (linear, concave, and convex). Clinical parameters including Keratinized mucosa Width (KMW), and soft tissue phenotype with Colorvue biotype probes were also recorded. RESULTS: Three different buccolingual patterns (linear, convex, and concave) were identified. Seven sites exhibited a linear profile, 10 sites displayed a concave shape, and 13 showed a convex profile. The linear profile had surface discrepancies similar to the neighboring teeth. In contrast, the convex profile revealed mid-buccal discrepancy localized only at the crestal aspect, while the concave had an extended divergence ranging from 1 to 5 mm below the soft tissue margin. Univariate and multiple logistic regression analyses did not reveal any statistically significant variables influencing profilometric analysis; however, when combining phenotype and KMW, thick phenotypes demonstrated a higher proportion of concavity (OR = 4.83) compared to thin ones, suggesting a significant trend. With every 1 mm of increase in KMW, the probability of showing a concavity decreased (p = 0.057). CONCLUSION: A 3D surface defect map represents a useful tool for objectively quantifying ridge defects and profiles by assessing profilometric and surface differences compared to adjacent dentition using a single intraoral scan. This method also indicates that KMW may play a critical role in preventing concavity defects. The 3D defect map can guide decision-making during soft tissue augmentation procedures by emphasizing the specific location of the defect and providing more detailed insights into its localization. These parameters can enable the tailoring of flap management and soft tissue grafting strategies to address the patient's individual needs.

Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data.

Background: The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology. Objective: This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census. Methods: The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles. Results: A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9%) or New Zealand (n=130, 11.8%), with most (n=620, 56.3%) aged 35-54 years and identifying as female (n=720, 65.3%). The top four occupational specialties were health informatics (n=179, 20.2%), health information management (n=175, 19.8%), health information technology (n=128, 14.4%), and health librarianship (n=104, 11.7%). Nearly all (n=797, 90%) participants identified as a manager or professional. Less than half (430/1019, 42.2%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26%) held a credential in a digital health area. While two-thirds (502/763, 65.7%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological. Conclusions: Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology.

Data management plan and REDCap mobile data capture for a multi-country Household Air Pollution Intervention Network (HAPIN) trial.

Background: Household air pollution (HAP) is a leading environmental risk factor accounting for about 1.6 million premature deaths mainly in low- and middle-income countries (LMICs). However, no multicounty randomized controlled trials have assessed the effect of liquefied petroleum gas (LPG) stove intervention on HAP and maternal and child health outcomes. The Household Air Pollution Intervention Network (HAPIN) was the first to assess this by implementing a common protocol in four LMICs. Objective: This manuscript describes the implementation of the HAPIN data management protocol via Research Electronic Data Capture (REDCap) used to collect over 50 million data points in more than 4000 variables from 80 case report forms (CRFs). Methods: We recruited 800 pregnant women in each study country (Guatemala, India, Peru, and Rwanda) who used biomass fuels in their households. Households were randomly assigned to receive LPG stoves and 18 months of free LPG supply (intervention) or to continue using biomass fuels (control). Households were followed for 18 months and assessed for primary health outcomes: low birth weight, severe pneumonia, and stunting. The HAPIN Data Management Core (DMC) implemented identical REDCap projects for each study site using shared variable names and timelines in local languages. Field staff collected data offline using tablets on the REDCap Mobile Application. Results: Utilizing the REDCap application allowed the HAPIN DMC to collect and store data securely, access data (near real-time), create reports, perform quality control, update questionnaires, and provide timely feedback to local data management teams. Additional REDCap functionalities (e.g. scheduling, data validation, and barcode scanning) supported the study. Conclusions: While the HAPIN trial experienced some challenges, REDCap effectively met HAPIN study goals, including quality data collection and timely reporting and analysis on this important global health trial, and supported more than 40 peer-reviewed scientific publications to date.

Using citizen science data for predicting the timing of ecological phenomena across regions.

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets.

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Individualized digitally designed surgical template for guided soft tissue surgery in cases with severe gingival enlargement: A clinical application in hereditary gingival fibromatosis.

AIM: The purpose of this study was to present the use of computer-assisted periodontal surgery utilizing a novel surgical guide for cases with severe gingival enlargement through a clinical application in a patient with hereditary gingival fibromatosis. MATERIALS AND METHODS: The treatment plan included nonsurgical periodontal therapy, surgical periodontal treatment, and regular periodontal maintenance before the initiation of orthodontic treatment. Due to the increased soft tissue thickness, a surgical guide with a novel design was fabricated to facilitate the periodontal surgery since most of the patient's teeth were malpositioned and underexposed due to fibromatosis. For this purpose, the patient's intraoral scan was merged with a CBCT image in order to plan surgical excisions based on the anatomy of the teeth and the bone contour. RESULTS: The customized surgical guide facilitated the gingivectomy by controlling not only the shape of the initial incisions but also their orientation toward the level of the cementoenamel junction, improving the efficiency of the clinical time compared with freehand surgery and assisting in the verification of the final soft tissue shape, based on the treatment plan. CONCLUSION: Digital technology through the superimposition of multiple data sets can assist in the diagnosis and multidisciplinary management of cases with gingival fibromatosis. The proposed design of the surgical guide can facilitate soft tissue surgery based on the digital treatment plan, leading to more predictable management of the soft tissue, especially in patients with severe gingival enlargement, as in cases with hereditary gingival fibromatosis or drug-induced gingival overgrowth.

Delivering Digital Health Solutions that Patients Need: A Call to Action.

Digital health solutions have the potential to complement traditional healthcare approaches and deliver improved health outcomes, but there are system-wide challenges that need to be addressed. These include fragmentation of the digital health landscape, regulatory processes that lack the agility to accommodate the fast pace of digital health advances, and inadequate transparency around data sharing and data governance. All of these challenges have led to mistrust, limited understanding and sharing of best practices, a lack of digital education and awareness, and insufficient patient and public engagement and involvement. In this paper, we argue that for digital health solutions to fulfil their potential, there needs to be a significant increase in early, meaningful, and sustained engagement with the people they intend to benefit. The uptake as well as the impact of digital solutions created in partnership with patients for patients are greater and more relevant to the communities they address.

A picture is worth a thousand words: Understanding the predictors of picture sharing in online consumer reviews.

Picture in online customer reviews conveys detailed product information to help potential customers make purchase decisions, which is an important form of digital data. Therefore, understanding the predictors of picture sharing in online customer reviews is crucial for retail businesses to achieve digital transformation strategies for competitive advantage, high quality service and sustainable performance. By analyzing 6211 online customer reviews of 16 products crawled from the famous online shopping site JD.com in China, this study tests the effects of reviewer rank (who), product type (what), time interval (when), review device (where), consumption satisfaction (why), and the level of involvement (how) on whether customers sharing picture and picture count in online customer reviews based on the 5W1H analysis framework. The results show that: (1) with or without picture and picture count both influence the helpfulness of online customer reviews; (2) product type, time interval, review device, consumption satisfaction and the level of involvement significantly affect whether consumers share picture; and (3) review device, consumption satisfaction and the level of involvement significantly affect picture count. The findings can help managers manage picture reviews to improve business performance.

Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.

PURPOSE OF REVIEW: The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research. RECENT FINDINGS: Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world.

Observation-based data-gathering method to support the assessment of the use of cultural ecosystem services in urban green spaces.

Urban green spaces (UGS), such as parks, gardens, forests, and green infrastructure, supply numerous regulating, provisioning and cultural ecosystem services to urban communities, which is key for their wellbeing. To effectively plan and design UGS, it is crucial to understand how people use them and the factors that influence their usage. The diverse range of factors includes cultural, socioeconomic, demographic, seasonal, and spatial aspects, which present a challenge for studying these areas. Data collection methods based on observation enable capturing of high-quality data that reflect the behaviours in UGS, providing valuable insights for urban planners and policymakers. Standardised protocols and frameworks facilitate knowledge gathering, allowing researchers and practitioners to build upon evidence for effective urban planning and policymaking. This work aims to develop a method based on a mobile app to collect observation-based data on UGS usage efficiently.•Mobile app to collect georeferenced information on observed activities, basic sociodemographic characteristics, time and seasonal factors, and park characteristics, including the spatial distribution of park recreational equipment.•App optimised for fast and accurate data collection.•Focused on cultural ecosystem services.

A realist review of digitally delivered child development assessment and screening tools: Psychometrics and considerations for future use.

BACKGROUND: Developmental screening improves the detection of developmental concerns, yet numerous children are not screened/assessed. Remote child developmental tool administration has been utilized to increase screening and assessment accessibility. METHOD: We conducted a realist review to: (1) identify existing multi-domain child development assessment and screening tools for children 0-5 years; (2) review psychometric data on their digital (i.e., only administered remotely) administration; and (3) explore contextual factors relevant to their digital administration. We searched APA PsycInfo, MEDLINE, CINAHL, and ERIC to identify tools and papers on their psychometrics. We reference-searched included articles and searched Google for relevant grey literature. RESULTS: Of 33 multi-domain child development tools identified in objective one, five tools (in five studies) were delivered digitally and compared to traditional (e.g., paper) delivery (i.e., objective two). Studies evaluated within-group equivalence reliability (k = 2) and between-group equivalence (k = 3). Within-group equivalence reliability was established for the Vineland Adaptive Behavior Scales, and domains (e.g., gross motor) of the Ages and Stages Questionnaires 2nd edition (ASQ-2) and Revised Prescreening Denver Questionnaire (R-PDQ). Between group equivalence was demonstrated for Developmental Neuropsychological Assessment, 2nd Edition (NEPSY-II) subtests and Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-3) items. In another between group evaluation, web-based and paper versions of the ASQ-2 were deemed generally equivalent. Digital Bayley-3 inter-observer reliability ranged from 0.82 to 1.0. Examiner support, time, tool modifications, family resources, and comfort promotion supported digital administration. CONCLUSION: Digitally delivered ASQ-2, R-PDQ, Vineland, and Bayley-3 and NEPSY-II components show promise for equivalence with traditional administration.

Tackling land's 'stubborn materiality': the interplay of imaginaries, data and digital technologies within farmland assetization.

The nature of farming is - still - an essentially biological, and thus volatile, system, which poses substantial challenges to its integration into financialized capitalism. Financial investors often seek stability and predictability of returns that are hardly compatible with agriculture - but which are increasingly seen as achievable through data and digital farming technologies. This paper investigates how farmland investment brokers engage with, perceive, and produce farming data for their investors within a co-constructive process. Tackling land's 'stubborn materiality' for investment, I argue, has material and immaterial components: it includes the re-imagination of farming as a financial asset that delivers reliable income streams for investors; and the re-engineering of farmland's concrete materialities with digital farming technologies. Farmland investment brokers develop investor-suitable farmland imaginaries, underpinned by storytelling as well as the calculative 'evidence' of (digital) data. At the same time, digital technologies have become a key tool for transforming farms into 'investment grade assets' endowed with the rich data on farm performance and financial returns requested by investors. I conclude that the assetization and digitization of farmland need to be seen as closely intertwined and mutually reinforcing processes and identify key areas for future research on this intersection.

Teachers' data literacy for learning analytics: a central predictor for digital data use in upper secondary schools.

Since schools increasingly use digital platforms that provide educational data in digital formats, teacher data use, and data literacy have become a focus of educational research. One main challenge is whether teachers use digital data for pedagogical purposes, such as informing their teaching. We conducted a survey study with N = 1059 teachers in upper secondary schools in Switzerland to investigate teacher digital data use and related factors such as the available technologies in schools. Descriptive analysis of the survey responses indicated that although more than half of Swiss upper-secondary teachers agreed with having data technologies at their disposal, only one-third showed a clear tendency to use these technologies, and only one-quarter felt positively confident in improving teaching in this way. An in-depth multilevel modeling showed that teachers' use of digital data could be predicted by differences between schools, teachers' positive beliefs towards digital technologies (will), self-assessed data literacy (skill), and access to data technologies (tool) as well as by general factors such as frequency of using digital devices in lessons by students. Teacher characteristics, such as age and teaching experience, were minor predictors. These results show that the provision of data technologies needs to be supplemented with efforts to strengthen teacher data literacy and use in schools.

Effect estimates can be accurately calculated with data digitally extracted from interrupted time series graphs.

Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide raw data for re-analysis, graphs are often included, from which time series data can be digitally extracted. However, the accuracy of effect estimates calculated from data digitally extracted from ITS graphs is currently unknown. Forty-three ITS with available datasets and time series graphs were included. Time series data from each graph was extracted by four researchers using digital data extraction software. Data extraction errors were analysed. Segmented linear regression models were fitted to the extracted and provided datasets, from which estimates of immediate level and slope change (and associated statistics) were calculated and compared across the datasets. Although there were some data extraction errors of time points, primarily due to complications in the original graphs, they did not translate into important differences in estimates of interruption effects (and associated statistics). Using digital data extraction to obtain data from ITS graphs should be considered in reviews including ITS. Including these studies in meta-analyses, even with slight inaccuracy, is likely to outweigh the loss of information from non-inclusion.

Crime, inequality and public health: a survey of emerging trends in urban data science.

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Dietary Patterns and Their Associations with Symptom Levels Among People with Multiple Sclerosis: A Real-World Digital Study.

INTRODUCTION: The objective of the study was to investigate long-term food intake patterns and establish possible associations between the inferred dietary habits and levels of reported symptoms among people with multiple sclerosis (MS) in Denmark. METHODS: The present study was designed as a prospective cohort study. Participants were invited to register daily food intake and MS symptoms and were observed during a period of 100 days. Dropout and inclusion probabilities were addressed using generalized linear models. Dietary clusters were identified among 163 participants using hierarchical clustering on principal component scores. Associations between the dietary clusters and the levels of self-assessed MS symptoms were estimated using inverse probability weighting. Furthermore, the effect of a person's position on the first and second principal dietary component axis on symptom burden was investigated. RESULTS: Three dietary clusters were identified: a Western dietary cluster, a plant-rich dietary cluster and a varied dietary cluster. Analyses further indicated a vegetables-fish-fruit-whole grain axis and a red-meat-processed-meat axis. The plant-rich dietary cluster showed reduction in symptom burden in nine pre-defined MS symptoms compared to the Western dietary cluster (between 19 and 90% reduction). This reduction was significant for pain and bladder dysfunction as well as across all nine symptoms (pooled p value = 0.012). Related to the two dietary axes, high intake of vegetables resulted in 32-74% reduction in symptom burden compared to low levels of vegetable intake. Across symptoms, this was significant (pooled p value = 0.015), also regarding walking difficulty and fatigue. CONCLUSIONS: Three dietary clusters were identified. Compared to levels of self-assessed MS-related symptoms, and adjusted for potential confounders, the results suggested less symptom burden with increased intake of vegetables. Although the research design limits the possibilities of establishing causal inference, the results indicate that general guidelines for healthy diet may be relevant as a tool in coping with MS symptoms.

Randomized evaluation of routine beta-blocker therapy after myocardial infarction quality of life (RQoL): design and rationale of a multicentre, prospective, randomized, open, blinded endpoint study.

Aims: Most cases of acute myocardial infarction (MI) in Sweden are treated with long-term ß-blocker therapy as secondary prevention. Case studies and patient reports have indicated negative effects of ß-blockers including symptoms of depression, fatigue, sexual dysfunction, and general low mood, all related to reduced quality of life (QoL). To date, no recent large-scale, randomized trial has explored the effects of ß-blockers on these factors. Methods and results: The ongoing Randomized Evaluation of Decreased Usage of beta-bloCkErs after myocardial infarction (REDUCE): quality of life (RQoL) study is a multicentre, prospective, randomized pre-specified substudy aiming to evaluate the effects of ß-blockers on self-reported measures of QoL. Following randomized allocation to long-term ß-blocker or no ß-blocker treatment, patients complete a total of six baseline measures pertaining to QoL, sexual functioning, and perceived side effects. Data collection is optionally carried out online through a unique and secure portal and repeated again at two follow-up time points. Recruitment began in July 2018. Data from the first 100 patients showed that at the first follow-up, 93% had completed the questionnaires, which decreased to 81% at the second follow-up. The method of digital data collection was utilized by over half of the patients recruited so far. Conclusion: Data from the first 100 patients indicate success in terms of study design and recruitment. The RQoL substudy investigates the effects of ß-blockers on self-reported measures of QoL in MI patients and will potentially contribute to the limited knowledge of QoL-related side effects reported in conjunction with ß-blocker use. Clinical trial registration: Eudra CT number, 2017-002336-17; Clinical trial.gov identifier, NCT03278509.

3D surface defect map for characterising the buccolingual profile of peri-implant tissues.

PURPOSE: To describe a novel, noninvasive, intraoral optical scanning-based approach for characterising the buccolingual profile of peri-implant tissues using a 3D surface defect map. MATERIALS AND METHODS: Intraoral optical scans of 20 isolated dental implants with peri-implant soft tissue dehiscence in 20 subjects were captured. The digital models were then imported into image analysis software, where an examiner (LM) performed a 3D surface defect map analysis characterising the buccolingual profile of the peri-implant tissues in respect to the adjacent teeth. Ten linear divergence points that were 0.5 mm apart in a corono-apical direction were identified at the midfacial aspect of the implants. Based on these points, the implants were grouped into three different buccolingual profiles. RESULTS: The method for creating the 3D surface defect map of isolated implant sites was outlined. Eight implants displayed pattern 1 (coronal profile of peri-implant tissues more lingual/palatal than their apical portion), six implants exhibited pattern 2 (opposite of pattern 1) and six sites showed pattern 3 (relatively uniform and "flat"). CONCLUSIONS: A novel method for assessing the buccolingual profile/position of peri-implant tissues using a single intraoral digital impression was proposed. The 3D surface defect map visualises the volumetric differences in the region of interest compared to the adjacent sites, allowing for objective quantification and reporting of profile/ridge deficiencies of isolated sites.

Epidemic intelligence data of Crimean-Congo haemorrhagic fever, European Region, 2012 to 2022: a new opportunity for risk mapping of neglected diseases.

BackgroundThe Epidemic Intelligence from Open Sources (EIOS) system, jointly developed by the World Health Organisation (WHO), the Joint Research Centre (JRC) of the European Commission and various partners, is a web-based platform that facilitate the monitoring of information on public health threats in near real-time from thousands of online sources.AimsTo assess the capacity of the EIOS system to strengthen data collection for neglected diseases of public health importance, and to evaluate the use of EIOS data for improving the understanding of the geographic extents of diseases and their level of risk.MethodsA Bayesian additive regression trees (BART) model was implemented to map the risk of Crimean-Congo haemorrhagic fever (CCHF) occurrence in 52 countries and territories within the European Region between January 2012 and March 2022 using data on CCHF occurrence retrieved from the EIOS system.ResultsThe model found a positive association between all temperature-related variables and the probability of CCHF occurrence, with an increased risk in warmer and drier areas. The highest risk of CCHF was found in the Mediterranean basin and in areas bordering the Black Sea. There was a general decreasing risk trend from south to north across the entire European Region.ConclusionThe study highlights that the information gathered by public health intelligence can be used to build a disease risk map. Internet-based sources could aid in the assessment of new or changing risks and planning effective actions in target areas.