Participatory Development and Pilot Testing of an Adolescent Health Promotion Chatbot

Background: The use of chatbots may increase engagement with digital behavior change interventions in youth by providing human-like interaction. Following a Person-Based Approach (PBA), integrating user preferences in digital tool development is crucial for engagement, whereas information on youth preferences for health chatbots is currently limited.Objective: The aim of this study was to gain an in-depth understanding of adolescents' expectations and preferences for health chatbots and describe the systematic development of a health promotion chatbot.Methods: Three studies in three different stages of PBA were conducted: (1) a qualitative focus group study (n = 36), (2) log data analysis during pretesting (n = 6), and (3) a mixed-method pilot testing (n = 73).Results: Confidentiality, connection to youth culture, and preferences when referring to other sources were important aspects for youth in chatbots. Youth also wanted a chatbot to provide small talk and broader support (e.g., technical support with the tool) rather than specifically in relation to health behaviors. Despite the meticulous approach of PBA, user engagement with the developed chatbot was modest.Conclusion: This study highlights that conducting formative research at different stages is an added value and that adolescents have different chatbot preferences than adults. Further improvement to build an engaging chatbot for youth may stem from using living databases.

Integration of sequence stratigraphy, rock physics and AI inversion to determine the prospective reservoir in the shelf environment of the Tarakan Basin, Indonesia

The Tarakan Basin is one hydrocarbon basin in Indonesia with approximately 40 discoveries and more than 1000 MBOE reserves. This study discusses an approach to integrate the sequence stratigraphy, rock physics and acoustic impedance (AI) inversion analysis to determine the prospective reservoirs in the basin. PRG-1 well data is used in the sequence stratigraphy and rock physic analysis. The sequence stratigraphy analysis of PRG-1 shows that there are three system tracts: transgressive, low stand tract and high stand system tracts. The integration of sequence stratigraphy, rock physics and log data analysis show that the prospective reservoir interval in PRG-1 well is located at a depth of 4730-4780 feet. It is characterized by low gamma ray, low NPHI, low density and high resistivity. The prospective interval was deposited in early Pliocene as Tarakan Formation in the low stand system tract of shelf depositional environment. The AI map shows that the distribution of the prospective is around the PRG-1 and in the eastern part of the area.

Technology Focus: Reservoir Surveillance (September 2021)

It is not unusual to compare a team of subsurface professionals to a team of detectives piecing together a sequence of events to solve a crime. To make sense of what is happening in a hydrocarbon reservoir, subsurface teams, like detectives, typically have incomplete, sparse data sets, sampled at different points in time and space. The data only provide a partial picture of what has happened and what is likely to happen in the future. In either case, surveillance is an essential tactic to build a mental model of the situation. Fortunately, both detectives and subsurface teams have growing surveillance toolboxes to help fill information gaps and narrow the range of possible scenarios. In the oil and gas industry, an endless set of questions can be asked to characterize the state and history of a hydrocarbon reservoir. Teams need to understand the capability of the reservoir to store fluids, stresses acting on the reservoir, what fluids exist and how they interact with each other and the rock, and how fluids are moving (or are likely to move) through the reservoir. Information, however, is rarely free, and different surveillance tools provide varying qualities of information, so it is essential for subsurface professionals to choose wisely in terms of which problems to solve and which tools to pull out of the toolbox. Ultimately, we need to apply the right tools to the right problems to maximize the value of the information we gather. In this feature, we will explore innovative approaches to help better understand the stress state of the reservoir, interactions between different fluids and rocks, and how to track the movement of specific fluid components throughout the reservoir. To do so, the authors of the papers highlighted in this month’s feature apply advanced log data analysis, experimental laboratory work, and compositional reservoir simulation, key tools that every subsurface team should have in its toolbox. Recommended additional reading at OnePetro: www.onepetro.org. SPE 201679 - A Fast Method To Estimate the Correlation Between Confining Stresses and Absolute Permeability of Propped Fractures by Faras Al Balushi, The Pennsylvania State University, et al. SPE 202224 - Downhole Surveillance During the Well Lifetime Using Distributed Temperature Sensing by Ludovic Paul Ricard, CSIRO, et al. SPE 201635 - Predicting Reservoir Fluid Properties From Advanced Mud Gas Data by Tao Yang, Equinor, et al.

A scoping review of the use of log data for evaluating mobile Apps (Preprint)

BACKGROUND There is a growing trend in the potential benefits and application of log data for the evaluation of mHealth Apps. However, the process by which insights may be derived from log data remains unstructured, resulting in underutilisation of mHealth data. OBJECTIVE We aimed to acquire an understanding of how log data analysis can be used to generate valuable insights in support of realistic evaluations of mobile Apps through a scoping review. This understanding is delineated according to publication trends, associated concepts and characteristics of log data, framework or processes required to develop insights from log data, and how these insights may be utilised towards evaluation of Apps. METHODS The PRISMA-ScR guidelines for a scoping review were followed. The Scopus database, the Journal of Medical Internet Research (JMIR), and grey literature (through a Google search) delivered 105 articles of which 33 articles were retained in the sample for analysis and synthesis. RESULTS A definition for log data is developed from its characteristics and articulated as: anonymous records of users’ real-time interactions with the application, collected objectively or automatically and often accessed from cloud-based storage. Publications for theoretical and empirical work on log data analysis have increased between 2010 and 2021 (100% and 95% respectively). The research approach is distributed between inductive (43%), deductive (30%), and a hybrid approach (27%). Research methods include mixed-methods (73%) and quantitative only (27%), although mixed-methods dominate since 2018. Only 30% of studies articulated the use of a framework or model to perform the log data analysis. Four main focus areas for log data analysis are identified as usability (40%), engagement (15%), effectiveness (15%), and adherence (15%). An average of one year of log data is used for analysis, with an average of three years from the launch of the App to the analysis. Collected indicators include user events or clicks made, specific features of the App, and timestamps of clicks. The main calculated indicators are features used or not used (24/33), frequency (21/33), and duration (18/33). Reporting the calculated indicators per ‘user or user group’ was the most used reference point. CONCLUSIONS Standardised terminology, processes, frameworks, and explicit benchmarks to utilise log data are lacking in literature. Thereby, the need for a conceptual framework that is able to standardise the log analysis of mobile Apps is determined. We provide a summary of concepts towards such a framework. CLINICALTRIAL NA

Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.

Statistical Applications in Educational Measurement

Educational measurement assigns numbers to individuals based on observed data to represent individuals' educational properties such as abilities, aptitudes, achievements, progress, and performance. The current review introduces a selection of statistical applications to educational measurement, ranging from classical statistical theory (e.g., Pearson correlation and the Mantel–Haenszel test) to more sophisticated models (e.g., latent variable, survival, and mixture modeling) and statistical and machine learning (e.g., high-dimensional modeling, deep and reinforcement learning). Three main subjects are discussed: evaluations for test validity, computer-based assessments, and psychometrics informing learning. Specific topics include item bias detection, high-dimensional latent variable modeling, computerized adaptive testing, response time and log data analysis, cognitive diagnostic models, and individualized learning. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.