Frequent User Research Articles

User Education Practices on Utilization of Electronic Resources at the Kenya Revenue Authority Library, Nairobi

This paper is slightly adapted from my master’s thesis. It addresses the first objective which was to determine the types of user education practices on the utilization of electronic resources at the Kenya Revenue Authority (KRA) library. The purpose of this paper was to assess how user education practices are planned, organized and implemented at the KRA Library in response to the challenge of orientation, training and instructing users on the use of information sources and services. User education is a life-long process that has no end. While seeking information services of a library, users need education to effectively use library resources, facilities and services. This ensures that users are aware of the available resources and how to access them to support their needs. However, electronic resources in organizations may not be fully utilized as a result of inadequate user education practices. The ultimate goal of librarians and libraries is to educate users to discover their information needs, encourage and motivate them to use library resources and services. The user is the most important component in a library or information system and is the last link or receiver of information in the communication cycle. The descriptive research design was used while both quantitative and qualitative approaches were used in this study. The census technique was applied to select the sample size from the study population. The study established that the types of user education programmes practiced at the KRA library include library orientation and bibliographic instruction among others. It recommends for extension of user education programs, frequent user surveys and integration of user education with collaborative library activities.

Deep Reinforcement Learning-Based Server Selection for Mobile Edge Computing

With Mobile Edge Computing (MEC), computational intensive applications can be offloaded to the nearby edge servers to support latency-sensitive applications on mobile devices. Different from the cloud, edge servers usually have limited resources, and then selecting which edge server to run the offloaded computation becomes an important issue. Although server selection has received considerable attention, not much work has been done to consider the limited coverage of the edge server and the frequent user movement, which introduce many dynamic changing factors affecting the workload of the edge server and making it hard to achieve long-term optimum in the edge server selection. To deal with these challenges, we model the problem of continuous server selection as a Markov Decision Process (MDP). The difficulty of this problem is that achieving long-term optimum requires future knowledge, such as user mobility, server workload, etc, which is not known a priori. We do not have such knowledge and thus cannot find the optimal policy through traditional methods. To address this problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Reinforcement Learning (DRL)</i> based algorithm to learn the selection policy based on the observed performance of past server selections. Specifically, a Long Short-Term Memory (LSTM) based neural network is exploited to encode the historical information which helps infer future knowledge of the dynamically changing factors. Then the DRL model selects the optimal server automatically based on the extracted system states. Extensive trace-driven evaluations demonstrate that the proposed DRL-based algorithm has the lowest overall cost compared to existing solutions.

Re‐ranking with multiple objective optimization in recommender system

AbstractThe ranking algorithm in the recommender system aims at optimizing accuracy during training so that it pays too much attention to the relevance of the individual and ignores the mutual influence between the items in the list. In response to this problem, we propose dither, a re‐ranking model for the recommender system. We deploy the re‐ranking algorithm as an independent module after the ranking algorithm to achieve the function of decoupling from it. Our model formalizes the re‐ranking problem as a multi‐objective optimization problem. It re‐ranks the initial ranking list by balancing multiple indicators to generate an improved list and updates the list during frequent user interactions with the system. Through a case study on the MovieLens 100 K data set, the workflow, and effects of the dither model are demonstrated. In addition, the re‐ranking algorithm shows the performance advantages of our model over existing methods.

Frequency and correlates of use of applications for monitoring and increasing health and well-being in middle-aged and older adults. Findings from the German Ageing Survey

AimTo clarify the frequency and correlates of using applications for monitoring and increase of health and well-being among middle-aged and older adults during the COVID-19 pandemic in Germany. MethodsData were used from the nationally representative German Ageing Survey (n = 3,174 individuals in the analytical sample; June/July 2020). The frequency of using applications for monitoring and increasing health and well-being (from daily to never) was used as main outcome measure. ResultsAmong individuals with access to the internet, 76% never used applications for monitoring and increase of health and well-being, whereas about 13% were rare and 11% were frequent users of such applications. Multinomial regressions showed that the likelihood of being a rare user (compared to never users of such applications) was positively associated with being male [RRR: 0.74, 95% CI: 0.59–0.93], the frequency of walks [e.g., several times a week compared to never: RRR: 2.76, 95% CI: 1.15–6.59] and worse self-rated health [RRR: 1.33, 95% CI: 1.11–1.59]. Furthermore, the likelihood of being a frequent user (compared to never users) was positively associated with younger age [RRR: 0.96, 95% CI: 0.94–0.98] and the frequency of walks [daily compared to never: RRR: 2.61, 95% CI: 1.07–6.35]. ConclusionsApplications for monitoring and increasing health and well-being are used by about one out of four middle-aged and older individuals with access to the internet in Germany during the COVID-19 pandemic. In international comparison, the proportion of users is rather low. Determining the factors associated with such use may help to address non-users.

Massive transfusion protocols in the Netherlands. Consensus or confusion?

IntroductionTransfusion strategy for trauma patients with massive haemorrhage is often incorporated in massive transfusion protocols (MTP). Albeit correct MTP use results in better patient outcome, research regarding the state of MTP knowledge is scarce. The objective of this study is therefore to assess knowledge of local MTP and massive transfusion strategy in the level 1 trauma centres in the Netherlands. Our hypothesis is that actual MTP knowledge is low and transfusion strategy differs. Materials and methodsSurveys were sent out in January 2020 to all trauma and vascular surgeons, anaesthesiologists, emergency department physicians of the largest level 1 trauma centre (locally, n = 113) and to one trauma surgeon, emergency physician and anaesthesiologist in each of the nine other governmentally assigned level 1 trauma centres in the Netherlands (nationally, n = 27). The respondents were subdivided into a frequent user group (MTP usage ≥ 4 times in 2019) and a non-frequent user group (MTP usage < 4 in 2019). Data are expressed as numbers and percentages. ResultsResponse rate was (n = 48; 42%) for the local survey and (n = 14; 52%) for the national survey. Locally, (n = 23; 48%) and (n = 25; 52%) respondents were defined frequent and non-frequent users respectively and national respondents all as frequent users. In total, (n = 13; 27%) of local respondents were aware of the current local composition of the MTP. Respondents indicated to transfuse erythrocytes first, followed by plasma and platelets (local non-frequent users n = 23; 92%, local frequent users n = 21; 91% and national frequent users n = 13; 93%). The indication for platelet transfusion was units erythrocytes transfused (local non-frequent users n = 10; 40% frequent users locally n = 11; 48% and nationally n = 5; 36%) and clinical view (local non-frequent users n = 9; 36%, frequent users locally n = 8; 35% and n<5 nationally. Whereas few respondents claimed (n = 5; 21% non-frequent users locally and n <5 nationally) to transfuse platelets based on platelet counts. Viscoelastic haemostatic assays were performed during MTP, but only by frequent users. ConclusionThe majority of physicians dealing with massive transfusion in trauma patients were not aware of the exact composition of the MTP and consensus regarding transfusion strategy and indication for platelet transfusion was low.

Medical masks’ and respirators’ pattern of use, adverse effects and errors among Portuguese health care professionals during the COVID-19 pandemic: A cross-sectional study

BackgroundDuring COVID-19 pandemic, a shortage of surgical masks (Mask) and respirators (Resp) was experienced worldwide. We aimed to assess its pattern of use, adverse effects and user errors by Portuguese health care professionals (HCP). MethodsA cross-sectional study was conducted through snowball convenience sample, collected by email/ social media to health care organizations. Participants answered an online anonymous survey in March 2021. ResultsMean age of 3052 respondents was 42.1 years old, 83.6% were female and 77.8% provided direct health care to COVID-19 patients. Mean time of use per shift was 6-8 hours in 40.8% of the participants. 28.0% reported never changing it during their shift. Resp use (vs Mask) was more associated with discomfort (58.2% vs 26.8%), affecting task performance (41.5 vs 18.9%) and communication (55.0 vs 40.9%), dyspnea (36.0 vs 14.4%), skin rash (37.5 vs 19.4%) and headache (37.5 vs 19.4%). Frequent user errors included touching the front while in use (70.1% Mask vs 66.3% Resp) and omitting hand hygiene before (61.8% Mask vs 55.0% Resp) or after use (61.3% Mask vs 57.0% Resp). Average number of errors was higher for Mask (4.3), than for Resp (3.2) (all: P < .001). ConclusionsMost HCP admitted an extended use of Mask/ Resp. Resp were more prone to adverse effects and Mask more prone to errors. Strategies to reinforce good practices should be considered.

Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Persistent user behavior monitoring, which deals with finding users that occur persistently over a measurement period, is one hot topic in traffic measurement. It is significant for many applications, such as anomaly detection. Former works concentrate on monitoring frequent user behavior, such as users occurring frequently either over one measurement period or on one monitor. They have paid little attention to detect persistent user behavior over a long measurement period on multiple monitors. However, persistent users do not necessarily appear frequently in a short measurement period, but appear persistently in a long measurement period. Due to limited resource on monitors, it is not practical to collect a tremendous amount of network traffic in a long measurement period on one single monitor. Moreover, since network attackers deliberately send packets flowing through the entire managed network, it is difficult to detect abnormal behavior on one single monitor. To solve the above challenges, a novel method for detecting persistent user behavior called DPU is proposed, and it contains both online distributed traffic collection in a long measurement period on multiple monitors and offline centralized user behavior detection on the central server. The key idea of DPU is that we design the compact distributed synopsis data structure to collect the relevant information with users occurring in a long measurement period on each monitor, and we can reconstruct user IDs using simple calculations and bit settings to find users with persistent behavior on the basis of estimated occurrence frequency of users on the central server when user IDs are unknown in advance. The experiments are conducted on real traffic to evaluate the performance of detecting persistent user behavior, and the experimental results illustrate that our method can improve about 30% estimation accuracy, 40% detection precision, and accelerate about 3 times in comparison with the related method.

Biosymbiotic, personalized, and digitally manufactured wireless devices for indefinite collection of high-fidelity biosignals.

Digital medicine, the ability to stream continuous information from the body to gain insight into health status, manage disease, and predict onset health problems, is only gradually developing. Key technological hurdles that slow the proliferation of this approach are means by which clinical grade biosignals are continuously obtained without frequent user interaction. To overcome these hurdles, solutions in power supply and interface strategies that maintain high-fidelity readouts chronically are critical. This work introduces a previously unexplored class of devices that overcomes the limitations using digital manufacturing to tailor geometry, mechanics, electromagnetics, electronics, and fluidics to create unique personalized devices optimized to the wearer. These elastomeric, three-dimensional printed, and laser-structured constructs, called biosymbiotic devices, enable adhesive-free interfaces and the inclusion of high-performance, far-field energy harvesting to facilitate continuous wireless and battery-free operation of multimodal and multidevice, high-fidelity biosensing in an at-home setting without user interaction.

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data.

BackgroundTracing frequent users of health care services is highly relevant to policymakers and clinicians, enabling them to avoid wasting scarce resources. Data collection on frequent users from all possible health care providers may be cumbersome due to patient privacy, competition, incompatible information systems, and the efforts involved.ObjectiveThis study explored the use of a single key source, emergency medical services (EMS) records, to trace and reveal frequent users’ health care consumption patterns.MethodsA retrospective study was performed analyzing EMS calls from the province of Drenthe in the Netherlands between 2012 and 2017. Process mining was applied to identify the structure of patient routings (ie, their consecutive visits to hospitals, nursing homes, and EMS). Routings are used to identify and quantify frequent users, recognizing frail elderly users as a focal group. The structure of these routes was analyzed at the patient and group levels, aiming to gain insight into regional coordination issues and workload distributions among health care providers.ResultsFrail elderly users aged 70 years or more represented over 50% of frequent users, making 4 or more calls per year. Over the period of observation, their annual number and the number of calls increased from 395 to 628 and 2607 to 3615, respectively. Structural analysis based on process mining revealed two categories of frail elderly users: low-complexity patients who need dialysis, radiation therapy, or hyperbaric medicine, involving a few health care providers, and high-complexity patients for whom routings appear chaotic.ConclusionsThis efficient approach exploits the role of EMS as the unique regional “ferryman,” while the combined use of EMS data and process mining allows for the effective and efficient tracing of frequent users’ utilization of health care services. The approach informs regional policymakers and clinicians by quantifying and detailing frequent user consumption patterns to support subsequent policy adaptations.

Concurrent Use of E-cigarettes, Combustible Cigarettes, and Marijuana.

Subgroups of adolescent single and dual e-cigarette and cigarette users have been identified, but usage patterns have changed in recent years, and there has been an increase in marijuana use. Research is needed with current data to identify subgroups of use including marijuana and determine their behavioral correlates. We cross classified ever and recent use of e-cigarettes, combustible cigarettes, and marijuana among US high school students in the 2019 Youth Risk Behavior Survey (YRBS), providing 8 different groups. Levels of 14 risk and protective factors were compared across groups in general linear models with demographic covariates using omnibus tests, pairwise comparisons, and planned contrasts. Replicability was tested through identical analyses for 2017 YRBS data. The nonuser group was 43.9% of the sample. The most frequent user groups were triple users (ever-use proportion: 16.9%), dual (e-cigarette and marijuana) users (15.8%), and exclusive e-cigarette users (13.2%). For risk profiles on levels of psychosocial variables, the triple-user group was typically elevated above all other groups. Exclusive e-cigarette users were above nonusers in risk profile but below dual users (both cigarettes and marijuana). Results were similar for ever use and recent use. The patterning of results in 2019 YRBS data were closely replicated in 2017 YRBS data. Co-occurrence of e-cigarette, cigarette, and marijuana use is currently substantial among adolescents and is associated with an elevated psychosocial risk profile. This has implications for both epidemiology and prevention studies. Further research is needed to study prevalence and identify pathways to triple use.

Digital Bookstore

The project's main goal is to build an online book store where users can search for and buy books based on title, author, and subject. The chosen books are shown in a tabular style and the customer may buy them online using a credit card. Using this Website, the user may buy a book online rather than going to a bookshop and spending time. Many online bookstores, such as Powell's and Amazon, were created using HTML. We suggest creating a comparable website with .NET and SQL Server. An online book store is a web application that allows customers to purchase ebooks. Through a web browser the customers can search for a book by its title or author, later can add it to the shopping cart and finally purchase using a credit card transaction. The client may sign in using his login credentials, or new clients can simply open an account. Customers must submit their full name, contact details, and shipping address. The user may also provide a review of a book by rating it on a scale of one to five. The books are classified into different types depending on their subject matter, such as software, databases, English, and architecture. Customers can shop online at the Online Book Store Website using a web browser. A client may create an account, sign in, add things to his shopping basket, and buy the product using his credit card information. As opposed to a frequent user, the Administrator has more abilities. He has the ability to add, delete, and edit book details, book categories, and member information, as well as confirm a placed order. This application was created with PHP and web programming languages. The Online Book Store is built using the Master page, data sets, data grids, and user controls.

Impact of a Remote Monitoring Programme Including Lifestyle Education Software in Type 2 Diabetes: Results of the Educ@dom Randomised Multicentre Study.

IntroductionTelemonitoring in type 2 diabetes (T2D) is mainly based on glucose monitoring. A new type of connected device which routinely gathers data on weight, physical activity and food intake could improve patients’ diabetes control. The main aim of this study was to assess the efficacy of an at-home interventional programme incorporating such devices and lifestyle education software on diabetes control, i.e., change in HbA1c, compared to standard care.MethodsThis multicentre study randomly assigned 282 people with T2D to either a telemonitoring group (TMG) or a control group (CG) for a 1-year intervention period. While routine follow-up was maintained in the CG, TMG subjects were provided with interactive lifestyle educational software (with artificial intelligence algorithms) and connected objects (blood glucose meters, scales and actimeters) for use in their own homes and were remotely monitored by their diabetologists. Changes in HbA1c were compared between groups using a mixed linear model.ResultsThe mean HbA1c dropped from 7.8 ± 0.8% (62 mmol/mol) to 7.4 ± 1.0% (57 mmol/mol) in the TMG and from 7.8 ± 0.8% (62 mmol/mol) to 7.6 ± 1.0% (60 mmol/mol) in the CG, resulting in an intergroup difference of − 0.16 (p = 0.06) in favour of TMG, after adjustment for confounding factors. Within TMG, the decrease in HbA1c was greater in frequent users: − 0.23% (p = 0.03) in the case of connections to telemonitoring synthesis above the median and − 0.21% (p = 0.05) in the case of connections to tele-education software above the median compared to the CG. Significant weight loss was observed in the TMG but only in women (p = 0.01).FindingsThe EDUC@DOM telemonitoring and tele-education device did not highlight a significant decrease in HbA1c levels compared to routine management although a slight, albeit significant improvement in glycaemic control was observed in the frequent user subgroup as well as significant weight loss but only in women. A high level of satisfaction with the connected device was recorded amongst all participants.Trial RegistrationThis trial was registered in the Clinical Trials Database on September 27, 2013, under no. NCT01955031 and bears ID-RCB number 2013-A00391-44.

A randomized controlled trial on a self-guided Internet-based intervention for gambling problems

The majority of individuals with problematic and pathological gambling remain untreated, and treatment barriers are high. Internet-based interventions can help to address existing barriers, and first studies suggest their potential for this target group. Within a randomized controlled trial (N = 150) with two assessment times (baseline and post-intervention), we aimed to investigate the feasibility, acceptance, and effectiveness of a self-guided Internet-based intervention targeted at gambling problems. We expected a significant reduction in gambling symptoms (primary outcome) and depressive symptoms as well gambling-specific dysfunctional thoughts (secondary outcomes) in the intervention group (IG) compared to a wait-list control group with access to treatment-as-usual (control group, CG) after the intervention period of 8 weeks. Results of the complete cases, per protocol, intention-to-treat (ITT), and frequent user analyses showed significant improvements in both groups for primary and secondary outcomes but no significant between-group differences (ITT primary outcome, F(1,147) = .11, p = .739, ηp2 < .001). Moderation analyses indicated that individuals in the IG with higher gambling and depressive symptoms, older age, and comorbid anxiety symptoms showed significant improvement relative to the CG. The intervention was positively evaluated (e.g., 96.5% rated the program as useful). Possible reasons for the nonsignificant between-group differences are discussed. Future studies should include follow-up assessments and larger samples to address limitations of the present study.Trial Registration ClinicalTrials.gov (NCT03372226), http://clinicaltrials.gov/ct2/show/NCT03372226, date of registration (13/12/2017).

SemRec – An efficient ensemble recommender with sentiment based clustering for social media text corpus

SummaryThe frequent user interactions happening in the form of textual contents like reviews, ratings, tags, blogs, testimonials, and so forth transformed the social media platform into a contextualized and personalized data warehouse focusing its users' unique likes and dislikes. The huge volume of social media content makes it difficult for the end users to consume relevant information by themselves. The need of a tool to deal with such scenario leads to the development of recommendation systems. This work proposes an ensemble multi‐stage recommender system with sentiment based clustering to deal with social media text corpus where each stage performing unique functionalities of information retrieval, natural language processing, user segmentation, prediction, and recommendation generation. The proposed system leverages a hybrid approach of content‐based, collaborative, and demographic filtering techniques to predict and recommend contents, products, or services according to user interests. The experimental results gathered using standard datasets are promising and found more efficient than the traditional approaches.

Correlation study between anatomopathological data and levels of blood morphine concentrations in heroin-related deaths.

Heroin-related mortality is a complex phenomenon involving several factors that make an individual more susceptible to opioid toxicity. Among these, pre-existing pathological conditions play an important role. The current paper reviewed 51 autopsied cases performed over the last 30years, each subject ("frequent heroin user") having taken heroin intravenously alone before death. For each case, total blood morphine concentration determined by gas chromatography-mass spectrometry (GC-MS) was obtained from the archives. For histopathological evaluation we examined microscopic sections of the heart, lung, brain, liver and kidney, and for each organ a score table with a numerical value from 1 to 5, as an indicator of increasing severity, was drawn up. Finally, we created a scoring system based on the Injury Severity Score (ISS), generated by the sum of the squares of the three highest organ scores. We investigated the relationship between total blood morphine value (mg/l) and pathological score by inserting the two variables in a linear regression model: Score=a+b·Morphinemia (p-value <0.001). The results demonstrated a statistically significant correlation between the two variables: individuals with low total blood morphine values tended to have a higher pathological score. Our study provides useful assessment tools for forensic practice, confirming the need to combine two important disciplines in drug death investigation: forensic pathology and toxicology.

A Housing First Approach to Limiting Frequent Emergency Room Use

This article explores the use of a Housing First policy to decrease emergency room use by Canadians suffering from homelessness, an emergency department frequent user population. We explore the research evidence behind the cost-effectiveness, efficacy, and feasibility of Housing First strategies, and an overview of how a Housing First approach was successfully implemented in Canada as a national policy. While Canadian Housing First policies have demonstrated success in addressing homelessness and decreasing costly healthcare utilization, we consider policy improvements that can be made to overcome future challenges.

Does socio-economic status influence the effect of multimorbidity on the frequent use of ambulatory care services in a universal healthcare system? A population-based cohort study

BackgroundFrequent healthcare users place a significant burden on health systems. Factors such as multimorbidity and low socioeconomic status have been associated with high use of ambulatory care services (emergency rooms, general practitioners and specialist physicians). However, the combined effect of these two factors remains poorly understood. Our goal was to determine whether the risk of being a frequent user of ambulatory care is influenced by an interaction between multimorbidity and socioeconomic status, in an entire population covered by a universal health system.MethodsUsing a linkage of administrative databases, we conducted a population-based cohort study of all adults in Quebec, Canada. Multimorbidity (defined as the number of different diseases) was assessed over a two-year period from April 1st 2012 to March 31st 2014 and socioeconomic status was estimated using a validated material deprivation index. Frequents users for a particular category of ambulatory services had a number of visits among the highest 5% in the total population during the 2014–15 fiscal year. We used ajusted logistic regressions to model the association between frequent use of health services and multimorbidity, depending on socioeconomic status.ResultsFrequent users (5.1% of the population) were responsible for 25.2% of all ambulatory care visits. The lower the socioeconomic status, the higher the burden of chronic diseases, and the more frequent the visits to emergency departments and general practitioners. Socioeconomic status modified the association between multimorbidity and frequent visits to specialist physicians: those with low socioeconomic status visited specialist physicians less often. The difference in adjusted proportions of frequent use between the most deprived and the least deprived individuals varied from 0.1% for those without any chronic disease to 5.1% for those with four or more chronic diseases. No such differences in proportions were observed for frequent visits to an emergency room or frequent visits to a general practitioner.ConclusionEven in a universal healthcare system, the gap between socioeconomic groups widens as a function of multimorbidity with regard to visits to the specialist physicians. Further studies are needed to better understand the differential use of specialized care by the most deprived individuals.

Distinguishing features in the assessment of mHealth apps

ABSTRACT Introduction: The unparalleled surge in digital health adoption during the COVID-19 pandemic has emphasized the potential of mHealth apps. However, the quality of available evidence is generally low, and regulatory frameworks have focused on apps with medical purposes only, overlooking apps with significant interactions with patients that may require stronger oversight. Areas covered: To support this expanded evidence generation process, we identified the reasons that distinguish mHealth apps compared to medical devices at large and that should differentially feature their assessment. mHealth apps are characterized by the iterative nature of the corresponding interventions, frequent user interactions with a non-linear relationship between technology usage, engagement and outcomes, significant organizational implications, as well as challenges associated with genericization, their broad diagnostic potential, and price setting. Expert Opinion: The renewed reliance experienced during the pandemic and the unprecedented injection of resources through recovery instruments can further boost the development of apps. Only robust evidence of the benefits of mHealth apps will persuade health-care professionals and beneficiaries to systematically deploy them. Regulatory bodies will need to question their current approaches by adopting comprehensive evaluation processes that adequately consider the specific features of mHealth apps.

Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.

Frequent emergency department (ED) use has been operationalized in research, clinical practice, and policy as number of visits to the ED, despite the fact that this definition lacks empirical evidence and theoretical foundation. To date, there are no studies that have attempted to understand ED use empirically, without arbitrary use of "cut-points." This study was conducted to identify the best-performing, empirically grounded definition of frequent ED use. The performance of machine learning supervised clustering algorithms based on the most common definitions of frequent ED use in peer-reviewed literature (i.e., 3+, 4+, 5+ visits per year) were compared to unsupervised clustering algorithms that take into account numerous systemic factors associated with patients' ED use. All ED visits for the State of Florida, 2011-2015, including more than 100 clinical and payment-related variables per visit were employed in the model. Supervised algorithms using number of visits to the ED, alone, were unable to differentiate patients into clusters, while unsupervised models using all patient data formed clusters in which patients within a given cluster were alike, and patients between clusters were different. Cluster size and characteristics were stable across years. The results of this study indicate that mean number of ED visits by patients differ between patient clusters, but this does not allow for accurate identification of ED patients. Machine learning algorithms using all systemic and biopsychosocial patient data can be used to identify and group patients for the purpose of developing and testing integrated, whole health interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mobile use induces local attentional precedence and is associated with limited socio-cognitive skills in preschoolers

Mobile touch screen devices (MTSDs; i.e., smartphones and tablets) are now being used at an early and neuroplastic age by an ever-growing number of children, with this use likely affecting cognitive development. In a cross-sectional study, we investigated whether frequent MTSD user preschoolers exhibit different attentional and socio-cognitive skills compared to non-users. In a second, experimental study, we tested whether exposure to digital and non-digital games is associated with differences in attentional performance, and whether game pace moderates observed effects. Findings of both studies indicate pre-existing and experimentally-induced MTSD use was associated with global precedence in selective attention tasks, but an atypical, local precedence in a divided attention task. Further, playing with a fast digital game eliminated the advantage of selective attention over divided attention observed in the non-digital and slow digital game conditions. MTSD use was not associated with emotion recognition but was associated with worse theory of mind. We argue that the observed correlates and effects of MTSD use, and specifically of games, can be explained by a combination of MTSD characteristics (e.g., screens are rich in local and multiple modes of information, relatively limited social experience) and game characteristics (e.g., fast speed). Our results may be informative for the design and optimization of game structure and function, and may even call for influencing parameters of MTSD use that could affect mental functioning in this sensitive age.