Data Overload Research Articles

Data literacy and the cognitive challenges of a data-rich business environment: an analysis of perceived data overload, technostress and their relationship to individual performance

PurposeConsidering the cognitive challenges associated with a data-rich business environment, this research aims to investigate the relationship between data literacy (DL), perceived data overload (PDO), and technostress (TS), besides the effect of these constructs on professional's individual performance (IP).Design/methodology/approachThrough survey research, the authors collected data from 321 professionals who work in data-rich and highly technological business environments. To test the hypotheses proposed, the authors developed the partial least squares structural equation modeling (PLS-SEM) procedures.FindingsThe results showed that DL is positively associated with IP and negatively with PDO. PDO is positively associated with TS and negatively with IP. The authors found no significant negative association between TS and IP.Research limitations/implicationsWith this research, the authors seek to contribute to the gap in the literature concerning two cognitive challenges associated with data-rich business environments: PDO and TS, analyzing from the point of view of the individual, and highlighting the importance of DL in this context.Practical implicationsThe results can assist managers in effectively being concerned with the DL level of their workforce. This is important considering not only the professionals' IP but also the cognitive challenges such as PDO and TS.Originality/valueThe innovation of this study lies in the empirical analysis of DL in the business context and its relationship with two cognitive challenges inherent in data-rich environments: PDO, and TS. Besides, the authors highlight the importance of understanding such phenomena in terms of IP.

ONBOARD: A Feasibility Study of a Telehealth-Based Continuous Glucose Monitoring Adoption Intervention for Adults with Type 1 Diabetes

Background: Continuous glucose monitoring (CGM) can improve glycemic control for adults with type 1 diabetes (T1D) but certain barriers interfere with consistent use including cost, data overload, alarm fatigue, physical discomfort, and unwanted social attention. This pilot study aimed to examine feasibility and acceptability of a behavioral intervention, ONBOARD (Overcoming Barriers and Obstacles to Adopting Diabetes Devices) to support adults with T1D in optimizing CGM use. Methods: Adults (18-50 years) with T1D in their first year of CGM use were invited to participate in a tailored, multicomponent telehealth-based intervention delivered over four 60-min sessions every 2-3 weeks. Participants completed surveys (demographics; diabetes distress, Diabetes Distress Scale for adults with type 1 diabetes; satisfaction with program) and provided CGM data at baseline and postintervention (3 months). Data were analyzed using paired t-tests and Wilcoxon signed-rank tests. Results: Twenty-two participants (age = 30.95 ± 8.32 years; 59% women; 91% non-Hispanic; 86% White, 5% Black, 9% other; 73% pump users) completed the study. ONBOARD demonstrated acceptability and a high rate of retention. Moderate effect sizes were found for reductions in diabetes distress (P = 0.01, r = -0.37) and increases in daytime spent in target range (70-180 mg/dL: P = 0.03, r = -0.35). There were no significant increases in hypoglycemia. Conclusions: Findings show preliminary evidence of feasibility, acceptability, and efficacy of ONBOARD for supporting adults with T1D in optimizing CGM use while alleviating diabetes distress. Further research is needed to examine ONBOARD in a larger sample over a longer period.

Incorporating contextual information into personalized mobile applications recommendation

With the rise of the mobile Internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. However, most of the existing recommended methods for apps ignore the app functional exclusiveness features, which makes it difficult to further improve the app recommendation performance. To solve this problem, we propose a personalized context-aware mobile app recommendation approach, called PCMARA. Specifically, (1) PCMARA explores the relationship between contextual information and function of apps and constructs the app contextual factors for app which represent the function of app. (2) PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate the adverse effects that ignore the app functional exclusiveness. (3) PCMARA comprehensively considers the contextual information of users and apps to generate a recommendation list for users based on the target users’ current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results demonstrate the superiority of PMARA over the benchmark methods.

THE CHALLENGES OF SECURITY THREAT IN NIGERIA CYBERSPACE

This research work was conducted to examine critically and systematically cyber threat intelligence challenges and prospects in Nigeria. It judges the value and relevance of cyber threat intelligence in the society where they are lacking in providing necessary information. Dealing with these challenges that may cause threat intelligence to be useless has become a major concern to Nigeria. The work was intended to achieve the following objectives: to examine the nature of cybersecurity in Nigeria, to analyse the cybersecurity threats that can disrupt the functioning of the country, to identify the challenges facing the Nigeria cyberspace and the conduct of a cyber threat intelligence analysis, to discuss the means by which cyber threat can be used to boost Nigeria’s National Security Policy, to make recommendations to preserve important intelligence capabilities while ensuring the protection of its critical infrastructures through the use of threat intelligence. The scope of the study was limited to the period 2009 – 2019. The research was analytical. Relevant data were collected from both primary and secondary sources of data. The data analysis used the percentage instrument and the following conclusions were drawn: that threat data overload, threat data quality, privacy and legal issues and interoperability issues are some of the challenges of cyber threat intelligence; also, the need to continually invest in research, build local cyber threat management infrastructure and enhance the ability to anticipate, detect, respond and contain information security threats is very crucial. Nigeria

Genetically optimized Fuzzy C-means data clustering of IoMT-based biomarkers for fast affective state recognition in intelligent edge analytics

IoMT sensors such as wearables, moodables, ingestible sensors and trackers have the potential to provide a proactive approach to healthcare. But grouping, traversing and selectively tapping the IoMT data traffic and its immediacy makes data management & decision analysis a pressing issue. Evidently, the selection process for real-world, time-constrained health problems involves looking at multivariate time-series data generated simultaneously from various wearables resulting in data overload and accuracy issues. Computational intelligence of edge analytics can extend predictive capability by quickly turning digital biomarker data into actions for remote monitoring and trigger alarm during emergency incidents without relying on backend servers. But the pervasive generation of data streams from IoMT levies significant issues in data visualization and exploratory data analysis. This paper presents a genetically optimized Fuzzy C-means data clustering technique for affective state recognition on the edge. Clustering segregates the biomarker data in chunks and generates a summarized data for each subject which is then genetically optimized to avoid stagnation in local optima. A multi-level convolution neural network is finally used to classify the affective states into the baseline, stress and amusement categories. The model is evaluated on the publicly available WESAD dataset and compares favorably to state-of-the-art with less time complexity. It demonstrates the use of data clustering technique for numerosity reduction of real-time data streams in intelligent edge analytics which facilitates fast analysis of affective state of the user.

Electronic Health Records' Support for Primary Care Physicians' Situation Awareness: A Metanarrative Review.

Situation awareness (SA) refers to people's perception and understanding of their dynamic environment. In primary care, reduced SA among physicians increases errors in clinical decision-making and, correspondingly, patients' risk of experiencing adverse outcomes. Our objective was to understand the extent to which electronic health records (EHRs) support primary care physicians (PCPs)' SA during clinical decision-making. We conducted a metanarrative review of papers in selected academic databases, including CINAHL and MEDLINE. Eligible studies included original peer-reviewed research published between January 2012 and August 2020 on PCP-EHR interactions. We iteratively queried, screened, and summarized literature focused on EHRs supporting PCPs' clinical decision-making and care management for adults. Then, we mapped findings to an established SA framework to classify external factors (individual, task, and system) affecting PCPs' levels of SA (1-Perception, 2-Comprehension, and 3-Projection) and identified SA barriers. From 1504 articles identified, we included and synthesized 19 studies. Study designs were largely noninterventional. Studies described EHR workflow misalignments, usability issues, and communication challenges. EHR information, including lab results and care plans, was characterized as incomplete, untimely, or irrelevant. Unmet information needs made it difficult for PCPs to obtain even basic SA, Level 1 SA. Prevalent barriers to PCPs developing SA with EHRs were errant mental models, attentional tunneling, and data overload. Based on our review, EHRs do not support the development of higher levels of SA among PCPs. Review findings suggest SA-oriented design processes for health information technology could improve PCPs' SA, satisfaction, and decision-making.

Improving Situational Awareness Using a Comprehensive Surveillance Strategy

ABSTRACT Having accurate, timely, and relevant situational awareness information is vital during an oil spill response to ensure informed operational and strategic decision making as well as effective coordination of field operations. Recent technological advancements have greatly enhanced situational awareness data collection capabilities such that numerous real-, or near real-time, data streams and inputs into the Incident Management Team are now possible. Although these advancements are generally beneficial, they can easily result in data overload and the subsequent burial or masking of critical information. This issue is compounded by the absence of standardized mechanisms or processes to assess and manage the data. Another area of concern is while surveillance is a key component of situational awareness, it is often comprised of random acts of surveillance rather than an objective driven, fit-for-purpose strategy. To address these issues, IPIECA, in coordination with the American Petroleum Institute (API), held stakeholder workshops in the United States and United Kingdom on situational awareness needs as well as the development of a comprehensive surveillance strategy. The results are being incorporated into an Oil Spill Surveillance Strategy technical support document currently under development by IPIECA. This paper will primarily focus on the key components of situational awareness as well as considerations for developing a comprehensive surveillance strategy. Additionally, it will discuss key findings of the workshops, exercise blind spots, data flow and timing, who should own the data collection process and other related topics.

Dictionaries Integrated into English Learning Apps: Critical Comments and Suggestions for Improvement

Digital applications to assist language learning are becoming increasingly popular. They typically incorporate one or two dictionaries to improve the service so that users avoid leaving the app to consult external resources. This paper deals with the two dictionaries used in a learning app for Chinese learners of English. Initially, it describes the functioning of the app as well as the two dictionaries that have different roles in the app. It then focuses on the one that is integrated into the course texts and can be activated by clicking on a word or a multiword unit. A number of deficiencies are discussed such as inconsistent treatment of words and senses, data overload, difficult access, and inconvenient location of the pop-up window that displays the lexicographical items. These deficiencies may impact negatively on the learners' motivation and the learning process in general. The paper traces the problems to the database that sustains the dictionary as well as the design of the user interfaces that filter the data offered to the users. Finally, and inspired by the classical Chinese Xun Gu tradition, it suggests an alternative, context-adapted approach that breaks with traditional features of the dictionary article and reduces the content of the pop-up window to an absolute minimum. The idea is to avoid a consultation process that interrupts the learners' reading flow and focus on learning.

Data denoising and compression of intelligent transportation system based on two‐dimensional discrete wavelet transform

SummaryTwo‐dimensional discrete wavelet transform is an important tool for digital image analysis. It is widely used in the field of image editing, such as image coding and compression and digital image processing. The realization of effective two‐dimensional discrete wavelet transform has important realities in data or image processing. This paper mainly introduces the research of intelligent transportation system data denoising and compression based on two‐dimensional discrete wavelet transform and intends to provide solutions to the problem of data overload in the process of intelligent transportation system data collection, transmission, and storage. This paper proposes the construction of two‐dimensional discrete wavelet transform, including separation calculation method and non‐separation calculation method, and proposes the construction of time–space data model of intelligent transportation system, including support vector machine (SVM), K nearest neighbor algorithm, and deep neural network algorithm. It is proposed to construct two‐dimensional wavelet transform to denoise and compress data of intelligent transportation systems. The experimental results in this paper show that the signal after denoising by two‐dimensional discrete wavelet transform is smoother, with a maximum difference of 0.57, and the denoising effect is better.

Rigour in the Management Case Study Method: A Study on Master's Dissertations

Many authors argue that the case study is a valid and advantageous research method for certain studies, having been used as a method of academic research in the field of social sciences as well as in management. This research method is an integrated system and offers the opportunity for a holistic view that combines data collection methods such as archival searches, interviews, questionnaires and observation. The data collection needed to build a case study is labour-intensive, can last months or even years, and data overload seems almost inevitable. The case study is an empirical inquiry that investigates a contemporary phenomenon within a real-life context. In this research method, qualitative data usually predominates, but quantitative data often appears in case studies. Some research studies consider the case study to be a research method for validating and constructing theory. Other research highlights that all social science studies must start with a theory based on a review of the literature relating to the subject under investigation and this theory must then be validated through the study of a specific object, phenomenon or social problem. The key point is that before a theory can be validated, it must be constructed. In other words, a theory or theoretical framework first emerges not through a deductive process but through the inductive approach of studying an empirical case or object, and finally all theories are initially based on a case or object. This type of research has traditionally been considered soft, due to the inexistence of criteria that allow to evaluate its validity as a method to study a phenomenon in its real context. The present work is part of this theme and intends to contribute to diminish the perception about the lack of rigour associated with the case study. The present research focuses on the analysis of the methodological rigour of defended master's dissertations. In this context an investigation was undertaken of master's dissertations in the management area of a higher education management institution that use the case study as the research method. In order to achieve our goals, four hundred and forty-two master's dissertations defended in management scientific master's degrees in a Portuguese higher education institution which specializes in the area of economic and business sciences were analysed. The results of the qualitative research indicate that 40.0% of the dissertations focus on case studies. The results obtained lead us to conclude that in general the dissertations analysed present a high level of methodological rigour with respect to the object of study and a moderate level with respect to data analysis and results. The most critical aspects are the way data are collected, namely the use of multiple sources of evidence in less than half of the cases, which makes triangulation of data impossible.
 

SISTEM PEREKOMENDASI DENGAN SINGULAR VALUE DECOMPOSITION DAN TEKNIK SIMILARITAS PEARSON CORRELATION

The growth of e-commerce has resulted in massive product information and huge volumes of data. This results in data overload problems. In the case of e-commerce, consumers or users spend a lot of time choosing the goods they need. The urgent question to be answered at this time is how to provide solutions related to intelligent information restrictions so that the existing information is truly information that is by preferences and needs. This research performs information filtering by applying the singular value decomposition method and the Pearson similarity technique to the book recommendation system. The data used is the Book-Crossing Dataset which is the reference dataset for many research recommendation systems. The resulting recommendations are then compared with e-commerce recommendations such as amazom.com. Based on the results of the study obtained data that the results of the recommendations in this study are very good and accurate.

Assessing the quality of the evidence underlying recommendations in type 2 diabetes' commonly used clinical practice guidelines.

In a world of data overload, clinical practice recommendations are needed to help practitioners and patients to take evidence-based decisions. However, in the field of type 2 diabetes mellitus (T2DM) recommendations on glycemic goals and treatment choice are controversial in spite of being supported by a common body of evidence. We hypothesize that internal and external validity of this body of evidence might not be as sound as expected. The aim of the current study is to appraise the studies supporting recommendations developed by influential medical societies dealing with glycemic goals and the choice of pharmacological treatment in adults with T2DM. Clinical practice recommendations and their references were extracted out of eight documents developed by influential scientific societies between 2016 and 2019. Internal and external validity of each study was then appraised with standard tools and in duplicate. A total of 114 recommendations and their underlying 233 citations were extracted. Out of these 233 citations 81 (35%) corresponded to randomized controlled trials (RCT) and 45 (20%) to systematic reviews. After systematical appraisal only four RCT (5%) and eight systematic reviews (17%) were considered to be unflawed. Indirectness (lack of generalizability) was the most common caveat identified in RCTs. Out of the 114 recommendations analyzed (32 dealing with glycemic goals and 82 with treatment choice), only 21 (18.4%) were supported by at least one high-quality study. Only one in five recommendations regarding glycemic goals or pharmacological treatment choice in T2DM is based on at least one high-quality study. Clinical practice recommendations dealing with areas of uncertainty should be formulated more transparently to enable real evidence-based decisions.

The Impact of Randomized Algorithm over Recommender System

In today’s world, the amount of information is increasing drastically. There are 2.5 quintillion bytes of data generated each day at a current time. With this rapid increase of data, it is important to filter the huge amount of data from the existing unnecessary available storage. To handle this issue, the researchers have incorporated the concept of recommender system. Recommender system tries to filter the users’ data to come up with the exact amount of information required according to the needs and preferences of the users. Hence there is an increase in the demand of recommender system to tackle the data overload. However, the existing recommender systems do perform well but face some issues in the corporate world. Cold start, user bias, precision and speed are some of the major problems faced by recommender system which are yet to be addressed. To minimize such issues, we incorporate the concept of randomization such that random factor can be integrated in design of recommender systems. In this paper, we have also outlined the advantages as well as the procedure of using a randomized algorithm for recommender system. The proposed approach would provide a platform for researchers to enhance the recommender system using the randomized algorithms in the future.

An Implicit Preference‐Aware Sequential Recommendation Method Based on Knowledge Graph

Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference‐aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.

Diversified Personalized Recommendation Optimization Based on Mobile Data

With the advent of the Internet of Things, especially the Internet of Vehicles, abundant environmental and mobile data can be generated continuously. A personalized recommender system is one of the important methods for solving the problem of big data overload. However, to make use of these mobile data from vehicles, traditional recommender services are confronted by severe challenges. Therefore, we study the diversified recommendation problem based on a real-world dataset, represented as a tensor with three dimensions of user, location and activity. As the tensor is rather sparse, we employ tensor decomposition to predict missing values. Additionally, we directly regard recommendation precision as an objective. In addition to precision, we also consider the recommendation novelty and coverage, providing a more comprehensive view of the recommender system. Thus, visitors can discover attractive spots that are less visited in a personalized manner, relieving traffic pressure at famous scenic spots and balancing overall transportation. By integrating all these objectives, we construct a many-objective recommendation model. To optimize this model, we propose a distributed parallel evolutionary algorithm employing the nondominated ranking and crowding distance. Compared with the state-of-the-art algorithms, the proposed algorithm performs well and is very efficient.

A window to the future: Proposal for a lexicography-assisted writing assistant

Abstract The paper initially discusses some of the challenges posed to contemporary lexicography and stresses the need to move upstream in the value chain to guarantee future work. Today’s lexicographers must accept that their product par excellence is not dictionaries, but lexicographical data that can either be presented to the users in the form of dictionaries or be integrated into various types of tools, platforms, and services. From this perspective, the paper describes the functionalities of various digital writing assistants and focuses on one of them, namely the Spanish-English Write Assistant. It illustrates some decisions that have to be taken to prepare a database to feed both this tool and a series of online dictionaries. A proposal on how a big amount of lexicographical data can be presented in a small pop-up window without resorting to data overload will be discussed. In this connection, alternative ways of doing user testing in a digital environment are introduced. Finally, the paper stresses the importance of a human-centered design and terminology.

Data Reduction Approaches to Athlete Monitoring in Professional Australian Football.

To apply data reduction methods to athlete-monitoring measures to address the issue of data overload for practitioners of professional Australian football teams. Data were collected from 45 professional Australian footballers from 1 club during the 2018 Australian Football League season. External load was measured in training and matches by 10-Hz OptimEye S5 and ClearSky T6 GPS units. Internal load was measured via the session rate of perceived exertion method. Perceptual wellness was measured via questionnaires completed before training sessions with players providing a rating (1-5 Likert scale) of muscle soreness, sleep quality, fatigue, stress, and motivation. Percentage of maximum speed was calculated relative to individual maximum velocity recorded during preseason testing. Derivative external training load measures (total daily, weekly, and monthly) were calculated. Principal-component analyses (PCAs) were conducted for Daily and Chronic measures, and components were identified via scree plot inspection (eigenvalue > 1). Components underwent orthogonal rotation with a factor loading redundancy threshold of 0.70. The Daily PCA identified components representing external load, perceived wellness, and internal load. The Chronic PCA identified components representing 28-d speed exposure, 28-d external load, 7-d external load, and 28-d internal load. Perceived soreness did not meet the redundancy threshold. Monitoring player exposure to maximum speed is more appropriate over chronic than short time frames to capture variations in between-matches training-cycle duration. Perceived soreness represents a distinct element of a player's perception of wellness. Summed-variable and single-variable approaches are novel methods of data reduction following PCA of athlete monitoring data.

Arbel mediko baten erabileraren azterketa: nolako elkarrekintza halako portaera-bisuala

Osasun-arlorako softwarean, beste esparru askotan bezala, erabilgarritasuna erronka handia da; batetik, datuak ugariak eta konplexuak direlako eta, bestetik, erabilera-testuingurua kritikoa delako. Jakina da langile klinikoek informazio kopuru egokia eskatzen dutela beren zereginak aurrera eramateko, eta, zentzu horretan, erabiltzaile-interfaze moldagarriak oso baliagarriak izan daitezke informazio-behar horiek asetzeko nahiz informazio-gainkargaren arazoari heltzeko. Erabiltzaile-interfazerako egokitzapenak inplementatu aurretik, baina, aldakorrak diren informazio-behar horiek identifikatu eta lehenetsi egin behar dira. Begi-arakatzaileak lagungarriak izan daitezke zeregin hauek erdiesteko, erabiltzaileen portaera bisuala antzeman baitezakete, zeina interesaren adierazle den. Zoritxarrez, arakatzaile horiek sistema hedatu batean erabiltzeak azpiegitura konplexuegia eskatzen du. Ekarpen honetan aztertu dugu ea erabiltzaileen portaera bisuala inferi ote daitekeen erabiltzaile horiek botiken segurtasunari lotutako arbel batean izandako elkarrekintzaren datuetatik abiatuz. Emaitzek aditzera eman dute bi alderdiak, hots, saguaren bidezko elkarrekintza eta portaera bisuala (karga kognitiboa), lotuta daudela; ezaugarri hauen arabera neurtuak, hurrenez hurren: saguaren ondoz ondoko bi kliken arteko denbora-tarteak eta sagu-pausatzeak, batetik, eta, bestetik, begiradaren finkapenaren iraupenak. Lanak eztabaidatzen du zer eragin izan dezakeen aurkikuntza horrek arbel medikoen egokitzapenen diseinuan.

Automatic milking systems and farmer wellbeing–exploring the effects of automation and digitalization in dairy farming

Abstract In recent years the concept of wellbeing, encompassing an individual's satisfaction with different aspects of their life, has received increased attention in literature. However, we find few studies of wellbeing in dairy farming. Therefore, the aim of this paper is to explore the wellbeing of Norwegian dairy farmers with automatic milking systems, and which factors are associated with wellbeing. We explore four dimensions of wellbeing; income, job satisfaction, mental health and family work balance. Data were collected from 739 Norwegian farmers using automatic milking systems. Linear regression, exploratory factor analysis and structural equation modelling were used to analyse the data. The results show that the following factors are associated with farmer wellbeing; gender, education, having a successor and colleagues, herd size and experience with automatic milking systems, together with training in use of the system and access to counselling. Our findings show that while being a female farmer is strongly associated with better family-work balance, it is negatively associated with mental health. Furthermore, we find that training in AMS, better management systems which avoids data overload, and access to extension services and colleagues are important for farmer wellbeing. Finally, our findings show that having a successor increases wellbeing, while increasing herd size reduces wellbeing. The findings have implications for farmers, extension services and for suppliers of automated systems to farmers. Suppliers and advisory services need to provide adequate training in AMS. Future advisory service models could offer back-office services monitoring key performance variables and help farmers interpret data for better decision making. Suppliers, preferrably in collaboration with extension services, should improve the AMS management software to avoid farmer technostress. Furthermore, suppliers and advisory services should facilitate networking among AMS-farmers to promote their wellbeing.

Role of wearable rhythm recordings in clinical decision making-The wEHRAbles project.

BackgroundMultiple wearable devices for rhythm analysis have been developed using either photoplethysmography (PPG) or handheld ECG.HypothesisThe aim of this survey was to assess impact of these technologies on physicians' clinical decision‐making regarding initiation of diagnostic steps, drug therapy, and invasive strategies.MethodsThe online survey included 10 questions on types of devices, advantages, and disadvantages of wearable devices as well as case scenarios for patients with supraventricular arrhythmias and atrial fibrillation (AF).ResultsA total of 417 physicians (median age 37 [IQR 32‐43] years) from 42 countries world‐wide completed the survey.When presented a tracing of a regular tachycardia by a symptomatic patient, most participants would trigger further diagnostic steps (90% for single‐lead ECG vs 83% for PPG, P < .001), while a single‐lead ECG would be sufficient to perform an invasive EP study in approximately half of participants (51% vs 22% for PPG, P < .001).When presented with a single‐lead ECG tracing suggesting AF, most participants (90%) would trigger further diagnostic steps. A symptomatic AF patient would trigger anticoagulation treatment to a higher extent as an asymptomatic patient (59% vs 21%, P < .001). PPG tracings would only rarely lead to therapeutic steps regardless of symptoms.Most participants would like scientific society recommendations on the use of wearable devices (62%).ConclusionsTracings from wearable rhythm devices suggestive of arrhythmias are most likely to trigger further diagnostic steps, and in the case of PPG recordings rarely therapeutic interventions. A majority of participants expect these devices to facilitate diagnostics and arrhythmia screening but fear data overload and expect scientific society recommendations on the use of wearables.