Message Count Research Articles

Secure Messaging Use and Wrong-Patient Ordering Errors Among Inpatient Clinicians

Use of secure messaging for clinician-to-clinician communication has increased exponentially over the past decade, but its association with clinician work is poorly understood. To investigate the association between secure messaging use and wrong-patient ordering errors. This cohort study included inpatient attending physicians, trainee physicians, and advanced practice practitioners (APPs) from 14 academic and community hospitals. Secure messaging volume was assessed over a 3-month period (February 1 to April 30, 2023). Secure messaging volume per clinician-day, measured as the count of secure messages sent and received by a clinician on a given clinician-day. Retract-and-reorder events were used to identify wrong-patient ordering errors, and the presence of any retract-and-reorder event on a clinician-day was the primary outcome. Multilevel logistic regression was used to examine the association between secure messaging volume and wrong-patient ordering errors after adjusting for clinician age, sex, patient load, order volume, and clinical service. A total of 3239 clinicians (median [IQR] age, 37 [32-46] years; 1791 female [55.3%]; 1680 attending physicians [51.2%], 560 trainee physicians [17.3%], and 999 APPs [30.8%]) with 75 546 clinician-days were included. Median secure messaging volume was 16 (IQR, 0-61) messages per day. Retract-and-reorder events were identified on 295 clinician-days (0.4%). Clinicians with secure messaging volume at the 75th percentile had a 10% higher odds of wrong-patient ordering errors compared with those at the 25th percentile (odds ratio [OR], 1.10; 95% CI, 1.01-1.20). After stratifying by clinician role, the association between secure messaging and wrong-patient ordering errors was observed only for attending physicians (OR, 1.20; 95% CI, 1.02-1.42) and APPs (OR, 1.18; 95% CI, 1.00-1.40). In this cohort study of inpatient clinicians, higher daily secure messaging was associated with increased odds of wrong-patient ordering errors. Although messaging may increase cognitive load and risk for wrong-patient ordering errors, these results do not provide conclusive evidence regarding the direct impact of secure messaging on errors, as increased messaging may also reflect greater care coordination, increased patient complexity, or communication of the presence of a wrong-patient ordering error.

Behaviors and Perceptions of Human-Chatbot Interactions Based on Top Active Users of a Commercial Social Chatbot

Natural language processing is enabling machines to communicate with humans naturally, yet the dynamics of extended user-chatbot interactions remain much unexplored. This study characterizes the conversational styles, demographics, psychologies, and emotional tendencies of the most active users (i.e., top 1% by message count) of a commercial chatbot platform (SimSimi.com), whom we refer to as superusers. We analyzed the linguistic patterns and topics of 1,988,971 messages written by 1,994 superusers over a period of three years. We further surveyed 76 users to observe their emotional dispositions and perceptions towards the chatbot. We find that SimSimi superusers empathize and humanize the chatbot more than less active users, and they show a higher tendency to share personal and negative feelings. Our findings suggest that chatbots require new design considerations for users who are vulnerable due to their high anthropomorphism and openness toward machines. Our work also shows that chatbots should have functions to offer social support when necessary.

Leveraging lightweight blockchain for secure collaborative computing in UAV Ad-Hoc Networks

Unmanned Aerial Vehicle (UAV) Ad-Hoc Networks (UANET) enable collaborative work among UAVs for versatile task execution, but they face security challenges due to physical vulnerabilities, software issues, and dynamic wireless networks. This paper proposes a secure collaborative computing framework based on blockchain technology. Specifically, we first design a lightweight blockchain scheme suitable for UANET and present an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on trust evaluation, aiming to reduce consensus overhead and establish trust relationships among UAVs. Furthermore, we devise a smart contract-based UAV task allocation strategy that considers both task execution efficiency and offloading security. This strategy enables UAVs to make optimal task-offloading decisions and facilitates collaborative computing according to smart contract rules. Simulation results demonstrate that our proposed consensus algorithm reduces average consensus latency by 47% and message count by 76% compared to PBFT-based algorithms. Additionally, the task allocation strategy decreases task costs by 48% compared to local computing strategies, achieving efficient and secure collaboration among UAVs in UANET. The real-world experiments with a UAV swarm further validate the efficiency and security of our framework, confirming its practical applicability in UANET.

Semantic SVM: Text and Media Analyzer

Chat analyzers are invaluable tools for understanding communication habits and patterns in digital conversations. This research paper presents a comprehensive overview of a chat analyzer’s capabilities, emphasizing its ability to provide insights into total message count, word usage, emoji frequency, and shared links. Furthermore, it explores the analyzer’s capacity to present data in various formats such as monthly timelines, identifying the most active days and months. Additionally, it examines weekly activity trends, shedding light on users’ engagement levels over time. The paper underscores the significance of such tools in enabling users to track their chat history effectively, offering valuable insights into their communication behaviors.

Semantic SVM: Text and Media Analyzer

Chat analyzers are invaluable tools for understanding communication habits and patterns in digital conversations. This research paper presents a comprehensive overview of a chat analyzer’s capabilities, emphasizing its ability to provide insights into total message count, word usage, emoji frequency, and shared links. Furthermore, it explores the analyzer’s capacity to present data in various formats such as monthly timelines, identifying the most active days and months. Additionally, it examines weekly activity trends, shedding light on users’ engagement levels over time. The paper underscores the significance of such tools in enabling users to track their chat history effectively, offering valuable insights into their communication behaviors.

A Bayesian approach using spatiotemporal features for suitable next hop selection in opportunistic networks

SummaryOpportunistic network (OppNet) belongs to the category of Mobile Ad‐hoc Networks (MANETs), a kind of Delay Tolerant Network (DTN), where the wireless nodes are completely mobile and the data transmission routes are dynamic. The major challenge in developing a routing model for such a network is the unpredictable nature of the movement of the nodes. In this paper, a spatiotemporal prediction model based on human mobility pattern is proposed using Bayesian posterior probability (BPPR) where several clusters are identified within the network and the day and time duration of nodes visiting those clusters are recorded. The Bayesian posterior probability is then used to determine the probability of the neighbor node visiting the destination's cluster. If the calculated probability for that node is higher than a specified threshold, the packet will be forwarded. A comparison of the results obtained on simulation is made with benchmark models—Epidemic, Prophet, HBPR, EDR, NexT, and EBC, to name a few, where it is found that on average the proposed model outperforms the other models in terms of delivery probability, hop count and number of messages dropped by around 23.89%, 24.8%, 24.4%, 37%, 11%, and 42% respectively, with varying number of nodes, TTL, message generation interval, and buffer size. Similar improvements have been observed in terms of the other two metrics. In terms of overhead ratio, the proposed model outperforms Epidemic, Prophet, HBPR, NexT, and EBC. However, as the number of nodes and TTL are varied, BPPR performs better than NexT by around 9% and 12%, respectively, in terms of average latency.

AI-Enabled Blockchain Consensus Node Selection in Cluster-Based Vehicular Networks

In the scenario of highly mobile nodes, applying blockchain not only needs to ensure high performance and security but also needs to consider the low stability of consensus nodes. We introduce a novel framework to reduce the negative impact of low-stable consensus nodes. In addition, we have developed an intelligent algorithm for selecting the optimal number of Mobile Consensus Nodes (MCNs) considering various parameters including Stability of Node (SoN). The results demonstrate that the proposed scheme improves the average reputation and stability of consensus nodes by 6.8% and 17.5%, respectively while reducing the average message counts by 33.9%.

Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.

Impact of Dispense Tracking Software on Inpatient Pharmacy Operations.

Background: When medications dispensed from a hospital inpatient pharmacy aren't able to be found at their intended destination (ie, a missing dose), this can result in delayed medication administration and rework to redispense the medication. Technology advancements in the medication use process have led to development of dose-tracking software that has the capability to track medication doses throughout the medication use cycle and document a medication's location to its destination. Objective: The primary objective of this study was to evaluate the impact of dose-tracking software on the number of inpatient pharmacy redispenses and nursing requests for missing medications. Secondary objectives included pharmacy staff satisfaction with dose-tracking software, its impact on workflow and patient safety, and compliance with dose-track scanning. Methods: The study design was a prospective, pre-post implementation to compare the requests for missing doses and associated dispenses of injectable medications during the set evaluation period. Dose-track scanning compliance data was collected and evaluated. A survey was also administered to staff to evaluate employee perception and satisfaction with usability and value of the software. Results: During the preimplementation period, 40 021 injectable doses were dispensed, and 9841 (24.6%) were documented as redispensed doses. After dose-tracking implementation, 42 975 total injectable doses were dispensed with 9839 (22.9%) being redispensed. The count of medication messages was 10 661 in the preperiod and 11 475 in the postperiod. The data were normalized using case mix index (CMI) and patient days to account for variation in severity of illness. Conclusion: Implementation of dose-tracking software showed a decrease in the percentage of redispensed injectable medications.

Characterizing Patient-Clinician Communication in Secure Medical Messages: Retrospective Study.

BackgroundPatient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care.ObjectiveThe aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester.MethodsWe performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity.ResultsDuring 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely “physician—primary care,” “registered nurse—specialty,” and “physician—specialty,” bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The “nurse practitioner or physician assistant—primary care” and “physician—primary care” categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds.ConclusionsOur study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients’ needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

Designing Online Class using Discord based on Community of Inquiry Framework

COVID-19 pandemic changes the landscape of education, where online learning becomes very important and can not be avoided. Therefore, this article discusses how a gamer's platform, Discord can be used as an interactive online class. A comparison of Discord with other online platforms is spelled out in a tabular form. The principle used to design the online class using Discord is based on Community of Inquiry (CoI). CoI consists of three elements which are teaching presence, social presence, and cognitive presence for a better educational experience. The design discussed in this article are Discord features design for the online class and class activity using Discord in developing the three elements in CoI. Data collection is done using open-ended surveys. They are 58 respondents with engineering and non-engineering students. Most of the students are familiar with Discord and agreed that Discord can be adapted for an online class. Thematic analysis is conducted to analyze the open-ended questions. The themes that can be identified are parallel channels, structure, all-in-one platform, facilitation, and learning environment. Another analysis is message counts in each student group to show how active the students in online class using Discord. In conclusion, Discord is the best platform to make students active and construct knowledge with peers.

Topic Modeling as a Tool for Analyzing Library Chat Transcripts

Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it impractical for librarians to go beyond simple quantitative analysis (e.g., chat duration, message count, word frequencies) with existing tools. As a stepping-stone toward a more sophisticated chat transcript analysis tool, this study investigated the application of different types of topic modeling techniques to analyze one academic library’s chat reference data collected from April 10, 2015, to May 31, 2019, with the goal of extracting the most accurate and easily interpretable topics. In this study, topic accuracy and interpretability—the quality of topic outcomes—were quantitatively measured with topic coherence metrics. Additionally, qualitative accuracy and interpretability were measured by the librarian author of this paper depending on the subjective judgment on whether topics are aligned with frequently asked questions or easily inferable themes in academic library contexts. This study found that from a human’s qualitative evaluation, Probabilistic Latent Semantic Analysis (pLSA) produced more accurate and interpretable topics, which is not necessarily aligned with the findings of the quantitative evaluation with all three types of topic coherence metrics. Interestingly, the commonly used technique Latent Dirichlet Allocation (LDA) did not necessarily perform better than pLSA. Also, semi-supervised techniques with human-curated anchor words of Correlation Explanation (CorEx) or guided LDA (GuidedLDA) did not necessarily perform better than an unsupervised technique of Dirichlet Multinomial Mixture (DMM). Last, the study found that using the entire transcript, including both sides of the interaction between the library patron and the librarian, performed better than using only the initial question asked by the library patron across different techniques in increasing the quality of topic outcomes.

PD03-07 EVALUATING MEDICAID UROLOGIC REFERRAL PATTERNS VIA ECONSULT IN A DIVERSE NON-INTEGRATED NETWORK OF COMMUNITY-BASED HEALTH SYSTEMS

You have accessJournal of UrologyHealth Services Research: Practice Patterns, Quality of Life and Shared Decision Making I (PD03)1 Sep 2021PD03-07 EVALUATING MEDICAID UROLOGIC REFERRAL PATTERNS VIA ECONSULT IN A DIVERSE NON-INTEGRATED NETWORK OF COMMUNITY-BASED HEALTH SYSTEMS Desiree Sanchez, Alvin Kwong, Waheed Baqai, Sajid Ahmed, and Stanley Frencher Desiree SanchezDesiree Sanchez More articles by this author , Alvin KwongAlvin Kwong More articles by this author , Waheed BaqaiWaheed Baqai More articles by this author , Sajid AhmedSajid Ahmed More articles by this author , and Stanley FrencherStanley Frencher More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001967.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Most patients in the US receive urologic care in their local communities; yet, the majority of published urologic patient data is derived from academic/academic-affiliated centers or large registries. This is problematic given the lack of racially diverse and underserved populations within academic cohorts, and inability to access granular data within registries. Telemedicine services like electronic consultation (eConsult), an electronic, asynchronous provider-specialist consultation system, are now used across various health systems including California’s largest Medicaid network, the Inland Empire Health Plan (IEHP). The IEHP mandates eConsult use for all specialty referrals for its 1.2 million patients. Here, we present the largest analysis of eConsult use in urology and characterize referral patterns in the safety net from a diverse non-integrated network of community-based health systems across Southern California communities. METHODS: We retrospectively reviewed all patients referred via eConsult from 2018-2020. Our main objective was to perform a descriptive analysis of all urologic eConsults within the IEHP since inception of the program. We categorized consult type etiology and measured the final eConsult outcome recommendation (e.g., face-to-face referral, no further action etc). To assess efficiency we measured message count, urologist response time and time to eConsult close. RESULTS: From 5/2018-8/2020, 1205 patients were referred for urologic eConsult. Median age was 53 (IQR 39, 61). Most patients were male (60%). BPH and/or LUTS or Retention and Gender-confirming surgery were the most and least common referral types, respectively (Figure 1). Majority of eConsults resulted in face-to-face recommendation (75%). Median total message count was 2 (IQR 2,3). Urologists responded within 0.4 days (IQR 0.1, 1.2) and spent <5 min to complete an eConsult; eConsults were closed in <1.5 days (IQR 0.3, 8.1). CONCLUSIONS: We report the largest analysis of eConsult use in urology. Its use allows for timely, efficient urologic access in the IEHP and presents a means to assess urologic needs in the community. We had a high rate of in-person conversion suggesting the need to study eConsult implementation and optimization strategies. Source of Funding: HH Lee Research Grant. © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e45-e45 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Desiree Sanchez More articles by this author Alvin Kwong More articles by this author Waheed Baqai More articles by this author Sajid Ahmed More articles by this author Stanley Frencher More articles by this author Expand All Advertisement Loading ...

FTTCNF- Novel Approach for Fault Tolerant Topology Control in Manet

In wireless networks it is observed that as nodes move unpredictably sudden link disconnections occur during transmission. This leads to frequent path changes and multiple path discoveries which subsequently increase transmission of control packets in network. The nodes in the network simply rebroadcast the received route request (RREQ) packet if they do not have the route to the required destination. In addition to this, frequent hello messages for neighbour set construction and maintenance also increase control message count in the network causing a flooding issue. In order to mitigate these problems, the proposed Fault Tolerant Topology Control Neuro Fuzzy method (FTTCNF), incorporates measures to improve the network stability and to reduce the control packets in the network. These measure 1.reduce control message transmissions among neighbours by finding a stable path 2. neighbour node distance is computed based on the reception of a signal strength Indication (RSSI), 3. path stability is decided by the link expiry time (LET) which can be used to predict the neighbour mobility deviations. These factors, ( above mentioned distance, path stability factor PSF, and LET) are subjected to the fuzzification process to identify the fuzzy rule values and are given as input to the neuron formation stage. Final neuron value is computed for all available paths and the maximum value path is chosen for data transmission. Energy level monitoring is also applied at each node to check the node’s current energy and should it go below the energy threshold level the node by itself, joining the routing process is avoided. Simulation results have proved that the proposed method significantly reduces the routing overhead and improves the stability of path during data transmission.

WITHDRAWN: Integration of blockchain and IoT for data storage and management

Abstract Blockchain is a fast-evolving, encrypted, immutable and decentralized technology. It is used to store different data types such as transactions, at its core. But it's not about the cryptomonetary transactions. It can also be used to store many other items, such as properties, IoT or even multimedia content, such as songs, photos and videos. On HyperLedger Fabric Blockchain, we are managing IoT info. We collect and securely send data from IoT sensors to our HyperLedger Blockchain node using the MQTT protocol. We process the data and link it to our ledger after collecting data from the sensor. We also assess our network’s efficiency, taking into account different parameters such as timeout for batch, batch size and message count.

A Novel Approach For Energy Optimization In Distributed Databases in Wireless Network Applications

Energy consumption is a significant factor in wireless networks with battery-powered nodes and transceivers as a dominant energy consumer .Also mutual exclusion is a fundamental problem in distributed systems.“Mutual exclusion” allows only one process at a time to access the shared resource and the others have to be locked and wait for their turns.. Retaining mutual exclusion, is proposed as a solution to guarantee the correctness of read/written information from/in the shared resource or critical sections (CS).Wireless Network is an unstable network formed dynamically by a connection of wireless mobile nodes without the use of an existing network substructure. Since energy consumption during communication is a major depletion parameter the number of communication must be reduced as much as possible to achieve extended battery life. Since battery technology does not grow as rapidly as CPU or memory does so there is a strong need for the presence of protocols which are as energy efficient as effective.In this paper we present a new fully distributed token–based mutual-exclusion algorithm for clustered Mobile ad-hoc network with message count reduction purpose which eventuates to power optimization. Therefore we use Raymond's algorithm (which is well-known in stepping down message complexity) within the clusters and perpetual Mobility of token (that is so efficient in high load network such as Manet's) between the clusters.

A Novel Approach For Energy Optimization In Distributed Databases In Wireless Network Applications

Energy consumption is a significant factor in wireless networks with battery-powered nodes and transceivers as a dominant energy consumer .Also mutual exclusion is a fundamental problem in distributed systems.“Mutual exclusion” allows only one process at a time to access the shared resource and the others have to be locked and wait for their turns.. Retaining mutual exclusion, is proposed as a solution to guarantee the correctness of read/written information from/in the shared resource or critical sections (CS).Wireless Network is an unstable network formed dynamically by a connection of wireless mobile nodes without the use of an existing network substructure. Since energy consumption during communication is a major depletion parameter the number of communication must be reduced as much as possible to achieve extended battery life. Since battery technology does not grow as rapidly as CPU or memory does so there is a strong need for the presence of protocols which are as energy efficient as effective.In this paper we present a new fully distributed token–based mutual-exclusion algorithm for clustered Mobile ad-hoc network with message count reduction purpose which eventuates to power optimization. Therefore we use Raymond's algorithm (which is well-known in stepping down message complexity) within the clusters and perpetual Mobility of token (that is so efficient in high load network such as Manet's) between the clusters.

Converting to Connect: A Rapid RE-AIM Evaluation of the Digital Conversion of a Clerkship Curriculum in the Age of COVID-19.

With the advent of the 2019 coronavirus pandemic, a decision was made to remove medical students from clinical rotations for their own safety. This forced students on a core emergency medicine (EM) rotation at McMaster University to immediately cease all in-person activities. An urgent need for a virtual curriculum emerged. A virtual curriculum consisting of asynchronous case-based learning on Slack, ask-me-anything webinars, and online e-modules was created to fill the need. We describe a program evaluation using the RE-AIM framework and a social networking analysis of participants. Medical students (n=23) and 11 facilitators (five residents, six faculty members) participated in this pilot study. Faculty members sent a mean (±SD) of 115 (±117) messages (n=6), and mean (±SD) message counts for students and residents were 49.96 (±25; n=23) and 39 (±38; n=5), respectively. A total of 62,237 words were written by the participants, with a mean of 1,831 per person. Each message consisted of a mean (±SD) of 25 words (±29). Students rapidly acquitted themselves to digital technology. Using the RE-AIM framework we highlight the feasibility of a virtual curriculum, discuss demands on faculty time, and reflect on strategies to engage learners. The use of asynchronous digital curricula creates opportunities for faculty-resident interaction and engagement. We report the successful deployment of a viable model for undergraduate EM training for senior medical students in the COVID-19 era of physical distancing.

A Fast Lock for Explicit Message Passing Architectures

Synchronization is a crucial issue for multi-threaded programs. Mutex locks are widely used in legacy programs and are still popular for the intuition semantics. The SW26010 architecture, deployed on the supercomputer Sunway TaihuLight, introduces a hardware-supported inter-core message passing mechanism and exposes explicit interfaces for developers to use its fast on-chip network. This emerging architectural feature brings both opportunities and challenges for mutex lock implementation. However, there is still no general lock mechanism, especially designed and optimized for architectures with this new feature. In this article, we propose mLock, a fast lock designed and optimized for architectures that support Explicit inter-core Message Passing (EMP). mLock uses partial cores as lock servers and leverages the fast on-chip network to implement high-performance mutual exclusive locks. In this article, we propose a series of novel techniques to improve the performance of EMP locks. First, we propose the concepts of chaining lock and hierarchical lock to reduce message count and mitigate network congestion. Second, we propose a fair lock approach to improve the fairness of EMP locks. Third, server reusing is introduced to reduce the number of lock servers. We implement and evaluate mLock on an SW26010 processor. Experimental results show that our proposed techniques can improve the performance of EMP locks by up to $16.2\times$ 16 . 2 × over a basic design.

Predictors of Chat Usage in an App-Based Health Coaching Platform: Implications for Treatment Program Planning

Introduction: Digital healthcare with smartphone application is a promising approach to bridge the gap between hospital and community care by facilitating preventive health behavior and chronic illness management. Such technology allows healthcare professionals to integrate online coaching and consultation as part of the treatment process.Objectives: This study seeks to understand the factors predicting users’ usage of the chat function in an app-based health coaching platform.Methods: A total of 394 employees from an oil and gas company participated in health screening as part of their human resource initiatives for employee wellness. Demographic variables, biometrics, psychometrics, and other variables were collected as part of the screening. Employees with existing chronic illness (i.e. diabetes and cardiovascular disease) or the risk of developing one, as indicated by their body mass index ([BMI] &gt; 27), blood pressure (&gt;140/90), HbA1c (&gt; 5.9), cholesterol (&gt; 5.2), and depression/ anxiety/ stress levels (moderate to severe), are enrolled into a 4-month online coaching program. Pearson correlation was performed between all the variables, together with users’ total chat count in four months. Subsequently, significant variables were entered into a multiple linear regression model using the forward method, with total message count as the dependent variable.Results: The analysis included 278 sample who have been offered the coaching program. Significant zero-order correlation was found between total chat count, waistline, family history of high cholesterol, self-identified areas of improvement, ongoing medical follow up, and cholesterol level. At the multivariate level (R2 = .119, F [1, 198] = 26.62, p &lt; .001), only total number of self- identified areas of improvement was found to be significant (β = .34, p&lt;.01).Conclusion: This study shows that the number of self-identified areas of improvement is an important factor that predicts user chat activity in online health coaching.International Journal of Human and Health Sciences Supplementary Issue: 2019 Page: 29