Actual Usage Data Research Articles

Understanding the elderly and their next of kin’s usage of mobile personal safety service

Purpose This study aims to understand elderly people and their family members’ perceptions and usage of a mobile personal safety service. Design/methodology/approach The study adopts both interviews and analysis of actual usage data to cross-validate the findings. Four family groups and 11 subjects participated in the study. Findings Elderly people are willing to learn to use the mobile safety service, and some elderly people explore more features than their younger family members. Family support facilitates their learning. Elderly people feel psychologically secure when using the mobile safety service. Privacy concerns are mitigated because they consider location sharing is necessary for the service. Research limitations/implications The current sample is small, as it involves collection of both interview data and actual usage data. Considering the emergency of the mobile personal safety service, the study is exploratory. Practical implications The findings suggest that elderly people are willing to learn and use technology such as the mobile safety service, which is relevant to their daily lives. Designers may think about how to highlight the relevancy aspect of technologies in elderly peoples’ lives. Originality/value This study is one of the earliest studies about elderly people’s usage of mobile safety service. This study reveals that elderly people are willing to learn to use the mobile safety service and explore its features. Technical support from family members and relevancy of the service in their daily lives may encourage them to use the technology.

Understanding Usage Behavior of Different Mobile Application Categories Based on Personality Traits

Abstract This study assessed the influence of personality traits on individuals’ mobile application usage behavior in different application categories. In the assessment of personality, this study used the well-known Big Five personality traits taxonomy. The Big Five personality traits were measured by using the Mini-International Personality Item Pool scale. Rather than using participants’ self-reports about their app usage behavior, this study utilized actual app usage data that were collected via app usage tracker installed in the participants’ mobile devices. Instead of focusing on a single category of app, this study assessed the participants’ actual behavior across 10 broad and distinct categories of apps. This study showed that personality traits do play an important role in usage behavior of different mobile application categories. Results of this research show that individuals who are extravert and open to experience are more likely to spend time using photography and video editing-related mobile apps. While emotionally stable individuals are less likely to access social networking apps, conscientious individuals tend to stay away from e-commerce-related apps. This research also found that agreeableness is negatively related with access and usage of health and lifestyle mobile apps as well as Internet searching and browsing apps. Application developers and marketers can use the findings of this research to increase users’ adoption and usage rate of their apps.

Placement of Charging Infrastructures for EVs using K-Mean Algorithm and its Validation using Real Usage Data

Global climate change is affecting human life more seriously than ever before. Countries around the world have identified cars as a significant source of pollution, leading to an increased interest in eco-friendly automobiles. Electric vehicles (EVs), which are characteristically eco-friendly, have become a choice for future transportation system. In this paper, we proposed a strategy for determining the appropriate placement of large-scale charging stations using K-mean algorithm. Also, the initial results were validated utilizing actual electricity usage data from the existing chargers. Currently in Korea, a typical public charging station has two to three chargers to support EV users. As EVs become more popular, new problems arise, such as charger hopping and/or long waiting lines. In order to address these new issues, a large-scale charging station concept which houses more than ten chargers, is suggested. In doing so, a strategic approach for selecting close-to-ideal locations for the charging stations is introduced to maximize the charging station’s effectiveness. In this study, Jeju Island, Korea, which has many EVs, was used as a testbed. With the wealth of EV chargers and their usage data, initial validation of the proposed methodology was made possible. During the evaluation of the best possible locations for the largescale charging stations, we considered the locations of tourist attractions and convenient support facilities, as well as the population. After the evaluations, the proposed locations were validated using actual long-term charger usage data on Jeju Island. The demonstrated strategy for identifying appropriate locations for large-scale charging stations can be used by other tourist heavy islands or even small countries.

Developing personas & use cases with user survey data: A study on the millennials’ media usage

The aim of the study is to investigate the characteristics of millennials on the usage of media (music, photo/video) and media-related devices. An online survey was conducted on 360 millennials. Based on the results, we developed representative use cases to be used in experience design projects based on the actual media usage data collected. We also classified the millennials into four specific groups by applying statistical cluster analysis techniques. The characteristics of each group were further identified. The results of this study will help designers to develop products and services that meets the needs of their target consumers.

Short-Term Energy Prediction for District-Level Load Management Using Machine Learning Based Approaches

This research explicates an analysis of short-term energy requirement forecasting for district-level by applying machine learning (ML) models. Two ML models employed for energy forecasting which are: I) One-step secant backpropagation neural network (OSSB-NN); and II) BFGS Quasi-Newton backpropagation (BFGS-QNB) neural network. The 7-day actual energy usage and environmental data are used for energy forecasting. The Pearson method is used for optimizing the sets of input feature variables. Furthermore, the input feature parameters sets are classified into two basic parts: i) feature selection-I (FS-I); and FS-II. FS-I comprises the seven environmental parameters, including various limits of energy consumption data sets and FS-II contains the different sixteen parameters including the environmental variables. The Grubbs model is used for the outlier’s detection of the datasets. Different hidden neurons of the models are selected to measure the forecasting performance at different epochs and mean absolute error (MAE). To measure the forecasting accuracy of the models, coefficient of variation (CV) and mean absolute percentage error (MAPE) indexes are applied. The best MAPE and CV of OSSB-NN and BFGS-QNB at FS-II state are 0.727%, 0.496% and 1.011%, 0.667% respectively, when the four hidden neurons are selected. The ML algorithm’s accuracy is validated and compared with existing Levenberg-Marquardt backpropagation (LMB) model. It is demonstrated that including the utilization of FS-II as one of the model’s input variables, and comparison with the LMB model, the energy forecasting performance is precise, accurate and can be increased.

Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data

BackgroundPersonal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.ObjectiveBy analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.MethodsWe gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.ResultsThe total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant.ConclusionsAlthough a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.

Maintaining accurate web usage models using updates from activity diagrams

ContextMarkov operational profile (Markov OP) is a type of usage models for large applications involving state transitions. Such usage models not only help us ensure and maximize product reliability, but can also be used to understand user behavior, and fine-tune system performance and usability. Web usage models can be constructed based on actual usage of the application by target users recorded in existing web logs. Such models constructed before maintenance and evolution may not reflect actual usage of the updated application accurately. At this point, the updated web application has not been deployed yet, so that its actual usage data could not be collected to construct a new Markov OP needed to test the newly updated web application. ObjectiveThis paper aims at maintaining accurate Markov OP using updates derived from activity diagrams used in web application maintenance and evolution. MethodMarkov OP shares some common characteristics with activity diagrams which describe the application in terms of user activities. We develop a method to update the initial Markov OP by analyzing its differences with the activity diagrams. ResultsWe have applied our method in a web application to provide an initial validation of its applicability and effectiveness. After the deployment of the updated web application and new usage data became available, a new Markov OP was constructed. We quantified inaccuracies of the initial Markov OP and the updated Markov OP using the new Markov OP as the reference standard, and quantitatively demonstrated that our method improves the accuracy of the initial Markov OP for the updated web application. ConclusionOur new method provides an effective and practical way to maintain accurate Markov OP over web application maintenance and evolution using existing activity diagrams.

The Impact of Time-of-Use (TOU) Rate Structure on Consumption Patterns of the Residential Customers

Load participation is vital for the smart grid development. As an effective tool to improve reliability, stability, and financial efficiency of the power grids, demand response (DR) has brought significant financial and technical benefits to power systems. As one of the price-based DR programs with less control costs, the time-of-use (TOU) program has been applied as the default rate structure by many utility companies. To avoid financial risks and make the most profit from the market, utility companies treat TOU as an effective strategy to change customers’ electricity consumption patterns. As reported in many literature, existing TOU programs are not as effective as expected in many developed countries due to the complexity of human behaviors and disparities of residential customers. To examine whether to obtain different outcomes of TOU on the residential customers in developing countries, actual utility usage data from residential consumers in Shanghai, China, are analyzed in this paper. The result shows current TOU in Shanghai, China, has similar trends as TOU in developed countries. With high penetration level of renewable energy, an effective TOU program is urgently needed in the utility industry. In recent years, a creative TOU pricing structure has been introduced at the Electrical Reliability Council of Texas deregulated market, and it shows that the introduced “zero pricing” strategy has a significant impact on customers’ consumption patterns. The purpose of this research is to examine the key reasons that underlie ineffectiveness/effectiveness of TOU programs at residential level.

Alarm Limits for Intraoperative Drug Infusions: A Report From the Multicenter Perioperative Outcomes Group.

Continuous medication infusions are commonly used during surgical procedures. Alarm settings for infusion pumps are considered important for patient safety, but limits are not created in a standardized manner from actual usage data. We estimated 90th and 95th percentile infusion rates from a national database for potential use as upper limit alarm settings. We extracted infusion rate data from 17 major hospitals using intraoperative records provided by Multicenter Perioperative Outcomes Group for adult surgery between 2008 and 2014. Seven infusions were selected for study: propofol, remifentanil, dexmedetomidine, norepinephrine, phenylephrine, nitroglycerin, and esmolol. Each dosage entry for an infusion during a procedure was included. We estimated the 50th, 90th, and 95th percentile levels for each infusion across institutions, and performed quantile regression to examine factors that might affect the percentiles rates, such as use in general anesthesia versus sedation. The median 90th and 95th percentile infusion rates (with interquartile range) for propofol were 150 (140-150) and 170 (150-200) μg/kg/min. Quantile regression demonstrated higher 90th and 95th percentile rates during sedation for gastrointestinal endoscopy than for all surgical procedures performed under general anesthesia. For selected vasoactive medications, the corresponding median 90th and 95th percentile rates (with interquartile range) were norepinephrine 14.0 (9.8-18.1) and 18.3 (12.6-23.9) μg/min, and phenylephrine 60 (55-80) and 80 (75-100) μg/min. Alarm settings based on infusion rate percentile limits would be triggered at predictable rates; ie, the 95th percentile would be exceeded and an alarm sounded during 1 in 20 infusion rate entries. As a result, institutions could establish pump alarm settings consistent with desired alarm frequency using their own or externally validated usage data. Further study will be needed to determine the optimal percentile for infusion alarm settings.

An evidence-based approach for investment in rapid-charging infrastructure

To date, real cost data for Electric Vehicle (EV) rapid charging infrastructure is largely missing in the literature, preventing development of economic models to encourage private investment and limiting policy decisions. A business model has been constructed using actual capital expenditure, operating costs and usage data from the Rapid Charge Network project (RCN) which can be used to assist future investment and policy decisions. The model is run under a wide spectrum of EV uptake scenarios to provide plausible answers to a variety of research, policy and investment questions, including minimum growth rates to break even under current policy. Using real-world data we have confirmed that a financial business opportunity does exist for investment in rapid chargers on main highways and have identified the operating area in which a profit can be made. However, since UK EV adoption is still at the Innovators stage in a niche market where innovations in technology, user practices, supporting infrastructure and functionality are still required to achieve wide user acceptance, the case is also made for continued fiscal incentives to encourage investment in rapid-charging infrastructure.

Potential for large-scale solar collector system to offset carbon-based heating in the Ontario greenhouse sector

ABSTRACTIn the Ontario greenhouse sector the misalignment of available solar radiation during the summer months and large heating demand during the winter months makes solar thermal collector systems an unviable option without some form of seasonal energy storage. Information obtained from Ontario greenhouse operators has shown that over 20% of annual natural gas usage occurs during the summer months for greenhouse pre-heating prior to sunrise. A transient model of the greenhouse microclimate and indoor conditioning systems is carried out using TRNSYS software and validated with actual natural gas usage data. A large-scale solar thermal collector system is then incorporated and found to reduce the annual heating energy demand by approximately 35%. The inclusion of the collector system correlates to a reduction of about 120 tonnes of CO2 equivalent emissions per acre of greenhouse per year. System payback period is discussed considering the benefits of a future Ontario carbon tax.

Wireless sensors linked to climate financing for globally affordable clean cooking

Data from 4,038 households in India show wireless sensors could make clean energy solutions affordable for those at the bottom of the energy pyramid. Three billion of the world’s poorest people mostly rely on solid biomass for cooking, with major consequences to health1 and environment2. We demonstrate the untapped potential of wireless sensors connected to the ‘internet of things’ to make clean energy solutions affordable for those at the bottom of the energy pyramid. This breakthrough approach is demonstrated by a 17-month field study with 4,038 households in India. Major findings include: self-reported data on cooking duration have little correlation with actual usage data from sensors; sensor data revealed that the distribution of high and low users varied over time, and the actual mitigation of climate pollution was only 25% of the projected mitigation; climate credits were shown to significantly incentivize the use of cleaner technologies.

Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing, tested in a California coastal drainage

AbstractBasin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km2 watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Seer Grid: Privacy and Utility Implications of Two-Level Load Prediction in Smart Grids

We propose “Seer Grid”, a novel two-level energy consumption prediction framework for smart grids, aimed to decrease the trade-off between privacy requirements (of the customer) and data utility requirements (of the energy company (EC)). The first-level prediction at the household level is performed by each smart meter (SM), and the predicted energy consumption pattern (instead of the actual energy usage data) is reported to a cluster head (CH). Then, a second-level prediction at the neighborhood level is done by the CH which predicts the energy spikes in the neighborhood or cluster and shares it with the EC. Our two-level prediction mechanism is designed such that it preserves the correlation between the predicted and actual energy consumption patterns at the cluster level and removes this correlation in the predicted data communicated by each SM to the CH. This maintains the usefulness of the cluster-level energy consumption data communicated to the EC, while preserving the privacy of the household-level energy consumption data against the CH (and thus the EC). Our evaluation results show that Seer Grid is successful in hiding private consumption patterns at the household-level while still being able to accurately predict energy consumption at the neighborhood-level.

Development and utilization of complementary communication channels for treatment decision making and survivorship issues among cancer patients: The CIS Research Consortium Experience

ObjectiveCancer patients and survivors are assuming active roles in decision-making and digital patient support tools are widely used to facilitate patient engagement. As part of Cancer Information Service Research Consortium's randomized controlled trials focused on the efficacy of eHealth interventions to promote informed treatment decision-making for newly diagnosed prostate and breast cancer patients, and post-treatment breast cancer, we conducted a rigorous process evaluation to examine the actual use of and perceived benefits of two complementary communication channels — print and eHealth interventions. MethodsThe three Virtual Cancer Information Service (V-CIS) interventions were developed through a rigorous developmental process, guided by self-regulatory theory, informed decision-making frameworks, and health communications best practices. Control arm participants received NCI print materials; experimental arm participants received the additional V-CIS patient support tool. Actual usage data from the web-based V-CIS was also obtained and reported. ResultsPrint materials were highly used by all groups. About 60% of the experimental group reported using the V-CIS. Those who did use the V-CIS rated it highly on improvements in knowledge, patient-provider communication and decision-making. ConclusionThe findings show that how patients actually use eHealth interventions either singularly or within the context of other communication channels is complex. Practice implicationsIntegrating rigorous best practices and theoretical foundations is essential and multiple communication approaches should be considered to support patient preferences.

Extending Dynamic Mapping to Reentry Practitioners

Although dynamic mapping is an increasingly popular tool for law enforcement, this technology is uncommon for reentry practitioners. This article introduces the Community Supervision Mapping System (CSMS), a web-based tool that routinely integrates Rhode Island Department of Corrections data into a user-friendly interface designed for those who supervise and provide service referrals to probationers. Using pre- and post-implementation survey data and actual usage data, we find that probation officers who adopt the new technology find the system useful and easy to use. We also learned that current usage is a better indicator of future use than is a respondent's reported intention to use the system.

Usage Data of Images from a Digital Library Informs Four Areas of Digital Library Management: Metadata Creation, System Design, Marketing and Promotion, and Content Selection

A Review of:&#x0D; Reilly, M., &amp; Thompson, S. (2014). Understanding ultimate use data and its implication for digital library management: A case study. Journal of Web Librarianship, 8(2), 196-213. http://dx.doi.org/10.1080/19322909.2014.901211 &#x0D; &#x0D; Abstract&#x0D; &#x0D; Objective – To investigate the implications of intended and actual usage data retrieved from a digital library on digital library management and design.&#x0D; &#x0D; Design – Case study.&#x0D; &#x0D; Setting – A digital library of predominantly high resolution images based at a large research university in the United States of America. &#x0D; &#x0D; Subjects – Responses from 917 users of an open access digital library.&#x0D; &#x0D; Methods – Researchers used a literature review to identify previous research on this topic and to inform the methodology for their research. Two distinct studies informed the methodology: research by Beaudoin (2009) that identified categories of both users and questions around usage was incorporated, and the ultimate use categories suggested by Chung and Yoon (2011) to compare against those used in this research. Researchers used data extracted via recorded system logs that are part of the statistics feature of the digital library. This feature is an in-house developed system, the Digital Cart Service (DCS). The logs tracked usage of 917 images recorded over a three year period, from 2011-2013. After eliminating personal information, researchers examined three fields: university affiliation, intended use, and description. After exporting the data from these three fields to a Microsoft Access database for text analysis, researchers normalized the data using a series of codes assigned to the responses. It is unclear how many description fields were used to yield more information.&#x0D; &#x0D; Main Results – Researchers identified five user-types among users of the digital library. The biggest user group was visitors, followed by university staff, while university faculty had the lowest usage. Visitors were found to use images for personal use, such as inspirational and artistic purposes. The products developed from images in the digital library were-wide ranging, and included image albums, research, artwork, and video productions. These findings have implications for four areas of practical management of digital libraries: metadata creation, system design, marketing and promotion, and content selection. Among the eight categories of intended uses recorded, the highest uses were found to be for personal use, followed by ‘other’ use. Researchers examined the ‘other’ use category and further divided it into 12 sub-categories. Of these sub-categories, the highest use was for publication and research, while the lowest use was for ‘gift’ and ‘industry.’&#x0D; &#x0D; Conclusion – Incorporating user-generated metadata and distributing it to digital library managers is found to produce enhanced metadata and to aid the promotion and awareness of collections. Usage data may inform marketing efforts, as it provides a more comprehensive picture of who uses digital libraries and why they use images retrieved from those libraries. Equally, usage data may reveal the least frequent users of digital libraries, which informs targeted user marketing campaigns. Finally, the authors find that usage data combined with user-generated metadata should form part of content selection criteria for digital library managers.

Understanding the continued use of intra-organizational blogs: An adaptive habituation model

As intra-organizational blogs are expected to provide a flexible, intra-organizational networking platform that can effectively facilitate knowledge sharing, it is worthwhile to address why employees embrace a blogging system and engage in continual blogging. Drawing upon the existing literature, this paper proposes a conceptual model that suggests the continued use of internal blogs by employees is strongly driven by the force of habituation, which is repeatedly adapted by the effects of network externalities and managerial incentives. To empirically test this “adaptive habituation model”, actual usage data are collected from the internal blogging platform of a large telecommunication company to measure all related constructs. Statistical results from regression analyses illustrate that the proposed model effectively explains why employees continue to use corporate internal blogging systems. Practically, our findings suggest that while managerial measures, such as a ranking mechanism, may help leverage the benefits of intra-organizational social networking applications, it may take some time for these effects to emerge.

How the burdens of ownership promote consumer usage of access-based services

Services that let customers access goods, such as car-sharing, are gaining increasing relevance as an alternative to ownership. These access-based services allow consumers to avoid the “burdens of ownership”, i.e., risks and responsibilities that come with owning a good. However, the interplay between consumers’ risk perception of ownership, access-based service usage, and the subsequent decision to reduce or forgo ownership has not been sufficiently investigated. Based on risk perception theory, we hypothesize the effects of different risk dimensions (financial, performance, social) on the intensity of access-based service usage, as well as the latter’s influence on ownership reduction. Using a unique dataset that links survey and actual usage data of car-sharing users, we test four corresponding hypotheses. The results reveal that access-based service usage is positively influenced by all three ownership risk perceptions. Moreover, a higher usage of an access-based service increases the likelihood that consumers subsequently reduce ownership.

A STRATEGY FOR ENERGY PERFORMANCE ANALYSIS AT THE EARLY DESIGN STAGE: PREDICTED VS. ACTUAL BUILDING ENERGY PERFORMANCE

Developments in information technology are providing methods to improve current design practices, where uncertainties about various design elements can be simulated and studied from the design inception. Energy and thermal simulations, improved design representations and enhanced collaboration using digital media are increasingly being used. With the expanding interest in energy-efficient building design, whole building energy simulation programs are increasingly employed in the design process to help architects and engineers determine which design strategies save energy and improve building performance. The purpose of this research was to investigate the potential of these programs to perform whole building energy analysis during the early stages of architectural design, and compare the results with the actual building energy performance. The research was conducted by simulating energy usage of a fully functional research laboratory building using two different simulation tools that are aimed for early schematic design. The results were compared with utility data of the building to identify the degree of closeness with which simulation results match the actual energy usage of the building. Results indicate that modeled energy data from one of the software programs was significantly higher than the measured, actual energy usage data, while the results from the second application were comparable, but did not correctly predict monthly energy loads for the building. This suggests that significant deviations may exist between modeled and actual energy consumption for buildings, and more importantly between different simulation software programs. Understanding the limitations and suitability of specific simulation programs is crucial for successful integration of performance simulations with the design process.