The Experts below are selected from a list of 43611 Experts worldwide ranked by ideXlab platform
John L Semple - One of the best experts on this subject based on the ideXlab platform.
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effect of home monitoring via Mobile App on the number of in person visits following ambulatory surgery a randomized clinical trial
JAMA Surgery, 2017Co-Authors: Kathleen A Armstrong, John L Semple, Peter C Coyte, Mitchell H Brown, Brett BeberAbstract:Importance In the age of information and patient-centered care, new methods of delivering postoperative care must be developed and evaluated. Objective To determine whether follow-up care delivered via a Mobile App can be used to avert in-person follow-up care visits compared with conventional, in-person follow-up care in the first 30 days following ambulatory surgery. Design, Setting, and Participants A randomized clinical trial was conducted from February 1 to August 31, 2015, among ambulatory patients undergoing breast reconstruction at an academic ambulatory care hospital. Patients were randomly assigned to receive follow-up care via a Mobile App or at an in-person visit during the first 30 days after the operation. Analysis was intention-to-treat. Main Outcomes and Measures The primary end point was the number of in-person follow-up visits during the first 30 days after the operation. Secondary end points were the number of telephone calls and emails to health care professionals, patient-reported convenience and satisfaction scores, and rates of complications. Results Of the 65 women in the study (mean [SD] age, 47.7 [13.4] years), 32 (49%) were in the Mobile App group, and 33 (51%) were in the in-person follow-up care group. Those in the Mobile App group attended a mean of 0.66 in-person visits, vs 1.64 in-person visits in the in-person follow-up care group, for a difference of 0.40 times fewer in-person visits (95% CI, 0.24-0.66;P Conclusions and Relevance Patients undergoing ambulatory breast reconstruction can use follow-up care via a Mobile App to avert in-person follow-up visits during the first 30 days after the operation. Mobile App follow-up care affects neither complication rates nor patient-reported satisfaction scores, but it improves patient-reported convenience scores. Trial Registration clinicaltrials.gov Identifier:NCT02318953
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replacing ambulatory surgical follow up visits with Mobile App home monitoring modeling cost effective scenarios
Journal of Medical Internet Research, 2014Co-Authors: Kathleen A Armstrong, John L Semple, Peter C CoyteAbstract:Background: Women’s College Hospital (WCH) offers specialized surgical procedures, including ambulatory breast reconstruction in post-mastectomy breast cancer patients. Most patients receiving ambulatory surgery have low rates of postoperative events necessitating clinic visits. Increasingly, Mobile monitoring and follow-up care is used to overcome the distance patients must travel to receive specialized care at a reduced cost to society. WCH has completed a feasibility study using a Mobile App (QoC Health Inc, Toronto) that suggests high patient satisfaction and adequate detection of postoperative complications. Objective: The proposed cost-effectiveness study models the replacement of conventional, in-person postoperative follow-up care with Mobile App follow-up care following ambulatory breast reconstruction in post-mastectomy breast cancer patients. Methods: This is a societal perspective cost-effectiveness analysis, wherein all costs are assessed irrespective of the payer. The patient/caregiver, health care system, and externally borne costs are calculated within the first postoperative month based on cost information provided by WCH and QoC Health Inc. The effectiveness of telemedicine and conventional follow-up care is measured as successful surgical outcomes at 30-days postoperative, and is modeled based on previous clinical trials containing similar patient populations and surgical risks. Results: This costing assumes that 1000 patients are enrolled in bring-your-own-device (BYOD) Mobile App follow-up per year and that 1.64 in-person follow-ups are attended in the conventional arm within the first month postoperatively. The total cost difference between Mobile App and in-person follow-up care is $245 CAD ($223 USD based on the current exchange rate), with in-person follow-up being more expensive ($381 CAD) than Mobile App follow-up care ($136 CAD). This takes into account the total of health care system, patient, and external borne costs. If we examine health care system costs alone, in-person follow-up is $38 CAD ($35 USD) more expensive than Mobile App follow-up care over the first postoperative month. The baseline difference in effect is modeled to be zero based on clinical trials examining the effectiveness of telephone follow-up care in similar patient populations. An incremental cost-effectiveness ratio (ICER) is not reportable in this scenario. An incremental net benefit (INB) is reportable, and reflects merely the cost difference between the two interventions for any willingness-to-pay value (INB=$245 CAD). The cost-effectiveness of Mobile App follow-up even holds in scenarios where all Mobile patients attend one in-person follow-up. Conclusions: Mobile App follow-up care is suitably targeted to low-risk postoperative ambulatory patients. It can be cost-effective from a societal and health care system perspective. [J Med Internet Res 2014;16(9):e213]
Alexander Ilic - One of the best experts on this subject based on the ideXlab platform.
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Mobile App adoption in different life stages: An empirical analysis
Pervasive and Mobile Computing, 2017Co-Authors: Remo Manuel Frey, Runhua Xu, Alexander IlicAbstract:Abstract The analysis of individuals’ current life stages is a powerful Approach for identifying und understanding patterns of human behavior. Different stages imply different preferences and consumer demands. Thus, life stages play an important role in marketing, economics, and sociology. However, such information is difficult to be obtained especially in the digital world. This work thus contributed to both theory and practice from two aspects. First, we conducted a large-scale empirical study with 1435 participants and showed that a person’s Mobile App adoption pattern is strongly influenced by her current life stage. Second, we presented a data-driven, highly-scalable, and real-time Approach of predicting an individual’s current life stage based on the Apps she has installed on smartphone. Result showed that our predictive models were able to predict life stages with 241.0% higher precision and 148.2% higher recall than a random guess on average.
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Mobile App adoption in different life stages: An empirical analysis; Pervasive and Mobile Computing
Pervasive and Mobile Computing, 2017Co-Authors: Remo Manuel Frey, Runhua Xu, Alexander IlicAbstract:The analysis of individuals’ current life stages is a powerful Approach for identifying und understanding patterns of human behavior. Different stages imply different preferences and consumer demands. Thus, life stages play an important role in marketing, economics, and sociology. However, such information is difficult to be obtained especially in the digital world. This work thus contributed to both theory and practice from two aspects. First, we conducted a large-scale empirical study with 1435 participants and showed that a person’s Mobile App adoption pattern is strongly influenced by her current life stage. Second, we presented a data-driven, highly-scalable, and real-time Approach of predicting an individual’s current life stage based on the Apps she has installed on smartphone. Result showed that our predictive models were able to predict life stages with 241.0% higher precision and 148.2% higher recall than a random guess on average.
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understanding the impact of personality traits on Mobile App adoption insights from a large scale field study
Computers in Human Behavior, 2016Co-Authors: Remo Manuel Frey, Elgar Fleisch, Alexander IlicAbstract:The sheer amount of available Apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on Mobile App adoption was examined through an empirical study involving 2043 Android users. A Mobile App was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed Apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of Mobile Apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed Apps. The predictive model is implemented in a prototype App and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any Mobile App. Personality has a significant impact on Mobile App adoption.A novel Approach is proposed to study Mobile App adoption on a large scale.A machine-learning model is developed to predict a smartphone user's personality.The predictive model can be integrated into any Mobile App.
Kathleen A Armstrong - One of the best experts on this subject based on the ideXlab platform.
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effect of home monitoring via Mobile App on the number of in person visits following ambulatory surgery a randomized clinical trial
JAMA Surgery, 2017Co-Authors: Kathleen A Armstrong, John L Semple, Peter C Coyte, Mitchell H Brown, Brett BeberAbstract:Importance In the age of information and patient-centered care, new methods of delivering postoperative care must be developed and evaluated. Objective To determine whether follow-up care delivered via a Mobile App can be used to avert in-person follow-up care visits compared with conventional, in-person follow-up care in the first 30 days following ambulatory surgery. Design, Setting, and Participants A randomized clinical trial was conducted from February 1 to August 31, 2015, among ambulatory patients undergoing breast reconstruction at an academic ambulatory care hospital. Patients were randomly assigned to receive follow-up care via a Mobile App or at an in-person visit during the first 30 days after the operation. Analysis was intention-to-treat. Main Outcomes and Measures The primary end point was the number of in-person follow-up visits during the first 30 days after the operation. Secondary end points were the number of telephone calls and emails to health care professionals, patient-reported convenience and satisfaction scores, and rates of complications. Results Of the 65 women in the study (mean [SD] age, 47.7 [13.4] years), 32 (49%) were in the Mobile App group, and 33 (51%) were in the in-person follow-up care group. Those in the Mobile App group attended a mean of 0.66 in-person visits, vs 1.64 in-person visits in the in-person follow-up care group, for a difference of 0.40 times fewer in-person visits (95% CI, 0.24-0.66;P Conclusions and Relevance Patients undergoing ambulatory breast reconstruction can use follow-up care via a Mobile App to avert in-person follow-up visits during the first 30 days after the operation. Mobile App follow-up care affects neither complication rates nor patient-reported satisfaction scores, but it improves patient-reported convenience scores. Trial Registration clinicaltrials.gov Identifier:NCT02318953
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replacing ambulatory surgical follow up visits with Mobile App home monitoring modeling cost effective scenarios
Journal of Medical Internet Research, 2014Co-Authors: Kathleen A Armstrong, John L Semple, Peter C CoyteAbstract:Background: Women’s College Hospital (WCH) offers specialized surgical procedures, including ambulatory breast reconstruction in post-mastectomy breast cancer patients. Most patients receiving ambulatory surgery have low rates of postoperative events necessitating clinic visits. Increasingly, Mobile monitoring and follow-up care is used to overcome the distance patients must travel to receive specialized care at a reduced cost to society. WCH has completed a feasibility study using a Mobile App (QoC Health Inc, Toronto) that suggests high patient satisfaction and adequate detection of postoperative complications. Objective: The proposed cost-effectiveness study models the replacement of conventional, in-person postoperative follow-up care with Mobile App follow-up care following ambulatory breast reconstruction in post-mastectomy breast cancer patients. Methods: This is a societal perspective cost-effectiveness analysis, wherein all costs are assessed irrespective of the payer. The patient/caregiver, health care system, and externally borne costs are calculated within the first postoperative month based on cost information provided by WCH and QoC Health Inc. The effectiveness of telemedicine and conventional follow-up care is measured as successful surgical outcomes at 30-days postoperative, and is modeled based on previous clinical trials containing similar patient populations and surgical risks. Results: This costing assumes that 1000 patients are enrolled in bring-your-own-device (BYOD) Mobile App follow-up per year and that 1.64 in-person follow-ups are attended in the conventional arm within the first month postoperatively. The total cost difference between Mobile App and in-person follow-up care is $245 CAD ($223 USD based on the current exchange rate), with in-person follow-up being more expensive ($381 CAD) than Mobile App follow-up care ($136 CAD). This takes into account the total of health care system, patient, and external borne costs. If we examine health care system costs alone, in-person follow-up is $38 CAD ($35 USD) more expensive than Mobile App follow-up care over the first postoperative month. The baseline difference in effect is modeled to be zero based on clinical trials examining the effectiveness of telephone follow-up care in similar patient populations. An incremental cost-effectiveness ratio (ICER) is not reportable in this scenario. An incremental net benefit (INB) is reportable, and reflects merely the cost difference between the two interventions for any willingness-to-pay value (INB=$245 CAD). The cost-effectiveness of Mobile App follow-up even holds in scenarios where all Mobile patients attend one in-person follow-up. Conclusions: Mobile App follow-up care is suitably targeted to low-risk postoperative ambulatory patients. It can be cost-effective from a societal and health care system perspective. [J Med Internet Res 2014;16(9):e213]
Bin Liu - One of the best experts on this subject based on the ideXlab platform.
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Can Machine Learning Help People Configure Their Mobile App Privacy Settings
2020Co-Authors: Bin LiuAbstract:Technologies such as Mobile Apps, web browsers, social networking sites, and IoT devices provide sophisticated services to users. At the same time, they are alsoincreasingly collecting privacy-sensitive data about them. In some domains, such as Mobile Apps, this trend has resulted in an increase in the breadth of privacy settingsmade available to users. These settings are necessary because not all users feel comfortable having their data collected by some of these technologies. On Mobilephones alone, the sheer number of Apps users download is staggering. The variety of sensitive data and functionality requested by these Apps has led to a demand formuch more specific privacy settings. The same is true in other domains as well, such as social networks, browsers, and various IoT technologies. The result of this situation is that users feel overwhelmed by all of the settings available to them, and are thus unable to take advantage of them effectively. This dissertation examines whether machine learning techniques can be utilized to help users manage an increasingly large number of privacy settings. It specificallyfocuses on Mobile App permissions. The research presented herein aims to simplify people’s tasks in regard to managing their large number of App privacy settings. Wepresent the methods we used for developing models of users’ privacy preferences, and describe the interactive assistant we designed based on these models to helpusers configure their settings using personalized recommendations. The objective of this work is to alleviate the burden placed on users while increasing alignmentbetween a their preferences and the privacy settings on their phones. This dissertation details three different studies. Specifically, in the first study, we used a dataset of Mobile App permission settings obtained from over 200K Androidusers, explored different machine learning models, and analyzed different combinations of features to predict users’ Mobile App permission settings. The study includesthe development and evaluation of profile-based models as well as individual prediction models. It also includes simulation studies, wherein we explored the viabilityof different interactive configuration scenarios by testing different ways of combining dialogue inputs from users with recommendations based on machine learningmodels. The results of these simulations suggest that by selectively prompting users to indicate how they would like to configure a relatively small percentage of their permission settings, it is possible to accurately predict many of their remaining permission settings. Another significant finding of this first study is that a relatively small number of privacy profiles derived from clusters of like-minded users can helppredict many of the permission settings that users in a given cluster prefer. The second study was designed to validate these findings in a field study with actual users. We designed an enhanced version of Android’s permission manager and collected rich information on users’ actual App permission settings. While results from this study involve a much smaller number of users, they were obtained usingprivacy nudges designed to increase user awareness of data being collected about them and as a result also their engagement with their permission settings. Using datacollected as part of this study, we were able to generate and analyze privacy profiles built for groups of like-minded users who exhibited similar privacy preferences. Resultsof this study confirm that a relatively small number of profiles (or clusters of users) can capture s large percentahe of users’ diverse privacy preferences and help predict many of their desired privacy settings. They also indicate that privacy nudges can be very effective in motivating users to engage with their permission settings and in deriving privacy profiles with strong predictive power. In the third study, we evaluated our profile-based preference models by developinga privacy assistant that helps users configure their App permission settings based on the developed profiles from our second study. We report on the results of a pilotstudy (N=72) conducted with actual Android users who used our privacy assistant on their smartphones while performing their regular daily activities. The results indicatethat participants accepted 78.7% of the recommendations made by the privacy assistant and kept 94.9% of these settings on their phones over the following sixdays, all while receiving daily nudges designed to motivate them to further review their settings. The dissertation also discusses the privacy profiles designed for this research and identifies essential attributes that separate people associated with different profiles (or clusters). A refined version of the Personalized Privacy Assistantwas released to the Google Play store and used to collect some additional data. In summary, through a series of three studies, this dissertation shows that using a small number of privacy decisions made by a given smartphone user, it is often possible to predict a large fraction of the Mobile App permission settings this user would want to have. The dissertation further shows how we have been able to effectively operationalize this finding in the form of personalized privacy assistants that can help users configure Mobile App permission settings on their smartphones.
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structural analysis of user choices for Mobile App recommendation
ACM Transactions on Knowledge Discovery From Data, 2016Co-Authors: Bin Liu, Hui Xiong, Neil Zhenqiang Gong, Martin EsterAbstract:Advances in smartphone technology have promoted the rapid development of Mobile Apps. However, the availability of a huge number of Mobile Apps in Application stores has imposed the challenge of finding the right Apps to meet the user needs. Indeed, there is a critical demand for personalized App recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of Mobile Apps. First, App markets have organized Apps in a hierarchical taxonomy. Second, Apps with similar functionalities are competing with each other. Although there are a variety of Approaches for Mobile App recommendations, these Approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of Apps as well as the competitive relationships among Apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large App adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.
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structural analysis of user choices for Mobile App recommendation
arXiv: Information Retrieval, 2016Co-Authors: Bin Liu, Hui Xiong, Neil Zhenqiang Gong, Martin EsterAbstract:Advances in smartphone technology have promoted the rapid development of Mobile Apps. However, the availability of a huge number of Mobile Apps in Application stores has imposed the challenge of finding the right Apps to meet the user needs. Indeed, there is a critical demand for personalized App recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of Mobile Apps. First, App markets have organized Apps in a hierarchical taxonomy. Second, Apps with similar functionalities are competing with each other. While there are a variety of Approaches for Mobile App recommendations, these Approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this paper, we provide a systematic study for addressing these challenges. Specifically, we develop a Structural User Choice Model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of Apps as well as the competitive relationships among Apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large App adoption data set collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art top-N recommendation methods by a significant margin.
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reconciling Mobile App privacy and usability on smartphones could user privacy profiles help
The Web Conference, 2014Co-Authors: Bin Liu, Jialiu Lin, Norman SadehAbstract:As they compete for developers, Mobile App ecosystems have been exposing a growing number of APIs through their software development kits. Many of these APIs involve accessing sensitive functionality and/or user data and require Approval by users. Android for instance allows developers to select from over 130 possible permissions. Expecting users to review and possibly adjust settings related to these permissions has proven unrealistic. In this paper, we report on the results of a study analyzing people's privacy preferences when it comes to granting permissions to different Mobile Apps. Our results suggest that, while people's Mobile App privacy preferences are diverse, a relatively small number of profiles can be identified that offer the promise of significantly simplifying the decisions Mobile users have to make. Specifically, our results are based on the analysis of settings of 4.8 million smartphone users of a Mobile security and privacy platform. The platform relies on a rooted version of Android where users are allowed to choose between "granting", "denying" or "requesting to be dynamically prompted" when it comes to granting 12 different Android permissions to Mobile Apps they have downloaded.
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modeling users Mobile App privacy preferences restoring usability in a sea of permission settings
Symposium On Usable Privacy and Security, 2014Co-Authors: Jialiu Lin, Bin Liu, Norman Sadeh, Jason HongAbstract:In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their Mobile App privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an App. Instead it relies on static code analysis to determine the purpose for which an App requests each of its permissions, distinguishing for instance between Apps relying on particular permissions to deliver their core functionality and Apps requesting these permissions to share information with advertising networks or social networks. Using privacy preferences that reflect people’s comfort with the purpose for which different Apps request their permissions, we use clustering techniques to identify privacy profiles. A major contribution of this work is to show that, while people’s Mobile App privacy preferences are diverse, it is possible to identify a small number of privacy profiles that collectively do a good job at capturing these diverse preferences.
Peter C Coyte - One of the best experts on this subject based on the ideXlab platform.
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effect of home monitoring via Mobile App on the number of in person visits following ambulatory surgery a randomized clinical trial
JAMA Surgery, 2017Co-Authors: Kathleen A Armstrong, John L Semple, Peter C Coyte, Mitchell H Brown, Brett BeberAbstract:Importance In the age of information and patient-centered care, new methods of delivering postoperative care must be developed and evaluated. Objective To determine whether follow-up care delivered via a Mobile App can be used to avert in-person follow-up care visits compared with conventional, in-person follow-up care in the first 30 days following ambulatory surgery. Design, Setting, and Participants A randomized clinical trial was conducted from February 1 to August 31, 2015, among ambulatory patients undergoing breast reconstruction at an academic ambulatory care hospital. Patients were randomly assigned to receive follow-up care via a Mobile App or at an in-person visit during the first 30 days after the operation. Analysis was intention-to-treat. Main Outcomes and Measures The primary end point was the number of in-person follow-up visits during the first 30 days after the operation. Secondary end points were the number of telephone calls and emails to health care professionals, patient-reported convenience and satisfaction scores, and rates of complications. Results Of the 65 women in the study (mean [SD] age, 47.7 [13.4] years), 32 (49%) were in the Mobile App group, and 33 (51%) were in the in-person follow-up care group. Those in the Mobile App group attended a mean of 0.66 in-person visits, vs 1.64 in-person visits in the in-person follow-up care group, for a difference of 0.40 times fewer in-person visits (95% CI, 0.24-0.66;P Conclusions and Relevance Patients undergoing ambulatory breast reconstruction can use follow-up care via a Mobile App to avert in-person follow-up visits during the first 30 days after the operation. Mobile App follow-up care affects neither complication rates nor patient-reported satisfaction scores, but it improves patient-reported convenience scores. Trial Registration clinicaltrials.gov Identifier:NCT02318953
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replacing ambulatory surgical follow up visits with Mobile App home monitoring modeling cost effective scenarios
Journal of Medical Internet Research, 2014Co-Authors: Kathleen A Armstrong, John L Semple, Peter C CoyteAbstract:Background: Women’s College Hospital (WCH) offers specialized surgical procedures, including ambulatory breast reconstruction in post-mastectomy breast cancer patients. Most patients receiving ambulatory surgery have low rates of postoperative events necessitating clinic visits. Increasingly, Mobile monitoring and follow-up care is used to overcome the distance patients must travel to receive specialized care at a reduced cost to society. WCH has completed a feasibility study using a Mobile App (QoC Health Inc, Toronto) that suggests high patient satisfaction and adequate detection of postoperative complications. Objective: The proposed cost-effectiveness study models the replacement of conventional, in-person postoperative follow-up care with Mobile App follow-up care following ambulatory breast reconstruction in post-mastectomy breast cancer patients. Methods: This is a societal perspective cost-effectiveness analysis, wherein all costs are assessed irrespective of the payer. The patient/caregiver, health care system, and externally borne costs are calculated within the first postoperative month based on cost information provided by WCH and QoC Health Inc. The effectiveness of telemedicine and conventional follow-up care is measured as successful surgical outcomes at 30-days postoperative, and is modeled based on previous clinical trials containing similar patient populations and surgical risks. Results: This costing assumes that 1000 patients are enrolled in bring-your-own-device (BYOD) Mobile App follow-up per year and that 1.64 in-person follow-ups are attended in the conventional arm within the first month postoperatively. The total cost difference between Mobile App and in-person follow-up care is $245 CAD ($223 USD based on the current exchange rate), with in-person follow-up being more expensive ($381 CAD) than Mobile App follow-up care ($136 CAD). This takes into account the total of health care system, patient, and external borne costs. If we examine health care system costs alone, in-person follow-up is $38 CAD ($35 USD) more expensive than Mobile App follow-up care over the first postoperative month. The baseline difference in effect is modeled to be zero based on clinical trials examining the effectiveness of telephone follow-up care in similar patient populations. An incremental cost-effectiveness ratio (ICER) is not reportable in this scenario. An incremental net benefit (INB) is reportable, and reflects merely the cost difference between the two interventions for any willingness-to-pay value (INB=$245 CAD). The cost-effectiveness of Mobile App follow-up even holds in scenarios where all Mobile patients attend one in-person follow-up. Conclusions: Mobile App follow-up care is suitably targeted to low-risk postoperative ambulatory patients. It can be cost-effective from a societal and health care system perspective. [J Med Internet Res 2014;16(9):e213]
