Bo-Jhang Ho, Nima Nikzad, Bharathan Balaji, Mani Srivastava
{"title":"Emu:用户研究的用户粘性模型。","authors":"Bo-Jhang Ho, Nima Nikzad, Bharathan Balaji, Mani Srivastava","doi":"10.1145/3123024.3124568","DOIUrl":null,"url":null,"abstract":"<p><p>Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed <i>Emu</i>, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the <i>Emu</i> API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.</p>","PeriodicalId":90688,"journal":{"name":"Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)","volume":"2017 ","pages":"959-964"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3123024.3124568","citationCount":"2","resultStr":"{\"title\":\"Emu: Engagement Modeling for User Studies.\",\"authors\":\"Bo-Jhang Ho, Nima Nikzad, Bharathan Balaji, Mani Srivastava\",\"doi\":\"10.1145/3123024.3124568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed <i>Emu</i>, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the <i>Emu</i> API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.</p>\",\"PeriodicalId\":90688,\"journal\":{\"name\":\"Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)\",\"volume\":\"2017 \",\"pages\":\"959-964\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1145/3123024.3124568\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3123024.3124568\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3123024.3124568","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.