Mu Lin, N. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, E. Berke, A. Campbell, Tanzeem Choudhury
{"title":"BeWell+:多维健康监测,社区引导用户反馈和能量优化","authors":"Mu Lin, N. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, E. Berke, A. Campbell, Tanzeem Choudhury","doi":"10.1145/2448096.2448106","DOIUrl":null,"url":null,"abstract":"Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"28 1","pages":"10:1-10:8"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"68","resultStr":"{\"title\":\"BeWell+: multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization\",\"authors\":\"Mu Lin, N. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, E. Berke, A. 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With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. 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BeWell+: multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization
Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.