東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Designing Self-Monitoring Technology...

Choe, Eun Kyoung.

Linked to FindBook

Google Book

Amazon

博客來

Designing Self-Monitoring Technology to Promote Data Capture and Reflection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Designing Self-Monitoring Technology to Promote Data Capture and Reflection./
Author:	Choe, Eun Kyoung.
Description:	175 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: A.
Contained By:	Dissertation Abstracts International76-02A(E).
Subject:	Information Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3641503
ISBN:	9781321278279

Designing Self-Monitoring Technology to Promote Data Capture and Reflection.
Choe, Eun Kyoung.

Designing Self-Monitoring Technology to Promote Data Capture and Reflection. - 175 p.

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: A.

Thesis (Ph.D.)--University of Washington, 2014.

This item must not be sold to any third party vendors.

Self-monitoring is a powerful means for self-reflection, which is important in health behavior change. More recently, researchers and companies began to offer numerous self-monitoring technologies for health. However, people---even experienced self-trackers such as Quantified-Selfers---have difficulty with continued tracking and gaining insights from their personal data. The goal of this dissertation is to provide insights on designing effective self-monitoring technology. In particular, I examine ways to support easy capturing of data and create persuasive feedback to nudge people toward positive behaviors.

ISBN: 9781321278279Subjects--Topical Terms:

1017528
Information Science.

Designing Self-Monitoring Technology to Promote Data Capture and Reflection.
LDR:04549nmm a2200349 4500 001 2057283
005 20150610073820.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781321278279
035 $a (MiAaPQ)AAI3641503
035 $a AAI3641503
040 $a MiAaPQ $c MiAaPQ
100 1 $a Choe, Eun Kyoung. $3 3171105
245 1 0 $a Designing Self-Monitoring Technology to Promote Data Capture and Reflection.
300 $a 175 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: A.
500 $a Advisers: Julie A. Kientz; Wanda Pratt.
502 $a Thesis (Ph.D.)--University of Washington, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Self-monitoring is a powerful means for self-reflection, which is important in health behavior change. More recently, researchers and companies began to offer numerous self-monitoring technologies for health. However, people---even experienced self-trackers such as Quantified-Selfers---have difficulty with continued tracking and gaining insights from their personal data. The goal of this dissertation is to provide insights on designing effective self-monitoring technology. In particular, I examine ways to support easy capturing of data and create persuasive feedback to nudge people toward positive behaviors.
520 $a As part of this research, I introduce a mobile self-monitoring technology, SleepTight, a lightweight self-monitoring application widget that helps people capture and reflect on sleep behaviors. The SleepTight system was designed based on two formative studies and theories of reactivity in self-monitoring research. I leveraged the Android platform's lock screen and home screen widgets to lower the capture burden and increase awareness. I conducted a 4-week deployment study to evaluate the efficacy of the SleepTight system and found that SleepTight's widgets served as visual reminders and helped participants collect more data, more accurately. Participants were also able to reflect on sleeping patterns and relationships among the captured factors.
520 $a I further examine effective ways to provide self-monitoring feedback by leveraging the Framing effects with an aim to nudge people toward positive health behaviors. The goal of self-monitoring is not simply to quantify individuals' behaviors, but to improve it. Therefore, self-monitoring feedback needs to convey information to help people make health-enhancing, self-beneficial decisions. To identify the type of framing that could best convey self-monitoring feedback, I conducted an online experiment to test the effects of three types of performance feedback framing---(1) valence, (2) presentation type, and (3) data unit---on individuals' self-efficacy. I identified that it is better to use a positive framing with data units (e.g., raw data, rate, percentage) that can increase the perception of one's performance capabilities to enhance individuals' self-efficacy. This work provides empirical guidance for creating influential, persuasive performance feedback, thereby helping people designing self-monitoring technologies to promote healthy behaviors.
520 $a In this dissertation, I discuss how we can successfully design self-monitoring technology to help people collect data easily, learn their behavioral patterns, and develop positive changes for improving health. Effective self-monitoring technology eases the capture burden, supports customization, prevents backfilling, and provides feedback in a positive light. Self-monitoring technology that adheres to these guidelines can enhance tracking adherence, data accuracy, data awareness, self-reflection, and self-efficacy. I verify these thesis statements through a mixed-method approach including formative studies, technology deployment study, and experimental study. This dissertation research expands our knowledge of how consumer health information technology should be designed to support self-monitoring and reflection. Once a motivated individual meets a well-designed self-monitoring technology, exciting possibilities will arise for gaining insights for health, wellness, and other aspects of life.
590 $a School code: 0250.
650 4 $a Information Science. $3 1017528
650 4 $a Computer Science. $3 626642
650 4 $a Design and Decorative Arts. $3 1024640
690 $a 0723
690 $a 0984
690 $a 0389
710 2 $a University of Washington. $b Information School. $3 2092649
773 0 $t Dissertation Abstracts International $g 76-02A(E).
790 $a 0250
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3641503