|
|
|
| Research on Participation Process and Underlying Mechanism of Quantified Self Based on Mobile Experience Sampling Method |
| Zhu Qinghua1, Xu Xiaoting2, Zhao Yuxiang3, Yang Mengqing4 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.School of Sociology and Population Studies, Nanjing University of Posts and Telecommunications, Nanjing 210023
3.School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094
4.School of Journalism and Communication, Nanjing Normal University, Nanjing 210097 |
|
|
|
|
Abstract As an important means of personal health management, the quantified self (QS) has been favored by many health consumers. It is of great significance for Chinese citizens to establish personal health awareness, and cultivate a healthy lifestyle and further practice the ‘Health China Action (2019-2030).’ However, existing studies do not focus on the specific participation process of QS from a micro perspective. In this regard, we use the mobile experience sampling method (mESM) and the ad hoc interview to obtain the data of 15 QS users. We identify the participation process, key information behaviors, and main barriers of QS in different stages. The results indicate that the participation process of QS can be divided into the initial, maintenance, and discovery stages. In the initial stage, the key information behaviors include the identification of tools, content, and goals, while the main barriers include tool selection and information overload. In the maintenance stage, the key information behaviors include data viewing, collection, and sharing, while the main barriers include delayed reminders, unstable systems, decentralized visualization, and lack of data standards. In the discovery stage, the key information behaviors focus on data comparison, reflection, and interpretation, while the main barriers include complex data connection, abnormal system feedback, and difficulty in understanding data. Moreover, the underlying mechanism of the participation process of QS in different stages is identified.
|
|
Received: 03 June 2021
|
|
|
|
1 中共中央 国务院印发《“健康中国2030”规划纲要》 [EB/OL]. (2016-10-25) [2020-10-15]. http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm.
2 健康中国行动(2019—2030年)[EB/OL]. (2019-07-15) [2020-10-24]. http://www.gov.cn/xinwen/2019-07/15/content_5409694.htm.
3 Etkin J. The hidden cost of personal quantification[J]. Journal of Consumer Research, 2016, 42(6): 967-984.
4 Fox S, Duggan M. Tracking for health[EB/OL]. [2020-12-23]. http://pewinternet.org/Reports/2013/Tracking-for-Health.aspx.
5 Swan M. The quantified self: fundamental disruption in big data science and biological discovery[J]. Big Data, 2013, 1(2): 85-99.
6 Crawford K, Lingel J, Karppi T. Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device[J]. European Journal of Cultural Studies, 2015, 18(4/5): 479-496.
7 刘咏梅, 剧晓红. 量化自我在健康领域的应用——基于大数据的角度[J]. 情报资料工作, 2018(4): 56-63.
8 朱启贞, 胡德华, 张彦斐. 量化自我理论在健康领域的应用[J]. 图书馆论坛, 2018, 38(2): 17-21.
9 Li I, Dey A, Forlizzi J. A stage-based model of personal informatics systems[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2010: 557-566.
10 Hassan L, Dias A, Hamari J. How motivational feedback increases user’s benefits and continued use: a study on gamification, quantified-self and social networking[J]. International Journal of Information Management, 2019, 46: 151-162.
11 Guo L. Quantified-self 2.0: using context-aware services for promoting gradual behaviour change[OL]. (2016-10-03). https://arxiv.org/ftp/arxiv/papers/1610/1610.00460.pdf.
12 Li I, Dey A K, Forlizzi J. Understanding my data, myself: supporting self-reflection with ubicomp technologies[C]// Proceedings of the 13th International Conference on Ubiquitous Computing. New York: ACM Press, 2011: 405-414.
13 Lupton D. Self-tracking modes: reflexive self-monitoring and data practices[J/OL]. SSRN Electronic Journal, (2014-08-19). http://dx.doi.org/10.2139/ssrn.2483549.
14 De Maeyer C, Jacobs A. Sleeping with technology—designing for personal health[C]// Proceedings of the 2013 AAAI Spring Symposium. Palo Alto: AAAI Press, 2013: 11-16.
15 Kevin K. What is the quantified self?[EB/OL]. (2007-10-05) [2021-01-04]. https://www.webcitation.org/66TEY49wv?url=http://quantifiedself.com/2007/page/3/.
16 Gary W. Quantified self[EB/OL]. (2012-03-27) [2021-01-04]. https://www.webcitation.org/66TEHdz4d?url=http://aether.com/quantifiedself.
17 Elsden C, Durrant A C, Kirk D S. The quantified past as PIM: remembering a data-driven life[OL]. https://chriselsden.files.wordpress.com/2015/03/pim-submission-2016-cam-ready.pdf.
18 Almalki M, Gray K, Martin-Sanchez F. Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical studies[J]. Journal of Medical Internet Research, 2016, 18(5): e131.
19 Gelonch O, Ribera M, Codern-Bové N, et al. Acceptability of a lifelogging wearable camera in older adults with mild cognitive impairment: a mixed-method study[J]. BMC Geriatrics, 2019, 19(1): 110.
20 Wikipedia. Quantified self[EB/OL]. [2021-01-04]. https://en.wikipedia.org/wiki/Quantified_self.
21 Lupton D. Self-tracking cultures: towards a sociology of personal informatics[C]// Proceedings of the 26th Australian Computer-Human Interaction Conference on Designing Futures: the Future of Design. New York: ACM Press, 2014: 77-86.
22 Rooksby J, Rost M, Morrison A, et al. Personal tracking as lived informatics[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2014: 1163-1172.
23 李东进, 张宇东. 消费领域的量化自我: 研究述评与展望[J]. 外国经济与管理, 2018, 40(1): 3-17.
24 Prochaska J O, Velicer W F. The transtheoretical model of health behavior change[J]. American Journal of Health Promotion, 1997, 12(1): 38-48.
25 小米社区. 小米手环[EB/OL]. [2020-12-23]. https://www.xiaomi.cn/board/5581007.
26 华为运动手环专区. 花粉俱乐部[EB/OL]. [2020-12-23]. https://club.huawei.com/forum-2848-1.html.
27 Wang Y F, Weber I, Mitra P. Quantified self meets social media: sharing of weight updates on Twitter[C]// Proceedings of the 6th International Conference on Digital Health Conference. New York: ACM Press, 2016: 93-97.
28 Pejovic V, Lathia N, Mascolo C, et al. Mobile-based experience sampling for behaviour research[M]// Emotions and Personality in Personalized Services. Cham: Springer, 2016: 141-161.
29 胡蓉, 唐振贵, 赵宇翔, 等. 移动经验取样法: 促进真实情境下的用户信息行为研究[J]. 情报学报, 2018, 37(10): 1046-1059.
30 Hektner J M, Schmidt J A, Csikszentmihalyi M. Experience sampling method[M]. Thousand Oaks: SAGE Publications, 2007.
31 Bales E, Sohn T, Setlur V. Planning, apps, and the high-end smartphone: exploring the landscape of modern cross-device reaccess[C]// Proceedings of the International Conference on Pervasive Computing. Heidelberg: Springer, 2011: 1-18.
32 Sharon T. Self-tracking for health and the quantified self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare[J]. Philosophy & Technology, 2017, 30(1): 93-121.
33 Lupton D. Lively data, social fitness and biovalue: the intersections of health self-tracking and social media[J/OL]. SSRN Electronic Journal, (2015-09-27). http://dx.doi.org/10.2139/ssrn.2666324.
34 Lupton D. The quantified self: a sociology of self-tracking[M]. Cambridge: Polity Press, 2016.
35 Yang N, van Hout G, Feijs L, et al. Facilitating physical activity through on-site quantified-self data sharing[J]. Sustainability, 2020, 12(12): 4904.
36 Carver C S, Scheier M F. On the self-regulation of behavior[M]. Cambridge: Cambridge University Press, 1998.
37 曾南, 应行仁. 非线性系统迭代学习算法[J]. 自动化学报, 1992, 18(2): 168-176. |
|
|
|
