隨著健康促進的風氣盛行以及穿戴式科技的發展，透過各種穿戴式裝置如智慧手環、智慧手錶、穿戴式心電儀、穿戴式腦波儀等來進行個人健康監控的現象逐漸普遍，大量可取得的個人數據促進了個人訊息 (Personal Informatics, PI) 技術的發展。透過數值的視覺化呈現，使用者可以從PI系統中了解自己的健康狀況，並刺激使用者注意個人的健康習慣，進而形成改善健康的動機。
現行的 PI 技術著重於如何利用有效的資料視覺化讓使用者得到可解讀的洞見，然而純粹呈現數據、圖表對於促進個人健康的效果有限。當使用者從PI系統中意識到自己的健康出現問題時，無法從中獲得改善的方向，使得健康促進的目的受到阻礙。這個現象引起了本論文的研究動機：從使用者研究角度探討現有的行動健康追蹤工具融合健康活動推薦服務的可行性，作為未來發展類似服務時的設計參考。
為了深入了解使用者與此新服務的互動方式以及過程中的行為改變，我們開發了一套結合健康追蹤功能與客製化健康活動推薦機制的手機APP — UrHealthRcmd。經過三週由20位20~29歲全職工作者參與的文化探針實驗，此研究發現結合群眾共享（Crowdsharing）概念的推薦機制能夠帶給使用者改善健康的動機，並有助於自我效能低的使用者提升他們的進行改變的信心。另外從交叉分析的結果顯示，自我效能依使用者認知的量測結果準確度和推薦項目適合度的不同扮演不同角色，相信量測結果且較願意選擇系統推薦項目的使用者擁有較高的自我效能。
綜上所述，此研究證實結合推薦機制的個人資訊工具（Personal Informatics tools）有促使使用者在意識到自身的健康狀態後，協助其採取行動來改善的潛力，且對於自我效能低的使用者較能發揮用處。根據研究發現，我們建議欲在現有的健康追蹤app上發展推薦機制的設計和開發人員，應充分表達個人健康狀況與推薦項目之關聯性，將有助於使用者更有效率地選擇有幫助的改善行動，另外需同時保有簡單的中等難度的活動，以滿足不同自我效能的使用者的需求。

With the prevalence of health promotion and the advance in wearable technology, health monitoring through various wearable devices such as smart bands, smart watches, wearable ECG (electrocardiogram), and wearable EEG (electroencephalography) becoming more common. Massive amount of the available personal data facilitates the development of Personal Informatics (PI) technology. With data visualization, the user can understand his or her health from the PI system and draw user's attention to personal health habits, thus forming a motivation to improve health.
The current PI technology focuses on how to effectively present data and give users interpretable insights. However, purely presented data and charts have limited effectiveness in promoting personal health. When the user realizes that there is a problem with his or her health from the PI system, s/he may have no idea about how to improve. Based on this phenomenon, the motivation of this study is to explore the feasibility of the existing mobile health tracking tool integrated with health activity recommendation service from the perspective of user research.
To understand how users interact with this new service and their behavioral changes in the process, we developed a mobile app, UrHealthRcmd, that combines health tracking and customized health activity recommendation mechanisms. After a three-week cultural probe assignment involving 20 full-time workers aged between 20 to 29, the study found that the recommendation mechanism adopted the crowdsharing concept can motivate users to improve their health and further help users with low self-efficacy to increase their confidence in making health behavior changes. The results from the cross-analysis show that self-efficacy plays a different role depending on user's perceived accuracy of measurement results and the perceived suitability of the recommended activity. Users who trust the measurement results and are more willing to choose the system recommended activity have higher self-efficacy.
In summary, this study confirms that PI tools embedded with a health recommendation service have potential to motivate users to take action for improving their health status. It’s more useful for users with low self-efficacy. According to the research findings, we suggest that designers and developers who want to develop such a system should fully express the association between personal health status and recommended activities, which will help users choose useful improvement actions more efficiently. In addition, it is necessary to add both simple and medium-difficulty activities to meet the needs of users with different self-efficacy level.

摘要 i
Abstract iii
謝誌 v
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
Chapter 2 Literature Review 5
2.1 Self-efficacy in Healthcare 5
2.2 Recommender System 6
2.3 Cultural Probe 7
Chapter 3 Research Method 10
3.1 System Design 10
3.2 Measurements of Self-Efficacy 11
3.3 Research Procedure 13
3.3.1 Participants recruitment 14
3.3.2 Experiment execution 15
3.3.3 Final interview 16
Chapter 4 System Architecture and Implementation 18
4.1 System Architecture 18
4.2 System Implementation 20
4.2.1 Physical and mental health tracking 20
4.2.2 Health activity recommendation 22
4.2.3 Recommendation effectiveness feedback 24
Chapter 5 Results 27
5.1 Data Organization 27
5.2 Data Analysis 31
5.2.1 The Effectiveness of Health Recommendation Service 31
5.2.2 Feedback of System Functionalities 32
5.2.3 Cross Analysis 36
Chapter 6 Discussions 48
Chapter 7 Conclusion 51
References 53
Appendices 56

Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175-185.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986.
Bandura, A. (1995). Self-efficacy in changing societies: Cambridge university press.
Bandura, A. (1997). Self-efficacy: The exercise of control: Macmillan.
Bandura, A. (2006). Guide for constructing self-efficacy scales. Self-efficacy beliefs of adolescents, 5(1), 307-337.
Bauman, A. E., Reis, R. S., Sallis, J. F., Wells, J. C., Loos, R. J., Martin, B. W., & Group, L. P. A. S. W. (2012). Correlates of physical activity: why are some people physically active and others not? The lancet, 380(9838), 258-271.
Becker, H., Stuifbergen, A., Oh, H. S., & Hall, S. (1993). Self-rated abilities for health practices: A health self-efficacy measure. Health Values: The Journal of Health Behavior, Education & Promotion.
Bernhaupt, R., Obrist, M., Weiss, A., Beck, E., & Tscheligi, M. (2008). Trends in the living room and beyond: results from ethnographic studies using creative and playful probing. Computers in Entertainment (CIE), 6(1), 5.
Clarke, J., Proudfoot, J., Birch, M.-R., Whitton, A. E., Parker, G., Manicavasagar, V., . . . Hadzi-Pavlovic, D. (2014). Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial. BMC psychiatry, 14(1), 272.
D'Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., . . . Alvarez-Jimenez, M. (2017). Artificial intelligence-assisted online social therapy for youth mental health. Frontiers in psychology, 8, 796.
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR mental health, 4(2), e19.
Gaver, B., Dunne, T., & Pacenti, E. (1999). Design: cultural probes. interactions, 6(1), 21-29.
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. Paper presented at the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999.
Hevey, D., Smith, M. l., & McGee, H. M. (1998). Self-efficacy and health behaviour: a review. The Irish Journal of Psychology, 19(2-3), 248-273.
Hoermann, S., McCabe, K. L., Milne, D. N., & Calvo, R. A. (2017). Application of synchronous text-based dialogue systems in mental health interventions: systematic review. Journal of medical Internet research, 19(8), e267.
Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106.
Isinkaye, F., Folajimi, Y., & Ojokoh, B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
Kim, H.-G., Cheon, E.-J., Bai, D.-S., Lee, Y. H., & Koo, B.-H. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry investigation, 15(3), 235.
Lee, D., Oh, K.-J., & Choi, H.-J. (2017). The chatbot feels you-a counseling service using emotional response generation. Paper presented at the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).
Lee, S. Y., Hwang, H., Hawkins, R., & Pingree, S. (2008). Interplay of negative emotion and health self-efficacy on the use of health information and its outcomes. Communication Research, 35(3), 358-381.
Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.
Mudde, A. N., Kok, G., & Strecher, V. J. (1995). Self-efficacy as a predictor for the cessation of smoking: Methodological issues and implications for smoking cessation programs. Psychology and Health, 10(5), 353-367.
Mullane, A., Laaksolahti, J., & Svanæs, D. (2014). Wearable probes for service design. Paper presented at the ServDes. 2014 Service Future; Proceedings of the fourth Service Design and Service Innovation Conference; Lancaster University; United Kingdom; 9-11 April 2014.
O'Leary, A. (1985). Self-efficacy and health. Behaviour research and therapy, 23(4), 437-451.
Ockene, J. K., Mermelstein, R. J., Bonollo, D. S., Emmons, K. M., Perkins, K. A., Voorhees, C. C., & Hollis, J. F. (2000). Relapse and maintenance issues for smoking cessation. Health Psychology, 19(1S), 17.
Servia-Rodríguez, S., Rachuri, K. K., Mascolo, C., Rentfrow, P. J., Lathia, N., & Sandstrom, G. M. (2017). Mobile sensing at the service of mental well-being: a large-scale longitudinal study. Paper presented at the Proceedings of the 26th International Conference on World Wide Web.
Strecher, V. J., McEvoy DeVellis, B., Becker, M. H., & Rosenstock, I. M. (1986). The role of self-efficacy in achieving health behavior change. Health education quarterly, 13(1), 73-92.
Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183.
Vandeput, S., Taelman, J., Spaepen, A., & Van Huffel, S. (2009). Heart rate variability as a tool to distinguish periods of physical and mental stress in a laboratory environment. Paper presented at the Proceedings of the 6th international workshop on biosignal interpretation (BSI), New Haven, CT.
WHO. (2003). Investing in Mental Health. Retrieved from https://www.who.int/mental_health/media/investing_mnh.pdf
WHO. (2017). Depression and Other Common Mental Disorders-Global Health Estimates. Retrieved from https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf
Woodward, K., Kanjo, E., Brown, D., McGinnity, T. M., Inkster, B., Macintyre, D. J., & Tsanas, A. (2019). Beyond Mobile Apps: A Survey of Technologies for Mental Well-being. arXiv preprint arXiv:1905.00288.