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.

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