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題名:穿戴健康科技研究:基於機器學習的研究趨勢分析暨使用者接受性非科技因素之角色
作者:金泰星
作者(外文):Tae Sung Kim
校院名稱:國立中興大學
系所名稱:科技管理研究所
指導教授:何建達
學位類別:博士
出版日期:2022
主題關鍵詞:健康信念模式健康資訊系統隱私計算理論行動健康照護主題模型穿戴健康技術Health belief modelHealth information systemsPrivacy calculus theorymHealthTopic modelingWearable health technology
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近年來,可穿戴技術在預防醫療保健領域開始發揮關鍵作用,透過配戴者提供生物反饋,可及早偵測出健康狀況異常,進而選擇適當的醫療程序,不啻是該領域強大的決策支援工具。然而,在綜整分析歷年文獻研究之後,發現就提高可穿戴技術的可接受度而言,有兩個主要研究缺口亟需補足。首先,由於每年發表的相關研究文章數量增長顯著,幾乎不可能迅速地了解近期具代表性的研究趨勢。再者,儘管衛生資訊科技有別於一般的資訊科技,但迄今為止,大多數研究主要仍集中在科技與其應用,而沒有適當關注非科技層面。
為了解決上述兩個問題,首先,運用機器學習為基礎的主題建模方法,透過彙整3,015 篇行動健康照護的研究文獻,並進行大數據的資料探勘與分析,以歸納與識別代表性研究主題。然後,依據健康、隱私和社會人口背景等主要面向重新配置各個分析因子,建構健康感知科技接受度模型,並應用結構方程模型執行經驗分析研究。
本研究的主要發現為研究學者和醫療保健政策制定者在制定戰略研究計畫時,如何做出明智決定提供具實用性的指導方針。而且,本研究為非科技層面因子如何影響消費者使用衛生資訊科技之意向提供更切合的證明與解釋。
Wearable technology has recently begun to play a key role in preventive healthcare and has become a powerful, decision-supporting tool. In particular, it provides immediate biofeedback (e.g., vital signs) to the wearer and then detects anomalous health conditions early enough to proceed with appropriate medical procedures. However, after an extensive literature review, two research gaps were identified and must be filled to increase the wearable technology acceptance rate. Firstly, due to the significant growth in the number of research articles published each year, it is nearly impossible to identify prevalent research trends effectively. Secondly, even though health information technology is distinctive from general information technology, so far, most acceptance research has been primarily focusing on the technology aspect without appropriate attention to the non-technology perspective.
To address these two questions mentioned above, first, a machine learning-based topic modeling method was utilized to identify prevalent research themes by analyzing big data of 3,015 mobile health research articles. Next, factors from health, privacy, and socio-demographic contexts were selectively reconfigured to formulate a health-aware technology acceptance model. Afterward, structural equation modeling was applied to perform empirical analysis.
The key findings of the current study create practical guidance for scholars and healthcare policymakers in forming an informed decision on strategic research plans. Furthermore, it demonstrates a more precise explanation of how non-technology elements affect consumers’ intention to use health information technology.
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