本研究應用解構計畫行為理論，評價中高齡者對『新北動健康』行動應用程式的使用意圖。研究對象為新北市地區年滿50歲以上的中高齡者，以自填式問卷進行調查，共分析有效問卷262份，研究結果如下：
一、中高齡者使用『新北動健康』行動應用程式之知覺有用性、知覺易用性、相容性、同儕影響、家人影響、自我效能、資源促成條件、科技促成條件、態度、主觀規範、知覺行為控制與使用行為意圖皆偏向中上程度。
二、中高齡者使用『新北動健康』行動應用程式之知覺有用性、知覺易用性、相容性、同儕影響、家人影響、自我效能、資源促成條件、科技促成條件、態度、主觀規範、知覺行為控制與使用『新北動健康』行動應用程式行為意圖皆達顯著正相關。
三、中高齡者使用『新北動健康』行動應用程式之知覺有用性與相容性能顯著預測其使用態度；同儕影響與家人影響能顯著預測其主觀規範；自我效能、資源促成條件與科技促成條件能顯著預測其知覺行為控制。
四、中高齡者使用『新北動健康』行動應用程式之態度、主觀規範、知覺行為控制能顯著預測其使用行為意圖。
本研究依結論提出對提供自我照護監測的『新北動健康』行動應用程式健康傳播與行銷建議，以提升中高齡者的使用意圖。

This study evaluated behavioral intentions to use the "Fit For Age" Mobile Application among middle-aged adults based on the Decomposed Theory of Planned Behavior. The participants were senior citizens aged over 50 years from New Taipei City. A total of 262 participants completed the self-administered questionnaire. The main results were as follows.
1.Middle-aged adults had average/high scores for their level of perceived usefulness, perceived ease of use, compatibility, peer influence, family influence, self-efficacy, resource facilitating conditions, technology facilitating conditions, attitudes, subjective norms, perceived behavioral control, and behavioral intentions to use "Fit For Age" Mobile Application.
2.Middle-aged adults’ perceived usefulness, perceived ease of use, compatibility, peer influence, family influence, self-efficacy, resource facilitating conditions, technology facilitating conditions, attitudes, subjective norms, perceived behavioral control were significantly positively correlated with behavioral intentions to use "Fit For Age" Mobile Application.
3.Middle-aged adults’ perceived usefulness and compatibility significantly predicted attitudes toward the usage of "Fit For Age" Mobile Application. Peer influence and family influence significantly predicted subjective norms of "Fit For Age" Mobile Application usage. Self-efficacy, resource facilitating conditions, and technology facilitating conditions significantly predicted perceived behavioral control of "Fit For Age" Mobile Application usage.
4.Middle-aged adults’ attitudes, subjective norms, and perceived behavioral control significantly predicted behavioral intentions to use "Fit For Age" Mobile Application.
Based on the findings, marketing strategies and recommendations for the "Fit For Age" Mobile Application usage among middle-aged adults are proposed.

參考文獻
一.中文文獻
王本正、許富榕（2016）。以延伸型整合性科技接受模式探討行動醫療App協助照護任務之接受度。福祉科技與服務管理學刊，4(4)，483-494。 doi:10.6283/jocsg.2016.4.4.483
池文海、林憬、王智永、張明暐（2011）。使用者採用GPS科技產品行為意圖之研究。創新與管理，8(2)，29-60。
李亭亭、施玉珊（2009）。運用創新擴散理論於促進護理資訊系統之推展。護理雜誌，56(3)，18-22。doi:10.6224/jn.56.3.18
李慶長、張銀益、黃柏翔（2015）。以計畫行為理論探討穿戴型裝置的使用意圖－以Google眼鏡為例。Electronic Commerce Studies，13(3)，315-334。
亞太智慧城市（2018）。入圍2018亞太智慧城市的公共衛生和社區服務類大獎。取自http://www.idc.asia/idcscapa/
拓墣產業研究所（2014）。從4G LTE看行動醫療商機。臺北市：拓墣科技股份有限公司。
林建羽、周玟慧（2016）。中高齡者使用行動通訊軟體與家庭成員互動感知需求、使用意願及互動內容之探究。高雄應用科技大學人文與社會科學學刊，2(1)，119-134。doi:10.6554/jktuhs.2016.0201.08
胡宗鳳、江文鉅（2017）。以整合性科技接受模式探討閱聽者下載新聞類行動應用程式之意願。管理資訊計算，6(1)，125-134。doi:10.6285/mic.6(1).10
財團法人資訊工業策進會（2017）。銀髮族的購物行為調查報告：逾6成持有智慧型手機、近2成有網購及電視購物經驗。取自 http://www.iii.org.tw/Press/NewsDtl.aspx?nsp_sqno=1917&fm_sqno=14
張紀萍（2015）。科技接受模式與應用－以慢性病長者遠距照護使用為例。護理雜誌，62(3)，11-16。doi:10.6224/jn.62.3.11
新北市政府衛生局（2018）。「新北動健康 智慧大平台」迎向數位健康城。取自https://www.health.ntpc.gov.tw/content/?type_id=19018&parent_id=19412
葉美春、阮明淑（2007）。使用者採用知識管理系統之影響因素研究－理論模型的比較取向。圖書資訊學刊，5(1&2)，69-90。 doi:10.6182/jlis.2007.5(1.2).069
趙正敏、馬志民、鄭博文（2016）。以整合性觀點探討護理人員對於混合式數位學習使用意向之研究。顧客滿意學刊，12(1)，1-31。
蘇矢立、林淑滿、林世鐸、林素蘭、廖培湧、黎煥中（2008）。利用網路或PDA手機照護系統對第2型糖尿病自我血糖監測的初步報告。中華民國糖尿病衛教學會會訊，4(4)，18-22。doi:10.6583/tade.2008.4(4).8
 
二.英文文獻
Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2), 71-80. doi:10.1016/j.iheduc.2008.05.002
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. Kuhl, J. & Beckman, J. (Eds.), Action-control: From cognition to behavior. 11-39. Springer-Verlag.
Ajzen, I. (1989). Attitude structure and behavior. Attitude structure and function, 241-274.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior & Human Decision Processes, 50(2), 179.
Al-Jumaili, A. A., Al-Rekabi, M. D., Alsawad, O. S., Allela, O. Q. B., Carnahan, R., Saaed, H., . . . Sorofman, B. (2017). Exploring Electronic Communication Modes Between Iraqi Faculty and Students of Pharmacy Schools Using the Technology Acceptance Model. Am J Pharm Educ, 81(5), 89. doi:10.5688/ajpe81589
Anderson, K., Burford, O., & Emmerton, L. (2016). App Chronic Disease Checklist: Protocol to Evaluate Mobile Apps for Chronic Disease Self-Management. JMIR Res Protoc, 5(4), e204. doi:10.2196/resprot.6194
Ashoorkhani, M., Bozorgi, A., Majdzadeh, R., Hosseini, H., Yoonessi, A., Ramezankhani, A., & Eftekhar, H. (2016). Comparing the effectiveness of the BPMAP (Blood Pressure Management Application) and usual care in self-management of primary hypertension and adherence to treatment in patients aged 30-60 years: study protocol for a randomized controlled trial. Trials, 17(1), 511. doi:10.1186/s13063-016-1638-0
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. doi:10.1037/0033-295X.84.2.191
Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory: Englewood Cliffs, N.J.: Prentice-Hall, c1986.
Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, IEEE Trans. Syst., Man, Cybern. A(4), 411-420. doi:10.1109/3468.852435
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers and education, 59(3), 1054-1064.
Davis, F. D. (1986). A Technology Acceptance Model for empirically testing new end-user information systems: theory and results. Paper presented at the massachusetts institute of technology, Massachusetts.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: an introduction to theory and research: Reading, Mass. : Addison-Wesley Pub. Co., [c1975].
Glass, R., & Li, S. (2010). Social influence and instant messaging adoption. Journal of Computer Information Systems, 51(2), 24-30.
Goyal, S., Lewis, G., Yu, C., Rotondi, M., Seto, E., & Cafazzo, J. A. (2016). Evaluation of a Behavioral Mobile Phone App Intervention for the Self-Management of Type 2 Diabetes: Randomized Controlled Trial Protocol. JMIR Res Protoc, 5(3), e174. doi:10.2196/resprot.5959
Hayashi, A., Yamaguchi, S., Waki, K., Fujiu, K., Hanafusa, N., Nishi, T., . . . Ohe, K. (2017). Testing the Feasibility and Usability of a Novel Smartphone-Based Self-Management Support System for Dialysis Patients: A Pilot Study. JMIR Res Protoc, 6(4), e63. doi:10.2196/resprot.7105
Henke. (1999). Promoting independence in older persons through the internet. Cyberpsychology & behavior, 2(6), 521-527.
Hung, S. Y., Ku, Y. C., & Chien, J. C. (2012). Understanding physicians' acceptance of the Medline system for practicing evidence-based medicine: a decomposed TPB model. Int J Med Inform, 81(2), 130-142. doi:10.1016/j.ijmedinf.2011.09.009
Kim, M. Y., Lee, S. Y., Jo, E. J., Lee, S. E., Kang, M. G., Song, W. J., . . . Chang, Y. S. (2016). Feasibility of a smartphone application based action plan and monitoring in asthma. Asia Pac Allergy, 6(3), 174-180. doi:10.5415/apallergy.2016.6.3.174
Masterson Creber, R. M., Maurer, M. S., Reading, M., Hiraldo, G., Hickey, K. T., & Iribarren, S. (2016). Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth, 4(2), e74. doi:10.2196/mhealth.5882
Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111-1119. doi:https://doi.org/10.1016/j.chb.2010.03.015
Reid, R., Trout, A. L., & Schartz, M. (2005). Self-Regulation Interventions for Children With Attention Deficit/Hyp emotivity Disorder. Exceptional Children, 71(4), 361-377.
Ribu, L., Holmen, H., Torbjornsen, A., Wahl, A. K., Grottland, A., Smastuen, M. C., . . . Arsand, E. (2013). Low-intensity self-management intervention for persons with type 2 diabetes using a mobile phone-based diabetes diary, with and without health counseling and motivational interviewing: protocol for a randomized controlled trial. JMIR Res Protoc, 2(2), e34. doi:10.2196/resprot.2768
Rogers, E. M. (1983). Diffusion of Innovations(4th ed.). New York, NY: Free Press.
Rogers, E. M., & Shoemaker, F. (1983). Diffusion of innovation: A cross-cultural approach. New York.
Rose, J., & Fogarty, G. J. (2010). Technology readiness and segmentation profile of mature consumers. Paper presented at the Paper session presented at the Proceedings of the 4th Biennial Conference of the Academy of World Business, Marketing and Management Development, , Oulu, Finland.
Shi, J., & Niu, Q. (2010). SNSs usage among Chinese internet users: an empirical study. Stud Health Technol Inform, 154, 150-154.
Specht, D.A.(1975). On the Evaluation of Causal Model.Social Science Research,4,113-133.
Taylor, S., & Todd, P. A. (1995a). Decomposition and crossover effects in the theory of planned behavior A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Taylor, S., & Todd, P. A. (1995b). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.
Tsai, M. (2012). The trends and adoption behaviors of smart phones in Taiwan: A comparison between persons over 45 years of age and youth under 25. Paper presented at the Paper presented at 2012 Proceedings of PICMET’12: Technology Management for Emerging Technologies Vancouver ,Canada.
Vermeulen, J., Neyens, J. C. L., Spreeuwenberg, M. D., Rossum, E. v., Sipers, W., Habets, H., . . . De Witte, L. P. (2013). User-centered development and testingof a monitoring system that provides feedbackregarding physical functioning to elderly people. Patient Preference & Adherence, 7, 843.
World Health Organization. (2016). mHealth: use of mobile wireless technologies for public health. Retrieved from 139th Executive Board, Geneva, Switzerland, Report by the Secretariat(27 May 2016).
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., . . . Li, S. (2017). Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR Mhealth Uhealth, 5(3), e35. doi:10.2196/mhealth.6522
Xie, B. (2007). Older Chinese, the Internet, and well-being. Care Management Journals: Journal of Long Term Home Health Care, 8(1), 33–38.
Zhou, J., Rau, P.-L. P., & Salvendy, G. (2012). Use and Design of Handheld Computers for Older Adults: A Review and Appraisal. International Journal of Human-Computer Interaction, 28(12), 799-826. doi:10.1080/10447318.2012.668129