研究生: |
陳亮融 Chen, Liang-Rong |
---|---|
論文名稱: |
高齡者使用穿戴式裝置影響因素之探討 Factors Affecting the Use of Wearable Devices among Older Adults |
指導教授: |
張少熙
Chang, Shao-Hsi |
口試委員: |
張少熙
Chang, Shao-Hsi 韓豐年 Han, Feng-Nien 李晶 Lee, Ching |
口試日期: | 2024/06/14 |
學位類別: |
碩士 Master |
系所名稱: |
體育與運動科學系 Department of Physical Education and Sport Sciences |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 97 |
中文關鍵詞: | 老年人 、智慧手錶 、智慧手環 、科技產品 、高齡者科技接受模式 |
英文關鍵詞: | Elderly, Smart Watch, Smart Wristband, Technology Products, Senior Technology Acceptance Model |
研究方法: | 調查研究 |
DOI URL: | http://doi.org/10.6345/NTNU202401212 |
論文種類: | 學術論文 |
相關次數: | 點閱:238 下載:21 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
運動結合科技已是全球趨勢,穿戴式裝置被預測為未來運動趨勢之首,亦被證實對 高齡者身體活動具有正面效益,然而高齡者可能面臨多方阻礙致使較低之科技使用率, 其中亦包含穿戴式裝置。本研究旨在探討高齡者使用穿戴式裝置運動之影響因素,以臺 北市 65 歲 (含) 以上具科技產品使用習慣之高齡者做為研究對象,採用問卷調查法透過 通訊軟體 Line 之高齡運動社群發放網路問卷,共計回收 397 份有效問卷,並以結構方 程模式檢驗研究假設。結果顯示科技焦慮對控制信念解釋力最高,控制信念對態度信念 解釋力最高,亦是影響高齡者使用穿戴式裝置運動的主要因素。建議未來研究可擴大範 圍,比較不同特性高齡族群使用穿戴式裝置運動之影響因素,亦可針對不同運動科技產 品進行探討,以瞭解高齡者對不同運動科技產品使用行為之影響因素。
Exercise combined with technology has become a global trend, and wearable devices are predicted to lead the future exercise trends, with proven positive effects on physical activities for older adults. However, older adults may encounter various obstacles, resulting in lower rates of technology usage, including wearable devices. This study aimed to investigate the influencing factors of older adults' use of wearable devices for exercise, based on the Senior Technology Acceptance Model (STAM). Targeted individuals aged 65 and above in Taipei City who have habitual use of technological products. A cross-sectional questionnaire survey was conducted through the LINE app's community for senior exercise, resulting in 397 valid responses. Structural Equation Modeling was used to examine the research hypotheses. The results indicated that Gerontechnology anxiety had the highest explanatory power for Control Beliefs, and Control Beliefs had the highest explanatory power for Attitudinal Beliefs, which is also the main factor influencing older adults' use of wearable devices for exercise. Future research could employ multiple comparisons to compare the influencing factors between different demographics of older adults. Additionally, exploring the influencing factors of older adults' usage behaviors regarding different sports technologies could provide further insights.
內政部 (2020,5月27日)。老人福利法。全國法規資訊庫。http://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=D0050037
王晨旭 (2019)。穿戴式智慧裝置對健康的助益。國泰醫訊。https://www.cgh.org.tw/rwd1320/store/F4/001-08.pdf
拓墣產業研究所 (2013)。Google Glass、Smartwatch 將掀起智慧穿戴式裝置科技革命。拓墣科技。
邱皓政 (2019)。量化研究與統計分析 (六版)。五南圖書。
洪新原、梁定澎、張嘉銘 (2005)。科技接受模式之彙總研究。資訊管理學報,12(4),211-234。http://dx.doi.org/10.6382/JIM.200510.0211
相子元 (2022)。運動科技,以人為本。人文與社會科學簡訊,23(2),57-64。https://www.nstc.gov.tw/nstc/attachments/d3770057-c606-43ba-bcd5-8df39ff8a1d9
財團法人臺灣網路資訊中心 (2022)。2022臺灣網路報告。作者。https://report.twnic.tw/2022/assets/download/TWNIC_TaiwanInternetReport_2022_CH.pdf
國家發展委員會 (2022)。中華民國人口推估 (2022年至2070年)。作者。https://pop-proj.ndc.gov.tw/download.aspx?uid=70&pid=70
張芳全 (2004)。教育在學率對預期壽命的結構方程模式檢定。國立臺北師範學院學報,17(2),153-186。
張家萱、周學雯、林麗娟 (2020)。不同目標設定方式對高齡者提升身體活動量之影響:以智慧健身手環為介入。體育學報,53(2),189-200。https://doi.org/10.6222/pej.202006_53(2).0004
張庭瑜 (2022,5月19日)。凱度洞察臺灣消費者智慧穿戴式裝置調查。凱度洞察臺灣。https://go.in.kantar.com/l/73302/2022-05-19/26bmlk8
教育部體育署 (2022)。中華民國111年運動現況調查。作者。https://isports.sa.gov.tw/apps/Download.aspx?SYS=TIS&MENU_CD=M07&ITEM_CD=T01&MENU_PRG_CD=4&ITEM_PRG_CD=2
陳寬裕 (2017)。應用統計分析:SPSS的運用。五南圖書。
臺北市政府民政局 (2023)。臺北市各行政區15歲以上現住人口數按年齡及教育程度分析。作者。https://ca.gov.taipei/News_Content.aspx?n=8693DC9620A1AABF&sms=D19E9582624D83CB&s=49EE949EED38EF73
臺北市政府社會局 (2024)。113年3月列冊關懷獨居長者總人數。作者。https://dosw.gov.taipei/cp.aspx?n=E15926774D45D8A6&s=5EAFD8C22BBE834F
臺灣傳播調查資料庫 (2022)。熟齡族群上網原因與數位媒體使用能力概況。作者。https://crctaiwan.dcat.nycu.edu.tw/epaper/第300期20221230.htm
數位發展部 (2022)。111年數位發展調查報告及摘要。作者。https://www-api.moda.gov.tw/File/Get/moda/zh-tw/qJEjIvRGm4LGlZw
衛生福利部 (2018)。106 年老人狀況調查報告。衛生福利部統計處。https://www.mohw.gov.tw/dl-48636-de32ad67-19c8-46d6-b96c-8826f6039fcb.html
衛生福利部 (2021,2月15日)。迎接醫材管理新紀元,穿戴式產品如何管。衛生福利部新聞稿。https://www.mohw.gov.tw/cp-5013-58006-1.html
衛生福利部 (2023)。社區據點彙整總表。衛生福利部社會及家庭署。https://ccare.sfaa.gov.tw/home/statistics
衛生福利部 (2024)。111 年老人狀況調查報告。衛生福利部統計處。https://dep.mohw.gov.tw/DOS/cp-5095-77509-113.html
賴弘基 (2020)。高齡學習者使用數位遊戲式學習影響因素之探討。福祉科技與服務管理學刊,8(1),58-71。https://doi.org/10.6283/JOCSG.202003_8(1).58
顏月珠 (1988)。戶外遊憩研究統計方法之探討。戶外遊憩研究,1(2),3-23。https://doi.org/10.6130/JORS.1988.1(2)1
Ajzen, I. (1985). Intentions to actions: a theory of planned behavior. In: J. Kuhl & J. Beckmann (Eds.) Action Control. Springer, Berlin, Heidelberg.https://doi.org/10.1007/978-3-642-69746-3_2
Auerswald, T., Meyer, J., von Holdt, K., & Voelcker-Rehage, C. (2020). Application of activity trackers among nursing home residents-A pilot and feasibility study on physical activity behavior, usage behavior, acceptance, usability and motivational impact. International Journal of Environmental Research and Public Health, 17(18), 6683.https://doi.org/10.3390/ijerph17186683
Bauman, A., Bull. F., Chey, T., Craig, C. L., Ainsworth, B. E., Sallis, J. F., Bowles, H. R., Hagstromer, M., Sjostrom, M., & Pratt, M. (2009). The international prevalence study on physical activity: Results from 20 countries. International Journal of Behavioral Nutrition and Physical Activity, 6(21). https://doi.org/10.1186/1479-5868-6-21
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.https://doi.org/10.1037/0033-2909.88.3.588
Bherer, L., Erickson, K. I., & Liu-Ambrose, T. (2013). A review of the effects of physical activity and exercise on cognitive and brain functions in older adults. Journal of Ageing Research, 657508. https://doi.org/10.1155/2013/657508
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., Chou, R., Dempsey, P. C., DiPietro, L., Ekelund, U., Firth, J., Friedenreich, C. M., Garcia, L., Gichu, M., Jago, R., Katzmarzyk, P. T., …Willumsen, J. F. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451-1462.https://doi.org/10.1136/bjsports-2020-102955
Cadmus-Bertram, L. A., Marcus, B. H., Patterson, R. E., Parker, B. A., & Morey, B. L. (2015). Randomized trial of a Fitbit-based physical activity intervention for women. American Journal of Preventive Medicine, 49(3), 414–418.https://doi.org/10.1016/j.amepre.2015.01.020
Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM). Ergonomics, 57(5), 635-652. https://doi.org/10.1080/00140139.2014.895855
Chen, K., & Lou, V. W. Q. (2020). Measuring senior technology acceptance: Development of a brief, 14-item scale. Innovation in Aging, 4(3), 1-12.https://doi.org/10.1093/geroni/igaa016
Chodzko-Zajko, W. J., Proctor, D. N., Fiatarone Singh, M. A., Minson, C. T., Nigg, C. R., Salem, G. J., & Skinner, J. S. (2009). American College of Sports Medicine position stand. Exercise and physical activity for older adults. Medicine and Science in Sports and Exercise, 41(7), 1510-1530. https://doi.org/10.1249/MSS.0b013e3181a0c95c
Chu,Y.-H., Chen, J.-S., & Wang, M.-C. (2019). Why do older adults use wearable devices: A case study adopting the senior technology acceptance model (STAM)[Conference paper]. 2019 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 2019, 1-8.https://doi.org/10.23919/PICMET.2019.8893767.
Cilliers L. (2020). Wearable devices in healthcare: Privacy and information security issues. Health Information Management: Journal of the Health Information Management Association of Australia, 49(2-3), 150-156. https://doi.org/10.1177/1833358319851684
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://www.jstor.org/stable/2632151
Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Sloan School of Management, Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/15192
Farage, M. A., Miller, K. W., Ajayi, F., & Hutchins, D. (2012). Design principles to accommodate older adults. Global Journal of Health Science, 4(2), 2–25. https://doi.org/10.5539/gjhs.v4n2p2
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley. https://people.umass.edu/aizen/f&a1975.html
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Grand View Research (2023). Wearable technology market size, share & trends analysis report by product (head & eyewear, wristwear), by application (consumer electronics, healthcare), by region (asia pacific, europe), and segment forecasts, 2023-2030. Author. https://www.grandviewresearch.com/industry-analysis/wearable-technology-market#
Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2018). Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9 million participants. The Lancet Global Health, 6(10), e1077-e1086. https://doi.org/https://doi.org/10.1016/S2214-109X(18)30357-7
Harris, M. T., Blocker, K. A., & Rogers, W. A. (2022). Older adults and smart technology: Facilitators and barriers to use. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.835927
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
Jakicic, J. M., & Otto, A. D. (2005). Physical activity considerations for the treatment and prevention of obesity. The American Journal of Clinical Nutrition, 82(1), 226S-229S. https://doi.org/10.1093/ajcn.82.1.226S
Jarvis, M.-A., Sartorius, B., & Chipps, J. (2020). Technology acceptance of older persons living in residential care. Information Development, 36(3), 339-353.
https://doi.org/10.1177/0266666919854164
Jia, P., Lu, Y., & Wajda, B. (2015). Designing for technology acceptance in an ageing society through multi-stakeholder collaboration. Procedia Manufacturing, 3, 3535-3542. https://doi.org/10.1016/j.promfg.2015.07.701
Knoblauch, M. (2014, May 13). The history of wearable tech, from the casino to the consumer. Mashale. https://mashable.com/archive/wearable-technology-history
Kohlbacher, F., Herstatt, C.,& Tim, S. (2011). Product Development for the Silver Market. In Kohlbacher, F., Herstatt, C. (Eds.). The Silver Market Phenomenon: Marketing and Innovation in the Aging Society, (pp. 3–13). Springer.
Koizumi, D., Rogers, N. L., Rogers, M. E., Islam, M. M., Kusunoki, M., & Takeshima, N. (2009). Efficacy of an accelerometer-guided physical activity intervention in community-dwelling older women. Journal of Physical Activity & Health, 6(4), 467-474. https://doi.org/10.1123/jpah.6.4.467
Kyytsönen M, Vehko T, Anttila H, Ikonen J (2023). Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digit Health 2(5): e0000245. https://doi.org/10.1371/journal.pdig.0000245
Laricchia, F. (2023). Number of connected wearable devices worldwide from 2019 to 2022. Statista. https://www.statista.com/statistics/487291/global-connected-wearable-devices/
Laricchia, F. (2023). Smartwatch market share worldwide from 2020 to 2022, by vendor. Statista. https://www.statista.com/statistics/1296818/smartwatch-market-share/
Legris, P., Ingham, J., & Collerette, C. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4
Lesch, M. F., Horrey, W. J., Wogalter, M. S., & Powell, W. R. (2011). Age-related differences in warning symbol comprehension and training effectiveness: Effects of familiarity, complexity, and comprehensibility. Ergonomics, 54(10), 879-890. https://doi.org/10.1080/00140139.2011.606924
Ma, Q., Chan, A. H. S., & Chen, K. (2016). Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Applied Ergonomics, 54, 62-71. https://doi.org/10.1016/j.apergo.2015.11.015
Mahendra, B. (2023, September 9). Development of Wearable Devices and Personal Health. Medium. https://medium.com/@bramahendramahendra1/development-of-wearable-devices-and-personal-health-63b855c06db1
McDonald, R. P., & Marsh, H. M. (1990). Choosing a multivariate model: Noncentrality and goodness-of-fit. Psychological Bulletin, 107, 247-255. https://doi.org/10.1037/0033-2909.107.2.247
Mercer, K., Giangregorio, L., Schneider, E., Chilana, P., Li, M., & Grindrod, K. (2016). Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: A mixed-methods evaluation. JMIR Mhealth and Uhealth, 4(1), e7. https://doi.org/10.2196/mhealth.4225
Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., & Walker, A. (2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. Quality & Safety in Health Care,14,26-33. https://doi.org/10.1136/qshc.2004.011155
Nelson, E. A., & Dannefer, D. (1992). Aged heterogeneity: Fact or fiction? The fate of diversity in gerontological research. The Gerontologist, 32(1), 17-23. https://doi.org/10.1093/geront/32.1.17
Nguyen, N. H., Hadgraft, N. T., Moore, M. M., Rosenberg, D. E., Lynch, C., Reeves, M. M., &Lynch, B. M. (2017). A qualitative evaluation of breast cancer survivors’ acceptance of and preferences for consumer wearable technology activity trackers. Supportive Care in Cancer, 25, 3375-3384. https://doi.org/10.1007/s00520-017-3756-y
Orlov, L. M. (2021). The future of wearables and older adults 2021. Aging and Health Technology Watch.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313(5), 459-460. https://doi.org/10.1001/jama.2014.14781
Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? critique and recommendations. Research in Nursing & Health, 29(5), 489-497. https://doi.org/10.1002/nur.20147
Rea, L. M., & Parker, R. A. (1997). Designing and conducting survey research: A comprehensive guide. Josey-Bass Publishers.
Renaud, K., & Van Biljon, J. (2008, October 6-8). Predicting technology acceptance and adoption by the elderly: A qualitative study [Conference paper]. The 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists. Wilderness, South Africa. https://doi.org/10.1145/1456659.1456684
Rosenberg, D., Kadokura, E. A., Bouldin, E. D., Miyawaki, C. E., Higano, C. S., & Hartzler, A. L. (2017). Acceptability of Fitbit for physical activity tracking within clinical care among men with prostate cancer. AMIA Annual Symposium Proceedings, 2016, 1050–1059.
Ruby, D. (2023, March 6). Smartwatch statistics 2023: How many people use smartwatches? Demandsage. https://www.demandsage.com/smartwatch-statistics/
Ryu, M. H., Kim, S., & Lee, E. (2009). Understanding the factors affecting online elderly user’s participation in video UCC services. Computers in Human Behavior, 25(3), 619-632. https://doi.org/10.1016/j.chb.2008.08.013
Schmidt, L. I., Jansen, C.-P., Depenbusch, J., Gabrian, M., Sieverding, M., & Wahl, H.-W. (2022). Using wearables to promote physical activity in old age. Zeitschrift für Gerontologie + Geriatrie 55(5)388-393. https://doi.org/10.1007/s00391-022-02083-x
Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Shin, H. R., Um, S. R., Yoon, H. J., Choi, E. Y., Shin, W. C., Lee, H. Y., & Kim, Y. S. (2023). Comprehensive senior technology acceptance model of daily living assistive technology for older adults with frailty: Cross-sectional study. Journal of Medical Internet Research, 25, e41935. https://doi.org/10.2196/41935
Smith, A. A., Li, R., & Tse, Z. T. (2023). Reshaping healthcare with wearable biosensors. Scientific Reports, 13(1), 1-16. https://doi.org/10.1038/s41598-022-26951-z
Šumak, B., & Heričko, M., Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077. https://doi.org/10.1016/j.chb.2011.08.005
Tan, L. F., Lim, Z. Y., Choe, R., Seetharaman, S., & Merchant, R. (2017). Screening for frailty and sarcopenia among older persons in medical outpatient clinics and its associations with healthcare burden. Journal of the American Medical Directors Association, 18(7), 583-587. https://doi.org/10.1016/j.jamda.2017.01.004
Tao, D., Chen, Z., Qin, M., & Cheng, M. (2023). Modeling consumer acceptance and usage behaviors of m-Health: An integrated model of self-Determination theory, task-technology fit, and the technology acceptance model. Healthcare (Basel, Switzerland), 11(11), 1550. https://doi.org/10.3390/healthcare11111550
Tenneti, R., Johnson, D., Goldenberg, L., Parker, R. A,, & Huppert, F. A. (2012). Towards a capabilities database to inform inclusive design: Experimental investigation of effective survey-based predictors of human-product interaction. Applied Ergonomics, 43(4), 713-726. https://doi.org/10.1016/j.apergo.2011.11.005
Thompson, W. R. (2015). Worldwide survey of fitness trends for 2016 10th anniversary edition. ACSM's Health & Fitness Journal 19(6), 9-18. https://doi.org/10.1249/FIT.0000000000000164
Thompson, W. R. (2016). Worldwide survey of fitness trends for 2017. ACSM's Health & Fitness Journal 20(6), 8-17. https://doi.org/10.1249/FIT.0000000000000252
Thompson, W. R. (2017). Worldwide survey of fitness trends for 2018. ACSM's Health & Fitness Journal 21(6), 10-19. https://doi.org/10.1249/FIT.0000000000000341
Thompson, W. R. (2018). Worldwide survey of fitness trends for 2019. ACSM's Health & Fitness Journal 22(6), 10-17. https://doi.org/10.1249/FIT.0000000000000438
Thompson, W. R. (2019). Worldwide survey of fitness trends for 2020. ACSM's Health & Fitness Journal 23(6), 10-18. https://doi.org/10.1249/FIT.0000000000000526
Thompson, W. R. (2021). Worldwide survey of fitness trends for 2021. ACSM's Health & Fitness Journal 25(6), 10-19. https://doi.org/10.1249/FIT.0000000000000631
Thompson, W. R. (2022). Worldwide survey of fitness trends for 2022. ACSM's Health & Fitness Journal 26(1), 11-20. https://doi.org/10.1249/FIT.0000000000000732
Thompson, W. R. (2023). Worldwide survey of fitness trends for 2023. ACSM's Health & Fitness Journal 27(1), 9-18. https://doi.org/10.1249/FIT.0000000000000834
Tomczyk, Ł., Mascia, M. L., Gierszewski, D., & Walker, C. (2023). Barriers to digital inclusion among older people: A intergenerational reflection on the need to develop digital competences for the group with the highest level of digital exclusion. Innoeduca. International Journal of Technology and Educational Innovation, 9(1), 5-26. https://doi.org/10.24310/innoeduca.2023.v9i1.16433
United Nations (1946). UN. Economic and Social Council (2nd sess.). International Health Conference, New York, United States.
United Nations Department of Economic and Social Affairs Population Division (2019). World population prospects 2019: Highlights (ST/ESA/SER.A/423).
US Department of Health and Human Services. (2018). Physical activity guidelines for Americans.(2nd ed.). Author. https://health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Werner, J. M., Carlson, M., Jordan-Marsh, M., & Clark, F. (2011). Predictors of computer use in community-dwelling, ethnically diverse older adults. Human Factors, 53(5), 431-447. https://doi.org/10.1177/0018720811420840
World Health Organization. (2018). Global action plan on physical activity 2018–2030: More active people for a healthier world. Author. https://www.who.int/publications/i/item/9789241514187
World Health Organization. (2021). Be he@lthy, be mobile: A handbook on how to implement mobile health for physical activity. Author. https://www.who.int/publications/i/item/9789240033474
Wilson, M.L., Huggins-Manley, A.C., Ritzhaupt, A.D., & Ruggles, K. (2023). Development of the abbreviated technology anxiety scale (ATAS). Behavior Research Methods, 55, 185-199. https://doi.org/10.3758/s13428-022-01820-9
Wu, J. and Lu, X. (2013). Effects of extrinsic and intrinsic motivators on using utilitarian, hedonic, and dual-purposed information systems: A meta-analysis, Journal of the Association for Information Systems, 14(3), https://doi.org/10.17705/1jais.00325
Yazdani‐Darki, M., Rahemi, Z., Adib‐Hajbaghery, M., & Izadi‐Avanji, F. S. (2020). Older adults’ barriers to use technology in daily life: A qualitative study. Nursing and Midwifery Studies, 9, 229. https://doi.org/10.4103/nms.nms_91_19
Zaman, S. B., Khan, R. K., Evans, R. G., Thrift, A. G., Maddison, R., & Islam, S. M. S. (2022). Exploring barriers to and enablers of the adoption of information and communication technology for the care of older adults with chronic diseases: Scoping review. JMIR Aging, 5(1), e25251. https://doi.org/10.2196/25251
Zhang, Z., Giordani, B., Margulis, A., & Chen, W. (2022). Efficacy and acceptability of using wearable activity trackers in older adults living in retirement communities: A mixed method study. BMC Geriatrics, 22. https://doi.org/10.1186/s12877-022-02931-w