背景：臺灣已於2025年邁入超高齡社會，高齡健康管理成為重要議題。目前多數運動科技產品仍在與高齡的需求進行磨合調整，評估高齡者對於新興運動科技設備接受程度之工具有需進一步與其他量表整合開發。
目的：開發適用於評估高齡者使用新興運動科技接受度的問卷，並以智慧型運動APP進行驗證，比較不同背景高齡者之科技接受度差異，且探討影響使用行為意願之關鍵因素及使用變化。
方法：以社區高齡者為研究對象，以專家效度刪去、整合三份已開發之高齡科技問卷，從 53 題原始題目中，建議修正語意及剔除7題不適題型，保留46題作為預試問卷。以124份有效問卷，經探索性因素分析進行題目剔除28題，最終收斂為13題五個構面的問卷，包括精神活力、科技焦慮、科技障礙、控制信念、態度信念，與增加另外5題做為評估用途的「使用行為」構面，其信度分析Cronbach's α值皆達 0.8以上之正式問卷 ; 並於社區運動課程進行驗證，並以課程三個時間點（第一週、第十二週、結束後一年）進行問卷填答。以獨立t檢定、單因子變異數分析，比較高齡者在不同背景下的科技接受度差異 ; 採卡方檢定、皮爾森積差相關分析，了解基本資料與構面間的相關性 ; 採多元逐步迴歸分析，篩選影響使用行為的關鍵因素 ; 以重複量測單因子變異數分析，觀察高齡者在時間及外部因素影響下之科技使用變化。
結果：正式測驗階段，共回收183份有效問卷，發現在不同背景因素下，男性相較於女性，在科技焦慮分數顯著高約17% (p<.05) ; 教育程度較低者相比中、高教育程度者，在控制信念分數分別顯著低約17%、19％ (p<.05)、態度信念分數皆低約13% (p<.05)、使用行為分數分別顯著低約17%、18％ (p<.05) ; 低度健康者相較於高度健康者，在精神活力分數顯著低約14% (p<.05) ; 有慢性病者相比於未有慢性病者，在科技焦慮分數顯著低約14% (p<.05) ; 受到身心因素影響社交者相較於未受影響者，在精神活力分數顯著低約4% (p<.05)。其次，發現在性別、年齡、教育程度、生活費支用、健康狀況、慢性病史、身心因素妨礙社交等個人變項與問卷之精神活力、科技焦慮、科技障礙、控制信念、態度信念、使用行為等構面間皆具有顯著相關 (p<.05)，更進一步迴歸分析出態度信念、教育程度、精神活力、科技障礙為影響使用行為的前四項關鍵因素，整體解釋量達 63.5% (p<.05)。其中共有66人完成參與三次有效問卷之驗證階段，結果顯示高齡者在第一週的科技障礙分數相較於第十二週顯著低約8% (p<.05)，以及第一週和第十二週的控制信念分數相較於結束後一年，分別顯著高約13%、11％ (p<.05)。
結論：本研究發展出適合評估社區高齡者且具有良好信效度之高齡運動科技接受度問卷，並發現在男性、教育程度較低、低度健康、有慢性病史、受身心因素影響社交等背景之社區高齡者可能對於使用智慧型運動APP會面臨更多挑戰 ; 建議實務上可提供個別化協助及排除使用障礙，以提升其健康程度並建立持續學習之概念，從而達到長期使用科技且規律運動之健康行為。

Background: As Taiwan approaches a super-aged society by 2025, health management for older adults has become a critical issue. Smart fitness applications are increasingly viewed as tools to support health promotion among the elderly. However, most exercise technologies are still under development to accommodate older users, and there remains a lack of validated instruments to assess their acceptance of such technologies.
Purpose: This study aimed to develop and validate a questionnaire for assessing older adults’ acceptance of emerging exercise technologies, to compare differences in acceptance across various demographic backgrounds, and to identify key factors influencing technology acceptance, using a smartphone fitness app as the verification tool.
Methods: Community-dwelling older adults aged 65 years and above were recruited through convenience sampling. The initial version of the questionnaire was developed by integrating items from three existing questionnaires. Expert review and content validation resulted in the removal of inappropriate items and refinement of semantics, producing a 46-item pilot questionnaire. In the pilot stage, 124 valid responses were collected. Exploratory factor analysis led to the removal of 28 items. The questionnaire was eventually reduced to 13 questions in five dimensions: psychological vitality, technological anxiety, technological barriers, control beliefs, and attitude beliefs. In addition, five questions in the "usage behavior" dimension were added for evaluation purposes. A total of 18 questions were completed which with Cronbach’s α values above 0.8, which were used for the formal questionnaire. The formal survey and validation application were conducted at three time points within a community exercise program: week 1, week 12, and one year post-program. Data analysis included independent t-tests and one-way ANOVA to compare differences in technology acceptance across demographic backgrounds, chi-square tests and Pearson correlations to examine relationships between demographic variables and dimensions, multiple stepwise regression to identify key predictors of usage behavior, and repeated measures ANOVA to observe changes in technology use over time and external factors.
Results: A total of 183 valid responses were collected in the formal phase. Male participants scored approximately 17% higher in technology anxiety compared with female participants (p <.05). Participants with lower education levels scored approximately 17% and 19% lower in control beliefs (p <.05), 13% lower in attitude beliefs (p <.05), and 17% and 18% lower in use behavior compared with participants with medium or higher education levels (p <.05). Participants with poor health scored approximately 14% lower in mental vitality compared with participants with good health (p <.05), while participants with chronic conditions scored approximately 14% lower in technology anxiety compared with participants without chronic conditions (p <.05). Participants affected by psychosocial limitations scored approximately 4% lower in mental vitality (p <.05). Significant correlations were found between demographic variables (gender, age, education, living expenses, health status, chronic disease history, psychosocial limitations) and all six dimensions (p <.05). Stepwise regression identified attitude beliefs, education level, mental vitality, and technology barriers as the four key predictors of use behavior, explaining 63.5% of the variance (p <.05). In the three-stage data analysis after community exercise course intervention (n = 66), participants showed an 8% decrease in technology barriers from week 1 to week 12 (p <.05), and control beliefs scores were approximately 13% and 11% higher at week 1 and week 12, respectively, compared to one year post-program (p <.05).
Conclusion: This study successfully developed and validated an assessment tool for evaluating older adults’ acceptance of exercise-related technologies, demonstrating good applicability among community-dwelling older adults and making it suitable for field application. The findings indicate that elderly individuals with different personal backgrounds exhibit significant variations in technology acceptance under different circumstances. For instance, older male participants, those of advanced age with lower education levels, poorer health status, chronic conditions, or social difficulties caused by physical and mental factors tend to experience anxiety when using sports technology products. The key determinants affecting the behavioral use of smart sports applications among the elderly are, in order, "attitude beliefs," "educational level," "mental vitality," and "technological barriers." It is recommended to encourage the elderly to participate in community-based exercise programs and hands-on training sessions may enhance health outcomes and promote the adoption of long-term technology-based health behaviors.

中文文獻
內政部（2024，8月23日）。113年第34週內政統計通報。https://www.moi.gov.tw/News_Content.aspx?n=2905&s=319132
吳仁宇 (2015)。運動健身，老而彌堅。中華民國內膜異位症婦女協會會刊，22(1-2)，11–13。
吳明蒼、蔡正育 (2021)。高齡者活躍老化影響因素之探討。運動休閒管理學報，18(2)，26–43。
吳舜堂、陳欽雨 (2017)。高齡長者社會支持，社會參與與活躍老化關係之研究。福祉科技與服務管理學刊，5(4)，331–352。
祁安美、陳雪芬 (2023)。高齡健康促進之在地老化與高齡友善政策 [Age-in-place and age-friendly policies for geriatric health promotion]。長期照護雜誌，26(1)，17–26。
許嘉麟、張韡瀚、黃美涓、陳皇仲、柯智裕、鄧復旦 (2008)。智慧型高齡者照護設備科技接受問卷之內容效度-以 “互動式隨身照護手錶” 為例。臺灣職能治療研究與實務雜誌，4(2)，104–115。
陳正芬 (2017)。成功老化或活躍老化？輸送基礎以及未來轉型之探討對〈社區據點服務品質與成功老化提升程度關連性之初探：政府角色認知的調節效果〉的對話與回應。公共行政學報，(53)，121–129。
陳宜清、林士凱 (2018)。以科技與接受整合模式驗證緊急救護使用行動 App 之行為意圖與使用行為。臺灣災害管理研討會，765–772。
陳肇堯 (2015)。高齡者科技接受度之研究—以南部地區為例。崑山科技大學學報，(10)，132–144。
國家發展委員會（2024，10月17日）。中華民國人口推估2024年至2070年報告。https://pop-proj.ndc.gov.tw/News.aspx?n=3&sms=10347
勞動部（2024，7月31日）。中高齡者及高齡者就業促進法。https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=N0090055
劉宗宜、邱榆淨、蔣鵬 (2016)。銀髮族購買具醫療照護功能之智慧型手錶關鍵因素。中原企管評論，14(1)，1–24。
黎沛姍、謝佳容 (2020)。活躍老化之概念分析－以高齡者之賦權為主體。台灣公共衛生雜誌，39(4)，386–396。
衛生福利部（2025，6月5日）。健康平均餘命指標統計。https://dep.mohw.gov.tw/DOS/cp-5082-55400-113.html
賴宜弘、黃芬芬、楊雪華 (2015)。科技接受模式中文版量表之編製與相關研究。 亞東學報，(35)，201–221。
謝智仱、林佩欣 (2015)。體感太極拳運動應用於社區老人之初探。長期照護雜誌，19(3)，283–294。
英文文獻
Abe, T., Nofuji, Y., Seino, S., Hata, T., Narita, M., Yokoyama, Y., Amano, H., Kitamura, A., Shinkai, S., & Fujiwara, Y. (2022a). Physical, social, and dietary behavioral changes during the COVID-19 crisis and their effects on functional capacity in older adults. Archives of Gerontology and Geriatrics, 101, 104708.
Abe, T., Seino, S., Tomine, Y., Nishi, M., Hata, T., Shinkai, S., Fujiwara, Y., & Kitamura, A. (2022b). Identifying the specific associations between participation in social activities and healthy lifestyle behaviours in older adults. Maturitas, 155, 24-31.
Ajzen, I. (1991). The Theory of planned behavior. Organizational Behavior and Human Decision Processes.
Alshammari, S. H., & Rosli, M. S. (2020). A review of technology acceptance models and theories. Innovative Teaching and Learning Journal (ITLJ), 4(2), 12-22.
Ammenwerth, E., Modre-Osprian, R., Fetz, B., Gstrein, S., Krestan, S., Dörler, J., Kastner, P., Welte, S., Rissbacher, C., & Pölzl, G. (2018). HerzMobil, an integrated and collaborative telemonitoring-based disease management program for patients with heart failure: a feasibility study paving the way to routine care. JMIR cardio, 2(1), e9936.
Anderson, M., & Perrin, A. (2017). Technology use among seniors. Washington, DC: Pew Research Center for Internet & Technology.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28), 2.
Bayles, M. P. (2023). ACSM's Exercise Testing and Prescription. Lippincott Williams & Wilkins.
Begde, A., Jain, M., Goodwin, M., Brayne, C., Barnes, L., Brooks, R., Green, E., Richardson, C., Dening, T., & Wilcockson, T. (2024). Exploring factors influencing willingness of older adults to use assistive technologies: evidence from the cognitive function and ageing study II. Information, Communication & Society, 27(2), 368-385.
Brooke, J. (1996). SUS: A quick and dirty usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis.
Cavallo, F., Esposito, R., Limosani, R., Manzi, A., Bevilacqua, R., Felici, E., Di Nuovo, A., Cangelosi, A., Lattanzio, F., & Dario, P. (2018). Robotic services acceptance in smart environments with older adults: user satisfaction and acceptability study. Journal of Medical Internet Research, 20(9), e9460.
Chen, K., & Chan, A. H.-s. (2013). Use or non-use of gerontechnology—A qualitative study. International Journal of Environmental Research and Public Health, 10(10), 4645-4666.
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.
Chen, K., & Lou, V. W. Q. (2020). Measuring senior technology acceptance: development of a brief, 14-item scale. Innovation in Aging, 4(3), igaa016.
Chen, Y., Yuan, J., Shi, L., Zhou, J., Wang, H., Li, C., Dong, E., & Zhao, L. (2024). Understanding the role of technology anxiety in the adoption of digital health technologies (Dhts) by older adults with chronic diseases in Shanghai: An extension of the unified theory of acceptance and use of technology (Utaut) model. Healthcare,
Chen, Y., Yuan, J., Shi, L., Zhou, J., Wang, H., Li, C., Dong, E., & Zhao, L. (2024). Understanding the role of technology anxiety in the adoption of digital health technologies (DHTs) by older adults with chronic diseases in Shanghai: An Extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Healthcare, 12(14), 1421.
Chen, Y.-R. R., & Schulz, P. J. (2016). The effect of information communication technology interventions on reducing social isolation in the elderly: a systematic review. Journal of Medical Internet Research, 18(1), e4596.
Chimento-Díaz, S., Sánchez-García, P., Franco-Antonio, C., Santano-Mogena, E., Espino-Tato, I., & Cordovilla-Guardia, S. (2022). Factors associated with the acceptance of new technologies for ageing in place by people over 64 years of age. International Journal of Environmental Research and Public Health, 19(5), 2947.
Cimperman, M., Brenčič, M. M., Trkman, P., & Stanonik, M. d. L. (2013). Older adults' perceptions of home telehealth services. Telemedicine and e-Health, 19(10), 786-790.
Conner, M., & Norman, P. (2017). Health behaviour: Current issues and challenges. In (Vol. 32, pp. 895-906): Taylor & Francis.
Daly, R. M., Gianoudis, J., Hall, T., Mundell, N. L., & Maddison, R. (2021). Feasibility, usability, and enjoyment of a home-based exercise program delivered via an exercise app for musculoskeletal health in community-dwelling older adults: short-term prospective pilot study. JMIR mHealth and uHealth, 9(1), e21094.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D., & Granić, A. (2024). Evolution of TAM. In The Technology Acceptance Model: 30 Years of TAM (pp. 19-57). Springer.
Dogra, S., Dunstan, D. W., Sugiyama, T., Stathi, A., Gardiner, P. A., & Owen, N. (2022). Active aging and public health: evidence, implications, and opportunities. Annual Review of Public Health, 43(1), 439-459.
Fang, Y., Chau, A. K., Wong, A., Fung, H. H., & Woo, J. (2018). Information and communicative technology use enhances psychological well-being of older adults: the roles of age, social connectedness, and frailty status. Aging & Mental Health, 22(11), 1516-1524.
Fausto, B. A., Azimipour, S., Charles, L., Yarborough, C., Grullon, K., Hokett, E., Duberstein, P. R., & Gluck, M. A. (2022). Cardio-dance exercise to improve cognition and mood in older African Americans: A propensity-matched cohort study. Journal of Applied Gerontology, 41(2), 496-505.
Fowler-Davis, S., Barnett, D., Kelley, J., & Curtis, D. (2020). Potential for digital monitoring to enhance wellbeing at home for people with mild dementia and their family carers. Journal of Alzheimer's Disease, 73(3), 867-872.
Gell, N. M., Rosenberg, D. E., Demiris, G., LaCroix, A. Z., & Patel, K. V. (2015). Patterns of technology use among older adults with and without disabilities. The Gerontologist, 55(3), 412-421.
Ghorayeb, A., Comber, R., & Gooberman-Hill, R. (2021). Older adults' perspectives of smart home technology: Are we developing the technology that older people want? International Journal of Human-Computer Studies, 147, 102571.
Giger, J. T., Pope, N. D., Vogt, H. B., Gutierrez, C., Newland, L. A., Lemke, J., & Lawler, M. J. (2015). Remote patient monitoring acceptance trends among older adults residing in a frontier state. Computers in Human Behavior, 44, 174-182.
Ha, J., & Park, H. K. (2020). Factors affecting the acceptability of technology in health care among older Korean adults with multiple chronic conditions: a cross-sectional study adopting the senior technology acceptance model. Clinical Interventions in Aging, 1873-1881.
Harris, M. T., & Rogers, W. A. (2023). Developing a healthcare technology acceptance model (H-TAM) for older adults with hypertension. Ageing & Society, 43(4), 814-834.
Holden, R. J. (2011). Lean thinking in emergency departments: a critical review. Annals of Emergency Medicine, 57(3), 265-278.
Hsieh, Y.-C., Tsai, W.-C., & Hsia, Y.-C. (2020). A study on technology anxiety among different ages and genders. In Q. Gao & J. Zhou (Eds.), Human aspects of IT for the aged population. Technology and society: 6th International Conference, ITAP 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part III (Vol. 12208, pp. 236–251). Springer. https://doi.org/10.1007/978-3-030-50232-4_17
Huang, G., & Ren, Y. (2020). Linking technological functions of fitness mobile apps with continuance usage among Chinese users: Moderating role of exercise self-efficacy. Computers in Human Behavior, 103, 151-160.
Jaana, M., & Paré, G. (2020). Comparison of mobile health technology use for self-tracking between older adults and the general adult population in Canada: cross-sectional survey. JMIR mHealth and uHealth, 8(11), e24718.
Jeng, M.-Y., Pai, F.-Y., & Yeh, T.-M. (2022). Antecedents for older adults’ intention to use smart health wearable devices-technology anxiety as a moderator. Behavioral Sciences, 12(4), 114.
Jokisch, M. R., Schmidt, L. I., & Doh, M. (2022). Acceptance of digital health services among older adults: Findings on perceived usefulness, self-efficacy, privacy concerns, ICT knowledge, and support seeking. Frontiers in Public Health, 10, 1073756.
Kalache, A., & Gatti, A. (2003). Active ageing: a policy framework. Advances in Gerontology, 11, 7-18.
Kampmeijer, R., Pavlova, M., Tambor, M., Golinowska, S., & Groot, W. (2016). The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Services Research, 16, 467-479.
Kavandi, H., & Jaana, M. (2020). Factors that affect health information technology adoption by seniors: A systematic review. Health & Social Care in the Community, 28(6), 1827-1842.
Kawaguchi, K., Nakagomi, A., Ide, K., & Kondo, K. (2024). Effects of a Mobile app to promote Social Participation on older adults: Randomized Controlled Trial. Journal of Medical Internet Research, 26, e64196.
Kim, J., Jeon, S.-W., Byun, H., & Yi, E. (2023). Exploring E Health Literacy and Technology Use Anxiety among Older Adults in Korea. Healthcare, 11(11), 1556. https://doi.org/10.3390/healthcare11111556
Kwon, S. (2016). Gerontechnology: Research, practice, and principles in the field of technology and aging. Springer Publishing Company.
Lee, J. Y., Chan, C. K. Y., Chua, S. S., Paraidathathu, T., Lee, K. K.-C., San San Tan, C., Nasir, N., & Lee, S. W. H. (2019). Using telemedicine to support care for people with type 2 diabetes mellitus: a qualitative analysis of patients’ perspectives. BMJ Open, 9(10), e026575.
Lee, P. G., Jackson, E. A., & Richardson, C. R. (2017). Exercise prescriptions in older adults. American Family Physician, 95(7), 425-432.
Li, N., Liu, C., Wang, N., Lin, S., Yuan, Y., Huang, F., & Zhu, P. (2024). Feasibility, usability and acceptability of a lifestyle-integrated multicomponent exercise delivered via a mobile health platform in community-dwelling pre-frail older adults: a short-term, mixed-methods, prospective pilot study. BMC Geriatrics, 24(1), 926.
Lin, C.-T., Shen, C.-C., Mao, T.-Y., & Yang, C.-C. (2019). Empirical investigation of sports management, behavior growth and usage of sports APP: new learning perspective. Polish Journal of Management Studies, 19(1), 225-234.
Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect users’ behavioral intention to transfer usage from the offline to the online channel. Computers in Human Behavior, 27(1), 355-364.
Mair, J. L., Hayes, L. D., Campbell, A. K., Buchan, D. S., Easton, C., & Sculthorpe, N. (2022). A personalized smartphone-delivered just-in-time adaptive intervention (JitaBug) to increase physical activity in older adults: mixed methods feasibility study. JMIR Formative Research, 6(4), e34662.
McGinty, J., Oest, J., & Divido, J. (2024). Digital immigrants: Leveraging the technology acceptance model to increase digital literacy in older adults. New Directions for Adult and Continuing Education, 2024(181), 9-18.
Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., Dijkstra, K., Fisk, A. D., Rogers, W. A., & Sharit, J. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710-1721.
Modave, F., Guo, Y., Bian, J., Gurka, M. J., Parish, A., Smith, M. D., Lee, A. M., & Buford, T. W. (2017). Mobile device accuracy for step counting across age groups. JMIR mHealth and uHealth, 5(6), e7870.
Moeini, B., Barati, M., Farhadian, M., & Ara, M. H. (2018). The association between social support and happiness among elderly in Iran. Korean Journal of Family Medicine, 39(4), 260.
Nebeker, C., & Zlatar, Z. Z. (2021). Learning from older adults to promote independent physical activity using mobile health (mHealth). Frontiers in Public Health, 9, 703910.
Newsome, A. N. M., Batrakoulis, A., Camhi, S. M., McAvoy, C., Sansone, J., Reed, R., & Contributors:. (2024). 2025 ACSM Worldwide Fitness Trends: Future Directions of the Health and Fitness Industry. ACSM's Health & Fitness Journal, 28(6), 11-25. https://doi.org/10.1249/fit.0000000000001017
Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing the elderly users’ adoption of smart-home services. IEEE Access, 6, 51238-51252.
Peek, S. T., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., Van Der Voort, C. S., Aarts, S., Van Hoof, J., Vrijhoef, H. J., & Wouters, E. J. (2016). Older adults' reasons for using technology while aging in place. Gerontology, 62(2), 226-237.
Peek, S. T., Wouters, E. J., Van Hoof, J., Luijkx, K. G., Boeije, H. R., & Vrijhoef, H. J. (2014). Factors influencing acceptance of technology for aging in place: a systematic review. International Journal of Medical Informatics, 83(4), 235-248.
Perez, H., Miguel-Cruz, A., Daum, C., Comeau, A. K., Rutledge, E., King, S., & Liu, L. (2022). Technology Acceptance of a Mobile Application to Support Family Caregivers in a Long-Term Care Facility. Applied Clinical Informatics, 13(05), 1181-1193.
Ruegsegger, G. N., & Booth, F. W. (2018). Health benefits of exercise. Cold Spring Harbor Perspectives in Medicine, 8(7), a029694.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
Schulz, R., Beach, S. R., Matthews, J. T., Courtney, K., Devito Dabbs, A., Person Mecca, L., & Sankey, S. S. (2014). Willingness to pay for quality of life technologies to enhance independent functioning among baby boomers and the elderly adults. The Gerontologist, 54(3), 363-374.
Schulz, R., Wahl, H.-W., Matthews, J. T., De Vito Dabbs, A., Beach, S. R., & Czaja, S. J. (2015). Advancing the aging and technology agenda in gerontology. The Gerontologist, 55(5), 724-734.
Seifert, A., Cotten, S. R., & Xie, B. (2021). A double burden of exclusion? Digital and social exclusion of older adults in times of COVID-19. The Journals of Gerontology: Series B, 76(3), e99-e103.
Shevtsova, D., Ahmed, A., Boot, I. W., Sanges, C., Hudecek, M., Jacobs, J. J., Hort, S., & Vrijhoef, H. J. (2024). Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors, 11(1), e47031.
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.
Sitar-Taut, A.-V., Sitar-Taut, D.-A., Cramariuc, O., Negrean, V., Sampelean, D., Rusu, L., Orasan, O., Fodor, A., Dogaru, G., & Cozma, A. (2018). Smart homes for older people involved in rehabilitation activities-reality or dream, acceptance or rejection. Balneo Research Journal , 9(3), 291-298.
Söderbacka, T., Nyström, L., & Fagerström, L. (2017). Older persons' experiences of what influences their vitality–a study of 65‐and 75‐year‐olds in F inland and S weden. Scandinavian Journal of Caring Sciences, 31(2), 378-387.
Suh, A., & Li, M. (2022). How the use of mobile fitness technology influences older adults’ physical and psychological well-being. Computers in Human Behavior, 131, 107205.
Tabira, K., Oguma, Y., Yoshihara, S., Shibuya, M., Nakamura, M., Doihara, N., Hirata, A., & Manabe, T. (2024). Digital Peer-Supported App Intervention to Promote Physical Activity Among Community-Dwelling Older Adults: Nonrandomized Controlled Trial. JMIR Aging, 7, e56184.
Tan, C. K. K., Lou, V. W., Cheng, C. Y. M., He, P. C., & Mor, Y. Y. (2023). Technology acceptance of a social robot (LOVOT) among single older adults in Hong Kong and Singapore: Protocol for a multimethod study. JMIR Research Protocols, 12(1), e48618.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 561-570.
Tournier, I. (2022). Learning and adaptation in older adults: An overview of main methods and theories. Learning, Culture and Social Interaction, 37, 100466.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178.
Wang, C., & Wang, X. (2023). Research on the Path of Embedding Digital Technology into the Integrated System of Smart Community Sports and Elderly Care Services. Journal of Sociology and Ethnology, 5(8), 79-86.
Wang, J., Du, Y., Coleman, D., Peck, M., Myneni, S., Kang, H., & Gong, Y. (2019). Mobile and connected health technology needs for older adults aging in place: cross-sectional survey study. JMIR Aging, 2(1), e13864.
Wang, J., Fu, Y., Lou, V., Tan, S. Y., & Chui, E. (2021). A systematic review of factors influencing attitudes towards and intention to use the long-distance caregiving technologies for older adults. International Journal of Medical Informatics, 153, 104536.
Wen, M.-H., Chen, P.-Y., Lin, S., Lien, C.-W., Tu, S.-H., Chueh, C.-Y., Wu, Y.-F., Tan Cheng Kian, K., Hsu, Y.-L., & Bai, D. (2024). Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention. Journal of Medical Internet Research, 26, e58380.
Yan, M., Filieri, R., Raguseo, E., & Gorton, M. (2021). Mobile apps for healthy living: Factors influencing continuance intention for health apps. Technological Forecasting and Social Change, 166, 120644.
Yang, C.-C., Li, C.-L., Yeh, T.-F., & Chang, Y.-C. (2022). Assessing Older Adults’ intentions to use a Smartphone: Using the meta–Unified theory of the acceptance and use of technology. International Journal of Environmental Research and Public Health, 19(9), 5403.
Yau, Y., & Hsiao, C.-H. (2022). The technology acceptance model and older adults’ exercise intentions—A systematic literature review. Geriatrics, 7(6), 124.
Yetisen, A. K., Martinez-Hurtado, J. L., Ünal, B., Khademhosseini, A., & Butt, H. (2018). Wearables in Medicine. Advanced Materials, 30(33), e1706910. https://doi.org/10.1002/adma.201706910
Yoon, D. K., Lee, S., Kim, D., Kim, C. O., & Jun, H. J. (2021). Technology anxiety on attitude towards technology: the moderating effect of age-friendly environment. Innovation in Aging, 5(Suppl 1), 659.
Zaidi, A., Gasior, K., Hofmarcher, M. M., Lelkes, O., Marin, B., Rodrigues, R., Schmidt, A., Vanhuysse, P., & Zolyomi, E. (2013). Active ageing index 2012. Concept, Methodology, and Final Results, Research Memo-randum, Methodology Report, European Centre Vienna. Available at.
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(1), 231.
Zhong, R., & Gao, T. (2022). Impact of walking states, self-reported daily walking amount and age on the gait of older adults measured with a smart-phone app: a pilot study. BMC Geriatrics, 22(1), 259.
Zhou, J., Zhang, B., Tan, R., Tseng, M.-L., & Zhang, Y. (2020). Exploring the systematic attributes influencing gerontechnology adoption for elderly users using a meta-analysis. Sustainability, 12(7), 2864.
Zhou, M., Zhao, L., Kong, N., Campy, K. S., Qu, S., & Wang, S. (2019). Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model. International Journal of Medical Informatics, 126, 118-127.