|
Abdi, J., Al-Hindawi, A., Ng, T., & Vizcaychipi, M. P. (2018). Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open, 8(2). https://doi.org/10.1136/bmjopen-2017-018815 Ahadzadeh, A. S., Pahlevan Sharif, S., Ong, F. S., & Khong, K. W. (2015). Integrating health belief model and technology acceptance model: An investigation of health-related internet use. Journal of Medical Internet Research, 17(2), e45. https://doi.org/10.2196/jmir.3564 Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: Boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469–490. https://doi.org/10.1287/isre.1100.0335 Angosto, S., García-Fernández, J., Valantine, I., & Grimaldi-Puyana, M. (2020). The intention to use fitness and physical activity apps: A systematic review. Sustainability, 12(16), 6641. https://doi.org/10.3390/su12166641 Ansah, J. P., Eberlein, R. L., Love, S. R., Bautista, M. A., Thompson, J. P., Malhotra, R., & Matchar, D. B. (2014). Implications of long-term care capacity response policies for an aging population: A simulation analysis. Health Policy, 116(1), 105–113. https://doi.org/10.1016/j.healthpol.2014.01.006 Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/s11747-011-0278-x Barkley, J. E., Lepp, A., Santo, A., Glickman, E., & Dowdell, B. (2020). The relationship between fitness app use and physical activity behavior is mediated by exercise identity. Computers in Human Behavior, 108, 106313. https://doi.org/10.1016/j.chb.2020.106313 Beard, J., Biggs, S., Bloom, D. E., Fried, L. P., Hogan, P. R., Kalache, A., Olshansky, S. J., & others. (2012). Global population ageing: Peril or promise? World Economic Forum. Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. The Journal of Computer Information Systems, 48(4), 69–76. Bloom, D. E., Canning, D., & Fink, G. (2010). Implications of population ageing for economic growth. Oxford Review of Economic Policy, 26(4), 583–612. https://doi.org/10.1093/oxrep/grq038 Bozan, K., Davey, B., & Parker, K. (2015). Social influence on health IT adoption patterns of the elderly: An institutional theory based use behavior approach. Procedia Computer Science, 63, 517–523. https://doi.org/10.1016/j.procs.2015.08.378 Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. https://doi.org/10.2307/25148690 Carrigan, M., & Szmigin, I. (1999). In pursuit of youth: What’s wrong with the older market? Marketing Intelligence & Planning, 17(5), 222–231. https://doi.org/10.1108/02634509910285637 Chand, M., & Tung, R. L. (2014). The aging of the world’s population and its effects on global business. Academy of Management Perspectives, 28(4), 409–429. https://doi.org/10.5465/amp.2012.0070 Chesbrough, H. W. (2003). The era of open innovation. MIT Sloan Management Review, 44(4), 35–41. Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777–786. https://doi.org/10.1016/j.chb.2016.06.007 Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684. https://doi.org/10.1016/j.chb.2010.06.016 Cimperman, M., Makovec Brenčič, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—Applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31. https://doi.org/10.1016/j.ijmedinf.2016.03.002 Commission of the European Communities. (2000). An evaluation of the bridge phase of TIDE. (Technology initiative for disabled and elderly people). http://aei.pitt.edu/38698/ Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115. Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (CREATE). Psychology and Aging, 21(2), 333–352. https://doi.org/10.1037/0882-7974.21.2.333 Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 53(5), 329–340. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–350. https://doi.org/10.2307/249008 Demiris, G., Rantz, M. J., Aud, M. A., Marek, K. D., Tyrer, H. W., Skubic, M., & Hussam, A. A. (2004). Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: A pilot study. Medical Informatics and the Internet in Medicine, 29(2), 87–94. https://doi.org/10.1080/14639230410001684387 Deng, Z. (2013). Understanding public users’ adoption of mobile health service. International Journal of Mobile Communications, 11(4), 351–373. https://doi.org/10.1504/IJMC.2013.055748 Department of Statistics. (2017). Ministry of the Interior Statistics, Republic of China. https://www.moi.gov.tw/files/news_file/week10624.pdf Diefenbach, M. A., Weinstein, N. D., & O’Reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8(2), 181–192. https://doi.org/10.1093/her/8.2.181 Dillard, A. J., Couper, M. P., & Zikmund-Fisher, B. J. (2010). Perceived risk of cancer and patient reports of participation in decisions about screening: The DECISIONS study. Medical Decision Making, 30(5_suppl), 96–105. https://doi.org/10.1177/0272989X10377660 Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080 Djellal, F., & Gallouj, F. (2006). Innovation in care services for the elderly. The Service Industries Journal, 26(3), 303–327. https://doi.org/10.1080/02642060600570943 Eisma, R., Dickinson, A., Goodman, J., Syme, A., Tiwari, L., & Newell, A. F. (2004). Early user involvement in the development of information technology-related products for older people. Universal Access in the Information Society, 3(2), 131–140. https://doi.org/10.1007/s10209-004-0092-z El-Amrawy, F., & Nounou, M. I. (2015). Are currently available wearable devices for activity tracking and heart rate monitoring accurate, precise, and medically beneficial? Healthcare Informatics Research, 21(4), 315–320. https://doi.org/10.4258/hir.2015.21.4.315 Ellis, R. D., & Allaire, J. C. (1999). Modeling computer interest in older adults: The role of age, education, computer knowledge, and computer anxiety. Human Factors, 41(3), 345–355. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704–1723. https://doi.org/10.1108/IMDS-03-2015-0087 Greaves, F., & Rozenblum, R. (2017). Social and consumer informatics. In A. Sheikh, K. M. Cresswell, A. Wright, & D. W. Bates (Eds.), Key Advances in Clinical Informatics (pp. 257–278). Academic Press. https://doi.org/10.1016/B978-0-12-809523-2.00018-2 Hair, J., Anderson, R., & Tatham, R. (1998). Multivariate data analysis (5th ed.). Prentice Hall. Heinz, M., Martin, P., Margrett, J. A., Yearns, M., Franke, W., Yang, H. I., Wong, J., & Chang, C. K. (2013). Perceptions of technology among older adults. Journal of Gerontological Nursing, 39(1), 42–51. Hollander, J. E., & Carr, B. G. (2020). Virtually perfect? Telemedicine for covid-19. New England Journal of Medicine, 382(18), 1679–1681. https://doi.org/10.1056/NEJMp2003539 Hsiao, C.-H., & Tang, K.-Y. (2015). Examining a model of mobile healthcare technology acceptance by the elderly in Taiwan. Journal of Global Information Technology Management, 18(4), 292–311. https://doi.org/10.1080/1097198X.2015.1108099 Hsiao, K.-L., & Chen, C.-C. (2018). What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telematics and Informatics, 35(1), 103–113. https://doi.org/10.1016/j.tele.2017.10.002 Huang, J.-C. (2013). Innovative health care delivery system—A questionnaire survey to evaluate the influence of behavioral factors on individuals’ acceptance of telecare. Computers in Biology and Medicine, 43(4), 281–286. https://doi.org/10.1016/j.compbiomed.2012.12.011 Hussein, S., & Manthorpe, J. (2005). An international review of the long-term care workforce: Policies and shortages. Journal of Aging & Social Policy, 17(4), 75–94. Intille, S. S. (2004). A new research challenge: Persuasive technology to motivate healthy aging. IEEE Transactions on Information Technology in Biomedicine, 8(3), 235–237. https://doi.org/10.1109/TITB.2004.835531 Isakadze, N., & Martin, S. S. (2020). How useful is the smartwatch ECG? Trends in Cardiovascular Medicine, 30(7), 442–448. https://doi.org/10.1016/j.tcm.2019.10.010 Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education Quarterly, 11(1), 1–47. https://doi.org/10.1177/109019818401100101 Jeyaraj, A., & Zadeh, A. H. (2020). Evolution of information systems research: Insights from topic modeling. Information & Management, 57(4), 103207. https://doi.org/10.1016/j.im.2019.103207 Jian, W.-S., Syed-Abdul, S., Sood, S. P., Lee, P., Hsu, M.-H., Ho, C.-H., Li, Y.-C., & Wen, H.-C. (2012). Factors influencing consumer adoption of USB-based personal health records in Taiwan. BMC Health Services Research, 12(1), 277. https://doi.org/10.1186/1472-6963-12-277 Kang, H. G., Mahoney, D. F., Hoenig, H., Hirth, V. A., Bonato, P., Hajjar, I., & Lipsitz, L. A. (2010). In situ monitoring of health in older adults: Technologies and issues. Journal of the American Geriatrics Society, 58(8), 1579–1586. https://doi.org/10.1111/j.1532-5415.2010.02959.x Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742–770. https://doi.org/10.1108/K-02-2017-0045 Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416. https://doi.org/10.1016/j.ijmedinf.2008.12.005 Kim, J., & Park, H.-A. (2012). Development of a health information technology acceptance model using consumers’ health behavior intention. Journal of Medical Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143 Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541. https://doi.org/10.1108/IntR-05-2014-0126 Kim, S., Park, H., & Lee, J. (2020). Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis. Expert Systems with Applications, 152, 113401. https://doi.org/10.1016/j.eswa.2020.113401 Kitchin, R. (2020). Civil liberties or public health, or civil liberties and public health? Using surveillance technologies to tackle the spread of COVID-19. Space and Polity, 24(3), 1–20. https://doi.org/10.1080/13562576.2020.1770587 Krasnova, H., Veltri, N. F., & Günther, O. (2012). Self-disclosure and privacy calculus on social networking sites: The role of culture. Business & Information Systems Engineering, 4(3), 127–135. https://doi.org/10.1007/s12599-012-0216-6 Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x Lee, H. (2020). Home IoT resistance: Extended privacy and vulnerability perspective. Telematics and Informatics, 49, 101377. https://doi.org/10.1016/j.tele.2020.101377 Lee, I.-M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. The Lancet, 380(9838), 219–229. https://doi.org/10.1016/S0140-6736(12)61031-9 Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88, 8–17. https://doi.org/10.1016/j.ijmedinf.2015.12.010 Li, J., Ma, Q., Chan, A. HS., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162–169. https://doi.org/10.1016/j.apergo.2018.10.006 Lin, Y.-Y., & Huang, C.-S. (2016). Aging in Taiwan: Building a society for active aging and aging in place. The Gerontologist, 56(2), 176–183. https://doi.org/10.1093/geront/gnv107 Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003 Lu, W.-H., Lee, W.-J., Chen, L.-K., & Hsiao, F.-Y. (2016). Comparisons of annual health care utilization, drug consumption, and medical expenditure between the elderly and general population in Taiwan. Journal of Clinical Gerontology and Geriatrics, 7(2), 44–47. https://doi.org/10.1016/j.jcgg.2015.08.002 Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114–120. https://doi.org/10.1016/j.chb.2016.08.007 Maddox, T. (2016). Smartwatch sales to hit $17.8 billion in 2020, but business use lagging. TechRepublic. https://www.techrepublic.com/article/smartwatch-sales-to-hit-17-8-billion-in-2020-but-business-use-lagging/ Mason, A. E., Hecht, F. M., Davis, S. K., Natale, J. L., Hartogensis, W., Damaso, N., Claypool, K. T., Dilchert, S., Dasgupta, S., Purawat, S., Viswanath, V. K., Klein, A., Chowdhary, A., Fisher, S. M., Anglo, C., Puldon, K. Y., Veasna, D., Prather, J. G., Pandya, L. S., … Smarr, B. L. (2022). Detection of COVID-19 using multimodal data from a wearable device: Results from the first TemPredict study. Scientific Reports, 12(1), 3463. https://doi.org/10.1038/s41598-022-07314-0 McCafferty, B. J., Hill, J. O., & Gunn, A. J. (2020). Obesity: Scope, lifestyle interventions, and medical management. Techniques in Vascular and Interventional Radiology, 23(1), 100653. https://doi.org/10.1016/j.tvir.2020.100653 McConnell, M. V., Turakhia, M. P., Harrington, R. A., King, A. C., & Ashley, E. A. (2018). Mobile health advances in physical activity, fitness, and atrial fibrillation: Moving hearts. Journal of the American College of Cardiology, 71(23), 2691–2701. https://doi.org/10.1016/j.jacc.2018.04.030 Michael, K., & Michael, M. G. (2015). Apple watch temptation: Just visit the app store. IEEE Consumer Electronics Magazine, 4(4), 120–122. https://doi.org/10.1109/MCE.2015.2463391 Miles, L. (2007). Physical activity and health. Nutrition Bulletin, 32(4), 314–363. https://doi.org/10.1111/j.1467-3010.2007.00668.x Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56, 103–114. https://doi.org/10.1016/j.dss.2013.05.010 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. https://doi.org/10.1016/j.chb.2010.06.020 Mothersbaugh, D. L., Foxx, W. K., Beatty, S. E., & Wang, S. (2012). Disclosure antecedents in an online service context: The role of sensitivity of information. Journal of Service Research, 15(1), 76–98. https://doi.org/10.1177/1094670511424924 National Development Council. (2021a). Population projection 2020 ~2070. https://pop-proj.ndc.gov.tw/main_en/download.aspx?uid=4105&pid=4104 National Development Council. (2021b). Population pyramid- Population projection. Population Pyramid - Population Projection. https://pop-proj.ndc.gov.tw/main_en/Pyramid.aspx?uid=4106&pid=4104 Nqweniso, S., Walter, C., du Randt, R., Aerts, A., Adams, L., Degen, J., Gall, S., Gani, Z., Joubert, N., Müller, I., Smith, D., Seelig, H., Steinmann, P., Probst-Hensch, N., Utzinger, J., Pühse, U., & Gerber, M. (2020). Prevention of overweight and hypertension through cardiorespiratory fitness and extracurricular sport participation among South African schoolchildren. Sustainability, 12(16), 6581. https://doi.org/10.3390/su12166581 Pai, F.-Y., & Huang, K.-I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660. https://doi.org/10.1016/j.techfore.2010.11.007 Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376–385. https://doi.org/10.1016/j.tele.2013.11.008 Park, S., Chung, K., & Jayaraman, S. (2014). Wearables: Fundamentals, advancements, and a roadmap for the future. In E. Sazonov (Ed.), Wearable Sensors: Fundamentals, Implementation and Applications (1st ed., pp. 1–23). Elsevier. https://doi.org/10.1016/B978-0-12-418662-0.00001-5 Prayoga, T., & Abraham, J. (2016). Behavioral intention to use IoT health device: The role of perceived usefulness, facilitated appropriation, big five personality traits, and cultural value orientations. International Journal of Electrical and Computer Engineering (IJECE), 6(4), 1751. https://doi.org/10.11591/ijece.v6i4.10546 Prentice-Dunn, S., & Rogers, R. W. (1986). Protection motivation theory and preventive health: Beyond the health belief model. Health Education Research, 1(3), 153–161. https://doi.org/10.1093/her/1.3.153 R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: R package for structural topic models. Journal of Statistical Software, 91, 1–40. Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114. Rogers, R. W. (1983). Cognitive and psychological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social psychophysiology: A sourcebook (pp. 153–176). Guilford. Rohm, A. J., & Milne, G. R. (2004). Just what the doctor ordered. Journal of Business Research, 57(9), 1000–1011. https://doi.org/10.1016/S0148-2963(02)00345-4 Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403 Saghafi-Asl, M., Aliasgharzadeh, S., & Asghari-Jafarabadi, M. (2020). Factors influencing weight management behavior among college students: An application of the Health Belief Model. PLOS ONE, 15(2), e0228058. https://doi.org/10.1371/journal.pone.0228058 Sallis, R., Young, D. R., Tartof, S. Y., Sallis, J. F., Sall, J., Li, Q., Smith, G. N., & Cohen, D. A. (2021). Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: A study in 48440 adult patients. British Journal of Sports Medicine, 55(19), 1099–1105. https://doi.org/10.1136/bjsports-2021-104080 Sauer, P. L., & Dick, A. (1993). Using moderator variables in structural equation models. In Leigh McAlister & Michael L. Rothschild (Eds.), NA - Advances in Consumer Research (Vol. 20, pp. 636–640). Association for Consumer Research. https://www.acrwebsite.org/volumes/7532/volumes/v20/NA- Steele, R., Lo, A., Secombe, C., & Wong, Y. K. (2009). Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. International Journal of Medical Informatics, 78(12), 788–801. https://doi.org/10.1016/j.ijmedinf.2009.08.001 Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: A comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183–200. Sun, Y., Wang, N., Shen, X.-L., & Zhang, J. X. (2015). Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52, 278–292. https://doi.org/10.1016/j.chb.2015.06.006 Tacken, M., Marcellini, F., Mollenkopf, H., Ruoppila, I., & Szeman, Z. (2005). Use and acceptance of new technology by older people. Findings of the international MOBILATE survey: ‘Enhancing mobility in later life.’ Gerontechnology, 3(3), 126–137. The Office of the National Coordinator for Health Information Technology. (2019). What is Health IT? | HealthIT.gov. https://www.healthit.gov/faq/what-health-it United Nations. (2015). World Population Ageing. United Nations, Department of Economic and Social Affairs, & Population Division. (2019). World population prospects highlights, 2019 revision. van der Heijden. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660 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 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, 36(1), 157–178. Von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), 791–805. https://doi.org/10.1287/mnsc.32.7.791 Walsh, K., & Callan, A. (2011). Perceptions, preferences, and acceptance of information and communication technologies in older-adult community care settings in Ireland: A Case-study and ranked-care program analysis. Ageing International, 36(1), 102–122. https://doi.org/10.1007/s12126-010-9075-y Wang, B. R., Park, J.-Y., Chung, K., & Choi, I. Y. (2014). Influential factors of smart health users according to usage experience and intention to use. Wireless Personal Communications, 79(4), 2671–2683. https://doi.org/10.1007/s11277-014-1769-0 Wildenbos, G. A., Peute, L., & Jaspers, M. (2018). Aging barriers influencing mobile health usability for older adults: A literature based framework (MOLD-US). International Journal of Medical Informatics, 114, 66–75. https://doi.org/10.1016/j.ijmedinf.2018.03.012 Wittenberg, R., Comas-Herrera, A., Pickard, L., & Hancock, R. (2004). Future demand for long-term care in the UK. A summary of projections of long-term care finance for older people to 2051. London School of Economics. Xiong, H., Cheng, Y., Zhao, W., & Liu, J. (2019). Analyzing scientific research topics in manufacturing field using a topic model. Computers & Industrial Engineering, 135, 333–347. https://doi.org/10.1016/j.cie.2019.06.010 Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269. https://doi.org/10.1016/j.tele.2015.08.007 Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006 Yun, J., & Geum, Y. (2020). Automated classification of patents: A topic modeling approach. Computers & Industrial Engineering, 147, 106636. https://doi.org/10.1016/j.cie.2020.106636 Zhang, M., Luo, M., Nie, R., & Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, 97–109. https://doi.org/10.1016/j.ijmedinf.2017.09.016
|