Bibliometric Analysis of Wearable Technology Studies in The Healtcare Industry

Yıl 2022, , 107 - 122, 31.08.2022

Esra Cengiz Tırpan , Tarık Semiz

https://doi.org/10.5824/ajite.2022.03.001.x

Öz

In recent years, it has been observed that expectations from health services and investments in this field are primarily directed towards studies for the early detection of diseases, more effective monitoring of health conditions, and studies to increase the general quality of life and healthy lifestyle. This study aims to present a bibliometric analysis by examining wearable technology studies in medicine. For wearable technology studies in the medical field, analyzes were performed with 616 articles listed in the Scopus database. The VOSviewer software created an international cooperation network, co-citation author network, and common word network. According to the analysis results, it is seen that the publications are distributed between 1997-2022. The country that contributed the most to these 616 studies in the United States (USA), with 216 publications, followed by China and the United Kingdom. In addition, the USA ranks first among the cooperating countries with the highest connection strength and number of connections. The top contributing author is Najafi, B. Wang J. is the most cited author in the co-citation network author analysis. According to the results of the common word analysis, 5 clusters were formed, and after the most repeated words "wearable technologies" and "wearable technology" were removed, "physical activity" and "machine learning" respectively words. This study is an essential resource to present the current issues of wearable technology studies in the field of health and to examine research trends.

Anahtar Kelimeler

Healthcare, Medicine, Wearable Technology, Bibliometric Analysis

Sağlık Sektöründe Giyilebilir Teknoloji Çalışmalarının Bibliyometrik Analizi

Öz

Son yıllarda sağlık hizmetlerinden beklentiler ve bu alanda yatırımlar daha çok hastalıkların önceden tespitine yönelik çalışmalara, sağlık durumlarının daha etkin izlenmesi, genel yaşam kalitesinin ve sağlıklı yaşam tarzını arttırılmasına yönelik çalışmalara doğru yöneldiği gözlenmektedir. Bu çalışmanın amacı, giyilebilir teknoloji çalışmalarını tıp alanı özelinde inceleyerek, bibliyometrik bir analiz ortaya koymaktır. Tıp alanındaki giyilebilir teknoloji çalışmaları için, Scopus veri tabanında listelenen 616 makale ile analizler gerçekleştirilmiştir. VOSviewer yazılımı ile, ülkeler arası iş birliği ağı, ortak atıf yazar ağı ve ortak kelime ağı oluşturulmuştur. Analiz sonuçlarına göre, yayınların 1997-2022 yılları arasında dağılım gösterdiği görülmektedir. Bu 616 çalışmaya en çok katkıda bulunan ülke 216 yayın ile Amerika Birleşik Devletleri (ABD), ardından sırası ile Çin ve Birleşik Krallık gelmektedir. Ayrıca, ABD en yüksek bağlantı gücü ve bağlantı sayısı ile iş birliği yapan ülkeler arasında birinci sırada yer almaktadır. En çok katkıda bulunan yazar, Najafi, B.’dir. Ortak atıf ağı yazar analizinde en çok atıfta bulunulan yazar ise Wang J. olarak karşımıza çıkmaktadır. Ortak kelime analizi sonuçlarına göre 5 küme oluşmuştur ve en çok tekrar edilen kelime “giyilebilir teknolojiler (wearable technologies)” ve “giyilebilir teknoloji (wearable technology)” kelimeleri çıkarıldıktan sonra sırasıyla “fiziksel aktivite (physical activity)” ve “makine öğrenmesi (machine learning)” kelimeleridir. Bu çalışma tıp alanında giyilebilir teknoloji uygulamalarının güncel konularını sunmak ve araştırma eğilimlerini incelemek için önemli bir kaynak niteliğindedir.

Anahtar Kelimeler

Sağlık, Giyilebilir Teknoloji, Tıp, Bibliyometrik Analiz

Kaynakça

• Akalın, B., & Veranyurt, Ü. (2020). Sağlıkta Dijitalleşme ve Yapay Zekâ. SDÜ Sağlık Yönetimi Dergisi, 2(2), 128-137.
• Ambrose, A. F., Paul, G., & Hausdorff, J. M. (2013). Risk Factors For Falls Among Older Adults: A Review Of The Literature. Maturitas, 75(1), 51-61.
• Amft, O., & Lukowicz, P. (2009). From Backpacks to Smartphones: Past, Present, And Future of Wearable Computers. IEEE Pervasive Computing, 8(3), 8-13.
• Awais, M., Palmerini, L., Bourke, A. K., Ihlen, E. A., Helbostad, J. L., & Chiari, L. (2016). Performance Evaluation Of State Of The Art Systems For Physical Activity Classification Of Older Subjects Using Inertial Sensors In A Real Life Scenario: A Benchmark Study. Sensors, 16(12), 2105.
• Burbano-Fernandez, M. F., & Ramirez-Gonzalez, G. (2018). Wearable Technology And Health: A Bibliometric Analysis Using Scimat. F1000Research, 7(1893), 1893.
• Büyükgöze, S. (2019). Sağlık 4.0’da Giyilebilir Teknolojilerden Sensör Yamalar Üzerine Bir Inceleme. Avrupa Bilim ve Teknoloji Dergisi, (17), 1239-1247.
• Burbano-Fernandez, M. F., & Ramirez-Gonzalez, G. (2018). Wearable Technology and Health: A Bibliometric Analysis Using Scimat. F1000Research, 7(1893), 1893.
• Chan, M., Estève, D., Fourniols, J.Y., Escriba, C. and Campo, E. (2012).  Smart Wearable Systems: Current Status and Future Challenges. Artificial Intelligence in Medicine, 3 (56), 137-156.
• Choi, Y., Jeon, Y.-M., Wang, L., & Kim, K. (2017). A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices. Sensors, 17(9), 1936.
• Choo, D., Dettman, S., Dowell, R., & Cowan, R. (2017). Talking to Toddlers: Drawing on Mothers' Perceptions of Using Wearable and Mobile Technology in the Home. Studies In Health Technology and Informatics, 239, 21-27.
• Cambrosio, A., Limoges, C., Courtial, J., & Laville, F. (1993). Historical Scientometrics? Mapping Over 70 Years Of Biological Safety Research With Coword Analysis. Scientometrics, 27(2), 119-143.
• Dehghani, M. (2020). A Bibliometric Review of Wearable Technologies. In Managing Medical Technological Innovations: Exploring Multiple Perspectives (pp. 3-34).
• Dooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017). Estimating Accuracy at Exercise Intensities: A Comparative Study Of Self-Monitoring Heart Rate And Physical Activity Wearable Devices. JMIR mHealth and uHealth, 5(3).
• Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How To Conduct A Bibliometric Analysis: An Overview And Guidelines. Journal of Business Research, 133, 285-296.
• Due B.L. (2014). The Future of Smart Glasses: An Essay About Challenges and Possibilities With Smart Glasses. Working Papers on Interaction and Communication, 1(2), 1-21.
• Ferreira, J. J., Fernandes, C. I., Rammal, H. G., & Veiga, P. M. (2021). Wearable Technology And Consumer Interaction: A Systematic Review And Research Agenda. Computers in Human Behavior, 118, 106710.
• Frank, H. A., Jacobs, K., & McLoone, H. (2017). The Effect of A Wearable Device Prompting High School Students Aged 17-18 Years To Break Up Periods Of Prolonged Sitting In Class. Work, 56(3), 475-482.
• Ghafar-Zadeh, E. (2015). Wireless Integrated Biosensors for Point-Of-Care Diagnostic Applications. Sensors, 15(2), 3236-3261.
• Ghosh, A., Torres, J. M. M., Danieli, M., & Riccardi, G. (2015). Detection of essential hypertension with physiological signals from wearable devices. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE.
• Gordt, K., Gerhardy, T., Najafi, B., & Schwenk, M. (2018). Effects Of Wearable Sensor-Based Balance and Gait Training On Balance, Gait, And Functional Performance In Healthy And Patient Populations: A Systematic Review And Meta-Analysis Of Randomized Controlled Trials. Gerontology, 64(1), 74-89.
• Hsieh, C.-Y., Liu, K.-C., Huang, C.-N., Chu, W.-C., & Chan, C.-T. (2017). Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model. Sensors, 17(2), 307.
• Kalantari, M. (2017). Consumers' Adoption of Wearable Technologies: Literature Review, Synthesis, And Future Research Agenda. International Journal of Technology Marketing, 12(3), 274-307.
• 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.
• Lee J., Kim D., Ryoo H.Y., Shin B.S. (2016). Sustainable Wearables: Wearable Technology for Enhancing the Quality Of Human Life. Sustainability, 8(5), 466. 
• Mohamed, A., Razak, A. Z. A., & Abdullah, Z. (2020). Most-Cited Research Publications on Educational Leadership and Management: A Bibliometric Analysis. International Online Journal of Educational Leadership, 4(2), 33-50.
• Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software Tools For Conducting Bibliometric Analysis In Science: An Up-To-Date Review. Profesional de la Información, 29(1).
• 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(11), 3375-3384.
• Ravikumar, S., Agrahari, A., & Singh, S. N. (2015). Mapping The Intellectual Structure Of Scientometrics: A Co-Word Analysis Of The Journal Scientometrics (2005–2010). Scientometrics, 102(1), 929-955.
• Small, H. (1999). Visualizing Science By Citation Mapping. Journal of the American society for Information Science, 50(9), 799-813.
• Sultan, N. (2015). Reflective Thoughts On The Potential And Challenges Of Wearable Technology For Healthcare Provision And Medical Education. International Journal of Information Management, 35(5), 521-526.
• Tehrani, K., & Andrew, M. (2014). Wearable Technology and Wearable Devices: Everything You Need to Know. Wearable Devices Magazine, WearableDevices. com, Mart 2014. Web. URL: http://www. wearabledevices. com/what-is-a-wearable-device.
• Thakkar, H. K., Chowdhury, S. R., Bhoi, A. K., & Barsocchi, P. (2022). Applications of wearable technologies in healthcare: an analytical study. In 5G IoT and Edge Computing for Smart Healthcare (pp. 279-299). Academic Press.
• Tey, C.-K., An, J., & Chung, W.-Y. (2017). A Novel Remote Rehabilitation System with The Fusion of Noninvasive Wearable Device and Motion Sensing For Pulmonary Patients. Computational and Mathematical Methods in Medicine, 2017.
• Van Eck, N. J., & Waltman, L. (2010). Software Survey: Vosviewer, A Computer Program for Bibliometric Mapping. Scientometrics, 84(2), 523-538.
• Van Eck, N. J., & Waltman, L. (2013). VOSviewer Manual. Leiden: Univeristeit Leiden, 1(1), 1-53.
• Van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. In Measuring scholarly impact (pp. 285-320). Springer, Cham.
• Valenza, G., Citi, L., Gentili, C., Lanata, A., Scilingo, E. P., & Barbieri, R. (2015). Characterization Of Depressive States in Bipolar Patients Using Wearable Textile Technology and Instantaneous Heart Rate Variability Assessment. IEEE Journal of Biomedical and Health Informatics, 19(1), 263-274.
• World Health Organization (WHO). (2015, Sept. 30). World report on ageing and health: Geneva: WHO. 
• Wright, R. & Keith, L. (2014). Wearable Technology: If the Tech Fits, Wear It. Journal of Electronic Resources in Medical Libraries, Vol. 11, No. 4, pp.204–216.
• Wu, M., & Luo, J. (2019). Wearable Technology Applications in Healthcare: A Literature Review. Online J. Nurs. Inform, 23(3).
• Yang, H.-K., Lee, J.-W., Lee, K.-H., Lee, Y.-J., Kim, K.-S., Choi, H.-J., & Kim, D.-J. (2008). Application for the wearable heart activity monitoring system: analysis of the autonomic function of HRV. Paper presented at the Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE.
• Zhang, M., Luo, M., Nie, R. and 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(1), 97-109.
• Zheng, X. S., Foucault, C., Matos da Silva, P., Dasari, S., Yang, T., & Goose, S. (2015, April). Eye-Wearable Technology for Machine Maintenance: Effects Of Display Position And Hands-Free Operation. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 2125-2134).