Mobile Technology Related Digital Therapuetics Trend

디지털치료제 관련 모바일 기술 동향

  • 임지연 (한국전자통신연구원 휴먼증강연구실)
  • Published : 2021.12.28

Abstract

Keywords

References

  1. 건강관리앱 품질 가이드라인 개발 연구, 임영이, 연미영, 여윤재, 박승주, 김도희, 이지연, 한국보건산업진흥원, 2021.11.08
  2. 디지털치료제 허가 심사 가이드라인, 식약처, 2020
  3. Velez, F. F., Colman, S., Kauffman, L., Ruetsch, C., & Anastassopoulos, K. (2021). Real-world reduction in healthcare resource utilization following treatment of opioid use disorder with reSET-O, a novel prescription digital therapeutic. Expert Review of Pharmacoeconomics & Outcomes Research, 21(1), 69-76. https://doi.org/10.1080/14737167.2021.1840357
  4. Christensen, H., Batterham, P. J., Gosling, J. A., Ritter band, L. M., Griffiths, K. M., Thorndike, F. P., ... & M ackinnon, A. J. (2016). Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial. The Lancet Psychiatry, 3(4), 333-341. https://doi.org/10.1016/S2215-0366(15)00536-2
  5. https://apps.apple.com/us/app/pear-reset-o/id1270975804
  6. Espie, C. A., Emsley, R., Kyle, S. D., Gordon, C., Drake, C. L., Siriwardena, A. N., ... & Luik, A. I. (2019). Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. JAMA psychiatry, 76(1), 21-30. https://doi.org/10.1001/jamapsychiatry.2018.2745
  7. Darden, M., Espie, C. A., Carl, J. R., Henry, A. L., Kanady, J. C., Krystal, A. D., & Miller, C. B. (2021). Cost-effectiveness of digital cognitive behavioral therapy(Sleepio) for insomnia: a Markov simulation model in the United States. Sleep, 44(4), zsaa223. https://doi.org/10.1093/sleep/zsaa223
  8. Erten Uyumaz, B., Feijs, L., & Hu, J. (2021). A review of digital cognitive behavioral therapy for insomnia (CBT-I apps): are they designed for engagement?. International journal of environmental research and public health, 18(6), 2929. https://doi.org/10.3390/ijerph18062929
  9. https://wowtale.net/2021/05/13/emocog-raised-funding-from-naver-d2sf/
  10. https://techcrunch.com/2016/12/01/neurotrack-takes-brain-scans-home/
  11. https://neurotrack.com/products
  12. https://medium.com/myachievement/behind-thebehaviorgram-300e583ee3840
  13. Chen, R., Jankovic, F., Marinsek, N., Foschini, L., Kourtis, L., Signorini, A., ... & Trister, A. (2019, July). Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2145-2155).
  14. Darcy, A., Daniels, J., Salinger, D., Wicks, P., & Robinson, A. (2021). Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study. JMIR Formative Research, 5(5), e27868. https://doi.org/10.2196/27868
  15. Cho, S. W., Wee, J. H., Yoo, S., Heo, E., Ryu, B., Kim, Y., ... & Kim, J. W. (2018). Effect of lifestyle modification using a smartphone application on obesity with obstructive sleep apnea: a short-term, randomized controlled study. Clinical and experimental otorhinolaryngology, 11(3), 192. https://doi.org/10.21053/ceo.2017.01284
  16. http://www.monews.co.kr/news/articleView.html?idxno=114371
  17. https://developers.liferecord.kr/api/info/ls#info_ms
  18. https://www.lark.com/about-lark
  19. https://welcome.livongo.com/#/
  20. Higgins, J. P. (2016). Smartphone applications for patients' health and fitness. The American journal of medicine, 129(1), 11-19. https://doi.org/10.1016/j.amjmed.2015.05.038
  21. Jake-Schoffman, D. E., Silfee, V. J., Waring, M. E., Boudreaux, E. D., Sadasivam, R. S., Mullen, S. P., ... & Pagoto, S. L. (2017). Methods for evaluating the content, usability, and efficacy of commercial mobile health apps. JMIR mHealth and uHealth, 5(12), e190. https://doi.org/10.2196/mhealth.8758
  22. Dagum, P. (2018). Digital biomarkers of cognitive function. NPJ digital medicine, 1(1), 1-3. https://doi.org/10.1038/s41746-017-0008-y
  23. Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research, 17(7), e175. https://doi.org/10.2196/jmir.4273
  24. Jacob, C., Sanchez-Vazquez, A., & Ivory, C. (2020). Social, organizational, and technological factors impacting clinicians' adoption of mobile health tools: systematic literature review. JMIR mHealth and uHealth, 8(2), e15935. https://doi.org/10.2196/15935
  25. Cheng, V. W. S., Davenport, T., Johnson, D., Vella, K., & Hickie, I. B. (2019). Gamification in apps and technologies for improving mental health and well-being: systematic review. JMIR mental health, 6(6), e13717. https://doi.org/10.2196/13717
  26. Liao, P., Greenewald, K., Klasnja, P., & Murphy, S. (2020). Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1-22.
  27. Wei, Y., Zheng, P., Deng, H., Wang, X., Li, X., & Fu, H. (2020). Design features for improving mobile health intervention user engagement: systematic review and thematic analysis. Journal of medical Internet research, 22(12), e21687. https://doi.org/10.2196/21687