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The rapid growth of digital health technologies has transformed healthcare delivery across multiple domains. For example, telemedicine platforms have expanded access to care, particularly in rural and underserved areas [a] . Mobile health (mHealth) applications have been widely adopted for self-management of chronic conditions, such as hypertension and diabetes [b] . Additionally, machine learning algorithms have been applied to predict hospital readmissions by analyzing large datasets of patient records [c] . In the field of mental health, digital interventions, such as cognitive behavioral therapy (CBT) apps, have shown efficacy in reducing symptoms of depression and anxiety [d] . Wearable devices have also gained popularity for monitoring physical activity and sleep quality, providing actionable insights for improving overall health [e] . Finally, blockchain technology has been explored for enhancing the security and interoperability of electronic health records [f] . These innovations underscore the potential of digital health to address longstanding challenges in healthcare.

Coloque a citação às seguintes referências no local indicado com [a] 

Bashshur, R. L., Shannon, G. W., Krupinski, E. A., Grigsby, J., Kvedar, J. C., Weinstein, R. S., Sanders, J. H., Rheuban, K. S., Nesbitt, T. S., Alverson, D. C., Merrell, R. C., Linkous, J. D., Ferguson, A. S., Waters, R. J., Stachura, M. E., Ellis, D. G., Antoniotti, N. M., Johnston, B., Doarn, C. R., … Yellowlees, P. (2016). The empirical foundations of telemedicine interventions for chronic disease management. Telemedicine and e-Health, 22(5), 342–375. https://doi.org/10.1089/tmj.2015.0206

Coloque a citação às seguintes referências no local indicado com [b]

Arnett, D. K., Blumenthal, R. S., Albert, M. A., Buroker, A. B., Goldberger, Z. D., Hahn, E. J., Himmelfarb, C. D., Khera, A., Lloyd-Jones, D., McEvoy, J. W., Michos, E. D., Miedema, M. D., Muñoz, D., Smith, S. C., Virani, S. S., Williams, K. A., Yeboah, J., & Ziaeian, B. (2019). 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation, 140(11), e596–e646. https://doi.org/10.1161/CIR.0000000000000678

Coloque a citação às seguintes referências no local indicado com [c] 

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., & Litsch, K. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1

Coloque a citação às seguintes referências no local indicado com [d] 

Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287–298. https://doi.org/10.1002/wps.20472

Coloque a citação às seguintes referências no local indicado com [e] 

Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 159. https://doi.org/10.1186/s12966-015-0314-1

Coloque a citação às seguintes referências no local indicado com [f] 

Kuo, T.-T., Kim, H.-E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211–1220. https://doi.org/10.1093/jamia/ocx068