The role of digital health technologies in COVID-19 surveillance and recovery: a specific case of long haulers

References

• Abroms, L. C. (2019). Public health in the era of social media. American Journal of Public Health, 109(S2), S130–S131. https://doi.org/10.2105/AJPH.2018.304947
   PubMedGoogle Scholar
• Barak, A., Boniel-Nissim, M., & Suler, J. (2008). Fostering empowerment in online support groups. Computers in Human Behavior, 24(5), 1867–1883. https://doi.org/10.1016/j.chb.2008.02.004
   Web of Science ®Google Scholar
• Barnett, I., & Torous, J. (2019). Ethics, transparency, and public health at the intersection of innovation and facebook's suicide prevention efforts. Annals of Internal Medicine, 170(8), 565–566. https://doi.org/10.7326/M19-0366
   PubMed Web of Science ®Google Scholar
• Baumgartner, J., Zannettou, S., Squire, M., Blackburn, J. (2020). The Pushshift Telegram Dataset. arXiv:2001.08438 [cs.SI]. http://arxiv.org/abs/2001.08438
   Google Scholar
• Blei, D. M., & Ng Mijjbe, A. Y. (2003). Latent Dirichlet allocation: Extracting topics from software engineering data. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.1016/B978-0-12-411519-4.00006-9
   Web of Science ®Google Scholar
• Boe, B. n.d. PRAW: The Python Reddit API Wrapper.
   Google Scholar
• Bradley, M. M., & Lang, P. J. (1999). Affective Norms for English Words (ANEW): Instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.
   Google Scholar
• Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., Rees, G., Emery, V. C., Stevens, M. M., Keegan, N., Short, M. J., Pillay, D., Manley, E., Cox, I. J., Heymann, D., Johnson, A. M., & McKendry, R. A. (2020). Digital technologies in the public-health response to COVID-19. Nature Medicine, 26(8), 1183–1192. https://doi.org/10.1038/s41591-020-1011-4
   PubMed Web of Science ®Google Scholar
• Burke, R. M., Killerby, M. E., Newton, S., Ashworth, C. E., Berns, A. L., Brennan, S., Bressler, J. M., Bye, E., Crawford, R., Harduar Morano, L., Lewis, N. M., Markus, T. M., Read, J. S., Rissman, T., Taylor, J., Tate, J. E., & Midgley, C. M, Case Investigation Form Working Group. (2020). Symptom profiles of a convenience sample of patients with COVID-19 – United States, January-April 2020. MMWR Morbidity and Mortality Weekly Report, 69(28), 904–908. https://doi.org/10.15585/mmwr.mm6928a2
   PubMed Web of Science ®Google Scholar
• Chandra Guntuku, S., Sherman, G., & Stokes, D. C., Agarwal, A. K., Seltzer, E., Merchant, R. M., & Ungar, L. H. (2020). Tracking mental health and symptom mentions on twitter during COVID-19. Journal of General Internal Medicine, 35, 2798–2800. https://doi.org/10.1007/s11606-020-05988-8
   PubMed Web of Science ®Google Scholar
• Choudhury, M. D. & De, S. (2014). Mental health discourse on Reddit: Self-disclosure, social support, and anonymity. In Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (pp. 71–80). Retrieved September 8, 2020, from https://www.researchgate.net/publication/287611387_Mental_health_discourse_on_reddit_Self-disclosure_social_support_and_anonymity
   Google Scholar
• Chung, J. E. (2013). Social interaction in online support groups: Preference for online social interaction over offline social interaction. Computers in Human Behavior, 29(4), 1408–1414. https://doi.org/10.1016/j.chb.2013.01.019
   Web of Science ®Google Scholar
• Conard, S. (2019). Best practices in digital health literacy. International Journal of Cardiology, 292, 277–279. https://doi.org/10.1016/j.ijcard.2019.05.070
   PubMed Web of Science ®Google Scholar
• COVID-19 Mobility Data Network. (2020). Facebook Data for Good Mobility Dashboard. Retrieved September 3, 2020, from https://visualization.covid19mobility.org/?date=2020-08-31&dates=2020-05-31_2020-08-31&region=WORLD
   Google Scholar
• del Rio, C., Collins, L. F., & Malani, P. (2020). Long-term health consequences of COVID-19. JAMA, 324(17), 1723–1724. https://doi.org/10.1001/jama.2020.19719
   PubMed Web of Science ®Google Scholar
• Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., Lahiri, D., & Lavie, C. J. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome, 14(5), 779–788. https://doi.org/10.1016/j.dsx.2020.05.035
   PubMed Web of Science ®Google Scholar
• Fagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., & Huiart, L. (2020). Digital health strategies to fight COVID-19 worldwide: Challenges, recommendations, and a call for papers. Journal of Medical Internet Research, 22(6), e19284. https://doi.org/10.2196/19284
   PubMed Web of Science ®Google Scholar
• Ferrara, E. (2020). What types of Covid-19 conspiracies are populated by twitter bots? arXiv:2004.09531 [cs.SI]. https://arxiv.org/abs/2004.09531
   Google Scholar
• Fowler, G. A. (2020, May 28). Wearables like Fitbit and Oura can detect coronavirus symptoms, new research shows. The Washington Post. Retrieved September 3, 2020.
   Google Scholar
• Gaur, M., Kursuncu, U., & Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018). Let me tell you about your mental health! In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18) (pp. 753–762). Association for Computing Machinery.
   Google Scholar
• Gennaro, M., Lorenzo, R. D., Conte, C., & Poletti, S. (2020). Role of inflammatory and clinical predictors.
   Google Scholar
• Gray, D. M., Joseph, J. J., & Olayiwola, J. N. (2020). Strategies for digital care of vulnerable patients in a COVID-19 world—Keeping in touch. JAMA Health Forum, 1(6), e200734. https://doi.org/10.1001/jamahealthforum.2020.0734
   PubMedGoogle Scholar
• Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., Polsky, D., Volpp, K. G., & Merchant, R. (2019). Studying expressions of loneliness in individuals using twitter: An observational study. BMJ Open, 9(11), e030355–8. https://doi.org/10.1136/bmjopen-2019-030355
   PubMed Web of Science ®Google Scholar
• Herridge, M. S., Cheung, A. M., Tansey, C. M., Matte-Martyn, A., Diaz-Granados, N., Al-Saidi, F., Cooper, A. B., Guest, C. B., Mazer, C. D., Mehta, S., Stewart, T. E., Barr, A., Cook, D., & Slutsky, A. S, Canadian Critical Care Trials Group (2003). One-year outcomes in survivors of the acute respiratory distress syndrome. New England Journal of Medicine, 348(8), 683–693. https://doi.org/10.1056/NEJMoa022450
   PubMed Web of Science ®Google Scholar
• Holtgrave, D. R., Valdiserri, R. O., Kalichman, S. C., del Rio, C., & Thompson, M. (2020). Core elements of a national COVID-19 strategy: Lessons learned from the US National HIV/AIDS Strategy. AIDS and Behavior, 24(12), 3279–3282. https://doi.org/10.1007/s10461-020-03045-3
   PubMed Web of Science ®Google Scholar
• Hussain, A., Ali, S., Ahmed, M., & Hussain, S. (2018). The anti-vaccination movement: A regression in modern medicine. Cureus, 10(7), e2919. https://doi.org/10.7759/cureus.2919
   PubMed Web of Science ®Google Scholar
• Jamieson, H., & Albarracín, K. (2020). The relation between media consumption and misinformation at the outset of the SARS-CoV-2 pandemic in the US. HKS Misinformation Review. https://doi.org/10.37016/mr-2020-012
   Google Scholar
• Jurkowitz, M., & Mitchell, A. (2020, March 25). Americans who get news mostly through social media are least likely to follow coronavirus coverage. Pew Research Center. Retrieved April 28, 2020, from https://www.journalism.org/2020/03/25/americans-who-primarily-get-news-through-social-media-are-least-likely-to-follow-covid-19-coverage-most-likely-to-report-seeing-made-up-news
   Google Scholar
• Koonin, L. M., Hoots, B., Tsang, C. A., Leroy, Z., Farris, K., Jolly, B., Antall, P., McCabe, B., Zelis, C. B. R., Tong, I., & Harris, A. M. (2020). Trends in the use of telehealth during the emergence of the COVID-19 pandemic – United States, January–March 2020. Morbidity and Mortality Weekly Report, 69(43), 1595–1599. https://doi.org/10.15585/mmwr.mm6943a3
   PubMed Web of Science ®Google Scholar
• Krelle, H., Dodson, J. A., & Horwitz, L. (2020). Virtual primary care—Is its expansion due to COVID-19 all upside? JAMA Health Forum, 1(7), e200900. https://doi.org/10.1001/jamahealthforum.2020.0900
   PubMedGoogle Scholar
• Laguarta, J., Hueto, F., & Subirana, B. (2020). COVID-19 artificial intelligence diagnosis using only cough recordings. IEEE Open Journal of Engineering in Medicine and Biology. https://doi.org/10.1109/ojemb.2020.3026928
   Google Scholar
• Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., & Chen, H. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance, 25(10), 2000199. https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000199
   Web of Science ®Google Scholar
• Mahase, E. (2020). Covid-19: What do we know about “long covid”? BMJ, 370, m2815. https://doi.org/10.1136/bmj.m2815
   PubMed Web of Science ®Google Scholar
• McNeil, D. G., Jr. (2020, March 18). Can smart thermometers track the spread of the coronavirus? The New York Times. Retrieved September 3, 2020, from https://www.nytimes.com/2020/03/18/health/coronavirus-fever-thermometers.html
   Google Scholar
• Merchant, R. M., & Asch, D. A. (2018). Protecting the value of medical science in the age of social media and “Fake News". JAMA, 320(23), 2415–2416. https://doi.org/10.1001/jama.2018.18416
   PubMed Web of Science ®Google Scholar
• Merchant, R. M., Elmer, S., & Lurie, N. (2011). Integrating social media into emergency-preparedness efforts. New England Journal of Medicine, 365(4), 289–291. https://doi.org/10.1056/NEJMp1103591
   PubMed Web of Science ®Google Scholar
• Moldofsky, H., & Patcai, J. (2011). Chronic widespread musculoskeletal pain, fatigue, depression and disordered sleep in chronic post-SARS syndrome; a case-controlled study. BMC Neurology, 11, 37. https://doi.org/10.1186/1471-2377-11-37
   PubMed Web of Science ®Google Scholar
• Murray, C. J. L., Alamro, N. M. S., Hwang, H., & Lee, U. (2020). Digital public health and COVID-19. The Lancet Public Health, 5(9), e469–e470. https://doi.org/10.1016/S2468-2667(20)30187-0
   PubMed Web of Science ®Google Scholar
• Nielsen, F. F. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv:1103.2903 [cs.IR]. http://www.imm.dtu.dk/∼fn/
   Google Scholar
• Oyebode, O., Ndulue, C., Mulchandani, D., Suruliraj, B., Adib, A., Orji, F. A., Milios, E., Matwin, S., Orji, R. (2020). COVID-19 pandemic: Identifying key issues using social media and natural language processing. arXiv:2008.10022 [cs.CL].
   Google Scholar
• Patient Led Research. n.d. Patient Led Research – for COVID-19. Retrieved September 3, 2020, from https://patientresearchcovid19.com/
   Google Scholar
• PHOSP-COVID. (2020). Home. Retrieved September 8, 2020, from https://www.phosp.org/
   Google Scholar
• RecoveryCorps.org. (2020). RecoveryCorps.org—Survivor Corps. Retrieved September 8, 2020, from https://www.survivorcorps.com/recoverycorps
   Google Scholar
• Sarker, A., Lakamana, S., Hogg-Bremer, W., Xie, A., Al-Garadi, M. A., & Yang, Y. C. (2020). Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource. Journal of the American Medical Informatics Association: Jamia, 27(8), 1310–1315. https://doi.org/10.1093/jamia/ocaa116
   PubMed Web of Science ®Google Scholar
• Schwartz, H. A., Giorgi, S., Sap, M., Crutchley, P., Eichstaedt, J. C., & Ungar, L. (2017). Dlatk: Differential language analysis toolkit. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstration, EMNLP 2017, 55–60.
   Google Scholar
• Sehra, S. T., George, M., Wiebe, D. J., Fundin, S., & Baker, J. F. (2020). Cell phone activity in categories of places and associations with growth in cases of COVID-19 in the US. JAMA Internal Medicine. https://doi.org/10.1001/jamainternmed.2020.4288
   Google Scholar
• Slomski, A. (2020). Telehealth success spurs a call for greater post-COVID-19 license portability. JAMA, 324(11), 1021–1022. https://doi.org/10.1001/jama.2020.9142
   PubMed Web of Science ®Google Scholar
• Stokes, D. C., Andy, A., Guntuku, S. C., Ungar, L. H., & Merchant, R. M. (2020). Public priorities and concerns regarding COVID-19 in an online discussion forum: Longitudinal topic modeling. Journal of General Internal Medicine, 35(7), 2244–2247. https://doi.org/10.1007/s11606-020-05889-w
   PubMed Web of Science ®Google Scholar
• Taquet, M., Luciano, S., Geddes, J. R., & Harrison, P. J. (2020). Bidirectional associations between COVID-19 and psychiatric disorder: Retrospective cohort studies of 62 354 COVID-19 cases in the USA. The Lancet Psychiatry. https://doi.org/10.1016/S2215-0366(20)30462-4
   Google Scholar
• Tenforde, M. W., Kim, S. S., Lindsell, C. J., Billig Rose, E., Shapiro, N. I., Files, D. C., Gibbs, K. W., Erickson, H. L., Steingrub, J. S., Smithline, H. A., Gong, M. N., Aboodi, M. S., Exline, M. C., Henning, D. J., Wilson, J. G., Khan, A., Qadir, N., Brown, S. M., Peltan, I. D., … Feldstein, L. R, IVY Network Investigators (2020). Symptom duration and risk factors for delayed return to usual health among outpatients with COVID-19 in a multistate health care systems network – United States, March–June 2020. Morbidity and Mortality Weekly Report, 69(30), 993–998. https://doi.org/10.15585/mmwr.mm6930e1
   PubMed Web of Science ®Google Scholar
• Torous, J., & Walker, R. (2019). Leveraging digital health and machine learning toward reducing suicide – From panacea to practical tool. JAMA Psychiatry, 76(10), 999. https://doi.org/10.1001/jamapsychiatry.2019.1231
   PubMed Web of Science ®Google Scholar
• Twitter Developer. (2020). Overview – Docs. Retrieved October 15, 2020, from https://developer.twitter.com/en/docs/labs/covid19-stream/overview
   Google Scholar
• van der Vaart, R., & Drossaert, C. (2017). Development of the digital health literacy instrument: Measuring a broad spectrum of health 1.0 and health 2.0 skills. Journal of Medical Internet Research, 19(1), e27. https://doi.org/10.2196/jmir.6709
   PubMedGoogle Scholar
• Villa, S., Jaramillo, E., Mangioni, D., Bandera, A., Gori, A., & Raviglione, M. C. (2020). Stigma at the time of the COVID-19 pandemic. Clinical Microbiology and Infection, 26(11), 1450–1452. https://doi.org/10.1016/j.cmi.2020.08.001
   PubMed Web of Science ®Google Scholar
• Vogels, E. A. (2020, April 30). What Americans are doing online during COVID-19. Pew Research Center. Retrieved September 8, 2020, from https://www.pewresearch.org/fact-tank/2020/04/30/from-virtual-parties-to-ordering-food-how-americans-are-using-the-internet-during-covid-19/
   Google Scholar
• Vogels, E. A., Perrin, A., Rainie, L., & Anderson, M. (2020, April 30). 53% of Americans say internet has been essential during COVID-19 outbreak. Pew Research Center. Retrieved October 15, 2020, from https://www.pewresearch.org/internet/2020/04/30/53-of-americans-say-the-internet-has-been-essential-during-the-covid-19-outbreak/
   Google Scholar
• Wang, L., Wang, Y., Ye, D., & Liu, Q. (2020). Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence. International Journal of Antimicrobial Agents, 55(6), 105948. https://doi.org/10.1016/j.ijantimicag.2020.105948
   PubMed Web of Science ®Google Scholar
• Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health, 2(8), e435–e440. https://doi.org/10.1016/S2589-7500(20)30142-4
   PubMedGoogle Scholar
• Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395(10225), 676. https://doi.org/10.1016/S0140-6736(20)30461-X
   PubMed Web of Science ®Google Scholar