Patient electronic communication data in clinical care: what is known and what is needed

References

• Allen, N. B., Nelson, B. W., Brent, D., & Auerbach, R. P. (2019). Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough? Journal of Affective Disorders, 250, 163–169. https://doi.org/10.1016/j.jad.2019.03.044
   PubMed Web of Science ®Google Scholar
• Alonzi, S., La Torre, A., & Silverstein, M. W. (2020). The psychological impact of preexisting mental and physical health conditions during the COVID-19 pandemic. Psychological Trauma: theory, Research, Practice, and Policy, 12(S1), S236–S238. https://doi.org/10.1037/tra0000840
   PubMedGoogle Scholar
• Areán, P. A., Ly, K. H., & Andersson, G. (2016). Mobile technology for mental health assessment. Dialogues in Clinical Neuroscience, 18(2), 163.
   PubMed Web of Science ®Google Scholar
• Brent, D., Emslie, G., Clarke, G., Wagner, K. D., Asarnow, J. R., Keller, M., Vitiello, B., Ritz, L., Iyengar, S., Abebe, K., Birmaher, B., Ryan, N., Kennard, B., Hughes, C., DeBar, L., McCracken, J., Strober, M., Suddath, R., Spirito, A., … Zelazny, J. (2008). Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA, 299(8), 901–913. https://doi.org/10.1001/jama.299.8.901
   PubMed Web of Science ®Google Scholar
• De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., & Kumar, M. (2016). Discovering shifts to suicidal ideation from mental health content in social media [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI Conference, 2016, 2098–2110.
   Google Scholar
• de Oliveira Araújo, F. J., de Lima, L. S. A., Cidade, P. I. M., Nobre, C. B., & Neto, M. L. R. (2020). Impact of Sars-Cov-2 And its reverberation in global higher education and mental health. Psychiatry Research, 288, 112977. https://doi.org/10.1016/j.psychres.2020.112977
   PubMed Web of Science ®Google Scholar
• Demiris, G., Afrin, L. B., Speedie, S., Courtney, K. L., Sondhi, M., Vimarlund, V., Lovis, C., Goossen, W., & Lynch, C. (2008). Patient-centered applications: Use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group. Journal of the American Medical Informatics Association, 15(1), 8–13. https://doi.org/10.1197/jamia.M2492
   PubMed Web of Science ®Google Scholar
• Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences of the United States of America, 115(44), 11203–11208. https://doi.org/10.1073/pnas.1802331115
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Busk, J., Þórarinsdóttir, H., Frost, M., Bardram, J. E., Vinberg, M., & Kessing, L. V. (2019). Objective smartphone data as a potential diagnostic marker of bipolar disorder. The Australian and New Zealand Journal of Psychiatry, 53(2), 119–128. https://doi.org/10.1177/0004867418808900
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Christensen, E. M., Frost, M., Bardram, J. E., Vinberg, M., & Kessing, L. V. (2020). Hypomania/Mania by DSM-5 definition based on daily smartphone-based patient-reported assessments. Journal of Affective Disorders, 264, 272–278. https://doi.org/10.1016/j.jad.2020.01.014
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Frost, M., Christensen, E. M., Bardram, J. E., Vinberg, M., & Kessing, L. V. (2020). The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial. Psychological Medicine, 50(5), 838–848. https://doi.org/10.1017/S0033291719000710
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Frost, M., Martiny, K., Tuxen, N., Rosenberg, N., Busk, J., Winther, O., Bardram, J. E., & Kessing, L. V. (2017). Reducing the rate and duration of Re-ADMISsions among patients with unipolar disorder and bipolar disorder using smartphone-based monitoring and treatment - the RADMIS trials: study protocol for two randomized controlled trials. Trials, 18(1), 277. https://doi.org/10.1186/s13063-017-2015-3
   PubMedGoogle Scholar
• Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2014a). Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Research, 217(1-2), 124–127. https://doi.org/10.1016/j.psychres.2014.03.009
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Vinberg, M., Frost, M., Christensen, E. M., Bardram, J., & Kessing, L. V. (2014b). Daily electronic monitoring of subjective and objective measures of illness activity in bipolar disorder using smartphones-the MONARCA II trial protocol: a randomized controlled single-blind parallel-group trial. BMC Psychiatry, 14(1), 309. https://doi.org/10.1186/s12888-014-0309-5
   PubMedGoogle Scholar
• Faurholt-Jepsen, M., Vinberg, M., Frost, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2015). Smartphone data as an electronic biomarker of illness activity in bipolar disorder. Bipolar Disorders, 17(7), 715–728. https://doi.org/10.1111/bdi.12332
   PubMed Web of Science ®Google Scholar
• Faurholt-Jepsen, M., Vinberg, M., Frost, M., Debel, S., Margrethe Christensen, E., Bardram, J. E., & Kessing, L. V. (2016). Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder: Smartphone data in bipolar disorder. International Journal of Methods in Psychiatric Research, 25(4), 309–323. https://doi.org/10.1002/mpr.1502
   PubMed Web of Science ®Google Scholar
• Fegert, J. M., Vitiello, B., Plener, P. L., & Clemens, V. (2020). Challenges and burden of the Coronavirus 2019 (COVID-19) pandemic for child and adolescent mental health: a narrative review to highlight clinical and research needs in the acute phase and the long return to normality. Child and Adolescent Psychiatry and Mental Health, 14, 20–11. https://doi.org/10.1186/s13034-020-00329-3
   PubMed Web of Science ®Google Scholar
• Frost, M., Doryab, A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E. (2013). 2013 ACM International Joint Conference, Supporting disease insight through data analysis: Refinements of the MONARCA self-assessment system. In Proceedings of the on Pervasive and Ubiquitous Computing (pp. 133–142). https://doi.org/10.1145/2493432.2493507
   Google Scholar
• Grünerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015). Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE Journal of Biomedical and Health Informatics, 19(1), 140–148. https://doi.org/10.1109/JBHI.2014.2343154-
   PubMed Web of Science ®Google Scholar
• Grünerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O (2014). Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. In Lukowicz, P. Proceedings of the 5th augmented human international conference (pp. 1–8). https://doi.org/10.1145/2582051.2582089
   Google Scholar
• Koeze, E., Popper, N. (2020). The virus changed the way we internet. The New York Times. Retrieved from https://www.nytimes.com/interactive/2020/04/07/technology/coronavirus-internet-use.html
   Google Scholar
• Lafferty, N. T., & Manca, A. (2015). Perspectives on social media in and as research: A synthetic review. International Review of Psychiatry (Abingdon, England), 27(2), 85–96. https://doi.org/10.3109/09540261.2015.1009419
   PubMed Web of Science ®Google Scholar
• McLaughlin, K. A. (2011). The public health impact of major depression: A call for interdisciplinary prevention efforts. Prevention Science, 12(4), 361–371. https://doi.org/10.1007/s11121-011-0231-8
   PubMed Web of Science ®Google Scholar
• Merikangas, K. R., He, J. P., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L., Benjet, C., Georgiades, K., & Swendsen, J. (2010). Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication--Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry, 49(10), 980–989. https://doi.org/10.1016/j.jaac.2010.05.017
   PubMed Web of Science ®Google Scholar
• Moser, D. A., Glaus, J., Frangou, S., & Schechter, D. S. (2020). Years of life lost due to the psychosocial consequences of COVID-19 mitigation strategies based on Swiss data. European Psychiatry: The Journal of the Association of European Psychiatrists, 63(1), e58. https://doi.org/10.1192/j.eurpsy.2020.56
   PubMed Web of Science ®Google Scholar
• Moukaddam, N., Truong, A., Cao, J., Shah, A., & Sabharwal, A. (2019). Findings from a trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) application: what do apps really tell us about patients with depression? Concordance between app-generated data and standard psychiatric questionnaires for depression and anxiety. Journal of Psychiatric Practice®, 25(5), 365–373. https://doi.org/10.1097/PRA.0000000000000420
   PubMed Web of Science ®Google Scholar
• Mühlbauer, E., Bauer, M., Ebner-Priemer, U., Ritter, P., Hill, H., Beier, F., Kleindienst, N., & Severus, E. (2018). Effectiveness of smartphone-based ambulatory assessment (SBAA-BD) including a predicting system for upcoming episodes in the long-term treatment of patients with bipolar disorders: study protocol for a randomized controlled single-blind trial. BMC Psychiatry, 18(1), 349. https://doi.org/10.1186/s12888-018-1929-y
   PubMedGoogle Scholar
• Niendam, T. A., Tully, L. M., Iosif, A.-M., Kumar, D., Nye, K. E., Denton, J. C., Zakskorn, L. N., Fedechko, T. L., & Pierce, K. M. (2018). Enhancing early psychosis treatment using smartphone technology: A longitudinal feasibility and validity study. Journal of Psychiatric Research, 96, 239–246. https://doi.org/10.1016/j.jpsychires.2017.10.017
   PubMed Web of Science ®Google Scholar
• Porras-Segovia, A., Molina-Madueño, R. M., Berrouiguet, S., López-Castroman, J., Barrigón, M. L., Pérez-Rodríguez, M. S., Marco, J. H., Díaz-Oliván, I., de León, S., Courtet, P., Artés-Rodríguez, A., & Baca-García, E. (2020). Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study. Journal of Affective Disorders, 274, 733–741. https://doi.org/10.1016/j.jad.2020.05.067
   PubMed Web of Science ®Google Scholar
• Rajkumar, R. P. (2020). COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry, 52, 102066. https://doi.org/10.1016/j.ajp.2020.102066
   PubMed Web of Science ®Google Scholar
• Shatte, A., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151
   PubMed Web of Science ®Google Scholar
• Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., McInnis, M., Ajilore, O., Nelson, P. C., Ryan, K., & Leow, A. (2018). Predicting mood disturbance severity with mobile phone keystroke metadata: A biaffect digital phenotyping study. Journal of Medical Internet Research, 20(7), e241. https://doi.org/10.2196/jmir.9775
   PubMed Web of Science ®Google Scholar