A
References
Abdullah, S., M. Matthews, E. Frank, G. Doherty, G. Gay, and T. Choudhury. 2016. Automatic detection of social rhythms in bipolar disorder. Journal of the American Medical Informatics Association 23(3):538-543.
Barnett, I., J. Torous, P. Staples, L. Sandoval, M. Keshavan, and J. P. Onnela. 2018. Relapse prediction in schizophrenia through digital phenotyping: A pilot study. Neuropsychopharmacology 43(8):1660-1666.
Ben-Zeev, D., R. Drake, and L. Marsch. 2015. Clinical technology specialists. BMJ 350:h945.
Ben-Zeev, D., R. M. Brian, G. Jonathan, L. Razzano, N. Pashka, E. Carpenter-Song, R. E. Drake, and E. A. Scherer. 2018. Mobile health (mhealth) versus clinic-based group intervention for people with serious mental illness: A randomized controlled trial. Psychiatric Services 69(9):978-985.
Birnbaum, M. L., S. K. Ernala, A. F. Rizvi, M. De Choudhury, and J. M. Kane. 2017. A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research 19(8):e289.
Corder, E. H., A. M. Saunders, W. J. Strittmatter, D. E. Schmechel, P. C. Gaskell, G. W. Small, A. D. Roses, J. L. Haines, and M. A. Pericak-Vance. 1993. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261(5123):921-923.
Croff, R. L., P. Witter IV, M. L. Walker, E. Francois, C. Quinn, T. C. Riley, N. F. Sharma, and J. A. Kaye. 2018. Things are changing so fast: Integrative technology for preserving cognitive health and community history. Gerontologist. https://doi.org/10.1093/geront/gny069.
De Choudhury M., S. Counts, and E. J. Horvitz. 2013. Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, April 27– May 02. New York: ACM Press. Pp. 3267-3276.
De Choudhury M., S. Counts, and E. J. Horvitz, and A. Hoff. 2014. Characterizing and predicting postpartum depression from shared Facebook data. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative
Work & Social Computing (CSCW’14), Baltimore, MD, February 15–19. New York: ACM Press. Pp. 626-638.
De Choudhury, M., E. Kiciman, M. Dredze, G. Coppersmith, and M. Kumar. 2016. Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI Conference, 2016, Pp. 2098-2110. http://doi.org/10.1145/2858036.2858207.
Dodge, H. H., J. Zhu, N. C. Mattek, D. Austin, J. Kornfeld, and J. A. Kaye. 2015. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLOS ONE 10(9):e0138095.
Dorsey, E. R., M. V. McConnell, S. Y. Shaw, A. D. Trister, and S. H. Friend. 2017. The use of smartphones for health research. Academic Medicine 92(2):157-160.
FDA (Food and Drug Administration). 2014. Providing regulatory submissions in electronic format—standardized study data: Guidance for industry. https://www.fda.gov/downloads/drugs/guidances/ucm292334.pdf (accessed October 18, 2018).
FDA. 2017a. Use of electronic records and electronic signatures in clinical investigations under 21 CFR part 11 questions and answers; guidance for industry. https://www.fda.gov/downloards/drugs/guidancecomplianceregulatoryinformation/guidances/ucm563785.pdf (accessed October 18, 2018).
FDA. 2017b. Use of real-world evidence to support regulatory decision-making for medical devices: Guidance for industry and Food and Drug Administration staff. Document no. 1500012. https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf (accessed October 18, 2018).
Holden, R. J., and B. T. Karsh. 2010. The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics 43(1):159-172.
Irizarry, T., J. Shoemake, M. L. Nilsen, S. Czaja, S. Beach, and A. DeVito Dabbs. 2017. Patient portals as a tool for health care engagement: A mixed-method study of older adults with varying levels of health literacy and prior patient portal use. Journal of Medical Internet Research 19(3):e99.
Kaye, J. A., S. A. Maxwell, N. Mattek, T. L. Hayes, H. Dodge, M. Pavel, H. B. Jimison, K. Wild, L. Boise, and T. A. Zitzelberger. 2011. Intelligent systems for assessing aging changes: Home-based, unobtrusive, and continuous assessment of aging. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 66(Suppl 1):i180-i190.
Lyons, B. E., D. Austin, A. Seelye, J. Petersen, J. Yeargers, T. Riley, N. Sharma, N. Mattek, K. Wild, H. Dodge, and J. A. Kaye. 2015. Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy. Frontiers in Aging Neuroscience 7:102.
Manji, H. K., T. R. Insel, and V. A. Narayan. 2014. Harnessing the informatics revolution for neuroscience drug R&D. Nature Reviews Drug Discovery 13(8):561-562.
Narayan, V. A., and H. K. Manji. 2016. Moving from “diagnose and treat” to “predict and pre-empt” in neuropsychiatric disorders. Nature Reviews Drug Discovery 15(2):71-72.
NASEM (National Academies of Sciences, Engineering, and Medicine). 2016. Statistical challenges in assessing and fostering the reproducibility of scientific results: Summary of a workshop. Washington, DC: The National Academies Press.
Neville, J., S. Kopko, K. Romero, B. Corrigan, B. Stafford, E. LeRoy, S. Broadbent, M. Cisneroz, E. Wilson, E. Reiman, H. Vanderstichele, S. P. Arnerić, and D. Stephenson. 2017. Accelerating drug development for Alzheimer’s disease through the use of data standards. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 3(2):273-283.
Onnela, J. P., and S. L. Rauch. 2016. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41(7):1691-1696.
Pew Research Center. 2017. Tech adoption climbs among older adults. http://www.pewinternet.org/2017/05/17/tech-adoption-climbs-among-older-adults (accessed October 18, 2018).
Pew Research Center. 2018. Social media use in 2018. http://www.pewinternet.org/2018/03/01/social-media-use-in-2018 (accessed October 18, 2018).
Piwek, L., D. A. Ellis, S. Andrews, and A. Joinson. 2016. The rise of consumer health wearables: Promises and barriers. PLOS Medicine 13(2):e1001953.
Prinz, F., T. Schlange, and K. Asadullah. 2011. Believe it or not: How much can we rely on published data on potential drug targets? Nature Reviews Drug Discovery 10(9):712.
Ramirez, V., E. Johnson, C. Gonzalez, V. Ramirez, B. Rubino, and G. Rossetti. 2016. Assessing the use of mobile health technology by patients: An observational study in primary care clinics. Journal of Medical Internet Research 4(2):e41.
Romero, K., V. Sinha, S. Allerheiligen, M. Danhof, J. Pinheiro, N. Kruhlak, Y. Wang, S. J. Wang, J. M. Sauer, J. F. Marier, B. Corrigan, J. Rogers, H. J. Lambers Heerspink, T. Gumbo, P. Vis, P. Watkins, T. Morrison, W. Gillespie, M. F. Gordon, D. Stephenson, D. Hanna, M. Pfister, R. Lalonde, and T. Colatsky. 2014. Modeling and simulation for medical product development and evaluation: Highlights from the FDA-C-Path-ISOP 2013 workshop. Journal of Pharmacokinetics and Pharmacodynamics 41(6):545-552.
Romero, K., K. Ito, J. A. Rogers, D. Polhamus, R. Qiu, D. Stephenson, R. Mohs, R. Lalonde, V. Sinha, Y. Wang, D. Brown, M. Isaac, S. Vamvakas, R. Hemmings, L. Pani, L. J. Bain, and B. Corrigan. 2015. The future is now: Model-based clinical trial design for Alzheimer’s disease. Clinical Pharmacology & Therapeutics 97(3):210-214.
Rothenhaus, T. 2015. Using data to increase patient engagement in health care. Harvard Business Review, June 30, 2015.
Seelye, A. M., K. V. Wild, N. Larimer, S. Maxwell, P. Kearns, and J. A. Kaye. 2012. Reactions to a remote-controlled video-communication robot in seniors’ homes: A pilot study of feasibility and acceptance. Telemed Journal and E Health 18(10):755-759.
Torous, J., and S. Roux. 2017. Patient-driven innovation for mobile mental health technology: Case report of symptom tracking in schizophrenia. Journal of Medical Internet Research 4(3):e27.
Torous, J., P. Staples, M. Shanahan, C. Lin, P. Peck, M. Keshavan, and J. P. Onnela. 2015. Utilizing a personal smartphone custom app to assess the Patient Health Questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. Journal of Medical Internet Research 2(1):e8.
Torous, J., M. V. Kiang, J. Lorme, and J.-P. Onnela. 2016. New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research. Journal of Medical Internet Research 3(2):e16.
Torous, J. B., S. R. Chan, S. Y. T. Gipson, J. W. Kim, T. Q. Nguyen, J. Luo, and P. Wang. 2018. A hierarchical framework for evaluation and informed decision making regarding smartphone apps for clinical care. Psychiatric Services 69(5):498-500.
Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3):425-478.
