Applied Clinical Informatics, Table of Contents Appl Clin Inform 2016; 07(02): 290-298DOI: 10.4338/ACI-2015-12-LE-0176 Letter to the Editor Schattauer GmbH The Quantified Brain: A Framework for Mobile Device-Based Assessment of Behavior and Neurological Function David E. Stark 1 Division of Biomedical Informatics, Department of Medicine, Mobilize Center, Department of Bioengineering, Stanford University, Stanford, CA , Rajiv B. Kumar 2 Department of Pediatrics, Stanford School of Medicine, Stanford, CA, Department of Clinical Informatics, Stanford Children’s Health, Palo Alto, CA , Christopher A. Longhurst 3 Department of Biomedical Informatics, UC San Diego, La Jolla, CA , Dennis P. Wall 4 Division of Systems Medicine, Department of Pediatrics and Psychiatry (by courtesy), Stanford University, Stanford, CA › Author Affiliations Recommend Article Abstract Citation: Stark DE; Kumar RB; Longhurst CA; Wall DP. The Quantified Brain: A Framework for Mobile Device Based Assessment of Behavior and Neurological Function. Full Text References References 1 Whiteford HA, Ferrari AJ, Degenhardt L, Feigin V, Vos T. The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS One 2015; 10 (02) e0116820. 2 Institute of Medicine. Measuring the Risks and Causes of Premature Death: Summary of Workshops. 2015. Washington (DC): National Academies Press (US); 3 Collins FS, Varmus H. A New Initiative on Precision Medicine. New England Journal of Medicine. 2015 4 Ashley EA. The precision medicine initiative: A new national effort. JAMA. 2015 5 Patient-Generated Health Data. [cited 2016 February 27]; Available from: https://www.healthit.gov/policy-researchers-implementers/patient-generated-health-data. 6 Gray K, Sockolow P, Gray K, Street B. Conceptual Models in Health Informatics Research : A Literature Review and Suggestions for Development. JMIR Med Inform 2016; 04 (01) e7. 7 Hunter NL, O’Callaghan KM, Califf RM. Engaging patients across the spectrum of medical product development: View from the us food and drug administration. JAMA 2015; 314 (23) 2499-500. 8 Kapur S, Phillips AG, Insel TR. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?. Molecular psychiatry 2012; 17 (12) 1174-1179. 9 Hobart JC, Cano SJ, Zajicek JP, Thompson AJ. Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations. The Lancet Neurology 2007; 06 (12) 1094-1105. 10 Jones W, Klin A. Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism. Nature 2013; 504 (7480): 427-431. 11 Acheson DT, Geyer MA, Risbrough VB. Psychophysiology in the study of psychological trauma: where are we now and where do we need to be?. Current topics in behavioral neurosciences 2014; 21: 157-183. 12 Orozco-Arroyave JR, Belalcazar-Bolanos EA, Arias-Londono JD, Vargas-Bonilla JF, Skodda S, Rusz J, Daqrouq K, Honig F, Noth E. Characterization Methods for the Detection of Multiple Voice Disorders: Neurological, Functional, and Laryngeal Diseases. IEEE journal of biomedical and health informatics 2015; 19 (06) 1820-1828. 13 Silbergleit AK, LeWitt PA, Peterson EL, Gardner GM. Quantitative Analysis of Voice in Parkinson Disease Compared to Motor Performance: A Pilot Study. Journal of Parkinson’s disease 2015; 05 (03) 517-524. 14 Topol EJ, Steinhubl SR, Torkamani A. Digital medical tools and sensors. JAMA 2015; 313 (04) 353-354. 15 Meeker M, Wu L. editors. Internet Trends D11 Conference. 2013 Kleiner Perkins Caufield Byers. 16 PubMed Search Results: ‘kinect’. [cited 2016 February 1]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=kinect. 17 Morrison C, Culmer P, Mentis H, Pincus T. Vision-based body tracking: turning Kinect into a clinical tool. Disability and rehabilitation Assistive technology. 2014; Dec 11: 1-5. 18 Morrison C, Huckvale K, Corish B, Dorn J, Kontschieder P, O’Hara K, Team AM, Criminisi A, Sellen A. Assessing multiple sclerosis with kinect: designing computer vision systems for real-world use. Human-Computer Interaction 2016; 1-36. 19 Kaye J, Mattek N, Dodge HH, Campbell I, Hayes T, Austin D, Hatt W, Wild K, Jimison H, Pavel M. Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimer’s & dementia : the journal of the Alzheimer’s Association 2014; 10 (01) 10-17. 20 Cook DJ, Schmitter-Edgecombe M, Dawadi P. Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques. IEEE journal of biomedical and health informatics 2015; 19 (06) 1882-1892. 21 Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB, Ribeiro S, Javitt DC, Copelli M, Corcoran CM. Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophrenia 2015; 01: 15030. 22 Madan A, Cebrian M, Lazer D, Pentland A. editors Social sensing for epidemiological behavior change. Proceedings of the 12th ACM international conference on Ubiquitous computing. 2010 ACM. 23 Bitsch JA, Ramos R, Ix T, Ferrer-Cheng PG, Wehrle K. Psychologist in a pocket: towards depression screening on mobile phones. Studies in health technology and informatics 2015; 211: 153-159. 24 Ben-Zeev D, Wang R, Abdullah S, Brian R, Scherer EA, Mistler LA, Hauser M, Kane JM, Campbell A, Choudhury T. Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia. Psychiatric services; (Washington, DC): 2015. Dec 15:appips201500130. 25 Looney D, Kidmose P, Mandic DP. Ear-EEG: user-centered and wearable BCI. Brain-Computer Interface Research: Springer Berlin Heidelberg. 2014: 41-50. 26 Open mHealth. 2016 [cited 2016 February 1]; Available from: http://www.openmhealth.org. 27 Bourne PE, Bonazzi V, Dunn M, Green ED, Guyer M, Komatsoulis G, Larkin J, Russell B. The NIH Big Data to Knowledge (BD2K) initiative. J Am Med Inform Assoc 2015; 22 (06) 1114. 28 Kumar S, Abowd GD, Abraham WT, al’Absi M, Beck JG, Chau DH, Condie T, Conroy DE, Ertin E, Estrin D, Ganesan D, Lam C, Marlin B, Marsh CB, Murphy SA, Nahum-Shani I, Patrick K, Rehg JM, Sharmin M, Shetty V, Sim I, Spring B, Srivastava M, Wetter DW. Center of excellence for mobile sensor data-to-knowledge (MD2K). J Am Med Inform Assoc 2015; 22 (06) 1137-1142. 29 Ku JP, Hicks JL, Hastie T, Leskovec J, Re C, Delp SL. The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility. J Am Med Inform Assoc 2015; 22 (06) 1120-1125. 30 Harris Center for Precision Wellness at the Icahn School of Medicine at Mount Sinai. 2016 [cited 2016 February 1]; Available from: http://precisionwellness.org. 31 Ya-Li Z, Xiao-Rong D, Poon CCY, Lo BPL, Heye Z, Xiao-Lin Z, Guang-Zhong Y, Ni Z, Yuan-Ting Z. Unobtrusive Sensing and Wearable Devices for Health Informatics. Biomedical Engineering, IEEE Transactions on 2014; 61 (05) 1538-1554. 32 Petersen C, DeMuro P. Legal and regulatory considerations associated with use of patient-generated health data from social media and mobile health (mHealth) devices. Applied clinical informatics 2015; 06 (01) 16-26. 33 Hudson KL, Collins FS. Bringing the Common Rule into the 21st Century. New England Journal of Medicine 2015; 373 (24) 2293-2296.
