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Appl Clin Inform 2016; 07(02): 290-298
DOI: 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 AffiliationsResearch reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number T15LM007033 (DES) and by the Hartwell Foundation’s iHART program (DPW). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Hartwell Foundation.
Further Information

Publication History

received: 18 December 2015

accepted: 28 March 2016

Publication Date:
16 December 2017 (online)

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Citation: Stark DE; Kumar RB; Longhurst CA; Wall DP. The Quantified Brain: A Framework for Mobile Device Based Assessment of Behavior and Neurological Function.

 

  • 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.
    CrossrefPubMedGoogle Scholar
  • 2 Institute of Medicine. Measuring the Risks and Causes of Premature Death: Summary of Workshops. 2015. Washington (DC): National Academies Press (US);
    Google Scholar
  • 3 Collins FS, Varmus H. A New Initiative on Precision Medicine. New England Journal of Medicine. 2015
    PubMedGoogle Scholar
  • 4 Ashley EA. The precision medicine initiative: A new national effort. JAMA. 2015
    PubMedGoogle Scholar
  • 5 Patient-Generated Health Data. [cited 2016 February 27]; Available from: https://www.healthit.gov/policy-researchers-implementers/patient-generated-health-data.
    PubMed
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 14 Topol EJ, Steinhubl SR, Torkamani A. Digital medical tools and sensors. JAMA 2015; 313 (04) 353-354.
    CrossrefPubMedGoogle Scholar
  • 15 Meeker M, Wu L. editors. Internet Trends D11 Conference. 2013 Kleiner Perkins Caufield Byers.
    PubMedGoogle Scholar
  • 16 PubMed Search Results: ‘kinect’. [cited 2016 February 1]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=kinect.
    PubMedGoogle Scholar
  • 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.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    Google Scholar
  • 25 Looney D, Kidmose P, Mandic DP. Ear-EEG: user-centered and wearable BCI. Brain-Computer Interface Research: Springer Berlin Heidelberg. 2014: 41-50.
    PubMedGoogle Scholar
  • 26 Open mHealth. 2016 [cited 2016 February 1]; Available from: http://www.openmhealth.org.
    PubMed
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 33 Hudson KL, Collins FS. Bringing the Common Rule into the 21st Century. New England Journal of Medicine 2015; 373 (24) 2293-2296.
    CrossrefPubMedGoogle Scholar