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1
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BinDhim NF, Shaman AM, Trevena L, Basyouni MH, Pont LG, Alhawassi TM. Depression screening via a smartphone app: cross-country user characteristics and feasibility. J Am Med Inform Assoc. 2015;22:29–34. doi:10.1136/amiajnl-2014-002840.
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Faurholt-Jepsen M, Frost M, Vinberg M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 2014;217:124–7. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24679993&retmode=ref&cmd=prlinks.
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Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull. 2014:1–10. doi:10.1093/schbul/sbu033. This is a significant paper as it demonstrates high levels of patient engagement with a smartphone apps and the immediate clinical applicability of current smartphone technology.
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The SmartCAT: an m-health platform for ecological momentary intervention in child anxiety treatment
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Pramana G, Parmanto B, Kendall PC, Silk JS. The SmartCAT: an m-health platform for ecological momentary intervention in child anxiety treatment. Telemed J E Health. 2013;20:419–27. doi:10.1089/tmj.2013.0214.
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This is a significant paper as it is one of the largest and most methodologically rigorous studies demonstrating the clinical applicability of smartphones and their ability to collect high volume, variety and velocity data related to clinical care
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Gustafson DH, McTavish FM, Chih M-Y, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatr. 2014;71:566–72. doi:10.1001/jamapsychiatry.2013.4642. This is a significant paper as it is one of the largest and most methodologically rigorous studies demonstrating the clinical applicability of smartphones and their ability to collect high volume, variety and velocity data related to clinical care.
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JAMA Psychiatr
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Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014. doi:10.1007/s11920-014-0523-3.
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Curr Psychiatry Rep
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Torous J, Friedman R, Keshvan M. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. J Med Internet Res. 2014;16. doi:10.2196/mhealth.2994.
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J Med Internet Res
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This paper provides the largest dataset, stratified by age and clinic setting, regarding mental health patients’ interest in smartphone monitoring
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Torous J, Chan SR, Tan SY, et al. Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics corresponding author. JMIR Ment Heal. 2014;1:1–7. doi:10.2196/mental.4004. This paper provides the largest dataset, stratified by age and clinic setting, regarding mental health patients’ interest in smartphone monitoring.
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J Med Internet Res
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Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med. 2014;4:363–71. doi:10.1007/s13142-014-0293-9.
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Bush NE, Skopp N, Smolenski D, Crumpton R, Fairall J. Behavioral screening measures delivered with a smartphone app: psychometric properties and user preference. J Nerv Ment Dis. 2013;201:991–5. doi:10.1097/NMD.0000000000000039.
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Markowetz A, Błaszkiewicz K, Montag C, Switala C, Schlaepfer TE. Psycho-informatics: big data shaping modern psychometrics. Med Hypotheses. 2014;82:405–11. doi:10.1016/j.mehy.2013.11.030.
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Selby EA, Yen S, Spirito A. Time varying prediction of thoughts of death and suicidal ideation in adolescents: weekly ratings over 6-month follow-up. J Clin Child Adolesc Psychol. 2013;42:481–95. doi:10.1080/15374416.2012.736356.
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Brain Topogr
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Ip EH, Zhang Q, Rejeski WJ, Harris TB, Kritchevsky S. Partially ordered mixed hidden Markov model for the disablement process of older adults. J Am Stat Assoc. 2013;108(2015):370–80. doi:10.1080/01621459.2013.770307.
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James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: Springer; 2013. This book summarizes a wide variety of the best and most basic methods available to predict and interpret data, written for non-specialists with a focus on application.
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An introduction to statistical learning
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James, G.1
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Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 2015. doi:10.1002/gps.4262.
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COI: 1:CAS:528:DC%2BD2sXlslWqsbw%3D, PID: 17456605, This was the first paper to demonstrate the construction of social networks from mobile phone communication data, also known as call detail records (CDRs), which is a form of high-volume passive data collected by telecom operators for billing purposes worldwide
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Onnela J-P, Saramäki J, Hyvönen J, et al. Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci U S A. 2007;104:7332–6. doi:10.1073/pnas.0610245104. This was the first paper to demonstrate the construction of social networks from mobile phone communication data, also known as call detail records (CDRs), which is a form of high-volume passive data collected by telecom operators for billing purposes worldwide.
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Proc Natl Acad Sci U S A
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Eagle N, Pentland A. Reality mining: sensing complex social systems. Pers Ubiquit Comput. 2006;10:255–68. doi:10.1007/s00779-005-0046-3. This is a significant paper as it was one of the first to propose mobile phone sensing in computer science in the academic research community.
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