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Cited by in F6Publishing
For: Schatz BR. National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors. Big Data 2015;3:219-29. [PMID: 26858915 DOI: 10.1089/big.2015.0021] [Cited by in Crossref: 20] [Cited by in F6Publishing: 10] [Article Influence: 2.9] [Reference Citation Analysis]
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2 Istepanian RS, Al-anzi T. m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics. Methods 2018;151:34-40. [DOI: 10.1016/j.ymeth.2018.05.015] [Cited by in Crossref: 35] [Cited by in F6Publishing: 13] [Article Influence: 8.8] [Reference Citation Analysis]
3 Nicholas J, Shilton K, Schueller SM, Gray EL, Kwasny MJ, Mohr DC. The Role of Data Type and Recipient in Individuals' Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR Mhealth Uhealth 2019;7:e12578. [PMID: 30950799 DOI: 10.2196/12578] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
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5 Collins FS, Riley WT. NIH's transformative opportunities for the behavioral and social sciences. Sci Transl Med 2016;8:366ed14. [PMID: 27881821 DOI: 10.1126/scitranslmed.aai9374] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 5.0] [Reference Citation Analysis]
6 Coda A, Sculley D, Santos D, Girones X, Brosseau L, Smith DR, Burns J, Rome K, Munro J, Singh-Grewal D. Harnessing interactive technologies to improve health outcomes in juvenile idiopathic arthritis. Pediatr Rheumatol Online J 2017;15:40. [PMID: 28511689 DOI: 10.1186/s12969-017-0168-y] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 2.8] [Reference Citation Analysis]
7 Freckmann G, Mende J. Continuous glucose monitoring: data management and evaluation by patients and health care professionals – current situation and developments. LaboratoriumsMedizin 2018;42:225-33. [DOI: 10.1515/labmed-2018-0119] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
8 Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 2018;114:57-65. [PMID: 29673604 DOI: 10.1016/j.ijmedinf.2018.03.013] [Cited by in Crossref: 139] [Cited by in F6Publishing: 49] [Article Influence: 34.8] [Reference Citation Analysis]
9 Crouthamel M, Quattrocchi E, Watts S, Wang S, Berry P, Garcia-Gancedo L, Hamy V, Williams RE. Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study. JMIR Mhealth Uhealth 2018;6:e177. [PMID: 30213779 DOI: 10.2196/mhealth.9656] [Cited by in Crossref: 34] [Cited by in F6Publishing: 27] [Article Influence: 8.5] [Reference Citation Analysis]
10 Vettoretti M, Cappon G, Acciaroli G, Facchinetti A, Sparacino G. Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications. J Diabetes Sci Technol. 2018;12:1064-1071. [PMID: 29783897 DOI: 10.1177/1932296818774078] [Cited by in Crossref: 36] [Cited by in F6Publishing: 20] [Article Influence: 9.0] [Reference Citation Analysis]
11 Navaz AN, Serhani MA, Al-Qirim N, Gergely M. Towards an efficient and Energy-Aware mobile big health data architecture. Comput Methods Programs Biomed 2018;166:137-54. [PMID: 30415713 DOI: 10.1016/j.cmpb.2018.10.008] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]