eHealth | 15 OCT 19

Dispositivos móviles y salud

Revisión sobre el uso de dispositivos móviles en salud
INDICE:  1. Texto principal | 2. Referencias bibliográficas
Referencias bibliográficas


1. Pew Research Center Global AttitudesProject. Smartphone ownership is growingrapidly around the world, but not alwaysequally. February 5, 2019 (

2. Buttorff C, Ruder T, Bauman M. Multiple chronic conditions in the United States. Santa Monica, CA: RAND, 2017 (

3. Agency for Healthcare Research andQuality. Multiple chronic conditions chartbook. April 2014 ( Apple. Apple Watch Series

4 (

5. Goel M, Saba E, Stiber M, et al. SpiroCall: measuring lung function over a phonecall. In: Proceedings of the 2016 CHI Conference on Human Factors in ComputingSystems — CHI ’16. Santa Clara, CA: ACMPress, 2016 (

6. Coppetti T, Brauchlin A, Müggler S, etal. Accuracy of smartphone apps for heartrate measurement. Eur J Prev Cardiol 2017;24:1287-93.

7. Statista. Wearable user penetrationrate in the United States, in 2017, by age(

8. Saleheen N, Ali AA, Hossain SM, et al.puffMarker: a multi-sensor approach forpinpointing the timing of first lapse insmoking cessation. Proc ACM Int ConfUbiquitous Comput 2015;2015:999-1010.

9. Empatica. Embrace2 seizure detection (

10. AliveCor. KardiaMobile (

11. MC10. BioStamp nPoint: wearablehealthcare technology & devices (

12. Gao J, Ertin E, Kumar S, al’Absi M.Contactless sensing of physiological signals using wideband RF probes. In: FortySeventh Asilomar Conference on Signals,Systems & Computers, Pacific Grove, CA,November 3–6, 2013. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2013:86-90.

13. Gonçalves C, Ferreira da Silva A,Gomes J, Simoes R. Wearable e-textiletechnologies: a review on sensors, actuators and control elements. Inventions2018;3:

14 ( McLaren R, Joseph F, Baguley C, Taylor D. A review of e-textiles in neurological rehabilitation: how close are we?J Neuroeng Rehabil 2016;13:59 .

15. Hafezi H, Robertson TL, Moon GD,Au-Yeung KY, Zdeblick MJ, Savage GM. Aningestible sensor for measuring medication adherence. IEEE Trans Biomed Eng2015;62:99-109.

16. Majumder S, Aghayi E, Noferesti M, etal. Smart homes for elderly healthcare —recent advances and research challenges.Sensors (Basel) 2017;

17(11):E2496.17. Array of Things. Introductory video(

18. Shiffman S, Stone AA, Hufford MR.Ecological momentary assessment. AnnuRev Clin Psychol 2008;4:1-32.

19. May M, Junghaenel DU, Ono M, StoneAA, Schneider S. Ecological momentaryassessment methodology in chronic painresearch: a systematic review. J Pain 2018;19:699-716.

20. Walz LC, Nauta MH, Aan Het Rot M.Experience sampling and ecological momentary assessment for studying the dailylives of patients with anxiety disorders:a systematic review. J Anxiety Disord2014;28:925-37.

21. Shiffman S. Ecological momentaryassessment. In: Sher KJ, ed. The Oxfordhandbook of substance use and substanceuse disorders. Vol. 2. New York: Oxford University Press, October 2016:466-512.

22. Brooks GC, Vittinghoff E, Iyer S, et al.Accuracy and usability of a self-administered 6-minute walk test smartphoneapplication. Circ Heart Fail 2015;8:905-13.

23. Singh S, Xu W. Robust detection ofParkinson’s disease using harvestedsmartphone voice data: a telemedicine approach. Telemed J E Health 2019 April 26(Epub ahead of print).

24. Moore RC, Swendsen J, Depp CA. Applications for self-administered mobilecognitive assessments in clinical research: a systematic review. Int J MethodsPsychiatr Res 2017;26(4):e1562.

25. Coravos A, Khozin S, Mandl KD. Developing and adopting safe and effectivedigital biomarkers to improve patient outcomes. NPJ Digit Med 2019;2(1):14.

26.. Noah B, Keller MS, Mosadeghi S, etal. Impact of remote patient monitoringon clinical outcomes: an updated metaanalysis of randomized controlled trials.NPJ Digit Med 2018;1:20172.

27. Luik AI, Kyle SD, Espie CA. Digital cognitive behavioral therapy (dCBT) forinsomnia: a state-of-the-science review.Curr Sleep Med Rep 2017;3:48-56.

28. Pear Therapeutics. Redefining medicine: prescription digital therapeutics forthe treatment of serious disease (

29. Akili Interactive. Akili achieves primary efficacy endpoint in pediatricADHD pivotal trial. December 4, 2017(

30. Omada Health. Digital therapeutics for chronic disease (

31. Barrett MA, Humblet O, Marcus JE, etal. Effect of a mobile health, sensor-driven asthma management platform onasthma control. Ann Allergy Asthma Immunol 2017;119(5):415.e1-421.e1.

32. Pourmand A, Davis S, Marchak A,Whiteside T, Sikka N. Virtual reality as aclinical tool for pain management. CurrPain Headache Rep 2018;22:53.

33. Freespira. A solution for panic attacktreatment: FDA-cleared, drug-free (

34. FDA clears mobile medical app tohelp those with opioid use disorder stayin recovery programs. News release of theFood and Drug Administration, SilverSpring, MD, December 10, 2018 (

35. Teva announces FDA approval of firstand only digital inhaler with built-in sensors — ProAir Digihaler (albuterol sulfate117 mcg) inhalation powder. Press releaseof Teva Pharmaceutical Industries, PetahTikva, Israel, December 21, 2018 (

36. Sheridan K. Prescription apps aregaining ground — and drug makers’backing — as digital therapeutics. Boston: STAT, July 25, 2018 (

37. Rovini E, Maremmani C, Cavallo F.How wearable sensors can support Parkinson’s disease diagnosis and treatment:a systematic review. Front Neurosci 2017;11:555.

38. Vargas-Cuentas NI, Roman-GonzalezA, Gilman RH, et al. Developing an eyetracking algorithm as a potential tool forearly diagnosis of autism spectrum disorder in children. PLoS One 2017;12(11):e0188826.

39. He L, Cao C. Automated depressionanalysis using convolutional neural networks from speech. J Biomed Inform2018;83:103-11.

40. Simblett S, Greer B, Matcham F, et al.Barriers to and facilitators of engagementwith remote measurement technology formanaging health: systematic review andcontent analysis of findings. J Med Internet Res 2018;20(7):e10480.

41. Inside wearables: how the science ofhuman behavior change offers the secretto long-term. Endeavour Partners, 2017(

42. Whitney RL, Ward DH, Marois MT,Schmid CH, Sim I, Kravitz RL. Patientperceptions of their own data in mHealthtechnology-enabled N-of-1 trials forchronic pain: qualitative study. JMIRMhealth Uhealth 2018;6(10):e10291.

43. Research 2 Guidance. mHealth economics — how mHealth publishers aremonetizing their apps. 2018 (

44. Notice of proposedrulemaking to improve the interoperability of health information. 2019 (

45. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR:a standards-based, interoperable appsplatform for electronic health records.J Am Med Inform Assoc 2016;23:899-908.

46. Milani RV, Bober RM, Lavie CJ. Therole of technology in chronic disease care.Prog Cardiovasc Dis 2016;58:579-83.

47. Adler-Milstein J, Nong P. Early experiences with patient generated health data:health system and patient perspectives.J Am Med Inform Assoc 2019 April 22(Epub ahead of print).

48. FDA Center for Devices and Radiological Health. 510(k) Clearances. October2018 (

49. FDA Center for Devices and Radiological Health. Digital health software precertification (Pre-Cert) program. October2018 (

50. Wen D, Zhang X, Liu X, Lei J. Evaluating the consistency of current mainstreamwearable devices in health monitoring:a comparison under free-living conditions.J Med Internet Res 2017;19(3):e68.

51. Perry B, Herrington W, Goldsack JC,et al. Use of mobile devices to measureoutcomes in clinical research, 2010-2016:a systematic literature review. Digit Biomark 2018;2:11-30.

52. Open mHealth. Open source data integration tools (

53. Sage Bionetworks. Parkinson’s disease digital biomarker DREAM challenge.2017 (!Synapse:syn8717496/wiki/422884).

54. Express Scripts. Express Scripts simplifies digital health technology marketplace for consumers and payers. May 16,2019 (

55. Xcertia. mHealth app guidelines(

56. NODE.Health home page (

57. Day S, Zweig M. 2018 Year end funding report: is digital health in a bubble?Rock Health, 2019 (

58. Centers for Medicare & Medicaid Services. Medicare program: revisions topayment policies under the physician feeschedule and other revisions to Part B forCY 2018: Medicare Shared Savings Program requirements: and Medicare Diabetes Prevention Program. Fed Regist 2017;82(219):52976 (

59. Richman B. Health regulation for thedigital age — correcting the mismatch.N Engl J Med 2018;379:1694-5.

60. Clinical Trials Transformation Initiative. CTTI recommendations: decentralizedclinical trials. 2018 (

61. ResearchKit. Introducing ResearchKit(

62. ResearchStack: an SDK for buildingresearch study apps on Android (

63. Tidepool. The place for your diabetes data (

64. Rosenbaum L. Swallowing a spy —the potential uses of digital adherencemonitoring. N Engl J Med 2018;378:101-3.

65. Baron KG, Abbott S, Jao N, Manalo N,Mullen R. Orthosomnia: are some patients taking the quantified self too far?J Clin Sleep Med 2017;13:351-4.

66. Korinek EV, Phatak SS, Martin CA, etal. Adaptive step goals and rewards: a longitudinal growth model of daily steps fora smartphone-based walking intervention. J Behav Med 2018;41:74-86.

67. Rainie L. Digital divides — feedingAmerica. Washington, DC: Pew ResearchCenter, February 9, 2017 (

68. StatCounter. Mobile operating systemmarket share in United States of America:June 2018-June 2019. Global Stats. 2019(

69. Comscore. iPhone users earn higherincome, engage more on apps than Android users. August 14, 2014 (

70. Centers for Disease Control and Prevention. Health Literacy: understanding literacy & numeracy. 2018 (

71. Rampey BD, Finnegan R, GoodmanM, et al. Skills of U.S. unemployed, young,and older adults in sharper focus: resultsfrom the Program for the International Assessment of Adult Competencies (PIAAC)2012/2014: first look (NCES 2016-039rev).Washington, DC: Department of Education, March 2016 (

72. Pasquale F. The black box society: thesecret algorithms that control money andinformation. Cambridge, MA: HarvardUniversity Press, 2015.

73. Consumer Reports. Guide to digitalsecurity & privacy. 2019 (

74. Hill D. Project HealthDesign: rethinking the power and potential of personalhealth records. Princeton, NJ: RobertWood Johnson Foundation, 2015 (

75. Toscos T, Drouin M, Pater J, FlanaganM, Pfafman R, Mirro MJ. Selection biasesin technology-based intervention research:patients’ technology use relates to bothdemographic and health-related inequities. J Am Med Inform Assoc 2019 June 7(Epub ahead of print).

76. Kumar S, Nilsen WJ, Abernethy A, etal. Mobile health technology evaluation:the mHealth evidence workshop. Am JPrev Med 2013;45:228-36.

77. Zhang X, Hailu B, Tabor DC, et al.Role of health information technology inaddressing health disparities: patient, clinician, and system perspectives. Med Care2019;57:Suppl 2:S115-S120.

78. Sheon AR, Van Winkle B, Solad Y,Atreja A. An algorithm for digital medicine testing: a NODE.Health perspectiveintended to help emerging technologycompanies and healthcare systems navigate the trial and testing period prior tofull-scale adoption. Digit Biomark 2018;2:139-54.

79. Makhni S, Atreja A, Sheon A, VanWinkle B, Sharp J, Carpenter N. The broken health information technology innovation pipeline: a perspective from theNODE health consortium. Digit Biomark2017;1:64-72 (

80. Pauwels E, Denton SW. Searching forprivacy in the Internet of Bodies. WilsonQuarterly. Spring 2018 (



Usted debe ingresar al sitio con su cuenta de usuario IntraMed para ver los comentarios de sus colegas o para expresar su opinión. Si ya tiene una cuenta IntraMed o desea registrase, ingrese aquí