Ida Sim, M.D., Ph.D.
From the Division of General Internal Medicine, University of California, San Francisco, San Francisco. Address reprint requests to Dr. Sim at the University of California, San Francisco, 1545 Divisadero St., Suite 308, San Francisco, CA 94143-0320, or at email@example.com.
1. Pew Research Center Global Attitudes Project. Smartphone ownership is growing rapidly around the world, but not always equally. February 5, 2019 (https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/. opens in new tab).
2. Buttorff C, Ruder T, Bauman M. Multiple chronic conditions in the United States. Santa Monica, CA: RAND, 2017 (http://www.rand.org/pubs/tools/TL221.html. opens in new tab).
3. Agency for Healthcare Research and Quality. Multiple chronic conditions chartbook. April 2014 (https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/prevention-chronic-care/decision/mcc/mccchartbook.pdf. opens in new tab).
4. Apple. Apple Watch Series 4 (https://www.apple.com/apple-watch-series-4/. opens in new tab).
5. Goel M, Saba E, Stiber M, et al. SpiroCall: measuring lung function over a phone call. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems — CHI ’16. Santa Clara, CA: ACM Press, 2016 (http://dl.acm.org/citation.cfm?doid=2858036.2858401. opens in new tab).
6. Coppetti T, Brauchlin A, Müggler S, et al. Accuracy of smartphone apps for heart rate measurement. Eur J Prev Cardiol 2017;24:1287-1293.
7. Statista. Wearable user penetration rate in the United States, in 2017, by age (https://www.statista.com/statistics/739398/us-wearable-penetration-by-age/. opens in new tab).
8. Saleheen N, Ali AA, Hossain SM, et al. puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. Proc ACM Int Conf Ubiquitous Comput 2015;2015:999-1010.
9. Empatica. Embrace2 seizure detection (https://www.empatica.com/. opens in new tab).
10. AliveCor. KardiaMobile (https://www.alivecor.com/. opens in new tab).
11. MC10. BioStamp nPoint: wearable healthcare technology & devices (https://www.mc10inc.com. opens in new tab).
12. Gao J, Ertin E, Kumar S, al’Absi M. Contactless sensing of physiological signals using wideband RF probes. In: Forty-Seventh 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-textile technologies: a review on sensors, actuators and control elements. Inventions 2018;3:14-14 (https://www.mdpi.com/2411-5134/3/1/14. opens in new tab).
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;13:59-59 .
15. Hafezi H, Robertson TL, Moon GD, Au-Yeung KY, Zdeblick MJ, Savage GM. An ingestible sensor for measuring medication adherence. IEEE Trans Biomed Eng 2015;62:99-109.
16. Majumder S, Aghayi E, Noferesti M, et al. Smart homes for elderly healthcare — recent advances and research challenges. Sensors (Basel) 2017;17(11):E2496-E2496.
17. Array of Things. Introductory video (https://arrayofthings.github.io/. opens in new tab).
18. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008;4:1-32.
19. May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological momentary assessment methodology in chronic pain research: 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 daily lives of patients with anxiety disorders: a systematic review. J Anxiety Disord 2014;28:925-937.
21. Shiffman S. Ecological momentary assessment. In: Sher KJ, ed. The Oxford handbook of substance use and substance use disorders. Vol. 2. New York: Oxford University Press, October 2016:466-512.
Google Scholar. opens in new tab
22. Brooks GC, Vittinghoff E, Iyer S, et al. Accuracy and usability of a self-administered 6-minute walk test smartphone application. Circ Heart Fail 2015;8:905-913.
23. Singh S, Xu W. Robust detection of Parkinson’s disease using harvested smartphone 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 mobile cognitive assessments in clinical research: a systematic review. Int J Methods Psychiatr Res 2017;26(4):e1562-e1562.
25. Coravos A, Khozin S, Mandl KD. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ Digit Med 2019;2(1):14-14.
26. Noah B, Keller MS, Mosadeghi S, et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digit Med 2018;1:20172-20172.
27. Luik AI, Kyle SD, Espie CA. Digital cognitive behavioral therapy (dCBT) for insomnia: a state-of-the-science review. Curr Sleep Med Rep 2017;3:48-56.
28. Pear Therapeutics. Redefining medicine: prescription digital therapeutics for the treatment of serious disease (https://peartherapeutics.com/. opens in new tab).
29. Akili Interactive. Akili achieves primary efficacy endpoint in pediatric ADHD pivotal trial. December 4, 2017 (https://www.akiliinteractive.com/news-collection/akili-achieves-primary-efficacy-endpoint-in-pediatric-adhd-pivotal-trial. opens in new tab).
30. Omada Health. Digital therapeutics for chronic disease (https://www.omadahealth.com/. opens in new tab).
31. Barrett MA, Humblet O, Marcus JE, et al. Effect of a mobile health, sensor-driven asthma management platform on asthma 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 a clinical tool for pain management. Curr Pain Headache Rep 2018;22:53-53.
33. Freespira. A solution for panic attack treatment: FDA-cleared, drug-free (https://freespira.com/. opens in new tab).
34. FDA clears mobile medical app to help those with opioid use disorder stay in recovery programs. News release of the Food and Drug Administration, Silver Spring, MD, December 10, 2018 (http://www.fda.gov/news-events/press-announcements/fda-clears-mobile-medical-app-help-those-opioid-use-disorder-stay-recovery-programs. opens in new tab).
35. Teva announces FDA approval of first and only digital inhaler with built-in sensors — ProAir Digihaler (albuterol sulfate 117 mcg) inhalation powder. Press release of Teva Pharmaceutical Industries, Petah Tikva, Israel, December 21, 2018 (https://ir.tevapharm.com/investors/press-releases/press-release-details/2018/Teva-Announces-FDA-Approval-of-First-and-Only-Digital-Inhaler-with-Built-In-Sensors--ProAir-Digihaler-albuterol-sulfate-117-mcg-Inhalation-Powder/default.aspx. opens in new tab).
36. Sheridan K. Prescription apps are gaining ground — and drug makers’ backing — as digital therapeutics. Boston: STAT, July 25, 2018 (https://www.statnews.com/2018/07/25/phone-apps-digital-therapeutics/. opens in new tab).
37. Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci 2017;11:555-555.
38. Vargas-Cuentas NI, Roman-Gonzalez A, Gilman RH, et al. Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PLoS One 2017;12(11):e0188826-e0188826.
39. He L, Cao C. Automated depression analysis using convolutional neural networks from speech. J Biomed Inform 2018;83:103-111.
40. Simblett S, Greer B, Matcham F, et al. Barriers to and facilitators of engagement with remote measurement technology for managing health: systematic review and content analysis of findings. J Med Internet Res 2018;20(7):e10480-e10480.
41. Inside wearables: how the science of human behavior change offers the secret to long-term. Endeavour Partners, 2017 (https://medium.com/@endeavourprtnrs/inside-wearable-how-the-science-of-human-behavior-change-offers-the-secret-to-long-term-engagement-a15b3c7d4cf3. opens in new tab).
42. Whitney RL, Ward DH, Marois MT, Schmid CH, Sim I, Kravitz RL. Patient perceptions of their own data in mHealth technology-enabled N-of-1 trials for chronic pain: qualitative study. JMIR Mhealth Uhealth 2018;6(10):e10291-e10291.
43. Research 2 Guidance. mHealth economics — how mHealth publishers are monetizing their apps. 2018 (https://research2guidance.com/product/mhealth-economics-how-mhealth-app-publishers-are-monetizing-their-apps/. opens in new tab).
44. HealthIT.gov. Notice of proposed rulemaking to improve the interoperability of health information. 2019 (https://www.healthit.gov/topic/laws-regulation-and-policy/notice-proposed-rulemaking-improve-interoperability-health. opens in new tab).
45. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016;23:899-908.
46. Milani RV, Bober RM, Lavie CJ. The role of technology in chronic disease care. Prog Cardiovasc Dis 2016;58:579-583.
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. October 2018 (https://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/DeviceApprovalsandClearances/510kClearances/default.htm. opens in new tab).
49. FDA Center for Devices and Radiological Health. Digital health software precertification (Pre-Cert) program. October 2018 (https://www.fda.gov/medicaldevices/digitalhealth/digitalhealthprecertprogram/default.htm. opens in new tab).
50. Wen D, Zhang X, Liu X, Lei J. Evaluating the consistency of current mainstream wearable devices in health monitoring: a comparison under free-living conditions. J Med Internet Res 2017;19(3):e68-e68.
51. Perry B, Herrington W, Goldsack JC, et al. Use of mobile devices to measure outcomes in clinical research, 2010-2016: a systematic literature review. Digit Biomark 2018;2:11-30.
52. Open mHealth. Open source data integration tools (http://www.openmhealth.org. opens in new tab).
53. Sage Bionetworks. Parkinson’s disease digital biomarker DREAM challenge. 2017 (https://www.synapse.org/#!Synapse:syn8717496/wiki/422884. opens in new tab).
54. Express Scripts. Express Scripts simplifies digital health technology marketplace for consumers and payers. May 16, 2019 (https://www.prnewswire.com/news-releases/express-scripts-simplifies-digital-health-technology-marketplace-for-consumers-and-payers-300851128.html. opens in new tab).
55. Xcertia. mHealth app guidelines (https://www.xcertia.org/. opens in new tab).
56. NODE.Health home page (http://nodehealth.org/. opens in new tab).
57. Day S, Zweig M. 2018 Year end funding report: is digital health in a bubble? Rock Health, 2019 (https://rockhealth.com/reports/2018-year-end-funding-report-is-digital-health-in-a-bubble/. opens in new tab).
58. Centers for Medicare & Medicaid Services. Medicare program: revisions to payment policies under the physician fee schedule and other revisions to Part B for CY 2018: Medicare Shared Savings Program requirements: and Medicare Diabetes Prevention Program. Fed Regist 2017;82(219):52976-52976 (https://www.federalregister.gov/documents/2017/11/15/2017-23953/medicare-program-revisions-to-payment-policies-under-the-physician-fee-schedule-and-other-revisions. opens in new tab).
59. Richman B. Health regulation for the digital age — correcting the mismatch. N Engl J Med 2018;379:1694-1695.
60. Clinical Trials Transformation Initiative. CTTI recommendations: decentralized clinical trials. 2018 (https://www.ctti-clinicaltrials.org/sites/www.ctti-clinicaltrials.org/files/dct_recommendations_final.pdf. opens in new tab).
61. ResearchKit. Introducing ResearchKit (http://researchkit.org/. opens in new tab).
62. ResearchStack: an SDK for building research study apps on Android (http://researchstack.org/. opens in new tab).
63. Tidepool. The place for your diabetes data (https://www.tidepool.org/users. opens in new tab).
64. Rosenbaum L. Swallowing a spy — the potential uses of digital adherence monitoring. N Engl J Med 2018;378:101-103.
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;13:351-354.
66. Korinek EV, Phatak SS, Martin CA, et al. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. J Behav Med 2018;41:74-86.
67. Rainie L. Digital divides — feeding America. Washington, DC: Pew Research Center, February 9, 2017 (http://www.pewinternet.org/2017/02/09/digital-divides-feeding-america/. opens in new tab).
68. StatCounter. Mobile operating system market share in United States of America: June 2018-June 2019. Global Stats. 2019 (http://gs.statcounter.com/os-market-share/mobile/united-states-of-america. opens in new tab).
69. Comscore. iPhone users earn higher income, engage more on apps than Android users. August 14, 2014 (http://www.comscore.com/Insights/Infographics/iPhone-Users-Earn-Higher-Income-Engage-More-on-Apps-than-Android-Users. opens in new tab).
70. Centers for Disease Control and Prevention. Health Literacy: understanding literacy & numeracy. 2018 (https://www.cdc.gov/healthliteracy/learn/UnderstandingLiteracy.html. opens in new tab).
71. Rampey BD, Finnegan R, Goodman M, et al. Skills of U.S. unemployed, young, and older adults in sharper focus: results from the Program for the International Assessment of Adult Competencies (PIAAC) 2012/2014: first look (NCES 2016-039rev). Washington, DC: Department of Education, March 2016 (https://nces.ed.gov/pubs2016/2016039rev.pdf. opens in new tab).
72. Pasquale F. The black box society: the secret algorithms that control money and information. Cambridge, MA: Harvard University Press, 2015.
73. Consumer Reports. Guide to digital security & privacy. 2019 (https://www.consumerreports.org/digital-security/online-security-and-privacy-guide/. opens in new tab).
74. Hill D. Project HealthDesign: rethinking the power and potential of personal health records. Princeton, NJ: Robert Wood Johnson Foundation, 2015 (https://www.rwjf.org/en/library/research/2010/10/project-healthdesign--rethinking-the-power-and-potential-of-pers.html. opens in new tab).
75. Toscos T, Drouin M, Pater J, Flanagan M, Pfafman R, Mirro MJ. Selection biases in technology-based intervention research: patients’ technology use relates to both demographic and health-related inequities. J Am Med Inform Assoc 2019 June 7 (Epub ahead of print).
76. Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med 2013;45:228-236.
77. Zhang X, Hailu B, Tabor DC, et al. Role of health information technology in addressing health disparities: patient, clinician, and system perspectives. Med Care 2019;57:Suppl 2:S115-S120.
78. Sheon AR, Van Winkle B, Solad Y, Atreja A. An algorithm for digital medicine testing: a NODE.Health perspective intended to help emerging technology companies and healthcare systems navigate the trial and testing period prior to full-scale adoption. Digit Biomark 2018;2:139-154.
79. Makhni S, Atreja A, Sheon A, Van Winkle B, Sharp J, Carpenter N. The broken health information technology innovation pipeline: a perspective from the NODE health consortium. Digit Biomark 2017;1:64-72 (https://www.karger.com/Article/FullText/479017. opens in new tab).
80. Pauwels E, Denton SW. Searching for privacy in the Internet of Bodies. Wilson Quarterly. Spring 2018 (https://www.wilsonquarterly.com/quarterly/living-with-artificial-intelligence/searching-for-privacy-in-the-internet-of-bodies/. opens in new tab).