[
Commentary Ahead of the Curve
]
Smart Technology in Lung Disease Clinical Trials Nancy L. Geller, PhD; Dong-Yun Kim, PhD; and Xin Tian, PhD
This article describes the use of smart technology by investigators and patients to facilitate lung disease clinical trials and make them less costly and more efficient. By “smart technology” we include various electronic media, such as computer databases, the Internet, and mobile devices. We first describe the use of electronic health records for identifying potential subjects and then discuss electronic informed consent. We give several examples of using the Internet and mobile technology in clinical trials. Interventions have been delivered via the World Wide Web or via mobile devices, and both have been used to collect outcome data. We discuss examples of new electronic devices that recently have been introduced to collect health data. While use of smart technology in clinical trials is an exciting development, comparison with similar interventions applied in a conventional manner is still in its infancy. We discuss advantages and disadvantages of using this omnipresent, powerful tool in clinical trials, as well as directions for CHEST 2016; 149(1):22-26
future research. KEY WORDS:
clinical trials; lung disease; mobile health; personal electronic devices; smart technology
Contemporary randomized controlled trials have become prohibitively expensive, thus limiting the number of conventional clinical trials that can be undertaken. One way to make clinical trials less expensive is through the use of computer technology. Over time, the use of computers has become increasingly a part of everyday life, and, consequently, their use in clinical trials has become more commonplace. We focus on use of smart technology: electronic health records (EHRs), web interventions, and mobile devices. These can now be used in clinical trials to enhance accrual and compliance, to collect health data, and to provide more accurate outcome measures. ABBREVIATIONS: EHR = electronic health record; eIC = electronic informed consent; LASST = Long-acting b Agonist Step Down Study; MICT = Mobile Devices and the Internet to Streamline an Asthma Clinical Trial AFFILIATIONS: From the Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD.
22 Commentary
Use of EHRs The Health Information Technology for Economic and Clinical Health Act of 2009 brought the rapid and broad adoption of EHRs to hospitals in the United States. As of 2014, more than three-quarters of the non-federal acute care hospitals adopted at least a basic EHR system, and almost all of the surveyed hospitals have certified EHR technology.1 Increased efficiency in handling patient information and care coordination are significant benefits of EHRs.2 The use of EHRs can also facilitate patient identification and recruitment for clinical trials.3 Traditionally, patient screening was done manually by clinical trial personnel. Manual
CORRESPONDENCE TO: Nancy L. Geller, PhD, Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 6701 Rockledge Dr, MSC 7913, Bethesda, MD 20892-7913; e-mail: [email protected] Published by Elsevier Inc. under license from the American College of Chest Physicians. DOI: http://dx.doi.org/10.1378/chest.15-1314
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review of patient records is labor-intensive and likely to be error-prone. Studies show that automating such searches leads to a dramatic increase in efficiency and accuracy. Beauharnais et al4 reported a 50% reduction in total screening time per patient. Ni et al5 found that an eligibility matching algorithm resulted in 85% workload reductions in patient chart review. While electronic patient screening has been used in the past,6 clinical trial recruitment support systems that specifically use EHRs have emerged in previous years.7,8 There are several feasibility trials that recruited patients with asthma or COPD using EHRs and clinical trial recruitment support systems, such as the Salford Lung Study9 and eLung.10 The broad adoption of EHRs offers great opportunities in clinical trials. However, for EHRs to be fully used, there are substantial issues to address. There is a lack of common terminology in different EHRs and lack of ability to connect electronic data capture and EHRs.11 There are other major obstacles such as nonuniform local regulations governing the use of EHRs and language barriers in multinational trials. Commercial EHR packages are expensive, especially for small clinics. Furthermore, clinicians may not adapt easily to a system they did not help design.
Electronic Informed Consent Use of electronic informed consent (eIC) is another technological innovation that benefits both the patient and the clinical trial investigator. An eIC can facilitate the enrollment process by allowing patients to sign up for a clinical trial from a remote location using smartphone applications, a web interface, a touch-screen terminal, or a combination of these. It also allows the investigator to use various interactive electronic formats such as text, clickable URLs, videos, and diagrams, all of which enhance the patients’ understanding of the content.12 For the investigator, eIC provides searchable data, such as the date and time that a subject signed the consent, unlike the scanned copy of the paper form. The design of the user interface and the choice of consent models are important issues to consider to ensure the success of this new consent medium.13,14 To address the privacy and security issues of eIC, the US Food and Drug Administration has issued a draft guidance.15
Web-Based and Short Message Service-Based Interventions Web-based interventions provide the opportunity to intervene in symptomatic subjects in a cost-effective
journal.publications.chestnet.org
manner.16 In the Puff City clinical trial, Joseph et al17 tested a multimedia web-based intervention in students with asthma at six Detroit high schools, randomizing them to an individually tailored program or to access generic asthma websites over a 180-day intervention period. The program was available on the high school’s computers, and students were allowed to access the material during the school day. The trial found that asthma outcomes were significantly improved in the intervention group in this traditionally hard-to-reach population.17 Some previous clinical trials use smartphones as a delivery mechanism via short message service (text messaging). Use of text messaging has certain advantages.18 A text message is immediate so it can be an effective tool for quick reminders. Also, it may be better suited to younger subjects. Text messages can reach patients in remote areas at low cost, which could be especially useful in bringing health care and clinical trials to an underserved population.19 They also help improve retention, as found in studies involving patients with HIV and TB.20 Interventions using text messages have been shown to be effective in several areas, including smoking cessation.21 Text message-based interventions have been found to be more effective when the messaging interval is not too frequent and the messages have personalized content.22 In a meta-analysis of pooled randomized or quasirandomized trials, Whittaker et al23 showed that a mobile phone-based intervention for smoking cessation was effective in increasing the 6-month cessation rate. The Nicotine Exit (NEXit) is a new randomized trial that will examine the effectiveness of a stand-alone text-based intervention to help university students in Sweden stop smoking.24 The 12-week intervention will occur simultaneously through Swedish universities served by 25 health-care centers. This trial has certain features worth noting, namely access to a large number of potential subjects, simple eligibility criteria, and a simple intervention. The health-care system in Sweden includes everyone and therefore facilitates wide publicity for volunteers. The trial had few eligibility criteria other than being a daily or weekly smoker, but participants are required to be willing to set a date within the next 4 weeks to stop smoking. This should help to decrease noncompliance and dropout. The intervention itself is straightforward, in the sense that successful delivery is assured (once a telephone number is confirmed), and the timing and number of the messages can be automated. The large number of eligible subjects should enable rapid completion of this trial.
23
Use of Mobile Health Technology to Streamline Data Collection The astronomical growth of the personal mobile device market and human connections through the Internet and social media provide an opportunity to invite mobile device users to be active partners in medical research. In 2014, 90% of American adults owned a cell phone and 64% were smartphone users, and this proportion is expected to reach 80% by 2017.25 This may have significant implications for future clinical trials. Mobile health technologies may overcome certain challenges and barriers that traditional clinical trials face, such as high costs, insufficient participation, lack of real-time data collection, poor adherence, and limited communication and feedback.26 Apps on mobile devices, for example, smartphones, tablets, and wearables, can reach millions of possible participants and provide real-time monitoring of a user’s health data (including BPs, glucose levels, cardiac rhythms, exercise behaviors, and lung functions). Research data may be collected and then electronically transferred to a central data center via the Internet or wireless devices. Applications with widely used mobile health technologies could be efficient and cost-effective breakthroughs for conducting clinical trials in lung diseases. The use of Mobile Devices and the Internet to Streamline an Asthma Clinical Trial (MICT) is a clinical trial in which participants aged 12 to 17 years with asthma as identified by their EHRs are randomized to MICT or a concurrent, very similar traditional trial, Long-acting b Agonist Step Down Study (LASST).27 Those randomized to MICT are provided an iPad to complete health information diaries and questionnaires in an online database at home. Aside from the initial and the final clinic visits, participants complete virtual FaceTime visits with the iPad. Participants also perform spirometry using a handheld spirometer with remote coaching via the iPad. The primary clinical endpoint of MICT is the asthma control test, which is conducted at the same intervals as in LASST. MICT uses web-based delivery of consent materials via a dynamic interactive multimedia platform viewed online. The video ends with a button which the participant selects if they are ready to enroll, and is followed by an informed consent interview via telephone. To complete the eIC process, the adolescent and parent are asked to complete and submit consent forms from their separate online accounts. A conventional informed consent is used in LASST.
24 Commentary
By conducting MICT and LASST concurrently, this project can make comparisons between MICT and the usual clinical trial approach used in LASST. The results will offer insights into whether MICT is noninferior and cost-effective. However, there are certain limitations of the MICT design, namely shorter length of the trial and a rather small sample size. MICT uses the so-called Zelen (opt-out consent) design, which randomizes patients before obtaining their consent.28 This could be problematic in an intention-to-treat analysis if a large proportion of randomized patients choose not to give consent. With three treatment groups in each trial, the study may not have adequate power to test the interactions between treatment groups and trials. One could devise a simple randomized controlled clinical trial to compare an intervention using a mobile device to the usual (visit-oriented) trial. For example, after informed consent, individuals with asthma could be randomized into a two-period crossover drug trial as well as randomized to whether or not they use a mobile device for virtual visits and to collect outcome data. Those randomized to the nonmobile device group would have visits scheduled to collect the same outcome data. The two drug interventions would be the same in the mobile device group and the nonmobile device group. The mobile device could also be used to enhance compliance to the drug interventions. If adequately powered for testing both drug interventions and mobile vs non-mobile device groups, such trial design would enable the investigation of the drug effect as well as the effectiveness of using a mobile device. Another promising application of mobile technology is ResearchKit, introduced by Apple in March 2015.29 ResearchKit is an open source software platform that will enable medical researchers to create apps to study various diseases. The hope is that this will facilitate participant recruitment in clinical trials and allow medical researchers to collect data from iPhones or iPhone-linked fitness monitors. Asthma Health app is one of the first five applications developed using the ResearchKit framework.30 This app provides personal medication reminders, tracks asthma symptoms and triggers, and allows recording of ED and other medical visits and physical activity. With participant permission, it records personal demographic data and sends the participants’ records to a central research facility. It may be a limitation that only iPhone users can participate in a ResearchKit study, which may bias study results. Studies using novel mobile technology enable remote data collection by not requiring active subject participation.
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For example, for patients with COPD, rather than a 6-min walk test conducted at the doctor’s office, the average number of steps recorded on a mobile device over a specific time period could be used as an outcome measure. Use of electronic devices could also minimize the need for patient visits.31 New applications have been developed to allow patients with COPD to perform a self-test of the 6-min walk test or lung functions at any convenient time from home.32,33 In a survey conducted by the Harris Poll in December 2014, almost two-thirds of adults expressed willingness to accept video contact as a “doctor’s visit.”34
Furthermore, the new applications use up battery at a rapid rate and there is the possibility that there are sizable gaps in a patient record because the device runs out of battery life. Mobile devices could be broken-down, lost, or stolen, which would interrupt the intervention or recording of data and result in loss of study data. In addition, training in the proper use of the electronic device in a clinical trial puts an additional burden on those conducting the trial as well as on the subjects. Therefore, the cost for staff and subject training should be carefully evaluated at the clinical trial design stage.
While mobile heath technologies provide a new way for researchers to carry out clinical trials, until now research has yielded inconsistent findings.35,36 A randomized controlled trial showed no improvement in asthma control or efficacy using mobile phone-based self-monitoring compared with paper-based monitoring.37 On the other hand, a pilot randomized trial of 2 months of self-care following discharge after lung transplantation using a pocket electronic device compared with paper reports found better self-care behavior in those with the device.38 More observations from randomized comparisons between interactive mobile-based and traditional trial strategies are needed to move this new paradigm of clinical research forward.
Automatic devices can collect a large stream of sequential data in a relatively short period of time. While a large dataset provides the opportunity for more meaningful discovery, it increases the need for data storage, retrieval, and processing. It may well be the case that after a certain point, additional data have only marginal utility. Without careful assessment of demonstrated needs for such data, it is possible to lose relevant data under tons of information debris.
Potential Pitfalls of Smart Technology Use in Clinical Trials While electronic delivery of more personalized interventions or reminders is a remarkable advance, it is not known how using these devices for interventions longer than a few weeks will fare. The uniqueness and enthusiasm of the approach might wear off, leading to decreased compliance over time. The lack of personal contact with the subject might mask such a problem. Thus, a negative result could be due to lack of compliance with the application rather than the new intervention itself. Just like many clinical trial interventions, use of electronic devices in clinical trials requires some pretesting to assure that the intervention is delivered appropriately. Another challenge is data privacy. Subjects must allow access to their data through their mobile device and while the researchers can assure they will use personal data only for research purposes, there is the chance of a third party gaining access to personal data, whether by accident or by intent. In contrast to the legal protection of information gathered in a health-care setting, protection and regulation of personal data collected from a mobile application such as ResearchKit are minimal.39,40
journal.publications.chestnet.org
Last, there is the problem of error propagation. Because of sheer size, examination of massive data sets for integrity and consistency becomes more costly, even if such an effort is aided by computers. If data are left unchecked, errors due to data entry mistakes, device malfunction, or misuse of devices introduce significant bias and increase the amount of noise in the data analysis.
Conclusions Use of smart technology in clinical trials is in its infancy and offers exciting potential. Experience to date is limited and involves short-term interventions primarily in young people. The popularity of personalized electronic devices as well as possible cost decrease and efficiency increase invites further assessment of this new technology in medical research, specifically in lung disease clinical trials.
Acknowledgments Financial/nonfinancial disclosures: N. L. G., D.-Y. K., and X. T. are employed by the National Institutes of Health. Other contributions: This paper is dedicated to our exceptional colleague, Gang Zheng, who had prepared an outline for this paper before his untimely death.
References 1. Charles D, Gabriel M, Searcy T. Adoption of electronic health record systems among U.S. non-federal acute care hospitals: 2008-2014. Health Information Technology (Health IT) website. http://healthit. gov/sites/default/files/data-brief/2014HospitalAdoptionDataBrief. pdf. ONC Data Brief, No. 23, April 2015. Accessed May 28, 2015. 2. Goetz Goldberg D, Kuzel AJ, Feng LB, DeShazo JP, Love LE. EHRs in primary care practices: benefits, challenges, and successful strategies. Am J Manag Care. 2012;18(2):e48-e54.
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3. Weng C, Batres C, Borda T, et al. A real-time screening alert improves patient recruitment efficiency. AMIA Annu Symp Proc. 2011;2011:1489-1498. 4. Beauharnais CC, Larkin ME, Zai AH, Boykin EC, Luttrell J, Wexler DJ. Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial. Clin Trials. 2012;9(2):198-203. 5. Ni Y, Wright J, Perentesis J, et al. Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients. BMC Med Inform Decis Mak. 2015;15(1):28. 6. Köpcke F, Prokosch HU. Employing computers for the recruitment into clinical trials: a comprehensive systematic review. J Med Internet Res. 2014;16(7):e161. 7. Embi PJ, Jain A, Clark J, Harris CM. Development of an electronic health record-based Clinical Trial Alert system to enhance recruitment at the point of care. AMIA Annu Symp Proc. 2005: 231-235. 8. Heinemann S, Thüring S, Wedeken S, et al. A clinical trial alert tool to recruit large patient samples and assess selection bias in general practice research. BMC Med Res Methodol. 2011;11:16. 9. Elkhenini HF, Davis KJ, Stein ND, et al. Using an electronic medical record (EMR) to conduct clinical trials: Salford Lung Study feasibility. BMC Med Inform Decis Mak. 2015;15(1):8. 10. van Staa TP, Dyson L, McCann G, et al. The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. Health Technol Assess. 2014;18(43):1-146. 11. Chute CG, Cohn SP, Campbell JR; ANSI Healthcare Informatics Standards Board Vocabulary Working Group and the ComputerBased Patient Records Institute Working Group on Codes and Structures. A framework for comprehensive health terminology systems in the United States: development guidelines, criteria for selection, and public policy implications. J Am Med Inform Assoc. 1998;5(6):503-510. 12. Rowbotham MC, Astin J, Greene K, Cummings SR. Interactive informed consent: randomized comparison with paper consents. PLoS ONE. 2013;8(3):e58603.
22. Finitsis DJ, Pellowski JA, Johnson BT. Text message intervention designs to promote adherence to antiretroviral therapy (ART): a meta-analysis of randomized controlled trials. PLoS ONE. 2014;9(2): e88166. 23. Whittaker R, McRobbie H, Bullen C, Borland R, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2012;11:CD006611. 24. Müssener U, Bendtsen M, Karlsson N, White IR, McCambridge J, Bendtsen P. SMS-based smoking cessation intervention among university students: study protocol for a randomised controlled trial (NEXit trial). Trials. 2015;16(1):140. 25. Smith A. U.S. Smartphone use in 2015. Pew Research Center website. http://www.pewinternet.org/2015/04/01/us-smartphoneuse-in-2015/. April 1, 2015. Accessed May 28, 2015. 26. Free C, Phillips G, Watson L, et al. The effectiveness of mobilehealth technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 2013;10(1): e1001363. 27. Blake K, Holbrook JT, Antal H, et al. Use of mobile devices and the internet for multimedia informed consent delivery and data entry in a pediatric asthma trial: study design and rationale. Contemp Clin Trials. 2015;42:105-118. 28. Zelen M. Randomized consent designs for clinical trials: an update. Stat Med. 1990;9(6):645-656. 29. ResearchKit. Now everybody can do their part to advance medical research. Apple Inc website. https://www.apple.com/researchkit/. Accessed May 28, 2015. 30. The Associate Press. ResearchKit: 5 things to know about Apple’s medical apps. http://phys.org/news/2015-03-researchkit-applemedical-apps.html. Accessed November 20, 2015. 31. Zeldovich L. Apple vs. asthma: how ResearchKit is ‘unshackling science’. Digital Trends website. http://www.digitaltrends.com/ sports/apple-vs-asthma-researchers-reveal-how-healthkit-isunshackling-science/. March 17, 2015. Accessed May 28, 2015.
13. Coiera E, Clarke R. e-Consent: The design and implementation of consumer consent mechanisms in an electronic environment. J Am Med Inform Assoc. 2004;11(2):129-140.
32. Roehrig M. RIIS develops Android app to help those with COPD inexpensively monitor health. Research Into Internet Systems website. http://riis.com/blog/riis-walk-app/. February 24, 2015. Accessed June 8, 2015.
14. Chalil Madathil K, Koikkara R, Obeid J, et al. An investigation of the efficacy of electronic consenting interfaces of research permissions management system in a hospital setting. Int J Med Inform. 2013;82(9):854-863.
33. Larson EC, Goel M, Boriello G, Heltshe S, Rosenfeld M, Patel SN. SpiroSmart: using a microphone to measure lung function on a mobile phone. In: Proceedings of the ACM Conference on Ubiquitous Computing; September 5-8, 2012; Pittsburgh, PA.
15. Use of electronic informed consent (eIC) in clinical investigations, questions and answers. US Food and Drug Administration website. http://www.fda.gov/downloads/ Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM436811.pdf. March 2015. Accessed May 28, 2015.
34. Epner A. Infographic – Telehealth Index: 2015 Consumer Survey. American Well website. https://www.americanwell.com/infographictelehealth-index-2015-consumer-survey/. January 21, 2015. Accessed May 28, 2015.
16. Lustria ML, Noar SM, Cortese J, Van Stee SK, Glueckauf RL, Lee J. A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun. 2013;18(9):1039-1069.
35. Black AD, Car J, Pagliari C, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8(1):e1000387.
17. Joseph CL, Peterson E, Havstad S, et al; Asthma in Adolescents Research Team. A web-based, tailored asthma management program for urban African-American high school students. Am J Respir Crit Care Med. 2007;175(9):888-895.
36. Elbert NJ, van Os-Medendorp H, van Renselaar W, et al. Effectiveness and cost-effectiveness of ehealth interventions in somatic diseases: a systematic review of systematic reviews and meta-analyses. J Med Internet Res. 2014;16(4):e110.
18. Bradley T. Email vs. IM vs. SMS: choosing the right one. PC World website. http://www.pcworld.com/article/248142/email_vs_im_vs_ sms_choosing_the_right_one.html. January 13, 2012. Accessed May 28, 2015.
37. Ryan D, Price D, Musgrave SD, et al. Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial. BMJ. 2012;344:e1756.
19. Mahmud N, Rodriguez J, Nesbit J. A text message-based intervention to bridge the healthcare communication gap in the rural developing world. Technol Health Care. 2010;18(2):137-144. 20. Nhavoto JA, Grönlund Å, Chaquilla WP. SMSaúde: Design, development, and implementation of a remote/mobile patient management system to improve retention in care for HIV/AIDS and tuberculosis patients. JMIR Mhealth Uhealth. 2015;3(1):e26. 21. Hall AK, Cole-Lewis H, Bernhardt JM. Mobile text messaging for health: a systematic review of reviews. Annu Rev Public Health. 2015;36:393-415.
26 Commentary
38. DeVito Dabbs A, Dew MA, Myers B, et al. Evaluation of a hand-held, computer-based intervention to promote early self-care behaviors after lung transplant. Clin Transplant. 2009;23(4): 537-545. 39. Mobile medical applications: guidance for industry and Food and Drug Administration staff. Food and Drug Administration website. http://www.fda.gov/downloads/MedicalDevices/ DeviceRegulationandGuidance/GuidanceDocuments/UCM263366. pdf. February 9, 2015. Accessed May 28, 2015. 40. Terry NP. Mobile health: assessing the barriers. Chest. 2015;147(5): 1429-1434.
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149#1 CHEST JANUARY 2016
]
Commentary Ahead of the Curve
]
Smart Technology in Lung Disease Clinical Trials Nancy L. Geller, PhD; Dong-Yun Kim, PhD; and Xin Tian, PhD
This article describes the use of smart technology by investigators and patients to facilitate lung disease clinical trials and make them less costly and more efficient. By “smart technology” we include various electronic media, such as computer databases, the Internet, and mobile devices. We first describe the use of electronic health records for identifying potential subjects and then discuss electronic informed consent. We give several examples of using the Internet and mobile technology in clinical trials. Interventions have been delivered via the World Wide Web or via mobile devices, and both have been used to collect outcome data. We discuss examples of new electronic devices that recently have been introduced to collect health data. While use of smart technology in clinical trials is an exciting development, comparison with similar interventions applied in a conventional manner is still in its infancy. We discuss advantages and disadvantages of using this omnipresent, powerful tool in clinical trials, as well as directions for CHEST 2016; 149(1):22-26
future research. KEY WORDS:
clinical trials; lung disease; mobile health; personal electronic devices; smart technology
Contemporary randomized controlled trials have become prohibitively expensive, thus limiting the number of conventional clinical trials that can be undertaken. One way to make clinical trials less expensive is through the use of computer technology. Over time, the use of computers has become increasingly a part of everyday life, and, consequently, their use in clinical trials has become more commonplace. We focus on use of smart technology: electronic health records (EHRs), web interventions, and mobile devices. These can now be used in clinical trials to enhance accrual and compliance, to collect health data, and to provide more accurate outcome measures. ABBREVIATIONS: EHR = electronic health record; eIC = electronic informed consent; LASST = Long-acting b Agonist Step Down Study; MICT = Mobile Devices and the Internet to Streamline an Asthma Clinical Trial AFFILIATIONS: From the Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD.
22 Commentary
Use of EHRs The Health Information Technology for Economic and Clinical Health Act of 2009 brought the rapid and broad adoption of EHRs to hospitals in the United States. As of 2014, more than three-quarters of the non-federal acute care hospitals adopted at least a basic EHR system, and almost all of the surveyed hospitals have certified EHR technology.1 Increased efficiency in handling patient information and care coordination are significant benefits of EHRs.2 The use of EHRs can also facilitate patient identification and recruitment for clinical trials.3 Traditionally, patient screening was done manually by clinical trial personnel. Manual
CORRESPONDENCE TO: Nancy L. Geller, PhD, Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 6701 Rockledge Dr, MSC 7913, Bethesda, MD 20892-7913; e-mail: [email protected] Published by Elsevier Inc. under license from the American College of Chest Physicians. DOI: http://dx.doi.org/10.1378/chest.15-1314
[
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]
review of patient records is labor-intensive and likely to be error-prone. Studies show that automating such searches leads to a dramatic increase in efficiency and accuracy. Beauharnais et al4 reported a 50% reduction in total screening time per patient. Ni et al5 found that an eligibility matching algorithm resulted in 85% workload reductions in patient chart review. While electronic patient screening has been used in the past,6 clinical trial recruitment support systems that specifically use EHRs have emerged in previous years.7,8 There are several feasibility trials that recruited patients with asthma or COPD using EHRs and clinical trial recruitment support systems, such as the Salford Lung Study9 and eLung.10 The broad adoption of EHRs offers great opportunities in clinical trials. However, for EHRs to be fully used, there are substantial issues to address. There is a lack of common terminology in different EHRs and lack of ability to connect electronic data capture and EHRs.11 There are other major obstacles such as nonuniform local regulations governing the use of EHRs and language barriers in multinational trials. Commercial EHR packages are expensive, especially for small clinics. Furthermore, clinicians may not adapt easily to a system they did not help design.
Electronic Informed Consent Use of electronic informed consent (eIC) is another technological innovation that benefits both the patient and the clinical trial investigator. An eIC can facilitate the enrollment process by allowing patients to sign up for a clinical trial from a remote location using smartphone applications, a web interface, a touch-screen terminal, or a combination of these. It also allows the investigator to use various interactive electronic formats such as text, clickable URLs, videos, and diagrams, all of which enhance the patients’ understanding of the content.12 For the investigator, eIC provides searchable data, such as the date and time that a subject signed the consent, unlike the scanned copy of the paper form. The design of the user interface and the choice of consent models are important issues to consider to ensure the success of this new consent medium.13,14 To address the privacy and security issues of eIC, the US Food and Drug Administration has issued a draft guidance.15
Web-Based and Short Message Service-Based Interventions Web-based interventions provide the opportunity to intervene in symptomatic subjects in a cost-effective
journal.publications.chestnet.org
manner.16 In the Puff City clinical trial, Joseph et al17 tested a multimedia web-based intervention in students with asthma at six Detroit high schools, randomizing them to an individually tailored program or to access generic asthma websites over a 180-day intervention period. The program was available on the high school’s computers, and students were allowed to access the material during the school day. The trial found that asthma outcomes were significantly improved in the intervention group in this traditionally hard-to-reach population.17 Some previous clinical trials use smartphones as a delivery mechanism via short message service (text messaging). Use of text messaging has certain advantages.18 A text message is immediate so it can be an effective tool for quick reminders. Also, it may be better suited to younger subjects. Text messages can reach patients in remote areas at low cost, which could be especially useful in bringing health care and clinical trials to an underserved population.19 They also help improve retention, as found in studies involving patients with HIV and TB.20 Interventions using text messages have been shown to be effective in several areas, including smoking cessation.21 Text message-based interventions have been found to be more effective when the messaging interval is not too frequent and the messages have personalized content.22 In a meta-analysis of pooled randomized or quasirandomized trials, Whittaker et al23 showed that a mobile phone-based intervention for smoking cessation was effective in increasing the 6-month cessation rate. The Nicotine Exit (NEXit) is a new randomized trial that will examine the effectiveness of a stand-alone text-based intervention to help university students in Sweden stop smoking.24 The 12-week intervention will occur simultaneously through Swedish universities served by 25 health-care centers. This trial has certain features worth noting, namely access to a large number of potential subjects, simple eligibility criteria, and a simple intervention. The health-care system in Sweden includes everyone and therefore facilitates wide publicity for volunteers. The trial had few eligibility criteria other than being a daily or weekly smoker, but participants are required to be willing to set a date within the next 4 weeks to stop smoking. This should help to decrease noncompliance and dropout. The intervention itself is straightforward, in the sense that successful delivery is assured (once a telephone number is confirmed), and the timing and number of the messages can be automated. The large number of eligible subjects should enable rapid completion of this trial.
23
Use of Mobile Health Technology to Streamline Data Collection The astronomical growth of the personal mobile device market and human connections through the Internet and social media provide an opportunity to invite mobile device users to be active partners in medical research. In 2014, 90% of American adults owned a cell phone and 64% were smartphone users, and this proportion is expected to reach 80% by 2017.25 This may have significant implications for future clinical trials. Mobile health technologies may overcome certain challenges and barriers that traditional clinical trials face, such as high costs, insufficient participation, lack of real-time data collection, poor adherence, and limited communication and feedback.26 Apps on mobile devices, for example, smartphones, tablets, and wearables, can reach millions of possible participants and provide real-time monitoring of a user’s health data (including BPs, glucose levels, cardiac rhythms, exercise behaviors, and lung functions). Research data may be collected and then electronically transferred to a central data center via the Internet or wireless devices. Applications with widely used mobile health technologies could be efficient and cost-effective breakthroughs for conducting clinical trials in lung diseases. The use of Mobile Devices and the Internet to Streamline an Asthma Clinical Trial (MICT) is a clinical trial in which participants aged 12 to 17 years with asthma as identified by their EHRs are randomized to MICT or a concurrent, very similar traditional trial, Long-acting b Agonist Step Down Study (LASST).27 Those randomized to MICT are provided an iPad to complete health information diaries and questionnaires in an online database at home. Aside from the initial and the final clinic visits, participants complete virtual FaceTime visits with the iPad. Participants also perform spirometry using a handheld spirometer with remote coaching via the iPad. The primary clinical endpoint of MICT is the asthma control test, which is conducted at the same intervals as in LASST. MICT uses web-based delivery of consent materials via a dynamic interactive multimedia platform viewed online. The video ends with a button which the participant selects if they are ready to enroll, and is followed by an informed consent interview via telephone. To complete the eIC process, the adolescent and parent are asked to complete and submit consent forms from their separate online accounts. A conventional informed consent is used in LASST.
24 Commentary
By conducting MICT and LASST concurrently, this project can make comparisons between MICT and the usual clinical trial approach used in LASST. The results will offer insights into whether MICT is noninferior and cost-effective. However, there are certain limitations of the MICT design, namely shorter length of the trial and a rather small sample size. MICT uses the so-called Zelen (opt-out consent) design, which randomizes patients before obtaining their consent.28 This could be problematic in an intention-to-treat analysis if a large proportion of randomized patients choose not to give consent. With three treatment groups in each trial, the study may not have adequate power to test the interactions between treatment groups and trials. One could devise a simple randomized controlled clinical trial to compare an intervention using a mobile device to the usual (visit-oriented) trial. For example, after informed consent, individuals with asthma could be randomized into a two-period crossover drug trial as well as randomized to whether or not they use a mobile device for virtual visits and to collect outcome data. Those randomized to the nonmobile device group would have visits scheduled to collect the same outcome data. The two drug interventions would be the same in the mobile device group and the nonmobile device group. The mobile device could also be used to enhance compliance to the drug interventions. If adequately powered for testing both drug interventions and mobile vs non-mobile device groups, such trial design would enable the investigation of the drug effect as well as the effectiveness of using a mobile device. Another promising application of mobile technology is ResearchKit, introduced by Apple in March 2015.29 ResearchKit is an open source software platform that will enable medical researchers to create apps to study various diseases. The hope is that this will facilitate participant recruitment in clinical trials and allow medical researchers to collect data from iPhones or iPhone-linked fitness monitors. Asthma Health app is one of the first five applications developed using the ResearchKit framework.30 This app provides personal medication reminders, tracks asthma symptoms and triggers, and allows recording of ED and other medical visits and physical activity. With participant permission, it records personal demographic data and sends the participants’ records to a central research facility. It may be a limitation that only iPhone users can participate in a ResearchKit study, which may bias study results. Studies using novel mobile technology enable remote data collection by not requiring active subject participation.
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For example, for patients with COPD, rather than a 6-min walk test conducted at the doctor’s office, the average number of steps recorded on a mobile device over a specific time period could be used as an outcome measure. Use of electronic devices could also minimize the need for patient visits.31 New applications have been developed to allow patients with COPD to perform a self-test of the 6-min walk test or lung functions at any convenient time from home.32,33 In a survey conducted by the Harris Poll in December 2014, almost two-thirds of adults expressed willingness to accept video contact as a “doctor’s visit.”34
Furthermore, the new applications use up battery at a rapid rate and there is the possibility that there are sizable gaps in a patient record because the device runs out of battery life. Mobile devices could be broken-down, lost, or stolen, which would interrupt the intervention or recording of data and result in loss of study data. In addition, training in the proper use of the electronic device in a clinical trial puts an additional burden on those conducting the trial as well as on the subjects. Therefore, the cost for staff and subject training should be carefully evaluated at the clinical trial design stage.
While mobile heath technologies provide a new way for researchers to carry out clinical trials, until now research has yielded inconsistent findings.35,36 A randomized controlled trial showed no improvement in asthma control or efficacy using mobile phone-based self-monitoring compared with paper-based monitoring.37 On the other hand, a pilot randomized trial of 2 months of self-care following discharge after lung transplantation using a pocket electronic device compared with paper reports found better self-care behavior in those with the device.38 More observations from randomized comparisons between interactive mobile-based and traditional trial strategies are needed to move this new paradigm of clinical research forward.
Automatic devices can collect a large stream of sequential data in a relatively short period of time. While a large dataset provides the opportunity for more meaningful discovery, it increases the need for data storage, retrieval, and processing. It may well be the case that after a certain point, additional data have only marginal utility. Without careful assessment of demonstrated needs for such data, it is possible to lose relevant data under tons of information debris.
Potential Pitfalls of Smart Technology Use in Clinical Trials While electronic delivery of more personalized interventions or reminders is a remarkable advance, it is not known how using these devices for interventions longer than a few weeks will fare. The uniqueness and enthusiasm of the approach might wear off, leading to decreased compliance over time. The lack of personal contact with the subject might mask such a problem. Thus, a negative result could be due to lack of compliance with the application rather than the new intervention itself. Just like many clinical trial interventions, use of electronic devices in clinical trials requires some pretesting to assure that the intervention is delivered appropriately. Another challenge is data privacy. Subjects must allow access to their data through their mobile device and while the researchers can assure they will use personal data only for research purposes, there is the chance of a third party gaining access to personal data, whether by accident or by intent. In contrast to the legal protection of information gathered in a health-care setting, protection and regulation of personal data collected from a mobile application such as ResearchKit are minimal.39,40
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Last, there is the problem of error propagation. Because of sheer size, examination of massive data sets for integrity and consistency becomes more costly, even if such an effort is aided by computers. If data are left unchecked, errors due to data entry mistakes, device malfunction, or misuse of devices introduce significant bias and increase the amount of noise in the data analysis.
Conclusions Use of smart technology in clinical trials is in its infancy and offers exciting potential. Experience to date is limited and involves short-term interventions primarily in young people. The popularity of personalized electronic devices as well as possible cost decrease and efficiency increase invites further assessment of this new technology in medical research, specifically in lung disease clinical trials.
Acknowledgments Financial/nonfinancial disclosures: N. L. G., D.-Y. K., and X. T. are employed by the National Institutes of Health. Other contributions: This paper is dedicated to our exceptional colleague, Gang Zheng, who had prepared an outline for this paper before his untimely death.
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