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Table.  
Characteristics of Eligible Participants
Characteristics of Eligible Participants
1.
Murthy  VH, Krumholz  HM, Gross  CP.  Participation in cancer clinical trials: race-, sex-, and age-based disparities.  JAMA. 2004;291(22):2720-2726.PubMedGoogle ScholarCrossref
2.
Loewenstein  G, Brennan  T, Volpp  KG.  Asymmetric paternalism to improve health behaviors.  JAMA. 2007;298(20):2415-2417.PubMedGoogle ScholarCrossref
3.
Johnson  EJ, Goldstein  D.  Medicine. Do defaults save lives?  Science. 2003;302(5649):1338-1339.PubMedGoogle ScholarCrossref
4.
Troxel  AB, Asch  DA, Mehta  SJ,  et al.  Rationale and design of a randomized trial of automated hovering for post–myocardial infarction patients: the HeartStrong program.  Am Heart J. In press.Google Scholar
5.
Kahneman  D, Knetsch  JL, Thaler  RH.  Experimental tests of the endowment effect and the Coase theorem.  J Polit Econ. 1990;98(6):1325-1348. doi:10.1086/261737.Google ScholarCrossref
Research Letter
October 2016

Participation Rates With Opt-out Enrollment in a Remote Monitoring Intervention for Patients With Myocardial Infarction

Author Affiliations
  • 1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 2Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
  • 3Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
  • 4Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 5Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, Pennsylvania
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Cardiol. 2016;1(7):847-848. doi:10.1001/jamacardio.2016.2374

Many research studies are limited by low participation rates, threatening the generalizability of findings because participants likely differ greatly from nonparticipants.1 Low rates of participation could be a function of the opt-in approach to enrollment, in which the default is to not participate. Behavioral economics research has shown that an opt-out approach can increase participation in retirement savings and organ donation while maintaining informed choice.2,3 We evaluated whether an opt-out approach to enrollment will increase participation in a remote monitoring intervention among patients with myocardial infarction.

Methods

This prospective cohort study compared enrollment rates in a remote monitoring intervention using an opt-in vs an opt-out approach. The intervention offered remote monitoring of medication adherence for patients recently discharged after myocardial infarction, as in a larger trial with opt-in enrollment. Those in the opt-in cohort were patients in a larger study4 with fee-for-service Medicare coverage who were discharged from the University of Pennsylvania Health System with a principal diagnosis of myocardial infarction. Participants were recruited from March 23, 2013, to January 4, 2016, in the 60 days after discharge by sending a recruitment letter to introduce the study, followed by up to 5 telephone calls by research staff (N.M. and D.T.) who obtained verbal consent to participate. Patients who consented were sent remote monitoring medication bottles in a system configured with reminders, financial incentives, and social support to promote adherence. Those in the opt-out cohort were similar patients discharged from the University of Pennsylvania Health System with myocardial infarction but with insurance types making them ineligible for the larger study. To create an opt-out frame, these patients received the remote monitoring medication bottles with the initial mailing to simulate participation as the default, but otherwise received the same recruitment processes and the same intervention if they agreed to participate.

The primary outcome was the proportion of patients mailed recruitment material who agreed to participate in the clinical study. We used a χ2 test to compare the intervention and control groups, with P < .05 considered statistically significant. We also compared the daily rates of opening the medication bottle for both groups in the 3 months after device set-up. Demographics and data on race/ethnicity were obtained from the patients’ electronic health record. We received approval from the University of Pennsylvania institutional review board and registered the protocol on clinicaltrials.gov (NCT02139202).

Results

We approached 235 patients in the opt-in cohort and 52 in the opt-out cohort (Table). The opt-in group included only patients with fee-for-service Medicare coverage, with 140 men (59.6%) and 192 patients (81.7%) older than 65 years. The opt-out group, whose members had a broader mix of insurance types, included 36 men (69.2%) and 12 patients (23.1%) older than 65 years. Thirty-seven patients (15.7%; 95% CI, 11.1%-20.4%) agreed to participate in the opt-in group compared with 20 patients (38.5%; 95% CI, 24.8%-52.1%) in the opt-out group (P < .001). Patient-days of opening the medication bottle were similar between participants in the opt-in and opt-out groups during the 3 months after device set-up (1512 of 1709 [88.5%] vs 1635 of 1820 patient-days [89.8%], respectively; P = .67).

Discussion

Results of this study suggest that an opt-out approach to enrollment can significantly increase participation in trials of health system interventions. More important, rates of adherence to use of the remote monitoring medication bottle after enrollment in the opt-out group were similar to those in the opt-in group, reflecting comparable engagement among the additional participants. Opt-out procedures may increase participation by shifting the perceived default to participation because the immediate provision of the remote monitoring medication bottles creates an endowment effect (which leads individuals to place more value on an item in their possession), or by implying that the social norm is to enroll in the trial.5 However, there was a greater loss of equipment in the opt-out group (21 of 37 participants in the opt-in group [56.8%] who received devices actually used them, vs 20 of 52 participants in the opt-out group [38.5%]; P = .09). and the effects may be limited to interventions with tangible devices, so larger studies are needed to confirm findings in other populations and conditions. For low-risk interventions, opt-out approaches can improve the efficiency of recruitment, expand the intervention to those who are otherwise less likely to participate, and increase generalizability of clinical trials to a broader population.

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Article Information

Corresponding Author: Shivan J. Mehta, MD, MBA, MSHP, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 1318 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104 (shivan.mehta@uphs.upenn.edu).

Published Online: September 7, 2016. doi:10.1001/jamacardio.2016.2374

Author Contributions: Dr Mehta had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Mehta, Troxel, Taylor, Asch, Volpp.

Acquisition, analysis, or interpretation of data: Mehta, Troxel, Marcus, Jameson, Taylor, Asch.

Drafting of the manuscript: Mehta, Marcus, Jameson, Taylor.

Critical revision of the manuscript for important intellectual content: Mehta, Troxel, Asch, Volpp.

Statistical analysis: Troxel, Marcus, Jameson.

Obtaining funding: Asch, Volpp.

Administrative, technical, or material support: Marcus, Jameson, Taylor.

Study supervision: Mehta, Marcus, Jameson, Taylor, Volpp.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Troxel reported serving on the Scientific Advisory Board of VAL Health. Drs Asch and Volpp reported being principals and owners of VAL Health. Dr Volpp reported serving as a consultant for CVS Caremark and receiving grants from CVS Caremark, Humana, Merck, Weight Watchers, and Discovery (South Africa). No other disclosures were reported.

Funding/Support: The study was supported by grant 1C1CMS331009, Health Care Innovation Award, from the Center for Medicare & Medicaid.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Murthy  VH, Krumholz  HM, Gross  CP.  Participation in cancer clinical trials: race-, sex-, and age-based disparities.  JAMA. 2004;291(22):2720-2726.PubMedGoogle ScholarCrossref
2.
Loewenstein  G, Brennan  T, Volpp  KG.  Asymmetric paternalism to improve health behaviors.  JAMA. 2007;298(20):2415-2417.PubMedGoogle ScholarCrossref
3.
Johnson  EJ, Goldstein  D.  Medicine. Do defaults save lives?  Science. 2003;302(5649):1338-1339.PubMedGoogle ScholarCrossref
4.
Troxel  AB, Asch  DA, Mehta  SJ,  et al.  Rationale and design of a randomized trial of automated hovering for post–myocardial infarction patients: the HeartStrong program.  Am Heart J. In press.Google Scholar
5.
Kahneman  D, Knetsch  JL, Thaler  RH.  Experimental tests of the endowment effect and the Coase theorem.  J Polit Econ. 1990;98(6):1325-1348. doi:10.1086/261737.Google ScholarCrossref
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