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Gibson LA, Stephens-Shields AJ, Hua SV, Orr JA, Lawman HG, Bleich SN, Volpp KG, Bleakley A, Thorndike AN, Roberto CA. Comparison of Sales From Vending Machines With 4 Different Food and Beverage Messages: A Randomized Trial. JAMA Netw Open 2024; 7:e249438. [PMID: 38717775 PMCID: PMC11079689 DOI: 10.1001/jamanetworkopen.2024.9438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/27/2024] [Indexed: 05/12/2024] Open
Abstract
Importance Point-of-sale food messaging can encourage healthier purchases, but no studies have directly compared multiple interventions in the field. Objective To examine which of 4 food and beverage messages would increase healthier vending machine purchases. Design, Setting, and Participants This randomized trial assessed 13 months (February 1, 2019, to February 29, 2020) of vending sales data from 267 machines and 1065 customer purchase assessments from vending machines on government property in Philadelphia, Pennsylvania. Data analysis was performed from March 5, 2020, to November 8, 2022. Interventions Study interventions were 4 food and beverage messaging systems: (1) beverage tax posters encouraging healthy choices because of the Philadelphia tax on sweetened drinks; (2) green labels for healthy products; (3) traffic light labels: green (healthy), yellow (moderately healthy), or red (unhealthy); or (4) physical activity equivalent labels (minutes of activity to metabolize product calories). Main Outcomes and Measures Sales data were analyzed separately for beverages and snacks. The main outcomes analyzed at the transaction level were calories sold and the health status (using traffic light criteria) of each item sold. Additional outcomes were analyzed at the monthly machine level: total units sold, calories sold, and units of each health status sold. The customer purchase assessment outcome was calories purchased per vending trip. Results Monthly sales data came from 150 beverage and 117 snack vending machines, whereas 1065 customers (558 [52%] male) contributed purchase assessment data. Traffic light labels led to a 30% decrease in the mean monthly number of unhealthy beverages sold (mean ratio [MR], 0.70; 95% CI, 0.55-0.88) compared with beverage tax posters. Physical activity labels led to a 34% (MR, 0.66; 95% CI, 0.51-0.87) reduction in the number of unhealthy beverages sold at the machine level and 35% (MR, 0.65; 95% CI, 0.50-0.86) reduction in mean calories sold. Traffic light labels also led to a 30-calorie reduction (b = -30.46; 95% CI, -49.36 to -11.56) per customer trip in the customer purchase analyses compared to physical activity labels. There were very few significant differences for snack machines. Conclusions and Relevance In this 13-month randomized trial of 267 vending machines, the traffic light and physical activity labels encouraged healthier beverage purchases, but no change in snack sales, compared with a beverage tax poster. Corporations and governments should consider such labeling approaches to promote healthier beverage choices. Trial Registration ClinicalTrials.gov Identifier: NCT06260176.
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Affiliation(s)
- Laura A. Gibson
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Center for Health Incentives and Behavioral Economics at the University of Pennsylvania’s Leonard Davis Institute of Health Economics, Philadelphia
| | - Alisa J. Stephens-Shields
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Sophia V. Hua
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Center for Health Incentives and Behavioral Economics at the University of Pennsylvania’s Leonard Davis Institute of Health Economics, Philadelphia
| | - Jennifer A. Orr
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Center for Health Incentives and Behavioral Economics at the University of Pennsylvania’s Leonard Davis Institute of Health Economics, Philadelphia
| | - Hannah G. Lawman
- Division of Chronic Disease and Injury Prevention, Philadelphia Department of Public Health, Philadelphia, Pennsylvania
- Now with Novo Nordisk Inc, Plainsboro Township, New Jersey
| | - Sara N. Bleich
- Department of Health Policy & Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Kevin G. Volpp
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Center for Health Incentives and Behavioral Economics at the University of Pennsylvania’s Leonard Davis Institute of Health Economics, Philadelphia
- Department of Health Care Management, University of Pennsylvania Wharton School, Philadelphia
| | - Amy Bleakley
- Department of Communication, University of Delaware, Newark
| | - Anne N. Thorndike
- Division of General Internal Medicine, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Christina A. Roberto
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Center for Health Incentives and Behavioral Economics at the University of Pennsylvania’s Leonard Davis Institute of Health Economics, Philadelphia
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Johnson C, Chen J, McGowan MP, Tricou E, Card M, Pettit AR, Klaiman T, Rader DJ, Volpp KG, Beidas RS. Family cascade screening for equitable identification of familial hypercholesterolemia: study protocol for a hybrid effectiveness-implementation type III randomized controlled trial. Implement Sci 2024; 19:30. [PMID: 38594685 PMCID: PMC11003060 DOI: 10.1186/s13012-024-01355-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 02/25/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Familial hypercholesterolemia (FH) is a heritable disorder affecting 1.3 million individuals in the USA. Eighty percent of people with FH are undiagnosed, particularly minoritized populations including Black or African American people, Asian or Asian American people, and women across racial groups. Family cascade screening is an evidence-based practice that can increase diagnosis and improve health outcomes but is rarely implemented in routine practice, representing an important care gap. In pilot work, we leveraged best practices from behavioral economics and implementation science-including mixed-methods contextual inquiry with clinicians, patients, and health system constituents-to co-design two patient-facing implementation strategies to address this care gap: (a) an automated health system-mediated strategy and (b) a nonprofit foundation-mediated strategy with contact from a foundation-employed care navigator. This trial will test the comparative effectiveness of these strategies on completion of cascade screening for relatives of individuals with FH, centering equitable reach. METHODS We will conduct a hybrid effectiveness-implementation type III randomized controlled trial testing the comparative effectiveness of two strategies for implementing cascade screening with 220 individuals with FH (i.e., probands) per arm identified from a large northeastern health system. The primary implementation outcome is reach, or the proportion of probands with at least one first-degree biological relative (parent, sibling, child) in the USA who is screened for FH through the study. Our secondary implementation outcomes include the number of relatives screened and the number of relatives meeting the American Heart Association criteria for FH. Our secondary clinical effectiveness outcome is post-trial proband cholesterol level. We will also use mixed methods to identify implementation strategy mechanisms for implementation strategy effectiveness while centering equity. DISCUSSION We will test two patient-facing implementation strategies harnessing insights from behavioral economics that were developed collaboratively with constituents. This trial will improve our understanding of how to implement evidence-based cascade screening for FH, which implementation strategies work, for whom, and why. Learnings from this trial can be used to equitably scale cascade screening programs for FH nationally and inform cascade screening implementation efforts for other genetic disorders. TRIAL REGISTRATION ClinicalTrials.gov, NCT05750667. Registered 15 February 2023-retrospectively registered, https://clinicaltrials.gov/study/NCT05750667 .
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Affiliation(s)
- Christina Johnson
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jinbo Chen
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mary P McGowan
- Family Heart Foundation, Fernandina Beach, FL, USA
- Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Eric Tricou
- Family Heart Foundation, Fernandina Beach, FL, USA
| | - Mary Card
- Family Heart Foundation, Fernandina Beach, FL, USA
| | | | - Tamar Klaiman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin G Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
- The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Rinad S Beidas
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Glanz K, Kather C, Chung A, Choi JR, Volpp KG, Clapp J. Qualitative study of perceptions of factors contributing to success or failure among participants in a US weight loss trial of financial incentives and environmental change strategies. BMJ Open 2024; 14:e078111. [PMID: 38553057 PMCID: PMC10982703 DOI: 10.1136/bmjopen-2023-078111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/08/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND The use of financial incentives and environmental change strategies to encourage health behaviour change is increasingly prevalent. However, the experiences of participants in incentive interventions are not well characterised. Examination of participant perceptions of financial incentives and environmental strategies can offer insights about how these interventions are facilitating or failing to encourage behaviour change. OBJECTIVE This study aimed to learn how participants in a randomised trial that tested financial incentives and environmental interventions to support weight loss perceived factors contributing to their success or failure in the trial. DESIGN Qualitative study with one-time interviews of trial participants with high and low success in losing weight, supplemented by study records of incentive payments and weight loss. PARTICIPANTS 24 trial participants (12 with substantial weight loss and 12 with no weight loss) stratified equally across the 4 trial arms (incentives, environmental strategies, combined and usual care) were interviewed. ANALYTICAL APPROACH Transcribed interviews were coded and interpreted using an iterative process. Explanation development was completed using an abductive approach. RESULTS Responses of trial participants who were very successful in losing weight differed in several ways from those who were not. Successful participants described more robust prior attempts at dietary and exercise modification, more active engagement with self-limitations, more substantial social support and a greater ability to routinise dietary and exercise changes than did participants who did not lose weight. Successful participants often stated that weight loss was its own reward, even without receiving incentives. Neither group could articulate the details of the incentive intervention or consistently differentiate incentives from study payments. CONCLUSIONS A number of factors distinguished successful from unsuccessful participants in this intervention. Participants who were successful tended to attribute their success to intrinsic motivation and prior experience. Making incentives more salient may make them more effective for participants with greater extrinsic motivation. TRIAL REGISTRATION NUMBER NCT02878343.
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Affiliation(s)
- Karen Glanz
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Collin Kather
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Annie Chung
- The Children's Hospital, Philadelphia, Pennsylvania, USA
| | - Ji Rebekah Choi
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kevin G Volpp
- Medical Ethics and Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Justin Clapp
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Tong J, Duan R, Li R, Luo C, Moore JH, Zhu J, Foster GD, Volpp KG, Yancy WS, Shaw PA, Chen Y. Publisher Correction: Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions. Sci Rep 2023; 13:22546. [PMID: 38110504 PMCID: PMC10728146 DOI: 10.1038/s41598-023-49737-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Affiliation(s)
- Jiayi Tong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chongliang Luo
- Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jingsan Zhu
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gary D Foster
- WW International, New York, NY, 10010, USA
- Center for Weight and eating Disorders, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - William S Yancy
- Department of Medicine, Duke University, Durham, NC, 27705, USA
| | - Pamela A Shaw
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Tong J, Duan R, Li R, Luo C, Moore JH, Zhu J, Foster GD, Volpp KG, Yancy WS, Shaw PA, Chen Y. Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions. Sci Rep 2023; 13:19078. [PMID: 37925516 PMCID: PMC10625563 DOI: 10.1038/s41598-023-41853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 08/31/2023] [Indexed: 11/06/2023] Open
Abstract
In response to the escalating global obesity crisis and its associated health and financial burdens, this paper presents a novel methodology for analyzing longitudinal weight loss data and assessing the effectiveness of financial incentives. Drawing from the Keep It Off trial-a three-arm randomized controlled study with 189 participants-we examined the potential impact of financial incentives on weight loss maintenance. Given that some participants choose not to weigh themselves because of small weight change or weight gains, which is a common phenomenon in many weight-loss studies, traditional methods, for example, the Generalized Estimating Equations (GEE) method tends to overestimate the effect size due to the assumption that data are missing completely at random. To address this challenge, we proposed a framework which can identify evidence of missing not at random and conduct bias correction using the estimating equation derived from pairwise composite likelihood. By analyzing the Keep It Off data, we found that the data in this trial are most likely characterized by non-random missingness. Notably, we also found that the enrollment time (i.e., duration time) would be positively associated with the weight loss maintenance after adjusting for the baseline participant characteristics (e.g., age, sex). Moreover, the lottery-based intervention was found to be more effective in weight loss maintenance compared with the direct payment intervention, though the difference was non-statistically significant. This framework's significance extends beyond weight loss research, offering a semi-parametric approach to assess missing data mechanisms and robustly explore associations between exposures (e.g., financial incentives) and key outcomes (e.g., weight loss maintenance). In essence, the proposed methodology provides a powerful toolkit for analyzing real-world longitudinal data, particularly in scenarios with data missing not at random, enriching comprehension of intricate dataset dynamics.
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Affiliation(s)
- Jiayi Tong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chongliang Luo
- Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jingsan Zhu
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gary D Foster
- WW International, New York, NY, 10010, USA
- Center for Weight and eating Disorders, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - William S Yancy
- Department of Medicine, Duke University, Durham, NC, 27705, USA
| | - Pamela A Shaw
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Volpp KG, Berkowitz SA, Sharma SV, Anderson CAM, Brewer LC, Elkind MSV, Gardner CD, Gervis JE, Harrington RA, Herrero M, Lichtenstein AH, McClellan M, Muse J, Roberto CA, Zachariah JPV. Food Is Medicine: A Presidential Advisory From the American Heart Association. Circulation 2023; 148:1417-1439. [PMID: 37767686 DOI: 10.1161/cir.0000000000001182] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Unhealthy diets are a major impediment to achieving a healthier population in the United States. Although there is a relatively clear sense of what constitutes a healthy diet, most of the US population does not eat healthy food at rates consistent with the recommended clinical guidelines. An abundance of barriers, including food and nutrition insecurity, how food is marketed and advertised, access to and affordability of healthy foods, and behavioral challenges such as a focus on immediate versus delayed gratification, stand in the way of healthier dietary patterns for many Americans. Food Is Medicine may be defined as the provision of healthy food resources to prevent, manage, or treat specific clinical conditions in coordination with the health care sector. Although the field has promise, relatively few studies have been conducted with designs that provide strong evidence of associations between Food Is Medicine interventions and health outcomes or health costs. Much work needs to be done to create a stronger body of evidence that convincingly demonstrates the effectiveness and cost-effectiveness of different types of Food Is Medicine interventions. An estimated 90% of the $4.3 trillion annual cost of health care in the United States is spent on medical care for chronic disease. For many of these diseases, diet is a major risk factor, so even modest improvements in diet could have a significant impact. This presidential advisory offers an overview of the state of the field of Food Is Medicine and a road map for a new research initiative that strategically approaches the outstanding questions in the field while prioritizing a human-centered design approach to achieve high rates of patient engagement and sustained behavior change. This will ideally happen in the context of broader efforts to use a health equity-centered approach to enhance the ways in which our food system and related policies support improvements in health.
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Underhill K, Bair EF, Dixon EL, Ferrell WJ, Linn KA, Volpp KG, Venkataramani AS. Public Views on Medicaid Work Requirements and Mandatory Premiums in Kentucky. JAMA Health Forum 2023; 4:e233656. [PMID: 37862033 PMCID: PMC10589806 DOI: 10.1001/jamahealthforum.2023.3656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/22/2023] [Indexed: 10/21/2023] Open
Abstract
Importance Federal and state policymakers continue to pursue work requirements and premiums as conditions of Medicaid participation. Opinion polling should distinguish between general policy preferences and specific views on quotas, penalties, and other elements. Objective To identify views of adults in Kentucky regarding the design of Medicaid work requirements and premiums. Design, Setting, and Participant A cross-sectional survey was conducted via telephone and the internet from June 27 through July 11, 2019, of 1203 Kentucky residents 9 months before the state intended to implement Medicaid work requirements and mandatory premiums. Statistical analysis was performed from October 2019 to August 2023. Main Outcomes and Measures Agreement, disagreement, or neutral views on policy components were the main outcomes. Recruitment for the survey used statewide random-digit dialing and an internet panel to recruit residents aged 18 years or older. Findings were weighted to reflect state demographics. Of 39 110 landlines called, 209 reached an eligible person (of whom 150 participated), 8654 were of unknown eligibility, and 30 247 were ineligible. Of 55 305 cell phone lines called, 617 reached an eligible person (of whom 451 participated), 29 951 were of unknown eligibility, and 24 737 were ineligible. Internet recruitment (602 participants) used a panel of adult Kentucky residents maintained by an external data collector. Results Percentages were weighted to resemble the adult population of Kentucky residents. Of the participants in the study, 52% (95% CI, 48%-55%) were women, 80% (95% CI, 77%-82%) were younger than 65 years, 41% (95% CI, 38%-45%) were enrolled in Medicaid, 36% (95% CI, 32%-39%) were Republican voters, 32% (95% CI, 29%-36%) were Democratic voters, 14% (95% CI, 11%-16%) were members of racial and ethnic minority groups (including but not limited to American Indian or Alaska Native, Asian, Black, Hispanic or Latinx, and Native Hawaiian or Pacific Islander), and 48% (95% CI, 44%-52%) were employed. Most participants supported work requirements generally (69% [95% CI, 66%-72%]) but did not support terminating benefits due to noncompliance (43% [95% CI, 39%-46%]) or requiring quotas of 20 or more hours per week (34% [95% CI, 31%-38%]). Support for monthly premiums (34% [95% CI, 31%-38%]) and exclusion penalties for premium nonpayment (22% [95% CI, 19%-25%]) was limited. Medicaid enrollees were significantly less supportive of these policies than nonenrollees. For instance, regarding work requirements, agreement was lower (64% [95% CI, 59%-69%] vs 72% [95% CI, 68%-77%]) and disagreement higher (26% [95% CI, 21%-31%] vs 20% [95% CI, 16%-24%]) among current Medicaid enrollees compared with nonenrollees (P = .04). Among Medicaid enrollees, some beliefs about work requirements varied significantly by employment status but not by political affiliation. Among nonenrollees, beliefs about work requirements, premiums, and Medicaid varied significantly by political affiliation but not by employment. Conclusions and Relevance This study suggests that even when public constituencies express general support for Medicaid work requirements or premiums, they may oppose central design features, such as quotas and termination of benefits. Program participants may also hold significantly different beliefs than nonparticipants, which should be understood before policies are changed.
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Affiliation(s)
- Kristen Underhill
- Cornell Law School, Ithaca, New York
- Department of Population Health Sciences, Weill Cornell Medical College, New York, New York
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Elizabeth F. Bair
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
| | - Erica L. Dixon
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
| | - William J. Ferrell
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
| | - Kristin A. Linn
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
| | - Kevin G. Volpp
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
- Wharton School, University of Pennsylvania, Philadelphia
- Corporal Michael J. Cresencz Department of Veterans Affairs Medical Center, Philadelphia, Pennsylvania
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Atheendar S. Venkataramani
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania. Philadelphia
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Hua SV, Petimar J, Mitra N, Roberto CA, Kenney EL, Thorndike AN, Rimm EB, Volpp KG, Gibson LA. Philadelphia Beverage Tax and Association With Prices, Purchasing, and Individual-Level Substitution in a National Pharmacy Chain. JAMA Netw Open 2023; 6:e2323200. [PMID: 37440231 PMCID: PMC10346119 DOI: 10.1001/jamanetworkopen.2023.23200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/29/2023] [Indexed: 07/14/2023] Open
Abstract
Importance Taxes on sweetened beverages are being implemented around the globe; an understanding of these taxes on individual-level behavior is necessary. Objective To evaluate the degree to which the sweetened beverage tax in Philadelphia, Pennsylvania, was associated with changes in beverage prices and individual-level purchasing over time at a national pharmacy chain in Philadelphia compared with Baltimore, Maryland. Design, Setting, and Participants Using a difference-in-differences approach and generalized linear mixed models, this cohort study examined beverage purchases made by loyalty cardholders at a national chain pharmacy retailer with stores in Philadelphia and Baltimore (control city) from before tax to after tax. Beverage sales (in US dollars) were linked by unique loyalty card numbers to enable longitudinal analyses. Data were collected from January 1, 2015, through December 31, 2017 (2 years before tax and 1 year after tax); data analyses were conducted from January through October 2022. Exposure Implementation of Philadelphia's 1.5 cents/oz tax on sweetened beverages. Main Outcomes and Measures The outcomes were the change in mean beverage price per-ounce and mean beverage volume purchased per cardholder transaction. Individual-level point-of-sale scanner data from all beverage purchases were analyzed. Results A total of 1188 unique beverages were purchased from the same stores before tax and after tax. There were 231 065 unique cardholders in Philadelphia and 82 517 in Baltimore. Mean prices of taxed beverages (n = 2 094 220) increased by 1.6 (95% CI, 1.3-2.0) cents/oz (106.7% pass-through) in Philadelphia compared with Baltimore from before tax to after tax. Philadelphia cardholders purchased 7.8% (95% CI -8.1% to -7.5%) fewer ounces of taxed beverages and 1.1% (95% CI, 0.6%-1.7%) more ounces of nontaxed beverages per transaction. Taxed beverages made up a smaller percentage of cardholders' overall beverage purchases after tax (-13.4% [95% CI, -14.2% to -12.6%]), while nontaxed beverages made up a larger share (9.3% [95% CI, 7.7%-10.7%]). Conclusions and Relevance In this longitudinal cohort study of the Philadelphia beverage tax, the tax was completely passed through to prices and was associated with a 7.8% decline in ounces of taxed beverages purchased at a national pharmacy chain.
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Affiliation(s)
- Sophia V. Hua
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Joshua Petimar
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christina A. Roberto
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Erica L. Kenney
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Anne N. Thorndike
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston
| | - Eric B. Rimm
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Kevin G. Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Laura A. Gibson
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Patel MS, Volpp KG, Small DS, Kanter GP, Park SH, Evans CN, Polsky D. Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial. Sci Rep 2023; 13:8258. [PMID: 37217585 DOI: 10.1038/s41598-023-35201-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 05/14/2023] [Indexed: 05/24/2023] Open
Abstract
Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to discharge. An enhanced model consisted of the standard model plus RPM data. Traditional parametric regression models (logit and lasso) were compared to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble). The main outcome was hospital readmission or death within 30 days of discharge. Prediction of 30-day hospital readmission significantly improved when including remotely-monitored patient data on activity patterns after hospital discharge and using nonparametric machine learning approaches. Wearables slightly outperformed smartphones but both had good prediction of 30-day hospital-readmission.
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Affiliation(s)
| | - Kevin G Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Wharton School, University of Pennsylvania, Philadelphia, PA, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Dylan S Small
- Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Genevieve P Kanter
- Sol Price School of Public Polocy, University of Southern California, Los Angeles, CA, USA
| | - Sae-Hwan Park
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chalanda N Evans
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Musicus AA, Gibson LA, Bellamy SL, Orr JA, Hammond D, Glanz K, Volpp KG, Schwartz MB, Bleakley A, Strasser AA, Roberto CA. Effects of Sugary Beverage Text and Pictorial Warnings: A Randomized Trial. Am J Prev Med 2023; 64:716-727. [PMID: 36764835 PMCID: PMC10121881 DOI: 10.1016/j.amepre.2023.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 02/10/2023]
Abstract
INTRODUCTION Multiple U.S. localities have introduced legislation requiring sugar-sweetened beverage (SSB) warnings. This study effects of different warning designs on beverage selections and perceptions. STUDY DESIGN The study was an RCT. SETTING/PARTICIPANTS An online virtual convenience store and survey were used with a nationally representative sample of primary caregivers of 6-11-year-olds (n=961). Data were collected in January 2020 and analyzed in May-July 2020. INTERVENTION Participants were randomized to view SSBs with 1 of 4 front-of-package label designs: (1) no-warning control, (2) health-related text warning, (3) sugar pictorial warning (image of beverage sugar content in cubes/teaspoons/packets with health-related warning text), or (4) health pictorial warning (image of possible health consequences of overconsuming SSBs with health-related warning text). MAIN OUTCOME MEASURES Outcomes included participants' beverage choice for their child and perceptions of beverages, their assigned labels, and warning policies. RESULTS Proportionally fewer participants chose a SSB in the sugar pictorial warning condition (-13.4 percentage points; 95% CI= -21.6 to -0.1 percentage points; p=0.007) and in the health pictorial warning condition (-14.7 percentage points; 95% CI= -22.8 to -0.1 percentage points; p=0.004) compared to the control. Sugar pictorial warnings led to more accurate added-sugar content estimates than all conditions and greater label trust and support for sugar-sweetened beverage warning policies than health pictorial warnings. CONCLUSIONS SSB warning policies may be most effective if they mandate images of beverages' added sugar content accompanied by warning text. TRIAL REGISTRATION This study is registered at www. CLINICALTRIALS gov NCT03648138.
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Affiliation(s)
- Aviva A Musicus
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
| | - Laura A Gibson
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Scarlett L Bellamy
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jennifer A Orr
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David Hammond
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Karen Glanz
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kevin G Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marlene B Schwartz
- Rudd Center for Food Policy and Health, University of Connecticut, Storrs, Connecticut
| | - Amy Bleakley
- Department of Communication, College of Arts and Sciences, University of Delaware, Newark, Delaware
| | - Andrew A Strasser
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Christina A Roberto
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Liao JM, Sun C, Yan XS, Patel MS, Small DS, Isenberg WM, Landa HM, Bond BL, Rareshide CAL, Volpp KG, Delgado MK, Lei VJ, Shen Z, Navathe AS. How Physician Self-Perceptions Affect the Impact of Peer Comparison Feedback on Opioid Prescribing. Am J Med Qual 2023; 38:129-136. [PMID: 37017283 DOI: 10.1097/jmq.0000000000000117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Peer comparison feedback is a promising strategy for reducing opioid prescribing and opioid-related harms. Such comparisons may be particularly impactful among underestimating clinicians who do not perceive themselves as high prescribers relative to their peers. But peer comparisons could also unintentionally increase prescribing among overestimating clinicians who do not perceive themselves as lower prescribers than peers. The objective of this study was to assess if the impact of peer comparisons varied by clinicians' preexisting opioid prescribing self-perceptions. Subgroup analysis of a randomized trial of peer comparison interventions among emergency department and urgent care clinicians was used. Generalized mixed-effects models were used to assess whether the impact of peer comparisons, alone or combined with individual feedback, varied by underestimating or overestimating prescriber status. Underestimating and overestimating prescribers were defined as those who self-reported relative prescribing amounts that were lower and higher, respectively, than actual relative baseline amounts. The primary outcome was pills per opioid prescription. Among 438 clinicians, 54% (n = 236) provided baseline prescribing self-perceptions and were included in this analysis. Overall, 17% (n = 40) were underestimating prescribers whereas 5% (n = 11) were overestimating prescribers. Underestimating prescribers exhibited a differentially greater decrease in pills per prescription compared to nonunderestimating clinicians when receiving peer comparison feedback (1.7 pills, 95% CI, -3.2 to -0.2 pills) or combined peer and individual feedback (2.8 pills, 95% CI, -4.8 to -0.8 pills). In contrast, there were no differential changes in pills per prescription for overestimating versus nonoverestimating prescribers after receiving peer comparison (1.5 pills, 95% CI, -0.9 to 3.9 pills) or combined peer and individual feedback (3.0 pills, 95% CI, -0.3 to 6.2 pills). Peer comparisons were more impactful among clinicians who underestimated their prescribing compared to peers. By correcting inaccurate self-perceptions, peer comparison feedback can be an effective strategy for influencing opioid prescribing.
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Affiliation(s)
- Joshua M Liao
- Department of Medicine, University of Washington School of Medicine, Seattle, WA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
| | - Chuxuan Sun
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | | | | | - Dylan S Small
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Charles A L Rareshide
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Kevin G Volpp
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - M Kit Delgado
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
- Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | | | | | - Amol S Navathe
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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12
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Parikh RB, Sedhom R, Ferrell WJ, Villarin K, Berwanger K, Scarborough B, Oyer R, Kumar P, Ganta N, Sivendran S, Chen J, Volpp KG, Bekelman JE. YIA23-006: BE-EPIC: Behavioral Economic Interventions to Embed Palliative Care in Community Oncology. J Natl Compr Canc Netw 2023. [DOI: 10.6004/jnccn.2022.7251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Affiliation(s)
- Ravi B. Parikh
- University of Pennsylvania, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Ramy Sedhom
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Bethann Scarborough
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, PA
| | - Randall Oyer
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, PA
| | | | | | - Shanthi Sivendran
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, PA
| | - Jinbo Chen
- University of Pennsylvania, Philadelphia, PA
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13
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Parikh RB, Sedhom R, Ferrell WJ, Villarin K, Berwanger K, Scarborough B, Oyer R, Kumar P, Ganta N, Sivendran S, Chen J, Volpp KG, Bekelman JE. Behavioural economic interventions to embed palliative care in community oncology (BE-EPIC): study protocol for the BE-EPIC randomised controlled trial. BMJ Open 2023; 13:e069468. [PMID: 36963789 PMCID: PMC10040061 DOI: 10.1136/bmjopen-2022-069468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/26/2023] Open
Abstract
INTRODUCTION Palliative care (PC) is a medical specialty focusing on providing relief from the symptoms and stress of serious illnesses such as cancer. Early outpatient specialty PC concurrent with cancer-directed treatment improves quality of life and symptom burden, decreases aggressive end-of-life care and is an evidence-based practice endorsed by national guidelines. However, nearly half of patients with advanced cancer do not receive specialty PC prior to dying. The objective of this study is to test the impact of an oncologist-directed default PC referral orders on rates of PC utilisation and patient quality of life. METHODS AND ANALYSIS This single-centre two-arm pragmatic randomised trial randomises four clinician-led pods, caring for approximately 250 patients who meet guideline-based criteria for PC referral, in a 1:1 fashion into a control or intervention arm. Intervention oncologists receive a nudge consisting of an electronic health record message indicating a patient has a default pended order for PC. Intervention oncologists are given an opportunity to opt out of referral to PC. Oncologists in pods randomised to the control arm will receive no intervention beyond usual practice. The primary outcome is completed PC visits within 12 weeks. Secondary outcomes are change in quality of life and absolute quality of life scores between the two arms. ETHICS AND DISSEMINATION This study has been approved by the Institutional Review Board at the University of Pennsylvania. Study results will be disseminated in peer-reviewed journals and scientific conferences using methods that describe the results in ways that key stakeholders can best understand and implement. TRIAL REGISTRATION NUMBER NCT05365997.
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Affiliation(s)
- Ravi B Parikh
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ramy Sedhom
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - William J Ferrell
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Katherine Villarin
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kara Berwanger
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bethann Scarborough
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, Pennsylvania, USA
| | - Randall Oyer
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, Pennsylvania, USA
| | - Pallavi Kumar
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Niharika Ganta
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Shanthi Sivendran
- The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health, Lancaster, Pennsylvania, USA
| | - Jinbo Chen
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kevin G Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Justin E Bekelman
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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14
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Patel MS, Milkman KL, Gandhi L, Graci HN, Gromet D, Ho H, Kay JS, Lee TW, Rothschild J, Akinola M, Beshears J, Bogard JE, Buttenheim A, Chabris C, Chapman GB, Choi JJ, Dai H, Fox CR, Goren A, Hilchey MD, Hmurovic J, John LK, Karlan D, Kim M, Laibson D, Lamberton C, Madrian BC, Meyer MN, Modanu M, Nam J, Rogers T, Rondina R, Saccardo S, Shermohammed M, Soman D, Sparks J, Warren C, Weber M, Berman R, Evans CN, Lee SH, Snider CK, Tsukayama E, Van den Bulte C, Volpp KG, Duckworth AL. A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit. Am J Health Promot 2023; 37:324-332. [PMID: 36195982 PMCID: PMC10798571 DOI: 10.1177/08901171221131021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE To evaluate if nudges delivered by text message prior to an upcoming primary care visit can increase influenza vaccination rates. DESIGN Randomized, controlled trial. SETTING Two health systems in the Northeastern US between September 2020 and March 2021. SUBJECTS 74,811 adults. INTERVENTIONS Patients in the 19 intervention arms received 1-2 text messages in the 3 days preceding their appointment that varied in their format, interactivity, and content. MEASURES Influenza vaccination. ANALYSIS Intention-to-treat. RESULTS Participants had a mean (SD) age of 50.7 (16.2) years; 55.8% (41,771) were female, 70.6% (52,826) were White, and 19.0% (14,222) were Black. Among the interventions, 5 of 19 (26.3%) had a significantly greater vaccination rate than control. On average, the 19 interventions increased vaccination relative to control by 1.8 percentage points or 6.1% (P = .005). The top performing text message described the vaccine to the patient as "reserved for you" and led to a 3.1 percentage point increase (95% CI, 1.3 to 4.9; P < .001) in vaccination relative to control. Three of the top five performing messages described the vaccine as "reserved for you." None of the interventions performed worse than control. CONCLUSIONS Text messages encouraging vaccination and delivered prior to an upcoming appointment significantly increased influenza vaccination rates and could be a scalable approach to increase vaccination more broadly.
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Affiliation(s)
- Mitesh S. Patel
- Department of Clinical Transformation and Behavioral Insights, Ascension, St. Louis, MO, USA
| | - Katherine L. Milkman
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Linnea Gandhi
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Heather N. Graci
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Dena Gromet
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Hung Ho
- Department of Marketing, The University of Chicago Booth School of Business, Chicago, IL, USA
| | - Joseph S. Kay
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy W. Lee
- School of Professional Studies, Northwestern University, Evanston, IL, USA
| | - Jake Rothschild
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Modupe Akinola
- Department of Management, Columbia Business School, Columbia University, New York, NY, USA
| | - John Beshears
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Jonathan E. Bogard
- Department of Behavioral Decision Making, Anderson School of Management, University of California, Los Angeles, CA, USA
| | - Alison Buttenheim
- Department of Family and Community Health, The University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | - Christopher Chabris
- Behavioral and Decision Sciences Program, Geisinger Health System, Danville, PA, USA
| | - Gretchen B. Chapman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - James J. Choi
- Department of Finance, Yale School of Management, Yale University, New Haven, CT, USA
| | - Hengchen Dai
- Department of Management and Organization, Anderson School of Management, University of California Los Angeles, Los Angeles, CA, USA
| | - Craig R. Fox
- Department of Management and Organization, Anderson School of Management, University of California Los Angeles, Los Angeles, CA, USA
| | - Amir Goren
- Behavioral Insights Team, Geisinger Health System, Danville, PA, USA
| | - Matthew D. Hilchey
- Department of Behavioural Science and Economics, University of Toronto, Toronto, ON, Canada
| | - Jillian Hmurovic
- Department of Marketing, Drexel University, Philadelphia, PA, USA
| | - Leslie K. John
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Dean Karlan
- Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Melanie Kim
- Department of Behavioural Science and Economics, University of Toronto, Toronto, ON, Canada
| | - David Laibson
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Cait Lamberton
- Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Brigitte C. Madrian
- Department of Finance, Marriott School of Business, Brigham Young University, Provo, UT, USA
| | - Michelle N. Meyer
- Behavioral and Decision Sciences Program, Geisinger Health System, Danville, PA, USA
| | - Maria Modanu
- Department of Management, Columbia Business School, Columbia University, New York, NY, USA
| | - Jimin Nam
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Todd Rogers
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Renante Rondina
- Department of Behavioural Science and Economics, University of Toronto, Toronto, ON, Canada
| | - Silvia Saccardo
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Maheen Shermohammed
- Behavioral and Decision Sciences Program, Geisinger Health System, Danville, PA, USA
| | - Dilip Soman
- Department of Behavioural Science and Economics, University of Toronto, Toronto, ON, Canada
| | - Jehan Sparks
- Department of Behavioral Decision Making, Anderson School of Management, University of California, Los Angeles, CA, USA
| | - Caleb Warren
- Department of Marketing, Eller College of Management, University of Arizona, Tucson, AZ, USA
| | - Megan Weber
- Department of Behavioral Decision Making, Anderson School of Management, University of California, Los Angeles, CA, USA
| | - Ron Berman
- Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Chalanda N. Evans
- Center for Digital Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Seung Hyeong Lee
- Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, MA, USA
| | - Christopher K. Snider
- Center for Health Care Innovation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli Tsukayama
- Business Administration Division, University of Hawaiì-West Òahu, Kapolei, HI, USA
| | | | - Kevin G. Volpp
- Penn Center for Health Incentives and Behavioral Economics, Departments of Medical Ethics and Health Policy and Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Angela L. Duckworth
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
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Dupuis R, Feuerstein-Simon R, Brown-Whitehorn TF, Spergel JM, Volpp KG, Marti XL, Troxel AB, Meisel ZF, Mollen CJ, Kenney EL, Block J, Gortmaker SL, Cannuscio CC. Food Allergy Management for Adolescents Using Behavioral Incentives: A Randomized Trial. Pediatrics 2023; 151:e2022058876. [PMID: 36683454 PMCID: PMC9890392 DOI: 10.1542/peds.2022-058876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/04/2022] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE We sought to evaluate the use of behavioral economics approaches to promote the carrying of epinephrine auto-injectors (EAIs) among adolescents with food allergies. We hypothesized that adolescents who receive frequent text message nudges (Intervention 1) or frequent text message nudges plus modest financial incentives (Intervention 2) would be more likely to carry their epinephrine than members of the usual care control group. METHODS We recruited 131 adolescents ages 15 to 19 with a food allergy and a current prescription for epinephrine to participate in a cohort multiple randomized controlled trial. Participants were randomly assigned to participate in Intervention 1, Intervention 2, or to receive usual care. The primary outcome was consistency of epinephrine-carrying, measured as the proportion of checkpoints at which a participant could successfully demonstrate they were carrying their EAI, with photo-documentation of the device. RESULTS During Intervention 1, participants who received the intervention carried their EAI 28% of the time versus 38% for control group participants (P = .06). During Intervention 2, participations who received the intervention carried their EAI 45% of the time versus 23% for control group participants (P = .002). CONCLUSIONS Text message nudges alone were unsuccessful at promoting EAI-carrying but text message nudges combined with modest financial incentives almost doubled EAI-carriage rates among those who received the intervention compared with the control group. However, even with the intervention, adolescents with food allergies carried their EAI <50% of the time. Alternative strategies for making EAIs accessible to adolescents at all times should be implemented.
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Affiliation(s)
- Roxanne Dupuis
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | | | | | | | | | | | | | | | - Erica L. Kenney
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jason Block
- Harvard Pilgrim/Harvard Medical School, Boston, Massachusetts
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Whitehouse CR, Knowles M, Long JA, Mitra N, Volpp KG, Xu C, Sabini C, Gerald N, Estrada I, Jones D, Kangovi S. Digital Health and Community Health Worker Support for Diabetes Management: a Randomized Controlled Trial. J Gen Intern Med 2023; 38:131-137. [PMID: 35581452 PMCID: PMC9113615 DOI: 10.1007/s11606-022-07639-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/22/2022] [Indexed: 01/22/2023]
Abstract
OBJECTIVE The aim of this study was to evaluate the effectiveness of a digital health intervention plus community health worker (CHW) support on self-monitoring of blood glucose and glycosylated hemoglobin (HbA1c) among adult Medicaid beneficiaries with diabetes. DESIGN Randomized controlled trial. SETTING Urban outpatient clinic. PARTICIPANTS Adult Medicaid beneficiaries living with diabetes and treated with insulin and who had a HbA1c ≥ 9%. INTERVENTION Participants were randomly assigned to one of three arms. Participants in the usual-care arm received a wireless glucometer if needed. Those in the digital arm received a lottery incentive for daily glucose monitoring. Those in the hybrid arm received the lottery plus support from a CHW if they had low adherence or high blood glucose levels. MAIN MEASURES The primary outcome was the difference in adherence to daily glucose self-monitoring at 3 months between the hybrid and usual-care arms. The secondary outcome was difference in HbA1c from baseline at 6 months. KEY RESULTS A total of 150 participants were enrolled in the study. A total of 102 participants (68%) completed the study. At 3 months, glucose self-monitoring rates in the hybrid versus usual-care arms were 0.72 vs 0.65, p = 0.23. At 6 months, change in HbA1c in the hybrid versus usual-care arms was - 0.74% vs - 0.49%, p = 0.69. CONCLUSION There were no statistically significant differences between the hybrid and usual care in glucose self-monitoring adherence or improvements in HbA1C. TRIAL REGISTRATION This trial is registered with clinicaltrials.gov identifier: NCT03939793.
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Affiliation(s)
- Christina R Whitehouse
- M. Louise Fitzpatrick College of Nursing, Villanova University, 800 E. Lancaster Avenue, Villanova, PA, 19085, USA.
- Clinical Practices of the University of Pennsylvania, Penn Medicine, Philadelphia, PA, USA.
| | - Molly Knowles
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Judith A Long
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Chang Xu
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Carolyn Sabini
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Norma Gerald
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Irene Estrada
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Denerale Jones
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya Kangovi
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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17
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Carter EW, Vadari HS, Stoll S, Rogers B, Resnicow K, Heisler M, Herman WH, Kim HM, McEwen LN, Volpp KG, Kullgren JT. Study protocol: Behavioral economics and self-determination theory to change diabetes risk (BEST Change). Contemp Clin Trials 2023; 124:107038. [PMID: 36460265 PMCID: PMC10259647 DOI: 10.1016/j.cct.2022.107038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND The Diabetes Prevention Program (DPP) and metformin can prevent or delay the onset of type 2 diabetes mellitus (T2DM) among patients with prediabetes. Yet, even when these evidence-based strategies are accessible and affordable, uptake is low. Thus, there is a critical need for effective, scalable, and sustainable approaches to increase uptake and engagement in these interventions. METHODS In this randomized controlled trial, we will test whether financial incentives and automated messaging to promote autonomous motivation for preventing T2DM can increase DPP participation, metformin use, or both among adults with prediabetes. Participants (n = 380) will be randomized to one of four study arms. Control Arm participants will receive usual care and educational text messages about preventing T2DM. Incentives Arm participants will receive the Control Arm intervention plus financial incentives for DPP participation or metformin use. Tailored Messages Arm participants will receive the Control Arm intervention plus tailored messages promoting autonomous motivation for preventing T2DM. Combined Arm participants will receive the Incentives Arm and Tailored Messages Arm interventions plus messages to increase the personal salience of financial incentives. The primary outcome is change in hemoglobin A1c from baseline to 12 months. Secondary outcomes are change in body weight, DPP participation, and metformin use. DISCUSSION If effective, these scalable and sustainable approaches to increase patient motivation to prevent T2DM can be deployed by health systems, health plans, and employers to help individuals with prediabetes lower their risk for developing T2DM.
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Affiliation(s)
- Eli W Carter
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America.
| | - Harita S Vadari
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Shelley Stoll
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Baylee Rogers
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Kenneth Resnicow
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Michele Heisler
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America; Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI, United States of America; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States of America; University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, MI, United States of America
| | - William H Herman
- Departments of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, MI, United States of America
| | - H Myra Kim
- VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States of America; Consulting for Statistics, Computing and Analytics Research, University of Michigan, Ann Arbor, MI, United States of America
| | - Laura N McEwen
- University of Michigan, Department of Internal Medicine- Metabolism, Endocrinology, and Diabetes, United States of America
| | - Kevin G Volpp
- Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, United States of America; Departments of Medicine and Health Care Management, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Jeffrey T Kullgren
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States of America; University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, MI, United States of America; Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
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18
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Affiliation(s)
- Kevin G Volpp
- Perelman School of Medicine, the Wharton School, Penn Center for Health Incentives and Behavioral Economics (CHIBE), University of Pennsylvania, Philadelphia
| | - Benjamin S Abella
- Center for Resuscitation Science and Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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19
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Adusumalli S, Kanter GP, Small DS, Asch DA, Volpp KG, Park SH, Gitelman Y, Do D, Leri D, Rhodes C, VanZandbergen C, Howell JT, Epps M, Cavella AM, Wenger M, Harrington TO, Clark K, Westover JE, Snider CK, Patel MS. Effect of Nudges to Clinicians, Patients, or Both to Increase Statin Prescribing: A Cluster Randomized Clinical Trial. JAMA Cardiol 2023; 8:23-30. [PMID: 36449275 PMCID: PMC9713674 DOI: 10.1001/jamacardio.2022.4373] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/29/2022] [Indexed: 12/02/2022]
Abstract
Importance Statins reduce the risk of major adverse cardiovascular events, but less than one-half of individuals in America who meet guideline criteria for a statin are actively prescribed this medication. Objective To evaluate whether nudges to clinicians, patients, or both increase initiation of statin prescribing during primary care visits. Design, Setting, and Participants This cluster randomized clinical trial evaluated statin prescribing of 158 clinicians from 28 primary care practices including 4131 patients. The design included a 12-month preintervention period and a 6-month intervention period between October 19, 2019, and April 18, 2021. Interventions The usual care group received no interventions. The clinician nudge combined an active choice prompt in the electronic health record during the patient visit and monthly feedback on prescribing patterns compared with peers. The patient nudge was an interactive text message delivered 4 days before the visit. The combined nudge included the clinician and patient nudges. Main Outcomes and Measures The primary outcome was initiation of a statin prescription during the visit. Results The sample comprised 4131 patients with a mean (SD) age of 65.5 (10.5) years; 2120 (51.3%) were male; 1210 (29.3%) were Black, 106 (2.6%) were Hispanic, 2732 (66.1%) were White, and 83 (2.0%) were of other race or ethnicity, and 933 (22.6%) had atherosclerotic cardiovascular disease. In unadjusted analyses during the preintervention period, statins were prescribed to 5.6% of patients (105 of 1876) in the usual care group, 4.8% (97 of 2022) in the patient nudge group, 6.0% (104 of 1723) in the clinician nudge group, and 4.7% (82 of 1752) in the combined group. During the intervention, statins were prescribed to 7.3% of patients (75 of 1032) in the usual care group, 8.5% (100 of 1181) in the patient nudge group, 13.0% (128 of 981) in the clinician nudge arm, and 15.5% (145 of 937) in the combined group. In the main adjusted analyses relative to usual care, the clinician nudge significantly increased statin prescribing alone (5.5 percentage points; 95% CI, 3.4 to 7.8 percentage points; P = .01) and when combined with the patient nudge (7.2 percentage points; 95% CI, 5.1 to 9.1 percentage points; P = .001). The patient nudge alone did not change statin prescribing relative to usual care (0.9 percentage points; 95% CI, -0.8 to 2.5 percentage points; P = .32). Conclusions and Relevance Nudges to clinicians with and without a patient nudge significantly increased initiation of a statin prescription during primary care visits. The patient nudge alone was not effective. Trial Registration ClinicalTrials.gov Identifier: NCT04307472.
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Affiliation(s)
| | | | - Dylan S. Small
- Wharton School, University of Pennsylvania, Philadelphia
| | - David A. Asch
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Wharton School, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Wharton School, University of Pennsylvania, Philadelphia
- Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
| | - Sae-Hwan Park
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Yevgeniy Gitelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - David Do
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Damien Leri
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Corinne Rhodes
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - John T. Howell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mika Epps
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ann M. Cavella
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Michael Wenger
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Kayla Clark
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
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20
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Feuerstein-Simon R, Strelau KM, Naseer N, Claycomb K, Kilaru A, Lawman H, Watson-Lewis L, Klusaritz H, Van Pelt AE, Penrod N, Srivastava T, Nelson HC, James R, Hall M, Weigelt E, Summers C, Paterson E, Aysola J, Thomas R, Lowenstein D, Advani P, Meehan P, Merchant RM, Volpp KG, Cannuscio CC. Design, Implementation, and Outcomes of a Volunteer-Staffed Case Investigation and Contact Tracing Initiative at an Urban Academic Medical Center. JAMA Netw Open 2022; 5:e2232110. [PMID: 36149656 PMCID: PMC9508658 DOI: 10.1001/jamanetworkopen.2022.32110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE The COVID-19 pandemic has claimed nearly 6 million lives globally as of February 2022. While pandemic control efforts, including contact tracing, have traditionally been the purview of state and local health departments, the COVID-19 pandemic outpaced health department capacity, necessitating actions by private health systems to investigate and control outbreaks, mitigate transmission, and support patients and communities. OBJECTIVE To investigate the process of designing and implementing a volunteer-staffed contact tracing program at a large academic health system from April 2020 to May 2021, including program structure, lessons learned through implementation, results of case investigation and contact tracing efforts, and reflections on how constrained resources may be best allocated in the current pandemic or future public health emergencies. DESIGN, SETTING, AND PARTICIPANTS This case series study was conducted among patients at the University of Pennsylvania Health System and in partnership with the Philadelphia Department of Public Health. Patients who tested positive for COVID-19 were contacted to counsel them regarding safe isolation practices, identify and support quarantine of their close contacts, and provide resources, such as food and medicine, needed during isolation or quarantine. RESULTS Of 5470 individuals who tested positive for COVID-19 and received calls from a volunteer, 2982 individuals (54.5%; median [range] age, 42 [18-97] years; 1628 [59.4%] women among 2741 cases with sex data) were interviewed; among 2683 cases with race data, there were 110 Asian individuals (3.9%), 1476 Black individuals (52.7%), and 817 White individuals (29.2%), and among 2667 cases with ethnicity data, there were 366 Hispanic individuals (13.1%) and 2301 individuals who were not Hispanic (82.6%). Most individuals lived in a household with 2 to 5 people (2125 of 2904 individuals with household data [71.6%]). Of 3222 unique contacts, 1780 close contacts (55.2%; median [range] age, 40 [18-97] years; 866 [55.3%] women among 1565 contacts with sex data) were interviewed; among 1523 contacts with race data, there were 69 Asian individuals (4.2%), 705 Black individuals (43.2%), and 573 White individuals (35.1%), and among 1514 contacts with ethnicity data, there were 202 Hispanic individuals (12.8%) and 1312 individuals (83.4%) who were not Hispanic. Most contacts lived in a household with 2 to 5 people (1123 of 1418 individuals with household data [79.2%]). Of 3324 cases and contacts who completed a questionnaire on unmet social needs, 907 (27.3%) experienced material hardships that would make it difficult for them to isolate or quarantine safely. Such hardship was significantly less common among White compared with Black participants (odds ratio, 0.20; 95% CI, 0.16-0.25). CONCLUSIONS AND RELEVANCE These findings demonstrate the feasibility and challenges of implementing a case investigation and contact tracing program at an academic health system. In addition to successfully engaging most assigned COVID-19 cases and close contacts, contact tracers shared health information and material resources to support isolation and quarantine, thus filling local public health system gaps and supporting local pandemic control.
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Affiliation(s)
- Rachel Feuerstein-Simon
- Department of Family and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Katherine M. Strelau
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
- Biomedical Graduate Studies, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Nawar Naseer
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
- Biomedical Graduate Studies, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kierstyn Claycomb
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Austin Kilaru
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Center for Emergency Care Policy and Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Hannah Lawman
- Philadelphia Department of Public Health, Philadelphia, Pennsylvania
- Now with Novo Nordisk, Plainsboro, New Jersey
| | | | - Heather Klusaritz
- Department of Family and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Amelia E. Van Pelt
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Nadia Penrod
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Tuhina Srivastava
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
- Biomedical Graduate Studies, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Hillary C.M. Nelson
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Richard James
- School of Nursing, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Moriah Hall
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Elaine Weigelt
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Courtney Summers
- Department of Family and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Emily Paterson
- Department of Family and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
| | - Jaya Aysola
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center For Health Equity Advancement, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Rosemary Thomas
- Center For Health Equity Advancement, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Deborah Lowenstein
- Center For Health Equity Advancement, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Preeti Advani
- Center For Health Equity Advancement, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Patricia Meehan
- Center For Health Equity Advancement, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Raina M. Merchant
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Center for Emergency Care Policy and Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz Department of Veterans Affairs Medical Center, Philadelphia, Pennsylvania
- Department of Health Care Management, Wharton School, University of Pennsylvania, Philadelphia
| | - Carolyn C. Cannuscio
- Department of Family and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
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21
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Asch DA, Troxel AB, Goldberg LR, Tanna MS, Mehta SJ, Norton LA, Zhu J, Iannotte LG, Klaiman T, Lin Y, Russell LB, Volpp KG. Remote Monitoring and Behavioral Economics in Managing Heart Failure in Patients Discharged From the Hospital: A Randomized Clinical Trial. JAMA Intern Med 2022; 182:643-649. [PMID: 35532915 PMCID: PMC9171555 DOI: 10.1001/jamainternmed.2022.1383] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
IMPORTANCE Close remote monitoring of patients following discharge for heart failure (HF) may reduce readmissions or death. OBJECTIVE To determine whether remote monitoring of diuretic adherence and weight changes with financial incentives reduces hospital readmissions or death following discharge with HF. DESIGN, SETTING, AND PARTICIPANTS The Electronic Monitoring of Patients Offers Ways to Enhance Recovery (EMPOWER) study, a 3-hospital pragmatic trial, randomized 552 adults recently discharged with HF to usual care (n = 280) or a compound intervention (n = 272) designed to inform clinicians of diuretic adherence and changes in patient weight. Patients were recruited from May 25, 2016, to April 8, 2019, and followed up for 12 months. Investigators were blinded to assignment but patients were not. Analysis was by intent to treat. INTERVENTIONS Participants randomized to the intervention arm received digital scales, electronic pill bottles for diuretic medication, and regret lottery incentives conditional on the previous day's adherence to both medication and weight measurement, with $1.40 expected daily value. Participants' physicians were alerted if participants' weights increased 1.4 kg in 24 hours or 2.3 kg in 72 hours or if diuretic medications were missed for 5 days. Alerts and weights were integrated into the electronic health record. Participants randomized to the control arm received usual care and no further study contact. MAIN OUTCOMES AND MEASURES Time to death or readmission for any cause within 12 months. RESULTS Of the 552 participants, 290 were men (52.5%); 291 patients (52.7%) were Black, 231 were White (41.8%), and 16 were Hispanic (2.9%); mean (SD) age was 64.5 (11.8) years. The mean (SD) ejection fraction was 43% (18.1%). Each month, approximately 75% of participants were 80% adherent to both medication and weight measurement. There were 423 readmissions and 26 deaths in the control group and 377 readmissions and 23 deaths in the intervention group. There was no significant difference between the 2 groups for the combined outcome of all-cause inpatient readmission or death (unadjusted hazard ratio, 0.91; 95% CI, 0.74-1.13; P = .40) and no significant differences in all-cause inpatient readmission or observation stay or death, all-cause cardiovascular readmission or death, time to first event, and total all-cause deaths. Participants in the intervention group were slightly more likely to spend fewer days in the hospital. CONCLUSIONS AND RELEVANCE In this randomized clinical trial, there was no reduction in the combined outcome of readmission or mortality in a year-long intensive remote monitoring program with incentives for patients previously hospitalized for HF. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02708654.
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Affiliation(s)
- David A Asch
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Andrea B Troxel
- Division of Biostatistics, NYU Grossman School of Medicine, New York, New York
| | - Lee R Goldberg
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Monique S Tanna
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Shivan J Mehta
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Laurie A Norton
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jingsan Zhu
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Lauren G Iannotte
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Tamar Klaiman
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Yuqing Lin
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
| | - Louise B Russell
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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22
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Thirumurthy H, Milkman KL, Volpp KG, Buttenheim AM, Pope DG. Association between statewide financial incentive programs and COVID-19 vaccination rates. PLoS One 2022; 17:e0263425. [PMID: 35353815 PMCID: PMC8966995 DOI: 10.1371/journal.pone.0263425] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 01/18/2022] [Indexed: 11/23/2022] Open
Abstract
To promote COVID-19 vaccination, many states in the US introduced financial incentives ranging from small, guaranteed rewards to lotteries that give vaccinated individuals a chance to win large prizes. There is limited evidence on the effectiveness of these programs and conflicting evidence from survey experiments and studies of individual states’ lotteries. To assess the effectiveness of COVID-19 vaccination incentive programs, we combined information on statewide incentive programs in the US with data on daily vaccine doses administered in each state. Leveraging variation across states in the daily availability of incentives, our difference-in-differences analyses showed that statewide programs were not associated with a significant change in vaccination rates. Furthermore, there was no significant difference in vaccination trends between states with and without incentives in any of the 14 days before or after incentives were introduced. Heterogeneity analyses indicated that neither lotteries nor guaranteed rewards were associated with significant change in vaccination rates.
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Affiliation(s)
- Harsha Thirumurthy
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Katherine L. Milkman
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kevin G. Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Alison M. Buttenheim
- Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Devin G. Pope
- Booth School of Business, University of Chicago, Chicago, Illinois, United States of America
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23
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Navathe AS, Liao JM, Yan XS, Delgado MK, Isenberg WM, Landa HM, Bond BL, Small DS, Rareshide CAL, Shen Z, Pepe RS, Refai F, Lei VJ, Volpp KG, Patel MS. The Effect Of Clinician Feedback Interventions On Opioid Prescribing. Health Aff (Millwood) 2022; 41:424-433. [PMID: 35254932 DOI: 10.1377/hlthaff.2021.01407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
An initial opioid prescription with a greater number of pills is associated with a greater risk for future long-term opioid use, yet few interventions have reliably influenced individual clinicians' prescribing. Our objective was to evaluate the effect of feedback interventions for clinicians in reducing opioid prescribing. The interventions included feedback on a clinician's outlier prescribing (individual audit feedback), peer comparison, and both interventions combined. We conducted a four-arm factorial pragmatic cluster randomized trial at forty-eight emergency department (ED) and urgent care (UC) sites in the western US, including 263 ED and 175 UC clinicians with 294,962 patient encounters. Relative to usual care, there was a significant decrease in pills per prescription both for peer comparison feedback (-0.8) and for the combination of peer comparison and individual audit feedback (-1.2). This decrease was sustained during follow-up. There were no significant changes for individual audit feedback alone, and no interventions changed the proportion of encounters with an opioid prescription.
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Affiliation(s)
- Amol S Navathe
- Amol S. Navathe , Corporal Michael J. Cresencz Veterans Affairs Medical Center and University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joshua M Liao
- Joshua M. Liao, University of Washington, Seattle, Washington, and University of Pennsylvania
| | - Xiaowei S Yan
- Xiaowei S. Yan, Sutter Health, Walnut Creek, California
| | | | | | | | - Barbara L Bond
- Barbara L. Bond, Sutter Health, Castro Valley, California
| | | | | | - Zijun Shen
- Zijun Shen, Sutter Health, San Francisco
| | | | | | | | | | - Mitesh S Patel
- Mitesh S. Patel, Corporal Michael J. Cresencz Veterans Affairs Medical Center and University of Pennsylvania
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24
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Harte R, Norton L, Whitehouse C, Lorincz I, Jones D, Gerald N, Estrada I, Sabini C, Mitra N, Long JA, Cappella J, Glanz K, Volpp KG, Kangovi S. Design of a randomized controlled trial of digital health and community health worker support for diabetes management among low-income patients. Contemp Clin Trials Commun 2022; 25:100878. [PMID: 34977421 PMCID: PMC8688867 DOI: 10.1016/j.conctc.2021.100878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 09/14/2021] [Accepted: 12/04/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Insulin-dependent diabetes is a challenging disease to manage and involves complex behaviors, such as self-monitoring of blood glucose. This can be especially challenging in the face of socioeconomic barriers and in the wake of the COVID-19 pandemic. Digital health self-monitoring interventions and community health worker support are promising and complementary best practices for improving diabetes-related health behaviors and outcomes. Yet, these strategies have not been tested in combination. This protocol paper describes the rationale and design of a trial that measures the combined effect of digital health and community health worker support on glucose self-monitoring and glycosylated hemoglobin. METHODS The study population was uninsured or publicly insured; lived in high-poverty, urban neighborhoods; and had poorly controlled diabetes mellitus with insulin dependence. The study consisted of three arms: usual diabetes care; digital health self-monitoring; or combined digital health and community health worker support. The primary outcome was adherence to blood glucose self-monitoring. The exploratory outcome was change in glycosylated hemoglobin. CONCLUSION The design of this trial was grounded in social justice and community engagement. The study protocols were designed in collaboration with frontline community health workers, the study aim was explicit about furthering knowledge useful for advancing health equity, and the population was focused on low-income people. This trial will advance knowledge of whether combining digital health and community health worker interventions can improve glucose self-monitoring and diabetes-related outcomes in a high-risk population.
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Affiliation(s)
- Rory Harte
- Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Lindsey Norton
- Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Christina Whitehouse
- Villanova University M. Louise Fitzpatrick College of Nursing, Villanova, PA, USA
| | - Ilona Lorincz
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Denerale Jones
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Norma Gerald
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Irene Estrada
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Carolyn Sabini
- Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
| | - Nandita Mitra
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Judith A. Long
- Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Joseph Cappella
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | - Karen Glanz
- Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin G. Volpp
- Penn Center for Health Incentives and Behavioral Economics, Departments of Medical Ethics and Health Policy and Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shreya Kangovi
- Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
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25
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Kohn R, Vachani A, Small D, Stephens-Shields AJ, Sheu D, Madden VL, Bayes BA, Chowdhury M, Friday S, Kim J, Gould MK, Ismail MH, Creekmur B, Facktor MA, Collins C, Blessing KK, Neslund-Dudas CM, Simoff MJ, Alleman ER, Epstein LH, Horst MA, Scott ME, Volpp KG, Halpern SD, Hart JL. Comparing Smoking Cessation Interventions among Underserved Patients Referred for Lung Cancer Screening: A Pragmatic Trial Protocol. Ann Am Thorac Soc 2022; 19:303-314. [PMID: 34384042 PMCID: PMC8867367 DOI: 10.1513/annalsats.202104-499sd] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/12/2021] [Indexed: 02/03/2023] Open
Abstract
Smoking burdens are greatest among underserved patients. Lung cancer screening (LCS) reduces mortality among individuals at risk for smoking-associated lung cancer. Although LCS programs must offer smoking cessation support, the interventions that best promote cessation among underserved patients in this setting are unknown. This stakeholder-engaged, pragmatic randomized clinical trial will compare the effectiveness of four interventions promoting smoking cessation among underserved patients referred for LCS. By using an additive study design, all four arms provide standard "ask-advise-refer" care. Arm 2 adds free or subsidized pharmacologic cessation aids, arm 3 adds financial incentives up to $600 for cessation, and arm 4 adds a mobile device-delivered episodic future thinking tool to promote attention to long-term health goals. We hypothesize that smoking abstinence rates will be higher with the addition of each intervention when compared with arm 1. We will enroll 3,200 adults with LCS orders at four U.S. health systems. Eligible patients include those who smoke at least one cigarette daily and self-identify as a member of an underserved group (i.e., is Black or Latinx, is a rural resident, completed a high school education or less, and/or has a household income <200% of the federal poverty line). The primary outcome is biochemically confirmed smoking abstinence sustained through 6 months. Secondary outcomes include abstinence sustained through 12 months, other smoking-related clinical outcomes, and patient-reported outcomes. This pragmatic randomized clinical trial will identify the most effective smoking cessation strategies that LCS programs can implement to reduce smoking burdens affecting underserved populations. Clinical trial registered with clinicaltrials.gov (NCT04798664). Date of registration: March 12, 2021. Date of trial launch: May 17, 2021.
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Affiliation(s)
- Rachel Kohn
- Palliative and Advanced Illness Research Center
- Department of Medicine
- Leonard Davis Institute of Health Economics
| | | | - Dylan Small
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | | | | | | | | | - Jannie Kim
- Palliative and Advanced Illness Research Center
| | - Michael K. Gould
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California
| | | | - Beth Creekmur
- Department of Research and Evaluation, Kaiser Permanente Southern California, Riverside, California
| | | | | | - Kristina K. Blessing
- Investigator Initiated Research Operations, Geisinger Health System, Danville, Pennsylvania
| | | | - Michael J. Simoff
- Henry Ford Cancer Institute, and
- Department of Pulmonary and Critical Care Medicine, Henry Ford Health System, Detroit, Michigan
| | | | - Leonard H. Epstein
- Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
| | - Michael A. Horst
- Lancaster General Health Research Institute, University of Pennsylvania Health System, Lancaster, Pennsylvania
| | - Michael E. Scott
- The Center for Black Health and Equity, Durham, North Carolina; and
| | - Kevin G. Volpp
- Department of Medicine
- Leonard Davis Institute of Health Economics
- Department of Medical Ethics and Health Policy, and
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Medicine, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
| | - Scott D. Halpern
- Palliative and Advanced Illness Research Center
- Department of Medicine
- Leonard Davis Institute of Health Economics
- Department of Biostatistics, Epidemiology and Informatics
- Department of Medical Ethics and Health Policy, and
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joanna L. Hart
- Palliative and Advanced Illness Research Center
- Department of Medicine
- Leonard Davis Institute of Health Economics
- Department of Medical Ethics and Health Policy, and
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Medicine, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
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26
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Russell LB, Huang Q, Lin Y, Norton LA, Zhu J, Iannotte LG, Asch DA, Mehta SJ, Tanna MS, Troxel AB, Volpp KG, Goldberg LR. The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study. Med Decis Making 2022; 42:975-984. [PMID: 35018863 DOI: 10.1177/0272989x211069980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
HIGHLIGHTS Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently.Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not.Acquiring essential supplementary data-in this trial, state data on hospital admission-can be a lengthy and difficult process.
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Affiliation(s)
- Louise B Russell
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
| | - Qian Huang
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
| | - Yuqing Lin
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
| | - Laurie A Norton
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
| | - Jingsan Zhu
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
| | - L G Iannotte
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shivan J Mehta
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Monique S Tanna
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Kevin G Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,The Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Lee R Goldberg
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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27
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Milkman KL, Gromet D, Ho H, Kay JS, Lee TW, Pandiloski P, Park Y, Rai A, Bazerman M, Beshears J, Bonacorsi L, Camerer C, Chang E, Chapman G, Cialdini R, Dai H, Eskreis-Winkler L, Fishbach A, Gross JJ, Horn S, Hubbard A, Jones SJ, Karlan D, Kautz T, Kirgios E, Klusowski J, Kristal A, Ladhania R, Loewenstein G, Ludwig J, Mellers B, Mullainathan S, Saccardo S, Spiess J, Suri G, Talloen JH, Taxer J, Trope Y, Ungar L, Volpp KG, Whillans A, Zinman J, Duckworth AL. Megastudies improve the impact of applied behavioural science. Nature 2021; 600:478-483. [PMID: 34880497 DOI: 10.1038/s41586-021-04128-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 10/13/2021] [Indexed: 11/09/2022]
Abstract
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens' decisions and outcomes1. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals2. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy-a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research3-6. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.
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Affiliation(s)
- Katherine L Milkman
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
| | - Dena Gromet
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Hung Ho
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.,Department of Marketing, Booth School of Business, University of Chicago, Chicago, IL, USA
| | - Joseph S Kay
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy W Lee
- Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.,McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Pepi Pandiloski
- Harris School of Public Policy, University of Chicago, Chicago, IL, USA
| | - Yeji Park
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Aneesh Rai
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Max Bazerman
- Department of Negotiation, Organizations & Markets, Harvard Business School, Harvard University, Boston, MA, USA
| | - John Beshears
- Department of Negotiation, Organizations & Markets, Harvard Business School, Harvard University, Boston, MA, USA
| | - Lauri Bonacorsi
- Pritzker School of Law, Northwestern University, Chicago, IL, USA
| | - Colin Camerer
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Edward Chang
- Department of Negotiation, Organizations & Markets, Harvard Business School, Harvard University, Boston, MA, USA
| | - Gretchen Chapman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Robert Cialdini
- Department of Psychology, Arizona State University, Tempe, AZ, USA
| | - Hengchen Dai
- Department of Management and Organizations, Anderson School of Management, University of California Los Angeles, Los Angeles, CA, USA
| | - Lauren Eskreis-Winkler
- Department of Behavioral Science, Booth School of Business, University of Chicago, Chicago, IL, USA
| | - Ayelet Fishbach
- Department of Behavioral Science, Booth School of Business, University of Chicago, Chicago, IL, USA
| | - James J Gross
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Samantha Horn
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alexa Hubbard
- Department of Psychology, New York University, New York, NY, USA
| | - Steven J Jones
- Department of Psychology, Rutgers University, New Brunswick, NJ, USA
| | - Dean Karlan
- Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | | | - Erika Kirgios
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Joowon Klusowski
- Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Ariella Kristal
- Department of Organizational Behavior, Harvard Business School, Harvard University, Boston, MA, USA
| | - Rahul Ladhania
- Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - George Loewenstein
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jens Ludwig
- Harris School of Public Policy, University of Chicago, Chicago, IL, USA
| | - Barbara Mellers
- Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Sendhil Mullainathan
- Department of Behavioral Science, Booth School of Business, University of Chicago, Chicago, IL, USA
| | - Silvia Saccardo
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jann Spiess
- Department of Operations, Information & Technology, Stanford Graduate School of Business, Stanford, CA, USA
| | - Gaurav Suri
- Department of Psychology, San Francisco State University, San Francisco, CA, USA
| | - Joachim H Talloen
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jamie Taxer
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Yaacov Trope
- Department of Psychology, New York University, New York, NY, USA
| | - Lyle Ungar
- Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin G Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ashley Whillans
- Department of Negotiation, Organizations & Markets, Harvard Business School, Harvard University, Boston, MA, USA
| | - Jonathan Zinman
- Department of Economics, Dartmouth College, Hanover, NH, USA
| | - Angela L Duckworth
- Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. .,Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
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28
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Russell LB, Volpp KG, Kwong PL, Cosgriff BS, Harhay MO, Zhu J, Halpern SD. Cost-Effectiveness of Four Financial Incentive Programs for Smoking Cessation. Ann Am Thorac Soc 2021; 18:1997-2006. [PMID: 33979562 PMCID: PMC8641815 DOI: 10.1513/annalsats.202012-1473oc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/12/2021] [Indexed: 11/20/2022] Open
Abstract
Rationale: A trial of four financial incentive programs, conducted at CVS Caremark, a large employer, documented their effectiveness in promoting sustained abstinence from smoking, but their cost-effectiveness is unknown, and the significant up-front cost of the incentives is a deterrent to their adoption. Objectives: To determine the cost-effectiveness of these incentives from the healthcare sector and employer perspectives. Methods: This study examines a decision model built with trial data, supplemented by data from the literature. Life-expectancy gains for quitters were projected on the basis of U.S. life tables. The two individual-oriented programs paid $800 for smoking cessation at 6 months; one required participants to deposit $150 at baseline. Payments in the two group-oriented programs varied with the group's success; again, one required participants to deposit $150. Results: Life-years, quality-adjusted life-years (QALYs), costs (2012 dollars), and cost-effectiveness ratios are described. From the healthcare sector perspective, costs ranged from $3,200 per life-year ($2,500 per QALY) for the competitive deposit program, compared with usual care, to $6,500 per life-year ($5,100 per QALY) for the individual reward program. From the employer perspective, costs ranged from $256,600 per life-year gained for the individual deposit program to $1,711,100 per life-year gained for the individual reward program; the cost per QALY ranged from $65,300 for the competitive deposit program to $128,800 for the individual reward program. Cost-effectiveness from the employer perspective improved with longer decision horizons. Including future medical costs reduced cost-effectiveness from both perspectives. Conclusions: Four financial incentive programs that paid smokers to quit are very cost-effective from the healthcare sector perspective. They are more expensive from the employer perspective but may be cost-effective for employers with longer decision horizons.
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Affiliation(s)
- Louise B. Russell
- Department of Medical Ethics and Health Policy
- Center for Health Incentives and Behavioral Economics
- Leonard Davis Institute of Health Economics, and
| | - Kevin G. Volpp
- Department of Medical Ethics and Health Policy
- Department of Medicine, and
- Center for Health Incentives and Behavioral Economics
- Leonard Davis Institute of Health Economics, and
- Department of Health Care Management, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Pui L. Kwong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Health Incentives and Behavioral Economics
| | | | - Michael O. Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Health Incentives and Behavioral Economics
| | - Jingsan Zhu
- Department of Medical Ethics and Health Policy
- Center for Health Incentives and Behavioral Economics
| | - Scott D. Halpern
- Department of Medical Ethics and Health Policy
- Department of Medicine, and
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Health Incentives and Behavioral Economics
- Leonard Davis Institute of Health Economics, and
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29
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Meer EA, Herriman M, Lam D, Parambath A, Rosin R, Volpp KG, Chaiyachati KH, McGreevey JD. Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool. Appl Clin Inform 2021; 12:1021-1028. [PMID: 34734403 DOI: 10.1055/s-0041-1736627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
OBJECTIVE We describe the design, implementation, and validation of an online, publicly available tool to algorithmically triage patients experiencing severe acute respiratory syndrome coronavirus (SARS-CoV-2)-like symptoms. METHODS We conducted a chart review of patients who completed the triage tool and subsequently contacted our institution's phone triage hotline to assess tool- and clinician-assigned triage codes, patient demographics, SARS-CoV-2 (COVID-19) test data, and health care utilization in the 30 days post-encounter. We calculated the percentage of concordance between tool- and clinician-assigned triage categories, down-triage (clinician assigning a less severe category than the triage tool), and up-triage (clinician assigning a more severe category than the triage tool) instances. RESULTS From May 4, 2020 through January 31, 2021, the triage tool was completed 30,321 times by 20,930 unique patients. Of those 30,321 triage tool completions, 51.7% were assessed by the triage tool to be asymptomatic, 15.6% low severity, 21.7% moderate severity, and 11.0% high severity. The concordance rate, where the triage tool and clinician assigned the same clinical severity, was 29.2%. The down-triage rate was 70.1%. Only six patients were up-triaged by the clinician. 72.1% received a COVID-19 test administered by our health care system within 14 days of their encounter, with a positivity rate of 14.7%. CONCLUSION The design, pilot, and validation analysis in this study show that this COVID-19 triage tool can safely triage patients when compared with clinician triage personnel. This work may signal opportunities for automated triage of patients for conditions beyond COVID-19 to improve patient experience by enabling self-service, on-demand, 24/7 triage access.
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Affiliation(s)
- Elana A Meer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Maguire Herriman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Doreen Lam
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Andrew Parambath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Roy Rosin
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Kevin G Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Department of Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Krisda H Chaiyachati
- Penn Medicine Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States.,Department of Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States.,Leonard Davis Institute, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - John D McGreevey
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Office of the Chief Medical Information Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States.,Center for Applied Health Informatics, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
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30
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Halpern SD, Chowdhury M, Bayes B, Cooney E, Hitsman BL, Schnoll RA, Lubitz SF, Reyes C, Patel MS, Greysen SR, Mercede A, Reale C, Barg FK, Volpp KG, Karlawish J, Stephens-Shields AJ. Effectiveness and Ethics of Incentives for Research Participation: 2 Randomized Clinical Trials. JAMA Intern Med 2021; 181:1479-1488. [PMID: 34542553 PMCID: PMC8453363 DOI: 10.1001/jamainternmed.2021.5450] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Incentivizing research participation is controversial and variably regulated because of uncertainty regarding whether financial incentives serve as undue inducements by diminishing peoples' sensitivity to research risks or unjust inducements by preferentially increasing enrollment among underserved individuals. OBJECTIVE To determine whether incentives improve enrollment in real randomized clinical trials (RCTs) or serve as undue or unjust inducements. DESIGN, SETTING, AND PARTICIPANTS Two RCTs of incentives that were embedded in 2 parent RCTs, 1 comparing smoking cessation interventions (conducted at smoking cessation clinics in 2 health systems) and 1 evaluating an ambulation intervention (conducted across wards of the Hospital of the University of Pennsylvania) included all persons eligible for the parent trials who did not have prior knowledge of the incentives trials. Recruitment occurred from September 2017 to August 2019 for the smoking trial and January 2018 through May 2019 for the ambulation trial; data were analyzed from January 2020 to July 2020. INTERVENTIONS Patients were randomly assigned to incentives of $0, $200, or $500 for participating in the smoking cessation trial and $0, $100, or $300 for the ambulation trial. MAIN OUTCOMES AND MEASURES The primary outcome of each incentive trial was the proportion of people assigned to each recruitment strategy that consented to participate. Each trial was powered to test the hypotheses that incentives served neither as undue inducements (based on the interaction between incentive size and perceived research risk, as measured using a 10-point scale, on the primary outcome), nor unjust inducements (based on the interaction between incentive size and participants' self-reported income). Noninferiority methods were used to test whether the data were compatible with these 2 effects of incentives and superiority methods to compare the primary and other secondary outcomes. RESULTS There were a total of 654 participants (327 women [50.0%]; mean [SD] age, 50.6 [12.1] years; 394 Black/African American [60.2%], 214 White [32.7%], and 24 multiracial individuals [3.7%]) in the smoking trial, and 642 participants (364 women [56.7%]; mean [SD] age, 46.7 [15.6] years; 224 Black/African American [34.9%], 335 White [52.2%], and 5 multiracial individuals [0.8%]) in the ambulation trial. Incentives significantly increased consent rates among those in the smoking trial in 47 of 216 (21.8%), 78 of 217 (35.9%), and 104 of 221 (47.1%) in the $0, $200, and $500 groups, respectively (adjusted odds ratio [aOR] for each increase in incentive, 1.70; 95% CI, 1.34-2.17; P < .001). Incentives did not increase consent among those in the ambulation trial: 98 of 216 (45.4%), 102 of 212 (48.1%), and 92 of 214 (43.0%) in the $0, $100, and $300 groups, respectively (aOR, 0.88; 95% CI, 0.64-1.22; P = .45). In neither trial was there evidence of undue or unjust inducement (upper confidence limits of ORs for undue inducement, 1.15 and 0.99; P < .001 showing noninferiority; upper confidence limits of ORs for unjust inducement, 1.21 and 1.26; P = .01 and P < .001, respectively). There were no significant effects of incentive size on the secondary outcomes in either trial, including time spent reviewing the risk sections of consent forms, perceived research risks, trial understanding, perceived coercion, or therapeutic misconceptions. CONCLUSIONS AND RELEVANCE In these 2 randomized clinical trials, financial incentives increased trial enrollment in 1 of 2 trials and did not produce undue or unjust inducement or other unintended consequences in either trial. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02697799.
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Affiliation(s)
- Scott D Halpern
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Marzana Chowdhury
- Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Brian Bayes
- Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Elizabeth Cooney
- Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Brian L Hitsman
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Robert A Schnoll
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Su Fen Lubitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Celine Reyes
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Mitesh S Patel
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Health Care Management, The Wharton School, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - S Ryan Greysen
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ashley Mercede
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Catherine Reale
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Frances K Barg
- Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Anthropology, University of Pennsylvania School of Arts and Sciences, Philadelphia
| | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Health Care Management, The Wharton School, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Jason Karlawish
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Penn Memory Center at the Penn Neuroscience Center, Philadelphia, Pennsylvania
| | - Alisa J Stephens-Shields
- Palliative and Advanced Illness Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Waddell KJ, Volpp KG, Chokshi NP, Small DS, Russell LB, Reale C, Patel MS. Association of COVID-19 Outbreak with Changes in Physical Activity Among Adults with Elevated Risk for Major Adverse Cardiovascular Events. J Gen Intern Med 2021; 36:3625-3628. [PMID: 33772441 PMCID: PMC7997555 DOI: 10.1007/s11606-021-06725-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/15/2021] [Indexed: 10/26/2022]
Affiliation(s)
- Kimberly J Waddell
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA. .,Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
| | - Kevin G Volpp
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA.,Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Wharton School, University of Pennsylvania, Philadelphia, PA, USA.,LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Neel P Chokshi
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Center for Digital Cardiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Dylan S Small
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA.,Wharton School, University of Pennsylvania, Philadelphia, PA, USA.,LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Louise B Russell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Catherine Reale
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA
| | - Mitesh S Patel
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA.,Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Wharton School, University of Pennsylvania, Philadelphia, PA, USA.,LDI Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
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Cordoza M, Basner M, Asch DA, Shea JA, Bellini LM, Carlin M, Ecker AJ, Malone SK, Desai SV, Katz JT, Bates DW, Small DS, Volpp KG, Mott CG, Coats S, Mollicone DJ, Dinges DF. Sleep and Alertness Among Interns in Intensive Care Compared to General Medicine Rotations: A Secondary Analysis of the iCOMPARE Trial. J Grad Med Educ 2021; 13:717-721. [PMID: 34721802 PMCID: PMC8527933 DOI: 10.4300/jgme-d-21-00045.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 07/08/2021] [Accepted: 07/22/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Medical interns are at risk for sleep deprivation from long and often rotating work schedules. However, the effects of specific rotations on sleep are less clear. OBJECTIVE To examine differences in sleep duration and alertness among internal medicine interns during inpatient intensive care unit (ICU) compared to general medicine (GM) rotations. METHODS This secondary analysis compared interns during a GM or ICU rotation from a randomized trial (2015-2016) of 12 internal medicine residency programs assigned to different work hour limit policies (standard 16-hour shifts or no shift-length limits). The primary outcome was sleep duration/24-hour using continuous wrist actigraphy over a 13-day period. Secondary outcomes assessed each morning during the concomitant actigraphy period were sleepiness (Karolinska Sleepiness Scale [KSS]), alertness (number of Brief Psychomotor Vigilance Test [PVT-B] lapses), and self-report of excessive sleepiness over past 24 hours. Linear mixed-effect models with random program intercept determined associations between each outcome by rotation, controlling for age, sex, and work hour policy followed. RESULTS Of 398 interns, 386 were included (n = 261 GM, n = 125 ICU). Average sleep duration was 7.00±0.08h and 6.84±0.10h, and number of PVT lapses were 5.5±0.5 and 5.7±0.7 for GM and ICU, respectively (all P > .05). KSS was 4.8±0.1 for both rotations. Compared to GM, ICU interns reported more days of excessive sleepiness from 12am-6am (2.6 vs 1.7, P < .001) and 6am-12pm (2.6 vs 1.9, P = .013) and had higher percent of days with sleep duration < 6 hours (27.6% vs 23.4%, P < .001). GM interns reported more days with no excessive sleepiness (5.3 vs 3.7, P < .001). CONCLUSIONS Despite ICU interns reporting more excessive sleepiness in morning hours and more days of insufficient sleep (<6 hours), overall sleep duration and alertness did not significantly differ between rotations.
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Affiliation(s)
- Makayla Cordoza
- Makayla Cordoza, PhD, RN, CCRN-K*, is a Lecturer, University of Pennsylvania
| | - Mathias Basner
- Mathias Basner, MD, PhD, MSc*, is a Professor, University of Pennsylvania
| | - David A. Asch
- David A. Asch, MD, MBA, is a Professor, University of Pennsylvania, and Practicing Physician, Corporal Michael J. Crescenz Veterans Affairs Medical Center
| | - Judy A. Shea
- Judy A. Shea, PhD, is a Professor, University of Pennsylvania
| | - Lisa M. Bellini
- Lisa M. Bellini, MD, is a Professor, University of Pennsylvania
| | - Michele Carlin
- Michele Carlin is a Project Manager, University of Pennsylvania
| | - Adrian J. Ecker
- Adrian J. Ecker is a Senior IT Project Leader, University of Pennsylvania
| | - Susan K. Malone
- Susan K. Malone, PhD, RN, is an Assistant Professor, New York University
| | - Sanjay V. Desai
- Sanjay V. Desai, MD, is a Professor, Johns Hopkins University
| | - Joel T. Katz
- Joel T. Katz, MD, is Vice Chair for Education, Brigham and Women's Hospital
| | - David W. Bates
- David W. Bates, MD, MSc, is Division of General Internal Medicine Chief, Brigham and Women's Hospital
| | - Dylan S. Small
- Dylan S. Small, PhD, is a Professor, University of Pennsylvania
| | - Kevin G. Volpp
- Kevin G. Volpp, MD, PhD, is a Professor, University of Pennsylvania, and Practicing Physician, Corporal Michael J. Crescenz Veterans Affairs Medical Center
| | | | - Sara Coats
- Sara Coats, BS, is Lead Project Coordinator, Pulsar Informatics
| | | | - David F. Dinges
- David F. Dinges, PhD, is a Professor, University of Pennsylvania; and iCOMPARE Research Group
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Reese PP, Barankay I, Putt M, Russell LB, Yan J, Zhu J, Huang Q, Loewenstein G, Andersen R, Testa H, Mussell AS, Pagnotti D, Wesby LE, Hoffer K, Volpp KG. Effect of Financial Incentives for Process, Outcomes, or Both on Cholesterol Level Change: A Randomized Clinical Trial. JAMA Netw Open 2021; 4:e2121908. [PMID: 34605920 PMCID: PMC8491106 DOI: 10.1001/jamanetworkopen.2021.21908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/15/2021] [Indexed: 11/14/2022] Open
Abstract
Importance Financial incentives may improve health behaviors. It is unknown whether incentives are more effective if they target a key process (eg, medication adherence), an outcome (eg, low-density lipoprotein cholesterol [LDL-C] levels), or both. Objective To determine whether financial incentives awarded daily for process (adherence to statins), awarded quarterly for outcomes (personalized LDL-C level targets), or awarded for process plus outcomes induce reductions in LDL-C levels compared with control. Design, Setting, and Participants A randomized clinical trial was conducted from February 12, 2015, to October 3, 2018; data analysis was performed from October 4, 2018, to May 27, 2021, at the University of Pennsylvania Health System, Philadelphia. Participants included 764 adults with an active statin prescription, elevated risk of atherosclerotic cardiovascular disease, suboptimal LDL-C level, and evidence of imperfect adherence to statin medication. Interventions Interventions lasted 12 months. All participants received a smart pill bottle to measure adherence and underwent LDL-C measurement every 3 months. In the process group, daily financial incentives were awarded for statin adherence. In the outcomes group, participants received incentives for achieving or sustaining at least a quarterly 10-mg/dL LDL-C level reduction. The process plus outcomes group participants were eligible for incentives split between statin adherence and quarterly LDL-C level targets. Main Outcomes and Measures Change in LDL-C level from baseline to 12 months, determined using intention-to-treat analysis. Results Of the 764 participants, 390 were women (51.2%); mean (SD) age was 62.4 (10.0) years, 310 (40.6%) had diabetes, 298 (39.0%) had hypertension, and mean (SD) baseline LDL-C level was 138.8 (37.6) mg/dL. Mean LDL-C level reductions from baseline to 12 months were -36.9 mg/dL (95% CI, -42.0 to -31.9 mg/dL) among control participants, -40.0 mg/dL (95% CI, -44.7 to -35.4 mg/dL) among process participants, -41.6 mg/dL (95% CI, -46.3 to -37.0 mg/dL) among outcomes participants, and -42.8 mg/dL (95% CI, -47.4 to -38.1 mg/dL) among process plus outcomes participants. In exploratory analysis among participants with diabetes and hypertension, no spillover effects of incentives were detected compared with the control group on hemoglobin A1c level and blood pressure over 12 months. Conclusions and Relevance In this randomized clinical trial, process-, outcomes-, or process plus outcomes-based financial incentives did not improve LDL-C levels vs control. Trial Registration ClinicalTrials.gov Identifier: NCT02246959.
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Affiliation(s)
- Peter P. Reese
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Iwan Barankay
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Management, Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia
| | - Mary Putt
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Louise B. Russell
- Leonard Davis Institute, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jiali Yan
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Jingsan Zhu
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Qian Huang
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - George Loewenstein
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Rolf Andersen
- The Heart Group, Lancaster General Health/Penn Medicine, Lancaster, Pennsylvania
- Research Institute, Lancaster General Health/Penn Medicine, Lancaster, Pennsylvania
| | - Heidi Testa
- The Heart Group, Lancaster General Health/Penn Medicine, Lancaster, Pennsylvania
- Research Institute, Lancaster General Health/Penn Medicine, Lancaster, Pennsylvania
| | - Adam S. Mussell
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - David Pagnotti
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Lisa E. Wesby
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Karen Hoffer
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Glanz K, Shaw PA, Kwong PL, Choi JR, Chung A, Zhu J, Huang QE, Hoffer K, Volpp KG. Effect of Financial Incentives and Environmental Strategies on Weight Loss in the Healthy Weigh Study: A Randomized Clinical Trial. JAMA Netw Open 2021; 4:e2124132. [PMID: 34491350 PMCID: PMC8424479 DOI: 10.1001/jamanetworkopen.2021.24132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
IMPORTANCE Modest weight loss can lead to meaningful risk reduction in adults with obesity. Although both behavioral economic incentives and environmental change strategies have shown promise for initial weight loss, to date they have not been combined, or compared, in a randomized clinical trial. OBJECTIVE To test the relative effectiveness of financial incentives and environmental strategies, alone and in combination, on initial weight loss and maintenance of weight loss in adults with obesity. DESIGN, SETTING, AND PARTICIPANTS This randomized clinical trial was conducted from 2015 to 2019 at 3 large employers in Philadelphia, Pennsylvania. A 2-by-2 factorial design was used to compare the effects of lottery-based financial incentives, environmental strategies, and their combination vs usual care on weight loss and maintenance. Interventions were delivered via website, text messages, and social media. Participants included adult employees with a body mass index (BMI; weight in kilograms divided by height in meters squared) of 30 to 55 and at least 1 other cardiovascular risk factor. Data analysis was performed from June to July 2021. INTERVENTIONS Interventions included lottery-based financial incentives based on meeting weight loss goals, environmental change strategies tailored for individuals and delivered by text messages and social media, and combined incentives and environmental strategies. MAIN OUTCOME AND MEASURES The primary outcome was weight change from baseline to 18 months, measured in person. RESULTS A total of 344 participants were enrolled, with 86 participants each randomized to the financial incentives group, environmental strategies group, combined financial incentives and environmental strategies group, and usual care (control) group. Participants had a mean (SD) age of 45.6 (10.5) years and a mean (SD) BMI of 36.5 (7.1); 247 participants (71.8%) were women, 172 (50.0%) were Black, and 138 (40.1%) were White. At the primary end point of 18 months, participants in the incentives group lost a mean of 5.4 lb (95% CI, -11.3 to 0.5 lb [mean, 2.45 kg; 95% CI, -5.09 to 0.23 kg]), those in the environmental strategies group lost a mean of a 2.2 lb (95% CI, -7.7 to 3.3 lb [mean, 1.00 kg; 95% CI, -3.47 to 1.49 kg]), and the combination group lost a mean of 2.4 lb (95% CI, -8.2 to 3.3 lb [mean, 1.09 kg; 95% CI, -3.69 to 1.49 kg]) more than participants in the usual care group. Financial incentives, environmental change strategies, and their combination were not significantly more effective than usual care. At 24 months, after 6 months without an intervention, the difference in the change from baseline was similar to the 18-month results, with no significant differences among groups. CONCLUSIONS AND RELEVANCE In this randomized clinical trial, across all study groups, participants lost a modest amount of weight but those who received financial incentives, environmental change, or the combined intervention did not lose significantly more weight than those in the usual care group. Employees with obesity may benefit from more intensive individualized weight loss strategies. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02878343.
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Affiliation(s)
- Karen Glanz
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- School of Nursing, University of Pennsylvania, Philadelphia
| | - Pamela A. Shaw
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Pui L. Kwong
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ji Rebekah Choi
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Annie Chung
- Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jingsan Zhu
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Qian Erin Huang
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Karen Hoffer
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Patel MS, Bachireddy C, Small DS, Harrison JD, Harrington TO, Oon AL, Rareshide CAL, Snider CK, Volpp KG. Effect of Goal-Setting Approaches Within a Gamification Intervention to Increase Physical Activity Among Economically Disadvantaged Adults at Elevated Risk for Major Adverse Cardiovascular Events: The ENGAGE Randomized Clinical Trial. JAMA Cardiol 2021; 6:1387-1396. [PMID: 34468691 DOI: 10.1001/jamacardio.2021.3176] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Importance Health promotion efforts commonly communicate goals for healthy behavior, but the best way to design goal setting among high-risk patients has not been well examined. Objective To test the effectiveness of different ways to set and implement goals within a behaviorally designed gamification intervention to increase physical activity. Design, Setting, and Participants Evaluation of the Novel Use of Gamification With Alternative Goal-setting Experiences was conducted from January 15, 2019, to June 1, 2020. The 24-week randomized clinical trial included a remotely monitored 8-week introductory intervention period, 8-week maintenance intervention period, and 8-week follow-up period. A total of 500 adults from lower-income neighborhoods in and around Philadelphia, Pennsylvania, who had either an atherosclerotic cardiovascular disease (ASCVD) condition or a 10-year ASCVD risk score greater than or equal to 7.5% were enrolled. Participants were paid for enrolling in and completing the trial. Interventions All participants used a wearable device to track daily steps, established a baseline level, and were then randomly assigned to an attention control or 1 of 4 gamification interventions that varied only on how daily step goals were set (self-chosen or assigned) and implemented (immediately or gradually). Main Outcome Measures The primary outcome was change in mean daily steps from baseline to the 8-week maintenance intervention period. Other outcomes included changes in minutes of moderate to vigorous physical activity. All randomly assigned participants were included in the intention-to-treat analysis. Results Of the 500 participants, 331 individuals (66.2%) were Black, 114 were White (22.8%), and 348 were women (69.6%). Mean (SD) age was 58.5 (10.8) years and body mass index was 33.2 (7.8). A total of 215 participants (43.0%) had an ASCVD condition. Compared with the control arm, participants with self-chosen and immediate goals had significant increases in the number of daily steps during the maintenance intervention period (1384; 95% CI, 805-1963; P < .001) that were sustained during the 8-week follow-up (1391; 95% CI, 785-1998; P < .001). This group also had significant increases in daily minutes of moderate to vigorous physical activity during the maintenance intervention (4.1; 95% CI, 1.8-6.4; P < .001) that were sustained during follow-up (3.5; 95% CI, 1.1-5.8; P = .004). No other gamification arms had consistent increases in physical activity compared with the control arm. No major adverse events were reported. Conclusions and Relevance In this trial among economically disadvantaged adults at elevated risk for major adverse cardiovascular events, a gamification intervention led to increases in physical activity that were sustained during 8 weeks of follow-up when goals were self-chosen and implemented immediately. Trial Registration ClinicalTrials.gov Identifier: NCT03749473.
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Affiliation(s)
- Mitesh S Patel
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,The Wharton School, University of Pennsylvania, Philadelphia.,Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia.,Penn Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia.,Crescenz Veterans Affairs Medical Center, Philadelphia.,Now with Ascension Health, St Louis, Missouri
| | - Chethan Bachireddy
- Virginia Department of Medical Assistance Services, Richmond.,Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond
| | - Dylan S Small
- The Wharton School, University of Pennsylvania, Philadelphia.,Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia.,Penn Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia.,Crescenz Veterans Affairs Medical Center, Philadelphia
| | | | | | - Ai Leen Oon
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
| | | | | | - Kevin G Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,The Wharton School, University of Pennsylvania, Philadelphia.,Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia.,Penn Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia.,Crescenz Veterans Affairs Medical Center, Philadelphia
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36
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Wang TT, Dixon EL, Bair EF, Ferrell W, Linn KA, Volpp KG, Underhill K, Venkataramani AS. Oral health and oral health care use among able-bodied adults enrolled in Medicaid in Kentucky after Medicaid expansion: A mixed methods study. J Am Dent Assoc 2021; 152:747-755. [PMID: 34454649 DOI: 10.1016/j.adaj.2021.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Oral health care use remains low among adult Medicaid recipients, despite the Patient Protection and Affordable Care Act's expansion increasing access to care in many states. It remains unclear the extent to which low use reflects either low demand for care or barriers to accessing care. The authors aimed to examine factors associated with low oral health care use among adults enrolled in Medicaid. METHODS The authors conducted a survey from May through September 2018 among able-bodied (n = 9,363) Medicaid recipients who were aged 19 through 65 years and nondisabled childless adults in Kentucky. The survey included questions on perceived oral health care use. Semistructured interviews were also conducted from May through November 2018 among a subset of participants (n = 127). RESULTS More than one-third (37.8%) of respondents reported fair or poor oral health, compared with 26.2% who reported fair or poor physical health. Although 47.6% of respondents indicated needing oral health care in the past 6 months, only one-half of this group reported receiving all of the care they needed. Self-reported barriers included lack of coverage for needed services and lack of access to care (for example, low provider availability and transportation difficulties). CONCLUSIONS Low rates of oral health care use can be attributed to a subset of the study population having low demand and another subset facing barriers to accessing care. Although Medicaid-covered services might be adequate for beneficiaries with good oral health, those with advanced dental diseases and a history of irregular care might benefit from coverage for more extensive restorative services. PRACTICAL IMPLICATIONS These results can inform dentists and policy makers about how to design effective interventions and policies to improve oral health care use and oral health outcomes.
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Mehta SJ, Reitz C, Niewood T, Volpp KG, Asch DA. Effect of Behavioral Economic Incentives for Colorectal Cancer Screening in a Randomized Trial. Clin Gastroenterol Hepatol 2021; 19:1635-1641.e1. [PMID: 32623005 PMCID: PMC7775888 DOI: 10.1016/j.cgh.2020.06.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 06/15/2020] [Accepted: 06/21/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Financial incentives might increase participation in prevention such as screening colonoscopy. We studied whether incentives informed by behavioral economics increase participation in risk assessment for colorectal cancer (CRC) and completion of colonoscopy for eligible adults. METHODS Employees of a large academic health system (50-64 y old; n = 1977) were randomly assigned to groups that underwent risk assessment for CRC screening and direct access colonoscopy scheduling (control), or risk assessment, direct access colonoscopy scheduling, a $10 loss-framed incentive to complete risk assessment, and a $25 unconditional incentive for colonoscopy completion (incentive). The primary outcome was the percentage of participants who completed screening colonoscopy within 3 months of initial outreach. Secondary outcomes included the percentage of participants who scheduled colonoscopy and the percentage who completed the risk assessment. RESULTS At 3 months, risk assessment was completed by 19.5% of participants in the control group (95% CI, 17.0-21.9%) and 31.9% of participants in the incentive group (95% CI, 29.0-34.8%) (P < .001). At 3 months, 0.7% of controls had completed a colonoscopy (95% CI, .2%-1.2%) compared with 1.2% of subjects in the incentive group (95% CI, .5%-1.9%) (P = .25). CONCLUSIONS In a randomized trial of participants who underwent risk assessment for CRC with vs without financial incentive, the financial incentive increased CRC risk assessment completion but did not result in a greater completion of screening colonoscopy. Clinicaltrials.gov no: NCT03068052.
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Affiliation(s)
- Shivan J. Mehta
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania,Center for Health Care Innovation, University of Pennsylvania,Center for Health Incentives and Behavioral Economics, University of Pennsylvania
| | - Catherine Reitz
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania,Center for Health Care Innovation, University of Pennsylvania,Center for Health Incentives and Behavioral Economics, University of Pennsylvania
| | - Tess Niewood
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania
| | - Kevin G. Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania,Center for Health Care Innovation, University of Pennsylvania,Center for Health Incentives and Behavioral Economics, University of Pennsylvania,Center for Health Equity Research and Promotion, Philadelphia VA Medical Center
| | - David A. Asch
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania,Center for Health Care Innovation, University of Pennsylvania,Center for Health Incentives and Behavioral Economics, University of Pennsylvania,Center for Health Equity Research and Promotion, Philadelphia VA Medical Center
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Huf SW, Asch DA, Volpp KG, Reitz C, Mehta SJ. Text Messaging and Opt-out Mailed Outreach in Colorectal Cancer Screening: a Randomized Clinical Trial. J Gen Intern Med 2021; 36:1958-1964. [PMID: 33511567 PMCID: PMC8298623 DOI: 10.1007/s11606-020-06415-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Routine screening reduces colorectal cancer mortality, but screening rates fall below national targets and are particularly low in underserved populations. OBJECTIVE To compare the effectiveness of a single text message outreach to serial text messaging and mailed fecal home test kits on colorectal cancer screening rates. DESIGN A two-armed randomized clinical trial. PARTICIPANTS An urban community health center in Philadelphia. Adults aged 50-74 who were due for colorectal cancer screening had at least one visit to the practice in the previously year, and had a cell phone number recorded. INTERVENTIONS Participants were randomized (1:1 ratio). Individuals in the control arm were sent a simple text message reminder as per usual practice. Those in the intervention arm were sent a pre-alert text message offering the options to opt-out of receiving a mailed fecal immunochemical test (FIT) kit, followed by up to three behaviorally informed text message reminders. MAIN MEASURES The primary outcome was participation in colorectal cancer screening at 12 weeks. The secondary outcome was the FIT kit return rate at 12 weeks. KEY RESULTS Four hundred forty participants were included. The mean age was 57.4 years (SD ± 6.1). 63.4% were women, 87.7% were Black, 19.1% were uninsured, and 49.6% were Medicaid beneficiaries. At 12 weeks, there was an absolute 17.3 percentage point increase in colorectal cancer screening in the intervention arm (19.6%), compared to the control arm (2.3%, p < 0.001). There was an absolute 17.7 percentage point increase in FIT kit return in the intervention arm (19.1%) compared to the control arm (1.4%, p < 0.001). CONCLUSIONS Serial text messaging with opt-out mailed FIT kit outreach can substantially improve colorectal cancer screening rates in an underserved population. TRIAL REGISTRATION clinicaltrials.gov ( https://clinicaltrials.gov/ct2/show/NCT03479645 ).
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Affiliation(s)
- Sarah W Huf
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA. .,The Commonwealth Fund, Harkness Fellowship, New York City, NY, USA. .,Department of Surgery and Cancer, Imperial College London, London, UK. .,Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
| | - David A Asch
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, USA
| | - Kevin G Volpp
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, USA
| | - Catherine Reitz
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shivan J Mehta
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Affiliation(s)
- Kevin G Volpp
- From the Perelman School of Medicine (K.G.V., C.C.C.), the Wharton School (K.G.V.), and the Center for Health Incentives and Behavioral Economics (K.G.V., C.C.C.), University of Pennsylvania; and the Corporal Michael J. Crescenz VA Medical Center (K.G.V.) - both in Philadelphia
| | - Carolyn C Cannuscio
- From the Perelman School of Medicine (K.G.V., C.C.C.), the Wharton School (K.G.V.), and the Center for Health Incentives and Behavioral Economics (K.G.V., C.C.C.), University of Pennsylvania; and the Corporal Michael J. Crescenz VA Medical Center (K.G.V.) - both in Philadelphia
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Mahraj K, Chaiyachati KH, Asch DA, Fala G, Do D, Lam D, Miller A, Mannion N, Stoloff V, Halbritter A, Huffenberger AM, Shuttleworth J, O’Donnell JA, Green-McKenzie J, Patel K, Rosin R, Kruse G, Brennan P, Volpp KG. Developing a Large-Scale Covid-19 Surveillance System to Reopen Campuses. NEJM Catalyst 2021. [PMCID: PMC8208605 DOI: 10.1056/cat.21.0049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
To open campuses safely, the University of Pennsylvania (Penn) and its health system (UPHS), with six hospitals and hundreds of outpatient practices, needed to develop an early warning system to identify the infected and exposed among Penn and UPHS campus members — 70,000 faculty, staff, and students who were at risk of transmitting severe acute respiratory syndrome coronavirus 2, or Covid-19. This warning system would help to minimize future spread by preventing individuals with concerning symptoms or recent exposures from coming into contact with others and, when necessary, streamline access to testing, self-isolation guidance, contact tracing, and medical care. The authors describe the challenges in designing, implementing, and continuously improving PennOpen Pass and the Red Pass Management System, a part-digital, part-human screening system. The lessons learned while developing and implementing PennOpen Pass provide key insights for the future of innovations in health care as we move toward improving the health of communities long after the pandemic.
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Affiliation(s)
- Katy Mahraj
- Director of Operations for the Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Krisda H. Chaiyachati
- Medical Director for PennOpen Pass, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - David A. Asch
- Executive Director for the Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Glenn Fala
- Associate Chief Information Officer, Software Development, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - David Do
- Assistant Professor of Clinical Neurology, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Doreen Lam
- Medical Student, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Amy Miller
- Information Technology Director, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nancy Mannion
- Interim Nurse Manager for PennLINKS at the Center for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Vanessa Stoloff
- Medical Director at University of Pennsylvania Student Health Service, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashlee Halbritter
- Director of Campus Health, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ann Marie Huffenberger
- Director of Operations for the Center for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Julie Shuttleworth
- University Operations Lead for PennOpen Pass, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Judith A. O’Donnell
- Professor of Clinical Medicine and Director of Infection Prevention and Control at Penn Presbyterian Medical Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Judith Green-McKenzie
- Professor of Medicine and Division of Occupational & Environmental Medicine, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Kash Patel
- Vice President and Chief Technology Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Roy Rosin
- Chief Innovation Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Greg Kruse
- Associate Vice President of Strategic Operations, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - P.J. Brennan
- Chief Medical Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Kevin G. Volpp
- Professor of Medicine and Director for the Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Khullar D, Colla CH, Volpp KG. Imagining a world without low-value services: progress, barriers, and the path forward. Am J Manag Care 2021; 27:137-139. [PMID: 33877771 DOI: 10.37765/ajmc.2021.88612] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Low-value services are a major problem in the US health care system. We believe that the coronavirus disease 2019 pandemic's unprecedented impact on the health system, and society writ large, offers an opportunity to reshape the conversation and incentives around low-value services. This article explores current barriers to and opportunities for accelerating progress toward high-value care delivery. We examine how financial and nonfinancial incentives, uncertainty in clinical decision-making, and insufficient partnering with patients and families contribute to the delivery of low-value care. We then explore potential solutions, including making it easier for clinicians to forgo low-value services and providing them with actionable information to make those decisions, expanding payer efforts to develop "value report cards," developing measures that map the adverse health and economic effects of low-value services, and training clinicians and health care leaders to engage in conversations with patients about the personal medical, financial, and psychological harms of low-value services.
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Affiliation(s)
- Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065.
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Patel MS, Small DS, Harrison JD, Hilbert V, Fortunato MP, Oon AL, Rareshide CAL, Volpp KG. Effect of Behaviorally Designed Gamification With Social Incentives on Lifestyle Modification Among Adults With Uncontrolled Diabetes: A Randomized Clinical Trial. JAMA Netw Open 2021; 4:e2110255. [PMID: 34028550 PMCID: PMC8144928 DOI: 10.1001/jamanetworkopen.2021.10255] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE Gamification is increasingly being used to promote healthy behaviors. However, it has not been well tested among patients with chronic conditions and over longer durations. OBJECTIVE To test the effectiveness of behaviorally designed gamification interventions to enhance support, collaboration, or competition to promote physical activity and weight loss among adults with uncontrolled type 2 diabetes. DESIGN, SETTING, AND PARTICIPANTS A 4-arm randomized clinical trial with a 1-year intervention was conducted from January 23, 2017, to January 27, 2020, with remotely monitored intervention. Analyses were conducted between February 10 and October 6, 2020. Participants included 361 adults with type 2 diabetes with hemoglobin A1c levels greater than or equal to 8% and body mass index greater than or equal to 25. INTERVENTIONS All participants received a wearable device, smart weight scale, and laboratory testing. Participants in the control group received feedback from their devices but no other interventions. Participants in the gamification arms conducted goal setting and were entered into a 1-year game designed using insights from behavioral economics with points and levels for achieving step goals and weight loss targets. The game varied by trial arm to promote either support, collaboration, or competition. MAIN OUTCOMES AND MEASURES Co-primary outcomes included daily step count, weight, and hemoglobin A1c level. Secondary outcome was low-density lipoprotein cholesterol level. Intention-to-treat analysis was used. RESULTS Participants had a mean (SD) age of 52.5 (10.1) years; hemoglobin A1c level, 9.6% (1.6%); daily steps, 4632 (2523); weight, 107.4 kg (20.8 kg); and body mass index, 37.1 (6.6). Of the 361 participants, 202 (56.0%) were women, 143 (39.6%) were White, and 185 (51.2%) were Black; with 87 (24.1%) randomized to control; 92 (25.4%) randomized to gamification with support and intervention; 95 (26.3%) randomized to gamification with collaboration; and 87 (24.1%) randomized to gamification with competition. Compared with the control group over 1 year, there was a significant increase in mean daily steps from baseline among participants receiving gamification with support (adjusted difference relative to control group, 503 steps; 95% CI, 103 to 903 steps; P = .01) and competition (606 steps; 95% CI, 201 to 1011 steps; P = .003) but not collaboration (280 steps; 95% CI, -115 to 674 steps; P = .16). All trial arms had significant reductions in weight and hemoglobin A1c levels from baseline, but there were no significant differences between any of the intervention arms and the control arm. There was only 1 adverse event reported that may have been related to the trial (arthritic knee pain). CONCLUSIONS AND RELEVANCE Among adults with uncontrolled type 2 diabetes, a behaviorally designed gamification intervention in this randomized clinical trial significantly increased physical activity over a 1-year period when designed to enhance either support or competition but not collaboration. No differences between intervention and control groups were found for other outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02961192.
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Affiliation(s)
- Mitesh S. Patel
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Wharton School, University of Pennsylvania, Philadelphia
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
- Department of Medicine, University of Pennsylvania, Philadelphia
| | - Dylan S. Small
- Wharton School, University of Pennsylvania, Philadelphia
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
| | | | - Victoria Hilbert
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
| | | | - Ai Leen Oon
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
| | | | - Kevin G. Volpp
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Wharton School, University of Pennsylvania, Philadelphia
- Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia
- Department of Medicine, University of Pennsylvania, Philadelphia
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Barankay I, Reese PP, Putt ME, Russell LB, Phillips C, Pagnotti D, Chadha S, Oyekanmi KO, Yan J, Zhu J, Volpp KG, Clapp JT. Qualitative Exploration of Barriers to Statin Adherence and Lipid Control: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2021; 4:e219211. [PMID: 33944923 PMCID: PMC8097500 DOI: 10.1001/jamanetworkopen.2021.9211] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 03/17/2021] [Indexed: 12/20/2022] Open
Abstract
Importance Financial incentives may improve health by rewarding patients for focusing on present actions-such as medication regimen adherence-that provide longer-term health benefits. Objective To identify barriers to improving statin therapy adherence and control of cholesterol levels with financial incentives and insights for the design of future interventions. Design, Setting, and Participants This qualitative study involved retrospective interviews with participants in a preplanned secondary analysis of a randomized clinical trial of financial incentives for statin therapy adherence. A total of 636 trial participants from several US insurer or employer populations and an academic health system were rank ordered by change in low-density lipoprotein cholesterol (LDLC) levels. Participants with the most LDLC level improvement (high-improvement group) and those with LDLC levels that did not improve (nonimprovement group) were purposively targeted, stratified across all trial groups, for semistructured telephone interviews that were performed from April 1 to June 30, 2018. Interviews were coded using a team-based, iterative approach. Data were analyzed from July 1, 2018, to October 31, 2020. Main Outcomes and Measures The primary outcome was mean change in LDLC level from baseline to 12 months; the secondary outcome, statin therapy adherence during the first 6 months. Results A total of 54 patients were interviewed, divided equally between high-improvement and nonimprovement groups, with a mean (SD) age of 43.5 (10.3) years; 36 (66.7%) were women, 28 (51.9%) had diabetes, and 18 (33.3%) had cardiovascular disease. Compared with the high-improvement group, the nonimprovement group had fewer interviewees with an annual income of greater than $50 000 (11 [40.7%] vs 22 [81.5%]), worse self-reported health (fair to poor, 13 [48.1%] vs 3 [11.1%]), more Black interviewees (16 [59.3%] vs 4 [14.8%]), and lower baseline LDLC levels (>160 mg/dL, 2 [7.4%] vs 25 [92.6%]). Participants in the nonimprovement group had a greater burden of chronic illness (≥2 chronic conditions, 13 [48.1%] vs 6 [22.2%]) and were less frequently employed (full-time, 6 [22.2%] vs 12 [44.4%]). In interviews, the nonimprovement group was less focused on risks of high LDLC levels, described less engagement in LDLC level management, articulated fewer specific nutritional choices for optimizing health, and recounted greater difficulty obtaining healthy food. Participants in both groups had difficulty describing the structure of the financial incentives but did recall features of the electronic pill containers used to track adherence and how those containers affected medication routines. Conclusions and Relevance Participants in a statin adherence trial whose LDLC levels did not improve found it more difficult to create medication routines and respond to financial incentives in the context of complex living conditions and a high burden of chronic illness. These findings suggest that future studies should be more attentive to socioeconomic circumstances of trial participants. Trial Registration ClinicalTrials.gov Identifier: NCT01798784.
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Affiliation(s)
- Iwan Barankay
- Department of Management, The Wharton School, University of Pennsylvania, Philadelphia
- Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Peter P. Reese
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Division of Renal Electrolyte and Hypertension, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
| | - Mary E. Putt
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Louise B. Russell
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
| | - Caitlin Phillips
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - David Pagnotti
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Sakshum Chadha
- currently a medical student at Rutgers New Jersey Medical School, Newark
| | - Kehinde O. Oyekanmi
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jiali Yan
- Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jingsan Zhu
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Equity Research and Promotion, Cresencz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
- Department of Health Care Management, The Wharton School, University of Pennsylvania, Philadelphia
| | - Justin T. Clapp
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Volpp KG, Kraut BH, Ghosh S, Neatherlin J. Minimal SARS-CoV-2 Transmission After Implementation of a Comprehensive Mitigation Strategy at a School - New Jersey, August 20-November 27, 2020. MMWR Morb Mortal Wkly Rep 2021; 70:377-381. [PMID: 33735161 PMCID: PMC7976619 DOI: 10.15585/mmwr.mm7011a2] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Parikh RB, Linn KA, Yan J, Maciejewski ML, Rosland AM, Volpp KG, Groeneveld PW, Navathe AS. A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data. PLoS One 2021; 16:e0247203. [PMID: 33606819 PMCID: PMC7894856 DOI: 10.1371/journal.pone.0247203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/02/2021] [Indexed: 11/30/2022] Open
Abstract
Background Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning clustering methods and linked comorbidity-based, sociodemographic, and psychobehavioral data to identify subgroups of high-risk Veterans and study long-term outcomes, hypothesizing that factors other than comorbidities would characterize several subgroups. Methods and findings In this cross-sectional study, we used data from the VA Corporate Data Warehouse, a national repository of VA administrative claims and electronic health data. To identify high-risk Veterans, we used the Care Assessment Needs (CAN) score, a routinely-used VA model that predicts a patient’s percentile risk of hospitalization or death at one year. Our study population consisted of 110,000 Veterans who were randomly sampled from 1,920,436 Veterans with a CAN score≥75th percentile in 2014. We categorized patient-level data into 119 independent variables based on demographics, comorbidities, pharmacy, vital signs, laboratories, and prior utilization. We used a previously validated density-based clustering algorithm to identify 30 subgroups of high-risk Veterans ranging in size from 50 to 2,446 patients. Mean CAN score ranged from 72.4 to 90.3 among subgroups. Two-year mortality ranged from 0.9% to 45.6% and was highest in the home-based care and metastatic cancer subgroups. Mean inpatient days ranged from 1.4 to 30.5 and were highest in the post-surgery and blood loss anemia subgroups. Mean emergency room visits ranged from 1.0 to 4.3 and were highest in the chronic sedative use and polysubstance use with amphetamine predominance subgroups. Five subgroups were distinguished by psychobehavioral factors and four subgroups were distinguished by sociodemographic factors. Conclusions High-risk Veterans are a heterogeneous population consisting of multiple distinct subgroups–many of which are not defined by clinical comorbidities–with distinct utilization and outcome patterns. To our knowledge, this represents the largest application of ML clustering methods to subgroup a high-risk population. Further study is needed to determine whether distinct subgroups may benefit from individualized interventions.
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Affiliation(s)
- Ravi B. Parikh
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America
- VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jiali Yan
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Matthew L. Maciejewski
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, United States of America
| | - Ann-Marie Rosland
- VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
| | - Kevin G. Volpp
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America
- VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Peter W. Groeneveld
- VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Amol S. Navathe
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America
- VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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Marcus SC, Reilly ME, Zentgraf K, Volpp KG, Olfson M. Effect of Escalating and Deescalating Financial Incentives vs Usual Care to Improve Antidepressant Adherence: A Pilot Randomized Clinical Trial. JAMA Psychiatry 2021; 78:222-224. [PMID: 32965464 PMCID: PMC7512125 DOI: 10.1001/jamapsychiatry.2020.3000] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 07/27/2020] [Indexed: 12/22/2022]
Affiliation(s)
- Steven C. Marcus
- University of Pennsylvania School of Social Policy and Practice, Philadelphia
| | - Megan E. Reilly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kelly Zentgraf
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mark Olfson
- Department of Psychiatry, Columbia University, New York, New York
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Affiliation(s)
- Kevin G Volpp
- Cresencz Philadelphia VA Medical Center, University of Pennsylvania Perelman School of Medicine and Wharton School, Philadelphia
| | - George Loewenstein
- Carnegie Mellon University, Department of Social and Decision Sciences, Pittsburgh, Pennsylvania
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Mehta SJ, Hume E, Troxel AB, Reitz C, Norton L, Lacko H, McDonald C, Freeman J, Marcus N, Volpp KG, Asch DA. Effect of Remote Monitoring on Discharge to Home, Return to Activity, and Rehospitalization After Hip and Knee Arthroplasty: A Randomized Clinical Trial. JAMA Netw Open 2020; 3:e2028328. [PMID: 33346847 PMCID: PMC7753899 DOI: 10.1001/jamanetworkopen.2020.28328] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/10/2020] [Indexed: 12/13/2022] Open
Abstract
Importance Hip and knee arthroplasty are the most common inpatient surgical procedures for Medicare beneficiaries in the US, with substantial variation in cost and quality. Whether remote monitoring incorporating insights from behavioral science might help improve outcomes and increase value of care remains unknown. Objective To evaluate the effect of activity monitoring and bidirectional text messaging on the rate of discharge to home and clinical outcomes in patients receiving hip or knee arthroplasty. Design, Setting, and Participants Randomized clinical trial conducted between February 7, 2018, and April 15, 2019. The setting was 2 urban hospitals at an academic health system. Participants were patients aged 18 to 85 years scheduled to undergo hip or knee arthroplasty with a Risk Assessment and Prediction Tool score of 6 to 8. Interventions Eligible patients were randomized evenly to receive usual care (n = 153) or remote monitoring (n = 147). Those in the intervention arm who agreed received a wearable activity monitor to track step count, messaging about postoperative goals and milestones, pain score tracking, and connection to clinicians as needed. Patients assigned to receive monitoring were further randomized evenly to remote monitoring alone or remote monitoring with gamification and social support. Remote monitoring was offered before surgery, began at hospital discharge, and continued for 45 days postdischarge. Main Outcomes and Measures The primary outcome was discharge status (home vs skilled nursing facility or inpatient rehabilitation). Prespecified secondary outcomes included change in average daily step count and rehospitalizations. Results A total of 242 patients were analyzed (124 usual care, 118 intervention); median age was 66 years (interquartile range, 58-73 years); 78.1% were women, 45.5% were White, 43.4% were Black; and 81.4% in the intervention arm agreed to receive monitoring. There was no significant difference in the rate of discharge to home between the usual care arm (57.3%; 95% CI, 48.5%-65.9%) and the intervention arm (56.8%; 95% CI, 47.9%-65.7%) and no significant increase in step count in those receiving remote monitoring plus gamification and social support compared with remote monitoring alone. There was a statistically significant reduction in rehospitalization rate in the intervention arm (3.4%; 95% CI, 0.1%-6.7%) compared with the usual care arm (12.2%; 95% CI, 6.4%-18.0%) (P = .01). Conclusions and Relevance In this study, the remote monitoring program did not increase rate of discharge to home after hip or knee arthroplasty, and gamification and social support did not increase activity levels. There was a significant reduction in rehospitalizations among those receiving the intervention, which may have resulted from goal setting and connection to the care team. Trial Registration ClinicalTrials.gov Identifier: NCT03435549.
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Affiliation(s)
- Shivan J. Mehta
- Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
| | - Eric Hume
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia
| | - Andrea B. Troxel
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Catherine Reitz
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
| | - Laurie Norton
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Hannah Lacko
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia
| | - Caitlin McDonald
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
| | - Jason Freeman
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Noora Marcus
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
| | - Kevin G. Volpp
- Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
- Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania
| | - David A. Asch
- Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia
- Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania
- Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania
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Russell LB, Norton LA, Pagnotti D, Sevinc C, Anderson S, Finnerty Bigelow D, Iannotte LG, Josephs M, McGilloway R, Barankay I, Putt ME, Reese PP, Asch DA, Goldberg LR, Mehta SJ, Tanna MS, Troxel AB, Volpp KG. Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists. Med Decis Making 2020; 41:9-20. [PMID: 33218296 DOI: 10.1177/0272989x20973160] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.
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Affiliation(s)
- Louise B Russell
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
| | - Laurie A Norton
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - David Pagnotti
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Christianne Sevinc
- Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA
| | - Sophia Anderson
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Darra Finnerty Bigelow
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren G Iannotte
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Josephs
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryan McGilloway
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Iwan Barankay
- Department of Management and Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Mary E Putt
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter P Reese
- The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Renal Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,The Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Lee R Goldberg
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shivan J Mehta
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Monique S Tanna
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Kevin G Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,The Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
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50
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Kim J, Troxel AB, Halpern SD, Volpp KG, Kahan BC, Morris TP, Harhay MO. Analysis of multicenter clinical trials with very low event rates. Trials 2020; 21:917. [PMID: 33168073 PMCID: PMC7654615 DOI: 10.1186/s13063-020-04801-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 10/10/2020] [Indexed: 11/15/2022] Open
Abstract
INTRODUCTION In a five-arm randomized clinical trial (RCT) with stratified randomization across 54 sites, we encountered low primary outcome event proportions, resulting in multiple sites with zero events either overall or in one or more study arms. In this paper, we systematically evaluated different statistical methods of accounting for center in settings with low outcome event proportions. METHODS We conducted a simulation study and a reanalysis of a completed RCT to compare five popular methods of estimating an odds ratio for multicenter trials with stratified randomization by center: (i) no center adjustment, (ii) random intercept model, (iii) Mantel-Haenszel model, (iv) generalized estimating equation (GEE) with an exchangeable correlation structure, and (v) GEE with small sample correction (GEE-small sample correction). We varied the number of total participants (200, 500, 1000, 5000), number of centers (5, 50, 100), control group outcome percentage (2%, 5%, 10%), true odds ratio (1, > 1), intra-class correlation coefficient (ICC) (0.025, 0.075), and distribution of participants across the centers (balanced, skewed). RESULTS Mantel-Haenszel methods generally performed poorly in terms of power and bias and led to the exclusion of participants from the analysis because some centers had no events. Failure to account for center in the analysis generally led to lower power and type I error rates than other methods, particularly with ICC = 0.075. GEE had an inflated type I error rate except in some settings with a large number of centers. GEE-small sample correction maintained the type I error rate at the nominal level but suffered from reduced power and convergence issues in some settings when the number of centers was small. Random intercept models generally performed well in most scenarios, except with a low event rate (i.e., 2% scenario) and small total sample size (n ≤ 500), when all methods had issues. DISCUSSION Random intercept models generally performed best across most scenarios. GEE-small sample correction performed well when the number of centers was large. We do not recommend the use of Mantel-Haenszel, GEE, or models that do not account for center. When the expected event rate is low, we suggest that the statistical analysis plan specify an alternative method in the case of non-convergence of the primary method.
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Affiliation(s)
- Jiyu Kim
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Scott D Halpern
- Palliative and Advanced Illness Research (PAIR) Center, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 304 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104-6021, USA
- Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin G Volpp
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Philadelphia VA Medical Center, Philadelphia, PA, USA
- Department of Health Care Management, Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Tim P Morris
- MRC Clinical Trials Unit at UCL, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Michael O Harhay
- Palliative and Advanced Illness Research (PAIR) Center, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 304 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104-6021, USA.
- Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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