Effect of Gamification, Financial Incentives, or Both to Increase Physical Activity Among Patients at High Risk of Cardiovascular Events: The BE ACTIVE Randomized Controlled Trial.

BACKGROUND: Physical activity is associated with a lower risk of major adverse cardiovascular events, but few individuals achieve guideline-recommended levels of physical activity. Strategies informed by behavioral economics increase physical activity, but their longer-term effectiveness is uncertain. We sought to determine the effect of behaviorally designed gamification, loss-framed financial incentives, or their combination on physical activity compared with attention control over 12-month intervention and 6-month postintervention follow-up periods. METHODS: Between May 2019 and January 2024, participants with clinical atherosclerotic cardiovascular disease or a 10-year risk of myocardial infarction, stroke, or cardiovascular death of ≥7.5% by the Pooled Cohort equation were enrolled in a pragmatic randomized clinical trial. Participants received a wearable device to track daily steps, established a baseline, selected a step goal increase, and were randomly assigned to control (n=151), behaviorally designed gamification (n=304), loss-framed financial incentives (n=302), or gamification+financial incentives (n=305). The primary outcome of the trial was the change in mean daily steps from baseline through the 12-month intervention period. RESULTS: A total of 1062 patients (mean±SD age, 67±8; 61% female; 31% non-White) were enrolled. Compared with control subjects, participants had significantly greater increases in mean daily steps from baseline during the 12-month intervention in the gamification arm (adjusted difference, 538.0 [95% CI, 186.2-889.9]; P=0.0027), financial incentives arm (adjusted difference, 491.8 [95% CI, 139.6-844.1]; P=0.0062), and gamification+financial incentives arm (adjusted difference, 868.0 [95% CI, 516.3-1219.7]; P<0.0001). During the 6-month follow-up, physical activity remained significantly greater in the gamification+financial incentives arm than in the control arm (adjusted difference, 576.2 [95% CI, 198.5-954]; P=0.0028), but it was not significantly greater in the gamification (adjusted difference, 459.8 [95% CI, 82.0-837.6]; P=0.0171) or financial incentives (adjusted difference, 327.9 [95% CI, -50.2 to 706]; P=0.09) arms after adjustment for multiple comparisons. CONCLUSIONS: Behaviorally designed gamification, loss-framed financial incentives, and the combination of both increased physical activity compared with control over a 12-month intervention period, with the largest effect in gamification+financial incentives. These interventions could be a useful component of strategies to reduce cardiovascular risk in high-risk patients. REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT03911141.

Gamification-augmented home-based exercise for peripheral artery disease: Rationale and design of the GAMEPAD Study.

BACKGROUND: Supervised exercise therapy improves walking performance, functional capacity, and quality of life in patients with peripheral artery disease (PAD). However, few patients with PAD are enrolled in supervised exercise programs, and there are a number of logistical and financial barriers to their participation. A home-based walking intervention is likely to be more accessible to patients with PAD, but no fully home-based walking program has demonstrated efficacy. Concepts from behavioral economics have been used to design scalable interventions that increase daily physical activity in patients with atherosclerotic vascular disease, but whether a similar program would be effective in patients with PAD is uncertain. STUDY DESIGN AND OBJECTIVES: GAMEPAD (NCT04536012) is a pragmatic, virtual, randomized controlled trial designed to evaluate the effectiveness of a gamification strategy informed by concepts from behavioral economics to increase daily physical activity in patients with PAD who are seen in cardiology and vascular surgery clinics affiliated with the University of Pennsylvania Health System. Patients are contacted by email or text message, and complete enrollment and informed consent on the Penn Way to Health online platform. A GAMEPAD substudy will evaluate the effectiveness of opt-in versus opt-out framing when approaching patients for study participation. Patients are then provided with a wearable fitness tracker, establish a baseline daily step count, set a goal to increase daily step count by 33%-50%, and are randomized 1:1 to the gamification or control arms. Interventions continue for 16 weeks, including a 4-week period during which goal step count is gradually increased in the gamification arm, with follow-up for an additional 8 weeks to evaluate the durability of behavior change. The trial has met its enrollment goal of 102 participants, with a primary endpoint of change from baseline in daily steps over the 16-week intervention period. Key secondary endpoints include change from baseline in daily steps over the 8-week postintervention follow-up period and changes in patient-reported measures of PAD symptoms and quality of life over the intervention and follow-up periods. CONCLUSIONS: GAMEPAD is a virtual, pragmatic randomized clinical trial of a novel, fully home-based walking intervention informed by concepts from behavioral economics to increase physical activity and PAD-specific quality of life in patients with PAD. Its results will have important implications for the application of behavioral economic concepts to scalable home-based strategies to promote physical activity in patients with PAD and other disease processes where physical activity is limited by exertional symptoms. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov; NCT04536012.

Differential Hospital Participation in Bundled Payments in Communities with Higher Shares of Marginalized Populations.

BACKGROUND: Medicare's voluntary bundled payment programs have demonstrated generally favorable results. However, it remains unknown whether uneven hospital participation in these programs in communities with greater shares of minorities and patients of low socioeconomic status results in disparate access to practice redesign innovations. OBJECTIVE: Examine whether communities with higher proportions of marginalized individuals were less likely to be served by a hospital participating in Bundled Payments for Care Improvement Advanced (BPCI-Advanced). DESIGN: Cross-sectional study using ordinary least squares regression controlling for patient and community factors. PARTICIPANTS: Medicare fee-for-service patients enrolled from 2015-2017 (pre-BPCI-Advanced) and residing in 2,058 local communities nationwide defined by Hospital Service Areas (HSAs). Each community's share of marginalized patients was calculated separately for each of the share of beneficiaries of Black race, Hispanic ethnicity, or dual eligibility for Medicare and Medicaid. MAIN MEASURES: Dichotomous variable indicating whether a given community had at least one hospital that ever participated in BPCI-Advanced from 2018-2022. KEY RESULTS: Communities with higher shares of dual-eligible individuals were less likely to be served by a hospital participating in BPCI-Advanced than communities with the lowest quartile of dual-eligible individuals (Q4: -15.1 percentage points [pp] lower than Q1, 95% CI: -21.0 to -9.1, p < 0.001). There was no consistent significant relationship between community proportion of Black beneficiaries and likelihood of having a hospital participating in BPCI-Advanced. Communities with higher shares of Hispanic beneficiaries were more likely to have a hospital participating in BPCI-Advanced than those in the lowest quartile (Q4: 19.2 pp higher than Q1, 95% CI: 13.4 to 24.9, p < 0.001). CONCLUSIONS: Communities with greater shares of dual-eligible beneficiaries, but not racial or ethnic minorities, were less likely to be served by a hospital participating in BPCI-Advanced Policymakers should consider approaches to incentivize more socioeconomically uniform participation in voluntary bundled payments.

Operationalizing Equity, Inclusion, and Access in Research Practice at a Large Academic Institution.

INTRODUCTION: Healthcare advances are hindered by underrepresentation in prospective research; sociodemographic, data, and measurement infidelity in retrospective research; and a paucity of guidelines surrounding equitable research practices. OBJECTIVE: The Joint Research Practices Working Group was created in 2021 to develop and disseminate guidelines for the conduct of inclusive and equitable research. METHODS: Volunteer faculty and staff from two research centers at the University of Pennsylvania initiated a multi-pronged approach to guideline development, including literature searches, center-level feedback, and mutual learning with local experts. RESULTS: We developed guidelines for (1) participant payment and incentives; (2) language interpretation and translation; (3) plain language in research communications; (4) readability of study materials; and (5) inclusive language for scientific communications. Key recommendations include (1) offer cash payments and multiple payment options to participants when required actions are completed; (2) identify top languages of your target population, map points of contact, and determine available interpretation and translation resources; (3) assess reading levels of materials and simplify language, targeting 6th- to 8th-grade reading levels; (4) improve readability through text formatting and style, symbols, and visuals; and (5) use specific, humanizing terms as adjectives rather than nouns. CONCLUSIONS: Diversity, inclusion, and access are critical values for research conduct that promotes justice and equity. These values can be operationalized through organizational commitment that combines bottom-up and top-down approaches and through partnerships across organizations that promote mutual learning and synergy. While our guidelines represent best practices at one time, we recognize that practices evolve and need to be evaluated continuously for accuracy and relevance. Our intention is to bring awareness to these critical topics and form a foundation for important conversations surrounding equitable and inclusive research practices.

A randomized controlled trial of gamification, financial incentives, or both to increase physical activity among patients with elevated risk for cardiovascular disease: rationale and design of the be active study.

BACKGROUND: Higher levels of physical activity are associated with improvements in cardiovascular health, and consensus guidelines recommend that individuals with or at risk for atherosclerotic cardiovascular disease (ASCVD) participate in regular physical activity. However, most adults do not achieve recommended levels of physical activity. Concepts from behavioral economics have been used to design scalable interventions that increase physical activity over short time periods, but the longer-term efficacy of these strategies is uncertain. STUDY DESIGN AND OBJECTIVES: BE ACTIVE (NCT03911141) is a pragmatic, virtual, randomized controlled trial designed to evaluate the effectiveness of 3 strategies informed by behavioral economic concepts to increase daily physical activity in patients with established ASCVD or 10-year ASCVD risk > 7.5% who are seen in primary care and cardiology clinics affiliated with the University of Pennsylvania Health System. Patients are contacted by email or text message, and complete enrollment and informed consent on the Penn Way to Health online platform. Patients are then provided with a wearable fitness tracker, establish a baseline daily step count, set a goal to increase daily step count by 33% to 50%, and are randomized 1:2:2:2 to control, gamification, financial incentives, or both gamification and financial incentives. Interventions continue for 12 months, with follow-up for an additional 6 months to evaluate the durability of behavior change. The trial has met its enrollment goal of 1050 participants, with a primary endpoint of change from baseline in daily steps over the 12-month intervention period. Key secondary endpoints include change from baseline in daily steps over the 6-month post-intervention follow-up period and change in moderate to vigorous physical activity over the intervention and follow-up periods. If the interventions prove effective, their effects on life expectancy will be compared with their costs in cost-effectiveness analysis. CONCLUSIONS: BE ACTIVE is a virtual, pragmatic randomized clinical trial powered to demonstrate whether gamification, financial incentives, or both are superior to attention control in increasing physical activity. Its results will have important implications for strategies to promote physical activity in patients with or at risk for ASCVD, as well as for the design and implementation of pragmatic virtual clinical trials within health systems.

Qualitative analysis of a remote monitoring intervention for managing heart failure.

BACKGROUND: Heart failure (HF) is one of the most common reasons for hospital admission and is a major cause of morbidity, mortality, and increasing health care costs. The EMPOWER study was a randomized trial that used remote monitoring technology to track patients' weight and diuretic adherence and a state-of-the-art approach derived from behavioral economics to motivate adherence to the reverse monitoring technology. OBJECTIVE: The goal was to explore patient and clinician perceptions of the program and its impact on perceived health outcomes and better understand why some patients or clinicians did better or worse than others in response to the intervention. APPROACH: This was a retrospective qualitative study utilizing semi-structured interviews with 43 patients and 16 clinicians to understand the trial's processes, reflecting on successes and areas for improvement for future iterations of behavioral economic interventions. KEY RESULTS: Many patients felt supported, and they appreciated the intervention. Many also appreciated the lottery intervention, and while it was not an incentive for enrolling for many respondents, it may have increased adherence during the study. Clinicians felt that the intervention integrated well into their workflow, but the number of alerts was burdensome. Additionally, responses to alerts varied considerably by provider, perhaps because there are no professional guidelines for alerts unaccompanied by severe symptoms. CONCLUSION: Our qualitative analysis indicates potential areas for additional exploration and consideration to design better behavioral economic interventions to improve cardiovascular health outcomes for patients with HF. Patients appreciated lottery incentives for adhering to program requirements; however, many were too far along in their disease progression to benefit from the intervention. Clinicians found the amount and frequency of electronic alerts burdensome and felt they did not improve patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02708654.

Spillover Effects of Medicare's Voluntary Bundled Payments for Joint Replacement Surgery to Patients Insured by Commercial Health Plans.

BACKGROUND: Under the Bundled Payments for Care Improvement (BPCI) program, bundled paymtents for lower-extremity joint replacement (LEJR) are associated with 2% to 4% cost savings with stable quality among Medicare fee-for-service beneficiaries. However, BPCI may prompt practice changes that benefit all patients, not just fee-for-service beneficiaries. OBJECTIVE: To examine the association between hospital participation in BPCI and LEJR outcomes for patients with commercial insurance or Medicare Advantage (MA). DESIGN: Quasi-experimental study using Health Care Cost Institute claims from 2011 to 2016. SETTING: LEJR at 281 BPCI hospitals and 562 non-BPCI hospitals. PATIENTS: 184 922 patients with MA or commercial insurance. MEASUREMENTS: Differential changes in LEJR outcomes at BPCI hospitals versus at non-BPCI hospitals matched on propensity score were evaluated using a difference-in-differences (DID) method. Secondary analyses evaluated associations by patient MA status and hospital characteristics. Primary outcomes were changes in 90-day total spending on LEJR episodes and 90-day readmissions; secondary outcomes were postacute spending and discharge to postacute care providers. RESULTS: Average episode spending decreased more at BPCI versus non-BPCI hospitals (change, -2.2% [95% CI, -3.6% to -0.71%]; P = 0.004), but differences in changes in 90-day readmissions were not significant (adjusted DID, -0.47 percentage point [CI, -1.0 to 0.06 percentage point]; P = 0.084). Participation in BPCI was also associated with differences in decreases in postacute spending and discharge to institutional postacute care providers. Decreases in episode spending were larger for hospitals with high baseline spending but did not vary by MA status. LIMITATION: Nonrandomized studies are subject to residual confounding and selection. CONCLUSION: Participation in BPCI was associated with modest spillovers in episode savings. Bundled payments may prompt hospitals to implement broad care redesign that produces benefits regardless of insurance coverage. PRIMARY FUNDING SOURCE: Leonard Davis Institute of Health Economics at the University of Pennsylvania.

Comparison of Pharmacy Claims and Electronic Pill Bottles for Measurement of Medication Adherence Among Myocardial Infarction Patients.

BACKGROUND: Medication adherence after myocardial infarction remains low. Pharmacy claims have typically been used to measure medication adherence, but electronic pill bottles may offer additional information. OBJECTIVE: The main objectives of this study were to compare the association of adherence measured by prescription claims and remote monitoring technologies with cardiovascular events. RESEARCH DESIGN: This study was a secondary analysis of a remote monitoring intervention to increase medication adherence in myocardial infarction patients. SUBJECTS: In total, 682 myocardial infarction patients were randomized to the intervention group with both medical and pharmacy benefits. MEASURES: Pharmacy claims adherence was measured using proportion of days covered (PDC) and GlowCap adherence (GC) was measured as the proportion of days the pill bottle was opened. We compared the association of PDC and GC adherence for statins with time to first vascular readmission or death and assessed model fit using Akaike information criterion and Bayesian information criterion and the likelihood ratio test. RESULTS: Higher PDC was significantly associated with a lower hazard rate for vascular readmissions or death (hazard ratio=0.435; P=0.009). There was also an association between GC adherence and vascular readmissions or death (hazard ratio=0.313; P≤0.001). Adding the GC adherence variable to the model using only PDC improved the model fit (likelihood ratio test, P=0.001), as well as vice versa (P=0.050). CONCLUSIONS: Pharmacy claims data provide useful but not complete data for medication adherence monitoring. New wireless technologies have the potential to provide additional data about clinical outcomes.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

BACKGROUND: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. OBJECTIVE: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients. DESIGN: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups. PARTICIPANTS: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries. MAIN MEASURES: Spending, utilization, and mortality. KEY RESULTS: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+). CONCLUSIONS: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.

Modeling lottery incentives for daily adherence.

Many health issues require adherence to recommended daily activities, such as taking medication to manage a chronic condition, walking a certain distance to promote weight loss, or measuring weights to assess fluid balance in heart failure. The cost of nonadherence can be high, with respect to both individual health outcomes and the healthcare system. Incentivizing adherence to daily activities can promote better health in patients and populations and potentially provide long-term cost savings. Multiple incentive structures are possible. We focus here on a daily lottery incentive in which payment occurs when both the participant's lottery number matches the number drawn and the participant adheres to the targeted daily behavior. Our objective is to model the lottery's effect on participants' probability to complete the targeted task, particularly over the short term. We combine two procedures for analyzing such binary time series: a parameter-driven regression model with an autocorrelated latent process and a comparative interrupted time series. We use the output of the regression model as the control generator for the comparative time series in order to create a quasi-experimental design.

Use of financial incentives and text message feedback to increase healthy food purchases in a grocery store cash back program: a randomized controlled trial.

BACKGROUND: The HealthyFood (HF) program offers members up to 25% cash back monthly on healthy food purchases. In this randomized controlled trial, we tested the efficacy of financial incentives combined with text messages in increasing healthy food purchases among HF members. METHODS: Members receiving the lowest (10%) cash back level were randomized to one of six arms: Arm 1 (Usual Care): 10% cash back, no weekly text, standard monthly text; Arm 2: 10% cash back, generic weekly text, standard monthly text; Arm 3: 10% cash back, personalized weekly text, standard monthly text; Arm 4: 25% cash back, personalized weekly text, standard monthly text; Arm 5: 10 + 15%NET cash back, personalized weekly text, standard monthly text; and, Arm 6: 10 + 15%NET cash back, personalized weekly text, unbundled monthly text. In the 10 + 15%NET cash back, the cash back amount was the baseline 10% plus 15% of the net difference between healthy and unhealthy spending. The generic text included information on HF and healthy eating, while the personalized text had individualized feedback on purchases. The standard monthly text contained the cash back amount. The unbundled monthly text included the amount lost due to unhealthy purchases. The primary outcome was the average monthly percent healthy food spending. Secondary outcomes were the percent unhealthy food spending, and the percent healthy and unhealthy food items. RESULTS: Of the members contacted, 20 opted out, and 2841 met all inclusion criteria. There were no between-arm differences in the examined outcomes. The largest mean (standard deviation) difference in percent healthy spending was between Arm 1 (24.8% [11%]) and Arm 2 (26.8% [13%]), and the largest mean difference in percent unhealthy spending was also between Arm 1 (24.4% [20%]) and Arm 2 (21.7% [17%]), but no differences were statistically significant after correction for multiple comparisons. CONCLUSIONS: None of the tested financial incentive structures or text strategies differentially affected food purchasing. Notably, more than doubling the cash back amount and introducing a financial disincentive for unhealthy purchases did not affect purchasing. These findings speak to the difficulty of changing shopping habits and to the need for innovative strategies to shift complex health behaviors. TRIAL REGISTRATION: NCT02486588 Increasing Engagement with a Healthy Food Benefit. The trial was prospectively registered on July 1, 2015.

Association Between the Implementation of a Population-Based Primary Care Payment System and Achievement on Quality Measures in Hawaii.

Importance: Hawaii Medical Service Association (HMSA), the Blue Cross Blue Shield of Hawaii, introduced Population-based Payments for Primary Care (3PC), a new capitation-based primary care payment system, in 2016. The effect of this system on quality measures has not been evaluated. Objective: To evaluate whether the 3PC system was associated with changes in quality, utilization, or spending in its first year. Design, Setting, and Participants: Observational study using HMSA claims and clinical registry data from January 1, 2012, to December 31, 2016, and a propensity-weighted difference-in-differences method to compare 77 225 HMSA members in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in the first wave of the 3PC and 222 233 members attributed to 312 PCPs and 14 physician organizations that continued in a fee-for-service model in 2016 but had 3PC start dates thereafter. Exposures: Participation in the 3PC system. Main Outcomes and Measures: The primary outcome was the change in a composite measure score reflecting the probability that a member achieved an eligible measure out of 13 pooled Healthcare Effectiveness Data and Information Set quality measures. Primary care visits and total cost of care were among 15 secondary outcomes. Results: In total, the study included 299 458 HMSA members (mean age, 42.1 years; 51.5% women) and 419 primary care physicians (mean age, 54.9 years; 34.8% women). The risk-standardized composite measure scores for 2012 to 2016 changed from 75.1% to 86.6% (+11.5 percentage points) in the 3PC group and 74.3% to 83.5% (+9.2 percentage points) in the non-3PC group (differential change, 2.3 percentage points [95% CI, 2.1 to 2.6 percentage points]; P < .001). Of 15 prespecified secondary end points for utilization and spending, 11 showed no significant difference. Compared with the non-3PC group, the 3PC system was associated with a significant reduction in the mean number of primary care visits (3.3 to 3.0 visits vs 3.3 to 3.1 visits; adjusted differential change, -3.9 percentage points [95% CI, -4.6 to -3.2 percentage points]; P < .001), but there was no significant difference in mean total cost of care ($3344 to $4087 vs $2977 to $3564; adjusted differential change, 1.0% [95% CI, -1.3% to 3.4%]; P = .39). Conclusions and Relevance: In its first year, the 3PC population-based primary care payment system in Hawaii was associated with small improvements in quality and a reduction in PCP visits but no significant difference in the total cost of care. Additional research is needed to assess longer-term outcomes as the program is more fully implemented and to determine whether results are generalizable to other health care markets.

Partners and Alerts in Medication Adherence: A Randomized Clinical Trial.

BACKGROUND: Poor medication adherence is common and limits the effectiveness of treatment. OBJECTIVE: To investigate how social supports, automated alerts, and their combination improve medication adherence. DESIGN: Four-arm, randomized clinical trial with a 6-month intervention. PARTICIPANTS: A total of 179 CVS health employees or adult dependents with CVS Caremark prescription coverage, a current daily statin prescription, a medication possession ratio less than 80%, and Internet access. INTERVENTIONS: Participants were randomly assigned to control, social support (partner), automated adherence alert messages (alert), or both social support and alerts (partner + alert). Participants in the social support arms were asked to name a medication adherence partner (MAP) to help them take their medication. Participants in the alert arms were sent emails, text messages, or automated phone calls if they had failed to adhere on the previous day and on one or both of the 2 days before that. In partner + alert, both participants and fully enrolled MAPs received alerts. MAIN MEASURES: Adherence measured by wireless pill bottle opening. KEY RESULTS: Compared to 36.0% adherence in control, adherence was significantly greater in the alert arm (52.9%, difference vs. control of 17.0%, 95% CI for difference 6.3 to 27.6%, P = 0.002) and the partner + alert arm (54.5%, difference vs. control of 18.6%, 95% CI for difference 6.6 to 30.5%, P = 0.003). Adherence in the partner arm was not statistically significantly greater than control (43.2%, difference vs. control of 7.2%, 95% CI of difference - 5.2% to 19.5%, P = 0.25). There were no statistically significant differences among the three treatment arms. Fewer participants invited a MAP in the partner + alert arm than the partner arm (P = 0.02). CONCLUSIONS: Automated alerts were effective at improving medication adherence. Assigning a medication adherence partner did not statistically significantly affect adherence rates. TRIAL REGISTRATION: ClinicalTrials.gov Number NCT01890018 [ https://clinicaltrials.gov /].

Social Incentives and Gamification to Promote Weight Loss: The LOSE IT Randomized, Controlled Trial.

BACKGROUND: Social networks influence obesity patterns, but interventions to leverage social incentives to promote weight loss have not been well evaluated. OBJECTIVE: To test the effectiveness of gamification interventions designed using insights from behavioral economics to enhance social incentives to promote weight loss. DESIGN: The Leveraging Our Social Experiences and Incentives Trial (LOSE IT) was a 36-week randomized, controlled trial with a 24-week intervention and 12-week follow-up. PARTICIPANTS: One hundred and ninety-six obese adults (body mass index ≥ 30) comprising 98 two-person teams. INTERVENTIONS: All participants received a wireless weight scale, used smartphones to track daily step counts, formed two-person teams with a family member or friend, and selected a weight loss goal. Teams were randomly assigned to control or one of two gamification interventions for 36 weeks that used points and levels to enhance collaborative social incentives. One of the gamification arms also had weight and step data shared regularly with each participant's primary care physician (PCP). MAIN OUTCOME MEASURES: The primary outcome was weight loss at 24 weeks. Secondary outcomes included weight loss at 36 weeks. KEY RESULTS: At 24 weeks, participants lost significant weight from baseline in the control arm (mean: - 3.9 lbs; 95% CI: - 6.1 to - 1.7; P < 0.001), the gamification arm (mean: - 6.6 lbs; 95% CI: - 9.4 to - 3.9; P < 0.001), and the gamification arm with PCP data sharing (mean: - 4.8 lbs; 95% CI: - 7.4 to - 2.3; P < 0.001). At 36 weeks, weight loss from baseline remained significant in the control arm (mean: - 3.5 lbs; 95% CI: - 6.1 to - 0.8; P = 0.01), the gamification arm (mean: - 6.3 lbs; 95% CI: - 9.2 to - 3.3; P < 0.001), and the gamification arm with PCP data sharing (mean: - 5.2 lbs; 95% CI: - 8.5 to - 2.0; P < 0.01). However, in the main adjusted model, there were no significant differences in weight loss between each of the intervention arms and control at either 12, 24, or 36 weeks. CONCLUSIONS: Using digital health devices to track behavior with a partner led to significant weight loss through 36 weeks, but the gamification interventions were not effective at promoting weight loss when compared to control. TRIAL REGISTRATION: clinicaltrials.gov Identifier: 02564445.

Automated Reminders and Physician Notification to Promote Immunosuppression Adherence Among Kidney Transplant Recipients: A Randomized Trial.

BACKGROUND: Immunosuppression nonadherence increases the risk for kidney transplant loss after transplantation. Wireless-enabled pill bottles have created the opportunity to monitor medication adherence in real time. Reminders may help patients with poor memory or organization. Provision of adherence data to providers may motivate patients to improve adherence and help providers identify adherence barriers. STUDY DESIGN: Randomized controlled trial. SETTING & PARTICIPANTS: Kidney transplant recipients (n=120) at a single center. INTERVENTION: Participants were provided wireless pill bottles to store tacrolimus and record bottle openings. Participants were randomly assigned 1:1:1 to adherence monitoring with customized reminders (including alarms, texts, telephone calls, and/or e-mails), monitoring with customized reminders plus provider notification (every 2 weeks, providers received notification if adherence decreased to <90% during that period), or wireless pill bottle use alone (control). OUTCOMES: The main outcome was bottle-measured tacrolimus adherence during the last 90 days of the 180-day trial. A secondary outcome was tacrolimus whole-blood concentrations at routine clinical visits. MEASUREMENTS: Adherence for the primary outcome was assessed via wireless pill bottle openings. RESULTS: Mean participant age was 50 years; 60% were men, and 40% were black. Mean adherence was 78%, 88%, and 55% in the reminders, reminders-plus-notification, and control arms (P<0.001 for comparison of each intervention to control). Mean tacrolimus levels were not significantly different between groups. LIMITATIONS: The study did not assess clinical end points. Participants and study coordinators were not blinded to intervention arm. CONCLUSIONS: Provider notification and customized reminders appear promising in helping patients achieve better medication adherence, but these strategies require evaluation in trials powered to detect differences in clinical outcomes.

Using Active Choice Within the Electronic Health Record to Increase Influenza Vaccination Rates.

BACKGROUND: Despite the benefits of influenza vaccination, each year more than half of adults in the United States do not receive it. OBJECTIVE: To evaluate the association between an active choice intervention in the electronic health record (EHR) and changes in influenza vaccination rates. DESIGN: Observational study. PATIENTS: Adults eligible for influenza vaccination with a clinic visit at one of three internal medicine practices at the University of Pennsylvania Health System between September 2010 and March 2013. INTERVENTION: The EHR confirmed patient eligibility during the clinic visit and, upon accessing the patient chart, prompted the physician and their medical assistant to actively choose to "accept" or "cancel" an order for the influenza vaccine. MAIN MEASURES: Change in influenza vaccination order rates at the intervention practice compared to two control practices for the 2012-2013 flu season, comparing trends during the prior two flu seasons adjusting for time trends and patient and clinic visit characteristics. KEY RESULTS: The sample (n = 45,926 patients) was 62.9% female, 35.9% white, and 54.4% black, with a mean age of 50.2 years. Trends were similar between practices during the 2 years in the pre-intervention period. Vaccination rates increased in both groups in the post-intervention year, but the intervention practice using active choice had a significantly greater increase than the control (adjusted difference-in-difference: 6.6 percentage points; 95% CI, 5.1-8.1; P < 0.001), representing a 37.3% relative increase compared to the pre-intervention period. More than 99.9% (9938/9941) of orders placed during the study period resulted in vaccination. CONCLUSIONS: Active choice through the EHR was associated with a significant increase in influenza vaccination rates.

Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults: A Randomized, Controlled Trial.

BACKGROUND: Financial incentive designs to increase physical activity have not been well-examined. OBJECTIVE: To test the effectiveness of 3 methods to frame financial incentives to increase physical activity among overweight and obese adults. DESIGN: Randomized, controlled trial. (ClinicalTrials.gov: NCT 02030119). SETTING: University of Pennsylvania. PARTICIPANTS: 281 adult employees (body mass index ≥27 kg/m2). INTERVENTION: 13-week intervention. Participants had a goal of 7000 steps per day and were randomly assigned to a control group with daily feedback or 1 of 3 financial incentive programs with daily feedback: a gain incentive ($1.40 given each day the goal was achieved), lottery incentive (daily eligibility [expected value approximately $1.40] if goal was achieved), or loss incentive ($42 allocated monthly upfront and $1.40 removed each day the goal was not achieved). Participants were followed for another 13 weeks with daily performance feedback but no incentives. MEASUREMENTS: Primary outcome was the mean proportion of participant-days that the 7000-step goal was achieved during the intervention. Secondary outcomes included the mean proportion of participant-days achieving the goal during follow-up and the mean daily steps during intervention and follow-up. RESULTS: The mean proportion of participant-days achieving the goal was 0.30 (95% CI, 0.22 to 0.37) in the control group, 0.35 (CI, 0.28 to 0.42) in the gain-incentive group, 0.36 (CI, 0.29 to 0.43) in the lottery-incentive group, and 0.45 (CI, 0.38 to 0.52) in the loss-incentive group. In adjusted analyses, only the loss-incentive group had a significantly greater mean proportion of participant-days achieving the goal than control (adjusted difference, 0.16 [CI, 0.06 to 0.26]; P = 0.001), but the adjusted difference in mean daily steps was not significant (861 [CI, 24 to 1746]; P = 0.056). During follow-up, daily steps decreased for all incentive groups and were not different from control. LIMITATION: Single employer. CONCLUSION: Financial incentives framed as a loss were most effective for achieving physical activity goals. PRIMARY FUNDING SOURCE: National Institute on Aging.

Rationale and design of a randomized trial of automated hovering for post-myocardial infarction patients: The HeartStrong program.

BACKGROUND: Coronary artery disease is the single leading cause of death in the United States, and medications can significantly reduce the rate of repeat cardiovascular events and treatment procedures. Adherence to these medications, however, is very low. METHODS: HeartStrong is a national randomized trial offering 3 innovations. First, the intervention is built on concepts from behavioral economics that we expect to enhance its effectiveness. Second, the implementation of the trial takes advantage of new technology, including wireless pill bottles and remote feedback, to substantially automate procedures. Third, the trial's design includes an enhancement of the standard randomized clinical trial that allows rapid-cycle innovation and ongoing program enhancement. RESULTS: Using a system involving direct data feeds from 6 insurance partners followed by mail, telephone, and email contact, we enrolled 1,509 patients discharged from the hospital with acute myocardial infarction in a 2:1 ratio of intervention:usual care. The intervention period lasts 1 year; the primary outcome is time to first fatal or nonfatal acute vascular event or revascularization, including acute myocardial infarction, unstable angina, stroke, acute coronary syndrome admission, or death. CONCLUSIONS: Our randomized controlled trial of the HeartStrong program will provide an evaluation of a state-of-the-art behavioral economic intervention with a number of important pragmatic features. These include a tailored intervention responding to patient activity, streamlining of consent and implementation processes using new technologies, outcomes centrally important to patients, and the ability to implement rapid-cycle innovation.

Two Randomized Controlled Pilot Trials of Social Forces to Improve Statin Adherence among Patients with Diabetes.

BACKGROUND: Medication nonadherence is an important obstacle to cardiovascular disease management. OBJECTIVE: To improve adherence through real-time feedback based on theories of how social forces influence behavior. DESIGN: Two randomized controlled pilot trials called PROMOTE and SUPPORT. Participants stored statin medication in wireless-enabled pill bottles that transmitted adherence data to researchers. PARTICIPANTS: Adults with diabetes and a history of low statin adherence based on pharmacy refills (i.e., Medication Possession Ratio [MPR] <80% in the pre-randomization screening period). INTERVENTION: In PROMOTE, each participant was randomized to 1) weekly messages in which that participant's statin adherence was compared to that of other participants (comparison), 2) weekly summaries of that participant's statin adherence (summary), or 3) control. In SUPPORT, each participant identified another person (the Medication Adherence Partner [MAP]) to receive reports about that participant's adherence, and was randomized to 1) daily reports to MAP, 2) weekly reports to MAP, 3) reports to MAP only if dose was missed, or 4) control. MAIN OUTCOMES MEASURE: Adherence measured by pill bottle. KEY RESULTS: Among 45,000 health plan members contacted by mail, <1% joined the trial. Participants had low baseline MPRs (median = 60%, IQR 41-72%) but high pill-bottle adherence (90% in PROMOTE, 92% in SUPPORT) during the trial. In PROMOTE (n = 201) and SUPPORT (n = 200), no intervention demonstrated significantly better adherence vs. CONTROL: In a subgroup of PROMOTE participants with the lowest pre-study MPR, pill-bottle-measured adherence in the comparison arm (89%) was higher than the control (86%) and summary (76%) arms, but differences were non-significant (p = 0.10). CONCLUSIONS: Interventions based on social forces did not improve medication adherence vs. control over a 3-month period. Given the low percentage of invited individuals who enrolled, the studies may have attracted participants who required little encouragement to improve adherence other than study participation.