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.

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.

Individual Versus Team-Based Financial Incentives to Increase Physical Activity: A Randomized, Controlled Trial.

BACKGROUND: More than half of adults in the United States do not attain the minimum recommended level of physical activity to achieve health benefits. The optimal design of financial incentives to promote physical activity is unknown. OBJECTIVE: To compare the effectiveness of individual versus team-based financial incentives to increase physical activity. DESIGN: Randomized, controlled trial comparing three interventions to control. PARTICIPANTS: Three hundred and four adult employees from an organization in Philadelphia formed 76 four-member teams. INTERVENTIONS: All participants received daily feedback on performance towards achieving a daily 7000 step goal during the intervention (weeks 1- 13) and follow-up (weeks 14- 26) periods. The control arm received no other intervention. In the three financial incentive arms, drawings were held in which one team was selected as the winner every other day during the 13-week intervention. A participant on a winning team was eligible as follows: $50 if he or she met the goal (individual incentive), $50 only if all four team members met the goal (team incentive), or $20 if he or she met the goal individually and $10 more for each of three teammates that also met the goal (combined incentive). MAIN MEASURES: Mean proportion of participant-days achieving the 7000 step goal during the intervention. KEY RESULTS: Compared to the control group during the intervention period, the mean proportion achieving the 7000 step goal was significantly greater for the combined incentive (0.35 vs. 0.18, difference: 0.17, 95 % confidence interval [CI]: 0.07-0.28, p <0.001) but not for the individual incentive (0.25 vs 0.18, difference: 0.08, 95 % CI: -0.02-0.18, p = 0.13) or the team incentive (0.17 vs 0.18, difference: -0.003, 95 % CI: -0.11-0.10, p = 0.96). The combined incentive arm participants also achieved the goal at significantly greater rates than the team incentive (0.35 vs. 0.17, difference: 0.18, 95 % CI: 0.08-0.28, p < 0.001), but not the individual incentive (0.35 vs. 0.25, difference: 0.10, 95 % CI: -0.001-0.19, p = 0.05). Only the combined incentive had greater mean daily steps than control (difference: 1446, 95 % CI: 448-2444, p ≤ 0.005). There were no significant differences between arms during the follow-up period (weeks 14- 26). CONCLUSIONS: Financial incentives rewarded for a combination of individual and team performance were most effective for increasing physical activity. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT02001194.

Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial.

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.

Exploring Wearables to Focus on the "Sweet Spot" of Physical Activity and Sleep After Hospitalization: Secondary Analysis.

BACKGROUND: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. OBJECTIVE: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. METHODS: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. RESULTS: For the full sample of 120 participants, we observed a "T-shaped" distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score -0.77, 95% CI -1.3 to -0.15; P=.02), and perceived physical health (z-score -0.73, 95% CI -1.3 to -0.13; P=.003), compared with those who were more physically active (≥5000 steps/day). CONCLUSIONS: Participants within the "sweet spot" that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the "sweet spot." Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a "sweet spot" of recommended levels for both sleep and activity needed for optimal recovery. TRIAL REGISTRATION: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279.

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial.

BACKGROUND: While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer. METHODS AND FINDINGS: Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention. CONCLUSIONS: Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients.

Time to address disparities in care by appointment time.

Quality of care systematically decreases over the course of the day. Ensuring that patients seen later in the day receive the same care as patients seen first thing in the morning has broad clinical and economic implications for our health care system. In this article, we outline feasible near-term solutions to direct clinicians and patients toward consistently better primary care decisions, throughout the day. These insights could be adapted to address similar challenges in other health care settings.

Association of Behavioral Phenotypes With Changes in Sleep During a Workplace Wellness Program.

PURPOSE: Examine changes in sleep duration by 3 behavioral phenotypes during a workplace wellness program with overweight and obese adults. DESIGN: Secondary analysis of a randomized clinical trial. SETTING: Remotely monitored intervention conducted across the United States. SUBJECTS: 553 participants with a body mass index ≥25. INTERVENTION: Participants were randomized to 1 of 4 study arms: control, gamification with support, gamification with collaboration, and gamification with competition to increase their physical activity. All participants were issued a wrist-worn wearable device to record their daily physical activity and sleep duration. MEASURES: The primary outcome was change in daily sleep duration from baseline during the 24 week intervention and follow-up period by study arm within behavioral phenotype class. ANALYSIS: Linear mixed effects regression. RESULTS: Participants who had a phenotype of less physically active and less social at baseline, in the gamification with collaboration arm, significantly increased their sleep duration during the intervention period (30.2 minutes [95% CI 6.9, 53.5], P = 0.01), compared to the control arm. There were no changes in sleep duration among participants who were more extroverted and motivated or participants who were less motivated and at-risk. CONCLUSIONS: Changes in sleep during a physical activity intervention varied by behavioral phenotype. Behavioral phenotypes may help to precisely identify who is likely to improve sleep duration during a physical activity intervention.

Effect of Behaviorally Designed Gamification With Social Incentives on Lifestyle Modification Among Adults With Uncontrolled Diabetes: A Randomized Clinical Trial.

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.

Effect of Behaviorally Designed Gamification With a Social Support Partner to Increase Mobility After Hospital Discharge: A Randomized Clinical Trial.

Importance: Hospitalization is associated with decreased mobility and functional decline. Behaviorally designed gamification can increase mobility in community settings but has not been tested among patients at risk for functional decline during a high-risk transition period after hospitalization. Objective: To test a behaviorally designed gamification intervention with a social support partner to increase patient mobility after hospital discharge. Design, Setting, and Participants: This study is a randomized clinical trial of a 12-week intervention without follow-up. Enrollment occurred from January 2018 to June 2019 at a referral hospital with a remote at-home monitoring intervention among patients living predominantly in 3 states (Pennsylvania, New Jersey, and Delaware). Participants included adult patients discharged from general medicine and oncology units to home. Data analysis was performed from October 2019 to March 2020. Interventions: All participants received a wearable device to track daily steps. The control group received feedback from the device but no other interventions. The intervention group entered into a 12-week game informed by behavioral economics to assign points and levels for achieving step goals and reinforced by a support partner who received updates on participant progress. Main Outcomes and Measures: The primary outcome was change in mean daily steps from baseline through the 12-week intervention. Secondary measures were change in functional status and urgent care utilization (ie, emergency department visits and hospital readmissions) within this period. Results: A total of 232 participants were enrolled in the study (118 randomized to control and 114 randomized to the intervention). Participants had a mean (SD) age of 40 (14) years, 141 (61%) were female, 101 (43%) were White, and 103 (44%) had an annual household income less than $50 000. Daily step counts increased from 3795 to 4652 steps (difference, 857 steps; 95% CI, 488 to 1224 steps) among intervention participants and increased from 3951 to 4499 steps (difference, 548 steps; 95% CI, 193 to 903 steps) among control participants. The change in mean daily step count from baseline was not significantly different for participants in the intervention group vs the control group (adjusted difference, 270 steps; 95% CI, -214 to 754 steps; P = .27). Among the subgroup of 76 participants with higher levels of social engagement, post hoc exploratory analyses showed a significant increase in mobility for intervention vs control (adjusted difference, 1125 steps; 95% CI, 409 to 1841 steps; P = .002). Fewer participants in this subgroup experienced functional decline (1 of 36 participants [4%] in the intervention group vs 5 of 40 participants [12%] in the control group) and hospital readmission at 30 days (3 of 36 participants [8%] in the intervention group vs 6 of 40 participants [15%] in the control group), but the differences were not statistically significant. There were no significant differences in these secondary outcomes for the overall sample. Conclusions and Relevance: Gamification with social incentives did not affect mobility or functional decline in all participants, but post hoc analysis suggests positive findings for both outcomes for patients with higher social engagement. Trial Registration: ClinicalTrials.gov Identifier: NCT03321279.

Framing Social Comparison Feedback With Financial Incentives for Physical Activity Promotion: A Randomized Trial.

BACKGROUND: Social comparison feedback is often used in physical activity interventions but the optimal design of feedback is unknown. METHODS: This 4-arm, randomized trial consisted of a 13-week intervention period and 13-week follow-up period. During the intervention, 4-person teams were entered into a weekly lottery valued at about $1.40/day and contingent on the team averaging ≥7000 steps per day. Social comparison feedback on performance was delivered weekly for 26 weeks, and varied by reference point (50th vs 75th percentile) and forgiveness in use of activity data (all 7 d or best 5 of 7 d). The primary outcome was the mean proportion of participant-days achieving the 7000-step goal. RESULTS: During the intervention period, the unadjusted mean proportion of participant-days that the goal was achieved was 0.47 (95% confidence interval [CI]: 0.38 to 0.56) in the 50th percentile arm, 0.38 (95% CI: 0.30 to 0.37) in the 75th percentile arm, 0.40 (95% CI: 0.31 to 0.49) in the 50th percentile with forgiveness arm, and 0.47 (95% CI: 0.38 to 0.55) in the 75th percentile with forgiveness arm. In adjusted models during the intervention and follow-up periods, there were no significant differences between arms. CONCLUSIONS: Changing social comparison feedback did not impact physical activity.

Scheduling primary care appointments online: Differences in availability based on health insurance.

BACKGROUND: Digital platforms that allow patients to go online or use smartphone applications to view and schedule physician appointments have not been well evaluated. METHODS: We conducted systematic searches for primary care physician appointments in 20 cities using ZocDoc, an online appointment scheduling platform. Availability was determined for three insurance types (self-pay, Medicare, and Medicaid) in states with and without Medicaid expansion. We collected data on physician characteristics, number of appointments available, and distance to clinics. RESULTS: The sample comprised 4150 physician observations across 17 states. Overall, the mean distance to clinic was 8.9 miles (SD: 8.4 miles), mean total number of appointments available within 3 days for the 10 closest physicians was 20.1 (SD: 27.1), and the mean number of physicians available within 5 miles was 5.4 (SD: 6.6). There were no differences in physician characteristics by insurance type. Access to appointments did not differ between Medicare and self-pay. However, compared to self-pay, appointments for Medicaid were further away (Mean difference in miles: 5.4, P < 0.001), and there were fewer physicians available within 5 miles (Mean difference in # of physicians: -4.9, P < 0.001). States that did not adopt Medicaid expansion had fewer appointments within proximity, but this differed similarly across insurance types. CONCLUSIONS: There were a substantial number of available appointments at close distances. However, Medicaid patients had less access to appointments within proximity than self-pay or Medicare patients.

Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial.

Importance: Statins are not prescribed to approximately 50% of patients who could benefit from them. Objective: To evaluate the effectiveness of an automated patient dashboard using active choice framing with and without peer comparison feedback on performance to nudge primary care physicians (PCPs) to increase guideline-concordant statin prescribing. Design, Setting, and Participants: This 3-arm cluster randomized clinical trial was conducted from February 21, 2017, to April 21, 2017, at 32 practice sites in Pennsylvania and New Jersey. Participants included 96 PCPs and 4774 patients not previously receiving statin therapy. Data were analyzed from April 25, 2017, to June 16, 2017. Interventions: Primary care physicians in the 2 intervention arms were emailed a link to an automated online dashboard listing their patients who met national guidelines for statin therapy but had not been prescribed this medication. The dashboard included relevant patient information, and for each patient, PCPs were asked to make an active choice to prescribe atorvastatin, 20 mg, once daily, atorvastatin at another dose, or another statin or not prescribe a statin and select a reason. The dashboard was available for 2 months. In 1 intervention arm, the email to PCPs also included feedback on their statin prescribing rate compared with their peers. Primary care physicians in the usual care group received no interventions. Main Outcomes and Measures: Statin prescription rates. Results: Patients had a mean (SD) age of 62.4 (8.3) years and a mean (SD) 10-year atherosclerotic cardiovascular disease risk score of 13.6 (8.2); 2625 (55.0%) were male, 3040 (63.7%) were white, and 1318 (27.6%) were black. In the active choice arm, 16 of 32 PCPs (50.0%) accessed the patient dashboard, but only 2 of 32 (6.3%) signed statin prescription orders. In the active choice with peer comparison arm, 12 of 32 PCPs (37.5%) accessed the patient dashboard and 8 of 32 (25.0%) signed statin prescription orders. Statins were prescribed in 40 of 1566 patients (2.6%) in the usual care arm, 116 of 1743 (6.7%) in the active choice arm, and 117 of 1465 (8.0%) in the active choice with peer comparison arm. In the main adjusted model, compared with usual care, there was a significant increase in statin prescribing in the active choice with peer comparison arm (adjusted difference in percentage points, 5.8; 95% CI, 0.9-13.5; P = .008), but not in the active choice arm (adjusted difference in percentage points, 4.1; 95% CI, -0.8 to 13.1; P = .11). Conclusions and Relevance: An automated patient dashboard using both active choice framing and peer comparison feedback led to a modest but significant increase in guideline-concordant statin prescribing rates. Trial Registration: ClinicalTrials.gov Identifier: NCT03021759.

A Randomized, Controlled Trial of Lottery-Based Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults.

PURPOSE: To evaluate the effect of lottery-based financial incentives in increasing physical activity. DESIGN: Randomized, controlled trial. SETTING: University of Pennsylvania Employees. PARTICIPANTS: A total of 209 adults with body mass index ≥27. INTERVENTIONS: All participants used smartphones to track activity, were given a goal of 7000 steps per day, and received daily feedback on performance for 26 weeks. Participants randomly assigned to 1 of the 3 intervention arms received a financial incentive for 13 weeks and then were followed for 13 weeks without incentives. Daily lottery incentives were designed as a "higher frequency, smaller reward" (1 in 4 chance of winning $5), "jackpot" (1 in 400 chance of winning $500), or "combined lottery" (18% chance of $5 and 1% chance of $50). MEASURES: Mean proportion of participant days step goals were achieved. ANALYSIS: Multivariate regression. RESULTS: During the intervention, the unadjusted mean proportion of participant days that goal was achieved was 0.26 in the control arm, 0.32 in the higher frequency, smaller reward lottery arm, 0.29 in the jackpot arm, and 0.38 in the combined lottery arm. In adjusted models, only the combined lottery arm was significantly greater than control ( P = .01). The jackpot arm had a significant decline of 0.13 ( P < .001) compared to control. There were no significant differences during follow-up. CONCLUSIONS: Combined lottery incentives were most effective in increasing physical activity.

Premium-Based Financial Incentives Did Not Promote Workplace Weight Loss In A 2013-15 Study.

Employers commonly use adjustments to health insurance premiums as incentives to encourage healthy behavior, but the effectiveness of those adjustments is controversial. We gave 197 obese participants in a workplace wellness program a weight loss goal equivalent to 5 percent of their baseline weight. They were randomly assigned to a control arm, with no financial incentive for achieving the goal, or to one of three intervention arms offering an incentive valued at $550. Two intervention arms used health insurance premium adjustments, beginning the following year (delayed) or in the first pay period after achieving the goal (immediate). A third arm used a daily lottery incentive separate from premiums. At twelve months there were no statistically significant differences in mean weight change either between the control group (whose members had a mean gain of 0.1 pound) and any of the incentive groups (delayed premium adjustment, -1.2 pound; immediate premium adjustment, -1.4 pound; daily lottery incentive, -1.0 pound) or among the intervention groups. The apparent failure of the incentives to promote weight loss suggests that employers that encourage weight reduction through workplace wellness programs should test alternatives to the conventional premium adjustment approach by using alternative incentive designs, larger incentives, or both.

A Randomized Trial of Social Comparison Feedback and Financial Incentives to Increase Physical Activity.

PURPOSE: To compare the effectiveness of different combinations of social comparison feedback and financial incentives to increase physical activity. DESIGN: Randomized trial (Clinicaltrials.gov number, NCT02030080). SETTING: Philadelphia, Pennsylvania. PARTICIPANTS: Two hundred eighty-six adults. INTERVENTIONS: Twenty-six weeks of weekly feedback on team performance compared to the 50th percentile (n = 100) or the 75th percentile (n = 64) and 13 weeks of weekly lottery-based financial incentive plus feedback on team performance compared to the 50th percentile (n = 80) or the 75th percentile (n = 44) followed by 13 weeks of only performance feedback. MEASURES: Mean proportion of participant-days achieving the 7000-step goal during the 13-week intervention. ANALYSIS: Generalized linear mixed models adjusting for repeated measures and clustering by team. RESULTS: Compared to the 75th percentile without incentives during the intervention period, the mean proportion achieving the 7000-step goal was significantly greater for the 50th percentile with incentives group (0.45 vs 0.27, difference: 0.18, 95% confidence interval [CI]: 0.04 to 0.32; P = .012) but not for the 75th percentile with incentives group (0.38 vs 0.27, difference: 0.11, 95% CI: -0.05 to 0.27; P = .19) or the 50th percentile without incentives group (0.30 vs 0.27, difference: 0.03, 95% CI: -0.10 to 0.16; P = .67). CONCLUSION: Social comparison to the 50th percentile with financial incentives was most effective for increasing physical activity.

The Hospital Medicine Reengineering Network (HOMERuN): a learning organization focused on improving hospital care.

Converting the health care delivery system into a learning organization is a key strategy for improving health outcomes. Although the collaborative learning organization approach has been successful in neonatal intensive care units and disease-specific collaboratives, there are few examples in general medicine and none in adult medicine that have leveraged the role of hospitalists nationally across multiple institutions to implement improvements. The authors describe the rationale for and early work of the Hospital Medicine Reengineering Network (HOMERuN), a collaborative of hospitals, hospitalists, and multidisciplinary care teams founded in 2011 that seeks to measure, benchmark, and improve the efficiency, quality, and outcomes of care in the hospital and afterwards. Robust and timely evaluation, with learning and refinement of approaches across institutions, should accelerate improvement efforts. The authors review HOMERuN's collaborative model, which focuses on a community-based participatory approach modified to include hospital-based staff as well as the larger community. HOMERuN's initial project is described, focusing on care transition measurement using perspectives from the patient, caregiver, and providers. Next steps and sustainability of the organization are discussed, including benchmarking, collaboration, and effective dissemination of best practices to stakeholders.