Comparing Smoking Cessation Interventions among Underserved Patients Referred for Lung Cancer Screening: A Pragmatic Trial Protocol

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.

The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

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.

Megastudies improve the impact of applied behavioural science

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 research^3-6. 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.

Cost-Effectiveness of Four Financial Incentive Programs for Smoking Cessation

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.

Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool

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.

Effectiveness and Ethics of Incentives for Research Participation: 2 Randomized Clinical Trials

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.

Sleep and Alertness Among Interns in Intensive Care Compared to General Medicine Rotations: A Secondary Analysis of the iCOMPARE Trial

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.

Effect of Financial Incentives for Process, Outcomes, or Both on Cholesterol Level Change: A Randomized Clinical Trial

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.

Effect of Financial Incentives and Environmental Strategies on Weight Loss in the Healthy Weigh Study: A Randomized Clinical Trial

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.

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

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.

Oral health and oral health care use among able-bodied adults enrolled in Medicaid in Kentucky after Medicaid expansion: A mixed methods study

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.

Effect of Behavioral Economic Incentives for Colorectal Cancer Screening in a Randomized Trial

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.

Text Messaging and Opt-out Mailed Outreach in Colorectal Cancer Screening: a Randomized Clinical Trial

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 ).

Developing a Large-Scale Covid-19 Surveillance System to Reopen Campuses

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.

Imagining a world without low-value services: progress, barriers, and the path forward

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.

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.

Qualitative Exploration of Barriers to Statin Adherence and Lipid Control: A Secondary Analysis of a Randomized Clinical Trial

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.

A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data

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≥75^th 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.

Effect of Remote Monitoring on Discharge to Home, Return to Activity, and Rehospitalization After Hip and Knee Arthroplasty: A Randomized Clinical Trial

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.

Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists

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.