Mitesh S. Patel

Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change

Several large technology companies including Apple, Google, and Samsung are entering the expanding market of population health with the introduction of wearable devices. This technology, worn in clothing or accessories, is part of a larger movement often referred to as the “quantified self.” The notion is that by recording and reporting information about behaviors such as physical activity or sleep patterns, these devices can educate and motivate individuals toward better habits and better health. The gap between recording information and changing behavior is substantial, however, and while these devices are increasing in popularity, little evidence suggests that they are bridging that gap. Only 1% to 2% of individuals in the United States have used a wearable device, but annual sales are projected to increase to more than

Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data

50 billion by 2018. 1 Some of these devices aim at individuals already motivated to change their health behaviors. Others are being considered by health care organizations, employers, insurers, and clinicians who see promise in using these devices to better engage less motivated individuals. Some of these devices may justify that promise, but less because of their technology and more because of the behavioral change strat

Association of the 2011 ACGME Resident Duty Hour Reforms With Mortality and Readmissions Among Hospitalized Medicare Patients

Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data Despite the potential of pedometers to increase physical activity and improve health,1 there is little evidence of broad adoption by the general population. In contrast, nearly twothirds of adults in the United States own a smartphone2 and technology advancements have enabled these devices to track health behaviors such as physical activity and provide convenient feedback.3 New wearable devices that may have more consumer appeal have also been developed. Even though these devices and applications might better engage individuals in their health, for example through workplace wellness programs,3 there has been little evaluation of their use.3-5 The objective of this study was to evaluate the accuracy of smartphone applications and wearable devices compared with direct observation of step counts, a metric successfully used in interventions to improve clinical outcomes.1

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

IMPORTANCE Patient outcomes associated with the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour reforms have not been evaluated at a national level. OBJECTIVE To evaluate the association of the 2011 ACGME duty hour reforms with mortality and readmissions. DESIGN, SETTING, AND PARTICIPANTS Observational study of Medicare patient admissions (6,384,273 admissions from 2,790,356 patients) to short-term, acute care, nonfederal hospitals (n = 3104) with principal medical diagnoses of acute myocardial infarction, stroke, gastrointestinal bleeding, or congestive heart failure or a Diagnosis Related Group classification of general, orthopedic, or vascular surgery. Of the hospitals, 96 (3.1%) were very major teaching, 138 (4.4%) major teaching, 442 (14.2%) minor teaching, 443 (14.3%) very minor teaching, and 1985 (64.0%) nonteaching. EXPOSURE Resident-to-bed ratio as a continuous measure of hospital teaching intensity. MAIN OUTCOMES AND MEASURES Change in 30-day all-location mortality and 30-day all-cause readmission, comparing patients in more intensive relative to less intensive teaching hospitals before (July 1, 2009-June 30, 2011) and after (July 1, 2011-June 30, 2012) duty hour reforms, adjusting for patient comorbidities, time trends, and hospital site. RESULTS In the 2 years before duty hour reforms, there were 4,325,854 admissions with 288,422 deaths and 602,380 readmissions. In the first year after the reforms, accounting for teaching hospital intensity, there were 2,058,419 admissions with 133,547 deaths and 272,938 readmissions. There were no significant postreform differences in mortality accounting for teaching hospital intensity for combined medical conditions (odds ratio [OR], 1.00; 95% CI, 0.96-1.03), combined surgical categories (OR, 0.99; 95% CI, 0.94-1.04), or any of the individual medical conditions or surgical categories. There were no significant postreform differences in readmissions for combined medical conditions (OR, 1.00; 95% CI, 0.97-1.02) or combined surgical categories (OR, 1.00; 95% CI, 0.98-1.03). For the medical condition of stroke, there were higher odds of readmissions in the postreform period (OR, 1.06; 95% CI, 1.001-1.13). However, this finding was not supported by sensitivity analyses and there were no significant postreform differences for readmissions for any other individual medical condition or surgical category. CONCLUSIONS AND RELEVANCE Among Medicare beneficiaries, there were no significant differences in the change in 30-day mortality rates or 30-day all-cause readmission rates for those hospitalized in more intensive relative to less intensive teaching hospitals in the year after implementation of the 2011 ACGME duty hour reforms compared with those hospitalized in the 2 years before implementation.

The Role of Behavioral Economic Incentive Design and Demographic Characteristics in Financial Incentive-Based Approaches to Changing Health Behaviors: A Meta-Analysis

Context Financial incentives are commonly used in workplace wellness programs aimed at increasing physical activity. The most effective approach to offering incentives, however, is not known. Contribution In this trial, the up-front allocation of a financial reward and subsequent loss when physical activity goals were not met resulted in greater daily exercise than no incentive. Providing a reward when goals were met, however, did not increase physical activity. Implication The manner in which financial incentives are offered may influence the success of health promotion programs. Higher levels of regular physical activity are associated with lower rates of cardiovascular disease, diabetes, obesity, hypertension, and all-cause mortality (15). However, more than half of adults in the United States do not attain the minimum recommended level of physical activity to have these health benefits (6, 7). The Centers for Disease Control and Prevention and many state public health departments have recommended the workplace as an environment to implement interventions to increase physical activity (811). But evidence suggests that most workplace physical activity interventions are not effective, particularly for more sedentary persons (1214). Workplace wellness programs are growing in popularity throughout the United States, and more than 80% of large employers now use some form of financial incentive for health promotion (1517). Beginning in 2014, the Patient Protection and Affordable Care Act increased the proportion of employee health insurance premiums that can be used as outcome-based wellness incentives from 20% to 30% and as high as 50% if tobacco use is targeted (18, 19). This provides a significant opportunity to use incentive-based programs to change health behaviors, but the optimal design of financial incentives to increase physical activity has not been well-examined (20). Behavioral economics incorporates principles from psychology to help understand why persons make decisions that are not in line with longer-term health goals. Many persons know physical activity is good for their health but do not do enough of it. Instead, they often deviate from these goals in a predictable manner and from a common set of decision errors (18, 19, 21). For example, persons tend to be more motivated by immediate rather than delayed gratification (22) and by losses rather than gains (23), and they tend to avoid the feeling of regret (24). These insights reveal that the design and delivery of an incentive has an important influence on its effectiveness. The objective of this study was to test the effectiveness of 3 financial incentive designs, each with the same expected economic value. In the gain-incentive group, participants received a fixed amount of money each day the step goal was achieved. This design follows traditional economic principles in that it is largely transactional: A certain constant reward is promised for a predetermined effort. Persons in the 2 other incentive groups were offered incentives of the same expected value, but those incentives were designed to leverage the fact that persons tend to be loss averse, are more engaged by variable reinforcement than by constant reinforcement, and tend to avoid the feeling of regret. Methods Design Overview We conducted a 26-week randomized, controlled trial between 6 March and 6 September 2014, consisting of 13-week intervention and follow-up periods. A total of 281 participants gave their informed consent and were randomly assigned to a control group or to 1 of 3 groups with different financial incentive designs, each with the same expected economic value. All participants were given a goal of achieving at least 7000 steps per day, and this target reflects several deliberate design elements. First, this level of physical activity is endorsed by the American College of Sports Medicine to be approximately equivalent to meeting the federal guidelines for the minimum recommended levels of physical activity needed to achieve health benefits (25, 26). Second, this level is 40% higher than the average daily step count of 5000 among U.S. adults (27, 28). Prior studies using an even higher goal of 10000 steps have found that more sedentary persons may be less likely to participate, and it was a priority in this study to engage as many persons as possible (12). Third, instead of simply asking participants to increase steps, a minimum threshold puts greater emphasis on encouraging more sedentary persons to be physically active and less emphasis on getting highly active persons to be even more active. Step counts were tracked using the Moves smartphone application (ProtoGeo Oy), which uses accelerometers within the phone and has been shown by our prior work to be accurate (29). Each participant was given a unique personal identification number to enter into the smartphone application and verify permission that the study team could access step-count data. Once the application was installed on the phone, the participant never had to reopen it, although they could as often as they wished. Instead, participants had to allow the application to run passively on the phone, have the phone powered on, and carry it with them (for example, in a pocket or on a belt clip or arm band) while they were active. The University of Pennsylvania Institutional Review Board approved this study. Setting and Participants Eligible participants were employees of the University of Pennsylvania in Philadelphia, Pennsylvania, were aged 18 years or older, and had a body mass index (BMI) of at least 27 kg/m2 (estimated from self-reported height and weight). We chose this BMI threshold to help ensure that our sample represented overweight or obese persons. Participants were recruited by e-mail from February to March 2014 and excluded if they were already participating in another physical activity study, were not able or willing to carry an iPhone (Apple) or Android (Google) smartphone with the mobile application installed, were pregnant or lactating, intended to become pregnant within 6 months, or stated that they could not complete the study. E-mails were sent to all University of Pennsylvania staff employees (approximately 10000 persons). All eligible participants provided electronic informed consent, completed a sociodemographic questionnaire, self-reported measures of height and weight, and reported recent physical activity using the long form of the International Physical Activity Questionnaire (30). Randomization and Interventions Participants enrolled online using Way to Health, an automated technology platform based at the University of Pennsylvania that integrates wireless devices, conducts clinical trial randomization and enrollment processes, delivers messaging (text message or e-mail) and surveys, automates transfers of financial incentives, and securely captures data for research purposes (31). Way to Health was used in prior behavioral intervention studies (3234). All participants received

Advancing Medical Education by Teaching Health Policy

25 for enrolling and

Medical student perceptions of education in health care systems

75 for participating through the primary end point at 13 weeks along with completion of a survey on their experience. However, there was no participation incentive for the follow-up period. Participants were mailed a bank check at the end of each month with all accumulated earnings. All participants selected whether they preferred to receive study communications by e-mail, text message, or both. Participants were electronically randomly assigned to the control group or to 1 of 3 intervention groups with an equivalent expected economic value of

Using Default Options Within the Electronic Health Record to Increase the Prescribing of Generic-Equivalent Medications: A Quasi-experimental Study

1.40, which is a value used in prior work (34). For 26 weeks, participants in all 4 groups received daily feedback on whether they had achieved the 7000-step goal in the prior day. The control group received no other intervention aside from daily feedback. For the 13-week intervention, the intervention groups included a gain incentive in which participants received

Teaching Residents to Provide Cost-Conscious Care: A National Survey of Residency Program Directors

1.40 for each day they met the goal, a loss incentive in which

Generic Medication Prescription Rates After Health System–Wide Redesign of Default Options Within the Electronic Health Record

1.40 was taken away from a monthly incentive (