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Purpose: This study aims to determine latent classes of study participants using baseline characteristics, explore the patterns within the groups, and determine whether the intervention had differential effects on weight loss across the groups. Design: Secondary analysis of a completed randomized clinical trial. Setting: Participants in a gamification intervention with social incentives who were recruited as pairs and given an intervention for 24 weeks. Participants were randomized to control, gamification, or gamification with primary care physician sharing arms. Participants: All 196 participants in the Lose It trial (recruited as 98 pairs). Measures: Outcome variable—participants' weight change after 24 and 36 weeks. Factors—intervention arm and latent class. Analysis: Latent class analysis on both participants' and teams' characteristics. This was followed by 1-sample t tests of weight at 24 and 36 weeks, stratified by latent class. Results: Three groups of participants were identified: "Kin teams," "Distant teams," and "Married teams." "Kin teams" lost more weight after the intervention in the gamification and gamification with PCP sharing arms. The "Distant teams" lost similar amounts of weight in all 3 arms but did not keep it off during maintenance. The "Married teams" lost the most weight across all 3 arms and kept it off following the intervention. Conclusions: Patient phenotypes can identify variations in response to a gamification weight loss intervention. Future intervention studies may benefit from leveraging this during participant recruitment and allocation.

Purpose: To evaluate the presence of gamification in popular mobile applications and whether principles from behavioral economics were incorporated in the design. Design: The top 50 ranked free health and fitness applications were downloaded. Gamification elements were predetermined through literature review, and applications were evaluated for their presence. Setting: App Store by Apple Inc. Measures: Presence of gamification, type of game element, and use of behavioral economic principles. Analysis: We classified the types and frequencies of targeted behaviors and features of gamification. Use of behavioral economic principles focused on designing rewards or points using loss aversion (allocated upfront and could be lost), variable reinforcement (not allocated constantly), and probability inflation (using drawings or lottery designs). Results: Gamification was used by 64% of mobile applications. Most applications that included gamification (97%) targeted behaviors related to physical activity and weight loss. Applications focused on other areas such as reproductive health, meditation, and sleep used gamification less often (11%). Game elements used most commonly included goal setting (78%), social influences (78%), and challenges (63%), while less common elements included points (6%) and levels (3%). No applications incorporated behavioral economics principles specified in the study. Conclusions: Gamification was commonly used by popular health and fitness mobile applications, but none used the specified behavioral economic principles to design rewards or points. Mobile applications could potentially improve their use if their design better leveraged principles from behavioral economics.

United States health expenditures continue to escalate at unsustainable rates. A recent movement around increasing price transparency has been suggested as a way of reducing the rate of increase in expenditures, with legislative efforts taking place at both the state and federal level. While this seems on the surface like a good idea, simply providing information on prices to physicians, particularly trainees, may not achieve the type of large changes in practice patterns that proponents expect. The manner in which price transparency is implemented will likely play a significant role in its effectiveness as an intervention. In this article, the authors review efforts of transparency and default options from other contexts and leverage insights from behavioral economics to provide recommendations for increasing the likelihood that price transparency will lead to physicians weighing the relative value of interventions.

AbstractThe degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.

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.

Objective. To evaluate the use of behavioral economics to design financial incentives to promote health behavior change and to explore associations with demographic characteristics. Data Source. Studies performed by the Center for Health Incentives and Behavioral Economics at the University of Pennsylvania published between January 2006 and March 2014. Study Inclusion and Exclusion Criteria. Randomized, controlled trials with available participant-level data. Studies that did not use financial incentives to promote health behavior change were excluded. Data Extraction. Participant-level data from seven studies were pooled. Data Synthesis. Meta-analysis on the pooled sample using a random-effects model with interaction terms to examine treatment effects and whether they varied by incentive structure or demographic characteristics. Results. The pooled study sample comprised 1403 participants, of whom 35% were female, 70% were white, 24% were black, and the mean age was 48 years (standard deviation 11.2 years). In the fully adjusted model, participants offered financial incentives had higher odds of behavior change (odds ratio [OR]: 3.96; p < .01) when compared to control. There were no significant interactions between financial incentives and gender, age, race, income, or education. When further adjusting for incentive structure, blacks had higher odds than whites of achieving behavior change (OR: 1.67; p< .05) with a conditional payment. Compared to lower-income participants, higher-income participants had lower odds of behavior change (OR: 0.46; p = .01) with a regret lottery. Conclusion. Financial incentives designed using concepts from behavioral economics were effective for promoting health behavior change. There were no large and consistent relationships between the effectiveness of financial incentives and observable demographic characteristics. Second-order examinations of incentive structure suggest potential relationships among the effectiveness of financial incentives, incentive structure, and the demographic characteristics of race and income.

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.