Applying Behavioral Economics to Improve Adolescent and Young Adult Health: A Developmentally-Sensitive Approach

Effectiveness of an mHealth App That Uses Financial Incentives and Gamification to Promote Health Behavior Change in Adolescents and Caregivers: Protocol for a Clinic-Based Randomized Controlled Trial

BACKGROUND

Adolescent and adult obesity continues to be a public health epidemic in the United States. Despite the popularity of mHealth apps with gamification among adolescents, there are insufficient studies to evaluate the efficacy of gamified mHealth apps and financial incentives to motivate sustained health behavior change in adolescents or their adult caregivers.

OBJECTIVE

This study aims to evaluate the effectiveness of gamification techniques and financial incentives used in the novel "CommitFit" mHealth app to motivate health behavior change and improve various mental and physical health metrics in adolescents and their caregivers.

METHODS

This study is a 3-month randomized controlled trial (RCT) with 30 adolescents (aged 13-15 years) and their adult caregivers (N=60). It evaluates "CommitFit," which uses gamification including points and leaderboards to motivate logging and achievement of self-selected health behavior goals (eg, more water, sleep, physical activity, fruits, or vegetables or fewer sugary beverages). The RCT had three arms, each with 10 dyads: (1) CommitFit-only users; (2) CommitFit$, where adolescents were paid US $0.05 for each point they earned; and (3) waitlist control. Intervention dyads used the app for 3 months and had the option to use it for the fourth month without prompts or extra financial incentives. User analytic software was used to evaluate the frequency of user logs and goal achievement. Monthly surveys evaluated self-reported change in the 5 CommitFit health behaviors. Changes in BMI and blood pressure were evaluated for all participants at 3 clinical visits. Mental health, gamification, and behavior economics surveys were completed during the clinical visits.

RESULTS

Recruitment began in August 2023 and was completed in 10 weeks. The research team successfully recruited and enrolled 30 dyads. Researchers emailed and called 89 caregivers on a physician-approved adolescent patient list, a 33% recruitment rate. Data collection and analysis will be conducted in the spring and summer of 2024. The results of this study are anticipated to be published between late 2024 and early 2025.

CONCLUSIONS

This RCT will expand knowledge of the effectiveness of gamification techniques, financial incentives, and mHealth apps to motivate sustained health behavior change among adolescents and caregivers. These results may offer new opportunities to caregivers, health insurers, health care systems, and clinicians to motivate health behavior change in adolescents and caregivers, with the ultimate goal of preventing or reducing obesity and obesity-related diseases. Additional gamification, mental health surveys, and app user analytics included in the study may provide further insight into the characteristics of adolescents or caregivers who would benefit the most from using a gamified mHealth app like CommitFit.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63505.

Adolescent-Preferred financial incentives to promote type 1 diabetes Self-Care: A discrete choice experiment

AIMS

This study aimed to quantify preferences for the characteristics of a financial incentives program that would motivate adolescent engagement in type 1 diabetes (T1D) self-care.

METHOD

We performed a discrete choice experiment with 12-18 year-olds with T1D from two pediatric hospital endocrinology clinics (n = 317). We identified key attributes of incentives: (1) monthly value of the reward, (2) payment structure, and (3) difficulty of incentivized behaviors. In twelve choice questions, adolescents chose the incentive option from a pair of profiles that was more likely to motivate them to increase adherence to recommended self-care. Options presented were tailored to adolescents' T1D technology use and perceived difficulty of completing each behavior. We analyzed data using a conditional logit model.

RESULTS

The value of the reward accounted for 60.8% of preferences. Adolescents were willing to accept lower value rewards when incentive payments used positive vs. negative reinforcement (-$10.88 (95% CI: -$12.60, -9.24)) and preferred higher incentives for performing hard vs. easier behaviors (+$14.92 (95% CI: +$12.66, +$17.28)).

CONCLUSIONS

Stated preferences can inform intervention design. Future research will evaluate the external validity of the discrete choice experiment-informed intervention design by assessing adolescent health and behavioral outcomes in a randomized controlled trial.

Pediatrician Preferences for Electronic Clinical Decision Support to Facilitate HIV Pre-Exposure Prophylaxis

PURPOSE

Pediatric primary care clinicians are often uncertain about patients' HIV infection risk and pre-exposure prophylaxis (PrEP) prescribing guidelines. This study was conducted to help identify ways to design and deliver useful electronic health record (EHR)-based alerts for PrEP to help mitigate this issue.

METHODS

Individual interviews and focus groups with pediatricians explored provider preferences for clinical decision support around PrEP in the EHR. Key themes were identified via the immersion-crystallization qualitative analytic technique.

RESULTS

Clinicians described ideal decision support tools as succinct, helpful in identifying patients at risk of acquiring HIV, providing automated follow-up, and linking to evidence-based prescribing guidelines. Concerns emerged about alert fatigue.

DISCUSSION

This study summarizes clinicians' preferences for EHR tool development to support PrEP provision among pediatricians with limited comfort and experience with prescribing PrEP. These findings can inform the development of PrEP decision support by implementing provider-centered feedback.

Promoting Sleep Duration in the Pediatric Setting Using a Mobile Health Platform: A Randomized Optimization Trial

Objective

Determine the optimal combination of digital health intervention component settings that increase average sleep duration by ≥30 minutes per weeknight.

Methods

Optimization trial using a 25 factorial design. The trial included 2 week run-in, 7 week intervention, and 2 week follow-up periods. Typically developing children aged 9-12y, with weeknight sleep duration <8.5 hours were enrolled (N=97). All received sleep monitoring and performance feedback. The five candidate intervention components (with their settings to which participants were randomized) were: 1) sleep goal (guideline-based or personalized); 2) screen time reduction messaging (inactive or active); 3) daily routine establishing messaging (inactive or active); 4) child-directed loss-framed financial incentive (inactive or active); and 5) caregiver-directed loss-framed financial incentive (inactive or active). The primary outcome was weeknight sleep duration (hours per night). The optimization criterion was: ≥30 minutes average increase in sleep duration on weeknights.

Results

Average baseline sleep duration was 7.7 hours per night. The highest ranked combination included the core intervention plus the following intervention components: sleep goal (either setting was effective), caregiver-directed loss-framed incentive, messaging to reduce screen time, and messaging to establish daily routines. This combination increased weeknight sleep duration by an average of 39.6 (95% CI: 36.0, 43.1) minutes during the intervention period and by 33.2 (95% CI: 28.9, 37.4) minutes during the follow-up period.

Conclusions

Optimal combinations of digital health intervention component settings were identified that effectively increased weeknight sleep duration. This could be a valuable remote patient monitoring approach to treat insufficient sleep in the pediatric setting.

Systematic review of health policy and behavioral economics: A neglected point in health promotion

BACKGROUND

Health policymakers use a variety of policy tools. These policies are either based on external factors or are based on paternal considerations; people may need to have help in the selection of items that may be regretted about them in the future. However, recent research on behavioral economics shows that major decision-making mistakes are not only limited to vulnerable groups but also are ubiquitous and systematic. The purpose of this study was on health policy making with a behavioral economics approach in health promotion.

MATERIALS AND METHODS

The eligible studies were obtained from Medline (PubMed), Web of Science, and Scopus databases. The search strategy uses a combination of keywords in the titles. The keywords of behavioral economics along with the keyword of health have been used to find related articles.

RESULTS

After deleting duplicate articles, a total of 38 articles were identified. After reviewing the title and abstract, 13 studies were omitted because they did not meet the inclusion criteria. Ten articles were removed from the found articles due to the unavailability of the full text and four articles were excluded because their method was quantitative. Finally, a total of 11 articles were eligible for including this review study.

CONCLUSION

Recent research on behavioral economics shows that decision-making errors are not limited to vulnerable groups but are ubiquitous and systematic. Forgotten income or negligible income is very high and is reasonably explained by transaction costs. Educational interventions often have little effect and do not benefit from basic cost-benefit tests. In addition, the seemingly insignificant aspects of choice-frameworks and assumptions-often have a profound effect on behavior.

Application of behavioral economics for understanding health behaviors among adolescents and young adults

PURPOSE OF REVIEW

Behavioral economics (BE) concepts have become well studied tools in addressing patient issues, such as weight loss, smoking cessation, and medication adherence. Although predominantly studied in adult populations, emerging literature has shown BE's utility for adolescent/young adult (AYA) populations, offering a practical framework to safeguard AYA health and influence healthy decision making.

RECENT FINDINGS

We identified substantive areas in which BE concepts have been applied in AYA populations (e.g., substance use) and outline how these concepts have been used as a tool to identify individuals at risk for poor outcomes and to leverage behavioral insights to improve health behaviors.

SUMMARY

BE research holds significant promise as a tool for clinicians and researchers to encourage healthy decision making in AYA populations. Yet, there are opportunities for BE research to expand further into current trends impacting adolescent health, such as electronic nicotine delivery systems, social media apps, and coronavirus disease 2019 vaccinations. Furthermore, the full degree of BE utility remains to be explored, as few studies demonstrate the translation of associative findings into direct interventions. Additional work is needed to formalize BE techniques into best practices that clinicians can implement in their daily practice.

Promoting Healthy Childhood Behaviors With Financial Incentives: A Narrative Review of Key Considerations and Design Features for Future Research

In the last decade, there has been a robust increase in research using financial incentives to promote healthy behaviors as behavioral economics and new monitoring technologies have been applied to health behaviors. Most studies of financial incentives on health behaviors have focused on adults, yet many unhealthy adult behaviors have roots in childhood and adolescence. The use of financial incentives is an attractive but controversial strategy in childhood. In this review, we first propose 5 general considerations in designing and applying incentive interventions to children. These include: 1) the potential impact of incentives on intrinsic motivation, 2) ethical concerns about incentives promoting undue influence, 3) the importance of child neurodevelopmental stage, 4) how incentive interventions may influence health disparities, and 5) how to finance effective programs. We then highlight empirical findings from randomized trials investigating key design features of financial incentive interventions, including framing (loss vs gain), timing (immediate vs delayed), and magnitude (incentive size) effects on a range of childhood behaviors from healthy eating to adherence to glycemic control in type 1 diabetes. Though the current research base on these subjects in children is limited, we found no evidence suggesting that loss-framed incentives perform better than gain-framed incentives in children and isolated studies from healthy food choice experiments support the use of immediate, small incentives versus delayed, larger incentives. Future research on childhood incentives should compare the effectiveness of gain versus loss-framing and focus on which intervention characteristics lead to sustained behavior change and habit formation.