Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults

Goals for Adherence with Low-cost Incentives (GOALS): a protocol for a randomized controlled trial evaluating the impact of small airtime incentives on ART adherence among young people living with HIV in Kampala, Uganda

BACKGROUND

Treatment outcomes of HIV-positive individuals are threatened by low antiretroviral therapy (ART) adherence, a problem that is particularly acute among youth. Incentives are a promising tool to support ART adherence, but traditional incentive designs rewarding uniformly high levels of the desired health behavior may demotivate those with low levels of the behavior. In this study, we investigate the effectiveness of alternative approaches to target-setting for incentive eligibility using subgoals (i.e., individual-specific, interim targets leading up to the optimal target).

METHODS / DESIGN

We will enroll 628 HIV-positive youth between ages 15 and 30 into a 3-year randomized controlled trial. Participants will be randomized 1:1:1:1 to a control arm or one of three intervention arms (n = 157 each) that allow them to enter a prize drawing for small incentives if their ART adherence meets the given goal. In the first arm (T1, assigned subgoal), goals will be externally assigned and adapted to their initial adherence level. In the second arm (T2, participatory subgoal), participants can set their own interim goals. In the third arm (T3, fixed goal), all participants must reach the same target goal of 90% adherence. T1 and T2 participants are required to reach 90% adherence by month 12 to participate in a larger prize drawing. The control group receives the usual standard of care. All four groups will receive weekly motivational messages; the three treatment groups will additionally receive reminders of their upcoming prize drawing. Adherence will be measured continuously throughout the intervention period using electronic devices and for 12 months post-intervention. Surveys will be conducted at baseline and every 6 months. Viral loads will be measured annually. The primary outcome is Wisepill-measured adherence and a binary measure for whether the person took at least 90% of their pills. The secondary outcome is the log-transformed viral load as a continuous measure.

DISCUSSION

Our study is one of the first to apply insights about the psychology and behavioral economics of goal-setting to the design of incentives, by testing whether conditioning the eligibility threshold for incentives on subgoals (interim goals leading up to the ultimate, high goal) improves motivation and adherence more than setting a uniformly highly goal, and a comparison group.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05378607. Date of registration: May 18, 2022.

Loss-Framed Adaptive Microcontingency Management for Preventing Prolonged Sedentariness: Development and Feasibility Study

BACKGROUND

A growing body of evidence shows that financial incentives can effectively reinforce individuals' positive behavior change and improve compliance with health intervention programs. A critical factor in the design of incentive-based interventions is to set a proper incentive magnitude. However, it is highly challenging to determine such magnitudes as the effects of incentive magnitude depend on personal attitudes and contexts.

OBJECTIVE

This study aimed to illustrate loss-framed adaptive microcontingency management (L-AMCM) and the lessons learned from a feasibility study. L-AMCM discourages an individual's adverse health behaviors by deducting particular expenses from a regularly assigned budget, where expenses are adaptively estimated based on the individual's previous responses to varying expenses and contexts.

METHODS

We developed a mobile health intervention app for preventing prolonged sedentary lifestyles. This app delivered a behavioral mission (ie, suggesting taking an active break for a while) with an incentive bid when 50 minutes of uninterrupted sedentary behavior happened. Participants were assigned to either the fixed (ie, deducting the monotonous expense for each mission failure) or adaptive (ie, deducting varying expenses estimated by the L-AMCM for each mission failure) incentive group. The intervention lasted 3 weeks.

RESULTS

We recruited 41 participants (n=15, 37% women; fixed incentive group: n=20, 49% of participants; adaptive incentive group: n=21, 51% of participants) whose mean age was 24.0 (SD 3.8; range 19-34) years. Mission success rates did not show statistically significant differences by group (P=.54; fixed incentive group mean 0.66, SD 0.24; adaptive incentive group mean 0.61, SD 0.22). The follow-up analysis of the adaptive incentive group revealed that the influence of incentive magnitudes on mission success was not statistically significant (P=.18; odds ratio 0.98, 95% CI 0.95-1.01). On the basis of the qualitative interviews, such results were possibly because the participants had sufficient intrinsic motivation and less sensitivity to incentive magnitudes.

CONCLUSIONS

Although our L-AMCM did not significantly affect users' mission success rate, this study configures a pioneering work toward adaptively estimating incentives by considering user behaviors and contexts through leveraging mobile sensing and machine learning. We hope that this study inspires researchers to develop incentive-based interventions.

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.

A Digital Intervention Using Daily Financial Incentives to Increase Medication Adherence in Severe Mental Illness: Single-Arm Longitudinal Pilot Study

BACKGROUND

Medication nonadherence is prevalent in severe mental illness and is associated with multiple negative outcomes. Mobile technology and financial incentives show promise to improve medication adherence; however, studies in mental health, especially with oral medications, are lacking.

OBJECTIVE

The aim of this paper is to assess the feasibility and effectiveness of offering financial incentives through a mobile app based on behavioral economics principles to improve medication adherence in severe mental illness.

METHODS

A 10-week, single-arm longitudinal pilot study was conducted. Patients earned rewards in the context of app-based adherence incentives. The reward was split into biweekly payments made in increments of US $15, minus any US $2 per day penalties for missed check-ins. Time-varying effect modeling was used to summarize the patients' response during the study.

RESULTS

A total of 25 patients were enrolled in this pilot study, of which 72% (n=18) were female, and 48% (n=12) were of a White racial background. Median age was 24 (Q1-Q3: 20.5-30) years. Participants were more frequently diagnosed with schizophrenia and related disorders (n=9, 36%), followed by major depressive disorder (n=8, 32%). App engagement and medication adherence in the first 2 weeks were higher than in the last 8 weeks of the study. At study endpoint, app engagement remained high (n=24, Z=-3.17; P<.001), but medication adherence was not different from baseline (n=24, Z=-0.59; P=.28).

CONCLUSIONS

Financial incentives were effectively delivered using an app and led to high engagement throughout the study and a significantly increased medication adherence for 2 weeks. Leveraging behavioral economics and mobile health technology can increase medication adherence in the short term.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04191876; https://clinicaltrials.gov/ct2/show/NCT04191876.

Investigating Rewards and Deposit Contract Financial Incentives for Physical Activity Behavior Change Using a Smartphone App: Randomized Controlled Trial

BACKGROUND

Financial incentive interventions for improving physical activity have proven to be effective but costly. Deposit contracts (in which participants pledge their own money) could be an affordable alternative. In addition, deposit contracts may have superior effects by exploiting the power of loss aversion. Previous research has often operationalized deposit contracts through loss framing a financial reward (without requiring a deposit) to mimic the feelings of loss involved in a deposit contract.

OBJECTIVE

This study aimed to disentangle the effects of incurring actual losses (through self-funding a deposit contract) and loss framing. We investigated whether incentive conditions are more effective than a no-incentive control condition, whether deposit contracts have a lower uptake than financial rewards, whether deposit contracts are more effective than financial rewards, and whether loss frames are more effective than gain frames.

METHODS

Healthy participants (N=126) with an average age of 22.7 (SD 2.84) years participated in a 20-day physical activity intervention. They downloaded a smartphone app that provided them with a personalized physical activity goal and either required a €10 (at the time of writing: €1=US $0.98) deposit up front (which could be lost) or provided €10 as a reward, contingent on performance. Daily feedback on incentive earnings was provided and framed as either a loss or gain. We used a 2 (incentive type: deposit or reward) × 2 (feedback frame: gain or loss) between-subjects factorial design with a no-incentive control condition. Our primary outcome was the number of days participants achieved their goals. The uptake of the intervention was a secondary outcome.

RESULTS

Overall, financial incentive conditions (mean 13.10, SD 6.33 days goal achieved) had higher effectiveness than the control condition (mean 8.00, SD 5.65 days goal achieved; P=.002; ηp2=0.147). Deposit contracts had lower uptake (29/47, 62%) than rewards (50/50, 100%; P<.001; Cramer V=0.492). Furthermore, 2-way analysis of covariance showed that deposit contracts (mean 14.88, SD 6.40 days goal achieved) were not significantly more effective than rewards (mean 12.13, SD 6.17 days goal achieved; P=.17). Unexpectedly, loss frames (mean 10.50, SD 6.22 days goal achieved) were significantly less effective than gain frames (mean 14.67, SD 5.95 days goal achieved; P=.007; ηp2=0.155).

CONCLUSIONS

Financial incentives help increase physical activity, but deposit contracts were not more effective than rewards. Although self-funded deposit contracts can be offered at low cost, low uptake is an important obstacle to large-scale implementation. Unexpectedly, loss framing was less effective than gain framing. Therefore, we urge further research on their boundary conditions before using loss-framed incentives in practice. Because of limited statistical power regarding some research questions, the results of this study should be interpreted with caution, and future work should be done to confirm these findings.

TRIAL REGISTRATION

Open Science Framework Registries osf.io/34ygt; https://osf.io/34ygt.

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

Each day, adolescents and young adults (AYAs) choose to engage in behaviors that impact their current and future health. Behavioral economics represents an innovative lens through which to explore decision-making among AYAs. Behavioral economics outlines a diverse set of phenomena that influence decision-making and can be leveraged to develop interventions that may support behavior change. Up to this point, behavioral economic interventions have predominantly been studied in adults. This article provides an integrative review of how behavioral economic phenomena can be leveraged to motivate health-related behavior change among AYAs. We contextualize these phenomena in the physical and social environments unique to AYAs and the neurodevelopmental changes they undergo, highlighting opportunities to intervene in AYA-specific contexts. Our review of the literature suggests behavioral economic phenomena leveraging social choice are particularly promising for AYA health. Behavioral economic interventions that take advantage of AYA learning and development have the potential to positively impact youth health and well-being over the lifespan.

Engineering a mobile platform to promote sleep in the pediatric primary care setting

Study Objectives

Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion. The objective of this study is to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep.

Methods

Children aged 10–12 years were enrolled (Study #1: N = 30; Study #2: N = 43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms.

Results

In study #1, 1514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: −8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms.

Conclusions

We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.

Clinical Trials

Both studies included in this manuscript were registered at clinicaltrials.gov:

-Study #1: NCT03263338

-Study #2: NCT03426644

Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement

Meta-analyses have shown that digital mental health apps can be efficacious in reducing symptoms of depression and anxiety. However, real-world usage of apps is typically not sustained over time, and no studies systematically examine which features increase sustained engagement with apps or the relationship between engagement features and clinical efficacy. We conducted a systematic search of the literature to identify empirical studies that (1) investigate standalone apps for depression and/or anxiety in symptomatic participants and (2) report at least one measure of engagement. Features intended to increase engagement were categorized using the persuasive system design (PSD) framework and principles of behavioral economics. Twenty-five studies with 4159 participants were included in the analysis. PSD features were commonly used, whereas behavioral economics techniques were not. Smartphone apps were efficacious in treating symptoms of anxiety and depression in randomized controlled trials, with overall small-to-medium effects (g = 0.2888, SE = 0.0999, z(15) = 2.89, p = 0.0119, Q(df = 14) = 41.93, p < 0.0001, I2 = 66.6%), and apps that employed a greater number of engagement features as compared to the control condition had larger effect sizes (β = 0.0450, SE = 0.0164, t(15) = 2.7344, p = 0.0161). We observed an unexpected negative association between PSD features and engagement, as measured by completion rate (β = -0.0293, SE = 0.0121, t(17) = 02.4142, p = 0.0281). Overall, PSD features show promise for augmenting app efficacy, though engagement, as reflected in study completion, may not be the primary factor driving this association. The results suggest that expanding the use of PSD features in mental health apps may increase clinical benefits and that other techniques, such as those informed by behavioral economics, are employed infrequently.

Engineering a Mobile Platform to Promote Sleep in the Pediatric Primary Care Setting

Background

Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion.

Purpose

Under the preparation phase of the MOST framework, to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep.

Methods

Children aged 10-12y were enrolled (Study #1: N=30; Study #2: N=43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms.

Results

In study #1, 1,514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: -8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms.

Conclusion

We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.

A randomized, controlled, behavioral intervention to promote walking after abdominal organ transplantation: results from the LIFT study

Kidney transplant recipients (KTRs) and liver transplant recipients (LTRs) have significant post-transplant weight gain and low physical activity. We conducted a home-based, remotely monitored intervention using wearable accelerometer devices to promote post-transplant physical activity. We randomized 61 KTRs and 66 LTRs within 24 months of transplant to: (i) control, (ii) accelerometer or (iii) intervention: accelerometer paired with financial incentives and health engagement questions to increase steps by 15% from baseline every 2 weeks. The primary outcome was weight change. A co-primary outcome for the two accelerometer arms was steps. Participants were recruited at a median of 9.5 [3-17] months post-transplant. At 3 months, there were no significant differences in weight change across the three arms. The intervention arm was more likely to achieve ≥7000 steps compared to control with device (OR 1.99, 95% CI: 1.03-3.87); effect remained significant after adjusting for demographics, allograft, time from transplant and baseline weight. Adherence to target step goals was 74% in the intervention arm, 84% of health engagement questions were answered correctly. A pilot study with financial incentives and health engagement questions was feasible and led KTRs and LTRs to walk more, but did not affect weight. A definitive trial is warranted.

Financial incentives for physical activity in adults: systematic review and meta-analysis

Objective

The use of financial incentives to promote physical activity (PA) has grown in popularity due in part to technological advances that make it easier to track and reward PA. The purpose of this study was to update the evidence on the effects of incentives on PA in adults.

Data sources

Medline, PubMed, Embase, PsychINFO, CCTR, CINAHL and COCH.

Eligibility criteria

Randomised controlled trials (RCT) published between 2012 and May 2018 examining the impact of incentives on PA.

Design

A simple count of studies with positive and null effects (‘vote counting’) was conducted. Random-effects meta-analyses were also undertaken for studies reporting steps per day for intervention and post-intervention periods.

Results

23 studies involving 6074 participants were included (64.42% female, mean age = 41.20 years). 20 out of 22 studies reported positive intervention effects and four out of 18 reported post-intervention (after incentives withdrawn) benefits. Among the 12 of 23 studies included in the meta-analysis, incentives were associated with increased mean daily step counts during the intervention period (pooled mean difference (MD), 607.1; 95% CI: 422.1 to 792.1). Among the nine of 12 studies with post-intervention daily step count data incentives were associated with increased mean daily step counts (pooled MD, 513.8; 95% CI:312.7 to 714.9).

Conclusion

Demonstrating rising interest in financial incentives, 23 RCTs were identified. Modest incentives ($1.40 US/day) increased PA for interventions of short and long durations and after incentives were removed, though post-intervention ‘vote counting’ and pooled results did not align. Nonetheless, and contrary to what has been previously reported, these findings suggest a short-term incentive ‘dose’ may promote sustained PA.

Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study

Background

The Carrot Rewards app was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards, and groceries for engaging in healthy behaviors such as walking.

Objective

This study examined whether a multicomponent intervention including goal setting, graded tasks, biofeedback, and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL).

Methods

This 12-week, quasi-experimental (single group pre-post) study included 78,882 participants; 44.39% (35,014/78,882) enrolled in the Carrot Rewards “Steps” walking program during the recruitment period (June 13–July 10, 2016). During the 2-week baseline (or “run-in”) period, we calculated participants’ mean steps per day. Thereafter, participants earned incentives in the form of loyalty points (worth Can $0.04 ) every day they reached their personalized daily step goal (ie, baseline mean+1000 steps=first daily step goal level). Participants earned additional points (Can $0.40) for meeting their step goal 10+ nonconsecutive times in a 14-day period (called a “Step Up Challenge”). Participants could earn up to Can $5.00 during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, aiming to gradually increase the daily step number by 3000. Only participants with ≥5 valid days (days with step counts: 1000-40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps per day (by week), analyzed using linear mixed-effects models.

Results

The mean age of 32,229 participants with valid baseline data was 33.7 (SD 11.6) years; 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6511.22. Over half of users (16,336/32,229, 50.69%) were categorized as “physically inactive,” accumulating <5000 daily steps at baseline. Results from mixed-effects models revealed statistically significant increases in mean daily step counts when comparing baseline with each study week (P<.001). Compared with baseline, participants walked 115.70 more steps (95% CI 74.59 to 156.81; P<.001) at study week 12. BC and NL users classified as “high engagers” (app engagement above sample median; 15,511/32,229, 48.13%) walked 738.70 (95% CI 673.81 to 803.54; P<.001) and 346.00 (95% CI 239.26 to 452.74; P<.001) more steps, respectively. Physically inactive, high engagers (7022/32,229, 21.08%) averaged an increase of 1224.66 steps per day (95% CI 1160.69 to 1288.63; P<.001). Effect sizes were modest.

Conclusions

Providing very small but immediate rewards for personalized daily step goal achievement as part of a multicomponent intervention increased daily step counts on a population scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC and NL provide evidence of replicability.