A Cluster-Randomized Trial on Small Incentives to Promote Physical Activity

Assessment of Step Tracker Mobile Applications for the Promotion of Physical Activity by Adolescents Based on Their Weight Status

The objectives of the present study were to analyze the adherence of normal weight and overweight/obese adolescents to intervention with mobile applications; to establish the differences in the evaluation of the mobile application and in the problematic use of the mobile phone between normal weight and overweight/obese adolescents; and to determine the relationship between the distance travelled, the evaluation of the mobile applications, and the conflictive use of the mobile phone. A quasi-experimental design was carried out with the participation of 70 adolescents between 12 and 16 years old (mean age: 14.25 ± 1.23 years old; 38 normal weight and 32 overweight/obese). The adolescents completed a 10-week intervention in which they used step tracker mobile applications for the promotion of physical activity, a minimum of three times per week. Problematic mobile phone use and adolescent ratings of the application used were measured. The results showed no significant differences in adolescents' adherence to the intervention according to the mobile application used (p = 0.191) or weight status (p = 0.202). In addition, significant differences were not found in the assessment of mobile applications within the group of overweight and obese adolescents: engagement (p = 0.471), functionality (p = 0.319), aesthetics (p = 0.378), information (p = 0.184), usability (p = 0.154), or perceived impact (p = 0.139), although differences were found in the assessment made by normal-weight adolescents in information (p = 0.029), usability (p = 0.029), and perceived impact (p = 0.044), where Pacer had better scores than Pokémon Go in the first two dimensions. No greater problematic mobile phone use was found after the intervention according to weight status (p = 0.311) nor the mobile application used (p = 0.985). It can be concluded that there is similar adherence among normal weight and overweight/obese adolescents to interventions with mobile applications to promote physical activity. It is noteworthy that adolescents, regardless of weight status, showed a positive perception towards the use of these mobile applications.

Evaluation of the Effectiveness of Behavioral Economic Incentive Programs for Goal Achievement on Healthy Diet, Weight Control and Physical Activity: A Systematic Review and Network Meta-analysis

BACKGROUND

Healthy diet, weight control and physical activity to reduce obesity can be motivated by financial incentives (FI). Behavioral-economic approaches may improve the incentivization effectiveness. This study compares and ranks the effectiveness of standard and behavioral incentivization for healthy diet, weight control, and physical activity promotion.

PURPOSE

To investigate whether behavioral-economic insights improve incentivization effectiveness.

METHODS

A systematic search of Medline and Scopus was performed from database inception to December 2020. Study characteristics, program designs, and risk ratio (RR) were extracted. A two-stage network meta-analysis pooled and ranked intervention effects.

RESULTS

There were 35 eligible RCTs. For diet-weight control, standard FI, deposit contract (deposit), lottery-based incentive (lottery), and standard-FI + lottery increased goal achievement compared to no-FI but only deposit was statistically significant with pooled RRs and 95% confidence intervals (CI) of 1.21 (0.94, 1.56), 1.79 (1.04, 3.05), 1.45 (0.99, 2.13), and 1.73 (0.83, 3.63). For physical activity, standard-FI, deposit, and lottery significantly increased goal achievement compared to no-FI, with pooled RRs of 1.38 (1.13, 1.68), 1.63 (1.24, 2.14) and 1.43 (1.14, 1.80), respectively. In a follow-up period for physical activity, only deposit significantly increased goal achievement compared to no-FI, with pooled RRs of 1.39 (1.11, 1.73).

CONCLUSION

Deposit, followed by lottery, were best for motivating healthy diet, weight control and physical activity at program end. Post-intervention, deposit then standard-FI were best for motivating physical activity. Behavioral insights can improve incentivization effectiveness, although lottery-based approaches may offer only short-term benefit regarding physical activity. However, the imprecise intervention effects were major concerns.

Effects of Mobile-Based Financial Incentive Interventions for Adults at Risk of Developing Hypertension: Feasibility Randomized Controlled Trial

BACKGROUND

Hypertension is the leading modifiable risk factor for cardiovascular disease and mortality. Adopting lifestyle modifications, like increasing physical activity (PA), can be an effective strategy in blood pressure (BP) control, but many adults do not meet the PA guidelines. Financial incentive interventions have the power to increase PA levels but are often limited due to cost. Further, mobile health technologies can make these programs more scalable. There is a gap in the literature about the most feasible and effective financial incentive PA framework; thus, pay-per-minute (PPM) and self-funded investment incentive (SFII) frameworks were explored.

OBJECTIVE

The aims were to (1) determine the feasibility (recruitment, engagement, and acceptability) of an 8-week mobile-based PPM and SFII hypertension prevention PA program and (2) explore the effects of PPM and SFII interventions relative to a control on the PA levels, BP, and PA motivation.

METHODS

In total, 55 adults aged 40-65 years not meeting the Canadian PA guidelines were recruited from Facebook and randomized into the following groups: financial incentive groups, PPM or SFII, receiving up to CAD $20 each (at the time of writing: CAD $1=US $0.74), or a control group without financial incentive. PPM participants received CAD $0.02 for each minute of moderate-to-vigorous PA (MVPA) per week up to the PA guidelines and the SFII received CAD $2.50 for each week they met the PA guidelines. Feasibility outcome measures (recruitment, engagement, and acceptability) were assessed. Secondary outcomes included changes in PA outcomes (MVPA and daily steps) relative to baseline were compared among PPM, SFII, and control groups at 4 and 8 weeks using linear regressions. Changes in BP and relative autonomy index relative to baseline were compared among the groups at follow-up.

RESULTS

Participants were randomized to the PPM (n=19), SFII (n=18), or control (n=18) groups. The recruitment, retention rate, and engagement were 77%, 75%, and 65%, respectively. The intervention received overall positive feedback, with 90% of comments praising the intervention structure, financial incentive, and educational materials. Relative to the control at 4 weeks, the PPM and SFII arms increased their MVPA with medium effect (PPM vs control: η²_p=0.06, mean 117.8, SD 514 minutes; SFII vs control: η²_p=0.08, mean 145.3, SD 616 minutes). At 8 weeks, PPM maintained a small effect in MVPA relative to the control (η²_p=0.01, mean 22.8, SD 249 minutes) and SFII displayed a medium effect size (η²_p=0.07, mean 113.8, SD 256 minutes). Small effects were observed for PPM and SFII relative to the control for systolic blood pressure (SBP) and diastolic blood pressure (DBP) (PPM: η²_p=0.12, Δmean SBP 7.1, SD 23.61 mm Hg; η²_p=0.04, Δmean DBP 3.5, SD 6.2 mm Hg; SFII: η²_p=0.01, Δmean SBP -0.4, SD 1.4 mm Hg; η²_p=0.02, Δmean DBP -2.3, SD 7.7 mm Hg) and relative autonomy index (PPM: η²_p=0.01; SFII: η²_p=0.03).

CONCLUSIONS

The feasibility metrics and preliminary findings suggest that a future full-scale randomized controlled trial examining the efficacy of PPM and SFII relative to a control is feasible, and studies with longer duration are warranted.

The Development of a Hypertension Prevention and Financial Incentive Mobile Health Program Using a “No-Code” Mobile App Builder: A Development and Usability Study (Preprint)

BACKGROUND

Regular physical activity (PA) is a key lifestyle component for hypertension prevention. Previous studies have shown that mobile health (mHealth) apps can be an effective tool for improving PA behaviors. However, adherence to and poor engagement with these apps is a challenge. A potential solution to overcome this challenge may be to combine financial incentives with innovative behavior theory, such as the Multiprocess Action Control (M-PAC) framework. Currently, there is a lack of PA financial incentive-driven M-PAC mHealth programs aimed at hypertension prevention.

OBJECTIVE

We aimed to describe the process of developing an 8-week mHealth PA and financial-incentive hypertension education program (Healthy Hearts) and to evaluate usability of the Healthy Hearts program.

METHODS

The first 2 stages of the Integrate, Design, Assess, and Share framework were used to guide the development of the Healthy Hearts program. The development process consisted of 2 phases. In phase 1, the research team met to discuss implementing the M-PAC framework to adopt an existing web-based hypertension prevention program to a mobile app. The app was developed using a no-code app development platform, Pathverse (Pathverse Inc), to help decrease overall development time. In phase 2, we created a prototype and conducted usability testing to evaluate lesson 1 of the Healthy Hearts program to further enhance the user experience. We used semistructured interviews and the mHealth App Usability Questionnaire to evaluate program acceptability and usability.

RESULTS

Intervention development among the research team successfully created an 8-week financial-incentive hypertension education program for adults aged 40-65 years who did not currently meet the Canadian Physical Activity Guidelines (<150 minutes of moderate to vigorous PA per week). This program lasted 8 weeks and comprised 25 lessons guided by the M-PAC framework. The program used various behavior change techniques to further support PA adherence. Usability testing of the first lesson was successful, with 6 participants recruited for 2 rounds of testing. Feedback was gathered to enhance the content, layout, and design of the Healthy Hearts program to prepare the mHealth program for feasibility testing. Results of round 1 of usability testing suggested that the content delivered in the lessons was long. Therefore, the content was divided into multiple lessons before round 2 of usability testing, where feedback was only on design preferences. A minimum viable product was created with these results.

CONCLUSIONS

The iterative development process and the usability assessments suggested by the Integrate, Design, Assess, and Share framework enabled participants to provide valuable feedback on the content, design, and layout of the program before advancing to feasibility testing. Furthermore, the use of the "no-code" app development tool enabled our team to rapidly make changes to the app based on user feedback during the iterative design process.

The Effect of a Future-Self Avatar Mobile Health Intervention (FutureMe) on Physical Activity and Food Purchases: Randomized Controlled Trial

BACKGROUND

Insufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking.

OBJECTIVE

We aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs.

METHODS

We conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences.

RESULTS

We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median -1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system.

CONCLUSIONS

Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04505124; https://clinicaltrials.gov/ct2/show/NCT04505124.

Usability Testing of a Social Media Chatbot for Increasing Physical Activity Behavior

Digital interventions for increasing physical activity behavior have shown great potential, especially those with social media. Chatbots, also known as conversational agents, have emerged in healthcare in relation to digital interventions and have proven effective in promoting physical activity among adults. The study’s objective is to explore users’ experiences with a social media chatbot. The concept and the prototype development of the social media chatbot MYA were realized in three steps: requirement analysis, concept development, and implementation. MYA’s design includes behavior change techniques effective in increasing physical activity through digital interventions. Participants in a usability study answered a survey with the Chatbot Usability Questionnaire (CUQ), which is comparable to the Systems Usability Scale. The mean CUQ score was below 68, the benchmark for average usability. The highest mean CUQ score was 64.5 for participants who thought MYA could help increase their physical activity behavior. The lowest mean CUQ score was 40.6 for participants aged between 50 and 69 years. Generally, MYA was considered to be welcoming, very easy to use, realistic, engaging, and informative. However, some technical issues were identified. A good and diversified user experience promotes prolonged chatbot use. Addressing identified issues will enhance users’ interaction with MYA.

Can a multi-level intervention approach, combining behavioural disciplines, novel technology and incentives increase physical activity at population-level?

BACKGROUND

Despite a global call for action and growing burden of non-communicable diseases (NCD) associated with physical inactivity, effective interventions to increase community-wide physical activity (PA) remain few. NCDs accounted for 80% of Singapore's disease burden (2015) and yet 40% of Singaporeans did not meet minimum recommended weekly PA despite evidence of the benefits to cardiorespiratory health, diabetes and cancer prevention.

METHODS

A large-scale public health intervention was initiated in 2015 to increase population-level PA through incidental daily walking. Intervention components included fitness trackers, redeemable rewards and gamification, implemented in a mutually-reinforcing manner within an eco-system supportive of PA and informed by real-time data analytics. Mean daily step count at baseline and post-intervention were compared across periods, and the influence of participant sub-groups characteristics on overall results, using significance tests. Standards for Reporting on Implementation Studies (StaRI) were adhered to.

RESULTS

Intervention reach increased fourfold from 129,677 participants in wave 1 (2015-16) to 690,233 in wave 3 (2017-18) amounting to a total of 1,184,410 Step Challenge participations. Mean days of fitness tracker use increased from 2.4 to 5.0 days/week among participants completing the Challenge in wave 1 and from 5.3 to 6.0 days/week in wave 3. The mean number of daily steps between pre-Challenge and Challenge periods increased by 4163 (sd=1360; p< 0.001) in wave 1, by 2242 (sd=334; p< 0.001) in wave 2 and by 1645 steps/day (sd=54; p< 0.001) in wave 3. Mean daily step increases between wave 1 and 3 also suggest that incidental PA was maintained, a finding supported by a 2017 national population survey showing that incidental PA among adults increased from 5% in 2010 to 14% in 2017 while moderate-intensity PA increased from 5 to 10% over the same period.

CONCLUSION

Population-level PA was effectively increased through multi-level interventions integrating technology, behavioural economics, gamification, marketing, communications and community linkages within a supportive context- and climate-appropriate environment. Responsive data analytics were instrumental to strengthen implementation by tailoring modalities that maximise effectiveness at population-level. Further analyses are needed to explore potential barriers, challenges or unmet needs in sub-groups with lower uptake to tailor future interventions for greater reach and impact.

Examining Responsiveness to an Incentive-Based Mobile Health App: Longitudinal Observational Study

Background

The Carrot Rewards app was developed as part of a public-private partnership to reward Canadians with loyalty points for downloading the app, referring friends, completing educational health quizzes, and health-related behaviors with long-term objectives of increasing health knowledge and encouraging healthy behaviors. During the first 3 months after program rollout in British Columbia, a number of program design elements were adjusted, creating observed differences between groups of users with respect to the potential impact of program features on user engagement levels.

Objective

This study examines the impact of reducing reward size over time and explored the influence of other program features such as quiz timing, health intervention content, and type of reward program on user engagement with a mobile health (mHealth) app.

Methods

Participants in this longitudinal, nonexperimental observational study included British Columbia citizens who downloaded the app between March and July 2016. A regression methodology was used to examine the impact of changes to several program design features on quiz offer acceptance and engagement with this mHealth app.

Results

Our results, based on the longitudinal app use of 54,917 users (mean age 35, SD 13.2 years; 65.03% [35,647/54,917] female), indicated that the key drivers of the likelihood of continued user engagement, in order of greatest to least impact, were (1) type of rewards earned by users (eg, movies [+355%; P<.001], air travel [+210%; P<.001], and grocery [+140%; P<.001] relative to gas), (2) time delay between early offers (−64%; P<.001), (3) the content of the health intervention (eg, healthy eating [−10%; P<.001] vs exercise [+20%, P<.001] relative to health risk assessments), and (4) changes in the number of points offered. Our results demonstrate that reducing the number of points associated with a particular quiz by 10% only led to a 1% decrease in the likelihood of offer response (P<.001) and that each of the other design features had larger impacts on participant retention than did changes in the number of points.

Conclusions

The results of this study demonstrate that this program, built around the principles of behavioral economics in the form of the ongoing awarding of a small number of reward points instantly following the completion of health interventions, was able to drive significantly higher engagement levels than those demonstrated in previous literature exploring the intersection of mHealth apps and financial incentives. Previous studies have demonstrated the presence of incentive matters to user engagement; however, our results indicate that the number of points offered for these reward point–based health interventions is less important than other program design features such as the type of reward points being offered, the timing of intervention and reward offers, and the content of the health interventions in driving continued engagement by users.

Effects of Incentives on Adherence to a Web-Based Intervention Promoting Physical Activity: Naturalistic Study

Background

Despite many advantages of web-based health behavior interventions such as wide accessibility or low costs, these interventions are often accompanied by high attrition rates, particularly in usage under real-life conditions. It would therefore be helpful to implement strategies such as the use of financial incentives to motivate program participation and increase adherence.

Objective

This naturalistic study examined real-life usage data of a 12-week web-based physical activity (PA) intervention (Fitness Coach) among insurants who participated in an additional incentive program (incentive group) and those who did not (nonincentive group). Users in the incentive group had the perspective of receiving €30 (about US $33) cash back at the end of the intervention.

Methods

Registration and real-life usage data as part of routine data management and evaluation of the Fitness Coach were analyzed between September 2016 and June 2018. Depending on the duration of use and the weekly recording of tasks, 4 adherence groups (low, occasional, strong, and complete adherence) were defined. Demographic characteristics were collected by a self-reported questionnaire at registration. We analyzed baseline predictors and moderators of complete adherence such as participation in the program, age, gender, and BMI using binary logistic regressions.

Results

A total of 18,613 eligible persons registered for the intervention. Of these, 15,482 users chose to participate in the incentive program (incentive group): mean age 42.4 (SD 14.4) years, mean BMI 24.5 (SD 4.0) kg/m², median (IQR) BMI 23.8 (21.7-26.4) kg/m²; 65.12% (10,082/15,482) female; and 3131 users decided not to use the incentive program (nonincentive group): mean age 40.7 (SD 13.4) years, mean BMI 26.2 (SD 5.0) kg/m², median BMI 25.3 (IQR 22.6-28.7) kg/m²; 72.18% (2260/3131) female. At the end of the intervention, participants in the incentive program group showed 4.8 times higher complete adherence rates than those in the nonincentive program group (39.2% vs 8.1%), also yielding significantly higher odds to complete the intervention (odds ratio [OR] 12.638) for the incentive program group. Gender significantly moderated the effect with men in the incentive group showing higher odds to be completely adherent than women overall and men in the nonincentive group (OR 1.761). Furthermore, older age and male gender were significant predictors of complete adherence for all participants, whereas BMI did not predict intervention completion.

Conclusions

This is the first naturalistic study in the field of web-based PA interventions that shows the potential of even small financial incentives to increase program adherence. Male users, in particular, seem to be strongly motivated by incentives to complete the intervention. Based on these findings, health care providers can use differentiated incentive systems to increase regular participation in web-based PA interventions.

The Impact of Financial Incentives on Physical Activity: A Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the effects of financial incentives on physical activity (PA).

DATA SOURCES

MEDLINE, Embase, 7 other databases, and 2 trial registries until July 17, 2019.

STUDY INCLUSION AND EXCLUSION CRITERIA

Randomized controlled trials with adults aged ≥18 years assessing the effect of financial incentives on PA. Any comparator was eligible provided the only difference between groups was the incentive strategy.

DATA EXTRACTION

Two independent reviewers extracted data and assessed study quality. Of 5765 records identified, 57 records (51 unique trials; n = 17 773 participants) were included.

DATA SYNTHESIS

Random-effects models pooling data for each of the 5 PA domains.

RESULTS

Financial incentives increase leisure time PA (gym or class attendance; standardized mean difference [95% CI], 0.46 [0.28-0.63], n = 5057) and walking behavior (steps walked; 0.25 [0.13-0.36], n = 3254). No change in total minutes of PA (0.52 [-0.09 to 1.12], n = 968), kilocalories expended (0.19 [-0.06 to 0.44], n = 247), or the proportion of participants meeting PA guidelines (risk ratio [95% CI] 1.53 [0.53-4.44], n = 650) postintervention was observed. After intervention has ceased, incentives sustain a slight increase in leisure time PA (0.10 [0.02-0.18], n = 2678) and walking behavior (0.11 [0.00-0.22], n = 2425).

CONCLUSIONS

Incentives probably improve leisure time PA and walking at intervention end, and small improvements may be sustained over time once incentives have ceased. They lead to little or no difference in kilocalories expended or minutes of PA. It is uncertain whether incentives change the likelihood of meeting PA guidelines because the certainty of the evidence is low.

Physical activity habit formation through a technology-based program

BACKGROUND

Only about half of adults in the United States meet the minimum federal guidelines for physical activity (PA), with less than one quarter getting an optimal amount of weekly activity. Programs to increase PA can improve health and increase worker productivity.

LOCAL PROBLEM

Clinic patients of a nurse practitioner-run employee health clinic in a self-insured health care system experienced health conditions associated with insufficient PA and wanted to form habits of adequate PA for health promotion.

METHODS

A quantitative design was used to assess pre- and postintervention measures in this quality improve project.

INTERVENTIONS

Patients of the clinic enrolled in a technology-based 3-month PA habit development program that included wearable technology, tracked step counts, daily text messaging, and weekly electronic newsletters. Biometric and habit measures were taken at baseline and at the conclusion of the 3-month program to determine effectiveness.

RESULTS

Participants who completed the program developed strong habits of PA, on average. Small improvements in blood pressure, weight, and body mass index occurred but were not clinically significant. Two thirds of participants dropped out, which was fewer than anticipated based on prior reports.

CONCLUSIONS

Habits make an activity less difficult to continue than to stop. Thus, PA habits developed through this innovative intervention should persist and lead to decreased risk of conditions such as diabetes, heart disease, cancers, and dementia. The self-insured employer should reap the benefits of employee's PA through increased productivity, decreased absenteeism, and lower health care costs.