The health benefits of physical activity (PA) are irrefutable and yet, widespread inactivity persists.1, 2 Interventions that increase PA on population-levels are needed to help tackle this important public health issue.3 As smartphone ownership increases (approaching 90% in the US),4 so has the number of mobile health applications (mHealth apps) in the major app stores (over 325,000; 30% are PA apps).5 Part of the appeal of mHealth apps is their potential to reach large populations at relatively low cost.6 Their effectiveness, however, is often limited by low user engagement with 90% of mHealth apps being deleted within 30 days.7–9 A still limited number of RCT studies suggest a dose-response relationship exists between engagement and effectiveness, with greater app use associated with larger PA improvements.10–12 Conversely, waning engagement has been linked with declining PA.10, 11, 13–15 Recent advances in behavioural science provide a new framework from which to develop practical solutions to address this notorious mHealth app engagement problem.
Behavioural economics, a branch of economics shaped by insights from psychology,16–18 has stimulated renewed interest in financial health incentive interventions, such as rewarding people to walk more.19 This increasingly common intervention is grounded in a behavioural economics concept called present bias which describes how individuals tend to place disproportionate emphasis on the present “cost” of a health behaviour (e.g., time) while discounting the future “benefits” of that behaviour (e.g., increased health).16 Behavioural economics suggests that providing timely financial incentives for behaviours with benefits that are largely delayed (e.g., PA) may encourage individuals to choose to engage in those behaviours rather than put them off.16, 18, 20 Individual-level financial incentives for PA (e.g., incentives for personal PA achievements) have shown promising results. A recent meta-analysis of RCTs concluded that financial incentives increased PA in the short-term by up to 4,000 steps per day, with some evidence of long-term (six or more months) and sustained (after incentives withdrawn) effects.21 A number of studies also suggest team-based incentives (e.g., incentives for group achievements) may be efficacious as well.22 Compared to individual incentives, team incentives have yielded better gym attendance, more PA, and greater weight loss in RCT settings.23–25 Interestingly, Patel et al. (2018) found that combining individual and team (i.e., combined) incentives was more efficacious than individual or team incentives alone.26
The notion of “aligning the thoughts or behaviours of individuals in a group” is another pertinent behavioural economics concept called herd behaviour.27 Herd behaviour describes how individuals are more likely to follow others in decision making instead of making independent decisions (e.g., “My friend is going for a walk, so I probably should too.”).21 The tendency for humans to want to behave in ways that are consistent with the people in their social networks may be leveraged in an mHealth context, for instance: (a) by providing feedback on peers’ progress, and/or (b) with team-based incentives. Recent evidence, though, suggests that adding a social component to mHealth interventions does not necessarily translate into positive effects.7, 11, 28, 29 It appears that mHealth features designed to increase social connectivity among participants with no prior relationship do not work as well as those delivered among people with existing relationships (e.g., work colleagues challenge each other in an online walking challenge).7, 26, 29, 30 Babcock et al. (2015) compared anonymous partners to partners with an existing social connection and found PA incentives were not as effective in the anonymous group, highlighting the importance of leveraging pre-existing social connections in mHealth interventions.24, 26, 30
Despite their popularity, very little is known about the effectiveness of commercial PA apps (or their design features) since few have undergone rigorous peer-reviewed evaluation. 10, 31 Among the 15 studies included in the recent Petersen et al. (2019) review of PA apps, for example, only five examined commercially available ones (e.g., Fitbit, ‘Zombie, Run!’) despite there being several hundred thousand in the major app stores. 31 Among these five, little consideration was given to the role of engagement as an effect moderator despite suggestions that intervention exposure is imperative and that greater engagement usually yields larger effects. 32 The Carrot Rewards app was a top tier Canadian app (i.e. 1.3 + million downloads, 500,000 + monthly active users (MAUs) as of May 2019) that rewarded users with loyalty points redeemable for consumer goods (e.g., gas, movies) for walking more. It was developed in partnership with the Public Health Agency of Canada as part of its Multi-Sectoral Partnership Approach to Healthy Living and Chronic Disease Prevention. 33 One of the stated objectives of the initiative was to conduct rigorous evaluations of the app intervention, including the impact of new features. 12, 34 In March 2018, Carrot Rewards launched a new social feature called ‘Step Together Challenges’ (STCs) to complement their existing walking program (called ‘Steps’) where individualized daily step goal achievements were rewarded with very small incentives ($0.04 CAD per day). STCs allowed users to invite a friend from their existing social network to participate in a collaborative walking challenge for bonus incentives ($0.40 CAD per week).
To enhance our understanding of mHealth interventions, and acknowledging how difficult it can be to conduct RCTs in fast paced commercial digital environments, non-RCT alternatives (e.g., quasi-experimental designs) have been recommended.31, 35, 36 Quasi-experimental evaluations of “top tier” commercial apps (i.e. the top 2% of apps reporting more than 500,000 MAUs)5 may provide particularly valuable insight into mHealth app engagement, it’s role in promoting health behaviours, and how it can be improved on a population scale. The primary objective of this study, then, was to examine the impact of adding team incentives to the Carrot Rewards app on mean daily step count. An important secondary objective was to determine whether a dose-response relationship existed between app engagement (i.e. STCs completed) and mean daily step count.