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Megastudies improve the impact of applied behavioural science

Abstract

Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes1. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals2. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy—a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research3,4,5,6. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.

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Fig. 1: Measured versus predicted changes in weekly gym visits induced by interventions.

Data availability

The data analysed in this paper were provided by 24 Hour Fitness and we have their legal permission to share the deidentified data. We have therefore made deidentified data available at https://osf.io/9av87/?view_only=8bb9282111c24f81a19c2237e7d7eba3. Furthermore, tables of all of the preregistration links for each of the substudies with the interventions and the prediction studies are available in Supplementary Tables 2 and 30.

Code availability

The code to replicate the analyses and figures in the paper and Supplementary Information is available online (https://osf.io/9av87/?view_only=8bb9282111c24f81a19c2237e7d7eba3).

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Acknowledgements

Support for this research was provided in part by the Robert Wood Johnson Foundation, the AKO Foundation, J. Alexander, M. J. Leder, W. G. Lichtenstein, the Pershing Square Fund for Research on the Foundations of Human Behavior from Harvard University and by Roybal Center grants (P30AG034546 and 5P30AG034532) from the National Institute on Aging. The views expressed here do not necessarily reflect the views of any of these individuals or entities. We thank 24 Hour Fitness for partnering with the Behavior Change for Good Initiative at the University of Pennsylvania to make this research possible.

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Authors and Affiliations

Authors

Contributions

K.L.M., D.G., A.R., M.B., J.B., L.B., E.C., G.C., R.C., H.D., L.E.-W., A.F., J.J.G., S.H., A.H., S.J.J., D.K., E.K., J.K., A.K., G.L., B.M., S.M., S.S., G.S., J.H.T., J.T., Y.T., L.U., K.G.V., A.W., J.Z. and A.L.D. designed the research. K.L.M., D.G., J.S.K., P.P., Y.P., A.L.D. and A.R. performed the research. H.H., T.W.L., P.P. and Y.P. analysed the data. K.L.M. and A.L.D wrote the paper. D.G., H.H., J.S.K., T.W.L., P.P., Y.P., A.R., M.B., J.B., C.C., G.C., H.D., A.F., J.J.G., D.K., T.K., E.K., J.K., R.L., J.L., B.M., S.M., S.S., J.S., A.W. and J.Z. provided feedback on the paper. K.L.M., D.G., J.S.K., T.K., R.L. and S.M. supervised data analysis. K.L.M., D.G., H.H., J.S.K. and T.W.L. prepared the Supplementary Information.

Corresponding authors

Correspondence to Katherine L. Milkman or Angela L. Duckworth.

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Competing interests

The authors declare no competing interests. The authors did not receive commercial benefits from the fitness chain or speaking/consulting fees related to any of the interventions presented here.

Additional information

Peer review information Nature thanks Charles Shearer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Measured vs. predicted change in likelihood of gym visit in a given week.

The measured change (blue) vs. change predicted by third-party observers (gold) in whether participants visited the gym that was induced by each of our megastudy’s 53 experimental conditions compared to a Placebo Control condition during a four-week intervention period is depicted here. Error bars represent 95% confidence intervals. See Extended Data Table 7 for complete OLS regression results graphed here in blue, Supplementary Information 11 for more details about the prediction data graphed here in gold, and Supplementary Table 1 for full descriptions of each treatment condition in our megastudy. Sample weights were included in the pooled third-party prediction data to ensure equal weighting of each of our three participant samples (professors, practitioners and prolific respondents). The superscripts a–e denote the different incentive amounts offered in different versions of the bonus for returning after missed workouts, higher incentives and rigidity rewarded conditions, which are described in Supplementary Table 1. In conditions with the same name, superscripts that come earlier in the alphabet indicate larger incentives.

Extended Data Table 1 Regression-estimated effects of each experimental condition on whether participants visited the gym in a given week during the four-week intervention period relative to the Planning, Reminders and Micro-Incentives to Exercise condition
Extended Data Table 2 Regression-estimated effects of each experimental condition on whether participants visited the gym in a given week during the four-week post-intervention period relative to the Placebo Control condition
Extended Data Table 3 The percentage of other conditions that each experimental condition outperformed for our dependent variable measuring whether participants visited the gym in a given week at p < .05 during the four-week intervention period
Extended Data Table 4 Participants’ mean age (in years), gender, length of gym membership (in weeks), and mean weekly gym visits in the four-week pre-intervention period across the 54 study conditions
Extended Data Table 5 Percentage of significant p-values and absolute difference in coefficients from pairwise comparisons of the 54 study conditions in our megastudy on each variable listed (alpha = .05)
Extended Data Table 6 Regression-estimated effects of each experimental condition on total weekly gym visits during the four-week intervention period relative to the Placebo Control condition
Extended Data Table 7 Regression-estimated effects of each experimental condition on whether participants visited the gym in a given week during the four-week intervention period relative to the Placebo Control condition
Extended Data Table 8 Regression-estimated effects of each experimental condition on total weekly gym visits during the four-week intervention period relative to the Planning, Reminders, and Micro-Incentives to Exercise condition
Extended Data Table 9 Regression-estimated effects of each experimental condition on total weekly gym visits during the four-week post-intervention period relative to the Placebo Control condition

Supplementary information

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Milkman, K.L., Gromet, D., Ho, H. et al. Megastudies improve the impact of applied behavioural science. Nature 600, 478–483 (2021). https://doi.org/10.1038/s41586-021-04128-4

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