Poster
in
Workshop: Causal Machine Learning for Real-World Impact

A Large-Scale Observational Study of the Causal Effects of a Behavioral Health Nudge

Achille Nazaret · Guillermo Sapiro

Abstract:

The Apple Watch encourages users to stand throughout the day by delivering a notification onto the users’ wrist if they have been sitting for the first 50 minutes of an hour. This simple behavioral intervention exemplifies the classical definition of nudge as a choice architecture that alters behavior without forbidding options or significantly changing economic incentives. In order to estimate from observational data the causal effect of the notification on the user's standing probability throughout the day, we introduce a novel regression discontinuity design for time series data with time-varying treatment. Using over 76 billions minutes of private and anonymous observational standing data from more than 160,000 subjects enrolled in the public Apple Heart and Movement Study from 2019 to 2022, we show that the nudge increases the probability of standing by up to 49.5% across all the studied population. The nudge is similarly effective for participants self-identified as male or female, and it is more effective in older people, increasing the standing probability in people over 75 years old by more than 60%. We also demonstrate that closing Apple Watch Activity Rings, another simple choice architecture that visualizes the participant's daily progress in Move, Exercise, and Stand, correlates with user's response to the intervention; for users who close their activity rings regularly, the standing nudge almost triples their probability of standing. This observational study, which is one of the largest of its kind exploring the causal effects of nudges in the general population, demonstrates the effectiveness of simple behavioral health interventions and introduces a novel application of regression discontinuity design extended here to time-varying treatments.

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