What can machine learning teach us about habit formation? Evidence from exercise and hygiene

We apply a machine learning technique to characterize habit formation in two large panel data sets with objective measures of 1) gym attendance (over 12 million observations) and 2) hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identifies context variables that best predict behavior for each individual. This approach also creates a time series of overall predictability for each individual. These time series predictability values are used to trace a habit formation curve for each individual, operationalizing the time of habit formation as the asymptotic limit of when behavior becomes highly predictable. Contrary to the popular belief in a magic number of days to develop a habit, we find that it typically takes months to form the habit of going to the gym but weeks to develop the habit of handwashing in the hospital. Furthermore, we find that gymgoers who are more predictable are less responsive to an intervention designed to promote more gym attendance, consistent with past experiments showing that habit formation generates insensitivity to reward devaluation.

Automated summary

We used machine learning to analyze two large panel data sets on gym attendance (over 12 million observations) and hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identified the best context variables predicting behavior for each individual. We also created time series of predictability values to trace habit formation curves, finding that it takes months to form the habit of going to the gym but weeks for handwashing in the hospital. Additionally, more predictable gymgoers were less responsive to interventions promoting more gym attendance, consistent with past experiments on habit formation and reward devaluation.