Satisfaction with Life Manifests in Physical Activity Patterns Captured with Smartphones

Satisfaction with life (SWL) plays a vital role in human prosperity, making it a key focus for practitioners and researchers alike. Research has shown that between-person differences in SWL relate to self-reported patterns in everyday activity including physical activeness and inactiveness during daytimes and overnight. However, these relationships rarely replicate in objective measures of activity, leaving real-life manifestations of SWL obscure. Here, we use an interpretable machine learning approach to examine how smartphone-captured patterns of everyday activity (1,232 behavioral features) relate to SWL in an international dataset (N = 2,272) combining four independently collected samples. The cross-validated results demonstrate that especially extremeness and variation in everyday activity patterns predict SWL (r_Md = .18, r_IQR = [.14, .21]) and that activity predictiveness varies especially with individuals’ personality. These findings enhance our understanding of real-life manifestations of SWL in everyday activity patterns, highlighting the potential of smartphone-based methods for well-being research and interventions.