Smartphone monitoring of smiling as a behavioral proxy of well-being in everyday life

TL;DR

This study demonstrates that passive smartphone video analysis of smiles can reliably measure well-being and positive affect in daily life, correlating well with traditional survey methods and physical activity.

Contribution

It introduces a scalable, objective method using deep learning to analyze natural smartphone interactions for assessing well-being.

Findings

Smile intensity patterns match national happiness data (r=0.92)

Diurnal rhythms align with established affective patterns (r=0.80)

Smile correlates with physical activity and light exposure

Abstract

Subjective well-being is a cornerstone of individual and societal health, yet its scientific measurement has traditionally relied on self-report methods prone to recall bias and high participant burden. This has left a gap in our understanding of well-being as it is expressed in everyday life. We hypothesized that candid smiles captured during natural smartphone interactions could serve as a scalable, objective behavioral correlate of positive affect. To test this, we analyzed 405,448 video clips passively recorded from 233 consented participants over one week. Using a deep learning model to quantify smile intensity, we identified distinct diurnal and daily patterns. Daily patterns of smile intensity across the week showed strong correlation with national survey data on happiness (r=0.92), and diurnal rhythms documented close correspondence with established results from the day reconstruction method (r=0.80). Higher daily mean smile intensity was significantly associated with more physical activity (Beta coefficient = 0.043, 95% CI [0.001, 0.085]) and greater light exposure (Beta coefficient = 0.038, [0.013, 0.063]), whereas no significant effects were found for smartphone use. These findings suggest that passive smartphone sensing could serve as a powerful, ecologically valid methodology for studying the dynamics of affective behavior and open the door to understanding this behavior at a population scale.