Abstract Digital habits remain poorly quantified despite ever-present concerns regarding problematic social media use. Through a mixed-methods study of 6,816 participants and 12,899 app-specific responses across six major platforms, we compare self-reported habit measures (via the Self-Reported Habit Index) with longitudinal behavioural tracking data collected over a 6 week period. Our analysis reveals three key insights: First, app usage shows strong habitual patterns (Facebook: mean SRHI = 4.88), exceeding established benchmarks for health-related habits like smoking. Second, we find weak correlations (r=0.28-0.32) between SRHI scores and actual usage metrics, exposing significant discrepancies in self-assessment accuracy. Third, machine learning demonstrates that simple behavioural history (sessions/hours) predicts future usage at least 64% more effectively than SRHI measures. These results challenge the predictive validity of self-report instruments in digital habit research and suggest observational data offers superior predictive power. Our findings have immediate implications for intervention design, platform accountability, and the methodological evolution of habi
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