Table 2.

Continuous and discrete parameter estimates of well-being and duration of smartphone use.

Path	Continuous drift parameters	Discrete drift parameters
	Estimate [95% CI]	1 day	3 days	7 days	30 days
Auto-regressions
Well-being → Well-being (drift_eta1_eta1)	− 0.736 [− 0.959 to − 0.549]	0.487 [0.389 to 0.584]	0.125 [0.064 to 0.207]	0.009 [0.002 to 0.026]	0.000 [0.000 to 0.000]
Duration of SP use → Duration of SP use (drift_eta2_eta2)	− 1.393 [− 1.624 to − 0.189]	0.254 [0.202 to 0.309]	0.021 [0.011 to 0.036]	0.001 [0.000 to 0.002]	0.000 [0.000 to 0.000]
Cross-regressions
Well-being → Duration of SP use (drift_eta2_eta1)	0.250 [0.101 to 0.402]	0.088 [0.035 to 0.141]	0.039 [0.014 to 0.071]	0.003 [0.001 to 0.009]	0.000 [0.000 to 0.000]
Duration of SP use → Well-being (drift_eta1_eta2)	0.128 [0.025−  0.221]	0.045 [0.009 to 0.077]	0.019 [0.004 to 0.036]	0.002 [0.000 to 0.004]	0.000 [0.000 to 0.000]

SP smartphone. All effects not including the value of zero in the 95% CI interval were significant at the 0.05 level. Continuous drift parameters include continuous (i.e., time-independent) autoregressive and cross-lagged estimates in the drift matrix, Discrete drift parameters report the discrete (i.e., time-dependent) standardized effects at intervals of 1 day, 3 days, 7 days, and 30 days.