CBTI Summary

Consort map

Demographic information

Characteristic	N	Overall, N = 3581	control, N = 1791	treatment, N = 1791	p-value2
age	358	36.34 ± 13.94 (18 - 73)	35.95 ± 13.84 (18 - 73)	36.72 ± 14.07 (18 - 71)	0.599
gender	358				0.792
female		286 (80%)	142 (79%)	144 (80%)
male		72 (20%)	37 (21%)	35 (20%)
occupation	358				0.658
civil		13 (3.6%)	4 (2.2%)	9 (5.0%)
clerk		57 (16%)	30 (17%)	27 (15%)
craft		12 (3.4%)	8 (4.5%)	4 (2.2%)
homemaker		26 (7.3%)	14 (7.8%)	12 (6.7%)
manager		28 (7.8%)	16 (8.9%)	12 (6.7%)
other		15 (4.2%)	5 (2.8%)	10 (5.6%)
professional		39 (11%)	16 (8.9%)	23 (13%)
retired		21 (5.9%)	10 (5.6%)	11 (6.1%)
service		12 (3.4%)	7 (3.9%)	5 (2.8%)
student		119 (33%)	60 (34%)	59 (33%)
unemploy		16 (4.5%)	9 (5.0%)	7 (3.9%)
marital	358				0.652
divorced		14 (3.9%)	5 (2.8%)	9 (5.0%)
married		97 (27%)	51 (28%)	46 (26%)
other		2 (0.6%)	1 (0.6%)	1 (0.6%)
separated		5 (1.4%)	1 (0.6%)	4 (2.2%)
single		235 (66%)	119 (66%)	116 (65%)
widowed		5 (1.4%)	2 (1.1%)	3 (1.7%)
marital_r	358				0.252
married		97 (27%)	51 (28%)	46 (26%)
other		26 (7.3%)	9 (5.0%)	17 (9.5%)
single		235 (66%)	119 (66%)	116 (65%)
education	358				0.914
post-secondary		52 (15%)	28 (16%)	24 (13%)
primary		2 (0.6%)	1 (0.6%)	1 (0.6%)
secondary		50 (14%)	24 (13%)	26 (15%)
university		254 (71%)	126 (70%)	128 (72%)
education_r	358				0.819
post-secondary		52 (15%)	28 (16%)	24 (13%)
secondary or below		52 (15%)	25 (14%)	27 (15%)
university		254 (71%)	126 (70%)	128 (72%)
family_income	358				0.502
0_10000		56 (16%)	27 (15%)	29 (16%)
10001_20000		75 (21%)	38 (21%)	37 (21%)
20001_30000		73 (20%)	42 (23%)	31 (17%)
30001_40000		60 (17%)	31 (17%)	29 (16%)
40000_above		94 (26%)	41 (23%)	53 (30%)
religion	358				0.110
buddhism		16 (4.5%)	7 (3.9%)	9 (5.0%)
catholic		17 (4.7%)	11 (6.1%)	6 (3.4%)
christianity		73 (20%)	30 (17%)	43 (24%)
nil		248 (69%)	130 (73%)	118 (66%)
other		3 (0.8%)	0 (0%)	3 (1.7%)
taoism		1 (0.3%)	1 (0.6%)	0 (0%)
religion_r	358				0.234
buddhism		16 (4.5%)	7 (3.9%)	9 (5.0%)
catholic		17 (4.7%)	11 (6.1%)	6 (3.4%)
christianity		73 (20%)	30 (17%)	43 (24%)
nil		248 (69%)	130 (73%)	118 (66%)
other		4 (1.1%)	1 (0.6%)	3 (1.7%)
source	358				0.233
bokss		15 (4.2%)	11 (6.1%)	4 (2.2%)
facebook		131 (37%)	63 (35%)	68 (38%)
instagram		12 (3.4%)	7 (3.9%)	5 (2.8%)
other		66 (18%)	28 (16%)	38 (21%)
refresh		134 (37%)	70 (39%)	64 (36%)
1Mean ± SD (Range); n (%)
2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Table

Characteristic	N	Overall, N = 3581	control, N = 1791	treatment, N = 1791	p-value2
isi	358	13.47 ± 3.37 (8 - 21)	13.53 ± 3.33 (8 - 21)	13.40 ± 3.42 (8 - 21)	0.719
who	358	39.61 ± 14.94 (0 - 84)	39.28 ± 14.84 (4 - 80)	39.93 ± 15.08 (0 - 84)	0.682
phq	358	8.51 ± 5.01 (0 - 25)	8.21 ± 4.98 (0 - 21)	8.80 ± 5.03 (0 - 25)	0.264
gad	358	7.78 ± 5.12 (0 - 21)	7.54 ± 5.03 (0 - 21)	8.02 ± 5.21 (0 - 21)	0.376
wsas	358	16.73 ± 9.85 (0 - 40)	16.77 ± 9.70 (0 - 39)	16.69 ± 10.03 (0 - 40)	0.936
wsas_1	358	3.49 ± 2.17 (0 - 8)	3.48 ± 2.16 (0 - 8)	3.50 ± 2.18 (0 - 8)	0.922
wsas_2	358	3.17 ± 2.26 (0 - 8)	3.22 ± 2.23 (0 - 8)	3.12 ± 2.29 (0 - 8)	0.658
wsas_3	358	3.50 ± 2.28 (0 - 8)	3.54 ± 2.27 (0 - 8)	3.46 ± 2.30 (0 - 8)	0.746
wsas_4	358	3.16 ± 2.30 (0 - 8)	3.14 ± 2.23 (0 - 8)	3.18 ± 2.38 (0 - 8)	0.855
wsas_5	358	3.41 ± 2.36 (0 - 8)	3.39 ± 2.33 (0 - 8)	3.42 ± 2.38 (0 - 8)	0.893
shps	358	83.53 ± 16.91 (35 - 145)	82.50 ± 15.69 (53 - 133)	84.55 ± 18.04 (35 - 145)	0.252
shps_arousal	358	3.10 ± 0.69 (1 - 5)	3.02 ± 0.68 (1 - 5)	3.18 ± 0.69 (1 - 5)	0.025
shps_schedule	358	3.55 ± 0.87 (1 - 6)	3.53 ± 0.81 (2 - 6)	3.58 ± 0.93 (1 - 6)	0.653
shps_behavior	358	2.05 ± 0.66 (1 - 4)	1.99 ± 0.61 (1 - 4)	2.12 ± 0.71 (1 - 4)	0.059
shps_environment	358	2.30 ± 0.82 (1 - 5)	2.33 ± 0.84 (1 - 5)	2.27 ± 0.80 (1 - 5)	0.473
dbas	358	96.11 ± 22.70 (30 - 148)	95.66 ± 23.30 (30 - 148)	96.55 ± 22.14 (37 - 146)	0.712
dbas_consequence	358	6.61 ± 1.75 (1 - 10)	6.59 ± 1.82 (1 - 10)	6.64 ± 1.68 (1 - 10)	0.772
dbas_worry	358	14.37 ± 3.23 (3 - 20)	14.20 ± 3.35 (3 - 20)	14.54 ± 3.11 (3 - 20)	0.319
dbas_expectation	358	7.03 ± 2.14 (1 - 10)	7.17 ± 2.09 (1 - 10)	6.89 ± 2.19 (1 - 10)	0.209
dbas_medication	358	3.19 ± 2.07 (0 - 9)	3.15 ± 2.04 (0 - 9)	3.24 ± 2.09 (0 - 9)	0.683
psas	358	38.44 ± 10.70 (16 - 72)	37.87 ± 10.65 (16 - 64)	39.02 ± 10.74 (17 - 72)	0.309
psas_somatic	358	1.88 ± 0.69 (1 - 5)	1.86 ± 0.66 (1 - 4)	1.91 ± 0.71 (1 - 5)	0.539
psas_cognitive	358	2.92 ± 0.85 (1 - 5)	2.87 ± 0.84 (1 - 5)	2.97 ± 0.86 (1 - 5)	0.270
psqi_global	358	10.36 ± 3.08 (2 - 19)	10.14 ± 3.13 (4 - 17)	10.58 ± 3.03 (2 - 19)	0.176
mic	358	16.15 ± 7.69 (0 - 36)	15.64 ± 7.68 (0 - 36)	16.65 ± 7.69 (0 - 35)	0.211
mic_attention	358	1.36 ± 0.72 (0 - 3)	1.30 ± 0.71 (0 - 3)	1.42 ± 0.73 (0 - 3)	0.110
mic_executive	358	1.31 ± 0.76 (0 - 3)	1.28 ± 0.77 (0 - 3)	1.35 ± 0.76 (0 - 3)	0.406
mic_memory	358	1.37 ± 0.73 (0 - 3)	1.33 ± 0.75 (0 - 3)	1.40 ± 0.71 (0 - 3)	0.397
nb_pcs	358	46.27 ± 8.63 (17 - 65)	46.33 ± 8.91 (17 - 63)	46.20 ± 8.38 (21 - 65)	0.879
nb_mcs	358	39.94 ± 9.95 (8 - 65)	39.90 ± 9.78 (8 - 62)	39.98 ± 10.14 (8 - 65)	0.935
1Mean ± SD (Range)
2Two Sample t-test

Plot

Data analysis

Table

Group	Characteristic	Beta	SE1	95% CI1	p-value
isi	(Intercept)	13.5	0.286	13.0, 14.1
	group
	control	—	—	—
	treatment	-0.128	0.404	-0.921, 0.664	0.751
	time_point
	1st	—	—	—
	2nd	-2.46	0.322	-3.09, -1.83	0.000
	3rd	-2.87	0.329	-3.52, -2.23	0.000
	group * time_point
	treatment * 2nd	-2.96	0.486	-3.91, -2.01	0.000
	treatment * 3rd	-2.96	0.494	-3.93, -2.00	0.000
	Pseudo R square	0.259
who	(Intercept)	39.3	1.220	36.9, 41.7
	group
	control	—	—	—
	treatment	0.648	1.726	-2.73, 4.03	0.707
	time_point
	1st	—	—	—
	2nd	2.92	1.194	0.578, 5.26	0.015
	3rd	3.74	1.221	1.35, 6.13	0.002
	group * time_point
	treatment * 2nd	5.61	1.808	2.06, 9.15	0.002
	treatment * 3rd	6.49	1.841	2.88, 10.1	0.000
	Pseudo R square	0.053
phq	(Intercept)	8.21	0.378	7.47, 8.95
	group
	control	—	—	—
	treatment	0.592	0.535	-0.456, 1.64	0.269
	time_point
	1st	—	—	—
	2nd	-0.779	0.333	-1.43, -0.127	0.020
	3rd	-0.658	0.341	-1.33, 0.009	0.054
	group * time_point
	treatment * 2nd	-1.73	0.506	-2.73, -0.743	0.001
	treatment * 3rd	-2.43	0.515	-3.43, -1.42	0.000
	Pseudo R square	0.039
gad	(Intercept)	7.54	0.381	6.79, 8.28
	group
	control	—	—	—
	treatment	0.480	0.539	-0.577, 1.54	0.373
	time_point
	1st	—	—	—
	2nd	-0.440	0.341	-1.11, 0.228	0.197
	3rd	-0.561	0.348	-1.24, 0.122	0.108
	group * time_point
	treatment * 2nd	-2.07	0.517	-3.09, -1.06	0.000
	treatment * 3rd	-2.40	0.527	-3.43, -1.37	0.000
	Pseudo R square	0.038
wsas	(Intercept)	16.8	0.748	15.3, 18.2
	group
	control	—	—	—
	treatment	-0.084	1.058	-2.16, 1.99	0.937
	time_point
	1st	—	—	—
	2nd	-0.819	0.694	-2.18, 0.541	0.238
	3rd	-0.156	0.710	-1.55, 1.24	0.826
	group * time_point
	treatment * 2nd	-2.95	1.053	-5.02, -0.890	0.005
	treatment * 3rd	-4.88	1.072	-6.98, -2.78	0.000
	Pseudo R square	0.034
wsas_1	(Intercept)	3.48	0.163	3.16, 3.80
	group
	control	—	—	—
	treatment	0.022	0.231	-0.429, 0.474	0.923
	time_point
	1st	—	—	—
	2nd	-0.055	0.165	-0.378, 0.268	0.739
	3rd	-0.065	0.169	-0.396, 0.265	0.699
	group * time_point
	treatment * 2nd	-0.531	0.249	-1.02, -0.042	0.034
	treatment * 3rd	-0.918	0.254	-1.42, -0.420	0.000
	Pseudo R square	0.023
wsas_2	(Intercept)	3.22	0.170	2.89, 3.56
	group
	control	—	—	—
	treatment	-0.106	0.240	-0.577, 0.365	0.659
	time_point
	1st	—	—	—
	2nd	-0.113	0.172	-0.450, 0.224	0.513
	3rd	0.160	0.176	-0.185, 0.505	0.363
	group * time_point
	treatment * 2nd	-0.384	0.260	-0.894, 0.126	0.141
	treatment * 3rd	-0.808	0.265	-1.33, -0.288	0.002
	Pseudo R square	0.017
wsas_3	(Intercept)	3.54	0.170	3.20, 3.87
	group
	control	—	—	—
	treatment	-0.078	0.240	-0.549, 0.393	0.745
	time_point
	1st	—	—	—
	2nd	-0.220	0.178	-0.569, 0.128	0.216
	3rd	-0.170	0.182	-0.526, 0.187	0.351
	group * time_point
	treatment * 2nd	-0.668	0.269	-1.19, -0.141	0.013
	treatment * 3rd	-0.898	0.274	-1.43, -0.361	0.001
	Pseudo R square	0.033
wsas_4	(Intercept)	3.14	0.169	2.81, 3.47
	group
	control	—	—	—
	treatment	0.045	0.239	-0.424, 0.514	0.852
	time_point
	1st	—	—	—
	2nd	-0.307	0.187	-0.673, 0.059	0.100
	3rd	0.015	0.191	-0.359, 0.389	0.938
	group * time_point
	treatment * 2nd	-0.436	0.282	-0.988, 0.116	0.122
	treatment * 3rd	-1.01	0.286	-1.57, -0.451	0.000
	Pseudo R square	0.025
wsas_5	(Intercept)	3.39	0.175	3.05, 3.73
	group
	control	—	—	—
	treatment	0.034	0.248	-0.452, 0.519	0.892
	time_point
	1st	—	—	—
	2nd	-0.140	0.178	-0.489, 0.209	0.432
	3rd	-0.119	0.182	-0.476, 0.238	0.513
	group * time_point
	treatment * 2nd	-0.897	0.269	-1.42, -0.369	0.001
	treatment * 3rd	-1.19	0.274	-1.73, -0.653	0.000
	Pseudo R square	0.040
shps	(Intercept)	82.5	1.350	79.9, 85.1
	group
	control	—	—	—
	treatment	2.05	1.909	-1.69, 5.79	0.283
	time_point
	1st	—	—	—
	2nd	-2.77	1.290	-5.29, -0.238	0.032
	3rd	-3.28	1.319	-5.87, -0.696	0.013
	group * time_point
	treatment * 2nd	-8.82	1.955	-12.7, -4.99	0.000
	treatment * 3rd	-12.1	1.990	-16.0, -8.20	0.000
	Pseudo R square	0.073
shps_arousal	(Intercept)	3.02	0.055	2.91, 3.13
	group
	control	—	—	—
	treatment	0.163	0.078	0.009, 0.316	0.038
	time_point
	1st	—	—	—
	2nd	-0.196	0.059	-0.311, -0.080	0.001
	3rd	-0.219	0.060	-0.338, -0.101	0.000
	group * time_point
	treatment * 2nd	-0.477	0.089	-0.652, -0.302	0.000
	treatment * 3rd	-0.565	0.091	-0.743, -0.387	0.000
	Pseudo R square	0.112
shps_schedule	(Intercept)	3.53	0.066	3.40, 3.66
	group
	control	—	—	—
	treatment	0.042	0.094	-0.143, 0.226	0.659
	time_point
	1st	—	—	—
	2nd	-0.101	0.060	-0.218, 0.017	0.093
	3rd	-0.134	0.061	-0.254, -0.014	0.029
	group * time_point
	treatment * 2nd	-0.345	0.091	-0.523, -0.167	0.000
	treatment * 3rd	-0.423	0.092	-0.604, -0.242	0.000
	Pseudo R square	0.045
shps_behavior	(Intercept)	1.99	0.051	1.89, 2.08
	group
	control	—	—	—
	treatment	0.132	0.072	-0.009, 0.273	0.067
	time_point
	1st	—	—	—
	2nd	0.024	0.051	-0.075, 0.124	0.629
	3rd	0.009	0.052	-0.092, 0.111	0.857
	group * time_point
	treatment * 2nd	-0.244	0.077	-0.394, -0.094	0.002
	treatment * 3rd	-0.333	0.078	-0.485, -0.180	0.000
	Pseudo R square	0.019
shps_environment	(Intercept)	2.33	0.061	2.21, 2.45
	group
	control	—	—	—
	treatment	-0.062	0.086	-0.230, 0.106	0.469
	time_point
	1st	—	—	—
	2nd	-0.058	0.060	-0.175, 0.059	0.331
	3rd	-0.057	0.061	-0.177, 0.063	0.352
	group * time_point
	treatment * 2nd	-0.085	0.091	-0.263, 0.092	0.346
	treatment * 3rd	-0.262	0.092	-0.442, -0.081	0.005
	Pseudo R square	0.021
dbas	(Intercept)	95.7	1.925	91.9, 99.4
	group
	control	—	—	—
	treatment	0.888	2.722	-4.45, 6.22	0.744
	time_point
	1st	—	—	—
	2nd	-4.14	1.917	-7.89, -0.379	0.031
	3rd	-8.36	1.960	-12.2, -4.52	0.000
	group * time_point
	treatment * 2nd	-18.1	2.902	-23.8, -12.4	0.000
	treatment * 3rd	-20.9	2.954	-26.7, -15.1	0.000
	Pseudo R square	0.138
dbas_consequence	(Intercept)	6.59	0.140	6.31, 6.86
	group
	control	—	—	—
	treatment	0.054	0.198	-0.335, 0.443	0.787
	time_point
	1st	—	—	—
	2nd	-0.336	0.141	-0.612, -0.061	0.017
	3rd	-0.669	0.144	-0.951, -0.388	0.000
	group * time_point
	treatment * 2nd	-1.11	0.213	-1.53, -0.693	0.000
	treatment * 3rd	-1.30	0.217	-1.72, -0.875	0.000
	Pseudo R square	0.117
dbas_worry	(Intercept)	14.2	0.283	13.6, 14.8
	group
	control	—	—	—
	treatment	0.341	0.401	-0.445, 1.13	0.395
	time_point
	1st	—	—	—
	2nd	-1.23	0.323	-1.86, -0.599	0.000
	3rd	-1.83	0.330	-2.47, -1.18	0.000
	group * time_point
	treatment * 2nd	-2.71	0.486	-3.66, -1.76	0.000
	treatment * 3rd	-2.89	0.494	-3.85, -1.92	0.000
	Pseudo R square	0.162
dbas_expectation	(Intercept)	7.17	0.172	6.84, 7.51
	group
	control	—	—	—
	treatment	-0.285	0.244	-0.763, 0.193	0.243
	time_point
	1st	—	—	—
	2nd	-0.343	0.176	-0.687, 0.001	0.051
	3rd	-0.768	0.179	-1.12, -0.416	0.000
	group * time_point
	treatment * 2nd	-1.25	0.266	-1.77, -0.728	0.000
	treatment * 3rd	-1.29	0.270	-1.82, -0.758	0.000
	Pseudo R square	0.111
dbas_medication	(Intercept)	3.15	0.161	2.83, 3.46
	group
	control	—	—	—
	treatment	0.089	0.228	-0.357, 0.535	0.695
	time_point
	1st	—	—	—
	2nd	0.366	0.164	0.045, 0.688	0.026
	3rd	0.309	0.168	-0.020, 0.638	0.066
	group * time_point
	treatment * 2nd	-0.664	0.248	-1.15, -0.177	0.008
	treatment * 3rd	-0.860	0.253	-1.35, -0.365	0.001
	Pseudo R square	0.015
psas	(Intercept)	37.9	0.811	36.3, 39.5
	group
	control	—	—	—
	treatment	1.15	1.146	-1.10, 3.40	0.316
	time_point
	1st	—	—	—
	2nd	-0.493	0.780	-2.02, 1.04	0.528
	3rd	-2.73	0.797	-4.30, -1.17	0.001
	group * time_point
	treatment * 2nd	-5.93	1.182	-8.24, -3.61	0.000
	treatment * 3rd	-5.30	1.203	-7.65, -2.94	0.000
	Pseudo R square	0.061
psas_somatic	(Intercept)	1.86	0.051	1.76, 1.96
	group
	control	—	—	—
	treatment	0.045	0.072	-0.096, 0.185	0.533
	time_point
	1st	—	—	—
	2nd	0.143	0.047	0.051, 0.236	0.003
	3rd	0.011	0.048	-0.084, 0.106	0.815
	group * time_point
	treatment * 2nd	-0.306	0.072	-0.447, -0.166	0.000
	treatment * 3rd	-0.243	0.073	-0.387, -0.100	0.001
	Pseudo R square	0.021
psas_cognitive	(Intercept)	2.87	0.063	2.75, 3.00
	group
	control	—	—	—
	treatment	0.099	0.090	-0.077, 0.275	0.269
	time_point
	1st	—	—	—
	2nd	-0.204	0.064	-0.329, -0.079	0.001
	3rd	-0.352	0.065	-0.480, -0.224	0.000
	group * time_point
	treatment * 2nd	-0.434	0.097	-0.623, -0.245	0.000
	treatment * 3rd	-0.418	0.098	-0.610, -0.225	0.000
	Pseudo R square	0.091
psqi_global	(Intercept)	10.1	0.240	9.67, 10.6
	group
	control	—	—	—
	treatment	0.441	0.339	-0.224, 1.11	0.194
	time_point
	1st	—	—	—
	2nd	-1.22	0.254	-1.72, -0.724	0.000
	3rd	-1.27	0.260	-1.78, -0.765	0.000
	group * time_point
	treatment * 2nd	-1.88	0.384	-2.63, -1.13	0.000
	treatment * 3rd	-2.66	0.391	-3.42, -1.89	0.000
	Pseudo R square	0.146
mic	(Intercept)	15.6	0.612	14.4, 16.8
	group
	control	—	—	—
	treatment	1.02	0.866	-0.680, 2.71	0.241
	time_point
	1st	—	—	—
	2nd	-0.098	0.522	-1.12, 0.925	0.851
	3rd	-0.304	0.534	-1.35, 0.743	0.570
	group * time_point
	treatment * 2nd	-2.72	0.794	-4.27, -1.16	0.001
	treatment * 3rd	-3.52	0.808	-5.10, -1.94	0.000
	Pseudo R square	0.021
mic_attention	(Intercept)	1.30	0.057	1.19, 1.41
	group
	control	—	—	—
	treatment	0.122	0.080	-0.035, 0.278	0.130
	time_point
	1st	—	—	—
	2nd	-0.022	0.055	-0.130, 0.087	0.693
	3rd	0.030	0.057	-0.081, 0.141	0.592
	group * time_point
	treatment * 2nd	-0.248	0.084	-0.412, -0.083	0.003
	treatment * 3rd	-0.384	0.085	-0.551, -0.217	0.000
	Pseudo R square	0.021
mic_executive	(Intercept)	1.28	0.058	1.17, 1.39
	group
	control	—	—	—
	treatment	0.067	0.082	-0.094, 0.228	0.415
	time_point
	1st	—	—	—
	2nd	-0.034	0.054	-0.140, 0.073	0.537
	3rd	-0.046	0.056	-0.155, 0.063	0.410
	group * time_point
	treatment * 2nd	-0.159	0.082	-0.321, 0.002	0.054
	treatment * 3rd	-0.275	0.084	-0.439, -0.110	0.001
	Pseudo R square	0.015
mic_memory	(Intercept)	1.33	0.057	1.22, 1.44
	group
	control	—	—	—
	treatment	0.066	0.081	-0.093, 0.224	0.417
	time_point
	1st	—	—	—
	2nd	0.032	0.051	-0.068, 0.131	0.537
	3rd	-0.060	0.052	-0.162, 0.042	0.250
	group * time_point
	treatment * 2nd	-0.276	0.077	-0.428, -0.124	0.000
	treatment * 3rd	-0.223	0.079	-0.377, -0.068	0.005
	Pseudo R square	0.017
nb_pcs	(Intercept)	46.3	0.659	45.0, 47.6
	group
	control	—	—	—
	treatment	-0.139	0.932	-1.96, 1.69	0.882
	time_point
	1st	—	—	—
	2nd	-0.871	0.590	-2.03, 0.286	0.141
	3rd	-0.820	0.604	-2.00, 0.363	0.175
	group * time_point
	treatment * 2nd	2.76	0.896	1.00, 4.52	0.002
	treatment * 3rd	3.24	0.912	1.45, 5.03	0.000
	Pseudo R square	0.016
nb_mcs	(Intercept)	39.9	0.770	38.4, 41.4
	group
	control	—	—	—
	treatment	0.085	1.089	-2.05, 2.22	0.938
	time_point
	1st	—	—	—
	2nd	2.00	0.739	0.556, 3.45	0.007
	3rd	2.28	0.755	0.801, 3.76	0.003
	group * time_point
	treatment * 2nd	3.57	1.119	1.38, 5.76	0.002
	treatment * 3rd	4.65	1.139	2.42, 6.88	0.000
	Pseudo R square	0.056
1SE = Standard Error, CI = Confidence Interval

Text

isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.53 (95% CI [12.97, 14.09], t(851) = 47.34, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(851) = -0.32, p = 0.751; Std. beta = -0.03, 95% CI [-0.21, 0.15])
The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.83], t(851) = -7.63, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
The effect of time point [3rd] is statistically significant and negative (beta = -2.87, 95% CI [-3.52, -2.23], t(851) = -8.72, p < .001; Std. beta = -0.64, 95% CI [-0.78, -0.50])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.91, -2.01], t(851) = -6.10, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.93, -2.00], t(851) = -6.00, p < .001; Std. beta = -0.66, 95% CI [-0.88, -0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.28 (95% CI [36.89, 41.68], t(851) = 32.19, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.65, 95% CI [-2.73, 4.03], t(851) = 0.38, p = 0.707; Std. beta = 0.04, 95% CI [-0.16, 0.24])
The effect of time point [2nd] is statistically significant and positive (beta = 2.92, 95% CI [0.58, 5.26], t(851) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
The effect of time point [3rd] is statistically significant and positive (beta = 3.74, 95% CI [1.35, 6.13], t(851) = 3.06, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.36])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 5.61, 95% CI [2.06, 9.15], t(851) = 3.10, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 6.49, 95% CI [2.88, 10.10], t(851) = 3.53, p < .001; Std. beta = 0.38, 95% CI [0.17, 0.60])

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 8.21 (95% CI [7.47, 8.95], t(851) = 21.71, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(851) = 1.11, p = 0.268; Std. beta = 0.11, 95% CI [-0.09, 0.32])
The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(851) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.66, 95% CI [-1.33, 9.22e-03], t(851) = -1.93, p = 0.053; Std. beta = -0.13, 95% CI [-0.26, 1.78e-03])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(851) = -3.43, p < .001; Std. beta = -0.33, 95% CI [-0.53, -0.14])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.43, 95% CI [-3.43, -1.42], t(851) = -4.71, p < .001; Std. beta = -0.47, 95% CI [-0.66, -0.27])

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.54 (95% CI [6.79, 8.28], t(851) = 19.76, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(851) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(851) = -1.29, p = 0.197; Std. beta = -0.09, 95% CI [-0.21, 0.04])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.56, 95% CI [-1.24, 0.12], t(851) = -1.61, p = 0.107; Std. beta = -0.11, 95% CI [-0.24, 0.02])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(851) = -4.01, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.40, 95% CI [-3.43, -1.37], t(851) = -4.56, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 16.77 (95% CI [15.30, 18.24], t(851) = 22.41, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(851) = -0.08, p = 0.937; Std. beta = -8.25e-03, 95% CI [-0.21, 0.20])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(851) = -1.18, p = 0.238; Std. beta = -0.08, 95% CI [-0.21, 0.05])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.16, 95% CI [-1.55, 1.24], t(851) = -0.22, p = 0.826; Std. beta = -0.02, 95% CI [-0.15, 0.12])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.89], t(851) = -2.81, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.88, 95% CI [-6.98, -2.78], t(851) = -4.55, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.27])

wsas_1

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas_1 with group and time_point (formula: wsas_1 ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.57) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.48 (95% CI [3.16, 3.80], t(851) = 21.35, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.43, 0.47], t(851) = 0.10, p = 0.923; Std. beta = 0.01, 95% CI [-0.19, 0.22])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.38, 0.27], t(851) = -0.33, p = 0.739; Std. beta = -0.02, 95% CI [-0.17, 0.12])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.07, 95% CI [-0.40, 0.27], t(851) = -0.39, p = 0.699; Std. beta = -0.03, 95% CI [-0.18, 0.12])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.53, 95% CI [-1.02, -0.04], t(851) = -2.13, p = 0.033; Std. beta = -0.24, 95% CI [-0.46, -0.02])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.92, 95% CI [-1.42, -0.42], t(851) = -3.61, p < .001; Std. beta = -0.42, 95% CI [-0.64, -0.19])

wsas_2

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas_2 with group and time_point (formula: wsas_2 ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.57) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.22 (95% CI [2.89, 3.56], t(851) = 18.98, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.11, 95% CI [-0.58, 0.36], t(851) = -0.44, p = 0.659; Std. beta = -0.05, 95% CI [-0.25, 0.16])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.11, 95% CI [-0.45, 0.22], t(851) = -0.66, p = 0.512; Std. beta = -0.05, 95% CI [-0.20, 0.10])
The effect of time point [3rd] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.18, 0.50], t(851) = 0.91, p = 0.362; Std. beta = 0.07, 95% CI [-0.08, 0.22])
The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.38, 95% CI [-0.89, 0.13], t(851) = -1.47, p = 0.140; Std. beta = -0.17, 95% CI [-0.39, 0.05])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.81, 95% CI [-1.33, -0.29], t(851) = -3.05, p = 0.002; Std. beta = -0.35, 95% CI [-0.58, -0.13])

wsas_3

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas_3 with group and time_point (formula: wsas_3 ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.54 (95% CI [3.20, 3.87], t(851) = 20.82, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-0.55, 0.39], t(851) = -0.33, p = 0.745; Std. beta = -0.03, 95% CI [-0.24, 0.17])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.22, 95% CI [-0.57, 0.13], t(851) = -1.24, p = 0.216; Std. beta = -0.10, 95% CI [-0.25, 0.06])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.17, 95% CI [-0.53, 0.19], t(851) = -0.93, p = 0.350; Std. beta = -0.07, 95% CI [-0.23, 0.08])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.67, 95% CI [-1.19, -0.14], t(851) = -2.48, p = 0.013; Std. beta = -0.29, 95% CI [-0.52, -0.06])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.90, 95% CI [-1.43, -0.36], t(851) = -3.28, p = 0.001; Std. beta = -0.39, 95% CI [-0.62, -0.16])

wsas_4

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas_4 with group and time_point (formula: wsas_4 ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.49) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.14 (95% CI [2.81, 3.47], t(851) = 18.56, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.42, 0.51], t(851) = 0.19, p = 0.852; Std. beta = 0.02, 95% CI [-0.19, 0.23])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.31, 95% CI [-0.67, 0.06], t(851) = -1.65, p = 0.100; Std. beta = -0.13, 95% CI [-0.30, 0.03])
The effect of time point [3rd] is statistically non-significant and positive (beta = 0.01, 95% CI [-0.36, 0.39], t(851) = 0.08, p = 0.938; Std. beta = 6.47e-03, 95% CI [-0.16, 0.17])
The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.44, 95% CI [-0.99, 0.12], t(851) = -1.55, p = 0.121; Std. beta = -0.19, 95% CI [-0.43, 0.05])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.01, 95% CI [-1.57, -0.45], t(851) = -3.53, p < .001; Std. beta = -0.44, 95% CI [-0.69, -0.20])

wsas_5

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas_5 with group and time_point (formula: wsas_5 ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.57) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.39 (95% CI [3.05, 3.73], t(851) = 19.36, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.45, 0.52], t(851) = 0.14, p = 0.892; Std. beta = 0.01, 95% CI [-0.19, 0.22])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.14, 95% CI [-0.49, 0.21], t(851) = -0.79, p = 0.431; Std. beta = -0.06, 95% CI [-0.20, 0.09])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.48, 0.24], t(851) = -0.65, p = 0.513; Std. beta = -0.05, 95% CI [-0.20, 0.10])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.90, 95% CI [-1.42, -0.37], t(851) = -3.33, p < .001; Std. beta = -0.38, 95% CI [-0.60, -0.15])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.19, 95% CI [-1.73, -0.65], t(851) = -4.34, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.27])

shps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps with group and time_point (formula: shps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.07. The model’s intercept, corresponding to group = control and time_point = 1st, is at 82.50 (95% CI [79.86, 85.15], t(851) = 61.11, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 2.05, 95% CI [-1.69, 5.79], t(851) = 1.07, p = 0.283; Std. beta = 0.11, 95% CI [-0.09, 0.31])
The effect of time point [2nd] is statistically significant and negative (beta = -2.77, 95% CI [-5.29, -0.24], t(851) = -2.14, p = 0.032; Std. beta = -0.15, 95% CI [-0.28, -0.01])
The effect of time point [3rd] is statistically significant and negative (beta = -3.28, 95% CI [-5.87, -0.70], t(851) = -2.49, p = 0.013; Std. beta = -0.17, 95% CI [-0.31, -0.04])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -8.82, 95% CI [-12.65, -4.99], t(851) = -4.51, p < .001; Std. beta = -0.47, 95% CI [-0.67, -0.27])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -12.10, 95% CI [-16.00, -8.20], t(851) = -6.08, p < .001; Std. beta = -0.65, 95% CI [-0.85, -0.44])

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.02 (95% CI [2.91, 3.13], t(851) = 54.51, p < .001). Within this model:

The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.98e-03, 0.32], t(851) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(851) = -3.31, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(851) = -3.62, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(851) = -5.34, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.39], t(851) = -6.21, p < .001; Std. beta = -0.72, 95% CI [-0.94, -0.49])

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.53 (95% CI [3.40, 3.66], t(851) = 53.15, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(851) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(851) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
The effect of time point [3rd] is statistically significant and negative (beta = -0.13, 95% CI [-0.25, -0.01], t(851) = -2.19, p = 0.029; Std. beta = -0.15, 95% CI [-0.28, -0.02])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(851) = -3.80, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.60, -0.24], t(851) = -4.57, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.99 (95% CI [1.89, 2.08], t(851) = 39.03, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.77e-03, 0.27], t(851) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(851) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
The effect of time point [3rd] is statistically non-significant and positive (beta = 9.30e-03, 95% CI [-0.09, 0.11], t(851) = 0.18, p = 0.857; Std. beta = 0.01, 95% CI [-0.13, 0.16])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(851) = -3.19, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.33, 95% CI [-0.49, -0.18], t(851) = -4.27, p < .001; Std. beta = -0.48, 95% CI [-0.71, -0.26])

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.33 (95% CI [2.21, 2.45], t(851) = 38.47, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(851) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(851) = -0.97, p = 0.330; Std. beta = -0.07, 95% CI [-0.22, 0.07])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(851) = -0.93, p = 0.352; Std. beta = -0.07, 95% CI [-0.22, 0.08])
The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(851) = -0.94, p = 0.346; Std. beta = -0.11, 95% CI [-0.32, 0.11])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(851) = -2.84, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.10])

dbas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas with group and time_point (formula: dbas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.14. The model’s intercept, corresponding to group = control and time_point = 1st, is at 95.66 (95% CI [91.89, 99.44], t(851) = 49.71, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.89, 95% CI [-4.45, 6.22], t(851) = 0.33, p = 0.744; Std. beta = 0.03, 95% CI [-0.16, 0.22])
The effect of time point [2nd] is statistically significant and negative (beta = -4.14, 95% CI [-7.89, -0.38], t(851) = -2.16, p = 0.031; Std. beta = -0.15, 95% CI [-0.28, -0.01])
The effect of time point [3rd] is statistically significant and negative (beta = -8.36, 95% CI [-12.20, -4.52], t(851) = -4.27, p < .001; Std. beta = -0.30, 95% CI [-0.44, -0.16])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -18.10, 95% CI [-23.79, -12.41], t(851) = -6.24, p < .001; Std. beta = -0.65, 95% CI [-0.85, -0.44])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -20.93, 95% CI [-26.72, -15.14], t(851) = -7.08, p < .001; Std. beta = -0.75, 95% CI [-0.96, -0.54])

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st, is at 6.59 (95% CI [6.31, 6.86], t(851) = 46.94, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(851) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(851) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
The effect of time point [3rd] is statistically significant and negative (beta = -0.67, 95% CI [-0.95, -0.39], t(851) = -4.66, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(851) = -5.22, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.30, 95% CI [-1.72, -0.87], t(851) = -6.00, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.43])

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.20 (95% CI [13.65, 14.76], t(851) = 50.11, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.44, 1.13], t(851) = 0.85, p = 0.395; Std. beta = 0.08, 95% CI [-0.11, 0.27])
The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(851) = -3.82, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
The effect of time point [3rd] is statistically significant and negative (beta = -1.83, 95% CI [-2.47, -1.18], t(851) = -5.54, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.28])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.66, -1.76], t(851) = -5.58, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.89, 95% CI [-3.85, -1.92], t(851) = -5.84, p < .001; Std. beta = -0.70, 95% CI [-0.93, -0.46])

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.17 (95% CI [6.84, 7.51], t(851) = 41.60, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(851) = -1.17, p = 0.243; Std. beta = -0.12, 95% CI [-0.31, 0.08])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.18e-03], t(851) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 4.82e-04])
The effect of time point [3rd] is statistically significant and negative (beta = -0.77, 95% CI [-1.12, -0.42], t(851) = -4.28, p < .001; Std. beta = -0.31, 95% CI [-0.46, -0.17])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(851) = -4.70, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.29, 95% CI [-1.82, -0.76], t(851) = -4.77, p < .001; Std. beta = -0.53, 95% CI [-0.74, -0.31])

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.15 (95% CI [2.83, 3.46], t(851) = 19.56, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(851) = 0.39, p = 0.694; Std. beta = 0.04, 95% CI [-0.17, 0.25])
The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(851) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.02, 0.64], t(851) = 1.84, p = 0.065; Std. beta = 0.14, 95% CI [-9.08e-03, 0.30])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(851) = -2.67, p = 0.007; Std. beta = -0.31, 95% CI [-0.53, -0.08])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.35, -0.36], t(851) = -3.40, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

psas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas with group and time_point (formula: psas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 37.87 (95% CI [36.28, 39.45], t(851) = 46.72, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 1.15, 95% CI [-1.10, 3.40], t(851) = 1.00, p = 0.315; Std. beta = 0.10, 95% CI [-0.10, 0.30])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.49, 95% CI [-2.02, 1.04], t(851) = -0.63, p = 0.527; Std. beta = -0.04, 95% CI [-0.18, 0.09])
The effect of time point [3rd] is statistically significant and negative (beta = -2.73, 95% CI [-4.30, -1.17], t(851) = -3.43, p < .001; Std. beta = -0.24, 95% CI [-0.38, -0.10])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -5.93, 95% CI [-8.24, -3.61], t(851) = -5.02, p < .001; Std. beta = -0.53, 95% CI [-0.74, -0.32])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -5.30, 95% CI [-7.65, -2.94], t(851) = -4.40, p < .001; Std. beta = -0.47, 95% CI [-0.68, -0.26])

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.86 (95% CI [1.76, 1.96], t(851) = 36.76, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(851) = 0.62, p = 0.532; Std. beta = 0.07, 95% CI [-0.14, 0.27])
The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(851) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
The effect of time point [3rd] is statistically non-significant and positive (beta = 0.01, 95% CI [-0.08, 0.11], t(851) = 0.23, p = 0.815; Std. beta = 0.02, 95% CI [-0.12, 0.16])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(851) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.10], t(851) = -3.33, p < .001; Std. beta = -0.36, 95% CI [-0.57, -0.15])

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.87 (95% CI [2.75, 3.00], t(851) = 45.30, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.27], t(851) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(851) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
The effect of time point [3rd] is statistically significant and negative (beta = -0.35, 95% CI [-0.48, -0.22], t(851) = -5.40, p < .001; Std. beta = -0.40, 95% CI [-0.54, -0.25])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(851) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.61, -0.22], t(851) = -4.25, p < .001; Std. beta = -0.47, 95% CI [-0.69, -0.25])

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.14 (95% CI [9.67, 10.61], t(851) = 42.25, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.22, 1.11], t(851) = 1.30, p = 0.194; Std. beta = 0.13, 95% CI [-0.06, 0.32])
The effect of time point [2nd] is statistically significant and negative (beta = -1.22, 95% CI [-1.72, -0.72], t(851) = -4.81, p < .001; Std. beta = -0.35, 95% CI [-0.49, -0.21])
The effect of time point [3rd] is statistically significant and negative (beta = -1.27, 95% CI [-1.78, -0.76], t(851) = -4.90, p < .001; Std. beta = -0.37, 95% CI [-0.51, -0.22])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.88, 95% CI [-2.63, -1.13], t(851) = -4.90, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.66, 95% CI [-3.42, -1.89], t(851) = -6.79, p < .001; Std. beta = -0.76, 95% CI [-0.98, -0.54])

mic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic with group and time_point (formula: mic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.64 (95% CI [14.44, 16.84], t(851) = 25.54, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 1.02, 95% CI [-0.68, 2.71], t(851) = 1.17, p = 0.240; Std. beta = 0.12, 95% CI [-0.08, 0.33])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-1.12, 0.92], t(851) = -0.19, p = 0.851; Std. beta = -0.01, 95% CI [-0.13, 0.11])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.35, 0.74], t(851) = -0.57, p = 0.569; Std. beta = -0.04, 95% CI [-0.16, 0.09])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.72, 95% CI [-4.27, -1.16], t(851) = -3.42, p < .001; Std. beta = -0.33, 95% CI [-0.51, -0.14])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -3.52, 95% CI [-5.10, -1.94], t(851) = -4.36, p < .001; Std. beta = -0.42, 95% CI [-0.61, -0.23])

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.30 (95% CI [1.19, 1.41], t(851) = 22.92, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(851) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(851) = -0.40, p = 0.693; Std. beta = -0.03, 95% CI [-0.17, 0.11])
The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(851) = 0.54, p = 0.592; Std. beta = 0.04, 95% CI [-0.11, 0.18])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(851) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.22], t(851) = -4.50, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.28 (95% CI [1.17, 1.39], t(851) = 22.01, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(851) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(851) = -0.62, p = 0.536; Std. beta = -0.04, 95% CI [-0.18, 0.09])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.15, 0.06], t(851) = -0.82, p = 0.409; Std. beta = -0.06, 95% CI [-0.20, 0.08])
The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.22e-03], t(851) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 2.81e-03])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.44, -0.11], t(851) = -3.27, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.33 (95% CI [1.22, 1.44], t(851) = 23.31, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(851) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(851) = 0.62, p = 0.537; Std. beta = 0.04, 95% CI [-0.09, 0.17])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.16, 0.04], t(851) = -1.15, p = 0.249; Std. beta = -0.08, 95% CI [-0.21, 0.05])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(851) = -3.56, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.38, -0.07], t(851) = -2.83, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 46.33 (95% CI [45.04, 47.63], t(851) = 70.33, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(851) = -0.15, p = 0.882; Std. beta = -0.02, 95% CI [-0.22, 0.19])
The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.29], t(851) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
The effect of time point [3rd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.00, 0.36], t(851) = -1.36, p = 0.174; Std. beta = -0.09, 95% CI [-0.22, 0.04])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.52], t(851) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.24, 95% CI [1.45, 5.03], t(851) = 3.55, p < .001; Std. beta = 0.36, 95% CI [0.16, 0.56])

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.90 (95% CI [38.39, 41.41], t(851) = 51.80, p < .001). Within this model:

The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(851) = 0.08, p = 0.938; Std. beta = 7.99e-03, 95% CI [-0.19, 0.21])
The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.56, 3.45], t(851) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
The effect of time point [3rd] is statistically significant and positive (beta = 2.28, 95% CI [0.80, 3.76], t(851) = 3.02, p = 0.003; Std. beta = 0.21, 95% CI [0.07, 0.35])
The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.38, 5.76], t(851) = 3.19, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.65, 95% CI [2.42, 6.88], t(851) = 4.08, p < .001; Std. beta = 0.44, 95% CI [0.23, 0.64])

Likelihood ratio tests

outcome	model	npar	AIC	BIC	logLik	deviance	Chisq	Df	p
isi	null	3	4,948.816	4,963.083	-2,471.408	4,942.816
isi	random	8	4,615.080	4,653.126	-2,299.540	4,599.080	343.736	5	0.000
who	null	3	7,061.290	7,075.557	-3,527.645	7,055.290
who	random	8	6,995.494	7,033.540	-3,489.747	6,979.494	75.796	5	0.000
phq	null	3	4,958.134	4,972.401	-2,476.067	4,952.134
phq	random	8	4,891.540	4,929.586	-2,437.770	4,875.540	76.594	5	0.000
gad	null	3	4,976.571	4,990.838	-2,485.286	4,970.571
gad	random	8	4,918.777	4,956.823	-2,451.389	4,902.777	67.794	5	0.000
wsas	null	3	6,146.880	6,161.148	-3,070.440	6,140.880
wsas	random	8	6,109.472	6,147.518	-3,046.736	6,093.472	47.408	5	0.000
wsas_1	null	3	3,584.240	3,598.507	-1,789.120	3,578.240
wsas_1	random	8	3,564.696	3,602.742	-1,774.348	3,548.696	29.544	5	0.000
wsas_2	null	3	3,643.765	3,658.032	-1,818.883	3,637.765
wsas_2	random	8	3,636.189	3,674.235	-1,810.094	3,620.189	17.576	5	0.004
wsas_3	null	3	3,691.251	3,705.518	-1,842.626	3,685.251
wsas_3	random	8	3,663.755	3,701.801	-1,823.877	3,647.755	37.496	5	0.000
wsas_4	null	3	3,717.105	3,731.372	-1,855.552	3,711.105
wsas_4	random	8	3,697.649	3,735.695	-1,840.825	3,681.649	29.456	5	0.000
wsas_5	null	3	3,732.053	3,746.320	-1,863.026	3,726.053
wsas_5	random	8	3,691.922	3,729.968	-1,837.961	3,675.922	50.131	5	0.000
shps	null	3	7,257.886	7,272.153	-3,625.943	7,251.886
shps	random	8	7,148.935	7,186.981	-3,566.467	7,132.935	118.951	5	0.000
shps_arousal	null	3	1,906.464	1,920.731	-950.232	1,900.464
shps_arousal	random	8	1,754.921	1,792.967	-869.461	1,738.921	161.543	5	0.000
shps_schedule	null	3	1,992.302	2,006.569	-993.151	1,986.302
shps_schedule	random	8	1,924.626	1,962.673	-954.313	1,908.626	77.675	5	0.000
shps_behavior	null	3	1,572.223	1,586.490	-783.111	1,566.223
shps_behavior	random	8	1,549.508	1,587.554	-766.754	1,533.508	32.715	5	0.000
shps_environment	null	3	1,860.080	1,874.347	-927.040	1,854.080
shps_environment	random	8	1,844.650	1,882.696	-914.325	1,828.650	25.430	5	0.000
dbas	null	3	7,979.820	7,994.087	-3,986.910	7,973.820
dbas	random	8	7,793.235	7,831.282	-3,888.618	7,777.235	196.585	5	0.000
dbas_consequence	null	3	3,459.555	3,473.822	-1,726.777	3,453.555
dbas_consequence	random	8	3,299.461	3,337.508	-1,641.731	3,283.461	170.093	5	0.000
dbas_worry	null	3	4,801.989	4,816.256	-2,397.995	4,795.989
dbas_worry	random	8	4,607.566	4,645.613	-2,295.783	4,591.566	204.423	5	0.000
dbas_expectation	null	3	3,794.146	3,808.413	-1,894.073	3,788.146
dbas_expectation	random	8	3,666.551	3,704.597	-1,825.276	3,650.551	137.594	5	0.000
dbas_medication	null	3	3,555.458	3,569.725	-1,774.729	3,549.458
dbas_medication	random	8	3,548.887	3,586.934	-1,766.444	3,532.887	16.571	5	0.005
psas	null	3	6,367.394	6,381.661	-3,180.697	6,361.394
psas	random	8	6,278.164	6,316.210	-3,131.082	6,262.164	99.230	5	0.000
psas_somatic	null	3	1,511.020	1,525.288	-752.510	1,505.020
psas_somatic	random	8	1,489.176	1,527.222	-736.588	1,473.176	31.844	5	0.000
psas_cognitive	null	3	2,071.703	2,085.970	-1,032.852	2,065.703
psas_cognitive	random	8	1,938.230	1,976.276	-961.115	1,922.230	143.473	5	0.000
psqi_global	null	3	4,467.524	4,481.792	-2,230.762	4,461.524
psqi_global	random	8	4,266.778	4,304.824	-2,125.389	4,250.778	210.747	5	0.000
mic	null	3	5,723.874	5,738.141	-2,858.937	5,717.874
mic	random	8	5,690.684	5,728.730	-2,837.342	5,674.684	43.190	5	0.000
mic_attention	null	3	1,744.527	1,758.794	-869.263	1,738.527
mic_attention	random	8	1,719.760	1,757.806	-851.880	1,703.760	34.767	5	0.000
mic_executive	null	3	1,743.908	1,758.175	-868.954	1,737.908
mic_executive	random	8	1,726.611	1,764.657	-855.305	1,710.611	27.297	5	0.000
mic_memory	null	3	1,677.918	1,692.186	-835.959	1,671.918
mic_memory	random	8	1,657.068	1,695.114	-820.534	1,641.068	30.850	5	0.000
nb_pcs	null	3	5,869.922	5,884.189	-2,931.961	5,863.922
nb_pcs	random	8	5,860.727	5,898.773	-2,922.363	5,844.727	19.195	5	0.002
nb_mcs	null	3	6,263.319	6,277.587	-3,128.660	6,257.319
nb_mcs	random	8	6,187.789	6,225.835	-3,085.895	6,171.789	85.530	5	0.000

Post hoc analysis

Table

outcome	time	control			treatment			between
outcome	time	n	estimate	within es	n	estimate	within es	p	es
isi	1st	179	13.53 ± 3.82		179	13.40 ± 3.82		0.751	0.045
isi	2nd	148	11.07 ± 3.76	0.865	109	7.98 ± 3.67	1.908	0.000	1.088
isi	3rd	139	10.66 ± 3.73	1.011	105	7.57 ± 3.66	2.054	0.000	1.089
who	1st	179	39.28 ± 16.33		179	39.93 ± 16.33		0.707	-0.062
who	2nd	148	42.20 ± 15.83	-0.279	109	48.46 ± 15.18	-0.815	0.001	-0.598
who	3rd	139	43.03 ± 15.64	-0.358	105	50.17 ± 15.09	-0.979	0.000	-0.683
phq	1st	179	8.21 ± 5.06		179	8.80 ± 5.06		0.269	-0.204
phq	2nd	148	7.43 ± 4.86	0.268	109	6.29 ± 4.60	0.865	0.055	0.393
phq	3rd	139	7.55 ± 4.79	0.227	105	5.72 ± 4.56	1.062	0.002	0.631
gad	1st	179	7.54 ± 5.10		179	8.02 ± 5.10		0.373	-0.162
gad	2nd	148	7.10 ± 4.91	0.148	109	5.50 ± 4.65	0.844	0.008	0.535
gad	3rd	139	6.98 ± 4.83	0.189	105	5.05 ± 4.61	0.996	0.002	0.646
wsas	1st	179	16.77 ± 10.01		179	16.69 ± 10.01		0.937	0.014
wsas	2nd	148	15.95 ± 9.66	0.135	109	12.92 ± 9.20	0.622	0.011	0.501
wsas	3rd	139	16.62 ± 9.53	0.026	105	11.65 ± 9.13	0.830	0.000	0.818
wsas_1	1st	179	3.48 ± 2.18		179	3.50 ± 2.18		0.923	-0.015
wsas_1	2nd	148	3.43 ± 2.12	0.038	109	2.92 ± 2.04	0.406	0.052	0.352
wsas_1	3rd	139	3.42 ± 2.10	0.045	105	2.52 ± 2.03	0.680	0.001	0.619
wsas_2	1st	179	3.22 ± 2.27		179	3.12 ± 2.27		0.659	0.070
wsas_2	2nd	148	3.11 ± 2.21	0.075	109	2.62 ± 2.13	0.329	0.073	0.325
wsas_2	3rd	139	3.38 ± 2.19	-0.106	105	2.47 ± 2.12	0.429	0.001	0.606
wsas_3	1st	179	3.54 ± 2.27		179	3.46 ± 2.27		0.745	0.050
wsas_3	2nd	148	3.32 ± 2.22	0.141	109	2.57 ± 2.15	0.568	0.007	0.477
wsas_3	3rd	139	3.37 ± 2.20	0.109	105	2.39 ± 2.14	0.683	0.001	0.625
wsas_4	1st	179	3.14 ± 2.26		179	3.18 ± 2.26		0.852	-0.027
wsas_4	2nd	148	2.83 ± 2.22	0.187	109	2.44 ± 2.16	0.452	0.157	0.238
wsas_4	3rd	139	3.15 ± 2.20	-0.009	105	2.19 ± 2.15	0.607	0.001	0.589
wsas_5	1st	179	3.39 ± 2.34		179	3.42 ± 2.34		0.892	-0.021
wsas_5	2nd	148	3.25 ± 2.28	0.090	109	2.39 ± 2.20	0.664	0.002	0.553
wsas_5	3rd	139	3.27 ± 2.25	0.076	105	2.12 ± 2.19	0.839	0.000	0.741
shps	1st	179	82.50 ± 18.06		179	84.55 ± 18.06		0.283	-0.182
shps	2nd	148	79.74 ± 17.48	0.245	109	72.97 ± 16.71	1.026	0.002	0.600
shps	3rd	139	79.22 ± 17.25	0.291	105	69.17 ± 16.59	1.363	0.000	0.891
shps_arousal	1st	179	3.02 ± 0.74		179	3.18 ± 0.74		0.038	-0.313
shps_arousal	2nd	148	2.83 ± 0.73	0.376	109	2.51 ± 0.70	1.294	0.000	0.605
shps_arousal	3rd	139	2.80 ± 0.72	0.421	105	2.40 ± 0.70	1.508	0.000	0.773
shps_schedule	1st	179	3.53 ± 0.89		179	3.58 ± 0.89		0.659	-0.079
shps_schedule	2nd	148	3.43 ± 0.86	0.193	109	3.13 ± 0.81	0.852	0.004	0.580
shps_schedule	3rd	139	3.40 ± 0.84	0.256	105	3.02 ± 0.81	1.066	0.000	0.730
shps_behavior	1st	179	1.99 ± 0.68		179	2.12 ± 0.68		0.067	-0.298
shps_behavior	2nd	148	2.01 ± 0.66	-0.055	109	1.90 ± 0.64	0.495	0.172	0.252
shps_behavior	3rd	139	1.99 ± 0.65	-0.021	105	1.79 ± 0.63	0.730	0.016	0.453
shps_environment	1st	179	2.33 ± 0.81		179	2.27 ± 0.81		0.469	0.119
shps_environment	2nd	148	2.27 ± 0.79	0.111	109	2.13 ± 0.76	0.274	0.129	0.282
shps_environment	3rd	139	2.28 ± 0.78	0.109	105	1.95 ± 0.75	0.608	0.001	0.618
dbas	1st	179	95.66 ± 25.75		179	96.55 ± 25.75		0.744	-0.053
dbas	2nd	148	91.53 ± 25.01	0.246	109	74.32 ± 24.04	1.323	0.000	1.024
dbas	3rd	139	87.30 ± 24.72	0.498	105	67.26 ± 23.90	1.743	0.000	1.193
dbas_consequence	1st	179	6.59 ± 1.88		179	6.64 ± 1.88		0.787	-0.044
dbas_consequence	2nd	148	6.25 ± 1.82	0.273	109	5.19 ± 1.76	1.174	0.000	0.858
dbas_consequence	3rd	139	5.92 ± 1.80	0.543	105	4.67 ± 1.74	1.597	0.000	1.011
dbas_worry	1st	179	14.20 ± 3.79		179	14.54 ± 3.79		0.395	-0.120
dbas_worry	2nd	148	12.97 ± 3.73	0.433	109	10.60 ± 3.65	1.386	0.000	0.834
dbas_worry	3rd	139	12.37 ± 3.70	0.642	105	9.83 ± 3.64	1.657	0.000	0.894
dbas_expectation	1st	179	7.17 ± 2.31		179	6.89 ± 2.31		0.243	0.185
dbas_expectation	2nd	148	6.83 ± 2.25	0.223	109	5.30 ± 2.16	1.033	0.000	0.996
dbas_expectation	3rd	139	6.41 ± 2.22	0.499	105	4.83 ± 2.15	1.335	0.000	1.022
dbas_medication	1st	179	3.15 ± 2.15		179	3.24 ± 2.15		0.695	-0.062
dbas_medication	2nd	148	3.51 ± 2.10	-0.255	109	2.94 ± 2.02	0.207	0.027	0.399
dbas_medication	3rd	139	3.46 ± 2.07	-0.215	105	2.69 ± 2.01	0.383	0.004	0.535
psas	1st	179	37.87 ± 10.84		179	39.02 ± 10.84		0.316	-0.169
psas	2nd	148	37.37 ± 10.50	0.072	109	32.60 ± 10.05	0.941	0.000	0.700
psas	3rd	139	35.13 ± 10.36	0.401	105	30.99 ± 9.98	1.177	0.002	0.607
psas_somatic	1st	179	1.86 ± 0.68		179	1.91 ± 0.68		0.533	-0.108
psas_somatic	2nd	148	2.00 ± 0.65	-0.347	109	1.74 ± 0.62	0.393	0.001	0.632
psas_somatic	3rd	139	1.87 ± 0.65	-0.027	105	1.67 ± 0.62	0.561	0.015	0.480
psas_cognitive	1st	179	2.87 ± 0.85		179	2.97 ± 0.85		0.269	-0.177
psas_cognitive	2nd	148	2.67 ± 0.82	0.365	109	2.33 ± 0.79	1.141	0.001	0.599
psas_cognitive	3rd	139	2.52 ± 0.82	0.630	105	2.20 ± 0.79	1.376	0.002	0.569
psqi_global	1st	179	10.14 ± 3.21		179	10.58 ± 3.21		0.194	-0.198
psqi_global	2nd	148	8.92 ± 3.14	0.547	109	7.48 ± 3.04	1.389	0.000	0.644
psqi_global	3rd	139	8.87 ± 3.11	0.570	105	6.65 ± 3.03	1.759	0.000	0.991
mic	1st	179	15.64 ± 8.19		179	16.65 ± 8.19		0.241	-0.223
mic	2nd	148	15.54 ± 7.85	0.022	109	13.84 ± 7.39	0.619	0.076	0.374
mic	3rd	139	15.33 ± 7.72	0.067	105	12.83 ± 7.32	0.840	0.010	0.550
mic_attention	1st	179	1.30 ± 0.76		179	1.42 ± 0.76		0.130	-0.251
mic_attention	2nd	148	1.28 ± 0.73	0.045	109	1.15 ± 0.70	0.556	0.164	0.260
mic_attention	3rd	139	1.33 ± 0.73	-0.063	105	1.07 ± 0.70	0.729	0.004	0.541
mic_executive	1st	179	1.28 ± 0.78		179	1.35 ± 0.78		0.415	-0.141
mic_executive	2nd	148	1.25 ± 0.75	0.071	109	1.15 ± 0.72	0.406	0.318	0.194
mic_executive	3rd	139	1.23 ± 0.74	0.096	105	1.03 ± 0.71	0.674	0.027	0.437
mic_memory	1st	179	1.33 ± 0.76		179	1.40 ± 0.76		0.417	-0.147
mic_memory	2nd	148	1.36 ± 0.74	-0.071	109	1.15 ± 0.70	0.549	0.020	0.472
mic_memory	3rd	139	1.27 ± 0.72	0.135	105	1.12 ± 0.69	0.636	0.085	0.353
nb_pcs	1st	179	46.33 ± 8.81		179	46.20 ± 8.81		0.882	0.027
nb_pcs	2nd	148	45.46 ± 8.48	0.169	109	48.08 ± 8.04	-0.366	0.012	-0.508
nb_pcs	3rd	139	45.51 ± 8.35	0.159	105	48.61 ± 7.97	-0.469	0.003	-0.601
nb_mcs	1st	179	39.90 ± 10.30		179	39.98 ± 10.30		0.938	-0.013
nb_mcs	2nd	148	41.90 ± 9.97	-0.310	109	45.56 ± 9.54	-0.862	0.003	-0.565
nb_mcs	3rd	139	42.18 ± 9.84	-0.353	105	46.91 ± 9.47	-1.072	0.000	-0.733

Between group

isi

1st

t(640.51) = -0.32, p = 0.751, Cohen d = 0.05, 95% CI (-0.92 to 0.67)

2st

t(756.98) = -6.60, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(772.27) = -6.48, p = 0.000, Cohen d = 1.09, 95% CI (-4.03 to -2.16)

who

1st

t(548.38) = 0.38, p = 0.707, Cohen d = -0.06, 95% CI (-2.74 to 4.04)

2st

t(687.48) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (2.42 to 10.09)

3rd

t(705.91) = 3.60, p = 0.000, Cohen d = -0.68, 95% CI (3.25 to 11.03)

phq

1st

t(501.28) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(637.10) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(655.28) = -3.04, p = 0.002, Cohen d = 0.63, 95% CI (-3.02 to -0.65)

gad

1st

t(507.04) = 0.89, p = 0.373, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(644.00) = -2.65, p = 0.008, Cohen d = 0.54, 95% CI (-2.77 to -0.41)

3rd

t(662.32) = -3.16, p = 0.002, Cohen d = 0.65, 95% CI (-3.12 to -0.73)

wsas

1st

t(522.74) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.70) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(680.22) = -4.13, p = 0.000, Cohen d = 0.82, 95% CI (-7.32 to -2.60)

wsas_1

1st

t(566.76) = 0.10, p = 0.923, Cohen d = -0.02, 95% CI (-0.43 to 0.48)

2st

t(703.92) = -1.94, p = 0.052, Cohen d = 0.35, 95% CI (-1.02 to 0.01)

3rd

t(722.02) = -3.36, p = 0.001, Cohen d = 0.62, 95% CI (-1.42 to -0.37)

wsas_2

1st

t(567.32) = -0.44, p = 0.659, Cohen d = 0.07, 95% CI (-0.58 to 0.37)

2st

t(704.39) = -1.79, p = 0.073, Cohen d = 0.32, 95% CI (-1.03 to 0.05)

3rd

t(722.48) = -3.29, p = 0.001, Cohen d = 0.61, 95% CI (-1.46 to -0.37)

wsas_3

1st

t(587.89) = -0.33, p = 0.745, Cohen d = 0.05, 95% CI (-0.55 to 0.39)

2st

t(721.02) = -2.71, p = 0.007, Cohen d = 0.48, 95% CI (-1.29 to -0.21)

3rd

t(738.54) = -3.49, p = 0.001, Cohen d = 0.62, 95% CI (-1.52 to -0.43)

wsas_4

1st

t(623.83) = 0.19, p = 0.852, Cohen d = -0.03, 95% CI (-0.43 to 0.51)

2st

t(746.46) = -1.42, p = 0.157, Cohen d = 0.24, 95% CI (-0.93 to 0.15)

3rd

t(762.56) = -3.44, p = 0.001, Cohen d = 0.59, 95% CI (-1.52 to -0.42)

wsas_5

1st

t(569.37) = 0.14, p = 0.892, Cohen d = -0.02, 95% CI (-0.45 to 0.52)

2st

t(706.12) = -3.06, p = 0.002, Cohen d = 0.55, 95% CI (-1.42 to -0.31)

3rd

t(724.17) = -4.04, p = 0.000, Cohen d = 0.74, 95% CI (-1.72 to -0.59)

shps

1st

t(536.47) = 1.07, p = 0.283, Cohen d = -0.18, 95% CI (-1.70 to 5.80)

2st

t(675.95) = -3.15, p = 0.002, Cohen d = 0.60, 95% CI (-10.99 to -2.55)

3rd

t(694.48) = -4.61, p = 0.000, Cohen d = 0.89, 95% CI (-14.34 to -5.77)

shps_arousal

1st

t(600.25) = 2.07, p = 0.038, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(730.24) = -3.50, p = 0.000, Cohen d = 0.61, 95% CI (-0.49 to -0.14)

3rd

t(747.32) = -4.39, p = 0.000, Cohen d = 0.77, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(510.10) = 0.44, p = 0.659, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(647.57) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(665.94) = -3.59, p = 0.000, Cohen d = 0.73, 95% CI (-0.59 to -0.17)

shps_behavior

1st

t(556.98) = 1.84, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(695.37) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(713.67) = -2.42, p = 0.016, Cohen d = 0.45, 95% CI (-0.36 to -0.04)

shps_environment

1st

t(552.78) = -0.72, p = 0.469, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(691.56) = -1.52, p = 0.129, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(709.92) = -3.29, p = 0.001, Cohen d = 0.62, 95% CI (-0.52 to -0.13)

dbas

1st

t(558.14) = 0.33, p = 0.744, Cohen d = -0.05, 95% CI (-4.46 to 6.23)

2st

t(696.40) = -5.58, p = 0.000, Cohen d = 1.02, 95% CI (-23.27 to -11.15)

3rd

t(714.68) = -6.39, p = 0.000, Cohen d = 1.19, 95% CI (-26.20 to -13.89)

dbas_consequence

1st

t(561.19) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(699.10) = -4.69, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(717.32) = -5.44, p = 0.000, Cohen d = 1.01, 95% CI (-1.69 to -0.80)

dbas_worry

1st

t(648.14) = 0.85, p = 0.395, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(761.55) = -5.10, p = 0.000, Cohen d = 0.83, 95% CI (-3.28 to -1.46)

3rd

t(776.46) = -5.37, p = 0.000, Cohen d = 0.89, 95% CI (-3.47 to -1.61)

dbas_expectation

1st

t(570.48) = -1.17, p = 0.243, Cohen d = 0.19, 95% CI (-0.76 to 0.19)

2st

t(707.06) = -5.52, p = 0.000, Cohen d = 1.00, 95% CI (-2.08 to -0.99)

3rd

t(725.07) = -5.58, p = 0.000, Cohen d = 1.02, 95% CI (-2.13 to -1.02)

dbas_medication

1st

t(571.42) = 0.39, p = 0.695, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.85) = -2.22, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(725.84) = -2.93, p = 0.004, Cohen d = 0.54, 95% CI (-1.29 to -0.25)

psas

1st

t(539.85) = 1.00, p = 0.316, Cohen d = -0.17, 95% CI (-1.10 to 3.40)

2st

t(679.29) = -3.70, p = 0.000, Cohen d = 0.70, 95% CI (-7.32 to -2.24)

3rd

t(697.80) = -3.16, p = 0.002, Cohen d = 0.61, 95% CI (-6.72 to -1.57)

psas_somatic

1st

t(526.35) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(665.55) = -3.26, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(684.09) = -2.44, p = 0.015, Cohen d = 0.48, 95% CI (-0.36 to -0.04)

psas_cognitive

1st

t(563.72) = 1.11, p = 0.269, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(701.31) = -3.29, p = 0.001, Cohen d = 0.60, 95% CI (-0.53 to -0.13)

3rd

t(719.48) = -3.08, p = 0.002, Cohen d = 0.57, 95% CI (-0.52 to -0.12)

psqi_global

1st

t(595.32) = 1.30, p = 0.194, Cohen d = -0.20, 95% CI (-0.23 to 1.11)

2st

t(726.63) = -3.70, p = 0.000, Cohen d = 0.64, 95% CI (-2.20 to -0.68)

3rd

t(743.89) = -5.60, p = 0.000, Cohen d = 0.99, 95% CI (-2.99 to -1.44)

mic

1st

t(489.94) = 1.17, p = 0.241, Cohen d = -0.22, 95% CI (-0.68 to 2.72)

2st

t(622.81) = -1.78, p = 0.076, Cohen d = 0.37, 95% CI (-3.58 to 0.18)

3rd

t(640.65) = -2.58, p = 0.010, Cohen d = 0.55, 95% CI (-4.41 to -0.60)

mic_attention

1st

t(548.17) = 1.52, p = 0.130, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(687.28) = -1.39, p = 0.164, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(705.72) = -2.85, p = 0.004, Cohen d = 0.54, 95% CI (-0.44 to -0.08)

mic_executive

1st

t(526.24) = 0.82, p = 0.415, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(665.43) = -1.00, p = 0.318, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(683.97) = -2.22, p = 0.027, Cohen d = 0.44, 95% CI (-0.39 to -0.02)

mic_memory

1st

t(506.57) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.44) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(661.75) = -1.72, p = 0.085, Cohen d = 0.35, 95% CI (-0.34 to 0.02)

nb_pcs

1st

t(508.29) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(645.46) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(663.80) = 2.94, p = 0.003, Cohen d = -0.60, 95% CI (1.03 to 5.16)

nb_mcs

1st

t(538.35) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.05 to 2.22)

2st

t(677.82) = 2.98, p = 0.003, Cohen d = -0.57, 95% CI (1.24 to 6.07)

3rd

t(696.34) = 3.80, p = 0.000, Cohen d = -0.73, 95% CI (2.29 to 7.18)

Within treatment group

isi

1st vs 2st

t(597.72) = -14.91, p = 0.000, Cohen d = 1.91, 95% CI (-6.13 to -4.71)

1st vs 3rd

t(599.39) = -15.83, p = 0.000, Cohen d = 2.05, 95% CI (-6.56 to -5.11)

who

1st vs 2st

t(574.56) = 6.27, p = 0.000, Cohen d = -0.82, 95% CI (5.85 to 11.19)

1st vs 3rd

t(575.30) = 7.42, p = 0.000, Cohen d = -0.98, 95% CI (7.53 to 12.94)

phq

1st vs 2st

t(560.18) = -6.60, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(560.56) = -7.99, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.33)

gad

1st vs 2st

t(562.06) = -6.45, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(562.48) = -7.50, p = 0.000, Cohen d = 1.00, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(567.00) = -4.76, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.22)

1st vs 3rd

t(567.53) = -6.27, p = 0.000, Cohen d = 0.83, 95% CI (-6.61 to -3.46)

wsas_1

1st vs 2st

t(579.65) = -3.13, p = 0.004, Cohen d = 0.41, 95% CI (-0.95 to -0.22)

1st vs 3rd

t(580.54) = -5.17, p = 0.000, Cohen d = 0.68, 95% CI (-1.36 to -0.61)

wsas_2

1st vs 2st

t(579.80) = -2.54, p = 0.023, Cohen d = 0.33, 95% CI (-0.88 to -0.11)

1st vs 3rd

t(580.70) = -3.27, p = 0.002, Cohen d = 0.43, 95% CI (-1.04 to -0.26)

wsas_3

1st vs 2st

t(585.18) = -4.40, p = 0.000, Cohen d = 0.57, 95% CI (-1.28 to -0.49)

1st vs 3rd

t(586.27) = -5.22, p = 0.000, Cohen d = 0.68, 95% CI (-1.47 to -0.67)

wsas_4

1st vs 2st

t(593.92) = -3.52, p = 0.001, Cohen d = 0.45, 95% CI (-1.16 to -0.33)

1st vs 3rd

t(595.39) = -4.66, p = 0.000, Cohen d = 0.61, 95% CI (-1.42 to -0.58)

wsas_5

1st vs 2st

t(580.35) = -5.13, p = 0.000, Cohen d = 0.66, 95% CI (-1.43 to -0.64)

1st vs 3rd

t(581.26) = -6.39, p = 0.000, Cohen d = 0.84, 95% CI (-1.71 to -0.91)

shps

1st vs 2st

t(571.12) = -7.88, p = 0.000, Cohen d = 1.03, 95% CI (-14.47 to -8.70)

1st vs 3rd

t(571.76) = -10.32, p = 0.000, Cohen d = 1.36, 95% CI (-18.31 to -12.46)

shps_arousal

1st vs 2st

t(588.28) = -10.04, p = 0.000, Cohen d = 1.29, 95% CI (-0.80 to -0.54)

1st vs 3rd

t(589.49) = -11.54, p = 0.000, Cohen d = 1.51, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(563.04) = -6.51, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(563.48) = -8.03, p = 0.000, Cohen d = 1.07, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(576.98) = -3.81, p = 0.000, Cohen d = 0.50, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(577.78) = -5.55, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(575.81) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(576.57) = -4.62, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas

1st vs 2st

t(577.30) = -10.20, p = 0.000, Cohen d = 1.32, 95% CI (-26.52 to -17.95)

1st vs 3rd

t(578.11) = -13.25, p = 0.000, Cohen d = 1.74, 95% CI (-33.64 to -24.95)

dbas_consequence

1st vs 2st

t(578.14) = -9.05, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(578.98) = -12.14, p = 0.000, Cohen d = 1.60, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(599.41) = -10.84, p = 0.000, Cohen d = 1.39, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(601.17) = -12.78, p = 0.000, Cohen d = 1.66, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(580.65) = -7.98, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(581.57) = -10.17, p = 0.000, Cohen d = 1.34, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(580.90) = -1.60, p = 0.222, Cohen d = 0.21, 95% CI (-0.66 to 0.07)

1st vs 3rd

t(581.83) = -2.92, p = 0.007, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas

1st vs 2st

t(572.11) = -7.23, p = 0.000, Cohen d = 0.94, 95% CI (-8.17 to -4.68)

1st vs 3rd

t(572.77) = -8.91, p = 0.000, Cohen d = 1.18, 95% CI (-9.80 to -6.26)

psas_somatic

1st vs 2st

t(568.10) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(568.65) = -4.24, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(578.83) = -8.80, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(579.69) = -10.47, p = 0.000, Cohen d = 1.38, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(587.05) = -10.77, p = 0.000, Cohen d = 1.39, 95% CI (-3.67 to -2.54)

1st vs 3rd

t(588.22) = -13.45, p = 0.000, Cohen d = 1.76, 95% CI (-4.50 to -3.36)

mic

1st vs 2st

t(556.39) = -4.71, p = 0.000, Cohen d = 0.62, 95% CI (-3.99 to -1.64)

1st vs 3rd

t(556.70) = -6.31, p = 0.000, Cohen d = 0.84, 95% CI (-5.02 to -2.63)

mic_attention

1st vs 2st

t(574.50) = -4.27, p = 0.000, Cohen d = 0.56, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(575.23) = -5.53, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(568.06) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(568.62) = -5.10, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(561.90) = -4.19, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(562.32) = -4.79, p = 0.000, Cohen d = 0.64, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(562.46) = 2.80, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.21)

1st vs 3rd

t(562.89) = 3.53, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(571.67) = 6.62, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(572.32) = 8.12, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(544.64) = -7.63, p = 0.000, Cohen d = 0.87, 95% CI (-3.09 to -1.82)

1st vs 3rd

t(549.35) = -8.72, p = 0.000, Cohen d = 1.01, 95% CI (-3.52 to -2.22)

who

1st vs 2st

t(532.59) = 2.44, p = 0.030, Cohen d = -0.28, 95% CI (0.57 to 5.26)

1st vs 3rd

t(535.51) = 3.06, p = 0.005, Cohen d = -0.36, 95% CI (1.34 to 6.14)

phq

1st vs 2st

t(525.62) = -2.34, p = 0.039, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(527.72) = -1.93, p = 0.108, Cohen d = 0.23, 95% CI (-1.33 to 0.01)

gad

1st vs 2st

t(526.51) = -1.29, p = 0.395, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(528.71) = -1.61, p = 0.216, Cohen d = 0.19, 95% CI (-1.25 to 0.12)

wsas

1st vs 2st

t(528.89) = -1.18, p = 0.477, Cohen d = 0.14, 95% CI (-2.18 to 0.54)

1st vs 3rd

t(531.36) = -0.22, p = 1.000, Cohen d = 0.03, 95% CI (-1.55 to 1.24)

wsas_1

1st vs 2st

t(535.13) = -0.33, p = 1.000, Cohen d = 0.04, 95% CI (-0.38 to 0.27)

1st vs 3rd

t(538.39) = -0.39, p = 1.000, Cohen d = 0.05, 95% CI (-0.40 to 0.27)

wsas_2

1st vs 2st

t(535.20) = -0.66, p = 1.000, Cohen d = 0.07, 95% CI (-0.45 to 0.23)

1st vs 3rd

t(538.47) = 0.91, p = 0.726, Cohen d = -0.11, 95% CI (-0.19 to 0.51)

wsas_3

1st vs 2st

t(537.95) = -1.24, p = 0.433, Cohen d = 0.14, 95% CI (-0.57 to 0.13)

1st vs 3rd

t(541.61) = -0.93, p = 0.701, Cohen d = 0.11, 95% CI (-0.53 to 0.19)

wsas_4

1st vs 2st

t(542.56) = -1.65, p = 0.201, Cohen d = 0.19, 95% CI (-0.67 to 0.06)

1st vs 3rd

t(546.93) = 0.08, p = 1.000, Cohen d = -0.01, 95% CI (-0.36 to 0.39)

wsas_5

1st vs 2st

t(535.48) = -0.79, p = 0.864, Cohen d = 0.09, 95% CI (-0.49 to 0.21)

1st vs 3rd

t(538.79) = -0.65, p = 1.000, Cohen d = 0.08, 95% CI (-0.48 to 0.24)

shps

1st vs 2st

t(530.89) = -2.14, p = 0.065, Cohen d = 0.25, 95% CI (-5.30 to -0.23)

1st vs 3rd

t(533.60) = -2.49, p = 0.026, Cohen d = 0.29, 95% CI (-5.87 to -0.69)

shps_arousal

1st vs 2st

t(539.56) = -3.31, p = 0.002, Cohen d = 0.38, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(543.46) = -3.62, p = 0.001, Cohen d = 0.42, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(526.98) = -1.68, p = 0.187, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(529.24) = -2.19, p = 0.058, Cohen d = 0.26, 95% CI (-0.25 to -0.01)

shps_behavior

1st vs 2st

t(533.78) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.07 to 0.12)

1st vs 3rd

t(536.87) = 0.18, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.11)

shps_environment

1st vs 2st

t(533.20) = -0.97, p = 0.661, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(536.21) = -0.93, p = 0.705, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

dbas

1st vs 2st

t(533.95) = -2.16, p = 0.063, Cohen d = 0.25, 95% CI (-7.90 to -0.37)

1st vs 3rd

t(537.05) = -4.26, p = 0.000, Cohen d = 0.50, 95% CI (-12.21 to -4.51)

dbas_consequence

1st vs 2st

t(534.36) = -2.39, p = 0.034, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(537.52) = -4.65, p = 0.000, Cohen d = 0.54, 95% CI (-0.95 to -0.39)

dbas_worry

1st vs 2st

t(545.58) = -3.82, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.60)

1st vs 3rd

t(550.45) = -5.54, p = 0.000, Cohen d = 0.64, 95% CI (-2.47 to -1.18)

dbas_expectation

1st vs 2st

t(535.63) = -1.95, p = 0.103, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(538.96) = -4.28, p = 0.000, Cohen d = 0.50, 95% CI (-1.12 to -0.41)

dbas_medication

1st vs 2st

t(535.76) = 2.23, p = 0.052, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(539.11) = 1.84, p = 0.132, Cohen d = -0.21, 95% CI (-0.02 to 0.64)

psas

1st vs 2st

t(531.38) = -0.63, p = 1.000, Cohen d = 0.07, 95% CI (-2.02 to 1.04)

1st vs 3rd

t(534.15) = -3.43, p = 0.001, Cohen d = 0.40, 95% CI (-4.30 to -1.17)

psas_somatic

1st vs 2st

t(529.42) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(531.95) = 0.23, p = 1.000, Cohen d = -0.03, 95% CI (-0.08 to 0.11)

psas_cognitive

1st vs 2st

t(534.71) = -3.20, p = 0.003, Cohen d = 0.37, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(537.92) = -5.40, p = 0.000, Cohen d = 0.63, 95% CI (-0.48 to -0.22)

psqi_global

1st vs 2st

t(538.92) = -4.81, p = 0.000, Cohen d = 0.55, 95% CI (-1.72 to -0.72)

1st vs 3rd

t(542.72) = -4.90, p = 0.000, Cohen d = 0.57, 95% CI (-1.78 to -0.76)

mic

1st vs 2st

t(523.83) = -0.19, p = 1.000, Cohen d = 0.02, 95% CI (-1.12 to 0.93)

1st vs 3rd

t(525.74) = -0.57, p = 1.000, Cohen d = 0.07, 95% CI (-1.35 to 0.75)

mic_attention

1st vs 2st

t(532.56) = -0.40, p = 1.000, Cohen d = 0.05, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(535.48) = 0.54, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.14)

mic_executive

1st vs 2st

t(529.40) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(531.93) = -0.82, p = 0.820, Cohen d = 0.10, 95% CI (-0.16 to 0.06)

mic_memory

1st vs 2st

t(526.44) = 0.62, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(528.63) = -1.15, p = 0.500, Cohen d = 0.13, 95% CI (-0.16 to 0.04)

nb_pcs

1st vs 2st

t(526.71) = -1.48, p = 0.281, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(528.93) = -1.36, p = 0.350, Cohen d = 0.16, 95% CI (-2.01 to 0.37)

nb_mcs

1st vs 2st

t(531.16) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.45)

1st vs 3rd

t(533.91) = 3.02, p = 0.005, Cohen d = -0.35, 95% CI (0.80 to 3.77)

Plot

Clinical significance

	T1			T2			T3
outcome	control1	treatment1	p-value2	control1	treatment1	p-value3	control1	treatment1	p-value3
isi	89%	85%	0.206	61%	31%	0.000	55%	29%	0.000
psqi	98%	98%	1.00	95%	80%	0.000	94%	67%	0.000
phq	31%	38%	0.148	32%	19%	0.019	29%	18%	0.041
gad	30%	33%	0.494	26%	17%	0.061	27%	16%	0.052
wsas	74%	72%	0.721	68%	55%	0.041	69%	49%	0.001
who	25%	18%	0.094	26%	39%	0.038	29%	47%	0.004
pcs	22%	22%	0.899	27%	18%	0.104	24%	11%	0.014
mcs	55%	50%	0.341	42%	29%	0.039	37%	25%	0.047
1%
2Pearson's Chi-squared test; Fisher's exact test
3Pearson's Chi-squared test

Insomnia group

	1st			2nd			3d
	control	treatment	p-value1	control	treatment	p-value1	control	treatment	p-value1
isi_group			0.828			0.000			0.000
None	0 (0%)	0 (0%)		24 (16%)	49 (45%)		33 (24%)	58 (55%)
Subthreshold	112 (63%)	109 (61%)		96 (65%)	53 (49%)		79 (57%)	40 (38%)
Moderate severity	67 (37%)	70 (39%)		26 (18%)	7 (6.4%)		27 (19%)	7 (6.7%)
Severe	0 (0%)	0 (0%)		2 (1.4%)	0 (0%)		0 (0%)	0 (0%)
1Pearson's Chi-squared test

Other method

method	control	treatment
none	33.8	29.5
exercise	33.1	40.0
relaxation	26.6	27.6
mindful	24.5	26.7
tcm	15.8	9.5
supplement	14.4	5.7
food therapy	11.5	6.7
insomnia web info	10.8	14.3
mental health web info	10.8	14.3
mental health book	5.8	2.9
insomnia course	5.0	12.4
mental health course	3.6	2.9
insomnia book	3.6	2.9
sleeping pill	2.9	1.0
other	2.2	1.0
acupuncture	0.7	4.8

ISI as Outcome

rowname	total_effect	total_se	direct_effect	direct_se	indirect_effect	indirect_se	indirect_p	percentage_mediate
shps	-1.86	0.36	-1.51	0.32	-0.35	0.16	0.03	18.94
dbas	-1.87	0.34	-1.46	0.33	-0.41	0.11	0.00	21.62
psas	-1.87	0.34	-1.53	0.31	-0.35	0.17	0.04	18.50

ISI as Mediator

rowname	total_effect	total_se	total_p	direct_effect	direct_se	direct_p	indirect_effect	indirect_se	percentage_mediate
who	4.11	1.51	0.01	1.51	1.45	0.27	2.61	0.55	63.62
phq	-0.64	0.45	0.15	0.23	0.44	0.60	-0.88	0.17	119.65
gad	-0.81	0.48	0.09	-0.04	0.46	0.92	-0.77	0.16	89.89
wsas	-2.31	0.92	0.01	-0.86	0.88	0.34	-1.45	0.31	62.74
psqi_global	-0.88	0.27	0.00	0.03	0.22	0.95	-0.91	0.17	101.51
mic	-0.84	0.78	0.27	0.18	0.76	0.82	-1.03	0.22	91.86
nb_pcs	1.63	0.82	0.04	0.66	0.81	0.39	0.97	0.22	59.40
nb_mcs	2.49	0.98	0.01	1.18	0.95	0.21	1.31	0.28	52.63