Analyses Using Total Scores
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## stepSet up
Reading the full data (intervention group, psychoeducation control group, and EC group) and calculating total scores
Full_data_all_t <- read_csv("MI_Data_B1W1M1.csv") %>%
rowwise() %>%
mutate(A_PRE_IUS_total = sum(B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5, B_IUS_6, B_IUS_7, B_IUS_8, B_IUS_9, B_IUS_10, B_IUS_11, B_IUS_12, na.rm = TRUE)) %>%
mutate(A_PRE_FI_total = sum(B_FI_friends, B_FI_strangers, B_FI_work, B_FI_education, B_FI_hobbies, na.rm = TRUE)) %>%
mutate(A_PRE_RTQ_total = sum(B_RTQ_1, B_RTQ_2, B_RTQ_3, B_RTQ_4, B_RTQ_5, B_RTQ_6, B_RTQ_7, B_RTQ_8, B_RTQ_9, B_RTQ_10, na.rm = TRUE)) %>%
mutate(A_PRE_ERQ_Rtotal = sum(B_ERQ_1, B_ERQ_3, B_ERQ_5, B_ERQ_7, B_ERQ_8, B_ERQ_10, na.rm = TRUE)) %>%
mutate(A_PRE_PHQ_total = sum(B_PHQ_1, B_PHQ_2, B_PHQ_3, B_PHQ_4, B_PHQ_5, B_PHQ_6, B_PHQ_7, B_PHQ_8, na.rm = TRUE)) %>%
mutate(A_PRE_GAD_total = sum(B_GAD_1, B_GAD_2, B_GAD_3, B_GAD_4, B_GAD_5, B_GAD_6, B_GAD_7, na.rm = TRUE)) %>%
mutate(B_POST_IUS_total = sum(POST_IUS_1, POST_IUS_2, POST_IUS_3, POST_IUS_4, POST_IUS_5, POST_IUS_6, POST_IUS_7, POST_IUS_8, POST_IUS_9, POST_IUS_10, POST_IUS_11, POST_IUS_12, na.rm = TRUE)) %>%
mutate(C_W1_IUS_total = sum(W1_IUS_1, W1_IUS_2, W1_IUS_3, W1_IUS_4, W1_IUS_5, W1_IUS_6, W1_IUS_7, W1_IUS_8, W1_IUS_9, W1_IUS_10, W1_IUS_11, W1_IUS_12, na.rm = TRUE)) %>%
mutate(C_W1_FI_total = sum(W1_FI_friends, W1_FI_strangers, W1_FI_work, W1_FI_education, W1_FI_hobbies, na.rm = TRUE)) %>%
mutate(C_W1_RTQ_total = sum(W1_RTQ_1, W1_RTQ_2, W1_RTQ_3, W1_RTQ_4, W1_RTQ_5, W1_RTQ_6, W1_RTQ_7, W1_RTQ_8, W1_RTQ_9, W1_RTQ_10, na.rm = TRUE)) %>%
mutate(C_W1_ERQ_Rtotal = sum(W1_ERQ_1, W1_ERQ_3, W1_ERQ_5, W1_ERQ_7, W1_ERQ_8, W1_ERQ_10, na.rm = TRUE)) %>%
mutate(C_W1_PHQ_total = sum(W1_PHQ_1, W1_PHQ_2, W1_PHQ_3, W1_PHQ_4, W1_PHQ_5, W1_PHQ_6, W1_PHQ_7, W1_PHQ_8, na.rm = TRUE)) %>%
mutate(C_W1_GAD_total = sum(W1_GAD_1, W1_GAD_2, W1_GAD_3, W1_GAD_4, W1_GAD_5, W1_GAD_6, W1_GAD_7, na.rm = TRUE)) %>%
mutate(D_M1_IUS_total = sum(M1_IUS_1, M1_IUS_2, M1_IUS_3, M1_IUS_4, M1_IUS_5, M1_IUS_6, M1_IUS_7, M1_IUS_8, M1_IUS_9, M1_IUS_10, M1_IUS_11, M1_IUS_12, na.rm = TRUE)) %>%
mutate(D_M1_FI_total = sum(M1_FI_friends, M1_FI_strangers, M1_FI_work, M1_FI_education, M1_FI_hobbies, na.rm = TRUE)) %>%
mutate(D_M1_RTQ_total = sum(M1_RTQ_1, M1_RTQ_2, M1_RTQ_3, M1_RTQ_4, M1_RTQ_5, M1_RTQ_6, M1_RTQ_7, M1_RTQ_8, M1_RTQ_9, M1_RTQ_10, na.rm = TRUE)) %>%
mutate(D_M1_ERQ_Rtotal = sum(M1_ERQ_1, M1_ERQ_3, M1_ERQ_5, M1_ERQ_7, M1_ERQ_8, M1_ERQ_10, na.rm = TRUE)) %>%
mutate(D_M1_PHQ_total = sum(M1_PHQ_1, M1_PHQ_2, M1_PHQ_3, M1_PHQ_4, M1_PHQ_5, M1_PHQ_6, M1_PHQ_7, M1_PHQ_8, na.rm = TRUE)) %>%
mutate(D_M1_GAD_total = sum(M1_GAD_1, M1_GAD_2, M1_GAD_3, M1_GAD_4, M1_GAD_5, M1_GAD_6, M1_GAD_7, na.rm = TRUE)) %>%
ungroup()## New names:
## Rows: 259 Columns: 207
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Prolific_ID, Group dbl (205): ...1, ID, B_IUS_1, B_IUS_2, B_IUS_3,
## B_IUS_4, B_IUS_5, B_IUS_6, B...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`Calculating mood means
Full_data_all <- mutate(Full_data_all_t, A_PRE_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(B_distressed_pleasant, B_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, B_POST_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(POST_distressed_pleasant, POST_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, C_W1_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(W1_distressed_pleasant, W1_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, D_M1_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(M1_distressed_pleasant, M1_anxious_relaxed)), na.rm = TRUE))Hypothesis 1
H1a: IUS and Mood association at PRE
#Distressed
PRE_IUS_Distress_lm <- lm(A_PRE_IUS_total ~ B_distressed_pleasant, data = Full_data_all)
summary(PRE_IUS_Distress_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_distressed_pleasant, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.8365 -5.8426 0.4123 6.3128 20.1076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.3702 0.7218 61.471 < 2e-16 ***
## B_distressed_pleasant -0.0559 0.0129 -4.334 2.11e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.571 on 257 degrees of freedom
## Multiple R-squared: 0.0681, Adjusted R-squared: 0.06448
## F-statistic: 18.78 on 1 and 257 DF, p-value: 2.105e-05anova(PRE_IUS_Distress_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_distressed_pleasant 1 1379.6 1379.58 18.781 2.105e-05 ***
## Residuals 257 18878.1 73.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Anxious
PRE_IUS_Anxiety_lm <- lm(A_PRE_IUS_total ~ B_anxious_relaxed, data = Full_data_all)
summary(PRE_IUS_Anxiety_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_anxious_relaxed, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.2289 -5.7679 0.5456 5.9105 20.4901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.00514 0.61670 71.355 < 2e-16 ***
## B_anxious_relaxed -0.05551 0.01021 -5.437 1.26e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.416 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1035, Adjusted R-squared: 0.1
## F-statistic: 29.56 on 1 and 256 DF, p-value: 1.262e-07anova(PRE_IUS_Anxiety_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_anxious_relaxed 1 2093.8 2093.83 29.564 1.262e-07 ***
## Residuals 256 18130.7 70.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Combined
PRE_IUS_mood_lm <- lm(A_PRE_IUS_total ~ A_PRE_mood_mean, data = Full_data_all)
summary(PRE_IUS_mood_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_mood_mean, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.2840 -6.0303 0.1752 6.0332 20.9727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.57405 0.67547 65.989 < 2e-16 ***
## A_PRE_mood_mean -0.06646 0.01228 -5.412 1.43e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.412 on 257 degrees of freedom
## Multiple R-squared: 0.1023, Adjusted R-squared: 0.0988
## F-statistic: 29.29 on 1 and 257 DF, p-value: 1.431e-07anova(PRE_IUS_mood_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_mood_mean 1 2072.2 2072.24 29.285 1.431e-07 ***
## Residuals 257 18185.4 70.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1H1a: IUS and mental health association at PRE
#Depression
PRE_IUS_PHQ_lm <- lm(A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
summary(PRE_IUS_PHQ_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.943 -4.553 0.081 4.910 22.117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.1756 1.5954 18.914 < 2e-16 ***
## A_PRE_PHQ_total 0.6707 0.0842 7.965 5.36e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.951 on 257 degrees of freedom
## Multiple R-squared: 0.198, Adjusted R-squared: 0.1949
## F-statistic: 63.44 on 1 and 257 DF, p-value: 5.356e-14anova(PRE_IUS_PHQ_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_PHQ_total 1 4010.8 4010.8 63.444 5.356e-14 ***
## Residuals 257 16246.9 63.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Anxiety
PRE_IUS_GAD_lm <- lm(A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
summary(PRE_IUS_GAD_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.3319 -4.8319 0.7045 4.1681 20.6317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.26836 1.43778 20.357 <2e-16 ***
## A_PRE_GAD_total 0.82727 0.08645 9.569 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.623 on 257 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2598
## F-statistic: 91.57 on 1 and 257 DF, p-value: < 2.2e-16anova(PRE_IUS_GAD_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_GAD_total 1 5321.7 5321.7 91.57 < 2.2e-16 ***
## Residuals 257 14935.9 58.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1H1b: IUS and BT at PRE
Hypothesis 2
H2a: difference in change in cognitive IUS over time between groups
IUS_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total")## Formatting table as needed
IUS_alltimepoints_long <- IUS_alltimepoints %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_alltimepoints_long, REML = TRUE)
summary(IUS_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_alltimepoints_long
##
## REML criterion at convergence: 7807.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4802 -0.3254 0.0609 0.4737 2.3749
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 72.64 8.523
## Residual 77.08 8.779
## Number of obs: 1036, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 1.1885 600.1493 35.363
## GroupECs -0.9483 2.0993 600.1493 -0.452
## GroupIntervention 1.0397 1.6929 600.1493 0.614
## TimeB_POST_IUS_total -3.8679 1.2059 768.0000 -3.207
## TimeC_W1_IUS_total -2.5472 1.2059 768.0000 -2.112
## TimeD_M1_IUS_total -6.1415 1.2059 768.0000 -5.093
## GroupECs:TimeB_POST_IUS_total 3.5879 2.1301 768.0000 1.684
## GroupIntervention:TimeB_POST_IUS_total -3.1321 1.7178 768.0000 -1.823
## GroupECs:TimeC_W1_IUS_total 1.8272 2.1301 768.0000 0.858
## GroupIntervention:TimeC_W1_IUS_total -2.8606 1.7178 768.0000 -1.665
## GroupECs:TimeD_M1_IUS_total 3.2415 2.1301 768.0000 1.522
## GroupIntervention:TimeD_M1_IUS_total -3.0138 1.7178 768.0000 -1.754
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.65162
## GroupIntervention 0.53937
## TimeB_POST_IUS_total 0.00139 **
## TimeC_W1_IUS_total 0.03499 *
## TimeD_M1_IUS_total 4.44e-07 ***
## GroupECs:TimeB_POST_IUS_total 0.09251 .
## GroupIntervention:TimeB_POST_IUS_total 0.06865 .
## GroupECs:TimeC_W1_IUS_total 0.39128
## GroupIntervention:TimeC_W1_IUS_total 0.09627 .
## GroupECs:TimeD_M1_IUS_total 0.12848
## GroupIntervention:TimeD_M1_IUS_total 0.07975 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS TC_W1_ TD_M1_ GEC:TB GI:TB_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TB_POST_IUS -0.507 0.287 0.356
## TmC_W1_IUS_ -0.507 0.287 0.356 0.500
## TmD_M1_IUS_ -0.507 0.287 0.356 0.500 0.500
## GEC:TB_POST 0.287 -0.507 -0.202 -0.566 -0.283 -0.283
## GI:TB_POST_ 0.356 -0.202 -0.507 -0.702 -0.351 -0.351 0.397
## GEC:TC_W1_I 0.287 -0.507 -0.202 -0.283 -0.566 -0.283 0.500 0.199
## GI:TC_W1_IU 0.356 -0.202 -0.507 -0.351 -0.702 -0.351 0.199 0.500 0.397
## GEC:TD_M1_I 0.287 -0.507 -0.202 -0.283 -0.283 -0.566 0.500 0.199 0.500
## GI:TD_M1_IU 0.356 -0.202 -0.507 -0.351 -0.351 -0.702 0.199 0.500 0.199
## GI:TC_ GEC:TD
## GroupECs
## GrpIntrvntn
## TB_POST_IUS
## TmC_W1_IUS_
## TmD_M1_IUS_
## GEC:TB_POST
## GI:TB_POST_
## GEC:TC_W1_I
## GI:TC_W1_IU
## GEC:TD_M1_I 0.199
## GI:TD_M1_IU 0.500 0.397anova (IUS_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 175.6 87.79 2 256 1.1390 0.32176
## Time 4324.7 1441.58 3 768 18.7032 1.022e-11 ***
## Group:Time 1012.0 168.66 6 768 2.1882 0.04221 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 39.70 – 44.36 | <0.001 |
| Group [ECs] | -0.95 | -5.07 – 3.17 | 0.652 |
| Group [Intervention] | 1.04 | -2.28 – 4.36 | 0.539 |
| Time [B_POST_IUS_total] | -3.87 | -6.23 – -1.50 | 0.001 |
| Time [C_W1_IUS_total] | -2.55 | -4.91 – -0.18 | 0.035 |
| Time [D_M1_IUS_total] | -6.14 | -8.51 – -3.78 | <0.001 |
|
Group [ECs] × Time [B_POST_IUS_total] |
3.59 | -0.59 – 7.77 | 0.092 |
|
Group [Intervention] × Time [B_POST_IUS_total] |
-3.13 | -6.50 – 0.24 | 0.069 |
|
Group [ECs] × Time [C_W1_IUS_total] |
1.83 | -2.35 – 6.01 | 0.391 |
|
Group [Intervention] × Time [C_W1_IUS_total] |
-2.86 | -6.23 – 0.51 | 0.096 |
|
Group [ECs] × Time [D_M1_IUS_total] |
3.24 | -0.94 – 7.42 | 0.128 |
|
Group [Intervention] × Time [D_M1_IUS_total] |
-3.01 | -6.38 – 0.36 | 0.080 |
| Random Effects | |||
| σ2 | 77.08 | ||
| Ï„00 ID | 72.64 | ||
| ICC | 0.49 | ||
| N ID | 259 | ||
| Observations | 1036 | ||
| Marginal R2 / Conditional R2 | 0.049 / 0.510 | ||
parameters::standardise_parameters(IUS_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.27 | [ 0.08, 0.46]
## GroupECs | -0.08 | [-0.41, 0.25]
## GroupIntervention | 0.08 | [-0.18, 0.35]
## TimeB_POST_IUS_total | -0.31 | [-0.50, -0.12]
## TimeC_W1_IUS_total | -0.20 | [-0.39, -0.01]
## TimeD_M1_IUS_total | -0.49 | [-0.68, -0.30]
## GroupECs:TimeB_POST_IUS_total | 0.29 | [-0.05, 0.62]
## GroupIntervention:TimeB_POST_IUS_total | -0.25 | [-0.52, 0.02]
## GroupECs:TimeC_W1_IUS_total | 0.15 | [-0.19, 0.48]
## GroupIntervention:TimeC_W1_IUS_total | -0.23 | [-0.50, 0.04]
## GroupECs:TimeD_M1_IUS_total | 0.26 | [-0.08, 0.59]
## GroupIntervention:TimeD_M1_IUS_total | -0.24 | [-0.51, 0.03]Baseline to post
IUS_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total")## Formatting table as needed
IUS_BP_long <- IUS_BP %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_BP <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long, REML = TRUE)
summary(IUS_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_BP_long
##
## REML criterion at convergence: 3656.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2320 -0.4094 0.0034 0.4135 3.2300
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 71.59 8.461
## Residual 28.73 5.360
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 0.9728 339.2570 43.202
## GroupECs -0.9483 1.7184 339.2570 -0.552
## GroupIntervention 1.0397 1.3858 339.2570 0.750
## TimeB_POST_IUS_total -3.8679 0.7363 256.0000 -5.253
## GroupECs:TimeB_POST_IUS_total 3.5879 1.3006 256.0000 2.759
## GroupIntervention:TimeB_POST_IUS_total -3.1321 1.0489 256.0000 -2.986
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.58141
## GroupIntervention 0.45364
## TimeB_POST_IUS_total 3.15e-07 ***
## GroupECs:TimeB_POST_IUS_total 0.00622 **
## GroupIntervention:TimeB_POST_IUS_total 0.00310 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS GEC:TB
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TB_POST_IUS -0.378 0.214 0.266
## GEC:TB_POST 0.214 -0.378 -0.150 -0.566
## GI:TB_POST_ 0.266 -0.150 -0.378 -0.702 0.397anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 21.31 10.66 2 256 0.3708 0.6905
## Time 1587.48 1587.48 1 256 55.2457 1.595e-12 ***
## Group:Time 787.45 393.72 2 256 13.7020 2.222e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_BP)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.12 – 43.94 | <0.001 |
| Group [ECs] | -0.95 | -4.32 – 2.43 | 0.581 |
| Group [Intervention] | 1.04 | -1.68 – 3.76 | 0.453 |
| Time [B_POST_IUS_total] | -3.87 | -5.31 – -2.42 | <0.001 |
|
Group [ECs] × Time [B_POST_IUS_total] |
3.59 | 1.03 – 6.14 | 0.006 |
|
Group [Intervention] × Time [B_POST_IUS_total] |
-3.13 | -5.19 – -1.07 | 0.003 |
| Random Effects | |||
| σ2 | 28.73 | ||
| Ï„00 ID | 71.59 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.062 / 0.731 | ||
parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.19 | [ 0.01, 0.38]
## GroupECs | -0.09 | [-0.42, 0.24]
## GroupIntervention | 0.10 | [-0.16, 0.37]
## TimeB_POST_IUS_total | -0.38 | [-0.52, -0.24]
## GroupECs:TimeB_POST_IUS_total | 0.35 | [ 0.10, 0.60]
## GroupIntervention:TimeB_POST_IUS_total | -0.30 | [-0.50, -0.10]plot_model(IUS_MEM_BP, type = "int")
Baseline to 1W
IUS_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total")## Formatting table as needed
IUS_B1W_long <- IUS_B1W %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1W <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long, REML = TRUE)
summary(IUS_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1W_long
##
## REML criterion at convergence: 3746.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9845 -0.3439 0.0235 0.4237 2.5991
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 69.60 8.342
## Residual 39.31 6.270
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 1.0136 363.5479 41.463
## GroupECs -0.9483 1.7904 363.5479 -0.530
## GroupIntervention 1.0397 1.4439 363.5479 0.720
## TimeC_W1_IUS_total -2.5472 0.8613 256.0000 -2.957
## GroupECs:TimeC_W1_IUS_total 1.8272 1.5213 256.0000 1.201
## GroupIntervention:TimeC_W1_IUS_total -2.8606 1.2269 256.0000 -2.332
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.59668
## GroupIntervention 0.47197
## TimeC_W1_IUS_total 0.00339 **
## GroupECs:TimeC_W1_IUS_total 0.23084
## GroupIntervention:TimeC_W1_IUS_total 0.02050 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_IUS_ -0.425 0.241 0.298
## GEC:TC_W1_I 0.241 -0.425 -0.169 -0.566
## GI:TC_W1_IU 0.298 -0.169 -0.425 -0.702 0.397anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 3.95 1.98 2 256 0.0503 0.950967
## Time 961.28 961.28 1 256 24.4509 1.381e-06 ***
## Group:Time 425.11 212.56 2 256 5.4065 0.005014 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1W)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.04 – 44.02 | <0.001 |
| Group [ECs] | -0.95 | -4.47 – 2.57 | 0.597 |
| Group [Intervention] | 1.04 | -1.80 – 3.88 | 0.472 |
| Time [C_W1_IUS_total] | -2.55 | -4.24 – -0.86 | 0.003 |
|
Group [ECs] × Time [C_W1_IUS_total] |
1.83 | -1.16 – 4.82 | 0.230 |
|
Group [Intervention] × Time [C_W1_IUS_total] |
-2.86 | -5.27 – -0.45 | 0.020 |
| Random Effects | |||
| σ2 | 39.31 | ||
| Ï„00 ID | 69.60 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.651 | ||
parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.14 | [-0.05, 0.32]
## GroupECs | -0.09 | [-0.42, 0.24]
## GroupIntervention | 0.10 | [-0.17, 0.37]
## TimeC_W1_IUS_total | -0.24 | [-0.40, -0.08]
## GroupECs:TimeC_W1_IUS_total | 0.17 | [-0.11, 0.46]
## GroupIntervention:TimeC_W1_IUS_total | -0.27 | [-0.50, -0.04]plot_model(IUS_MEM_B1W, type = "int")
Baseline to 1M
IUS_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total")## Formatting table as needed
IUS_B1M_long <- IUS_B1M %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1M <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1M_long, REML = TRUE)
summary(IUS_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1M_long
##
## REML criterion at convergence: 4070.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92417 -0.36721 0.08337 0.54180 1.88810
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.84 7.735
## Residual 108.95 10.438
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 1.2619 454.8351 33.307
## GroupECs -0.9483 2.2289 454.8351 -0.425
## GroupIntervention 1.0397 1.7975 454.8351 0.578
## TimeD_M1_IUS_total -6.1415 1.4337 256.0000 -4.284
## GroupECs:TimeD_M1_IUS_total 3.2415 2.5325 256.0000 1.280
## GroupIntervention:TimeD_M1_IUS_total -3.0138 2.0423 256.0000 -1.476
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.671
## GroupIntervention 0.563
## TimeD_M1_IUS_total 2.6e-05 ***
## GroupECs:TimeD_M1_IUS_total 0.202
## GroupIntervention:TimeD_M1_IUS_total 0.141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmD_M1_IUS_ -0.568 0.322 0.399
## GEC:TD_M1_I 0.322 -0.568 -0.226 -0.566
## GI:TD_M1_IU 0.399 -0.226 -0.568 -0.702 0.397anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 42.2 21.1 2 256 0.1937 0.8240
## Time 4229.7 4229.7 1 256 38.8236 1.896e-09 ***
## Group:Time 688.0 344.0 2 256 3.1574 0.0442 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1M)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 39.55 – 44.51 | <0.001 |
| Group [ECs] | -0.95 | -5.33 – 3.43 | 0.671 |
| Group [Intervention] | 1.04 | -2.49 – 4.57 | 0.563 |
| Time [D_M1_IUS_total] | -6.14 | -8.96 – -3.32 | <0.001 |
|
Group [ECs] × Time [D_M1_IUS_total] |
3.24 | -1.73 – 8.22 | 0.201 |
|
Group [Intervention] × Time [D_M1_IUS_total] |
-3.01 | -7.03 – 1.00 | 0.141 |
| Random Effects | |||
| σ2 | 108.95 | ||
| Ï„00 ID | 59.84 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.070 / 0.400 | ||
parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.23 | [ 0.05, 0.42]
## GroupECs | -0.07 | [-0.40, 0.26]
## GroupIntervention | 0.08 | [-0.19, 0.34]
## TimeD_M1_IUS_total | -0.46 | [-0.67, -0.25]
## GroupECs:TimeD_M1_IUS_total | 0.24 | [-0.13, 0.61]
## GroupIntervention:TimeD_M1_IUS_total | -0.22 | [-0.52, 0.07]plot_model(IUS_MEM_B1M, type = "int")
H2a: difference in change in growth mindsets over time between groups
GM_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM")## Formatting table as needed
GM_alltimepoints_long <- GM_alltimepoints %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM, C_W1_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_alltimepoints_long, REML = TRUE)
summary(GM_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_alltimepoints_long
##
## REML criterion at convergence: 3110.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1305 -0.5023 -0.0609 0.4554 3.6922
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.3098 1.1445
## Residual 0.7768 0.8814
## Number of obs: 996, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1403 463.0767 22.391 < 2e-16
## GroupECs -0.4015 0.2478 463.0767 -1.620 0.10589
## GroupIntervention -0.2386 0.1999 463.0767 -1.194 0.23316
## TimeB_POST_GM -0.4906 0.1211 728.1342 -4.052 5.62e-05
## TimeC_W1_GM -0.3130 0.1218 729.2028 -2.569 0.01039
## TimeD_M1_GM -0.3899 0.1264 733.4076 -3.085 0.00211
## GroupECs:TimeB_POST_GM 0.5306 0.2138 728.1342 2.481 0.01332
## GroupIntervention:TimeB_POST_GM -0.1807 0.1730 728.5210 -1.044 0.29664
## GroupECs:TimeC_W1_GM 0.2371 0.2162 729.5489 1.097 0.27310
## GroupIntervention:TimeC_W1_GM -0.2772 0.1739 729.4128 -1.595 0.11122
## GroupECs:TimeD_M1_GM 0.3500 0.2231 733.1339 1.569 0.11704
## GroupIntervention:TimeD_M1_GM -0.2387 0.1798 733.2890 -1.327 0.18488
##
## (Intercept) ***
## GroupECs
## GroupIntervention
## TimeB_POST_GM ***
## TimeC_W1_GM *
## TimeD_M1_GM **
## GroupECs:TimeB_POST_GM *
## GroupIntervention:TimeB_POST_GM
## GroupECs:TimeC_W1_GM
## GroupIntervention:TimeC_W1_GM
## GroupECs:TimeD_M1_GM
## GroupIntervention:TimeD_M1_GM
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS TC_W1_ TD_M1_ GEC:TB GI:TB_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmB_POST_GM -0.431 0.244 0.303
## TimeC_W1_GM -0.429 0.243 0.301 0.497
## TimeD_M1_GM -0.413 0.234 0.290 0.479 0.477
## GEC:TB_POST 0.244 -0.431 -0.171 -0.566 -0.281 -0.271
## GI:TB_POST_ 0.302 -0.171 -0.430 -0.700 -0.348 -0.335 0.396
## GEC:TC_W1_G 0.242 -0.427 -0.170 -0.280 -0.564 -0.269 0.495 0.196
## GI:TC_W1_GM 0.300 -0.170 -0.428 -0.348 -0.701 -0.335 0.197 0.494 0.395
## GEC:TD_M1_G 0.234 -0.414 -0.164 -0.271 -0.271 -0.567 0.479 0.190 0.474
## GI:TD_M1_GM 0.290 -0.164 -0.414 -0.337 -0.336 -0.703 0.191 0.478 0.189
## GI:TC_ GEC:TD
## GroupECs
## GrpIntrvntn
## TmB_POST_GM
## TimeC_W1_GM
## TimeD_M1_GM
## GEC:TB_POST
## GI:TB_POST_
## GEC:TC_W1_G
## GI:TC_W1_GM
## GEC:TD_M1_G 0.190
## GI:TD_M1_GM 0.477 0.398anova (GM_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.6999 2.3499 2 256.22 3.0251 0.05029 .
## Time 21.1646 7.0549 3 731.30 9.0818 6.584e-06 ***
## Group:Time 10.6523 1.7754 6 731.29 2.2855 0.03414 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.87 – 3.42 | <0.001 |
| Group [ECs] | -0.40 | -0.89 – 0.08 | 0.106 |
| Group [Intervention] | -0.24 | -0.63 – 0.15 | 0.233 |
| Time [B_POST_GM] | -0.49 | -0.73 – -0.25 | <0.001 |
| Time [C_W1_GM] | -0.31 | -0.55 – -0.07 | 0.010 |
| Time [D_M1_GM] | -0.39 | -0.64 – -0.14 | 0.002 |
|
Group [ECs] × Time [B_POST_GM] |
0.53 | 0.11 – 0.95 | 0.013 |
|
Group [Intervention] × Time [B_POST_GM] |
-0.18 | -0.52 – 0.16 | 0.297 |
|
Group [ECs] × Time [C_W1_GM] |
0.24 | -0.19 – 0.66 | 0.273 |
|
Group [Intervention] × Time [C_W1_GM] |
-0.28 | -0.62 – 0.06 | 0.111 |
|
Group [ECs] × Time [D_M1_GM] |
0.35 | -0.09 – 0.79 | 0.117 |
|
Group [Intervention] × Time [D_M1_GM] |
-0.24 | -0.59 – 0.11 | 0.185 |
| Random Effects | |||
| σ2 | 0.78 | ||
| Ï„00 ID | 1.31 | ||
| ICC | 0.63 | ||
| N ID | 259 | ||
| Observations | 996 | ||
| Marginal R2 / Conditional R2 | 0.037 / 0.641 | ||
parameters::standardise_parameters(GM_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | 0.33 | [ 0.14, 0.52]
## GroupECs | -0.27 | [-0.61, 0.06]
## GroupIntervention | -0.16 | [-0.43, 0.10]
## TimeB_POST_GM | -0.33 | [-0.50, -0.17]
## TimeC_W1_GM | -0.21 | [-0.38, -0.05]
## TimeD_M1_GM | -0.27 | [-0.44, -0.10]
## GroupECs:TimeB_POST_GM | 0.36 | [ 0.08, 0.65]
## GroupIntervention:TimeB_POST_GM | -0.12 | [-0.35, 0.11]
## GroupECs:TimeC_W1_GM | 0.16 | [-0.13, 0.45]
## GroupIntervention:TimeC_W1_GM | -0.19 | [-0.42, 0.04]
## GroupECs:TimeD_M1_GM | 0.24 | [-0.06, 0.54]
## GroupIntervention:TimeD_M1_GM | -0.16 | [-0.40, 0.08]Baseline to post
GM_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM")## Formatting table as needed
GM_BP_long <- GM_BP %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_BP <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long, REML = TRUE)
summary(GM_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_BP_long
##
## REML criterion at convergence: 1673.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5473 -0.4356 -0.0320 0.4087 2.9611
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.471 1.2128
## Residual 0.614 0.7836
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1402 341.6190 22.400 < 2e-16
## GroupECs -0.4015 0.2477 341.6189 -1.621 0.10599
## GroupIntervention -0.2386 0.1998 341.6189 -1.194 0.23319
## TimeB_POST_GM -0.4906 0.1076 254.5431 -4.558 8.03e-06
## GroupECs:TimeB_POST_GM 0.5306 0.1901 254.5431 2.791 0.00566
## GroupIntervention:TimeB_POST_GM -0.1932 0.1540 255.1798 -1.255 0.21079
##
## (Intercept) ***
## GroupECs
## GroupIntervention
## TimeB_POST_GM ***
## GroupECs:TimeB_POST_GM **
## GroupIntervention:TimeB_POST_GM
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS GEC:TB
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmB_POST_GM -0.384 0.217 0.269
## GEC:TB_POST 0.217 -0.384 -0.153 -0.566
## GI:TB_POST_ 0.268 -0.152 -0.382 -0.699 0.396anova (GM_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.0352 1.0176 2 256.34 1.6574 0.1926771
## Time 16.3667 16.3667 1 254.86 26.6558 4.9e-07 ***
## Group:Time 8.8331 4.4165 2 254.92 7.1931 0.0009142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_BP)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.87 – 3.42 | <0.001 |
| Group [ECs] | -0.40 | -0.89 – 0.09 | 0.106 |
| Group [Intervention] | -0.24 | -0.63 – 0.15 | 0.233 |
| Time [B_POST_GM] | -0.49 | -0.70 – -0.28 | <0.001 |
|
Group [ECs] × Time [B_POST_GM] |
0.53 | 0.16 – 0.90 | 0.005 |
|
Group [Intervention] × Time [B_POST_GM] |
-0.19 | -0.50 – 0.11 | 0.210 |
| Random Effects | |||
| σ2 | 0.61 | ||
| Ï„00 ID | 1.47 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.043 / 0.718 | ||
parameters::standardise_parameters(GM_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | 0.27 | [ 0.09, 0.46]
## GroupECs | -0.27 | [-0.60, 0.06]
## GroupIntervention | -0.16 | [-0.43, 0.10]
## TimeB_POST_GM | -0.33 | [-0.48, -0.19]
## GroupECs:TimeB_POST_GM | 0.36 | [ 0.11, 0.61]
## GroupIntervention:TimeB_POST_GM | -0.13 | [-0.34, 0.07]plot_model(GM_MEM_BP, type = "int")
Baseline to 1W
GM_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM")## Formatting table as needed
GM_B1W_long <- GM_B1W %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1W <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1W_long, REML = TRUE)
summary(GM_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1W_long
##
## REML criterion at convergence: 1747.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2664 -0.5109 -0.1518 0.4940 2.7870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.038 1.019
## Residual 1.010 1.005
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1390 403.7332 22.603 <2e-16 ***
## GroupECs -0.4015 0.2455 403.7332 -1.635 0.1027
## GroupIntervention -0.2386 0.1980 403.7332 -1.205 0.2289
## TimeC_W1_GM -0.3160 0.1390 251.9742 -2.273 0.0239 *
## GroupECs:TimeC_W1_GM 0.2376 0.2470 253.3522 0.962 0.3370
## GroupIntervention:TimeC_W1_GM -0.2757 0.1985 252.4706 -1.389 0.1659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TimeC_W1_GM -0.493 0.279 0.346
## GEC:TC_W1_G 0.278 -0.490 -0.195 -0.563
## GI:TC_W1_GM 0.345 -0.196 -0.492 -0.701 0.394anova (GM_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.0659 2.5329 2 256.72 2.5080 0.0834210 .
## Time 12.1159 12.1159 1 253.25 11.9969 0.0006256 ***
## Group:Time 4.6763 2.3381 2 253.08 2.3152 0.1008383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1W)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.87 – 3.41 | <0.001 |
| Group [ECs] | -0.40 | -0.88 – 0.08 | 0.103 |
| Group [Intervention] | -0.24 | -0.63 – 0.15 | 0.229 |
| Time [C_W1_GM] | -0.32 | -0.59 – -0.04 | 0.023 |
|
Group [ECs] × Time [C_W1_GM] |
0.24 | -0.25 – 0.72 | 0.337 |
|
Group [Intervention] × Time [C_W1_GM] |
-0.28 | -0.67 – 0.11 | 0.165 |
| Random Effects | |||
| σ2 | 1.01 | ||
| Ï„00 ID | 1.04 | ||
| ICC | 0.51 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.524 | ||
parameters::standardise_parameters(GM_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------
## (Intercept) | 0.24 | [ 0.06, 0.43]
## GroupECs | -0.28 | [-0.61, 0.06]
## GroupIntervention | -0.16 | [-0.43, 0.10]
## TimeC_W1_GM | -0.22 | [-0.41, -0.03]
## GroupECs:TimeC_W1_GM | 0.16 | [-0.17, 0.50]
## GroupIntervention:TimeC_W1_GM | -0.19 | [-0.46, 0.08]plot_model(GM_MEM_B1W, type = "int")
Baseline to 1M
GM_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM")## Formatting table as needed
GM_B1M_long <- GM_B1M %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1M <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1M_long, REML = TRUE)
summary(GM_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1M_long
##
## REML criterion at convergence: 1639.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3875 -0.5367 -0.1142 0.4999 2.8661
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.0920 1.0450
## Residual 0.8918 0.9444
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1368 375.5454 22.964 <2e-16 ***
## GroupECs -0.4015 0.2416 375.5454 -1.662 0.0974 .
## GroupIntervention -0.2386 0.1949 375.5454 -1.224 0.2216
## TimeD_M1_GM -0.3459 0.1366 235.6703 -2.533 0.0120 *
## GroupECs:TimeD_M1_GM 0.3073 0.2410 235.5102 1.275 0.2036
## GroupIntervention:TimeD_M1_GM -0.2840 0.1943 235.3935 -1.462 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TimeD_M1_GM -0.450 0.255 0.316
## GEC:TD_M1_G 0.255 -0.451 -0.179 -0.567
## GI:TD_M1_GM 0.317 -0.179 -0.451 -0.703 0.398anova (GM_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3400 2.1700 2 254.05 2.4332 0.089803 .
## Time 11.8930 11.8930 1 235.41 13.3353 0.000321 ***
## Group:Time 5.5615 2.7807 2 235.41 3.1180 0.046079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1M)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.87 – 3.41 | <0.001 |
| Group [ECs] | -0.40 | -0.88 – 0.07 | 0.097 |
| Group [Intervention] | -0.24 | -0.62 – 0.14 | 0.221 |
| Time [D_M1_GM] | -0.35 | -0.61 – -0.08 | 0.012 |
|
Group [ECs] × Time [D_M1_GM] |
0.31 | -0.17 – 0.78 | 0.203 |
|
Group [Intervention] × Time [D_M1_GM] |
-0.28 | -0.67 – 0.10 | 0.144 |
| Random Effects | |||
| σ2 | 0.89 | ||
| Ï„00 ID | 1.09 | ||
| ICC | 0.55 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.039 / 0.568 | ||
parameters::standardise_parameters(GM_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------
## (Intercept) | 0.26 | [ 0.07, 0.44]
## GroupECs | -0.28 | [-0.61, 0.05]
## GroupIntervention | -0.17 | [-0.43, 0.10]
## TimeD_M1_GM | -0.24 | [-0.43, -0.05]
## GroupECs:TimeD_M1_GM | 0.21 | [-0.12, 0.55]
## GroupIntervention:TimeD_M1_GM | -0.20 | [-0.47, 0.07]plot_model(GM_MEM_B1M, type = "int")
H2b: difference in change in PHQ over time between groups
PHQ_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total")## Formatting table as needed
PHQ_alltimepoints_long <- PHQ_alltimepoints %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_alltimepoints_long, REML = TRUE)
summary(PHQ_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_alltimepoints_long
##
## REML criterion at convergence: 5050.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2687 -0.4532 -0.0281 0.4903 2.4898
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.66 4.545
## Residual 26.75 5.172
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.4245 0.6687 556.5908 26.056
## GroupECs 0.5355 1.1812 556.5908 0.453
## GroupIntervention 1.2260 0.9526 556.5908 1.287
## TimeC_W1_PHQ_total -1.3302 0.7104 512.0000 -1.872
## TimeD_M1_PHQ_total -3.1226 0.7104 512.0000 -4.396
## GroupECs:TimeC_W1_PHQ_total 0.5702 1.2548 512.0000 0.454
## GroupIntervention:TimeC_W1_PHQ_total -0.6504 1.0119 512.0000 -0.643
## GroupECs:TimeD_M1_PHQ_total 0.9426 1.2548 512.0000 0.751
## GroupIntervention:TimeD_M1_PHQ_total -1.0910 1.0119 512.0000 -1.078
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.6505
## GroupIntervention 0.1986
## TimeC_W1_PHQ_total 0.0617 .
## TimeD_M1_PHQ_total 1.34e-05 ***
## GroupECs:TimeC_W1_PHQ_total 0.6497
## GroupIntervention:TimeC_W1_PHQ_total 0.5207
## GroupECs:TimeD_M1_PHQ_total 0.4529
## GroupIntervention:TimeD_M1_PHQ_total 0.2815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ TD_M1_ GEC:TC GI:TC_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_PHQ_ -0.531 0.301 0.373
## TmD_M1_PHQ_ -0.531 0.301 0.373 0.500
## GEC:TC_W1_P 0.301 -0.531 -0.211 -0.566 -0.283
## GI:TC_W1_PH 0.373 -0.211 -0.531 -0.702 -0.351 0.397
## GEC:TD_M1_P 0.301 -0.531 -0.211 -0.283 -0.566 0.500 0.199
## GI:TD_M1_PH 0.373 -0.211 -0.531 -0.351 -0.702 0.199 0.500 0.397anova (PHQ_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 38.69 19.34 2 256 0.7232 0.4862
## Time 1164.82 582.41 2 512 21.7752 8.392e-10 ***
## Group:Time 76.29 19.07 4 512 0.7130 0.5833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 17.42 | 16.11 – 18.74 | <0.001 |
| Group [ECs] | 0.54 | -1.78 – 2.85 | 0.650 |
| Group [Intervention] | 1.23 | -0.64 – 3.10 | 0.199 |
| Time [C_W1_PHQ_total] | -1.33 | -2.72 – 0.06 | 0.062 |
| Time [D_M1_PHQ_total] | -3.12 | -4.52 – -1.73 | <0.001 |
|
Group [ECs] × Time [C_W1_PHQ_total] |
0.57 | -1.89 – 3.03 | 0.650 |
|
Group [Intervention] × Time [C_W1_PHQ_total] |
-0.65 | -2.64 – 1.34 | 0.521 |
|
Group [ECs] × Time [D_M1_PHQ_total] |
0.94 | -1.52 – 3.41 | 0.453 |
|
Group [Intervention] × Time [D_M1_PHQ_total] |
-1.09 | -3.08 – 0.90 | 0.281 |
| Random Effects | |||
| σ2 | 26.75 | ||
| Ï„00 ID | 20.66 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.044 / 0.461 | ||
parameters::standardise_parameters(PHQ_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.15 | [-0.04, 0.33]
## GroupECs | 0.08 | [-0.25, 0.41]
## GroupIntervention | 0.17 | [-0.09, 0.44]
## TimeC_W1_PHQ_total | -0.19 | [-0.39, 0.01]
## TimeD_M1_PHQ_total | -0.45 | [-0.64, -0.25]
## GroupECs:TimeC_W1_PHQ_total | 0.08 | [-0.27, 0.43]
## GroupIntervention:TimeC_W1_PHQ_total | -0.09 | [-0.38, 0.19]
## GroupECs:TimeD_M1_PHQ_total | 0.13 | [-0.22, 0.49]
## GroupIntervention:TimeD_M1_PHQ_total | -0.16 | [-0.44, 0.13]Baseline to 1W
PHQ_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total")## Formatting table as needed
PHQ_B1W_long <- PHQ_B1W %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1W <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1W_long, REML = TRUE)
summary(PHQ_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1W_long
##
## REML criterion at convergence: 3213.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09297 -0.46311 -0.00509 0.43990 2.68855
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 25.22 5.022
## Residual 13.65 3.695
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.4245 0.6056 360.3455 28.775
## GroupECs 0.5355 1.0696 360.3455 0.501
## GroupIntervention 1.2260 0.8626 360.3455 1.421
## TimeC_W1_PHQ_total -1.3302 0.5076 256.0000 -2.621
## GroupECs:TimeC_W1_PHQ_total 0.5702 0.8965 256.0000 0.636
## GroupIntervention:TimeC_W1_PHQ_total -0.6504 0.7230 256.0000 -0.900
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupECs 0.6169
## GroupIntervention 0.1561
## TimeC_W1_PHQ_total 0.0093 **
## GroupECs:TimeC_W1_PHQ_total 0.5253
## GroupIntervention:TimeC_W1_PHQ_total 0.3692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_PHQ_ -0.419 0.237 0.294
## GEC:TC_W1_P 0.237 -0.419 -0.167 -0.566
## GI:TC_W1_PH 0.294 -0.167 -0.419 -0.702 0.397anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 20.499 10.250 2 256 0.7507 0.4730687
## Time 211.676 211.676 1 256 15.5036 0.0001062 ***
## Group:Time 27.054 13.527 2 256 0.9908 0.3727148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1W)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 17.42 | 16.23 – 18.61 | <0.001 |
| Group [ECs] | 0.54 | -1.57 – 2.64 | 0.617 |
| Group [Intervention] | 1.23 | -0.47 – 2.92 | 0.156 |
| Time [C_W1_PHQ_total] | -1.33 | -2.33 – -0.33 | 0.009 |
|
Group [ECs] × Time [C_W1_PHQ_total] |
0.57 | -1.19 – 2.33 | 0.525 |
|
Group [Intervention] × Time [C_W1_PHQ_total] |
-0.65 | -2.07 – 0.77 | 0.369 |
| Random Effects | |||
| σ2 | 13.65 | ||
| Ï„00 ID | 25.22 | ||
| ICC | 0.65 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.656 | ||
parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.02 | [-0.17, 0.21]
## GroupECs | 0.09 | [-0.25, 0.42]
## GroupIntervention | 0.20 | [-0.07, 0.47]
## TimeC_W1_PHQ_total | -0.21 | [-0.37, -0.05]
## GroupECs:TimeC_W1_PHQ_total | 0.09 | [-0.19, 0.37]
## GroupIntervention:TimeC_W1_PHQ_total | -0.10 | [-0.33, 0.12]plot_model(PHQ_MEM_B1W, type = "int")
Baseline to 1M
PHQ_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total")## Formatting table as needed
PHQ_B1M_long <- PHQ_B1M %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1M <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long, REML = TRUE)
summary(PHQ_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1M_long
##
## REML criterion at convergence: 3443.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38575 -0.50846 -0.02286 0.56387 2.22387
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 17.49 4.182
## Residual 32.01 5.658
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.4245 0.6834 455.1887 25.499
## GroupECs 0.5355 1.2070 455.1887 0.444
## GroupIntervention 1.2260 0.9734 455.1887 1.259
## TimeD_M1_PHQ_total -3.1226 0.7772 256.0000 -4.018
## GroupECs:TimeD_M1_PHQ_total 0.9426 1.3728 256.0000 0.687
## GroupIntervention:TimeD_M1_PHQ_total -1.0910 1.1071 256.0000 -0.985
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.658
## GroupIntervention 0.209
## TimeD_M1_PHQ_total 7.72e-05 ***
## GroupECs:TimeD_M1_PHQ_total 0.493
## GroupIntervention:TimeD_M1_PHQ_total 0.325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmD_M1_PHQ_ -0.569 0.322 0.399
## GEC:TD_M1_P 0.322 -0.569 -0.226 -0.566
## GI:TD_M1_PH 0.399 -0.226 -0.569 -0.702 0.397anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 40.51 20.25 2 256 0.6327 0.5320
## Time 1156.78 1156.78 1 256 36.1358 6.308e-09 ***
## Group:Time 75.29 37.65 2 256 1.1760 0.3102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1M)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 17.42 | 16.08 – 18.77 | <0.001 |
| Group [ECs] | 0.54 | -1.84 – 2.91 | 0.658 |
| Group [Intervention] | 1.23 | -0.69 – 3.14 | 0.208 |
| Time [D_M1_PHQ_total] | -3.12 | -4.65 – -1.60 | <0.001 |
|
Group [ECs] × Time [D_M1_PHQ_total] |
0.94 | -1.75 – 3.64 | 0.493 |
|
Group [Intervention] × Time [D_M1_PHQ_total] |
-1.09 | -3.27 – 1.08 | 0.325 |
| Random Effects | |||
| σ2 | 32.01 | ||
| Ï„00 ID | 17.49 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.060 / 0.392 | ||
parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.15 | [-0.03, 0.34]
## GroupECs | 0.07 | [-0.25, 0.40]
## GroupIntervention | 0.17 | [-0.10, 0.43]
## TimeD_M1_PHQ_total | -0.43 | [-0.64, -0.22]
## GroupECs:TimeD_M1_PHQ_total | 0.13 | [-0.24, 0.50]
## GroupIntervention:TimeD_M1_PHQ_total | -0.15 | [-0.45, 0.15]plot_model(PHQ_MEM_B1M, type = "int")
H2b: difference in change in GAD over time between groups
# Merging across timepoints
GAD_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total")## Formatting table as needed
GAD_alltimepoints_long <- GAD_alltimepoints %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_alltimepoints_long, REML = TRUE)
summary(GAD_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_alltimepoints_long
##
## REML criterion at convergence: 4917.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4172 -0.4747 -0.0001 0.5239 3.2139
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.27 4.389
## Residual 21.80 4.669
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.4717 0.6224 533.2553 24.857
## GroupECs -0.4517 1.0994 533.2553 -0.411
## GroupIntervention 0.8001 0.8866 533.2553 0.902
## TimeC_W1_GAD_total -1.0094 0.6413 512.0000 -1.574
## TimeD_M1_GAD_total -2.7453 0.6413 512.0000 -4.281
## GroupECs:TimeC_W1_GAD_total 0.7494 1.1328 512.0000 0.662
## GroupIntervention:TimeC_W1_GAD_total -0.4760 0.9136 512.0000 -0.521
## GroupECs:TimeD_M1_GAD_total 1.6053 1.1328 512.0000 1.417
## GroupIntervention:TimeD_M1_GAD_total -0.6334 0.9136 512.0000 -0.693
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.681
## GroupIntervention 0.367
## TimeC_W1_GAD_total 0.116
## TimeD_M1_GAD_total 2.23e-05 ***
## GroupECs:TimeC_W1_GAD_total 0.509
## GroupIntervention:TimeC_W1_GAD_total 0.603
## GroupECs:TimeD_M1_GAD_total 0.157
## GroupIntervention:TimeD_M1_GAD_total 0.488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ TD_M1_ GEC:TC GI:TC_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_GAD_ -0.515 0.292 0.362
## TmD_M1_GAD_ -0.515 0.292 0.362 0.500
## GEC:TC_W1_G 0.292 -0.515 -0.205 -0.566 -0.283
## GI:TC_W1_GA 0.362 -0.205 -0.515 -0.702 -0.351 0.397
## GEC:TD_M1_G 0.292 -0.515 -0.205 -0.283 -0.566 0.500 0.199
## GI:TD_M1_GA 0.362 -0.205 -0.515 -0.351 -0.702 0.199 0.500 0.397anova (GAD_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 8.44 4.22 2 256 0.1935 0.8242
## Time 687.11 343.55 2 512 15.7595 2.281e-07 ***
## Group:Time 85.55 21.39 4 512 0.9811 0.4174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.47 | 14.25 – 16.69 | <0.001 |
| Group [ECs] | -0.45 | -2.61 – 1.71 | 0.681 |
| Group [Intervention] | 0.80 | -0.94 – 2.54 | 0.367 |
| Time [C_W1_GAD_total] | -1.01 | -2.27 – 0.25 | 0.116 |
| Time [D_M1_GAD_total] | -2.75 | -4.00 – -1.49 | <0.001 |
|
Group [ECs] × Time [C_W1_GAD_total] |
0.75 | -1.47 – 2.97 | 0.508 |
|
Group [Intervention] × Time [C_W1_GAD_total] |
-0.48 | -2.27 – 1.32 | 0.602 |
|
Group [ECs] × Time [D_M1_GAD_total] |
1.61 | -0.62 – 3.83 | 0.157 |
|
Group [Intervention] × Time [D_M1_GAD_total] |
-0.63 | -2.43 – 1.16 | 0.488 |
| Random Effects | |||
| σ2 | 21.80 | ||
| Ï„00 ID | 19.27 | ||
| ICC | 0.47 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.486 | ||
parameters::standardise_parameters(GAD_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.16 | [-0.03, 0.35]
## GroupECs | -0.07 | [-0.40, 0.26]
## GroupIntervention | 0.12 | [-0.15, 0.39]
## TimeC_W1_GAD_total | -0.16 | [-0.35, 0.04]
## TimeD_M1_GAD_total | -0.42 | [-0.62, -0.23]
## GroupECs:TimeC_W1_GAD_total | 0.12 | [-0.23, 0.46]
## GroupIntervention:TimeC_W1_GAD_total | -0.07 | [-0.35, 0.20]
## GroupECs:TimeD_M1_GAD_total | 0.25 | [-0.10, 0.59]
## GroupIntervention:TimeD_M1_GAD_total | -0.10 | [-0.37, 0.18]Baseline to 1W
# Merging across timepoints
GAD_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total")## Formatting table as needed
GAD_B1W_long <- GAD_B1W %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1W <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1W_long, REML = TRUE)
summary(GAD_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1W_long
##
## REML criterion at convergence: 3156.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.97971 -0.46162 -0.05959 0.48166 2.72962
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.31 4.723
## Residual 12.31 3.508
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.4717 0.5714 361.7397 27.075
## GroupECs -0.4517 1.0094 361.7397 -0.448
## GroupIntervention 0.8001 0.8140 361.7397 0.983
## TimeC_W1_GAD_total -1.0094 0.4818 256.0000 -2.095
## GroupECs:TimeC_W1_GAD_total 0.7494 0.8511 256.0000 0.881
## GroupIntervention:TimeC_W1_GAD_total -0.4760 0.6864 256.0000 -0.693
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupECs 0.6548
## GroupIntervention 0.3263
## TimeC_W1_GAD_total 0.0372 *
## GroupECs:TimeC_W1_GAD_total 0.3794
## GroupIntervention:TimeC_W1_GAD_total 0.4886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_GAD_ -0.422 0.239 0.296
## GEC:TC_W1_G 0.239 -0.422 -0.168 -0.566
## GI:TC_W1_GA 0.296 -0.168 -0.422 -0.702 0.397anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 9.323 4.662 2 256 0.3788 0.685040
## Time 96.944 96.944 1 256 7.8782 0.005388 **
## Group:Time 25.452 12.726 2 256 1.0342 0.356993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_B1W)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.47 | 14.35 – 16.59 | <0.001 |
| Group [ECs] | -0.45 | -2.43 – 1.53 | 0.655 |
| Group [Intervention] | 0.80 | -0.80 – 2.40 | 0.326 |
| Time [C_W1_GAD_total] | -1.01 | -1.96 – -0.06 | 0.037 |
|
Group [ECs] × Time [C_W1_GAD_total] |
0.75 | -0.92 – 2.42 | 0.379 |
|
Group [Intervention] × Time [C_W1_GAD_total] |
-0.48 | -1.82 – 0.87 | 0.488 |
| Random Effects | |||
| σ2 | 12.31 | ||
| Ï„00 ID | 22.31 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.649 | ||
parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.05 | [-0.14, 0.24]
## GroupECs | -0.08 | [-0.41, 0.26]
## GroupIntervention | 0.14 | [-0.14, 0.41]
## TimeC_W1_GAD_total | -0.17 | [-0.33, -0.01]
## GroupECs:TimeC_W1_GAD_total | 0.13 | [-0.16, 0.41]
## GroupIntervention:TimeC_W1_GAD_total | -0.08 | [-0.31, 0.15]plot_model(GAD_MEM_B1W, type = "int")
Baseline to 1M
# Merging across timepoints
GAD_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total")## Formatting table as needed
GAD_B1M_long <- GAD_B1M %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1M <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1M_long, REML = TRUE)
summary(GAD_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1M_long
##
## REML criterion at convergence: 3351.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.49775 -0.51016 -0.03334 0.55438 2.48053
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 16.32 4.039
## Residual 25.74 5.073
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.4717 0.6298 445.0075 24.564
## GroupECs -0.4517 1.1125 445.0075 -0.406
## GroupIntervention 0.8001 0.8972 445.0075 0.892
## TimeD_M1_GAD_total -2.7453 0.6968 256.0000 -3.940
## GroupECs:TimeD_M1_GAD_total 1.6053 1.2308 256.0000 1.304
## GroupIntervention:TimeD_M1_GAD_total -0.6334 0.9926 256.0000 -0.638
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.684930
## GroupIntervention 0.372967
## TimeD_M1_GAD_total 0.000105 ***
## GroupECs:TimeD_M1_GAD_total 0.193332
## GroupIntervention:TimeD_M1_GAD_total 0.523997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmD_M1_GAD_ -0.553 0.313 0.388
## GEC:TD_M1_G 0.313 -0.553 -0.220 -0.566
## GI:TD_M1_GA 0.388 -0.220 -0.553 -0.702 0.397anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 11.22 5.61 2 256 0.2180 0.8043
## Time 674.00 674.00 1 256 26.1900 6.079e-07 ***
## Group:Time 84.65 42.32 2 256 1.6446 0.1951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_B1M)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.47 | 14.23 – 16.71 | <0.001 |
| Group [ECs] | -0.45 | -2.64 – 1.73 | 0.685 |
| Group [Intervention] | 0.80 | -0.96 – 2.56 | 0.373 |
| Time [D_M1_GAD_total] | -2.75 | -4.11 – -1.38 | <0.001 |
|
Group [ECs] × Time [D_M1_GAD_total] |
1.61 | -0.81 – 4.02 | 0.193 |
|
Group [Intervention] × Time [D_M1_GAD_total] |
-0.63 | -2.58 – 1.32 | 0.524 |
| Random Effects | |||
| σ2 | 25.74 | ||
| Ï„00 ID | 16.32 | ||
| ICC | 0.39 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.046 / 0.416 | ||
parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.17 | [-0.02, 0.36]
## GroupECs | -0.07 | [-0.40, 0.26]
## GroupIntervention | 0.12 | [-0.15, 0.39]
## TimeD_M1_GAD_total | -0.42 | [-0.62, -0.21]
## GroupECs:TimeD_M1_GAD_total | 0.24 | [-0.12, 0.61]
## GroupIntervention:TimeD_M1_GAD_total | -0.10 | [-0.39, 0.20]plot_model(GAD_MEM_B1M, type = "int")
H2b: difference in change in mood over time between groups
Mood_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "B_POST_mood_mean", "C_W1_mood_mean", "D_M1_mood_mean")## Formatting tables as needed
Mood_alltimepoints_long <- Mood_alltimepoints %>%
pivot_longer(cols = c("A_PRE_mood_mean", "B_POST_mood_mean", "C_W1_mood_mean", "D_M1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_alltimepoints_long, REML = TRUE)
summary(Mood_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_alltimepoints_long
##
## REML criterion at convergence: 9998.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9245 -0.4840 0.0543 0.5791 3.4075
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 892.9 29.88
## Residual 982.7 31.35
## Number of obs: 995, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 37.3160 4.2065 599.2037 8.871
## GroupECs 2.9840 7.4301 599.2037 0.402
## GroupIntervention -7.6850 5.9920 599.2037 -1.283
## TimeB_POST_mood_mean 19.9717 4.3060 729.2427 4.638
## TimeC_W1_mood_mean -6.2501 4.3456 731.4162 -1.438
## TimeD_M1_mood_mean -6.6068 4.4744 736.8688 -1.477
## GroupECs:TimeB_POST_mood_mean -19.9917 7.6059 729.2427 -2.628
## GroupIntervention:TimeB_POST_mood_mean 9.4911 6.1432 729.5454 1.545
## GroupECs:TimeC_W1_mood_mean -8.6947 7.6949 731.6309 -1.130
## GroupIntervention:TimeC_W1_mood_mean 1.2499 6.2004 731.5383 0.202
## GroupECs:TimeD_M1_mood_mean -14.4877 7.9567 737.8488 -1.821
## GroupIntervention:TimeD_M1_mood_mean -0.1891 6.3771 736.7884 -0.030
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.68812
## GroupIntervention 0.20015
## TimeB_POST_mood_mean 4.17e-06 ***
## TimeC_W1_mood_mean 0.15079
## TimeD_M1_mood_mean 0.14022
## GroupECs:TimeB_POST_mood_mean 0.00876 **
## GroupIntervention:TimeB_POST_mood_mean 0.12279
## GroupECs:TimeC_W1_mood_mean 0.25887
## GroupIntervention:TimeC_W1_mood_mean 0.84030
## GroupECs:TimeD_M1_mood_mean 0.06904 .
## GroupIntervention:TimeD_M1_mood_mean 0.97635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS TC_W1_ TD_M1_ GEC:TB GI:TB_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmB_POST_m_ -0.512 0.290 0.359
## TmC_W1_md_m -0.507 0.287 0.356 0.495
## TmD_M1_md_m -0.493 0.279 0.346 0.481 0.478
## GEC:TB_POST 0.290 -0.512 -0.203 -0.566 -0.280 -0.272
## GI:TB_POST_ 0.359 -0.203 -0.511 -0.701 -0.347 -0.337 0.397
## GEC:TC_W1__ 0.286 -0.506 -0.201 -0.280 -0.565 -0.270 0.494 0.196
## GrI:TC_W1__ 0.355 -0.201 -0.506 -0.347 -0.701 -0.335 0.197 0.494 0.396
## GEC:TD_M1__ 0.277 -0.489 -0.194 -0.271 -0.269 -0.562 0.478 0.190 0.472
## GrI:TD_M1__ 0.346 -0.196 -0.492 -0.338 -0.335 -0.702 0.191 0.480 0.189
## GI:TC_ GEC:TD
## GroupECs
## GrpIntrvntn
## TmB_POST_m_
## TmC_W1_md_m
## TmD_M1_md_m
## GEC:TB_POST
## GI:TB_POST_
## GEC:TC_W1__
## GrI:TC_W1__
## GEC:TD_M1__ 0.188
## GrI:TD_M1__ 0.476 0.395anova (Mood_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2110 1055 2 258.46 1.0734 0.34336
## Time 104490 34830 3 734.51 35.4434 < 2e-16 ***
## Group:Time 15983 2664 6 734.42 2.7107 0.01307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 37.32 | 29.06 – 45.57 | <0.001 |
| Group [ECs] | 2.98 | -11.60 – 17.56 | 0.688 |
| Group [Intervention] | -7.68 | -19.44 – 4.07 | 0.200 |
| Time [B_POST_mood_mean] | 19.97 | 11.52 – 28.42 | <0.001 |
| Time [C_W1_mood_mean] | -6.25 | -14.78 – 2.28 | 0.151 |
| Time [D_M1_mood_mean] | -6.61 | -15.39 – 2.17 | 0.140 |
|
Group [ECs] × Time [B_POST_mood_mean] |
-19.99 | -34.92 – -5.07 | 0.009 |
|
Group [Intervention] × Time [B_POST_mood_mean] |
9.49 | -2.56 – 21.55 | 0.123 |
|
Group [ECs] × Time [C_W1_mood_mean] |
-8.69 | -23.80 – 6.41 | 0.259 |
|
Group [Intervention] × Time [C_W1_mood_mean] |
1.25 | -10.92 – 13.42 | 0.840 |
|
Group [ECs] × Time [D_M1_mood_mean] |
-14.49 | -30.10 – 1.13 | 0.069 |
|
Group [Intervention] × Time [D_M1_mood_mean] |
-0.19 | -12.70 – 12.33 | 0.976 |
| Random Effects | |||
| σ2 | 982.69 | ||
| Ï„00 ID | 892.93 | ||
| ICC | 0.48 | ||
| N ID | 259 | ||
| Observations | 995 | ||
| Marginal R2 / Conditional R2 | 0.079 / 0.517 | ||
parameters::standardise_parameters(Mood_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.15, 0.21]
## GroupECs | 0.07 | [-0.26, 0.39]
## GroupIntervention | -0.17 | [-0.43, 0.09]
## TimeB_POST_mood_mean | 0.44 | [ 0.26, 0.63]
## TimeC_W1_mood_mean | -0.14 | [-0.33, 0.05]
## TimeD_M1_mood_mean | -0.15 | [-0.34, 0.05]
## GroupECs:TimeB_POST_mood_mean | -0.44 | [-0.78, -0.11]
## GroupIntervention:TimeB_POST_mood_mean | 0.21 | [-0.06, 0.48]
## GroupECs:TimeC_W1_mood_mean | -0.19 | [-0.53, 0.14]
## GroupIntervention:TimeC_W1_mood_mean | 0.03 | [-0.24, 0.30]
## GroupECs:TimeD_M1_mood_mean | -0.32 | [-0.67, 0.03]
## GroupIntervention:TimeD_M1_mood_mean | -4.20e-03 | [-0.28, 0.27]Baseline to post
Mood_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "B_POST_mood_mean")## Formatting tables as needed
Mood_BP_long <- Mood_BP %>%
pivot_longer(cols = c("A_PRE_mood_mean", "B_POST_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_BP <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_BP_long, REML = TRUE)
summary(Mood_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_BP_long
##
## REML criterion at convergence: 5079
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1547 -0.3855 0.0476 0.4451 3.3781
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1035.6 32.18
## Residual 513.4 22.66
## Number of obs: 517, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 37.316 3.823 353.535 9.762
## GroupECs 2.984 6.752 353.535 0.442
## GroupIntervention -7.685 5.445 353.535 -1.411
## TimeB_POST_mood_mean 19.972 3.112 255.115 6.417
## GroupECs:TimeB_POST_mood_mean -19.992 5.498 255.115 -3.637
## GroupIntervention:TimeB_POST_mood_mean 9.350 4.443 255.465 2.105
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.658816
## GroupIntervention 0.159037
## TimeB_POST_mood_mean 6.72e-10 ***
## GroupECs:TimeB_POST_mood_mean 0.000334 ***
## GroupIntervention:TimeB_POST_mood_mean 0.036311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TB_POS GEC:TB
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmB_POST_m_ -0.407 0.230 0.286
## GEC:TB_POST 0.230 -0.407 -0.162 -0.566
## GI:TB_POST_ 0.285 -0.161 -0.406 -0.701 0.397anova (Mood_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 677.9 339.0 2 255.99 0.6603 0.5176
## Time 30949.7 30949.7 1 255.29 60.2843 1.989e-13 ***
## Group:Time 14453.2 7226.6 2 255.32 14.0761 1.588e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_BP)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 37.32 | 29.81 – 44.83 | <0.001 |
| Group [ECs] | 2.98 | -10.28 – 16.25 | 0.659 |
| Group [Intervention] | -7.68 | -18.38 – 3.01 | 0.159 |
| Time [B_POST_mood_mean] | 19.97 | 13.86 – 26.09 | <0.001 |
|
Group [ECs] × Time [B_POST_mood_mean] |
-19.99 | -30.79 – -9.19 | <0.001 |
|
Group [Intervention] × Time [B_POST_mood_mean] |
9.35 | 0.62 – 18.08 | 0.036 |
| Random Effects | |||
| σ2 | 513.40 | ||
| Ï„00 ID | 1035.58 | ||
| ICC | 0.67 | ||
| N ID | 259 | ||
| Observations | 517 | ||
| Marginal R2 / Conditional R2 | 0.079 / 0.695 | ||
parameters::standardise_parameters(Mood_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.18 | [-0.36, 0.00]
## GroupECs | 0.07 | [-0.25, 0.40]
## GroupIntervention | -0.19 | [-0.45, 0.07]
## TimeB_POST_mood_mean | 0.49 | [ 0.34, 0.64]
## GroupECs:TimeB_POST_mood_mean | -0.49 | [-0.75, -0.23]
## GroupIntervention:TimeB_POST_mood_mean | 0.23 | [ 0.02, 0.44]plot_model(Mood_MEM_BP, type = "int")
Baseline to 1W
Mood_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "C_W1_mood_mean")## Formatting tables as needed
Mood_B1W_long <- Mood_B1W %>%
pivot_longer(cols = c("A_PRE_mood_mean", "C_W1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_B1W <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_B1W_long, REML = TRUE)
summary(Mood_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_B1W_long
##
## REML criterion at convergence: 5220.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1570 -0.4429 0.1159 0.5899 2.4716
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 866.2 29.43
## Residual 1117.7 33.43
## Number of obs: 509, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 37.316 4.326 424.920 8.626
## GroupECs 2.984 7.641 424.920 0.390
## GroupIntervention -7.685 6.162 424.920 -1.247
## TimeC_W1_mood_mean -6.440 4.640 252.527 -1.388
## GroupECs:TimeC_W1_mood_mean -8.265 8.220 253.359 -1.005
## GroupIntervention:TimeC_W1_mood_mean 1.112 6.623 253.086 0.168
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupECs 0.696
## GroupIntervention 0.213
## TimeC_W1_mood_mean 0.166
## GroupECs:TimeC_W1_mood_mean 0.316
## GroupIntervention:TimeC_W1_mood_mean 0.867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_md_m -0.525 0.297 0.369
## GEC:TC_W1__ 0.296 -0.524 -0.208 -0.564
## GrI:TC_W1__ 0.368 -0.208 -0.524 -0.701 0.395anova (Mood_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2236.2 1118.1 2 257.40 1.0004 0.36916
## Time 8711.4 8711.4 1 253.43 7.7944 0.00564 **
## Group:Time 1553.5 776.7 2 253.35 0.6950 0.50003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_B1W)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 37.32 | 28.82 – 45.82 | <0.001 |
| Group [ECs] | 2.98 | -12.03 – 18.00 | 0.696 |
| Group [Intervention] | -7.68 | -19.79 – 4.42 | 0.213 |
| Time [C_W1_mood_mean] | -6.44 | -15.56 – 2.68 | 0.166 |
|
Group [ECs] × Time [C_W1_mood_mean] |
-8.26 | -24.42 – 7.89 | 0.315 |
|
Group [Intervention] × Time [C_W1_mood_mean] |
1.11 | -11.90 – 14.12 | 0.867 |
| Random Effects | |||
| σ2 | 1117.65 | ||
| Ï„00 ID | 866.19 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 509 | ||
| Marginal R2 / Conditional R2 | 0.014 / 0.445 | ||
parameters::standardise_parameters(Mood_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 0.14 | [-0.05, 0.33]
## GroupECs | 0.07 | [-0.27, 0.40]
## GroupIntervention | -0.17 | [-0.44, 0.10]
## TimeC_W1_mood_mean | -0.14 | [-0.35, 0.06]
## GroupECs:TimeC_W1_mood_mean | -0.19 | [-0.55, 0.18]
## GroupIntervention:TimeC_W1_mood_mean | 0.02 | [-0.27, 0.32]plot_model(Mood_MEM_B1W, type = "int")
Baseline to 1M
Mood_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "D_M1_mood_mean")## Formatting tables as needed
Mood_B1M_long <- Mood_B1M %>%
pivot_longer(cols = c("A_PRE_mood_mean", "D_M1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_B1M <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_B1M_long, REML = TRUE)
summary(Mood_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_B1M_long
##
## REML criterion at convergence: 5011.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91923 -0.51813 0.07265 0.59296 2.42299
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 893.6 29.89
## Residual 1156.5 34.01
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 37.3160 4.3979 411.7355 8.485
## GroupECs 2.9840 7.7682 411.7355 0.384
## GroupIntervention -7.6850 6.2646 411.7355 -1.227
## TimeD_M1_mood_mean -6.6766 4.8807 243.7920 -1.368
## GroupECs:TimeD_M1_mood_mean -14.1983 8.6884 245.7580 -1.634
## GroupIntervention:TimeD_M1_mood_mean 0.1323 6.9573 243.9696 0.019
## Pr(>|t|)
## (Intercept) 3.93e-16 ***
## GroupECs 0.701
## GroupIntervention 0.221
## TimeD_M1_mood_mean 0.173
## GroupECs:TimeD_M1_mood_mean 0.104
## GroupIntervention:TimeD_M1_mood_mean 0.985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmD_M1_md_m -0.508 0.288 0.357
## GEC:TD_M1__ 0.286 -0.504 -0.200 -0.562
## GrI:TD_M1__ 0.357 -0.202 -0.508 -0.702 0.394anova (Mood_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2302.4 1151.2 2 260.11 0.9954 0.3709745
## Time 13435.3 13435.3 1 245.36 11.6170 0.0007637 ***
## Group:Time 3690.3 1845.2 2 245.06 1.5954 0.2049198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_B1M)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 37.32 | 28.67 – 45.96 | <0.001 |
| Group [ECs] | 2.98 | -12.28 – 18.25 | 0.701 |
| Group [Intervention] | -7.68 | -19.99 – 4.62 | 0.221 |
| Time [D_M1_mood_mean] | -6.68 | -16.27 – 2.91 | 0.172 |
|
Group [ECs] × Time [D_M1_mood_mean] |
-14.20 | -31.27 – 2.87 | 0.103 |
|
Group [Intervention] × Time [D_M1_mood_mean] |
0.13 | -13.54 – 13.80 | 0.985 |
| Random Effects | |||
| σ2 | 1156.52 | ||
| Ï„00 ID | 893.64 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.447 | ||
parameters::standardise_parameters(Mood_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 0.15 | [-0.04, 0.34]
## GroupECs | 0.07 | [-0.27, 0.40]
## GroupIntervention | -0.17 | [-0.44, 0.10]
## TimeD_M1_mood_mean | -0.15 | [-0.36, 0.06]
## GroupECs:TimeD_M1_mood_mean | -0.31 | [-0.69, 0.06]
## GroupIntervention:TimeD_M1_mood_mean | 2.90e-03 | [-0.30, 0.30]plot_model(Mood_MEM_B1M, type = "int")
Hypothesis 3
# Baseline to 1W/1M changes (creating new columns)
changeinvariables <- mutate(Full_data_all,
IUS_BP_change = B_POST_IUS_total - A_PRE_IUS_total,
IUS_B1W_change = C_W1_IUS_total - A_PRE_IUS_total,
IUS_B1M_change = D_M1_IUS_total - A_PRE_IUS_total,
PHQ_B1W_change = C_W1_PHQ_total - A_PRE_PHQ_total,
PHQ_B1M_change = D_M1_PHQ_total - A_PRE_PHQ_total,
GAD_B1W_change = C_W1_GAD_total - A_PRE_GAD_total,
GAD_B1M_change = D_M1_GAD_total - A_PRE_GAD_total,
Mood_BP_change = B_POST_mood_mean - A_PRE_mood_mean,
Mood_B1W_change = C_W1_mood_mean - A_PRE_mood_mean,
Mood_B1M_change = D_M1_mood_mean - A_PRE_mood_mean)# Separating out each group
Intervention_group <- changeinvariables %>%
filter(Group == "Intervention")
Psychoed_group <- changeinvariables %>%
filter(Group == "Controls")
ECs_group <- changeinvariables %>%
filter(Group == "ECs")H3: IUS mediating change in PHQ over time between groups
Full sample at 1 week: Group to change in PHQ via change in IUS
Mediation.PHQchange.1W <-
'#regressions
PHQ_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
PHQ_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.PHQ.1W <- sem(Mediation.PHQchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 13 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1W_change ~
## Group (c1) 0.003 0.065 0.043 0.966 0.003 0.003
## IUS_B1W_change ~
## Group (a1) -0.158 0.067 -2.346 0.019 -0.158 -0.142
## PHQ_B1W_change ~
## IUS_B1W_c (b1) 0.409 0.084 4.879 0.000 0.409 0.409
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change -0.006 0.134 -0.041 0.967 -0.006 -0.006
## .IUS_B1W_change 0.313 0.142 2.212 0.027 0.313 0.314
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.829 0.092 8.978 0.000 0.829 0.833
## .IUS_B1W_change 0.976 0.153 6.391 0.000 0.976 0.980
##
## R-Square:
## Estimate
## PHQ_B1W_change 0.167
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.065 0.031 -2.092 0.036 -0.065 -0.058
## direct 0.003 0.065 0.043 0.966 0.003 0.003
## total -0.062 0.070 -0.880 0.379 -0.062 -0.056Intervention group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.intervention.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.intervention.1W <- sem(Mediation.PHQ.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.097 0.014 6.784 0.000 0.097 0.549
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.008 0.015 0.534 0.593 0.008 0.047
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.278 0.123 2.254 0.024 0.278 0.278
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.815 0.247 -7.340 0.000 -1.815 -1.823
## .IUS_B1W_change -0.154 0.311 -0.495 0.621 -0.154 -0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.601 0.093 6.493 0.000 0.601 0.607
## .IUS_B1W_change 0.988 0.223 4.429 0.000 0.988 0.998
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.393
## IUS_B1W_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.002 0.004 0.555 0.579 0.002 0.013
## direct 0.097 0.014 6.784 0.000 0.097 0.549
## total 0.100 0.015 6.827 0.000 0.100 0.562Psychoeducation group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.psychoed.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.psychoed.1W <- sem(Mediation.PHQ.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.118 0.011 10.736 0.000 0.118 0.722
## IUS_B1W_change ~
## A_PRE_PHQ (a1) -0.007 0.013 -0.558 0.577 -0.007 -0.043
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.302 0.080 3.785 0.000 0.302 0.302
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -2.048 0.178 -11.515 0.000 -2.048 -2.058
## .IUS_B1W_change 0.123 0.243 0.507 0.612 0.123 0.124
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.402 0.062 6.491 0.000 0.402 0.406
## .IUS_B1W_change 0.989 0.233 4.249 0.000 0.989 0.998
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.594
## IUS_B1W_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.002 0.004 -0.563 0.573 -0.002 -0.013
## direct 0.118 0.011 10.736 0.000 0.118 0.722
## total 0.115 0.011 10.108 0.000 0.115 0.709EC group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.ECs.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.ECs.1W <- sem(Mediation.PHQ.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.104 0.008 12.929 0.000 0.104 0.602
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.036 0.013 2.654 0.008 0.036 0.206
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.503 0.051 9.905 0.000 0.503 0.503
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.876 0.150 -12.504 0.000 -1.876 -1.895
## .IUS_B1W_change -0.643 0.303 -2.118 0.034 -0.643 -0.649
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.254 0.073 3.473 0.001 0.254 0.259
## .IUS_B1W_change 0.938 0.464 2.024 0.043 0.938 0.957
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.741
## IUS_B1W_change 0.043
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.018 0.007 2.505 0.012 0.018 0.104
## direct 0.104 0.008 12.929 0.000 0.104 0.602
## total 0.122 0.009 13.339 0.000 0.122 0.706Full sample at 1 month: Group to change in PHQ via change in IUS
Mediation.PHQchange.1M <-
'#regressions
PHQ_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
PHQ_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.PHQ.1M <- sem(Mediation.PHQchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1M_change ~
## Group (c1) -0.002 0.053 -0.041 0.967 -0.002 -0.002
## IUS_B1M_change ~
## Group (a1) -0.100 0.068 -1.468 0.142 -0.100 -0.090
## PHQ_B1M_change ~
## IUS_B1M_c (b1) 0.652 0.052 12.636 0.000 0.652 0.652
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.004 0.119 0.036 0.971 0.004 0.004
## .IUS_B1M_change 0.200 0.149 1.338 0.181 0.200 0.200
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.572 0.064 8.991 0.000 0.572 0.574
## .IUS_B1M_change 0.988 0.122 8.130 0.000 0.988 0.992
##
## R-Square:
## Estimate
## PHQ_B1M_change 0.426
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.065 0.044 -1.475 0.140 -0.065 -0.059
## direct -0.002 0.053 -0.041 0.967 -0.002 -0.002
## total -0.068 0.067 -1.007 0.314 -0.068 -0.061Intervention group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.intervention.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.intervention.1M <- sem(Mediation.PHQ.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.082 0.010 8.324 0.000 0.082 0.464
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.005 0.015 -0.334 0.738 -0.005 -0.028
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.565 0.081 6.974 0.000 0.565 0.565
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.535 0.186 -8.247 0.000 -1.535 -1.543
## .IUS_B1M_change 0.093 0.290 0.322 0.748 0.093 0.094
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.476 0.075 6.303 0.000 0.476 0.481
## .IUS_B1M_change 0.990 0.184 5.381 0.000 0.990 0.999
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.519
## IUS_B1M_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.009 -0.330 0.741 -0.003 -0.016
## direct 0.082 0.010 8.324 0.000 0.082 0.464
## total 0.079 0.014 5.820 0.000 0.079 0.448Psychoeducation group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.psychoed.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.psychoed.1M <- sem(Mediation.PHQ.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.066 0.011 5.895 0.000 0.066 0.403
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.023 0.017 -1.375 0.169 -0.023 -0.142
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.615 0.070 8.805 0.000 0.615 0.615
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.143 0.198 -5.783 0.000 -1.143 -1.149
## .IUS_B1M_change 0.403 0.273 1.474 0.140 0.403 0.404
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.524 0.079 6.655 0.000 0.524 0.529
## .IUS_B1M_change 0.971 0.183 5.306 0.000 0.971 0.980
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.471
## IUS_B1M_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.014 0.011 -1.295 0.195 -0.014 -0.087
## direct 0.066 0.011 5.895 0.000 0.066 0.403
## total 0.051 0.015 3.474 0.001 0.051 0.316EC group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.ECs.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.ECs.1M <- sem(Mediation.PHQ.ECs.1M, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.ECs.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.067 0.016 4.242 0.000 0.067 0.386
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.009 0.023 -0.419 0.676 -0.009 -0.055
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.727 0.058 12.599 0.000 0.727 0.727
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.202 0.265 -4.537 0.000 -1.202 -1.214
## .IUS_B1M_change 0.170 0.382 0.445 0.656 0.170 0.172
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.346 0.120 2.882 0.004 0.346 0.353
## .IUS_B1M_change 0.977 0.297 3.293 0.001 0.977 0.997
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.647
## IUS_B1M_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.007 0.017 -0.414 0.679 -0.007 -0.040
## direct 0.067 0.016 4.242 0.000 0.067 0.386
## total 0.060 0.024 2.534 0.011 0.060 0.346H3: IUS mediating change in GAD over time between groups
Full sample at 1 week: Group to change in GAD via change in IUS
Mediation.GADchange.1W <-
'#regressions
GAD_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
GAD_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1W <- sem(Mediation.GADchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 14 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1W_change ~
## Group (c1) 0.024 0.066 0.364 0.716 0.024 0.022
## IUS_B1W_change ~
## Group (a1) -0.158 0.067 -2.346 0.019 -0.158 -0.142
## GAD_B1W_change ~
## IUS_B1W_c (b1) 0.453 0.080 5.691 0.000 0.453 0.453
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change -0.048 0.134 -0.356 0.722 -0.048 -0.048
## .IUS_B1W_change 0.313 0.142 2.212 0.027 0.313 0.314
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.794 0.108 7.357 0.000 0.794 0.797
## .IUS_B1W_change 0.976 0.153 6.391 0.000 0.976 0.980
##
## R-Square:
## Estimate
## GAD_B1W_change 0.203
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.071 0.034 -2.095 0.036 -0.071 -0.064
## direct 0.024 0.066 0.364 0.716 0.024 0.022
## total -0.047 0.071 -0.664 0.506 -0.047 -0.043Intervention group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.intervention.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.intervention.1W <- sem(Mediation.GAD.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.108 0.014 7.546 0.000 0.108 0.589
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.004 0.017 -0.233 0.815 -0.004 -0.022
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.286 0.121 2.372 0.018 0.286 0.286
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.750 0.208 -8.401 0.000 -1.750 -1.759
## .IUS_B1W_change 0.065 0.299 0.217 0.828 0.065 0.065
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.573 0.106 5.421 0.000 0.573 0.578
## .IUS_B1W_change 0.990 0.223 4.434 0.000 0.990 1.000
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.422
## IUS_B1W_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.005 -0.224 0.823 -0.001 -0.006
## direct 0.108 0.014 7.546 0.000 0.108 0.589
## total 0.106 0.014 7.366 0.000 0.106 0.583Psychoeducation group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.psychoed.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.psychoed.1W <- sem(Mediation.GAD.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.122 0.010 12.186 0.000 0.122 0.706
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.009 0.013 -0.694 0.488 -0.009 -0.053
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.343 0.065 5.253 0.000 0.343 0.343
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.891 0.163 -11.636 0.000 -1.891 -1.900
## .IUS_B1W_change 0.142 0.231 0.614 0.539 0.142 0.143
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.407 0.075 5.397 0.000 0.407 0.410
## .IUS_B1W_change 0.988 0.232 4.253 0.000 0.988 0.997
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.590
## IUS_B1W_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.004 -0.703 0.482 -0.003 -0.018
## direct 0.122 0.010 12.186 0.000 0.122 0.706
## total 0.119 0.011 11.113 0.000 0.119 0.687EC group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.ECs.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.ECs.1W <- sem(Mediation.GAD.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.135 0.012 11.312 0.000 0.135 0.657
## IUS_B1W_change ~
## A_PRE_GAD (a1) 0.027 0.017 1.622 0.105 0.027 0.131
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.503 0.039 13.030 0.000 0.503 0.503
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -2.035 0.166 -12.255 0.000 -2.035 -2.055
## .IUS_B1W_change -0.405 0.312 -1.297 0.195 -0.405 -0.409
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.224 0.053 4.212 0.000 0.224 0.228
## .IUS_B1W_change 0.963 0.469 2.055 0.040 0.963 0.983
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.772
## IUS_B1W_change 0.017
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.014 0.009 1.595 0.111 0.014 0.066
## direct 0.135 0.012 11.312 0.000 0.135 0.657
## total 0.149 0.013 11.412 0.000 0.149 0.723Full sample at 1 month: Group to change in GAD via change in IUS
Mediation.GADchange.1M <-
'#regressions
GAD_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
GAD_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1M <- sem(Mediation.GADchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 15 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1M_change ~
## Group (c1) 0.025 0.050 0.490 0.624 0.025 0.022
## IUS_B1M_change ~
## Group (a1) -0.100 0.068 -1.468 0.142 -0.100 -0.090
## GAD_B1M_change ~
## IUS_B1M_c (b1) 0.678 0.050 13.527 0.000 0.678 0.678
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change -0.049 0.115 -0.426 0.670 -0.049 -0.049
## .IUS_B1M_change 0.200 0.149 1.338 0.181 0.200 0.200
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.541 0.068 7.997 0.000 0.541 0.543
## .IUS_B1M_change 0.988 0.122 8.130 0.000 0.988 0.992
##
## R-Square:
## Estimate
## GAD_B1M_change 0.457
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.068 0.046 -1.479 0.139 -0.068 -0.061
## direct 0.025 0.050 0.490 0.624 0.025 0.022
## total -0.043 0.067 -0.646 0.518 -0.043 -0.039Intervention group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.intervention.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.intervention.1M <- sem(Mediation.GAD.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.100 0.011 9.396 0.000 0.100 0.546
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.004 0.014 -0.268 0.788 -0.004 -0.021
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.572 0.075 7.597 0.000 0.572 0.572
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.622 0.170 -9.555 0.000 -1.622 -1.630
## .IUS_B1M_change 0.063 0.247 0.256 0.798 0.063 0.063
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.384 0.062 6.200 0.000 0.384 0.388
## .IUS_B1M_change 0.990 0.184 5.379 0.000 0.990 1.000
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.612
## IUS_B1M_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.002 0.008 -0.266 0.790 -0.002 -0.012
## direct 0.100 0.011 9.396 0.000 0.100 0.546
## total 0.097 0.013 7.632 0.000 0.097 0.534Psychoeducation group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.psychoed.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.psychoed.1M <- sem(Mediation.GAD.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.072 0.010 7.252 0.000 0.072 0.417
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.025 0.019 -1.300 0.194 -0.025 -0.143
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.671 0.065 10.368 0.000 0.671 0.671
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.117 0.154 -7.270 0.000 -1.117 -1.122
## .IUS_B1M_change 0.383 0.272 1.405 0.160 0.383 0.384
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.452 0.075 5.988 0.000 0.452 0.456
## .IUS_B1M_change 0.970 0.180 5.388 0.000 0.970 0.980
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.544
## IUS_B1M_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.017 0.013 -1.247 0.213 -0.017 -0.096
## direct 0.072 0.010 7.252 0.000 0.072 0.417
## total 0.056 0.017 3.219 0.001 0.056 0.321EC group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.ECs.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.ECs.1M <- sem(Mediation.GAD.ECs.1M, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.ECs.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.067 0.024 2.783 0.005 0.067 0.327
## IUS_B1M_change ~
## A_PRE_GAD (a1) 0.002 0.018 0.122 0.903 0.002 0.011
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.673 0.051 13.210 0.000 0.673 0.673
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.013 0.366 -2.765 0.006 -1.013 -1.023
## .IUS_B1M_change -0.033 0.294 -0.113 0.910 -0.033 -0.034
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.427 0.142 3.008 0.003 0.427 0.436
## .IUS_B1M_change 0.980 0.301 3.256 0.001 0.980 1.000
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.564
## IUS_B1M_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.001 0.012 0.122 0.903 0.001 0.007
## direct 0.067 0.024 2.783 0.005 0.067 0.327
## total 0.069 0.027 2.562 0.010 0.069 0.334H3: IUS mediating change in mood over time between groups
Full sample at post: Group to change in mood via change in IUS
Mediation.Moodchange.post <-
'#regressions
Mood_BP_change ~ c1 * Group
IUS_BP_change ~ a1 * Group
Mood_BP_change ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.post <- sem(Mediation.Moodchange.post, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 15 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_BP_change ~
## Group (c1) 0.086 0.070 1.220 0.223 0.086 0.077
## IUS_BP_change ~
## Group (a1) -0.195 0.070 -2.780 0.005 -0.195 -0.176
## Mood_BP_change ~
## IUS_BP_ch (b1) -0.305 0.073 -4.157 0.000 -0.305 -0.304
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change -0.164 0.148 -1.110 0.267 -0.164 -0.163
## .IUS_BP_change 0.388 0.131 2.960 0.003 0.388 0.389
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change 0.900 0.151 5.946 0.000 0.900 0.894
## .IUS_BP_change 0.965 0.148 6.504 0.000 0.965 0.969
##
## R-Square:
## Estimate
## Mood_BP_change 0.106
## IUS_BP_change 0.031
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.060 0.023 2.577 0.010 0.060 0.053
## direct 0.086 0.070 1.220 0.223 0.086 0.077
## total 0.145 0.071 2.048 0.041 0.145 0.130Intervention group at post: pre mood to post mood via change in IU
Mediation.Mood.intervention.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.post <- sem(Mediation.Mood.intervention.post, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.014 0.003 4.715 0.000 0.014 0.600
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.530 0.596 0.001 0.051
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.162 0.093 -1.751 0.080 -0.162 -0.162
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.407 0.147 -2.767 0.006 -0.407 -0.407
## .IUS_BP_change -0.035 0.099 -0.357 0.721 -0.035 -0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.624 0.128 4.889 0.000 0.624 0.624
## .IUS_BP_change 0.988 0.224 4.416 0.000 0.988 0.997
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.376
## IUS_BP_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.495 0.621 -0.000 -0.008
## direct 0.014 0.003 4.715 0.000 0.014 0.600
## total 0.014 0.003 4.724 0.000 0.014 0.591Psychoeducation group at post: pre mood to post mood via change in IU
Mediation.Mood.psychoed.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.post <- sem(Mediation.Mood.psychoed.post, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.016 0.002 7.911 0.000 0.016 0.695
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.002 0.002 0.698 0.485 0.002 0.067
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.241 0.069 -3.506 0.000 -0.241 -0.241
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.589 0.119 -4.939 0.000 -0.589 -0.592
## .IUS_BP_change -0.057 0.138 -0.412 0.681 -0.057 -0.057
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.477 0.083 5.756 0.000 0.477 0.482
## .IUS_BP_change 0.986 0.196 5.026 0.000 0.986 0.996
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.518
## IUS_BP_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.647 0.518 -0.000 -0.016
## direct 0.016 0.002 7.911 0.000 0.016 0.695
## total 0.015 0.002 7.039 0.000 0.015 0.679EC group at post: pre mood to post mood via change in IU
Mediation.Mood.ECs.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.ECs.post <- sem(Mediation.Mood.ECs.post, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.ECs.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.022 0.002 13.400 0.000 0.022 0.868
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.001 0.003 0.236 0.814 0.001 0.025
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.063 0.060 -1.052 0.293 -0.063 -0.063
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.895 0.104 -8.601 0.000 -0.895 -0.904
## .IUS_BP_change -0.026 0.178 -0.147 0.883 -0.026 -0.026
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.240 0.063 3.801 0.000 0.240 0.245
## .IUS_BP_change 0.979 0.226 4.336 0.000 0.979 0.999
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.755
## IUS_BP_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.231 0.817 -0.000 -0.002
## direct 0.022 0.002 13.400 0.000 0.022 0.868
## total 0.022 0.002 13.364 0.000 0.022 0.866Full sample at 1 week: Group to change in mood via change in IUS
Mediation.Moodchange.1W <-
'#regressions
Mood_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
Mood_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.1W <- sem(Mediation.Moodchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 15 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_B1W_change ~
## Group (c1) -0.026 0.074 -0.354 0.723 -0.026 -0.023
## IUS_B1W_change ~
## Group (a1) -0.158 0.067 -2.346 0.019 -0.158 -0.142
## Mood_B1W_change ~
## IUS_B1W_c (b1) -0.221 0.090 -2.451 0.014 -0.221 -0.219
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang 0.076 0.166 0.456 0.648 0.076 0.075
## .IUS_B1W_change 0.313 0.142 2.212 0.027 0.313 0.314
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang 0.965 0.120 8.070 0.000 0.965 0.953
## .IUS_B1W_change 0.976 0.153 6.391 0.000 0.976 0.980
##
## R-Square:
## Estimate
## Mood_B1W_chang 0.047
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.035 0.019 1.870 0.062 0.035 0.031
## direct -0.026 0.074 -0.354 0.723 -0.026 -0.023
## total 0.009 0.072 0.119 0.906 0.009 0.008Intervention group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.intervention.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.1W <- sem(Mediation.Mood.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.011 0.002 4.421 0.000 0.011 0.457
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.492 0.623 0.001 0.051
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.133 0.114 -1.173 0.241 -0.133 -0.134
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.311 0.110 -2.833 0.005 -0.311 -0.313
## .IUS_B1W_change -0.036 0.114 -0.311 0.756 -0.036 -0.036
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.768 0.155 4.947 0.000 0.768 0.779
## .IUS_B1W_change 0.988 0.220 4.496 0.000 0.988 0.997
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.221
## IUS_B1W_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.478 0.633 -0.000 -0.007
## direct 0.011 0.002 4.421 0.000 0.011 0.457
## total 0.011 0.003 4.150 0.000 0.011 0.450Psychoeducation group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.psychoed.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.1W <- sem(Mediation.Mood.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.478 0.000 0.010 0.444
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.633 0.527 0.001 0.062
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.198 0.120 -1.648 0.099 -0.198 -0.196
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.361 0.135 -2.670 0.008 -0.361 -0.359
## .IUS_B1W_change -0.053 0.133 -0.397 0.691 -0.053 -0.053
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.781 0.126 6.223 0.000 0.781 0.776
## .IUS_B1W_change 0.987 0.235 4.206 0.000 0.987 0.996
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.224
## IUS_B1W_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.554 0.579 -0.000 -0.012
## direct 0.010 0.002 4.478 0.000 0.010 0.444
## total 0.010 0.002 4.164 0.000 0.010 0.431EC group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.ECs.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.ECs.1W <- sem(Mediation.Mood.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.011 0.003 3.198 0.001 0.011 0.434
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.003 0.002 2.046 0.041 0.003 0.136
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.078 0.244 -0.320 0.749 -0.078 -0.078
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.434 0.178 -2.433 0.015 -0.434 -0.439
## .IUS_B1W_change -0.140 0.154 -0.906 0.365 -0.140 -0.141
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.795 0.129 6.163 0.000 0.795 0.815
## .IUS_B1W_change 0.962 0.476 2.021 0.043 0.962 0.982
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.185
## IUS_B1W_change 0.018
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.314 0.754 -0.000 -0.011
## direct 0.011 0.003 3.198 0.001 0.011 0.434
## total 0.011 0.003 3.239 0.001 0.011 0.424Full sample at 1 month: Group to change in mood via change in IUS
Mediation.Moodchange.1M <-
'#regressions
Mood_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
Mood_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.1M <- sem(Mediation.Moodchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_B1M_change ~
## Group (c1) -0.046 0.075 -0.607 0.544 -0.046 -0.039
## IUS_B1M_change ~
## Group (a1) -0.100 0.068 -1.468 0.142 -0.100 -0.090
## Mood_B1M_change ~
## IUS_B1M_c (b1) -0.452 0.119 -3.812 0.000 -0.452 -0.425
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.215 0.177 1.213 0.225 0.215 0.203
## .IUS_B1M_change 0.200 0.149 1.338 0.181 0.200 0.200
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.925 0.102 9.074 0.000 0.925 0.821
## .IUS_B1M_change 0.988 0.122 8.130 0.000 0.988 0.992
##
## R-Square:
## Estimate
## Mood_B1M_chang 0.179
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.045 0.033 1.382 0.167 0.045 0.038
## direct -0.046 0.075 -0.607 0.544 -0.046 -0.039
## total -0.000 0.079 -0.005 0.996 -0.000 -0.000Intervention group at 1 month: pre mood to 1 month mood via change in IU
Mediation.Mood.intervention.1M <-
'#regressions
D_M1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1M_change ~ a1 * A_PRE_mood_mean
D_M1_mood_mean ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.1M <- sem(Mediation.Mood.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 13 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.288 0.000 0.010 0.417
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.003 0.002 1.160 0.246 0.003 0.111
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.329 0.135 -2.428 0.015 -0.329 -0.320
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.209 0.115 -1.806 0.071 -0.209 -0.204
## .IUS_B1M_change -0.077 0.120 -0.645 0.519 -0.077 -0.078
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.785 0.140 5.613 0.000 0.785 0.753
## .IUS_B1M_change 0.978 0.183 5.338 0.000 0.978 0.988
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.247
## IUS_B1M_change 0.012
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.001 -1.014 0.311 -0.001 -0.036
## direct 0.010 0.002 4.288 0.000 0.010 0.417
## total 0.009 0.003 3.594 0.000 0.009 0.381Psychoeducation group at 1 month: pre mood to 1 month mood via change in IU
Mediation.Mood.psychoed.1M <-
'#regressions
D_M1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1M_change ~ a1 * A_PRE_mood_mean
D_M1_mood_mean ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.1M <- sem(Mediation.Mood.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.816 0.000 0.010 0.422
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.786 0.432 0.001 0.060
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.342 0.145 -2.365 0.018 -0.342 -0.332
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.274 0.131 -2.092 0.036 -0.274 -0.268
## .IUS_B1M_change -0.051 0.121 -0.423 0.672 -0.051 -0.051
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.764 0.093 8.253 0.000 0.764 0.728
## .IUS_B1M_change 0.987 0.197 5.013 0.000 0.987 0.996
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.272
## IUS_B1M_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.698 0.485 -0.000 -0.020
## direct 0.010 0.002 4.816 0.000 0.010 0.422
## total 0.009 0.002 4.193 0.000 0.009 0.403Exploratory analyses
E1: Growth mindsets moderating the association between group and PHQ changes across time
# 1 week
moderation_GM_PHQ_1W <- lm(PHQ_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1W)##
## Call:
## lm(formula = PHQ_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0159 -2.0823 0.7306 3.0574 12.9841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1921 1.2297 -1.783 0.0758 .
## GroupECs -0.8065 2.0501 -0.393 0.6944
## GroupIntervention 0.3160 1.7306 0.183 0.8553
## A_PRE_GM 0.2744 0.3565 0.770 0.4423
## GroupECs:A_PRE_GM 0.5426 0.6425 0.845 0.3991
## GroupIntervention:A_PRE_GM -0.3104 0.5212 -0.596 0.5520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.226 on 253 degrees of freedom
## Multiple R-squared: 0.01907, Adjusted R-squared: -0.0003146
## F-statistic: 0.9838 on 5 and 253 DF, p-value: 0.4282anova(moderation_GM_PHQ_1W)## Analysis of Variance Table
##
## Response: PHQ_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 54.1 27.054 0.9905 0.3728
## A_PRE_GM 1 34.0 33.971 1.2438 0.2658
## Group:A_PRE_GM 2 46.3 23.135 0.8470 0.4299
## Residuals 253 6910.3 27.313# 1 month
moderation_GM_PHQ_1M <- lm(PHQ_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1M)##
## Call:
## lm(formula = PHQ_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.700 -3.261 1.451 4.711 22.300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.0267 1.8888 -2.132 0.034 *
## GroupECs 2.3436 3.1490 0.744 0.457
## GroupIntervention 1.5199 2.6582 0.572 0.568
## A_PRE_GM 0.2878 0.5476 0.526 0.600
## GroupECs:A_PRE_GM -0.4691 0.9868 -0.475 0.635
## GroupIntervention:A_PRE_GM -0.8757 0.8005 -1.094 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.028 on 253 degrees of freedom
## Multiple R-squared: 0.01432, Adjusted R-squared: -0.00516
## F-statistic: 0.7351 on 5 and 253 DF, p-value: 0.5977anova(moderation_GM_PHQ_1M)## Analysis of Variance Table
##
## Response: PHQ_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 150.6 75.293 1.1684 0.3125
## A_PRE_GM 1 8.9 8.889 0.1379 0.7106
## Group:A_PRE_GM 2 77.4 38.696 0.6005 0.5493
## Residuals 253 16303.8 64.442E1: Growth mindsets moderating the association between group and GAD changes across time
# 1 week
moderation_GM_GAD_1W <- lm(GAD_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1W)##
## Call:
## lm(formula = GAD_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.5676 -2.0544 0.4949 2.4807 15.5291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4066 1.1658 0.349 0.7275
## GroupECs -2.5470 1.9436 -1.310 0.1912
## GroupIntervention -2.0325 1.6407 -1.239 0.2166
## A_PRE_GM -0.4508 0.3380 -1.334 0.1835
## GroupECs:A_PRE_GM 1.1370 0.6091 1.867 0.0631 .
## GroupIntervention:A_PRE_GM 0.4991 0.4941 1.010 0.3134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.955 on 253 degrees of freedom
## Multiple R-squared: 0.02205, Adjusted R-squared: 0.002722
## F-statistic: 1.141 on 5 and 253 DF, p-value: 0.3392anova(moderation_GM_GAD_1W)## Analysis of Variance Table
##
## Response: GAD_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 50.9 25.452 1.0367 0.3561
## A_PRE_GM 1 1.0 0.977 0.0398 0.8420
## Group:A_PRE_GM 2 88.2 44.079 1.7955 0.1682
## Residuals 253 6211.2 24.550# 1 month
moderation_GM_GAD_1M <- lm(GAD_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1M)##
## Call:
## lm(formula = GAD_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.151 -2.289 1.296 4.085 19.507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.6309 1.6911 -1.556 0.121
## GroupECs 0.9127 2.8193 0.324 0.746
## GroupIntervention 1.3990 2.3799 0.588 0.557
## A_PRE_GM -0.0364 0.4903 -0.074 0.941
## GroupECs:A_PRE_GM 0.2474 0.8835 0.280 0.780
## GroupIntervention:A_PRE_GM -0.7031 0.7167 -0.981 0.328
##
## Residual standard error: 7.187 on 253 degrees of freedom
## Multiple R-squared: 0.02077, Adjusted R-squared: 0.001419
## F-statistic: 1.073 on 5 and 253 DF, p-value: 0.3756anova(moderation_GM_GAD_1M)## Analysis of Variance Table
##
## Response: GAD_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 169.3 84.646 1.6387 0.1963
## A_PRE_GM 1 32.5 32.506 0.6293 0.4284
## Group:A_PRE_GM 2 75.4 37.704 0.7299 0.4829
## Residuals 253 13068.5 51.654E1: Growth mindsets moderating the association between group and mood changes across time
# post
moderation_GM_mood_BP <- lm(Mood_BP_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_BP)##
## Call:
## lm(formula = Mood_BP_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.57 -19.14 -3.97 15.23 171.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.2623 7.5685 3.074 0.00235 **
## GroupECs -31.6421 12.6179 -2.508 0.01278 *
## GroupIntervention 7.2041 10.6547 0.676 0.49957
## A_PRE_GM -1.0475 2.1943 -0.477 0.63353
## GroupECs:A_PRE_GM 4.0985 3.9542 1.036 0.30097
## GroupIntervention:A_PRE_GM 0.6891 3.2076 0.215 0.83007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.17 on 252 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1039, Adjusted R-squared: 0.08617
## F-statistic: 5.847 on 5 and 252 DF, p-value: 3.945e-05anova(moderation_GM_mood_BP)## Analysis of Variance Table
##
## Response: Mood_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29097 14548.3 14.0609 1.625e-06 ***
## A_PRE_GM 1 0 0.0 0.0000 0.9988
## Group:A_PRE_GM 2 1150 575.1 0.5558 0.5743
## Residuals 252 260736 1034.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_GM_mood_1W <- lm(Mood_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_1W)##
## Call:
## lm(formula = Mood_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -185.320 -23.963 2.028 25.016 185.316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3130 11.2877 0.028 0.978
## GroupECs -15.5416 18.8441 -0.825 0.410
## GroupIntervention 0.8914 16.0669 0.055 0.956
## A_PRE_GM -2.1258 3.2638 -0.651 0.515
## GroupECs:A_PRE_GM 2.3073 5.8749 0.393 0.695
## GroupIntervention:A_PRE_GM -0.2204 4.8097 -0.046 0.963
##
## Residual standard error: 47.61 on 244 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.00881, Adjusted R-squared: -0.0115
## F-statistic: 0.4338 on 5 and 244 DF, p-value: 0.8248anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2952 1476.0 0.6511 0.5224
## A_PRE_GM 1 1516 1515.6 0.6686 0.4143
## Group:A_PRE_GM 2 449 224.5 0.0990 0.9057
## Residuals 244 553135 2266.9# 1 month
moderation_GM_mood_1W <- lm(Mood_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_1W)##
## Call:
## lm(formula = Mood_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -150.984 -28.262 4.266 26.821 183.538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.407 12.401 -0.194 0.846
## GroupECs -27.357 20.391 -1.342 0.181
## GroupIntervention -14.301 17.187 -0.832 0.406
## A_PRE_GM -1.226 3.614 -0.339 0.735
## GroupECs:A_PRE_GM 4.281 6.404 0.668 0.505
## GroupIntervention:A_PRE_GM 4.774 5.228 0.913 0.362
##
## Residual standard error: 48.65 on 222 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.02057, Adjusted R-squared: -0.001493
## F-statistic: 0.9323 on 5 and 222 DF, p-value: 0.4608anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7881 3940.5 1.6652 0.1915
## A_PRE_GM 1 904 904.4 0.3822 0.5371
## Group:A_PRE_GM 2 2246 1122.9 0.4745 0.6228
## Residuals 222 525329 2366.3E2: Association between IUS and baseline functional impairment
# Total FI scale
PRE_IUS_FI_lm <- lm(A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
summary(PRE_IUS_FI_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.4978 -4.2115 0.3611 5.1474 16.9780
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.5947 1.7806 13.25 <2e-16 ***
## A_PRE_FI_total 1.2379 0.1141 10.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.354 on 257 degrees of freedom
## Multiple R-squared: 0.314, Adjusted R-squared: 0.3113
## F-statistic: 117.6 on 1 and 257 DF, p-value: < 2.2e-16anova(PRE_IUS_FI_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_FI_total 1 6360.1 6360.1 117.61 < 2.2e-16 ***
## Residuals 257 13897.6 54.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Friends item
PRE_IUS_friends_lm <- lm(A_PRE_IUS_total ~ B_FI_friends, data = Full_data_all)
summary(PRE_IUS_friends_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_friends, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.7419 -5.1496 0.6416 6.0252 19.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.2760 1.3886 23.963 < 2e-16 ***
## B_FI_friends 3.2329 0.4657 6.942 3.16e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.159 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1584, Adjusted R-squared: 0.1551
## F-statistic: 48.2 on 1 and 256 DF, p-value: 3.163e-11anova(PRE_IUS_friends_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_friends 1 3208.3 3208.3 48.195 3.163e-11 ***
## Residuals 256 17041.8 66.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Strangers item
PRE_IUS_strangers_lm <- lm(A_PRE_IUS_total ~ B_FI_strangers, data = Full_data_all)
summary(PRE_IUS_strangers_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_strangers, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.857 -4.948 1.075 6.143 20.189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.7198 1.7331 20.033 < 2e-16 ***
## B_FI_strangers 2.0457 0.4489 4.557 8.03e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.548 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07504, Adjusted R-squared: 0.07142
## F-statistic: 20.77 on 1 and 256 DF, p-value: 8.034e-06anova(PRE_IUS_strangers_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_strangers 1 1517.6 1517.59 20.768 8.034e-06 ***
## Residuals 256 18707.0 73.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Work item
PRE_IUS_work_lm <- lm(A_PRE_IUS_total ~ B_FI_work, data = Full_data_all)
summary(PRE_IUS_work_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_work, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.9966 -4.3259 0.0034 5.8388 18.9211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.914 1.450 20.627 <2e-16 ***
## B_FI_work 4.082 0.451 9.051 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.705 on 243 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.2521, Adjusted R-squared: 0.249
## F-statistic: 81.92 on 1 and 243 DF, p-value: < 2.2e-16anova(PRE_IUS_work_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_work 1 4863.8 4863.8 81.92 < 2.2e-16 ***
## Residuals 243 14427.5 59.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Education item
PRE_IUS_education_lm <- lm(A_PRE_IUS_total ~ B_FI_education, data = Full_data_all)
summary(PRE_IUS_education_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_education, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.9221 -4.9864 0.0136 5.9976 18.0457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.8900 1.3789 23.853 < 2e-16 ***
## B_FI_education 3.0321 0.4192 7.233 5.79e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.153 on 249 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1736, Adjusted R-squared: 0.1703
## F-statistic: 52.32 on 1 and 249 DF, p-value: 5.793e-12anova(PRE_IUS_education_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_education 1 3478.3 3478.3 52.323 5.793e-12 ***
## Residuals 249 16552.6 66.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Hobbies item
PRE_IUS_hobbies_lm <- lm(A_PRE_IUS_total ~ B_FI_hobbies, data = Full_data_all)
summary(PRE_IUS_hobbies_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_hobbies, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.3848 -5.5183 0.0813 5.4372 17.6152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.2068 1.2818 25.907 < 2e-16 ***
## B_FI_hobbies 3.1780 0.4149 7.659 3.85e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.022 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1864, Adjusted R-squared: 0.1832
## F-statistic: 58.66 on 1 and 256 DF, p-value: 3.853e-13anova(PRE_IUS_hobbies_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_hobbies 1 3775.2 3775.2 58.662 3.853e-13 ***
## Residuals 256 16474.9 64.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E2: Change in functional impairment over time across groups
# Merging across timepoints
FI_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total", "D_M1_FI_total")## Formatting table as needed
FI_alltimepoints_long <- FI_alltimepoints %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_alltimepoints_long, REML = TRUE)
summary(FI_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_alltimepoints_long
##
## REML criterion at convergence: 4585.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5128 -0.3758 0.0291 0.5036 2.2680
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.546 3.090
## Residual 15.332 3.916
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.95283 0.48446 593.28918 30.865
## GroupECs -0.09283 0.85573 593.28918 -0.108
## GroupIntervention 0.35785 0.69010 593.28918 0.519
## TimeC_W1_FI_total -0.55660 0.53785 512.00000 -1.035
## TimeD_M1_FI_total -2.25472 0.53785 512.00000 -4.192
## GroupECs:TimeC_W1_FI_total 0.07660 0.95003 512.00000 0.081
## GroupIntervention:TimeC_W1_FI_total -0.79291 0.76615 512.00000 -1.035
## GroupECs:TimeD_M1_FI_total 0.35472 0.95003 512.00000 0.373
## GroupIntervention:TimeD_M1_FI_total -0.37635 0.76615 512.00000 -0.491
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.914
## GroupIntervention 0.604
## TimeC_W1_FI_total 0.301
## TimeD_M1_FI_total 3.26e-05 ***
## GroupECs:TimeC_W1_FI_total 0.936
## GroupIntervention:TimeC_W1_FI_total 0.301
## GroupECs:TimeD_M1_FI_total 0.709
## GroupIntervention:TimeD_M1_FI_total 0.623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ TD_M1_ GEC:TC GI:TC_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_FI_t -0.555 0.314 0.390
## TmD_M1_FI_t -0.555 0.314 0.390 0.500
## GEC:TC_W1_F 0.314 -0.555 -0.221 -0.566 -0.283
## GI:TC_W1_FI 0.390 -0.221 -0.555 -0.702 -0.351 0.397
## GEC:TD_M1_F 0.314 -0.555 -0.221 -0.283 -0.566 0.500 0.199
## GI:TD_M1_FI 0.390 -0.221 -0.555 -0.351 -0.702 0.199 0.500 0.397anova (FI_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.24 0.122 2 256 0.0079 0.9921
## Time 605.46 302.728 2 512 19.7448 5.49e-09 ***
## Group:Time 23.37 5.842 4 512 0.3811 0.8222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.95 | 14.00 – 15.90 | <0.001 |
| Group [ECs] | -0.09 | -1.77 – 1.59 | 0.914 |
| Group [Intervention] | 0.36 | -1.00 – 1.71 | 0.604 |
| Time [C_W1_FI_total] | -0.56 | -1.61 – 0.50 | 0.301 |
| Time [D_M1_FI_total] | -2.25 | -3.31 – -1.20 | <0.001 |
|
Group [ECs] × Time [C_W1_FI_total] |
0.08 | -1.79 – 1.94 | 0.936 |
|
Group [Intervention] × Time [C_W1_FI_total] |
-0.79 | -2.30 – 0.71 | 0.301 |
|
Group [ECs] × Time [D_M1_FI_total] |
0.35 | -1.51 – 2.22 | 0.709 |
|
Group [Intervention] × Time [D_M1_FI_total] |
-0.38 | -1.88 – 1.13 | 0.623 |
| Random Effects | |||
| σ2 | 15.33 | ||
| Ï„00 ID | 9.55 | ||
| ICC | 0.38 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.037 / 0.407 | ||
parameters::standardise_parameters(FI_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 0.19 | [ 0.00, 0.37]
## GroupECs | -0.02 | [-0.35, 0.31]
## GroupIntervention | 0.07 | [-0.20, 0.34]
## TimeC_W1_FI_total | -0.11 | [-0.32, 0.10]
## TimeD_M1_FI_total | -0.45 | [-0.65, -0.24]
## GroupECs:TimeC_W1_FI_total | 0.02 | [-0.35, 0.38]
## GroupIntervention:TimeC_W1_FI_total | -0.16 | [-0.45, 0.14]
## GroupECs:TimeD_M1_FI_total | 0.07 | [-0.30, 0.44]
## GroupIntervention:TimeD_M1_FI_total | -0.07 | [-0.37, 0.22]Baseline to 1W
# Merging across timepoints
FI_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total")## Formatting table as needed
FI_B1W_long <- FI_B1W %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1W <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1W_long, REML = TRUE)
summary(FI_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1W_long
##
## REML criterion at convergence: 2917.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6197 -0.4865 0.0175 0.4849 2.7271
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.554 3.091
## Residual 9.586 3.096
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.95283 0.42493 409.87229 35.189
## GroupECs -0.09283 0.75058 409.87229 -0.124
## GroupIntervention 0.35785 0.60531 409.87229 0.591
## TimeC_W1_FI_total -0.55660 0.42529 256.00000 -1.309
## GroupECs:TimeC_W1_FI_total 0.07660 0.75121 256.00000 0.102
## GroupIntervention:TimeC_W1_FI_total -0.79291 0.60581 256.00000 -1.309
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupECs 0.902
## GroupIntervention 0.555
## TimeC_W1_FI_total 0.192
## GroupECs:TimeC_W1_FI_total 0.919
## GroupIntervention:TimeC_W1_FI_total 0.192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TC_W1_ GEC:TC
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmC_W1_FI_t -0.500 0.283 0.351
## GEC:TC_W1_F 0.283 -0.500 -0.199 -0.566
## GI:TC_W1_FI 0.351 -0.199 -0.500 -0.702 0.397anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.086 0.043 2 256 0.0045 0.995524
## Time 72.728 72.728 1 256 7.5869 0.006301 **
## Group:Time 20.828 10.414 2 256 1.0864 0.338986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM_B1W)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.95 | 14.12 – 15.79 | <0.001 |
| Group [ECs] | -0.09 | -1.57 – 1.38 | 0.902 |
| Group [Intervention] | 0.36 | -0.83 – 1.55 | 0.555 |
| Time [C_W1_FI_total] | -0.56 | -1.39 – 0.28 | 0.191 |
|
Group [ECs] × Time [C_W1_FI_total] |
0.08 | -1.40 – 1.55 | 0.919 |
|
Group [Intervention] × Time [C_W1_FI_total] |
-0.79 | -1.98 – 0.40 | 0.191 |
| Random Effects | |||
| σ2 | 9.59 | ||
| Ï„00 ID | 9.55 | ||
| ICC | 0.50 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.505 | ||
parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | 0.07 | [-0.12, 0.26]
## GroupECs | -0.02 | [-0.36, 0.32]
## GroupIntervention | 0.08 | [-0.19, 0.35]
## TimeC_W1_FI_total | -0.13 | [-0.32, 0.06]
## GroupECs:TimeC_W1_FI_total | 0.02 | [-0.32, 0.35]
## GroupIntervention:TimeC_W1_FI_total | -0.18 | [-0.45, 0.09]plot_model(FI_MEM_B1W, type = "int")
Baseline to 1M
# Merging across timepoints
FI_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "D_M1_FI_total")## Formatting table as needed
FI_B1M_long <- FI_B1M %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1M <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1M_long, REML = TRUE)
summary(FI_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1M_long
##
## REML criterion at convergence: 3116.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73980 -0.43311 0.05596 0.52370 2.09643
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.596 3.098
## Residual 16.669 4.083
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.95283 0.49778 451.70879 30.039
## GroupECs -0.09283 0.87925 451.70879 -0.106
## GroupIntervention 0.35785 0.70907 451.70879 0.505
## TimeD_M1_FI_total -2.25472 0.56081 256.00000 -4.020
## GroupECs:TimeD_M1_FI_total 0.35472 0.99059 256.00000 0.358
## GroupIntervention:TimeD_M1_FI_total -0.37635 0.79886 256.00000 -0.471
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupECs 0.916
## GroupIntervention 0.614
## TimeD_M1_FI_total 7.65e-05 ***
## GroupECs:TimeD_M1_FI_total 0.721
## GroupIntervention:TimeD_M1_FI_total 0.638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpECs GrpInt TD_M1_ GEC:TD
## GroupECs -0.566
## GrpIntrvntn -0.702 0.397
## TmD_M1_FI_t -0.563 0.319 0.395
## GEC:TD_M1_F 0.319 -0.563 -0.224 -0.566
## GI:TD_M1_FI 0.395 -0.224 -0.563 -0.702 0.397anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.40 0.70 2 256 0.0419 0.9589
## Time 588.19 588.19 1 256 35.2863 9.248e-09 ***
## Group:Time 9.59 4.79 2 256 0.2875 0.7503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM_B1M)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.95 | 13.97 – 15.93 | <0.001 |
| Group [ECs] | -0.09 | -1.82 – 1.63 | 0.916 |
| Group [Intervention] | 0.36 | -1.04 – 1.75 | 0.614 |
| Time [D_M1_FI_total] | -2.25 | -3.36 – -1.15 | <0.001 |
|
Group [ECs] × Time [D_M1_FI_total] |
0.35 | -1.59 – 2.30 | 0.720 |
|
Group [Intervention] × Time [D_M1_FI_total] |
-0.38 | -1.95 – 1.19 | 0.638 |
| Random Effects | |||
| σ2 | 16.67 | ||
| Ï„00 ID | 9.60 | ||
| ICC | 0.37 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.050 / 0.397 | ||
parameters::standardise_parameters(FI_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 0.20 | [ 0.01, 0.39]
## GroupECs | -0.02 | [-0.35, 0.31]
## GroupIntervention | 0.07 | [-0.20, 0.33]
## TimeD_M1_FI_total | -0.43 | [-0.64, -0.22]
## GroupECs:TimeD_M1_FI_total | 0.07 | [-0.30, 0.44]
## GroupIntervention:TimeD_M1_FI_total | -0.07 | [-0.37, 0.23]plot_model(FI_MEM_B1M, type = "int")
Extra exploratory: IUS and GM association at PRE
PRE_IUS_GM_lm <- lm(A_PRE_IUS_total ~ A_PRE_GM, data = Full_data_all)
summary(PRE_IUS_GM_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_GM, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.939 -5.868 0.918 6.097 18.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.6537 1.2714 30.402 < 2e-16 ***
## A_PRE_GM 1.2142 0.3875 3.134 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.713 on 257 degrees of freedom
## Multiple R-squared: 0.03681, Adjusted R-squared: 0.03306
## F-statistic: 9.82 on 1 and 257 DF, p-value: 0.001926anova(PRE_IUS_GM_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_GM 1 745.6 745.58 9.8203 0.001926 **
## Residuals 257 19512.1 75.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1