library(gamlss)
library(ggsci)
library(viridis)
library(car)
library(sjPlot)
library(mosaic)
library(tidyverse)

getwd()
## [1] "/Users/mike.xiao/Documents/GitHub/NIMH_project_repo"

Data Prep

#get physical activity person summaries from GGIR part 5
part5 <- read_csv('Data/HBN/actigraphy/GGIR_summaries/part5_personsummary_WW_L50M125V500_T5A5.csv')
plotdata <- part5 %>% dplyr::select(ID, ACC_day_mg_pla, ACC_spt_mg_pla, ACC_day_spt_mg_pla, dur_day_total_IN_min_pla, dur_day_total_LIG_min_pla,
                             dur_day_total_MOD_min_pla, dur_day_total_VIG_min_pla)

Load in GGIR Part5 subject level summaries, picked out these physical activity variables of interest:

Variable name Description
ACC_day_mg_pla Average acceleration for activity outside sleep window across all days, calculated from 5s epoch data
ACC_spt_mg_pla Average acceleration for activity within sleep window across all days, calculated from 5s epoch data
ACC_day_spt_mg_pla Average acceleration across entire period of wear across all days, calculated from 5s epoch data
dur_day_total_IN_min_pla Average duration spent in inactivity (<50mg) per day outside of sleep window
dur_day_total_LIG_min_pla Average duration spent in light activity (50-125mg) per day outside of sleep window
dur_day_total_MOD_min_pla Average duration spent in moderate activity (125-500mg) per day outside of sleep window
dur_day_total_VIG_min_pla Average duration spent in vigorous activity (>500mg) within a day outside of sleep window


Make MVPA duration variable by adding moderate and vigorous duration variables:

#create MVPA duration variable
plotdata <- plotdata %>% mutate(dur_day_total_MVPA = dur_day_total_MOD_min_pla + dur_day_total_VIG_min_pla)

Load and join basic phenotypic variables and pubertal status:

#get age and sex from HBN demo data
demo <- read_csv('Data/HBN/pheno/Basic_Demos.csv')
demo <- demo %>% select(EID, Age, Sex) %>% rename(ID = EID)

#get pubertal status from male and female datasets
pubertal_m <- read_csv('Data/HBN/pheno/PPS_M_20200814.csv', na = c('NULL'))
pubertal_f <- read_csv('Data/HBN/pheno/PPS_F_20200814.csv', na = c('NULL'))

pubertal <- bind_rows(pubertal_m, pubertal_f)
pubertal <- pubertal %>% rename(ID = EID)

#join age, sex, pubertal status to activity data
plotdata <- left_join(plotdata, demo)
plotdata <- left_join(plotdata, pubertal)

Checking sample sizes:

909 subjects passed GGIR part5.

missing_demo <- plotdata %>% filter(is.na(Sex))
plotdata <- filter(plotdata, ! ID %in% missing_demo$ID)

866 of these subjects have sex and age variables.

missing_puberty <- plotdata %>% filter(is.na(PPS_M_Score) & is.na(PPS_F_Score))
plotdata <- filter(plotdata, ! ID %in% missing_puberty$ID)

694 of these subjects have sex, age, and pubertal status variables.

#recode sex variable into character
plotdata <- plotdata %>%
  mutate(
    Sex = as.factor(Sex),
    Sex = recode(Sex, `0` = 'Male', `1` = 'Female'))

Checking age and sex in sample:

table(plotdata$Sex)
## 
##   Male Female 
##    439    255
#554M 313F (63.9% male)

summary(plotdata$Age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   5.036   7.746   9.836  10.456  12.737  21.419
#Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#5.036   7.535   9.510  10.293  12.481  21.482

Subset data to subjects who have at least 3 days of 95% wear:

#load in 3 days of 95% wear data
act10 <- read_csv('Data/HBN/actigraphy/epoch/HBN_actigraphy_600s_95wear_3day.csv')
goodIDs <- unique(act10$ID)

#filter to subjects with at least 3 days of 95% wear
plotdata <- filter(plotdata, ID %in% goodIDs)

519 of subjects have sex, age, and pubertal status, and 3 days of 95% wear. This sample is used for all further analysis shown below.

Check how age and sex compares in this sample.

table(plotdata$Sex)
## 
##   Male Female 
##    329    190
#329M #190F (63.4% male)

summary(plotdata$Age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   5.036   7.683   9.516  10.275  12.427  21.419
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#5.036   7.683   9.516  10.275  12.427  21.419

Recode pubertal status (males)

Following guidelines on data dictionary:

  • 3=Prepubertal
  • 4,5=Beginning Pubertal
  • 6,7,8=Mid-Pubertal
  • 9,10,11=Advanced Pubertal
  • 12=Postpubertal


Females are already on a 1-5 scale.

plotdata <- plotdata %>%
  mutate(PPS_M_Score = ifelse(PPS_M_Score == 3, 1, 
                              ifelse(PPS_M_Score %in% c(4,5), 2,
                                     ifelse(PPS_M_Score %in% c(6,7,8), 3,
                                            ifelse(PPS_M_Score %in% c(9,10, 11), 4,
                                                   ifelse(PPS_M_Score == 12, 5, NA))))),
         PPS_Score_Combined = ifelse(is.na(PPS_M_Score), PPS_F_Score, PPS_M_Score))

mean activity count (mAC) variable

Made a mistake earlier, I defined mLAC as log(1 + ACC_day_mg_pla). However, log(mean(X)) =/= mean(log(X)). Can’t recover mean(log(X)) from mean(X).

ACC_day_mg_pla (renamed as mAC for “mean activity count”) is already normally distributed, no need to log transform.

hist(plotdata$ACC_day_mg_pla)

Some notes about choice of activity count variable:

TLAC value depends on the epoch the person uses to sum. So if he uses 5 second means, the value would be 12 times as great as if he used 60s means.

Note however, that mean AC calculated for the whole day using 5 second and 60 second epochs would be the same.

Outliers

Function to identify outliers based on median +/- threshold * IQR

find_outliers <- function(x, threshold = 2.22) {
  
  qtiles = summary(x)
  
  median = qtiles[[3]]
  iqr = qtiles[[5]] - qtiles[[2]]
  
  print(paste0('median = ', median))
  print(paste0('iqr = ', iqr))
  
  lb <- median - threshold * iqr
  ub <- median + threshold * iqr
  
  print(paste0('lowerbound = ', lb, ', upperbound = ', ub))
  
  return(ifelse(x > ub | x < lb, 1, 0))
  
  
}
mAC

Remove outliers for mAC based on median +/- 3 * IQR:

age_mAC <- plotdata %>% 
  dplyr::select(ID, Sex, Age, ACC_day_mg_pla, dur_day_total_IN_min_pla, dur_day_total_LIG_min_pla,
dur_day_total_MVPA, PPS_M_Score, PPS_F_Score, PPS_Score_Combined) %>%
  rename(mAC = ACC_day_mg_pla,
         sed_dur = dur_day_total_IN_min_pla,
         LA_dur = dur_day_total_LIG_min_pla,
         MVPA_dur = dur_day_total_MVPA) %>%
  mutate(mAC_outliers = find_outliers(mAC, 3))
## [1] "median = 57.8642203"
## [1] "iqr = 27.5556727"
## [1] "lowerbound = -24.8027978, upperbound = 140.5312384"
age_mAC_outliers <- age_mAC %>% filter(mAC_outliers == 1)
cleaned_data <- age_mAC %>% filter(mAC_outliers == 0)

Found and removed 1 outlier.

Exploratory Plots

Distributions

mAC

Sedentary duration

Light activity duration

MVPA duration

Age

PPS score

Age vs PPS score

Violin with quantiles

GAMLSS
#function to run LMS regression, then extract and append fitted percentile values into current dataframe
tidy_cent <- function(data, xvar, yvar, cent) {
  
  f1 = as.formula(paste0(yvar, "~pb(", xvar, ")"))
  f2 = as.formula(paste0("~pb(", xvar, ")"))
  
  #run lms.bct quantile regression
  model = gamlss(f1, sigma.formula = f2, family=BCT, data=data) 
  
  xvar = data[[xvar]]
  oxvar = xvar[order(xvar)]
  
  #extract fitted values for each quantile
  #qtiles <- cent %>% map_dfc(setNames, object = list(numeric()))
  qtiles = vector("list", length(cent))
  names(qtiles) = cent
  
  for (i in cent) {
    
    qfit = qBCT(i/100, 
                mu = fitted(model, "mu")[order(xvar)], 
                sigma = fitted(model, "sigma")[order(xvar)], 
                nu = fitted(model, "nu")[order(xvar)], 
                tau = fitted(model, "tau")[order(xvar)])
    
    qtiles[[as.character(i)]] <- qfit
    
  
  }
  
  qtile_df = bind_cols(qtiles)
  
  ID_df = tibble(ID = data$ID[order(xvar)])
  
  #dataframe with fitted quantile values
  result = cbind(ID_df, qtile_df)
  
  #append to current dataframe
  return(left_join(data, result, by = 'ID'))
  
}


age_PPS <- cleaned_data %>% select(ID, Sex, Age, PPS_Score_Combined)

age_PPS_male <- tidy_cent(filter(age_PPS, Sex == 'Male'), xvar = "PPS_Score_Combined", yvar = "Age", cent = c(5, 10, 25, 50, 75, 90, 95))
## GAMLSS-RS iteration 1: Global Deviance = 1354.898 
## GAMLSS-RS iteration 2: Global Deviance = 1349.733 
## GAMLSS-RS iteration 3: Global Deviance = 1348.92 
## GAMLSS-RS iteration 4: Global Deviance = 1348.809 
## GAMLSS-RS iteration 5: Global Deviance = 1348.795 
## GAMLSS-RS iteration 6: Global Deviance = 1348.79 
## GAMLSS-RS iteration 7: Global Deviance = 1348.79
age_PPS_female <- tidy_cent(filter(age_PPS, Sex == 'Female'), xvar = "PPS_Score_Combined", yvar = "Age", cent = c(5, 10, 25, 50, 75, 90, 95))
## GAMLSS-RS iteration 1: Global Deviance = 749.5505 
## GAMLSS-RS iteration 2: Global Deviance = 749.0072 
## GAMLSS-RS iteration 3: Global Deviance = 749.1831 
## GAMLSS-RS iteration 4: Global Deviance = 749.4601 
## GAMLSS-RS iteration 5: Global Deviance = 749.705 
## GAMLSS-RS iteration 6: Global Deviance = 749.9052 
## GAMLSS-RS iteration 7: Global Deviance = 750.0645 
## GAMLSS-RS iteration 8: Global Deviance = 750.1731 
## GAMLSS-RS iteration 9: Global Deviance = 750.1927 
## GAMLSS-RS iteration 10: Global Deviance = 750.1948 
## GAMLSS-RS iteration 11: Global Deviance = 750.1952
age_PPS_long <- rbind(age_PPS_male, age_PPS_female) %>%
  select(-Age) %>%
  gather(4:10, key = percentile, value = Age) %>%
  mutate(percentile = factor(percentile, levels = c(5, 10, 25, 50, 75, 90, 95)))

ggplot() + 
  geom_point(data = age_PPS, aes(x = PPS_Score_Combined, y = Age, color = Sex), position = position_jitter(), alpha = 0.5, size = 1) +
  geom_line(data = age_PPS_long, aes(x = PPS_Score_Combined, y = Age, color = Sex, linetype = percentile), size = 0.7) +
  theme_minimal(base_size = 12) +
  guides(colour = guide_legend(reverse=T),
         linetype = guide_legend(reverse=T)) +
  scale_color_manual(values = c("#0D0887FF", "#7E03A8FF")) +
  facet_wrap(~Sex, scales = 'free_x') +
  ggtitle('PPS vs Age')

Loess Smoother

Trying another smoother.

Age vs mAC (colored by pubertal status)

## GAMLSS-RS iteration 1: Global Deviance = 2737.05 
## GAMLSS-RS iteration 2: Global Deviance = 2731.531 
## GAMLSS-RS iteration 3: Global Deviance = 2730.774 
## GAMLSS-RS iteration 4: Global Deviance = 2730.805 
## GAMLSS-RS iteration 5: Global Deviance = 2730.814 
## GAMLSS-RS iteration 6: Global Deviance = 2730.815 
## GAMLSS-RS iteration 7: Global Deviance = 2730.816
## GAMLSS-RS iteration 1: Global Deviance = 1464.543 
## GAMLSS-RS iteration 2: Global Deviance = 1464.83 
## GAMLSS-RS iteration 3: Global Deviance = 1465.385 
## GAMLSS-RS iteration 4: Global Deviance = 1465.752 
## GAMLSS-RS iteration 5: Global Deviance = 1466.048 
## GAMLSS-RS iteration 6: Global Deviance = 1466.151 
## GAMLSS-RS iteration 7: Global Deviance = 1466.165 
## GAMLSS-RS iteration 8: Global Deviance = 1466.17 
## GAMLSS-RS iteration 9: Global Deviance = 1466.171 
## GAMLSS-RS iteration 10: Global Deviance = 1466.172

Age vs Sedentary Duration (colored by pubertal status)

## GAMLSS-RS iteration 1: Global Deviance = 3791.756 
## GAMLSS-RS iteration 2: Global Deviance = 3792.039 
## GAMLSS-RS iteration 3: Global Deviance = 3792.081 
## GAMLSS-RS iteration 4: Global Deviance = 3792.088 
## GAMLSS-RS iteration 5: Global Deviance = 3792.09 
## GAMLSS-RS iteration 6: Global Deviance = 3792.091
## GAMLSS-RS iteration 1: Global Deviance = 2170.475 
## GAMLSS-RS iteration 2: Global Deviance = 2169.06 
## GAMLSS-RS iteration 3: Global Deviance = 2168.914 
## GAMLSS-RS iteration 4: Global Deviance = 2168.883 
## GAMLSS-RS iteration 5: Global Deviance = 2168.874 
## GAMLSS-RS iteration 6: Global Deviance = 2168.87 
## GAMLSS-RS iteration 7: Global Deviance = 2168.867 
## GAMLSS-RS iteration 8: Global Deviance = 2168.866 
## GAMLSS-RS iteration 9: Global Deviance = 2168.865

Age vs Light Activity Duration (colored by pubertal status)

## GAMLSS-RS iteration 1: Global Deviance = 3123.728 
## GAMLSS-RS iteration 2: Global Deviance = 3122.456 
## GAMLSS-RS iteration 3: Global Deviance = 3122.622 
## GAMLSS-RS iteration 4: Global Deviance = 3122.816 
## GAMLSS-RS iteration 5: Global Deviance = 3122.976 
## GAMLSS-RS iteration 6: Global Deviance = 3123.075 
## GAMLSS-RS iteration 7: Global Deviance = 3123.142 
## GAMLSS-RS iteration 8: Global Deviance = 3123.197 
## GAMLSS-RS iteration 9: Global Deviance = 3123.24 
## GAMLSS-RS iteration 10: Global Deviance = 3123.273 
## GAMLSS-RS iteration 11: Global Deviance = 3123.298 
## GAMLSS-RS iteration 12: Global Deviance = 3123.317 
## GAMLSS-RS iteration 13: Global Deviance = 3123.331 
## GAMLSS-RS iteration 14: Global Deviance = 3123.341 
## GAMLSS-RS iteration 15: Global Deviance = 3123.349 
## GAMLSS-RS iteration 16: Global Deviance = 3123.354 
## GAMLSS-RS iteration 17: Global Deviance = 3123.359 
## GAMLSS-RS iteration 18: Global Deviance = 3123.362 
## GAMLSS-RS iteration 19: Global Deviance = 3123.364 
## GAMLSS-RS iteration 20: Global Deviance = 3123.365
## GAMLSS-RS iteration 1: Global Deviance = 1835.799 
## GAMLSS-RS iteration 2: Global Deviance = 1840.707 
## GAMLSS-RS iteration 3: Global Deviance = 1840.5 
## GAMLSS-RS iteration 4: Global Deviance = 1840.443 
## GAMLSS-RS iteration 5: Global Deviance = 1840.427 
## GAMLSS-RS iteration 6: Global Deviance = 1840.422 
## GAMLSS-RS iteration 7: Global Deviance = 1840.421 
## GAMLSS-RS iteration 8: Global Deviance = 1840.421

Age vs MVPA Duration (colored by pubertal status)

## GAMLSS-RS iteration 1: Global Deviance = 3237.57 
## GAMLSS-RS iteration 2: Global Deviance = 3231.779 
## GAMLSS-RS iteration 3: Global Deviance = 3230.921 
## GAMLSS-RS iteration 4: Global Deviance = 3230.821 
## GAMLSS-RS iteration 5: Global Deviance = 3230.806 
## GAMLSS-RS iteration 6: Global Deviance = 3230.806
## GAMLSS-RS iteration 1: Global Deviance = 1779.308 
## GAMLSS-RS iteration 2: Global Deviance = 1781.167 
## GAMLSS-RS iteration 3: Global Deviance = 1780.964 
## GAMLSS-RS iteration 4: Global Deviance = 1780.832 
## GAMLSS-RS iteration 5: Global Deviance = 1780.756 
## GAMLSS-RS iteration 6: Global Deviance = 1780.708 
## GAMLSS-RS iteration 7: Global Deviance = 1780.679 
## GAMLSS-RS iteration 8: Global Deviance = 1780.66 
## GAMLSS-RS iteration 9: Global Deviance = 1780.648 
## GAMLSS-RS iteration 10: Global Deviance = 1780.64 
## GAMLSS-RS iteration 11: Global Deviance = 1780.635 
## GAMLSS-RS iteration 12: Global Deviance = 1780.631 
## GAMLSS-RS iteration 13: Global Deviance = 1780.628 
## GAMLSS-RS iteration 14: Global Deviance = 1780.626 
## GAMLSS-RS iteration 15: Global Deviance = 1780.625 
## GAMLSS-RS iteration 16: Global Deviance = 1780.624

Pubertal status vs mAC

Pubertal status vs Sedentary Duration

Pubertal status vs Light Activity Duration

Pubertal status vs MVPA duration

ADHD Combined Type

Distributions

NOTE: y-axis scale is free

by Age and Sex

by PPS and Sex

SWAN_Hyperactive

Distributions

##                 
##                  Male Female
##   Q1 [-3 to 0]     91     80
##   Q2 [0 to 0.67]  113     58
##   Q3 [0.67 to 3]  122     50
by Age and Sex

by PPS and Sex

Diurnal Surface Plots

Don’t know how to make these, learn about the models used to generate these coefficient surfaces.

Testing

Fitting 4 OLS models for each outcome across both genders.

  1. Y ~ Age
  2. Y ~ PPS_score
  3. Y ~ Age + PPS_score
  4. Y ~ Age + PPS_score + Age*PPS_score

mAC

##         Age PPS_M_Score 
##    2.998889    2.998889
##             Age     PPS_M_Score Age:PPS_M_Score 
##        6.370316       17.161635       29.817177
##         Age PPS_F_Score 
##    3.556056    3.556056
##             Age     PPS_F_Score Age:PPS_F_Score 
##        10.40104        13.63360        32.00549
Males
  mAC mAC mAC mAC
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 102.91
(97.16 – 108.66) 		<0.001 80.93
(77.23 – 84.64) 		<0.001 101.28
(94.95 – 107.60) 		<0.001 111.31
(98.94 – 123.68) 		<0.001
Age -3.95
(-4.48 – -3.43) 		<0.001 -3.49
(-4.41 – -2.58) 		<0.001 -4.40
(-5.73 – -3.08) 		<0.001
PPS_M_Score -10.21
(-11.92 – -8.50) 		<0.001 -1.68
(-4.41 – 1.05) 		0.226 -7.26
(-13.77 – -0.75) 		0.029
Age * PPS_M_Score 0.44
(-0.03 – 0.90) 		0.064
Observations 328 328 328 328
R2 / R2 adjusted 0.400 / 0.398 0.298 / 0.296 0.402 / 0.399 0.409 / 0.403
Females
  mAC mAC mAC mAC
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 83.88
(78.50 – 89.26) 		<0.001 69.75
(65.80 – 73.71) 		<0.001 83.44
(77.63 – 89.25) 		<0.001 83.98
(71.08 – 96.88) 		<0.001
Age -3.01
(-3.51 – -2.51) 		<0.001 -2.85
(-3.79 – -1.91) 		<0.001 -2.91
(-4.53 – -1.29) 		<0.001
PPS_F_Score -6.78
(-8.19 – -5.37) 		<0.001 -0.50
(-2.95 – 1.95) 		0.688 -0.69
(-5.50 – 4.12) 		0.777
Age * PPS_F_Score 0.02
(-0.39 – 0.43) 		0.927
Observations 190 190 190 190
R2 / R2 adjusted 0.430 / 0.427 0.322 / 0.319 0.431 / 0.425 0.431 / 0.422

Sedentary Duration

##         Age PPS_M_Score 
##    2.998889    2.998889
##             Age     PPS_M_Score Age:PPS_M_Score 
##        6.370316       17.161635       29.817177
##         Age PPS_F_Score 
##    3.556056    3.556056
##             Age     PPS_F_Score Age:PPS_F_Score 
##        10.40104        13.63360        32.00549
Males
  sed_dur sed_dur sed_dur sed_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 455.20
(426.93 – 483.47) 		<0.001 565.96
(547.80 – 584.11) 		<0.001 467.01
(435.97 – 498.05) 		<0.001 408.31
(347.77 – 468.86) 		<0.001
Age 20.32
(17.72 – 22.91) 		<0.001 16.99
(12.51 – 21.48) 		<0.001 22.32
(15.82 – 28.81) 		<0.001
PPS_M_Score 53.63
(45.27 – 61.99) 		<0.001 12.16
(-1.24 – 25.56) 		0.075 44.77
(12.91 – 76.63) 		0.006
Age * PPS_M_Score -2.55
(-4.82 – -0.29) 		0.027
Observations 328 328 328 328
R2 / R2 adjusted 0.421 / 0.419 0.328 / 0.326 0.426 / 0.423 0.435 / 0.430
Females
  sed_dur sed_dur sed_dur sed_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 484.64
(447.17 – 522.10) 		<0.001 562.05
(535.71 – 588.40) 		<0.001 494.33
(454.03 – 534.64) 		<0.001 555.00
(465.99 – 644.01) 		<0.001
Age 17.68
(14.21 – 21.16) 		<0.001 14.10
(7.56 – 20.65) 		<0.001 7.19
(-3.97 – 18.34) 		0.205
PPS_F_Score 42.05
(32.63 – 51.47) 		<0.001 10.98
(-6.03 – 27.99) 		0.204 -10.82
(-44.01 – 22.38) 		0.521
Age * PPS_F_Score 2.16
(-0.67 – 5.00) 		0.134
Observations 190 190 190 190
R2 / R2 adjusted 0.349 / 0.345 0.292 / 0.288 0.354 / 0.347 0.362 / 0.352

Light Activity Duration

##         Age PPS_M_Score 
##    2.998889    2.998889
##             Age     PPS_M_Score Age:PPS_M_Score 
##        6.370316       17.161635       29.817177
##         Age PPS_F_Score 
##    3.556056    3.556056
##             Age     PPS_F_Score Age:PPS_F_Score 
##        10.40104        13.63360        32.00549
Males
  LA_dur LA_dur LA_dur LA_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 213.56
(203.26 – 223.87) 		<0.001 182.22
(175.68 – 188.77) 		<0.001 214.38
(203.01 – 225.75) 		<0.001 238.26
(216.13 – 260.40) 		<0.001
Age -5.29
(-6.24 – -4.35) 		<0.001 -5.52
(-7.16 – -3.88) 		<0.001 -7.69
(-10.06 – -5.31) 		<0.001
PPS_M_Score -12.64
(-15.65 – -9.62) 		<0.001 0.84
(-4.07 – 5.75) 		0.737 -12.43
(-24.08 – -0.78) 		0.037
Age * PPS_M_Score 1.04
(0.21 – 1.87) 		0.014
Observations 328 328 328 328
R2 / R2 adjusted 0.271 / 0.268 0.173 / 0.170 0.271 / 0.266 0.284 / 0.278
Females
  LA_dur LA_dur LA_dur LA_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 213.16
(199.45 – 226.87) 		<0.001 186.70
(176.84 – 196.55) 		<0.001 217.66
(202.95 – 232.37) 		<0.001 208.71
(176.06 – 241.36) 		<0.001
Age -4.79
(-6.06 – -3.51) 		<0.001 -6.45
(-8.83 – -4.06) 		<0.001 -5.43
(-9.52 – -1.33) 		0.010
PPS_F_Score -9.11
(-12.63 – -5.59) 		<0.001 5.09
(-1.12 – 11.30) 		0.107 8.31
(-3.87 – 20.48) 		0.180
Age * PPS_F_Score -0.32
(-1.36 – 0.72) 		0.545
Observations 190 190 190 190
R2 / R2 adjusted 0.227 / 0.222 0.122 / 0.117 0.237 / 0.229 0.239 / 0.226

MVPA duration

##         Age PPS_M_Score 
##    2.998889    2.998889
##             Age     PPS_M_Score Age:PPS_M_Score 
##        6.370316       17.161635       29.817177
##         Age PPS_F_Score 
##    3.556056    3.556056
##             Age     PPS_F_Score Age:PPS_F_Score 
##        10.40104        13.63360        32.00549
Males
  MVPA_dur MVPA_dur MVPA_dur MVPA_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 174.40
(162.39 – 186.40) 		<0.001 139.39
(131.89 – 146.89) 		<0.001 171.84
(158.61 – 185.06) 		<0.001 188.78
(162.87 – 214.68) 		<0.001
Age -6.29
(-7.40 – -5.19) 		<0.001 -5.57
(-7.48 – -3.66) 		<0.001 -7.11
(-9.89 – -4.33) 		<0.001
PPS_M_Score -16.24
(-19.69 – -12.78) 		<0.001 -2.64
(-8.35 – 3.07) 		0.364 -12.05
(-25.68 – 1.58) 		0.083
Age * PPS_M_Score 0.74
(-0.23 – 1.71) 		0.136
Observations 328 328 328 328
R2 / R2 adjusted 0.279 / 0.277 0.208 / 0.205 0.281 / 0.276 0.286 / 0.279
Females
  MVPA_dur MVPA_dur MVPA_dur MVPA_dur
Predictors Estimates p Estimates p Estimates p Estimates p
(Intercept) 141.58
(129.55 – 153.60) 		<0.001 117.06
(108.44 – 125.68) 		<0.001 142.45
(129.46 – 155.44) 		<0.001 141.48
(112.62 – 170.34) 		<0.001
Age -4.96
(-6.08 – -3.85) 		<0.001 -5.29
(-7.40 – -3.18) 		<0.001 -5.18
(-8.79 – -1.56) 		0.005
PPS_F_Score -10.66
(-13.74 – -7.58) 		<0.001 0.99
(-4.50 – 6.47) 		0.723 1.33
(-9.43 – 12.10) 		0.807
Age * PPS_F_Score -0.03
(-0.95 – 0.88) 		0.941
Observations 190 190 190 190
R2 / R2 adjusted 0.291 / 0.287 0.198 / 0.194 0.291 / 0.284 0.291 / 0.280