+
+
+
+
+
+
+
+
+
+
+
+
+
+
data_dir <- '~/Box/CogNeuroLab/Wearables/data/'
+fit <- read_csv(paste0(data_dir, 'fitbit/fitbitSleepStatsGroup.csv'))
+act <- read_csv(paste0(data_dir, 'actiwatch/wa_act_combined.csv'))
+act_cl <- read_csv(paste0(data_dir, 'actiwatch/actiware_manual_clean/wa_act_combined.csv'))
+
+df_cx <- readr::read_csv(paste0(data_dir, 'df_main.csv'))
+
+head(act)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
df_cx %>%
+ mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
+ mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
+ mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
+ mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
+ group_by(age_group, dominant) %>%
+ summarise(n = n())
+
+
+
+
+
+
+
+
+df_cx %>%
+ mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
+ mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
+ mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
+ filter(handedness == "Right") %>%
+ mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
+ group_by(age_group, dominant) %>%
+ summarise(n = n())
+
+
+
+
+
+
+
+
+df_cx %>%
+ mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
+ mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
+ mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
+ filter(handedness == "Left") %>%
+ mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
+ group_by(age_group, dominant) %>%
+ summarise(n = n())
+
+
+
+
+
+
+
+
+
+
+
+
exclude = c('10023', '10076', '20182', '20184', '20176', '10151', '10139')
+df <- df[!df$subject %in% exclude, ]
+
+df_means <- df %>%
+ select(subject_id, analysis_name_cl, where(is.numeric)) %>%
+ group_by(subject, analysis_name_cl) %>%
+ summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
+
+head(df_means)
+
+
+
+
+
+
+
+
NA
+
+
+
+
+
Correlations
+
+
+
+
library(corrr)
+library(purrr)
+library(dplyr)
+
+df %>%
+ group_by(subject) %>%
+ select(where(is.numeric)) %>%
+ group_by(subject) %>%
+ summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
+ corrr::correlate(method = 'spearman') %>%
+ corrr::focus(matches('sleep_duration'), mirror = TRUE)
+
+
+
+
+
+
+
+
NA
+
+
+
+
+
+
+
df %>%
+ group_by(subject) %>%
+ filter(handedness == 2) %>%
+ select(where(is.numeric)) %>%
+ group_by(subject) %>%
+ summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
+ corrr::correlate(method = 'spearman') %>%
+ corrr::focus(matches('sleep_duration'), mirror = TRUE)
+
+
+
+
+
+
+
+
+
+
+
+
df %>% #all
+ group_by(subject) %>%
+ select(where(is.numeric)) %>%
+ group_by(subject) %>%
+ summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
+ corrr::correlate(method = 'spearman') %>%
+ corrr::focus(matches('bed'), mirror = TRUE)
+
+
+
+
+
+
+
+
+
+
+
+
df %>% #right handed only
+ group_by(subject) %>%
+ filter(handedness == 2) %>%
+ select(where(is.numeric)) %>%
+ group_by(subject) %>%
+ summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
+ corrr::correlate(method = 'spearman') %>%
+ corrr::focus(matches('bed'), mirror = TRUE)
+
+
+
+
+
+
+
+
NA
+
+
+
+
+
+
+
library(RColorBrewer)
+
+df_means %>%
+ ggplot(aes(x = sleep_duration_cl, y = sleep_duration_fit, color = analysis_name_cl)) +
+ geom_point() +
+ stat_smooth(method = 'lm') +
+ scale_color_brewer(palette = 'Set1')
+
+
+
+
+
+
NA
+NA
+
+
+
+
+
+
+
df_means %>%
+ ggplot(aes(x = time_in_bed_cl, y = time_in_bed_fit, color = analysis_name_cl)) +
+ geom_point() +
+ stat_smooth(method = 'lm') +
+ scale_color_brewer(palette = 'Set1')
+
+
+
+
+
+
+
+
+
+
+
# excluded subject 10139
+df_means %>%
+ ggplot(aes(x = time_in_bed_cl)) +
+ geom_boxplot()
+
+
+
+
+
+
+df_means$subject[df_means$time_in_bed_cl > 700]
+
+
+
[1] 20108
+
+
+
+
+
Repeated measures correlations
+
Documentation
+
Repeated Measures Correlation, Bakdash1 and Marusich, 2017
+
+
Time in Bed
+
+
+
+
library(rmcorr)
+
+colourCount = length(unique(df$subject))
+getPalette = colorRampPalette(brewer.pal(9, "Spectral"))
+
+my.rmc <- rmcorr(participant = subject, measure1 = time_in_bed_act, measure2 = time_in_bed_fit, dataset = df)
+plot(my.rmc, overall = TRUE)
+
+
+
+
+
+
+
+
+
+
+
ggplot2::ggplot(df, ggplot2::aes(x = time_in_bed_act, y = time_in_bed_fit, group = factor(subject),
+ color = factor(subject))) +
+ ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
+ ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time In Bed') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject') + ggsave(paste0(data_dir, 'CARE2021/figures/timeinbedrmcorr.png'), height = 5, width = 8, dpi = 300)
+
+
+
+
+
+
+
+
+
+
Time in Bed: Cleaned Actiwatch data
+
+
+
+
my.rmc <- rmcorr(participant = subject, measure1 = time_in_bed_cl, measure2 = time_in_bed_fit, dataset = df[df$age_group == 1,])
+
+ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = time_in_bed_cl, y = time_in_bed_fit, group = factor(subject),
+ color = factor(subject))) +
+ ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
+ ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time In Bed') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')
+
+
+
+
+
+
+
+
+
+
Time in bed: correlations
+
+
+
+
#raw
+rmcorr(subject, time_in_bed_act, time_in_bed_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.1353624
+
+degrees of freedom
+721
+
+p-value
+0.0002620058
+
+95% confidence interval
+0.06296976 0.2063381
+
+
+
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, handedness == 2))
+
+
+
+Repeated measures correlation
+
+r
+0.1405546
+
+degrees of freedom
+671
+
+p-value
+0.0002544933
+
+95% confidence interval
+0.06556382 0.2139666
+
+
+
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.1816943
+
+degrees of freedom
+331
+
+p-value
+0.000865625
+
+95% confidence interval
+0.07537021 0.2839334
+
+
+
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.07252395
+
+degrees of freedom
+339
+
+p-value
+0.1815221
+
+95% confidence interval
+-0.03425988 0.1776706
+
+
+
# manually cleaned
+rmcorr(subject, time_in_bed_cl, time_in_bed_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.1597513
+
+degrees of freedom
+721
+
+p-value
+1.590169e-05
+
+95% confidence interval
+0.08775993 0.2300826
+
+
+
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, handedness == 2))
+
+
+
+Repeated measures correlation
+
+r
+0.1674614
+
+degrees of freedom
+671
+
+p-value
+1.258939e-05
+
+95% confidence interval
+0.09295126 0.2401063
+
+
+
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.1786162
+
+degrees of freedom
+331
+
+p-value
+0.001062066
+
+95% confidence interval
+0.07220616 0.2810059
+
+
+
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.1483393
+
+degrees of freedom
+339
+
+p-value
+0.006062129
+
+95% confidence interval
+0.04249153 0.2508941
+
+
+
+
+
+
Sleep time
+
+
+
+
my.rmc <- rmcorr(participant = subject, measure1 = sleep_duration_act, measure2 = sleep_duration_fit, dataset = df)
+
+ggplot2::ggplot(df, ggplot2::aes(x = sleep_duration_act, y = sleep_duration_fit, group = factor(subject),
+ color = factor(subject))) +
+ ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
+ ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time Asleep') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject') + ggsave(paste0(data_dir, 'CARE2021/figures/sleeptimermcorr.png'), height = 5, width = 8, dpi = 300)
+
+
+
+
+
+
+
+
+
+
Sleep Time: Cleaned Actiwatch data
+
+
+
+
my.rmc <- rmcorr(participant = subject, measure1 = sleep_duration_cl, measure2 = sleep_duration_fit, dataset = df[df$age_group == 1,], nreps=500)
+
+ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = sleep_duration_cl, y = sleep_duration_fit, group = factor(subject),
+ color = factor(subject))) +
+ ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
+ ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time Asleep') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')
+
+
+
+
+
+
+
+
+
+
+
#raw
+rmcorr(subject, sleep_duration_act, sleep_duration_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.2741189
+
+degrees of freedom
+721
+
+p-value
+6.280332e-14
+
+95% confidence interval
+0.2052111 0.3403224
+
+
+
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, handedness == 2))
+
+
+
+Repeated measures correlation
+
+r
+0.2620646
+
+degrees of freedom
+671
+
+p-value
+4.950899e-12
+
+95% confidence interval
+0.190148 0.3311838
+
+
+
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.3734459
+
+degrees of freedom
+331
+
+p-value
+1.845944e-12
+
+95% confidence interval
+0.2767884 0.4626223
+
+
+
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.09787818
+
+degrees of freedom
+339
+
+p-value
+0.07105332
+
+95% confidence interval
+-0.008732025 0.2022883
+
+
+
# manually cleaned
+rmcorr(subject, sleep_duration_cl, sleep_duration_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.3072433
+
+degrees of freedom
+721
+
+p-value
+2.854961e-17
+
+95% confidence interval
+0.2396034 0.371915
+
+
+
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, handedness == 2))
+
+
+
+Repeated measures correlation
+
+r
+0.291924
+
+degrees of freedom
+671
+
+p-value
+1.092137e-14
+
+95% confidence interval
+0.2211215 0.3596654
+
+
+
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.3609073
+
+degrees of freedom
+331
+
+p-value
+1.110282e-11
+
+95% confidence interval
+0.2633529 0.4511551
+
+
+
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))
+
+
+
+Repeated measures correlation
+
+r
+0.17582
+
+degrees of freedom
+339
+
+p-value
+0.001113192
+
+95% confidence interval
+0.07062353 0.2771487
+
+
+
+
+
+
+
rmcorr(subject, sleep_duration_act, time_in_bed_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.2704232
+
+degrees of freedom
+721
+
+p-value
+1.393078e-13
+
+95% confidence interval
+0.2013845 0.3367884
+
+
+
+
+
+
+
Efficiency
+
+
+
+
my.rmc <- rmcorr(participant = subject, measure1 = efficiency_act, measure2 = efficiency_fit, dataset = df[df$age_group == 1,], nreps=500)
+
+ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = efficiency_act, y = efficiency_fit, group = factor(subject),
+ color = factor(subject))) +
+ ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
+ ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Sleep Efficiency') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')
+
+
+
+
+
+
+
+
+
+
+
rmcorr(subject, efficiency_act, efficiency_fit, df)
+
+
+
+Repeated measures correlation
+
+r
+0.06349924
+
+degrees of freedom
+721
+
+p-value
+0.08797509
+
+95% confidence interval
+-0.009560122 0.1358843
+
+
+
+
+
+
Next steps:
+
Need correlation value for each subject, then see how that’s related to measures of subjective sleep quality, handedness, arm assignment, percent watch off, beiwe, fitbit device
+
+
+
+
corrdf <- df %>%
+ group_by(subject) %>%
+ summarize(sleep_time = cor(sleep_duration_act, sleep_duration_fit, method = 'spearman'), time_in_bed = cor(time_in_bed_act, time_in_bed_fit, method = 'spearman'))
+
+head(corrdf)
+
+
+
+
+
+
+
+
NA
+
+
+
+
+
dfcor <- merge(df, corrdf, by = 'subject')
+
+head(dfcor)
+
+
+
+
+
+
+
+
NA
+
+
+
+
+
+
+
unique(dfcor$subject[dfcor$time_in_bed < 0.5])
+
+
+
[1] 10011 10012 10014 10033 10038 10040 10045 10062 10140 10146 10147 10148 10149 20103 20105 20108 20113 20115
+[19] 20120 20122 20123 20124 20125 20127 20131 20142 20154 20155 20157
+
+
+
length(unique(dfcor$subject[dfcor$time_in_bed < 0.5]))
+
+
+
[1] 29
+
+
+
unique(dfcor$subject[dfcor$sleep_time < 0.5])
+
+
+
[1] 10011 10033 10038 10045 10140 10147 10148 10149 20103 20105 20113 20115 20120 20122 20123 20124 20125 20131
+[19] 20142 20154 20155
+
+
+
length(unique(dfcor$subject[dfcor$sleep_time < 0.5]))
+
+
+
[1] 21
+
+
+
+
+
+
+
+require(gridExtra)
+
+plot1 <- dfcor %>%
+ ggplot() +
+ geom_boxplot(aes(x = fitbit_device, y = sleep_time, fill = fitbit_device, group = fitbit_device)) +
+ ylab('Sleep Time Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1')
+
+plot2 <- dfcor %>%
+ ggplot() +
+ geom_boxplot(aes(x = fitbit_device, y = time_in_bed, fill = fitbit_device, group = fitbit_device)) +
+ ylab('Time In Bed Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + theme(axis.text.x = element_text(angle = 90))
+
+grid.arrange(plot1, plot2, ncol=2)
+
+
+
+
+
+
+
+
+
+
+
dfcor$handedness <- factor(dfcor$handedness, labels = c("Right", "Left", "Both"))
+
+plot1 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = handedness, y = sleep_time, fill = handedness, group = handedness)) +
+ ylab('Sleep Time Correlation (r)') + xlab('Handedness') +
+ scale_fill_brewer(palette = 'Set1')
+
+plot2 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = handedness, y = time_in_bed, fill = handedness, group = handedness)) +
+ ylab('Time In Bed Correlation (r)') + xlab('Handedness') +
+ scale_fill_brewer(palette = 'Set1')
+
+pnew <- arrangeGrob(plot1, plot2, ncol=2)
+ggsave("~/Desktop/handedness.png", pnew, height = 5, width = 10, units = "in")
+
+plot(pnew)
+
+
+
+
+
+
+
+
+
+
+
+
+
dfcor$actiwatch_arm <- factor(dfcor$actiwatch_arm, labels = c("Right", "Left"))
+
+plot1 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = actiwatch_arm, y = sleep_time, fill = handedness, group = handedness)) +
+ ylab('Sleep Time Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")
+
+plot2 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = actiwatch_arm, y = time_in_bed, fill = handedness, group = handedness)) +
+ ylab('Time In Bed Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")
+
+pnew <- arrangeGrob(plot1, plot2, ncol=2)
+ggsave("~/Desktop/actiwatchassignment.png", pnew, height = 5, width = 10, units = "in")
+
+plot(pnew)
+
+
+
+
+
+
+
+
+
+
+
+
+
dfcor$dominant_assignment <- factor(ifelse(as.character(dfcor$actiwatch_arm) == as.character(dfcor$handedness), 1, 0), labels = c("Dominant", "Non-Dominant"))
+
+df_means$dominant_assignment <- factor(ifelse(as.character(df_means$actiwatch_arm) == as.character(df_means$handedness), 1, 0), labels = c("Dominant", "Non-Dominant"))
+
+plot1 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = dominant_assignment, y = sleep_time, fill = dominant_assignment, group = dominant_assignment)) +
+ ylab('Sleep Time Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")
+
+plot2 <- dfcor %>%
+ filter(handedness != "Both") %>%
+ ggplot() +
+ geom_boxplot(aes(x = dominant_assignment, y = time_in_bed, fill = dominant_assignment, group = dominant_assignment)) +
+ ylab('Time In Bed Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")
+
+pnew <- arrangeGrob(plot1, plot2, ncol=2)
+ggsave("~/Desktop/actiwatchassignment_dominant.png", pnew, height = 5, width = 10, units = "in")
+
+summary(lm(sleep_duration_act ~ sleep_duration_fit + dominant_assignment, data = df_means))
+
+
+
+Call:
+lm(formula = sleep_duration_act ~ sleep_duration_fit + dominant_assignment,
+ data = df_means)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-118.901 -42.467 4.308 41.687 176.725
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 469.1405 87.8572 5.340 6.77e-06 ***
+sleep_duration_fit -0.0311 0.2008 -0.155 0.878
+dominant_assignmentNon-Dominant 8.1282 22.4001 0.363 0.719
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 66.78 on 33 degrees of freedom
+Multiple R-squared: 0.005072, Adjusted R-squared: -0.05523
+F-statistic: 0.08412 on 2 and 33 DF, p-value: 0.9195
+
+
+
summary(lm(time_in_bed_act ~ time_in_bed_fit + dominant_assignment, data = df_means))
+
+
+
+Call:
+lm(formula = time_in_bed_act ~ time_in_bed_fit + dominant_assignment,
+ data = df_means)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-113.165 -54.558 3.155 31.991 179.772
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 571.5440 95.8090 5.965 1.07e-06 ***
+time_in_bed_fit -0.1331 0.2043 -0.651 0.519
+dominant_assignmentNon-Dominant 4.7718 23.9942 0.199 0.844
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 71.18 on 33 degrees of freedom
+Multiple R-squared: 0.0152, Adjusted R-squared: -0.04448
+F-statistic: 0.2547 on 2 and 33 DF, p-value: 0.7766
+
+
+
plot(pnew)
+
+
+
+
+
+
+
+
+
+
+
+
+
dfcor$age_group <- factor(dfcor$age_group, labels = c("Young Adults", "Older Adults"))
+
+plot1 <- dfcor %>%
+ ggplot() +
+ geom_boxplot(aes(x = age_group, y = sleep_time, fill = age_group, group = age_group)) +
+ ylab('Sleep Time Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Age Group")
+
+plot2 <- dfcor %>%
+ ggplot() +
+ geom_boxplot(aes(x = age_group, y = time_in_bed, fill = age_group, group = age_group)) +
+ ylab('Time In Bed Correlation (r)') +
+ scale_fill_brewer(palette = 'Set1') + xlab("Age Group")
+
+pnew <- arrangeGrob(plot1, plot2, ncol=2)
+ggsave("~/Desktop/agegroup.png", pnew, height = 5, width = 10, units = "in")
+
+
+
+
+
+
+
+
write.csv(dfcor, paste0(data_dir, 'sleep_summary_condition_correlation.csv'))
+
+
+
+
+
+
+
+
Bland Altman plots
+
About the package: blandr Interpretation: Statology
+
e.g.
+
The average of the values in the Difference column turns out to be 0.5. (middle line) The standard deviation of values in the Difference column turns out to be 1.235. The upper and lower limits of the confidence interval for the average difference can be calculated as:
+
Upper Limit: x + 1.96s = 0.5 + 1.961.235 = 2.92 Lower Limit: x – 1.96s = 0.5 – 1.961.235 = -1.92
+
On average, instrument A weighs frogs to be 0.5 grams heavier than instrument B. 95% of the differences in weight between the two instruments are expected to fall in the range of -1.92 grams and 2.92 grams.
+
First look at which device may be under or over estimating - Fitbit underestimating sleep time and time in bed
+
+
+
+
+# fitbit underestimating sleep time
+df %>%
+ ggplot(aes(x = sleep_duration_act, y = sleep_duration_fit)) +
+ geom_point() +
+ stat_smooth(method = "lm")
+
+
+
+
+
+
+df %>%
+ ggplot(aes(x = sleep_duration_cl, y = sleep_duration_fit)) +
+ geom_point() +
+ stat_smooth(method = "lm")
+
+
+
+
+
+
+#fitbit underestimating time in bed
+df %>%
+ ggplot(aes(x = time_in_bed_act, y = time_in_bed_fit)) +
+ geom_point() +
+ stat_smooth(method = "lm")
+
+
+
+
+
+
+df %>%
+ ggplot(aes(x = time_in_bed_cl, y = time_in_bed_fit)) +
+ geom_point() +
+ stat_smooth(method = "lm")
+
+
+
+
+
+
+
+
+
+
+
library(blandr)
+
+df.stats <- blandr.statistics(df$sleep_duration_act , df$sleep_duration_fit)
+p1 <- blandr.draw(df$sleep_duration_act , df$sleep_duration_fit ) + ggtitle(paste("Raw Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$sleep_duration_cl , df$sleep_duration_fit)
+p2 <- blandr.draw(df$sleep_duration_cl , df$sleep_duration_fit) + ggtitle(paste("Cleaned Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$sleep_duration_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
+p3 <- blandr.draw(df$sleep_duration_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Non-Dominant, \n \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$sleep_duration_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
+p4 <- blandr.draw(df$sleep_duration_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$sleep_duration_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
+p5 <- blandr.draw(df$sleep_duration_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$sleep_duration_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
+p6 <- blandr.draw(df$sleep_duration_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+gridExtra::grid.arrange(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)
+
+
+
+
+
+
+pall <- gridExtra::arrangeGrob(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)
+
+ggsave("~/Box/CogNeuroLab/Wearables/results/sleep_duration_bland_altman.png", pall, scale = 1.5, dpi = 300)
+
+
+
+
+
+
+
library(blandr)
+
+df.stats <- blandr.statistics(df$time_in_bed_act , df$time_in_bed_fit)
+p1 <- blandr.draw(df$time_in_bed_act , df$time_in_bed_fit ) + ggtitle(paste("Raw Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$time_in_bed_cl , df$time_in_bed_fit)
+p2 <- blandr.draw(df$time_in_bed_cl , df$time_in_bed_fit) + ggtitle(paste("Cleaned Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$time_in_bed_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
+p3 <- blandr.draw(df$time_in_bed_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$time_in_bed_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
+p4 <- blandr.draw(df$time_in_bed_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$time_in_bed_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
+p5 <- blandr.draw(df$time_in_bed_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+df.stats <- blandr.statistics(df$time_in_bed_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
+p6 <- blandr.draw(df$time_in_bed_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))
+
+gridExtra::grid.arrange(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)
+
+
+
+
+
+
+pall <- gridExtra::arrangeGrob(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)
+
+ggsave("~/Box/CogNeuroLab/Wearables/results/time_in_bed_bland_altman.png", pall, scale = 1.5, dpi = 300)
+
+
+
+
+
+
Correspondance with Sleep Diary Data
+
+
+
+
+
+
for (participant in unique(dfn1$subject)[1]) {
+ print(participant)
+ n_pages <- dfn1 %>%
+ filter(subject == participant) %>%
+ drop_na(date) %>%
+ summarise(ceiling(length(date) / 4))
+ print(n_pages)
+
+ rawact <-
+ read_csv(act_files[grep(participant, act_files)], col_names = c("date", "activity"))
+
+ rawact <- rawact %>%
+ mutate(clock_time = as.numeric(
+ lubridate::hour(lubridate::ymd_hms(date)) + lubridate::minute(lubridate::ymd_hms(date)) /
+ 60
+ )) %>%
+ mutate(date = format(ymd_hms(date), "%Y-%m-%d"))
+
+
+ p <- dfn1 %>%
+ filter(subject == participant) %>%
+ drop_na(date) %>%
+ ggplot() +
+ geom_vline(aes(
+ xintercept = bed_time_clock_act,
+ color = 'Actiwatch',
+ linetype = 'dashed'
+ )) +
+ geom_vline(aes(
+ xintercept = bed_time_clock_fit,
+ color = 'Fitbit',
+ linetype = 'dotted'
+ )) +
+ geom_vline(aes(
+ xintercept = bed_time_clock,
+ color = 'Self-Report',
+ linetype = 'longdash'
+ )) + #, y = 1, color = 'Self-Report', shape = 'Bed Time')) +
+ geom_vline(aes(
+ xintercept = wake_time_clock_act,
+ color = 'Actiwatch',
+ linetype = 'dashed'
+ )) +
+ geom_vline(aes(
+ xintercept = wake_time_clock_fit,
+ color = 'Fitbit',
+ linetype = 'dotted'
+ )) +
+ geom_vline(aes(
+ xintercept = wake_time_clock,
+ color = 'Self-Report',
+ linetype = 'longdash'
+ )) +
+ geom_line(data = rawact,
+ aes(x = clock_time, y = activity, size = 0.2)) +
+ theme_minimal() + theme_readable(base_size = 12) +
+ labs(color = "Method") + ggtitle(participant) +
+ theme(axis.text.y = element_blank(), axis.title.y = element_blank()) +
+ scale_x_continuous(limits = c(0, 24), breaks = seq(0, 24, 3)) + xlab("Hour") +
+ facet_wrap_paginate(. ~ date, nrow = 2, ncol = 2, page = as.numeric(n_pages))
+
+ pdf(
+ paste0(
+ "~/Box/CogNeuroLab/Wearables/results/diary_comparison/",
+ as.character(participant),
+ ".pdf"
+ ),
+ onefile = T
+ )
+ print(p)
+ dev.off()
+
+}
+
+
+
+
[1] "10011"
+
+
+
+
+
+
+
+
+
+
+
+
---
title: "Sleep Summary Stats Comparison"
author: "Megan McMahon"
date: "3/12/2021"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)

library(readr)
library(tidyverse)
library(reshape2)
library(lubridate)

```

```{r}
data_dir <- '~/Box/CogNeuroLab/Wearables/data/'
fit <- read_csv(paste0(data_dir, 'fitbit/fitbitSleepStatsGroup.csv'))
act <- read_csv(paste0(data_dir, 'actiwatch/wa_act_combined.csv'))
act_cl <- read_csv(paste0(data_dir, 'actiwatch/actiware_manual_clean/wa_act_combined.csv'))

df_cx <- readr::read_csv(paste0(data_dir, 'df_main.csv'))

head(act)
```

```{r read}

#clean actiware datasets

clean_actiware_combined_df <- function(df) {
  TimeInBed <- df %>%
    filter(interval_type == 'REST') %>%
    select(subject_id, start_date, duration)
  
  dfnew <- df %>%
    dplyr::filter(interval_type == 'SLEEP') %>%
    merge(TimeInBed, by = c('subject_id', 'start_date')) %>%
    rename(
      time_in_bed = duration.y,
      sleep_duration = duration.x) %>%
    mutate(
      bed_time = as.character(format(as_datetime(paste(start_date, start_time), format = '%m/%d/%Y %I:%M:%S %p'), format = '%Y-%m-%d %H:%M')),
      wake_time = as.character(format(as_datetime(paste(end_date, end_time), format = '%m/%d/%Y %I:%M:%S %p'), format = '%Y-%m-%d %H:%M'))
    ) %>%
    select(-X31,
           -(number_of_scores:avg_scheduled),
           -interval_type,
           -interval_number) %>%
    mutate(
      date = format(
        lubridate::round_date(
          lubridate::parse_date_time(paste0(start_date, start_time), '%m/%d/%Y %I:%M:%S %p'),
          unit = 'd'
        ),
        '%Y-%m-%d'
      ),
      joinid = paste0(subject_id, '_', date)
    )
  
  return(dfnew)
}

act <- clean_actiware_combined_df(act)
act_cl <- clean_actiware_combined_df(act_cl)
head(act)
```


```{r}
#fitbit subject numbers are different for subjects who used Beiwe, so need to match them with their full subject number from Actiware
match_subject_number <- function(data, pad_digits, match_to){
    for (i in unique(data$subject[data$subject < 10000])){
    subno <- data$subject[grep(paste0('[1-2]', stringr::str_pad(i, pad_digits, side = 'left', pad = '0')), match_to)][1]
    if (! is.na(subno)){
      data$subject[data$subject == i] <- subno
    }
    }
  return(data)
}
fit <- match_subject_number(fit, 4, act$subject_id)
head(fit)
```

```{r}
# match date formats between fitbit and actiwatch datasets
act_cl <- act_cl %>%
  mutate(cleaned = ifelse(analysis_name == "Manual", 1, 0))

actall <- act %>%
  merge(act_cl, by = c('joinid', 'subject_id'), suffixes = c('_act', '_cl'))

head(actall)
```

```{r}
fitnew <- fit %>% 
  mutate(date = format(lubridate::parse_date_time(StartTime, '%m/%d/%Y %I:%M:%S %p'), format = '%Y-%m-%d'), 
         joinid = paste0(subject, '_', format(date, format = '%Y-%m-%d')),
         bed_time = as.character(format(lubridate::parse_date_time(StartTime, '%m/%d/%Y %I:%M:%S %p'), format = '%Y-%m-%d %H:%M'))) %>%
  rename(sleep_duration = TotalMinutesAsleep, 
         time_in_bed = TotalTimeInBed, 
         efficiency = Efficiency, 
         min_after_wake = MinutesAfterWakeup, 
         onset_latency = MinutesToFallAsleep, 
         awake_count = AwakeCount, 
         awake_duration = AwakeDuration, 
         restless_count = RestlessCount, 
         restless_duration = RestlessDuration) %>%
  mutate(wake_time = as.character(lubridate::as_datetime(bed_time, format = '%Y-%m-%d %H:%M') + lubridate::minutes(sleep_duration))) %>%
  select(-X1, -Duration, -MinutesAsleep, -TimeInBed) %>%
  rename_at(vars(-subject, -date, -joinid), ~ paste0(., '_fit')) %>%
  filter(rowSums(is.na(.)) != ncol(.)-1)

fitnew %>%
  select(bed_time_fit, wake_time_fit) %>%
  head()
```


```{r}
df <- merge(actall, fitnew, by = 'joinid', all = T)

df$subject <- substring(df$joinid, 0, 5)

write_csv(df, paste0(data_dir, 'sleep-summary-stats_devices-actiwatch-fitbit.csv'))

df %>%
  select(subject, joinid, matches('sleep_duration'))

```

```{r}
df <- merge(df, df_cx, by = 'subject')

```

```{r}
df_cx %>%
  mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
  mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
  mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
  mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
  group_by(age_group, dominant) %>%
  summarise(n = n())

df_cx %>%
  mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
  mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
  mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
  filter(handedness == "Right") %>%
  mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
  group_by(age_group, dominant) %>%
  summarise(n = n())

df_cx %>%
  mutate(age_group = recode(age_group, "Young Adults", "Older Adults")) %>%
  mutate(handedness = recode(handedness, "Left", "Right", "Both")) %>%
  mutate(actiwatch_arm = recode(actiwatch_arm, "Left", "Right")) %>%
  filter(handedness == "Left") %>%
  mutate(dominant = ifelse(handedness == actiwatch_arm, 1, 0)) %>%
  group_by(age_group, dominant) %>%
  summarise(n = n())
```


```{r}
exclude = c('10023', '10076', '20182', '20184', '20176', '10151', '10139')
df <- df[!df$subject %in% exclude, ]

df_means <- df %>%
  select(subject_id, analysis_name_cl, where(is.numeric)) %>%
  group_by(subject, analysis_name_cl) %>%
  summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))

head(df_means)

```

## Correlations

```{r}
library(corrr)
library(purrr)
library(dplyr)

df %>%
  group_by(subject) %>%
  select(where(is.numeric)) %>%
  group_by(subject) %>%
  summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
  corrr::correlate(method = 'spearman') %>%
  corrr::focus(matches('sleep_duration'), mirror = TRUE) 

```

```{r}
df %>%
  group_by(subject) %>%
  filter(handedness == 2) %>%
  select(where(is.numeric)) %>%
  group_by(subject) %>%
  summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
  corrr::correlate(method = 'spearman') %>%
  corrr::focus(matches('sleep_duration'), mirror = TRUE) 
```

```{r}
df %>% #all
  group_by(subject) %>%
  select(where(is.numeric)) %>%
  group_by(subject) %>%
  summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
  corrr::correlate(method = 'spearman') %>%
  corrr::focus(matches('bed'), mirror = TRUE) 
```

```{r}
df %>% #right handed only
  group_by(subject) %>%
  filter(handedness == 2) %>%
  select(where(is.numeric)) %>%
  group_by(subject) %>%
  summarise_at(vars(-subject_id), funs(mean(., na.rm=TRUE))) %>%
  corrr::correlate(method = 'spearman') %>%
  corrr::focus(matches('bed'), mirror = TRUE) 

```


```{r}
library(RColorBrewer)

df_means %>%
  ggplot(aes(x = sleep_duration_cl, y = sleep_duration_fit, color = analysis_name_cl)) +
  geom_point() +
  stat_smooth(method = 'lm') +
  scale_color_brewer(palette = 'Set1')


```

```{r}
df_means %>%
  ggplot(aes(x = time_in_bed_cl, y = time_in_bed_fit, color = analysis_name_cl)) +
  geom_point() +
  stat_smooth(method = 'lm') +
  scale_color_brewer(palette = 'Set1')

```

```{r}
# excluded subject 10139
df_means %>%
  ggplot(aes(x = time_in_bed_cl)) +
  geom_boxplot() 

df_means$subject[df_means$time_in_bed_cl > 700]
```

### Repeated measures correlations

[Documentation](https://cran.r-project.org/web/packages/rmcorr/rmcorr.pdf)

[Repeated Measures Correlation, Bakdash1 and Marusich, 2017](https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00456/full)

#### Time in Bed
```{r}
library(rmcorr)

colourCount = length(unique(df$subject))
getPalette = colorRampPalette(brewer.pal(9, "Spectral"))

my.rmc <- rmcorr(participant = subject, measure1 = time_in_bed_act, measure2 = time_in_bed_fit, dataset = df)
plot(my.rmc, overall = TRUE)

```

```{r}
ggplot2::ggplot(df, ggplot2::aes(x = time_in_bed_act, y = time_in_bed_fit, group = factor(subject),
      color = factor(subject))) +
      ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
      ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time In Bed') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject') + ggsave(paste0(data_dir, 'CARE2021/figures/timeinbedrmcorr.png'), height = 5, width = 8, dpi = 300)

```

#### Time in Bed: Cleaned Actiwatch data
```{r}
my.rmc <- rmcorr(participant = subject, measure1 = time_in_bed_cl, measure2 = time_in_bed_fit, dataset = df[df$age_group == 1,])

ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = time_in_bed_cl, y = time_in_bed_fit, group = factor(subject),
      color = factor(subject))) +
      ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
      ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time In Bed') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')

```
#### Time in bed: correlations
```{r}
#raw
rmcorr(subject, time_in_bed_act, time_in_bed_fit, df)
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, handedness == 2))
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
rmcorr(subject, time_in_bed_act, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))

# manually cleaned
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, df)
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, handedness == 2))
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
rmcorr(subject, time_in_bed_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))

```

#### Sleep time

```{r}
my.rmc <- rmcorr(participant = subject, measure1 = sleep_duration_act, measure2 = sleep_duration_fit, dataset = df)

ggplot2::ggplot(df, ggplot2::aes(x = sleep_duration_act, y = sleep_duration_fit, group = factor(subject),
      color = factor(subject))) +
      ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
      ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time Asleep') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject') + ggsave(paste0(data_dir, 'CARE2021/figures/sleeptimermcorr.png'), height = 5, width = 8, dpi = 300)

```

#### Sleep Time: Cleaned Actiwatch data

```{r}
my.rmc <- rmcorr(participant = subject, measure1 = sleep_duration_cl, measure2 = sleep_duration_fit, dataset = df[df$age_group == 1,], nreps=500)

ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = sleep_duration_cl, y = sleep_duration_fit, group = factor(subject),
      color = factor(subject))) +
      ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
      ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Time Asleep') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')

```

```{r}
#raw
rmcorr(subject, sleep_duration_act, sleep_duration_fit, df)
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, handedness == 2))
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
rmcorr(subject, sleep_duration_act, sleep_duration_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))

# manually cleaned
rmcorr(subject, sleep_duration_cl, sleep_duration_fit, df)
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, handedness == 2))
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 1) & (handedness == 2)))
rmcorr(subject, sleep_duration_cl, time_in_bed_fit, filter(df, (actiwatch_arm == 2) & (handedness == 2)))

```

```{r}
rmcorr(subject, sleep_duration_act, time_in_bed_fit, df)

```

### Efficiency

```{r}
my.rmc <- rmcorr(participant = subject, measure1 = efficiency_act, measure2 = efficiency_fit, dataset = df[df$age_group == 1,], nreps=500)

ggplot2::ggplot(df[df$age_group == 1,], ggplot2::aes(x = efficiency_act, y = efficiency_fit, group = factor(subject),
      color = factor(subject))) +
      ggplot2::geom_point(ggplot2::aes(colour = factor(subject))) +
      ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values), linetype = 1) + xlab('Actiwatch 2.0') + ylab('Fitbit') + ggtitle('Sleep Efficiency') + scale_color_manual(values = getPalette(colourCount)) + labs(color = 'Subject')

```

```{r}
rmcorr(subject, efficiency_act, efficiency_fit, df)

```

### Next steps: 
Need correlation value for each subject, then see how that's related to measures of subjective sleep quality, handedness, arm assignment, percent watch off, beiwe, fitbit device

```{r}
corrdf <- df %>%
  group_by(subject) %>%
  summarize(sleep_time = cor(sleep_duration_act, sleep_duration_fit, method = 'spearman'), time_in_bed = cor(time_in_bed_act, time_in_bed_fit, method = 'spearman'))

head(corrdf)

```
```{r}
dfcor <- merge(df, corrdf, by = 'subject')

head(dfcor)

```

```{r}
unique(dfcor$subject[dfcor$time_in_bed < 0.5])
length(unique(dfcor$subject[dfcor$time_in_bed < 0.5]))

unique(dfcor$subject[dfcor$sleep_time < 0.5])
length(unique(dfcor$subject[dfcor$sleep_time < 0.5]))

```


```{r}

require(gridExtra)

plot1 <- dfcor %>%
  ggplot() + 
  geom_boxplot(aes(x = fitbit_device, y = sleep_time, fill = fitbit_device, group = fitbit_device)) +
  ylab('Sleep Time Correlation (r)') +
  scale_fill_brewer(palette = 'Set1')

plot2 <- dfcor %>%
  ggplot() + 
  geom_boxplot(aes(x = fitbit_device, y = time_in_bed, fill = fitbit_device, group = fitbit_device)) +
  ylab('Time In Bed Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + theme(axis.text.x = element_text(angle = 90))

grid.arrange(plot1, plot2, ncol=2)

```

```{r}
dfcor$handedness <- factor(dfcor$handedness, labels = c("Right", "Left", "Both"))

plot1 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = handedness, y = sleep_time, fill = handedness, group = handedness)) +
  ylab('Sleep Time Correlation (r)') + xlab('Handedness') +
  scale_fill_brewer(palette = 'Set1') 

plot2 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = handedness, y = time_in_bed, fill = handedness, group = handedness)) +
  ylab('Time In Bed Correlation (r)') + xlab('Handedness') +
  scale_fill_brewer(palette = 'Set1')

pnew <- arrangeGrob(plot1, plot2, ncol=2)
ggsave("~/Desktop/handedness.png", pnew, height = 5, width = 10, units = "in")

plot(pnew)
```

```{r}
dfcor$actiwatch_arm <- factor(dfcor$actiwatch_arm, labels = c("Right", "Left"))

plot1 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = actiwatch_arm, y = sleep_time, fill = handedness, group = handedness)) +
  ylab('Sleep Time Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")

plot2 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = actiwatch_arm, y = time_in_bed, fill = handedness, group = handedness)) +
  ylab('Time In Bed Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")

pnew <- arrangeGrob(plot1, plot2, ncol=2)
ggsave("~/Desktop/actiwatchassignment.png", pnew, height = 5, width = 10, units = "in")

plot(pnew)
```

```{r}
dfcor$dominant_assignment <- factor(ifelse(as.character(dfcor$actiwatch_arm) == as.character(dfcor$handedness), 1, 0), labels = c("Dominant", "Non-Dominant"))

df_means$dominant_assignment <- factor(ifelse(as.character(df_means$actiwatch_arm) == as.character(df_means$handedness), 1, 0), labels = c("Dominant", "Non-Dominant"))

plot1 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = dominant_assignment, y = sleep_time, fill = dominant_assignment, group = dominant_assignment)) +
  ylab('Sleep Time Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")

plot2 <- dfcor %>%
  filter(handedness != "Both") %>%
  ggplot() + 
  geom_boxplot(aes(x = dominant_assignment, y = time_in_bed, fill = dominant_assignment, group = dominant_assignment)) +
  ylab('Time In Bed Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Actiwatch Arm")

pnew <- arrangeGrob(plot1, plot2, ncol=2)
ggsave("~/Desktop/actiwatchassignment_dominant.png", pnew, height = 5, width = 10, units = "in")

summary(lm(sleep_duration_act ~ sleep_duration_fit + dominant_assignment, data = df_means))
summary(lm(time_in_bed_act ~ time_in_bed_fit + dominant_assignment, data = df_means))

plot(pnew)
```

```{r}
dfcor$age_group <- factor(dfcor$age_group, labels = c("Young Adults", "Older Adults"))

plot1 <- dfcor %>%
  ggplot() + 
  geom_boxplot(aes(x = age_group, y = sleep_time, fill = age_group, group = age_group)) +
  ylab('Sleep Time Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Age Group")

plot2 <- dfcor %>%
  ggplot() + 
  geom_boxplot(aes(x = age_group, y = time_in_bed, fill = age_group, group = age_group)) +
  ylab('Time In Bed Correlation (r)') +
  scale_fill_brewer(palette = 'Set1') + xlab("Age Group")

pnew <- arrangeGrob(plot1, plot2, ncol=2)
ggsave("~/Desktop/agegroup.png", pnew, height = 5, width = 10, units = "in")

```


```{r}
write.csv(dfcor, paste0(data_dir, 'sleep_summary_condition_correlation.csv'))

```

## Bland Altman plots

About the package: [blandr](https://cran.r-project.org/web/packages/blandr/vignettes/introduction.html)
Interpretation: [Statology](https://www.statology.org/bland-altman-plot/)

e.g.

The average of the values in the Difference column turns out to be 0.5. (middle line)
The standard deviation of values in the Difference column turns out to be 1.235.
The upper and lower limits of the confidence interval for the average difference can be calculated as:

Upper Limit: x + 1.96*s = 0.5 + 1.96*1.235 = 2.92
Lower Limit: x – 1.96*s = 0.5 – 1.96*1.235 = -1.92

On average, instrument A weighs frogs to be 0.5 grams heavier than instrument B.
95% of the differences in weight between the two instruments are expected to fall in the range of -1.92 grams and 2.92 grams.

First look at which device may be under or over estimating
- Fitbit underestimating sleep time and time in bed

```{r}

# fitbit underestimating sleep time
df %>%
  ggplot(aes(x = sleep_duration_act, y = sleep_duration_fit)) +
  geom_point() + 
  stat_smooth(method = "lm")

df %>%
  ggplot(aes(x = sleep_duration_cl, y = sleep_duration_fit)) +
  geom_point() + 
  stat_smooth(method = "lm")

#fitbit underestimating time in bed
df %>%
  ggplot(aes(x = time_in_bed_act, y = time_in_bed_fit)) +
  geom_point() + 
  stat_smooth(method = "lm")

df %>%
  ggplot(aes(x = time_in_bed_cl, y = time_in_bed_fit)) +
  geom_point() + 
  stat_smooth(method = "lm")

```

```{r}
library(blandr)

df.stats <- blandr.statistics(df$sleep_duration_act , df$sleep_duration_fit)
p1 <- blandr.draw(df$sleep_duration_act , df$sleep_duration_fit ) + ggtitle(paste("Raw Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$sleep_duration_cl , df$sleep_duration_fit)
p2 <- blandr.draw(df$sleep_duration_cl , df$sleep_duration_fit) + ggtitle(paste("Cleaned Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$sleep_duration_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
p3 <- blandr.draw(df$sleep_duration_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Non-Dominant, \n \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$sleep_duration_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
p4 <- blandr.draw(df$sleep_duration_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$sleep_duration_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
p5 <- blandr.draw(df$sleep_duration_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$sleep_duration_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
p6 <- blandr.draw(df$sleep_duration_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$sleep_duration_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

gridExtra::grid.arrange(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)

pall <- gridExtra::arrangeGrob(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)

ggsave("~/Box/CogNeuroLab/Wearables/results/sleep_duration_bland_altman.png", pall, scale = 1.5, dpi = 300)
```

```{r}
library(blandr)

df.stats <- blandr.statistics(df$time_in_bed_act , df$time_in_bed_fit)
p1 <- blandr.draw(df$time_in_bed_act , df$time_in_bed_fit ) + ggtitle(paste("Raw Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$time_in_bed_cl , df$time_in_bed_fit)
p2 <- blandr.draw(df$time_in_bed_cl , df$time_in_bed_fit) + ggtitle(paste("Cleaned Actiwatch, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$time_in_bed_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
p3 <- blandr.draw(df$time_in_bed_act[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$time_in_bed_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
p4 <- blandr.draw(df$time_in_bed_act[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Raw, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$time_in_bed_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)])
p5 <- blandr.draw(df$time_in_bed_cl[(df$actiwatch_arm == 1) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 1) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Non-Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

df.stats <- blandr.statistics(df$time_in_bed_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)])
p6 <- blandr.draw(df$time_in_bed_cl[(df$actiwatch_arm == 2) & (df$handedness == 2)] , df$time_in_bed_fit[(df$actiwatch_arm == 2) & (df$handedness == 2)]) + ggtitle(paste("Cleaned, RH, Dominant, \n bias=", signif(df.stats$bias, 4), "lower=", signif(df.stats$lowerLOA, 4), "upper=", signif(df.stats$upperLOA, 4)))

gridExtra::grid.arrange(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)

pall <- gridExtra::arrangeGrob(p1, p2, p3, p4, p5, p6, nrow=3, ncol=2)

ggsave("~/Box/CogNeuroLab/Wearables/results/time_in_bed_bland_altman.png", pall, scale = 1.5, dpi = 300)
```

## Correspondance with Sleep Diary Data

```{r, include = FALSE, echo = FALSE}
diary <- read_csv(paste0(data_dir, 'sleep_diaries/sleep_diaries_all.csv')) 

if (diary$record_id < 1000){
  for (subject in unique(diary$record_id)){
    i <- as.numeric(match(str_pad(subject, 4, pad = "0"), substring(df$subject, 2, 5)))
    if (!is.na(i)){
      diary$record_id[diary$record_id == subject] <- df$subject[i]
    }
  }
}

diary <- diary %>%
  filter(record_id > 300)

diary$record_id

write.csv(diary, paste0(data_dir, 'sleep_diaries/sleep_diaries_all.csv'), row.names = F)
```

```{r}
dfn <- df %>%
  mutate(
    bed_time_clock_act = as.numeric(hour(
      as_datetime(bed_time_act, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(bed_time_act, format = '%Y-%m-%d %H:%M')
    ) / 60),
    bed_time_clock_fit = as.numeric(hour(
      as_datetime(bed_time_fit, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(bed_time_fit, format = '%Y-%m-%d %H:%M')
    ) / 60),
    bed_time_clock_cl = as.numeric(hour(
      as_datetime(bed_time_cl, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(bed_time_cl, format = '%Y-%m-%d %H:%M')
    ) / 60),
        wake_time_clock_act = as.numeric(hour(
      as_datetime(wake_time_act, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(wake_time_act, format = '%Y-%m-%d %H:%M')
    ) / 60),
    wake_time_clock_fit = as.numeric(hour(
      as_datetime(wake_time_fit, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(wake_time_fit, format = '%Y-%m-%d %H:%M')
    ) / 60),
    wake_time_clock_cl = as.numeric(hour(
      as_datetime(wake_time_cl, format = '%Y-%m-%d %H:%M')
    ) + minute(
      as_datetime(wake_time_cl, format = '%Y-%m-%d %H:%M')
    ) / 60)
  )


diary2 <- diary %>%
  mutate(
    date = format(round_date(diary$timestamp), '%Y-%m-%d'),
    subject = record_id,
    joinid = paste0(subject, '_', date),
    bed_time_clock = as.numeric(
      lubridate::hour(diary$bed_time) + lubridate::minute(bed_time) / 60
    ),
    wake_time_clock = as.numeric(
      lubridate::hour(lubridate::hms(wakeup_time)) + lubridate::minute(lubridate::hms(diary$wakeup_time)) /
        60
    )
  )

head(diary2)



dfn1 <- dfn %>%
  merge(diary2, by = c('subject', 'date'))

library(ggforce)

act_files <- list.files("~/Box/CogNeuroLab/Wearables/data/actiwatch/act_files/preproc/", full.names = T)


for (participant in unique(dfn1$subject)[1]) {
  print(participant)
  n_pages <- dfn1 %>%
    filter(subject == participant) %>%
    drop_na(date) %>%
    summarise(ceiling(length(date) / 4))
  print(n_pages)
  
  rawact <-
    read_csv(act_files[grep(participant, act_files)], col_names = c("date", "activity"))
  
  rawact <- rawact %>%
    mutate(clock_time = as.numeric(
      lubridate::hour(lubridate::ymd_hms(date)) + lubridate::minute(lubridate::ymd_hms(date)) /
        60
    )) %>%
    mutate(date = format(ymd_hms(date), "%Y-%m-%d"))
  
  
  p <- dfn1 %>%
    filter(subject == participant) %>%
    drop_na(date) %>%
    ggplot() +
    geom_vline(aes(
      xintercept = bed_time_clock_act,
      color = 'Actiwatch',
      linetype = 'dashed'
    )) +
    geom_vline(aes(
      xintercept = bed_time_clock_fit,
      color = 'Fitbit',
      linetype = 'dotted'
    )) +
    geom_vline(aes(
      xintercept = bed_time_clock,
      color = 'Self-Report',
      linetype = 'longdash'
    )) + #, y = 1, color = 'Self-Report', shape = 'Bed Time')) +
    geom_vline(aes(
      xintercept = wake_time_clock_act,
      color = 'Actiwatch',
      linetype = 'dashed'
    )) +
    geom_vline(aes(
      xintercept = wake_time_clock_fit,
      color = 'Fitbit',
      linetype = 'dotted'
    )) +
    geom_vline(aes(
      xintercept = wake_time_clock,
      color = 'Self-Report',
      linetype = 'longdash'
    )) +
    geom_line(data = rawact,
              aes(x = clock_time, y = activity, size = 0.2)) +
    theme_minimal() + theme_readable(base_size = 12) +
    labs(color = "Method") + ggtitle(participant) +
    theme(axis.text.y = element_blank(), axis.title.y = element_blank()) +
    scale_x_continuous(limits = c(0, 24), breaks = seq(0, 24, 3)) + xlab("Hour") +
    facet_wrap_paginate(. ~ date, nrow = 2, ncol = 2, page = as.numeric(n_pages))
  
  pdf(
    paste0(
      "~/Box/CogNeuroLab/Wearables/results/diary_comparison/",
      as.character(participant),
      ".pdf"
    ),
    onefile = T
  )
  print(p)
  dev.off()
  
}


```


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