library(plotly)
library(hms)
library(lubridate)
library(tibbletime)
library(kableExtra)
library(tidyverse)

Intro

3 subjects (CC, CG, NJ) wore the two ActiGraph wGT3X-BT devices concurrently on their non-dominant (left) wrist for several hours over 3-4 days. Idle Sleep Mode (ISM) was enabled in one device and disabled in the other. Here’s a quick look at the data:

CC

Day Worn Hours worn Time worn
Day 2 4 hours 2pm-6pm
Day 3 7 hours 10am-5pm
Day 8 9 hours 9am-6pm

ISM On

ISM Off

CG

Day Worn Hours worn Time worn
Day 1 4 hours 2pm-6pm
Day 2 4 hours 2pm-6pm
Day 4 11 hours 9am-8pm
Day 11 7 hours 10am-5pm

ISM On

ISM Off

NJ

Day Worn Hours worn Time worn
Day 1 4 hours 2pm-6pm
Day 2 4 hours 2pm-6pm
Day 3 4 hours 1pm-5pm

ISM On

ISM Off

Exploratory Plots

#minute level data

#1) import
data60 <- map(list.files('output_csv/meta/csv', full.names = T), read_csv)

#2) add ID column
IDs <- list.files('output_csv/meta/csv') %>% str_replace('.csv.RData.csv', '')
data60 <- map2(data60, IDs, ~ .x %>% mutate(ID = .y))

#3) rowbind, make timezone America/New_York, convert ENMO units from g to mg
data60 <- bind_rows(data60) %>%
  mutate(timestamp = with_tz(timestamp, "America/New_York"),
         ENMO = ENMO * 1000)


#3.5) create MVPA summaries for Mike plot

data60_milham <- data60 %>%
  separate(ID, c('ID', 'ISM'), sep = '_') %>%
  mutate(day = day(timestamp))

data60_milham <- data60_milham %>%
  select(-anglez) %>%
  spread(key = ISM, value = ENMO) %>%
  rename(ENMO_ISM_On = yes,
         ENMO_ISM_Off = no)

#write a function to calculate minutes of MVPA per day

calc_MVPA <- function(ENMO, threshold) {
  
  return(length(ENMO[ENMO > threshold]))
  
}

data60_milham_plot <- map(c(100,120,125), ~ data60_milham %>%
                                              group_by(ID, day) %>%
                                              summarise(MVPA_duration_ISM_Off = calc_MVPA(ENMO_ISM_Off, .x),
                                                        MVPA_duration_ISM_On = calc_MVPA(ENMO_ISM_On, .x),
                                                        MVPA_threshold = .x)
                            )
  

data60_milham_plot <- bind_rows(data60_milham_plot)

#subset to valid days for each subject
CC = c(26, 27, 2)
CG = c(25, 26, 28, 5)
NJ = c(25, 26, 27)

data60_milham_plot <- data60_milham_plot %>% filter(ID == "CC" & day %in% CC | 
                                                    ID == "CG" & day %in% CG | 
                                                    ID == "NJ" & day %in% NJ)



data60_milham_plot_sub <- data60_milham_plot %>% 
  group_by(ID, MVPA_threshold) %>% 
  summarise(MVPA_duration_ISM_Off = mean(MVPA_duration_ISM_Off),
            MVPA_duration_ISM_On = mean(MVPA_duration_ISM_On))
  

#4) spread data for plotly
data60 <- data60 %>% select(-anglez) %>% spread(key = ID, value = ENMO)
#day level summaries
day_summaries <- read_csv('output_csv/results/part2_daysummary.csv')

#make exploratory plots to compare ISM vs No ISM data
create_plot <- function(subID, date_start, date_end) {
  
  ISM_on = paste0(subID, '_yes')
  ISM_off = paste0(subID, '_no')
  
  
  plotdata <- as_tbl_time(data60, index = timestamp) %>%
    filter_time(date_start ~ date_end) 
  
  plot <- plot_ly(type = 'scatter', mode = 'lines') %>%
      add_trace(x = ~plotdata[['timestamp']], y = ~plotdata[[ISM_on]], name = "ISM On", opacity = 0.7, line=list(color='#a32c3f', opacity = 0.7)) %>%
      add_trace(x = ~plotdata[['timestamp']], y = ~plotdata[[ISM_off]], name = "ISM_Off", opacity = 0.7, line=list(color='#0067a0')) %>%
    layout(
      xaxis = list(title = ''), 
      yaxis = list(title = 'ENMO (mg)')
    )
    
  return(plot)
}

ISM-Off watches consistently produced higher ENMOs than ISM-On watches.

CC

CC’s watches looked perfectly in sync from day 1 to day 8.

Day 2

create_plot('CC', '2022-04-26 13:00', '2022-04-26 19:00')

Day 3

create_plot('CC', '2022-04-27 10:00', '2022-04-27 18:00')

Day 8

create_plot('CC', '2022-05-02 8:40', '2022-05-02 19:00')

CG

CG’s watches looked off by 10 seconds on setup (ISM On was 10 seconds ahead), and off by 25 seconds by day 11.

Day 1

create_plot('CG', '2022-04-25 13:00', '2022-04-25 19:00')

Day 2

create_plot('CG', '2022-04-26 13:40', '2022-04-26 19:30')

Day 4

create_plot('CG', '2022-04-28 8:40', '2022-04-28 21:00')

Day 11

create_plot('CG', '2022-05-05 9:50', '2022-05-05 17:30')

NJ

NJ’s watches looked off by 5 seconds on setup (ISM On was 5 seconds ahead), and off by 10 seconds around the end of day 3.

Day 1

create_plot('NJ', '2022-04-25 13:30', '2022-04-25 18:30')

Day 2

create_plot('NJ', '2022-04-26 13:30', '2022-04-26 19:10')

Day 3

create_plot('NJ', '2022-04-27 13:00', '2022-04-27 18:00')

GGIR Part 2 Summary Comparison

There were differences in summary values across the board.

subID ISM calendar_date bodylocation N valid hours N hours weekday measurementday L5hr_ENMO_mg_0-24hr L5_ENMO_mg_0-24hr M5hr_ENMO_mg_0-24hr M5_ENMO_mg_0-24hr L10hr_ENMO_mg_0-24hr L10_ENMO_mg_0-24hr M10hr_ENMO_mg_0-24hr M10_ENMO_mg_0-24hr mean_ENMO_mg_1-6am mean_ENMO_mg_0-24hr p95.83333_ENMO_mg_0-24hr p97.91667_ENMO_mg_0-24hr [0,50)_ENMO_mg_0-24hr [50,100)_ENMO_mg_0-24hr [100,150)_ENMO_mg_0-24hr [150,200)_ENMO_mg_0-24hr [200,250)_ENMO_mg_0-24hr [250,300)_ENMO_mg_0-24hr [300,350)_ENMO_mg_0-24hr [350,400)_ENMO_mg_0-24hr [400,8e+03)_ENMO_mg_0-24hr MVPA_E5S_T125_ENMO_0-24hr MVPA_E1M_T125_ENMO_0-24hr MVPA_E5M_T125_ENMO_0-24hr MVPA_E5S_B1M80%_T125_ENMO_0-24hr MVPA_E5S_B5M80%_T125_ENMO_0-24hr MVPA_E5S_B10M80%_T125_ENMO_0-24hr
CC no 2022-04-27 04:00:00 not extracted 9.75 24 Wednesday 3 18.333333 0.5005000 11.166667 41.72319 18.333333 0.8449028 8.333333 30.495639 1.1275000 13.216724 68.20417 91.40208 1329.750 85.916667 18.583333 4.583333 0.6666667 0.2500000 0.0000000 0.0000000 0.2500000 11.833333 1 0 0.000000 0.000000 0
CC yes 2022-04-27 04:00:00 not extracted 9.75 24 Wednesday 3 3.666667 0.0000000 11.166667 22.02367 22.000000 0.0000000 9.166667 15.342958 0.0000000 6.393067 47.00000 68.20000 1385.583 43.083333 9.416667 1.166667 0.4166667 0.2500000 0.0000000 0.0000000 0.0833333 4.166667 0 0 0.000000 0.000000 0
CC no 2022-05-02 04:00:00 not extracted 10.50 24 Monday 8 1.000000 0.6081944 13.500000 41.58981 22.833333 1.1102361 9.333333 39.198625 0.6081944 19.600799 79.10000 108.20417 1311.083 91.250000 26.166667 7.833333 2.2500000 0.7500000 0.1666667 0.1666667 0.3333333 19.833333 10 5 3.333333 0.000000 0
CC yes 2022-05-02 04:00:00 not extracted 10.50 24 Monday 8 2.333333 0.0000000 10.500000 19.23258 22.666667 0.0001111 8.833333 17.543236 0.0000000 7.902888 58.10417 86.30208 1368.833 50.666667 16.000000 3.250000 0.6666667 0.2500000 0.0833333 0.1666667 0.0833333 9.000000 1 0 0.000000 0.000000 0
CC no 2022-05-03 04:00:00 not extracted 6.75 24 Tuesday 9 18.833333 1.5965000 13.500000 41.47628 18.666667 2.5627361 8.666667 23.349319 3.8206944 11.548542 109.40000 125.40000 1344.750 22.750000 63.666667 7.666667 0.7500000 0.1666667 0.0833333 0.0000000 0.1666667 30.916667 21 5 12.083333 0.000000 0
CC yes 2022-05-03 04:00:00 not extracted 6.75 24 Tuesday 9 19.666667 0.0000000 13.500000 38.16503 20.833333 0.0000000 10.500000 19.222056 0.0000000 8.010758 106.30000 124.10000 1343.833 27.666667 60.916667 6.583333 0.7500000 0.0000000 0.0000000 0.0000000 0.2500000 28.416667 18 5 12.583333 0.000000 0
CG no 2022-04-26 04:00:00 not extracted 6.25 24 Tuesday 2 4.500000 1.7376111 14.500000 19.05992 1.000000 1.8267361 13.833333 12.425681 1.8695278 6.320897 27.60000 59.20208 1404.917 16.833333 10.583333 5.916667 1.5833333 0.0833333 0.0000000 0.0000000 0.0833333 13.500000 8 5 8.083333 7.916667 0
CG yes 2022-04-26 04:00:00 not extracted 7.25 24 Tuesday 2 2.666667 0.0000000 14.500000 27.24414 22.666667 0.0024028 13.500000 14.807472 0.0000000 6.171979 39.80000 71.00417 1395.250 23.833333 9.416667 9.416667 1.4166667 0.5000000 0.0000000 0.0833333 0.0833333 16.250000 13 10 9.166667 8.916667 0
CG no 2022-04-28 04:00:00 not extracted 12.50 24 Thursday 4 20.500000 0.4915833 12.833333 13.97725 20.500000 0.7037917 10.333333 11.839528 0.9155278 5.874543 31.90000 50.70000 1409.333 17.916667 7.250000 3.250000 1.2500000 0.2500000 0.0833333 0.1666667 0.5000000 8.333333 3 0 1.000000 0.000000 0
CG yes 2022-04-28 04:00:00 not extracted 12.50 24 Thursday 4 2.500000 0.0000000 12.833333 20.61422 22.166667 0.0024722 10.333333 17.405667 0.0000000 8.121470 41.40000 62.30000 1396.167 29.333333 8.416667 3.500000 1.7500000 0.4166667 0.0833333 0.0000000 0.3333333 9.416667 4 0 1.166667 0.000000 0
CG no 2022-04-29 04:00:00 not extracted 4.00 24 Friday 5 2.000000 0.0000000 14.833333 10.62483 21.666667 0.0394028 9.833333 5.592542 0.0000000 3.123299 17.60417 34.70625 1417.250 8.583333 11.166667 2.500000 0.5000000 0.0000000 0.0000000 0.0000000 0.0000000 8.583333 6 0 3.666667 0.000000 0
CG yes 2022-05-04 04:00:00 not extracted 4.50 24 Wednesday 10 2.666667 0.0000000 17.166667 10.09692 22.500000 0.0151250 13.833333 5.086514 0.0000000 2.564861 15.20000 30.80208 1420.667 6.083333 10.250000 1.500000 1.2500000 0.1666667 0.0833333 0.0000000 0.0000000 7.416667 6 0 3.500000 0.000000 0
CG no 2022-05-05 04:00:00 not extracted 8.25 24 Thursday 11 2.833333 0.0000000 11.500000 17.35628 22.833333 0.0044028 9.833333 11.599958 0.0000000 4.838715 32.40417 56.10208 1404.167 27.416667 5.333333 1.666667 0.6666667 0.4166667 0.1666667 0.0000000 0.1666667 4.666667 3 0 0.000000 0.000000 0
CG yes 2022-05-05 04:00:00 not extracted 8.25 24 Thursday 11 4.000000 0.0000000 11.000000 25.99731 20.000000 0.0089167 8.500000 17.343583 0.0000000 7.230208 47.30000 74.50208 1384.583 37.250000 11.916667 3.083333 1.6666667 0.5833333 0.3333333 0.1666667 0.4166667 10.333333 4 0 1.000000 0.000000 0
NJ no 2022-04-25 14:00:00 not extracted 8.75 14 Monday 1 18.166667 1.1255000 3.333333 21.40821 18.000000 2.6478472 22.833333 11.831157 11.7592500 9.685177 44.20139 64.83542 1392.333 36.416667 6.083333 2.666667 1.4166667 0.5000000 0.1666667 0.0833333 0.3333333 6.833333 3 0 0.000000 0.000000 0
NJ yes 2022-04-25 14:00:00 not extracted 8.75 14 Monday 1 19.000000 0.0000000 14.000000 19.28403 18.000000 1.0014491 8.000000 9.805079 10.2594444 8.037513 46.56944 69.50417 1385.583 40.250000 8.500000 2.666667 1.0000000 1.0000000 0.5833333 0.0000000 0.4166667 8.750000 4 0 0.000000 0.000000 0
NJ no 2022-04-26 04:00:00 not extracted 6.00 24 Tuesday 2 18.666667 0.3934444 13.666667 14.43725 18.666667 0.8499583 8.666667 8.078431 1.3796111 3.949010 23.50000 40.60000 1418.667 15.250000 3.500000 1.833333 0.1666667 0.0000000 0.0833333 0.1666667 0.3333333 4.083333 0 0 0.000000 0.000000 0
NJ yes 2022-04-26 04:00:00 not extracted 6.00 24 Tuesday 2 6.833333 0.0000000 13.666667 17.71956 19.500000 0.0000000 11.500000 8.861500 0.0000000 3.692292 27.00417 51.80000 1408.667 22.083333 5.666667 2.083333 0.8333333 0.1666667 0.0833333 0.0000000 0.4166667 6.083333 1 0 0.000000 0.000000 0
NJ no 2022-04-27 04:00:00 not extracted 6.00 24 Wednesday 3 5.000000 0.3392778 13.333333 28.88986 2.166667 0.3799722 13.333333 16.047000 0.4117778 6.998310 40.40417 68.30000 1393.083 34.916667 7.083333 2.666667 1.4166667 0.4166667 0.0833333 0.0833333 0.2500000 6.500000 2 0 1.500000 0.000000 0
NJ yes 2022-04-27 04:00:00 not extracted 6.00 24 Wednesday 3 6.833333 0.0000000 13.000000 19.27428 19.500000 0.0000000 11.000000 9.642153 0.0000000 4.017564 33.00000 55.50208 1403.167 28.416667 5.750000 1.916667 0.3333333 0.0000000 0.0833333 0.0833333 0.2500000 4.666667 2 0 1.083333 0.000000 0


Part 5 Summary Example

We didn’t have enough data to produce part 5 summaries for most days.

ID filename window_number weekday calendar_date sleeponset sleeponset_ts wakeup wakeup_ts night_number daysleeper cleaningcode guider sleeplog_used acc_available nonwear_perc_day nonwear_perc_spt nonwear_perc_day_spt dur_spt_sleep_min dur_spt_wake_IN_min dur_spt_wake_LIG_min dur_spt_wake_MOD_min dur_spt_wake_VIG_min dur_day_IN_unbt_min dur_day_LIG_unbt_min dur_day_MOD_unbt_min dur_day_VIG_unbt_min dur_day_MVPA_bts_10_min dur_day_MVPA_bts_5_10_min dur_day_MVPA_bts_1_5_min dur_day_IN_bts_30_min dur_day_IN_bts_20_30_min dur_day_IN_bts_10_20_min dur_day_LIG_bts_10_min dur_day_LIG_bts_5_10_min dur_day_LIG_bts_1_5_min dur_day_total_IN_min dur_day_total_LIG_min dur_day_total_MOD_min dur_day_total_VIG_min dur_day_min dur_spt_min dur_day_spt_min N_atleast5minwakenight sleep_efficiency ACC_spt_sleep_mg ACC_spt_wake_IN_mg ACC_spt_wake_LIG_mg ACC_spt_wake_MOD_mg ACC_spt_wake_VIG_mg ACC_day_IN_unbt_mg ACC_day_LIG_unbt_mg ACC_day_MOD_unbt_mg ACC_day_VIG_unbt_mg ACC_day_MVPA_bts_10_mg ACC_day_MVPA_bts_5_10_mg ACC_day_MVPA_bts_1_5_mg ACC_day_IN_bts_30_mg ACC_day_IN_bts_20_30_mg ACC_day_IN_bts_10_20_mg ACC_day_LIG_bts_10_mg ACC_day_LIG_bts_5_10_mg ACC_day_LIG_bts_1_5_mg ACC_day_total_IN_mg ACC_day_total_LIG_mg ACC_day_total_MOD_mg ACC_day_total_VIG_mg ACC_day_mg ACC_spt_mg ACC_day_spt_mg quantile_mostactive60min_mg quantile_mostactive30min_mg L5TIME L5VALUE M5TIME M5VALUE L5TIME_num M5TIME_num L10TIME L10VALUE M10TIME M10VALUE L10TIME_num M10TIME_num Nbouts_day_MVPA_bts_10 Nbouts_day_MVPA_bts_5_10 Nbouts_day_MVPA_bts_1_5 Nbouts_day_IN_bts_30 Nbouts_day_IN_bts_20_30 Nbouts_day_IN_bts_10_20 Nbouts_day_LIG_bts_10 Nbouts_day_LIG_bts_5_10 Nbouts_day_LIG_bts_1_5 Nblocks_spt_sleep Nblocks_spt_wake_IN Nblocks_spt_wake_LIG Nblocks_spt_wake_MOD Nblocks_spt_wake_VIG Nblocks_day_IN_unbt Nblocks_day_LIG_unbt Nblocks_day_MOD_unbt Nblocks_day_VIG_unbt Nblocks_day_MVPA_bts_10 Nblocks_day_MVPA_bts_5_10 Nblocks_day_MVPA_bts_1_5 Nblocks_day_IN_bts_30 Nblocks_day_IN_bts_20_30 Nblocks_day_IN_bts_10_20 Nblocks_day_LIG_bts_10 Nblocks_day_LIG_bts_5_10 Nblocks_day_LIG_bts_1_5 Nblocks_day_total_IN Nblocks_day_total_LIG Nblocks_day_total_MOD Nblocks_day_total_VIG boutcriter.in boutcriter.lig boutcriter.mvpa boutdur.in boutdur.lig boutdur.mvpa bout.metric daytype
not extracted CG_no.csv.RData 11 Thursday 2022-05-05 20.34444 20:20:40 32.68611 08:41:10 11 0 2 sleeplog 0 1 29.23517 100.00000 65.62500 0.00000 739.9167 0.5833333 0.0000000 0 91.33333 28.25000 5.583333 0.1666667 0 0 3.500000 493.8333 26.33333 26.91667 10.83333 0 12.75 600.0833 86.66667 12.416667 0.3333333 699.5000 740.5000 1440 0 0.0000000 NA 0.0361978 44.21429 NA NA 8.537044 55.56372 142.0507 454.850 NA NA 168.2619 9.703274 10.37278 7.968112 64.77462 NA 57.45425 6.264102 54.90760 148.3376 484.2750 15.040636 0.0709993 7.342708 48.80000 72.80208 2022-05-05 04:00:05 0 2022-05-05 15:30:05 17.35628 0.0013889 11.50139 2022-05-05 04:00:05 1.3346111 2022-05-05 14:20:05 15.957556 0.0013889 10.334722 0 0 3 6 1 2 1 0 11 0 6 4 0 0 163 180 50 2 0 0 3 6 1 2 1 0 11 456 504 94 4 0.9 0.8 0.8 30_20_10 10_5_1 10_5_1 6 WD
not extracted CG_no.csv.RData 4 Thursday 2022-04-28 20.38472 20:23:05 31.00000 07:00:00 4 0 2 sleeplog 0 1 11.20681 94.20385 47.91667 36.91667 600.0000 0.0000000 0.0000000 0 31.91667 10.58333 4.916667 0.1666667 0 0 2.333333 712.7500 28.83333 11.58333 0.00000 0 0.00 738.2500 52.08333 12.250000 0.5000000 803.0833 636.9167 1440 0 0.0579615 0.4699774 0.0000000 NA NA NA 5.369452 57.78898 150.8119 592.900 NA NA 177.5429 7.491500 14.16879 10.875540 NA NA NA 4.278892 48.97520 153.1619 651.4167 9.851562 0.0272406 5.506233 31.90000 50.70000 2022-04-28 04:00:05 0 2022-04-28 16:50:05 13.97761 0.0013889 12.83472 2022-04-28 04:00:05 1.1067639 2022-04-28 14:20:05 11.839639 0.0013889 10.334722 0 0 2 4 1 1 0 0 0 1 2 0 0 0 54 76 37 1 0 0 2 4 1 1 0 0 0 403 425 85 4 0.9 0.8 0.8 30_20_10 10_5_1 10_5_1 6 WD
not extracted CG_yes.csv.RData 3 Thursday 2022-04-28 22.00000 22:00:00 31.00000 07:00:00 4 0 5 nosleeplog_accnotworn 0 0 16.66667 100.00000 47.91667 0.00000 539.9167 0.0833333 0.0000000 0 113.08333 41.83333 6.666667 0.0833333 0 0 6.833333 659.7500 0.00000 69.25000 0.00000 0 2.50 793.3333 89.41667 16.916667 0.3333333 900.0000 540.0000 1440 0 0.0000000 NA 0.0025158 39.20000 NA NA 11.784451 49.03765 142.5613 491.000 NA NA 156.6000 8.620601 NA 13.097232 NA NA 65.96000 6.470252 48.05778 148.7261 775.8000 13.560870 0.0085648 8.478756 43.70417 67.90208 2022-04-28 04:00:05 0 2022-04-28 16:50:05 20.61464 0.0013889 12.83472 2022-04-28 04:00:05 1.6481806 2022-04-28 14:20:05 17.413042 0.0013889 10.334722 0 0 5 10 0 6 0 0 2 0 3 1 0 0 237 284 52 1 0 0 5 10 0 6 0 0 2 608 656 97 4 0.9 0.8 0.8 30_20_10 10_5_1 10_5_1 6 WD
not extracted NJ_no.csv.RData 2 Tuesday 2022-04-26 18.14306 18:08:35 37.96111 13:57:40 2 1 2 sleeplog 0 1 0.00000 90.82627 75.00000 234.33333 952.0000 2.3333333 0.4166667 0 56.16667 19.66667 4.750000 0.3333333 0 0 0.000000 128.7500 21.00000 16.25000 0.00000 0 4.00 207.3333 37.58333 5.666667 0.3333333 250.9167 1189.0833 1440 0 0.1970706 2.4424609 0.0873512 56.23214 150.4000 NA 8.034866 51.05805 153.3509 640.275 NA NA NA 9.417023 10.79762 11.128205 NA NA 48.10417 6.022548 50.39268 150.6515 640.2750 16.777350 0.7143178 3.513264 24.50000 42.10000 2022-04-26 04:00:05 0 2022-04-26 17:10:05 14.80278 0.0013889 13.16806 2022-04-26 04:00:05 0.0018333 2022-04-26 13:30:05 8.431833 0.0013889 9.501389 0 0 0 2 1 1 0 0 3 2 13 13 3 0 116 136 31 3 0 0 0 2 1 1 0 0 3 234 258 37 3 0.9 0.8 0.8 30_20_10 10_5_1 10_5_1 6 WD
not extracted NJ_yes.csv.RData 2 Tuesday 2022-04-26 18.17778 18:10:40 37.96250 13:57:45 2 1 2 sleeplog 0 1 0.00000 90.97929 75.00000 224.16667 959.9167 2.5000000 0.5000000 0 75.50000 28.83333 7.833333 0.4166667 0 0 0.000000 125.0000 0.00000 10.58333 0.00000 0 4.75 198.2500 45.50000 8.750000 0.4166667 252.9167 1187.0833 1440 0 0.1888382 0.0124535 0.0713690 54.41000 125.3167 NA 8.201545 51.91676 148.9457 634.660 NA NA NA 10.308600 NA 9.396850 NA NA 60.59649 6.546238 52.14597 147.2362 634.6600 20.651829 0.2274342 3.814705 28.70417 52.80417 2022-04-26 04:00:05 0 2022-04-26 17:20:05 17.89331 0.0013889 13.33472 2022-04-26 04:00:05 0.0000000 2022-04-26 13:10:05 9.155292 0.0013889 9.168056 0 0 0 2 0 1 0 0 3 7 21 16 4 0 150 173 47 5 0 0 0 2 0 1 0 0 3 248 278 54 5 0.9 0.8 0.8 30_20_10 10_5_1 10_5_1 6 WD

MVPA comparison

Subject Level

Day level