I posted a similar question but I am still struggling to make sure my syntax is accurate. I used the Cosinor package in R to determine the amplitude value of each participant, based on heart rate per day across a 24 hour period.

Data was collected for every day for 6 weeks per subject.

  • However, some participants have less days than other due to data loss - range is 1-42 days with average of 16

my data sample:

structure(list(User.ID = c(21256L, 21256L, 21256L, 21256L, 21256L, 
                           21256L, 21256L, 21258L, 21258L, 21258L, 
    21258L, 21258L, 21258L, 21259L), HR = c(93.12272727, 95.60333333, 
    98.29333333, 118.9666667, 84.46666667, 78.36666667, 95.03333333, 
    97.80333333, 80.03333333, 94.03333333, 97.88, 82.86, 85.86333333, 
    81.59666667), Weekday = structure(c(7L, 5L, 1L, 3L, 4L, 2L, 1L, 
    5L, 1L, 3L, 4L, 2L, 5L, 7L), 
    .Label = c("Fri", "Mon", "Sat", "Sun", "Thu", "Tue", "Wed"), 
    class = "factor"), 
    YearDay = c(45L, 46L, 47L, 48L, 49L, 50L, 54L, 46L, 47L, 48L, 49L,  
    50L, 53L, 45L), 
    Whoop_ID = c(6003L, 6003L, 6003L, 6003L, 6003L, 6003L, 6003L, 
    6004L, 6004L, 6004L, 6004L, 6004L, 6004L, 6001L), 
    MESOR = c(127.8198669, 82.53790266, 80.196328, 87.94691954, 
    78.24139419, 85.76274123, 85.24568034, 78.76045577, 78.13040009, 
    82.96056761, 80.7242717, 81.09227575, 79.80820793, 76.53899579), 
    amp = c(39.62989516, 15.90807964, 17.40106027, 20.93586176, 
    11.64670832, 17.36869616, 12.79682425, 15.60118873, 18.1036377, 
    17.95736176, 14.5964711, 12.81719691, 19.65582833, 19.85491777), 
    acr = c(-2.096524329, -0.531214487, -0.419872151, -0.885908809, 
    -0.614661956, -0.498619211, -0.347860598, -0.424370362, 
    -0.09063145, -0.414970962, -0.390604738, -0.415772215, 
    -6.021379968, -0.627923711), 
    Group.x = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L), 
    .Label = c("Decrease", "Increase"), 
    class = "factor"), 
    DayofIntervention = c(0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 0),
    MRI.Day.of.Year = c(54, 54, 54, 54, 54, 54, 54, 53, 53, 53, 53, 
    53, 53, 57), 
    Intervention = c("PRE", "PRE", "PRE", "PRE", "PRE", "PRE", "POST",  
    "PRE", "PRE", "PRE", "PRE", "PRE", "POST", "PRE")), 
    row.names = c(NA, -14L), 
    groups = structure(list(User.ID = c(21256L, 21258L, 21259L), 
    .rows = list(1:7, 8:13, 14L)), 
    row.names = c(NA, -3L), 
    class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE), 
    class = c("grouped_df", "tbl_df", "tbl", "data.frame"))

Which looks like:

# A tibble: 14 x 12
# Groups:   User.ID [3]
   User.ID    HR Weekday YearDay Whoop_ID MESOR   amp     acr Group.x  DayofIntervention MRI.Day.of.Year Intervention
     <int> <dbl> <fct>     <int>    <int> <dbl> <dbl>   <dbl> <fct>                <dbl>           <dbl> <chr>       
 1   21256  93.1 Wed          45     6003 128.   39.6 -2.10   Decrease                 0              54 PRE         
 2   21256  95.6 Thu          46     6003  82.5  15.9 -0.531  Decrease                 1              54 PRE         
 3   21256  98.3 Fri          47     6003  80.2  17.4 -0.420  Decrease                 2              54 PRE         
 4   21256 119.  Sat          48     6003  87.9  20.9 -0.886  Decrease                 3              54 PRE         
 5   21256  84.5 Sun          49     6003  78.2  11.6 -0.615  Decrease                 4              54 PRE         
 6   21256  78.4 Mon          50     6003  85.8  17.4 -0.499  Decrease                 5              54 PRE         
 7   21256  95.0 Fri          54     6003  85.2  12.8 -0.348  Decrease                 6              54 POST        
 8   21258  97.8 Thu          46     6004  78.8  15.6 -0.424  Decrease                 0              53 PRE         
 9   21258  80.0 Fri          47     6004  78.1  18.1 -0.0906 Decrease                 1              53 PRE         
10   21258  94.0 Sat          48     6004  83.0  18.0 -0.415  Decrease                 2              53 PRE         
11   21258  97.9 Sun          49     6004  80.7  14.6 -0.391  Decrease                 3              53 PRE         
12   21258  82.9 Mon          50     6004  81.1  12.8 -0.416  Decrease                 4              53 PRE         
13   21258  85.9 Thu          53     6004  79.8  19.7 -6.02   Decrease                 5              53 POST        
14   21259  81.6 Wed          45     6001  76.5  19.9 -0.628  Decrease                 0              57 PRE

Assumptions:

  1. I want to assume that measurements that are further apart in time are less correlated than measurements that closer (random slope).

  2. I want to assume that amplitude measurements from the same participants on the same weekday are more correlated than measurements on different weekdays, ( random intercept for Weekday grouping factor nested with User.ID.)

  • There are 2 groups (Group.x) "Decrease" and "Increase" that can be thought of as "control and intervention," respectively.

  • The DayofIntervention variable counts the number of days data was collected, sequentially, per subject. The Intervention variable determines if the subject is performing a task PRE or POST having intervention.

  1. I would like to account for Pre/Post intervention timepoints as well but I'm not sure how, syntactically.

my model (with what I think is random slope + intercepts:

mixed.model = lmer (amp ~ Group.x * DayofIntervention + 
    (1 | Weekday) + (1 + DayofIntervention | User.ID) + 
    (User.ID / Weekday), data = DF)