10. Sleep Analysis

Sleep analysis

Sleep analysis in GGIR comes at three stages:

1. The discrimination of sustained inactivity and wakefulness periods, discussed in chapter 8.

2. Identification of time windows that guide the eventual sleep detection, as discussed chapter 9.

3. Assess overlap between the windows identified in step 1 and 2, which we use to define the Sleep Period Time window (SPT) or the time in bed window (TimeInBed) as discussed in this chapter.

In the previous two chapters you learnt about the first two steps in this chapter we will discuss the last step.

Sleep Period Time (SPT) window or Time in Bed

Here we have two scenarios:

1. If the guider reflects an approximation of the Sleep Period Time window, which is the window between sleep onset and waking up at the end of the night, then any SIB that fully or partially overlaps with the guider is considered sleep.

2. If a guider reflects the Time in Bed then any SIB that fully overlaps with the guider is considered sleep. In this scenario sleep latency and sleep efficiency can be estimated and are included in the GGIR part 4 report.

In both cases the start of the first SIB is then considered sleep onset and the end of the last SIB is considered waking up.

For all guiders, other than “HorAngle”, parameter sleepwindowType is automatically set to “SPT” corresponding to scenario 1, such that no attempt is made to estimate sleep latency or sleep efficiency.

If you use as guider a sleeplog that reflects the Time in Bed you will need to set parameter sleepwindowType = "TimeInBed" to tell GGIR to follow scenario 2.

Quality assurance

Cleaningcode

To monitor possible problems with the sleep detection, the output variable cleaningcode is stored per night. Cleaningcode per night (noon-noon or 6pm-6pm as described above) can have one of the following values:

• 0: sleep log available and SPT is identified.

• 1: sleep log not available, and alternative guider used (HDCZA by default) and SPT is identified with that.

• 2: not enough valid accelerometer data in the present night, where parameter includenightcrit is used to define how many valid hours are need.

• 3: no accelerometer data available.

• 4: there are no nights to be analysed for this person.

• 5: SPT estimated based on guider only, because either no SIB was found during the entire guider window which complicates defining the start and end of the SPT, or the user specified the ID number of the recording and the night number in the data_cleaning_file, more about this further down, to tell GGIR to rely on the guider and not rely on the accelerometer data for this particular night.

• 6: no sleep log available and also alternative guider (HDCZA/HorAngle) failed for this specific night then use average guider estimates from other nights in the recording as guider for this night. If HDCZA/HorAngle estimates are also not available during the entire recording then use L5+/-12 estimate for this night.

Visual inspection of classifications

As overlap between sib and guiders is difficult to review in a quantitative way, GGIR offers the option to export a visualisation, with parameter do.visual = TRUE. To manage the number of visualisations generated it is possible to tell GGIR to only show outliers. Here, outliers are defined as a difference between guider edge and the final classification of sleep onset and wakeup time larger than parameter criterror. When you set parameter outliers.only = TRUE only nights considered to be an outlier will be displayed.

This functionality is useful when reviewing classifications in large data sets that use sleep logs. Visual inspection of outliers in this way can for example help to identify data entry errors for sleep logs.

Data cleaning file

After data quality check you may observe that some adjustments are needed. Parameter data_cleaning_file (path to a csv file you create) allows you to specify individuals and nights for whom part4 should entirely rely on the guider. The first column of this csv file should have column name ID and there should be a column relyonguider_part4 to specify the night. The night_part4 allows you to tell GGIR which night(s) should be omitted in part 4.

Sleep metrics available in GGIR

For a full overview of all sleep variables in part 4 see: https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html#42_Output_part_4

Among these we assume that most are intuitive:

• sleep onset and wakeup

• Sleep duration during SPT, which is the accumulate sleep time (sustained inactivity bouts classified as sleep)

• WASO, the time spent in wakefulness after sleep onset. However, there are possible a few concepts that need clarifications:

Sleep Regularity Index (SRI)

This is a measure of sleep regularity between successive days, as first described by Phillips and colleagues. The SRI can have a value between -100 and 100, where 100 reflects perfect regularity (identical days), 0 reflects random pattern, and -100 reflects perfect reversed regularity. The SRI is proposed to only be calculated based on seven, or a multitude of seven, consecutive days of data without missing values. This to avoid a possible role of imbalanced data to the final estimate. However, this renders many datasets unsuitable for analysis and leads to a painful loss in sample size and statistical power.

Sleep Regularity Index – Dealing with unbalanced data

To address this, I implemented the SRI in GGIR per day-pair. Per day-pair GGIR now stores the SRI value and the fraction of the 30 second epoch-pairs between both days that are valid. This fraction can be found in the output under the variable name SriFractionValid. By default, day-pairs are excluded if this fraction is below 0.66. For those familiar with GGIR this threshold is coupled with the 16-hour default value for parameter “includenightcrit”. For example, if you set parameter “includenightcrit = 12”, the fraction threshold will be: 12 / 24 = 0.5. Note that I have implemented the SRI calculation such that it accounts for the missing values in the denominator. As a result, the SRI value interpretation remains unchanged.

The 30 second epoch setting is automatically applied, even if the rest of the GGIR process works with a different epoch duration.

The day-pair level estimates are stored as variable SleepRegularityIndex in the GGIR part 4 .csv-report on sleep. Further, GGIR also stores the person-level aggregates such as: the plain average over all valid days, the average of all valid weekend days, and the average of all valid week days. No GGIR input arguments are needed to invoke the SRI calculation. The calculation is automatically performed after updating GGIR and processing your data.

Sleep Regularity Index – Benefits of the revised approach

It enables the user to study the day-pair to day-pair variation in SRI, and the role of day-pair inclusion criteria.

The access to SRI at day-pair level makes it possible to account for an imbalanced datasets via multilevel regression analysis applied to the output of GGIR, with day-pair as one of the model levels.

Nap detection

Any references to daytime nap detection in GGIR is based on experimental functionality that requires ongoing investigation. Once this functionality has matured we will expand the documentation accordingly.

Key parameters

All parameters that are part of the params_sleep category as discussed in section “Sleep parameters” in https://cran.r-project.org/web/packages/GGIR/vignettes/GGIRParameters.html

and

• do.visual, outliers.only, and criterror.
• excludefirstlast.
• def.noc.sleep
• includenightcrit
• data_cleaning_file

Related output

GGIR stores two type of output: The cleaned and the full output. In the cleaned output invalid nights have been removed, while in the full output these nights are included.

More specifically, a night is excluded from the ‘cleaned’ results based on the following criteria:

• If the study proposed a sleep log to the individuals, then nights are excluded for which the sleep log was not used as a guider. In other words: nights with cleaningcode not equal to 0 or variable sleep log used equals FALSE).

• If the study did not propose a sleep log to the individuals, then all nights are removed with cleaningcode higher than 1.

Filename	Folder	Content
part4_nightsummary_cleaned.csv	results	cleaned night level results
part4_summary_cleaned.csv	results	cleaned person level results
part4_nigthsummary_full.csv	results	full night level results

Be aware that if using the full output and working with wrist accelerometer data, missing entries in a sleep log that asks for Time in Bed will be replaced by HDCZA estimates of SPT. Therefore, extra caution should be taken when working with the full output.

Notice that part 4 is focused on sleep research. In the chapters we will discuss the analysis done by part 5. There, the choice of guider may be considered less important, by which any estimate of the time in bed is considered useful. So, you may see that a night that is excluded from the cleaned results in part 4 still appears in the cleaned results for part 5.