How to read sleep graphs

1. What it is

Sleep graph is the main outcome of sleep tracking - it shows analysed data from all the sensors that were used during the night.

2. Sleep graph screen

The graph screen shows up to three graphs:

Graph screen also shows statistics about your night, along with an option to rate and annotate the record.

The app uses Actigraphy – activity-based sleep tracking – as the method of estimating your sleep phases. We have shown in a study that our approach produces a very good match with Polysonography – the de-facto golder standard for clinical sleep tracking with a fraction of the costs. See How does Sleep as Android compare to the sleeplab.

2.1. Statistics

numbers
Figure 1. Sleep record statistics
Sleep record end date

Why end and not start?

Deep sleep
  • Top value (%) shows deep sleep ratio (how big a portion of the night did you spend in deep sleep).

  • Bottom value (hours:minutes) shows deep sleep duration.

Duration
  • Top value (hours:minutes) shows sleep duration (sum of all sleep phases, awakes are not counted).

  • Bottom value shows the surplus or deficit compared to your Ideal daily sleep income.

Snoring
  • Top value (%) shows snoring ratio (how big a portion of the night did you spend snoring).

  • Bottom value (hours:minutes) shows snoring duration.

2.2. Actigraph

acti
Figure 2. Actigraph

Actigraph shows the intensity of your nightly movements. The higher the peak, the more you’ve been moving.

2.3. Hypnogram

phase
Figure 3. Hypnogram

The hypnogram shows your sleep phases progress during the night, estimated from actigraphic data and other inputs (e.g. awake periods).

  • Awake: light green, the highest column reaching the top of the graph.

  • REM phase and light sleep: shown as medium green, REM has lower columns.

  • Deep sleep: shown as the lowest, dark green column.

red
Figure 4. Red sections

Red sections on your graph indicate that the app did not receive data from sensors at that time. This usually happens when using wearables due to lost connection to the wearable. The phone’s accelerometer can also (very rarely) malfunction.

2.4. Noise graph

snore
Figure 5. Noise graph

The noise graph shows how much noise (sleep talk, snoring, environmental) was there throughout the night.

When sound recognition is enabled, sounds (cry, laugh, sneeze or cough, snoring and talking) are marked with icons.

2.5. Colored lines

hrbr
Figure 6. Oxygen level (Oximeter) + heart rate
light
Figure 7. Light level + heart rate
sonar breath rate
Figure 8. Breath rate (sonar)
  • Blue line = blood oxygen level if you are using oximeter. Blue dots with numbers indicate maximum and minimum (read more about breath rate monitoring)

  • Red line = heart rate through the night. Red dots with numbers inside are the maximum and minimum heart rate (read more about heart rate monitoring).

  • Orange line = light in your room in LUX units (read more about light awake detection).

  • Turquoise (Blue-green) line = breath rate if you are using sonar Blue dots with numbers indicate maximum and minimum (read more about breath rate monitoring).

  • Dashed line = smart period prior to alarm time (read more about Smart wake up).

2.6. Markers and Icons

Besides deep sleep, REM phase and light sleep, there are several other events depicted in the sleep graphs.

Icons on Actigraph

ic action pause Tracking paused
ic action time Alarm / snoozed alarm
ic action sunrise Sunrise / sunset
ic action noise Snoring event
ic action cpap Low breath rate detected (Apnea event)
ic battery 60 Low battery (switching to stand-by mode)

Icons on Noise graph

ic action talk Sleep talking
ic action sick Cough and sneeze
ic action baby Baby cry
ic action laughLaugh
ic action mic Sleep noise recorded
ic action dream Lucid dreaming

3. Guide

3.1. Editing graphs

For a guide on how to edit a graph, please see Graph editing.

3.2. How should the graphs look?

As a general rule of thumb that applies to healthy individuals:

A healthy sleep (for a monophasic sleeper) is 7-8 hours long and consists of 5 sleep cycles where the first lasts for 70-100 minutes and the consequent cycles get longer but lighter. Each cycle consists of 4 stages lasting usually 5-15 minutes. Stage 1 and 2 are considered light sleep and this is the best time to be woken up in the morning.

A healthy sleep cycle looks like a 10-30 minutes of light sleep (high peaks) followed by an area of deep sleep (low peaks or no peaks) lasting 40-100 minutes.
Different resources on sleep may provide different figures though.

So deep sleep % may actually range between 30%-70%. Figures out of this range may indicate either incorrect sleep tracking setup or some sleep issues. For example very low deep sleep % may indicate either sleep deprivation or issues in your life style such as higher alcohol or caffeine intake, not enough sport etc.

3.3. Comparing Sleep as Android graphs to sleep lab

Ever wondered, how precise the sleep tracking with only a mobile phone could be?
We had the opportunity to compare our algorithms with sleep-lab clinical study, and the results are very promising!
The chances the smart alarm will be triggered properly (not in deep sleep) is 96%.
Lucid cues have a 50% change to hit REM phase.
Awake periods just from movement intensity changes (no other awake heuristic like sound detection, light detection, HR monitoring) can be detected with 30% success.

You can read more details about the study on our blog post here.
If you are interested how the REM detection with Sleep as Android works, you can read it here.

FAQ

Added awakes by mistake

  • It is possible to revert up to 5 changes done during editing a graph.

  • Uu can use Undo banner (appears each time you change a graph):

awake mistake
Figure 1. Undo banner after using scissors icon
  • You can use ⋮ menu → Undo option

How does Sleep as Android (actigraphy) compare to Polysomnography?

We use a different input than polysomnographists, and define our own sleep phases, reflecting an objective aspect of sleep, easy to measure with common devices. One naturally needs to ask whether there is any relationship between the EEG-phases and our ACT-phases.

Fortunately, several research teams raised similar questions before (See this one, or this one, or this one, or this one). They measured a bunch of people on a traditional polysomnograph and recorded their physical activity at the same time (By filming them and then counting the movements manually, or by using accelerometer readings). The published analyses show that there indeed is a significant statistical relationship between EEG-phases and body movements.

You can also read about comparison of Sleep as Android algorithms and Sleep lab results on our blog here.

I do not trust the results, it is fake / generating random data

Accelerometric sensors are really sensitive, which is great for sleep tracking. Normally, what you see when you leave the phone on the table gets immediately dwarfed when you do some more significant move. Just leave phone on the table for a while and you will see a dramatic development, but then move the phone and you will see all the development is really tiny in comparison to the new peak.

So what you see is random noise, given by very small vibrations of the table or in very calm areas by seismic movement. We mark the data relatively, so you always get it distinguished into light and deep sleep. But the algorithm works well only in conditions that are assumed by it, i.e. in the bed with relatively large movement peaks.
To be more specific, if you leave the phone on a table, you can get values perhaps on the scale of 0.000001 to 0.000009 m/s2 (The value is made up here, but it is physically very small). In the bed, you may get values from 1 to 9 m/s2 (which is physically large). The algorithm sees though just that the high value is 9 times higher than the low value, in both cases.
We had to do this because every accelerometer (in different cell phones) measures differently, so we couldn’t assume any standard conversion formula that would respond to absolute values.

So if you use the phone in the bed, it is in fact drastically different from measuring on a calm spot, just like the table.

Please do not hesitate to ask for any clarification at support@urbandroid.org.

My graphs are flat

There can be several reasons why your graphs are flat.

The actigraphy is almost flat

When you can see some movement on the actigraphy, but the graph is unusually flat:
. Sonar - make sure the signal is strong enough by keeping sonar volume at max at Settings → Sleep tracking → Test sensor → blue sliding bar.
- you can also try different frequency by choosing other frequency from the ddrop down menu list in Settings → Sleep tracking → Test sensor → Frequency
- keep the phone closer to you bed
- try different positioning of the phone
. Accelerometer - try keeping the phone closer to you.

The actigraphy is flat, or red
  • Disable all system restrictions applied to Sleep as Android, or any companion app for tracking with a wearable: https://dontkillmyapp.com/

Too much awakes (false-positive)

  • When there is too much awakes falsely estimated on your graph, use Left ☰ _menu → ic bug Report a bug, and send us the application log.

  • Most often the awakes are driven by significant HR peaks (awakes align with HR red line graph), you can try disable this type of awake detection in Settings → Sleep tracking → Awake detection → Heart rate monitoring.

  • Other common reason is phone screen turned on, you can try to disblae Awake when using phone awake detection in Settings → Sleep tracking → Awake detection → Awake when using phone.

Tracking crashes, stops suddenly

If the tracking stops completely after few minutes, the background processes are restricted by your system.

  • Make sure no system restrictions are applied to Sleep as Android, or any companion app for a tracking with wearable: Check our guide here.

  • If the guide won’t help, send us your log using Leftmenu → ic bug Report a bug.

Why is there a red bar / section / block in my sleep graph?

The red block indicates that something went wrong with tracking at that time and the device stopped providing sensor data for some reason. Usually those are some non-standard battery optimizations or battery savers, the battery gets too low so we preserve it for the alarm or connectivity issue if you use a wearable.

1. Battery restrictions

Make sure no system restrictions are applied to Sleep, or any involved apps like wearable companion app).
See our guide here, and follow the instructions.

2. Too low battery

When the battery is too low (usually below 10%), data collecting is terminated to preserve enough battery for alarm.
When the battery was too low, there is a battery icon is displayed on the graph:

low battery
Figure 1. Low battery graph

3. Connectivity issues with a wearable

When the connection with the wearable is lost, you can see red sections on the graph. The app always tries to reach the wearable again.
The graph can look like this:

red wearable
Figure 2. Connection lost during tracking
  1. Opt-out from any battery restrictions is applied by your system (https://dontkillmyapp.com/)

  2. Pair the wearable with your phone in System settings.

  3. Make sure the BT is not lost, and try lowering the distance between the phone and the wearable.

  4. Try settings the device as Trusted device.