Tags allow you to find out how various lifestyle events affect your sleep.
Tags can be added:
while sleep tracking, by tapping the button
in the rating screen, after you finish sleep tracking
in any sleep record, by tapping the big button
You can add tags to any sleep record by including it in the comment section of the record. Any word can become a tag if you prefix it with #. Emoji also count as tags. See also editing tags.
There are also predefined icon tags, and a handful of automatically added tags.
The tags are arranged in the order based on the frequency of use.
You can choose from the list of pre-defined tags with icons, which include the most common lifestyle events which have some relevance to sleep.
Some tags are calculated and added to your sleep record automatically. You can turn this feature off in Settings → Sleep → More → Stats → Automatic tagging.
|Automatically added tags|
#newmoon (that night was new moon)
We calculate the 4 most frequent places in the past year of your data history. One year should be sufficient so that you can e.g. watch your sleep at your holiday home where you go regularly in the summer.
Should you move, your new home will gradually become #geo3 → #geo2 → #geo1 → #home after some time as it becomes dominant in your data.
You can also reset your home location any time using Settings → Privacy → Reset home location.
|Location permission needs to be granted.|
The location we use is coarse and we store it even coarser, +/- 5 km. In effect, we identify your location as a square and then match those squares to set up the location tags.
You can completely turn off location storing in Settings → Privacy → Don’t store sleep location.
If you use a wearable or sonar, a #watch or #sonar tag will appear on your graph.
If sleep tracking was started automatically for your (you did not hit the Sleep tracking button) an #auto tag will be added.
You can see more on this feature here.
You can create your own personal tag, the only requirement is using the hash mark notation at the start, followed by any number of alphanumeric character (For example #headache or #travel).
It is also possible to track frequency of your events. For example #caffeine_3x – meaning 3 coffees, #alcohol_2x – 2 shots, #sport_3x – 3 hours of sport. This allows to describe your lifestyle events more precisely.
|Support for automated analysis of the frequency information is not yet included in the app, but it is planned in coming versions.|
You can add frequencies easily but pressing the tag button multiple times. To delete a tag on the other hand — long press the tag icon.
In Stats, there is a collapsible Tags section which shows average sleep measures so that you can easily compare your overall average measures (sleep length, deep sleep, snoring…) with your tagged results. If you observe e.g. a significant drop in deep sleep % after drinking coffee, you may consider to reduce caffeine consumption (especially before sleep) in order to improve your deep sleep %.
Added awakes by mistake
It is possible to revert up to 5 changes done during editing a graph.
You can use Undo banner (appears each time you change a graph):
You can use ⋮ menu → Undo option
Duration is shorter, lenght is wrong
The Sleep Duration is total sum of all your sleep phases (Light, REM, and Deep), not counting the awake phases - because when awake, you are actually not sleeping.
So on default settings, the Sleep duration is always a bit shorter than the duration of tracking.
If you wish your Sleep duration is the same as tracking duration:
Disable the awake detection in Settings → Sleep tracking → Awake detection.
You can also try to adjust the sensitivity of each type of settings to get optimal results. In most cases, too much awake periods are caused by significant HR peaks.
If you are not sure, where those awake periods come from, please use Left ☰ menu → Report a bug, and send us the application log.
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 email@example.com.
My graphs are flat
There can be several reasons why your graphs are 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.
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 → 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 Left ☰ menu → 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.
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.
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:
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:
Opt-out from any battery restrictions is applied by your system (https://dontkillmyapp.com/)
Pair the wearable with your phone in System settings.
Make sure the BT is not lost, and try lowering the distance between the phone and the wearable.
Try settings the device as Trusted device.