261 lines
34 KiB
Markdown
261 lines
34 KiB
Markdown
# Fitbit Sleep Intraday
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Sensor parameters description for `[FITBIT_SLEEP_INTRADAY]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[TABLE]`| Database table name or file path where the sleep intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file.
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The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together.
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We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity).
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??? example "Example of the structure of source data with Fitbit’s sleep API Version 1"
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=== "JSON"
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|device_id |fitbit_data |
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|---------------------------------------- |--------------------------------------------------------- |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 2, "awakeDuration": 3, "awakeningsCount": 10, "dateOfSleep": "2020-10-07", "duration": 8100000, "efficiency": 91, "endTime": "2020-10-07T18:10:00.000", "isMainSleep": true, "logId": 14147921940, "minuteData": [{"dateTime": "15:55:00", "value": "3"}, {"dateTime": "15:56:00", "value": "3"}, {"dateTime": "15:57:00", "value": "2"},...], "minutesAfterWakeup": 0, "minutesAsleep": 123, "minutesAwake": 12, "minutesToFallAsleep": 0, "restlessCount": 8, "restlessDuration": 9, "startTime": "2020-10-07T15:55:00.000", "timeInBed": 135}, {"awakeCount": 0, "awakeDuration": 0, "awakeningsCount": 1, "dateOfSleep": "2020-10-07", "duration": 3780000, "efficiency": 100, "endTime": "2020-10-07T10:52:30.000", "isMainSleep": false, "logId": 14144903977, "minuteData": [{"dateTime": "09:49:00", "value": "1"}, {"dateTime": "09:50:00", "value": "1"}, {"dateTime": "09:51:00", "value": "1"},...], "minutesAfterWakeup": 1, "minutesAsleep": 62, "minutesAwake": 0, "minutesToFallAsleep": 0, "restlessCount": 1, "restlessDuration": 1, "startTime": "2020-10-07T09:49:00.000", "timeInBed": 63}], "summary": {"totalMinutesAsleep": 185, "totalSleepRecords": 2, "totalTimeInBed": 198}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 3, "awakeDuration": 21, "awakeningsCount": 16, "dateOfSleep": "2020-10-08", "duration": 19260000, "efficiency": 89, "endTime": "2020-10-08T06:01:30.000", "isMainSleep": true, "logId": 14150613895, "minuteData": [{"dateTime": "00:40:00", "value": "3"}, {"dateTime": "00:41:00", "value": "3"}, {"dateTime": "00:42:00", "value": "3"},...], "minutesAfterWakeup": 0, "minutesAsleep": 275, "minutesAwake": 33, "minutesToFallAsleep": 0, "restlessCount": 13, "restlessDuration": 25, "startTime": "2020-10-08T00:40:00.000", "timeInBed": 321}], "summary": {"totalMinutesAsleep": 275, "totalSleepRecords": 1, "totalTimeInBed": 321}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 1, "awakeDuration": 3, "awakeningsCount": 8, "dateOfSleep": "2020-10-09", "duration": 19320000, "efficiency": 96, "endTime": "2020-10-09T05:57:30.000", "isMainSleep": true, "logId": 14161136803, "minuteData": [{"dateTime": "00:35:30", "value": "2"}, {"dateTime": "00:36:30", "value": "1"}, {"dateTime": "00:37:30", "value": "1"},...], "minutesAfterWakeup": 0, "minutesAsleep": 309, "minutesAwake": 13, "minutesToFallAsleep": 0, "restlessCount": 7, "restlessDuration": 10, "startTime": "2020-10-09T00:35:30.000", "timeInBed": 322}], "summary": {"totalMinutesAsleep": 309, "totalSleepRecords": 1, "totalTimeInBed": 322}}
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=== "PLAIN_TEXT"
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All columns are mandatory, however, all except `device_id`, `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "minutesData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1. Actually, you only need to make sure rows extracted from the same "minutesData" block are assigned with the same unique `type_episode_id` value.
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|device_id |type_episode_id |local_date_time |duration |level |is_main_sleep |type |
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|------------------------------------ |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-07 15:55:00 |60 |awake |0 |classic |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-07 15:56:00 |60 |awake |0 |classic |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-07 15:57:00 |60 |restless |0 |classic |
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??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2"
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=== "JSON"
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|device_id |fitbit_data |
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|---------------------------------------- |--------------------------------------------------------- |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-10","duration":3600000,"efficiency":92,"endTime":"2020-10-10T16:37:00.000","infoCode":2,"isMainSleep":false,"levels":{"data":[{"dateTime":"2020-10-10T15:36:30.000","level":"restless","seconds":60},{"dateTime":"2020-10-10T15:37:30.000","level":"asleep","seconds":660},{"dateTime":"2020-10-10T15:48:30.000","level":"restless","seconds":60},...], "summary":{"asleep":{"count":0,"minutes":56},"awake":{"count":0,"minutes":0},"restless":{"count":3,"minutes":4}}},"logId":26315914306,"minutesAfterWakeup":0,"minutesAsleep":55,"minutesAwake":5,"minutesToFallAsleep":0,"startTime":"2020-10-10T15:36:30.000","timeInBed":60,"type":"classic"},{"dateOfSleep":"2020-10-10","duration":22980000,"efficiency":88,"endTime":"2020-10-10T08:10:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-10T01:46:30.000","level":"light","seconds":420},{"dateTime":"2020-10-10T01:53:30.000","level":"deep","seconds":1230},{"dateTime":"2020-10-10T02:14:00.000","level":"light","seconds":360},...], "summary":{"deep":{"count":3,"minutes":92,"thirtyDayAvgMinutes":0},"light":{"count":29,"minutes":193,"thirtyDayAvgMinutes":0},"rem":{"count":4,"minutes":33,"thirtyDayAvgMinutes":0},"wake":{"count":28,"minutes":65,"thirtyDayAvgMinutes":0}}},"logId":26311786557,"minutesAfterWakeup":0,"minutesAsleep":318,"minutesAwake":65,"minutesToFallAsleep":0,"startTime":"2020-10-10T01:46:30.000","timeInBed":383,"type":"stages"}],"summary":{"stages":{"deep":92,"light":193,"rem":33,"wake":65},"totalMinutesAsleep":373,"totalSleepRecords":2,"totalTimeInBed":443}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-11","duration":41640000,"efficiency":89,"endTime":"2020-10-11T11:47:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-11T00:12:30.000","level":"wake","seconds":450},{"dateTime":"2020-10-11T00:20:00.000","level":"light","seconds":870},{"dateTime":"2020-10-11T00:34:30.000","level":"wake","seconds":780},...], "summary":{"deep":{"count":4,"minutes":52,"thirtyDayAvgMinutes":62},"light":{"count":32,"minutes":442,"thirtyDayAvgMinutes":364},"rem":{"count":6,"minutes":68,"thirtyDayAvgMinutes":58},"wake":{"count":29,"minutes":132,"thirtyDayAvgMinutes":94}}},"logId":26589710670,"minutesAfterWakeup":1,"minutesAsleep":562,"minutesAwake":132,"minutesToFallAsleep":0,"startTime":"2020-10-11T00:12:30.000","timeInBed":694,"type":"stages"}],"summary":{"stages":{"deep":52,"light":442,"rem":68,"wake":132},"totalMinutesAsleep":562,"totalSleepRecords":1,"totalTimeInBed":694}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-12","duration":28980000,"efficiency":93,"endTime":"2020-10-12T09:34:30.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-12T01:31:00.000","level":"wake","seconds":600},{"dateTime":"2020-10-12T01:41:00.000","level":"light","seconds":60},{"dateTime":"2020-10-12T01:42:00.000","level":"deep","seconds":2340},...], "summary":{"deep":{"count":4,"minutes":63,"thirtyDayAvgMinutes":59},"light":{"count":27,"minutes":257,"thirtyDayAvgMinutes":364},"rem":{"count":5,"minutes":94,"thirtyDayAvgMinutes":58},"wake":{"count":24,"minutes":69,"thirtyDayAvgMinutes":95}}},"logId":26589710673,"minutesAfterWakeup":0,"minutesAsleep":415,"minutesAwake":68,"minutesToFallAsleep":0,"startTime":"2020-10-12T01:31:00.000","timeInBed":483,"type":"stages"}],"summary":{"stages":{"deep":63,"light":257,"rem":94,"wake":69},"totalMinutesAsleep":415,"totalSleepRecords":1,"totalTimeInBed":483}}
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=== "PLAIN_TEXT"
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All columns are mandatory, however, all except `device_id`, `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "data" and "shortData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1. Actually, you only need to make sure rows extracted from the same "minutesData" block are assigned with the same unique `type_episode_id` value.
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|device_id |type_episode_id |local_date_time |duration |level |is_main_sleep |type |
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|------------------------------------ |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-10 15:36:30 |60 |restless |0 |classic |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-10 15:37:30 |660 |asleep |0 |classic |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |0 |2020-10-10 15:48:30 |60 |restless |0 |classic |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |... |... |... |... |... |... |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |1 |2020-10-10 01:46:30 |420 |light |1 |stages |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |1 |2020-10-10 01:53:30 |1230 |deep |1 |stages |
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## RAPIDS provider
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!!! info "Available time segments"
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- Available for all time segments
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!!! info "File Sequence"
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```bash
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- data/raw/{pid}/fitbit_sleep_intraday_raw.csv
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- data/raw/{pid}/fitbit_sleep_intraday_parsed.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv
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- data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv
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- data/processed/features/{pid}/fitbit_sleep_intraday.csv
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```
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Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `RAPIDS` provider|
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|`[FEATURES]` | Features to be computed from sleep intraday data, see table below |
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|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS grouped them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`).
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|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. Fitbit provides 2 types of sleep: `main`, `nap`.
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|`[INCLUDE_SLEEP_LATER_THAN]`| All resampled sleep rows (bin interval: one minute) that started after this time will be included in the feature computation. It is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. If a segment is longer than one day, this value is for every day.
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|`[REFERENCE_TIME]`| The reference point from which the `[ROUTINE]` features are to be computed. Chosen from `MIDNIGHT` and `START_OF_THE_SEGMENT`, default is `MIDNIGHT`. If you have multiple time segments per day it might be more informative to set this flag to `START_OF_THE_SEGMENT`.
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]`:
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|Feature |Units |Description |
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|------------------------------- |-------------- |-------------------------------------------------------------|
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|countepisode`[LEVEL][TYPE]` |episodes |Number of `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]` can also be `all` when ``LEVELS_AND_TYPES_COMBINING_ALL`` is True, which ignores the levels and groups by sleep types.
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|sumduration`[LEVEL][TYPE]` |minutes |Total duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|maxduration`[LEVEL][TYPE]` |minutes | Longest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|minduration`[LEVEL][TYPE]` |minutes | Shortest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|avgduration`[LEVEL][TYPE]` |minutes | Average duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|medianduration`[LEVEL][TYPE]` |minutes | Median duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|stdduration`[LEVEL][TYPE]` |minutes | Standard deviation duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_LEVELS]`:
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|Feature |Units |Description |
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|-------------------------- |-------------- |-------------------------------------------------------------|
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|ratiocount`[LEVEL]` |-|Ratio between the **count** of episodes of a single sleep `[LEVEL]` and the **count** of all episodes of all levels during both `main` and `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` episodes were `rem`? (e.g., $countepisode[remstages][all] / countepisode[all][all]$)
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|ratioduration`[LEVEL]` |-|Ratio between the **duration** of episodes of a single sleep `[LEVEL]` and the **duration** of all episodes of all levels during both `main` and `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` time was `rem`? (e.g., $sumduration[remstages][all] / sumduration[all][all]$)
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_TYPES]`:
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|Feature |Units |Description |
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|-------------------------- |-------------- |-------------------------------------------------------------|
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|ratiocountmain |- |Ratio between the **count** of all `main` episodes (independently of the levels inside) divided by the **count** of all `main` and `nap` episodes. This answers the question: what percentage of all sleep episodes (`main` and `nap`) were `main`? We do not provide the ratio for `nap` because is complementary. ($countepisode[all][main] / countepisode[all][all]$)
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|ratiodurationmain |- |Ratio between the **duration** of all `main` episodes (independently of the levels inside) divided by the **duration** of all `main` and `nap` episodes. This answers the question: what percentage of all sleep time (`main` and `nap`) was `main`? We do not provide the ratio for `nap` because is complementary. ($sumduration[all][main] / sumduration[all][all]$)
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[WITHIN_LEVELS]`:
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|Feature |Units |Description |
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|--------------------------------- |-------------- |-------------------------------------------------------------|
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|ratiocount`[TYPE]`within`[LEVEL]` |- |Ratio between the **count** of episodes of a single sleep `[LEVEL]` during `main` sleep divided by the **count** of episodes of a single sleep `[LEVEL]` during `main` **and** `nap`. This answers the question: are `rem` episodes more frequent during `main` than `nap` sleep? We do not provide the ratio for `nap` because is complementary. ($countepisode[remstages][main] / countepisode[remstages][all]$)
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|ratioduration`[TYPE]`within`[LEVEL]` |- |Ratio between the **duration** of episodes of a single sleep `[LEVEL]` during `main` sleep divided by the **duration** of episodes of a single sleep `[LEVEL]` during `main` **and** `nap`. This answers the question: is `rem` time more frequent during `main` than `nap` sleep? We do not provide the ratio for `nap` because is complementary. ($countepisode[remstages][main] / countepisode[remstages][all]$)
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[WITHIN_TYPES]`:
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|Feature |Units|Description|
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| - |- | - |
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|ratiocount`[LEVEL]`within`[TYPE]` |-|Ratio between the **count** of episodes of a single sleep `[LEVEL]` and the **count** of all episodes of all levels during either `main` or `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` episodes were `rem` during `main`/`nap` sleep time? (e.g., $countepisode[remstages][main] / countepisode[all][main]$)
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|ratioduration`[LEVEL]`within`[TYPE]` |-|Ratio between the **duration** of episodes of a single sleep `[LEVEL]` and the **duration** of all episodes of all levels during either `main` or `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` time was `rem` during `main`/`nap` sleep time? (e.g., $sumduration[remstages][main] / sumduration[all][main]$)
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][ROUTINE]`:
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|Feature |Units |Description |
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|--------------------------------- |-------------- |-------------------------------------------------------------|
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|starttimefirstmainsleep |minutes |Start time (in minutes since `REFERENCE_TIME`) of the first main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastmainsleep |minutes |End time (in minutes since `REFERENCE_TIME`) of the last main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|starttimefirstnap |minutes |Start time (in minutes since `REFERENCE_TIME`) of the first nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastnap |minutes |End time (in minutes since `REFERENCE_TIME`) of the last nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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!!! note "Assumptions/Observations"
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1. Deleting values from `[SLEEP_LEVELS]` or `[SLEEP_TYPES]` will only change the features you receive from `[LEVELS_AND_TYPES]`. For example if `STAGES` only contains `[rem, light]` you will not receive `countepisode[wake|deep][TYPE]` or sum, max, min, avg, median, or std `duration`. These values will not influence `RATIOS` or `ROUTINE` features.
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2. Any `[LEVEL]` grouping is done within the elements of each class `CLASSIC`, `STAGES`, and `UNIFIED`. That is, we never combine `CLASSIC` or `STAGES` types to compute features when `LEVELS_AND_TYPES_COMBINING_ALL` is True or when computing `RATIOS`.
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## PRICE provider
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!!! info "Available time segments"
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- Available for any time segments larger or equal to one day
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!!! info "File Sequence"
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```bash
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- data/raw/{pid}/fitbit_sleep_intraday_raw.csv
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- data/raw/{pid}/fitbit_sleep_intraday_parsed.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv
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- data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv
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||
- data/processed/features/{pid}/fitbit_sleep_intraday.csv
|
||
```
|
||
|
||
|
||
Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE]`:
|
||
|
||
|Key | Description |
|
||
|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `PRICE` provider |
|
||
|`[FEATURES]` | Features to be computed from sleep intraday data, see table below
|
||
|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS grouped them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`).
|
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|`[DAY_TYPE]` | The features of this provider can be computed using daily averages/standard deviations that were extracted on `WEEKEND` days only, `WEEK` days only, or `ALL` days|
|
||
|`[GROUP_EPISODES_WITHIN]` | This parameter contains 2 values: `[START_TIME]` and `[LENGTH]`. Only `main` sleep episodes that intersect or contain the period between [`START_TIME`, `START_TIME` + `LENGTH`] are taken into account to compute the features described below. Both `[START_TIME]` and `[LENGTH]` are in minutes. `[START_TIME]` is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. `[LENGTH]` is a number smaller than 1440 (24 hours). |
|
||
|
||
|
||
Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][PRICE]`:
|
||
|
||
|Feature |Units |Description |
|
||
|------------------------------------- |----------------- |-------------------------------------------------------------|
|
||
|avgduration`[LEVEL]`main`[DAY_TYPE]` |minutes | Average duration of daily `LEVEL` sleep episodes. You can include daily average that were computed on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|avgratioduration`[LEVEL]`withinmain`[DAY_TYPE]` |- | Average ratio between daily `LEVEL` time and in-bed time inferred from `main` sleep episodes. `LEVEL` is one of `SLEEP_LEVELS` (e.g. awake-classic or rem-stages). In-bed time is the total duration of all `main` sleep episodes for each day. You can include daily ratios that were computed on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|avgstarttimeofepisodemain`[DAY_TYPE]` |minutes | Average start time of the first `main` sleep episode of each day in a time segment. You can include daily start times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|avgendtimeofepisodemain`[DAY_TYPE]` |minutes | Average end time of the last `main` sleep episode of each day in a time segment. You can include daily end times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|avgmidpointofepisodemain`[DAY_TYPE]` |minutes | Average mid time between the start of the first `main` sleep episode and the end of the last `main` sleep episode of each day in a time segment. You can include episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|stdstarttimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of start time of the first `main` sleep episode of each day in a time segment. You can include daily start times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|stdendtimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of end time of the last `main` sleep episode of each day in a time segment. You can include daily end times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|stdmidpointofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of mid time between the start of the first `main` sleep episode and the end of the last `main` sleep episode of each day in a time segment. You can include episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
||
|socialjetlag |minutes | Difference in minutes between the avgmidpointofepisodemain (average mid time between bedtime and wake time) of weekends and weekdays.
|
||
|meanssdstarttimeofepisodemain |minutes squared | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive start times.
|
||
|meanssdendtimeofepisodemain |minutes squared | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive end times.
|
||
|meanssdmidpointofepisodemain |minutes squared | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive mid times.
|
||
|medianssdstarttimeofepisodemain |minutes squared | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive start times.
|
||
|medianssdendtimeofepisodemain |minutes squared | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive end times.
|
||
|medianssdmidpointofepisodemain |minutes squared | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive mid times.
|
||
|
||
|
||
|
||
!!! note "Assumptions/Observations"
|
||
1. These features are based on descriptive statistics computed across daily values (start/end/mid times of sleep episodes). This is the reason why they are only available on time segments that are longer than 24 hours (we need at least 1 day to get the average).
|
||
2. Even though Fitbit provides 2 types of sleep episodes (`main` and `nap`), only `main` sleep episodes are considered.
|
||
3. How do we assign sleep episodes to specific dates?
|
||
|
||
`START_TIME` and `LENGTH` control the dates that sleep episodes belong to. For a pair of `[START_TIME]` and `[LENGTH]`, sleep episodes (blue boxes) can only be placed at the following places:
|
||
|
||
<figure>
|
||
<img src="../../img/features_fitbit_sleep_intraday.png" max-width="100%" />
|
||
<figcaption>Relationship between sleep episodes and the given times`([START_TIME], [LENGTH])`</figcaption>
|
||
</figure>
|
||
|
||
- If the end time of a sleep episode is before `[START_TIME]`, it will belong to the day before its start date (e.g. sleep episode #1).
|
||
|
||
- if (1) the start time or the end time of a sleep episode are between (overlap) `[START_TIME]` and `[START_TIME] + [LENGTH]` or (2) the start time is before `[START_TIME]` and the end time is after `[START_TIME] + [LENGTH]`, it will belong to its start date (e.g. sleep episode #2, #3, #4, #5).
|
||
|
||
- If the start time of a sleep episode is after `START_TIME] + [LENGTH]`, it will belong to the day after its start date (e.g. sleep episode #6).
|
||
|
||
Only `main` sleep episodes that intersect or contain the period between `[START_TIME]` and `[START_TIME] + [LENGTH]` will be included in the feature computation. If we process the following `main` sleep episodes:
|
||
|
||
| episode |start|end|
|
||
|-|-|-|
|
||
|1|2021-02-01 12:00|2021-02-01 15:00|
|
||
|2|2021-02-01 21:00|2021-02-02 03:00|02-01
|
||
|3|2021-02-02 05:00|2021-02-02 08:00|02-01
|
||
|4|2021-02-02 11:00|2021-02-02 14:00|
|
||
|5|2021-02-02 19:00|2021-02-03 06:00|02-02
|
||
|
||
And our parameters:
|
||
|
||
- `[INCLUDE_EPISODES_INTERSECTING][START_TIME]` = 1320 (today's 22:00)
|
||
|
||
- `[INCLUDE_EPISODES_INTERSECTING][LENGTH]` = 720 (tomorrow's 10:00, or 22:00 + 12 hours)
|
||
|
||
Only sleep episodes 2, 3,and 5 would be considered.
|
||
|
||
4. Time related features represent the number of minutes between the start/end/midpoint of sleep episodes and the assigned day's midnight.
|
||
|
||
5. All `main` sleep episodes are chunked within the requested [time segments](../../setup/configuration/#time-segments) which need to be at least 24 hours or more long (1, 2, 3, 7 days, etc.). Then, daily features will be extracted and averaged across the length of the time segment, for example:
|
||
|
||
The daily features extracted on 2021-02-01 will be:
|
||
|
||
- starttimeofepisodemain (bedtime) is `21 * 60` (episode 2 start time 2021-02-01 21:00)
|
||
|
||
- endtimeofepisodemain (wake time) is `32 * 60 `(episode 3 end time 2021-02-02 08:00 + 24)
|
||
|
||
- midpointofepisodemain (midpoint sleep) is `[(21 * 60) + (32 * 60)] / 2`
|
||
|
||
|
||
The daily features extracted on 2021-02-02 will be:
|
||
|
||
- starttimeofepisodemain (bedtime) is `19 * 60` (episode 5 start time 2021-02-01 19:00)
|
||
|
||
- endtimeofepisodemain (wake time) is `30 * 60 `(episode 5 end time 2021-02-03 06:00 + 24)
|
||
|
||
- midpointofepisodemain (midpoint sleep) is `[(19 * 60) + (30 * 60)] / 2`
|
||
|
||
And `avgstarttimeofepisodemain[DAY_TYPE]` will be `([21 * 60] + [19 * 60]) / 2`
|
||
|
||
|
||
|
||
|
||
|
||
|