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Fitbit Sleep Intraday

Sensor parameters description for [FITBIT_SLEEP_INTRADAY]:

Key                              Description
[TABLE] Database table name or file path where the sleep intraday data is stored. The configuration keys in Device Data Source Configuration control whether this parameter is interpreted as table or file.

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.

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).

??? example "Example of the structure of source data with Fitbit’s sleep API Version 1"

=== "JSON"

    |device_id                                |fitbit_data                                               |
    |---------------------------------------- |--------------------------------------------------------- |
    |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}}
    |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}}
    |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}}

=== "PLAIN_TEXT"

    All columns are mandatory, however, all except `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.

    |type_episode_id  |local_date_time     |duration  |level      |is_main_sleep  |type           |
    |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
    |0                |2020-10-07 15:55:00 |60        |awake      |0              |classic        |
    |0                |2020-10-07 15:56:00 |60        |awake      |0              |classic        |
    |0                |2020-10-07 15:57:00 |60        |restless   |0              |classic        |

??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2"

=== "JSON"

    |device_id                                |fitbit_data                                               |
    |---------------------------------------- |--------------------------------------------------------- |
    |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}}
    |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}}
    |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}}

=== "PLAIN_TEXT"

    All columns are mandatory, however, all except `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.

    |type_episode_id  |local_date_time     |duration  |level      |is_main_sleep  |type           |
    |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
    |0                |2020-10-10 15:36:30 |60        |restless   |0              |classic        |
    |0                |2020-10-10 15:37:30 |660       |asleep     |0              |classic        |
    |0                |2020-10-10 15:48:30 |60        |restless   |0              |classic        |
    |...              |...                 |...       |...        |...            |...            |
    |1                |2020-10-10 01:46:30 |420       |light      |1              |stages         |
    |1                |2020-10-10 01:53:30 |1230      |deep       |1              |stages         |

RAPIDS provider

!!! info "Available time segments" - Available for all time segments

!!! info "File Sequence" bash - data/raw/{pid}/fitbit_sleep_intraday_raw.csv - data/raw/{pid}/fitbit_sleep_intraday_parsed.csv - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv - data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv - data/processed/features/{pid}/fitbit_sleep_intraday.csv

Parameters description for [FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]:

Key                              Description
[COMPUTE] Set to True to extract FITBIT_SLEEP_INTRADAY features from the RAPIDS 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).
[SLEEP_TYPES] Types of sleep to be included in the feature extraction computation. Fitbit provides 2 types of sleep: main, nap.
[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.
[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.

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]:

Feature                                           Units Description
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.
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.
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.
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.
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.
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.
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.

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS] RATIOS [ACROSS_LEVELS]:

Feature                            Units Description
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])
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])

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS] RATIOS [ACROSS_TYPES]:

Feature                            Units Description
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])
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])

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS] RATIOS [WITHIN_LEVELS]:

Feature                            Units Description
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])
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])

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS] RATIOS [WITHIN_TYPES]:

Feature                                                    Units Description
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])
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])

Features description for [FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][ROUTINE]:

Feature Units Description
starttimefirstmainsleep minutes Start time (in minutes since REFERENCE_TIME) of the first main sleep episode after INCLUDE_EPISODES_LATER_THAN.
endtimelastmainsleep minutes End time (in minutes since REFERENCE_TIME) of the last main sleep episode after INCLUDE_EPISODES_LATER_THAN.
starttimefirstnap minutes Start time (in minutes since REFERENCE_TIME) of the first nap episode after INCLUDE_EPISODES_LATER_THAN.
endtimelastnap minutes End time (in minutes since REFERENCE_TIME) of the last nap episode after INCLUDE_EPISODES_LATER_THAN.

!!! note "Assumptions/Observations" 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. 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.

PRICE provider

!!! info "Available time segments" - Available for any time segments larger or equal to one day

!!! info "File Sequence" bash - data/raw/{pid}/fitbit_sleep_intraday_raw.csv - data/raw/{pid}/fitbit_sleep_intraday_parsed.csv - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv - data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv - 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).
[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 avgstarttimeofepisodemain (bed 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). 1. Even though Fitbit provides 2 types of sleep episodes (main and nap), only main sleep episodes are considered. 2. We sum 24 to any start or bed times that happen after midnight so they can be averaged with other values happening before midnight. 2. main sleep episodes belong to the day they start at and are only included in the feature computation if they start or end (overlap) between [START_TIME] and [START_TIME] + [LENGTH]. For example:

    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|
    |3|2021-02-02 05:00|2021-02-02 08:00|
    |4|2021-02-02 11:00|2021-02-02 14:00|
    |5|2021-02-02 19:00|2021-02-03 06:00|

    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.

3. 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 `[(32 * 60) - (21 * 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 `[(30 * 60) - (19 * 60)] / 2`

    And `avgstarttimeofepisodemain[DAY_TYPE]` will be `([21 * 60] + [19 * 60]) / 2`