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 of the structure of source data with Fitbit’s sleep API Version 1
| 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}} |
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.
| device_id | type_episode_id | local_date_time | duration | level | is_main_sleep | type |
|---|---|---|---|---|---|---|
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-07 15:55:00 | 60 | awake | 0 | classic |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-07 15:56:00 | 60 | awake | 0 | classic |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-07 15:57:00 | 60 | restless | 0 | classic |
Example of the structure of source data with Fitbit’s sleep API Version 1.2
| 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}} |
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.
| device_id | type_episode_id | local_date_time | duration | level | is_main_sleep | type |
|---|---|---|---|---|---|---|
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-10 15:36:30 | 60 | restless | 0 | classic |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-10 15:37:30 | 660 | asleep | 0 | classic |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 0 | 2020-10-10 15:48:30 | 60 | restless | 0 | classic |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | … | … | … | … | … | … |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 1 | 2020-10-10 01:46:30 | 420 | light | 1 | stages |
| a748ee1a-1d0b-4ae9-9074-279a2b6ba524 | 1 | 2020-10-10 01:53:30 | 1230 | deep | 1 | stages |
RAPIDS provider¶
Available time segments
- Available for all time segments
File Sequence
- 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. |
Assumptions/Observations
- Deleting values from
[SLEEP_LEVELS]or[SLEEP_TYPES]will only change the features you receive from[LEVELS_AND_TYPES]. For example ifSTAGESonly contains[rem, light]you will not receivecountepisode[wake|deep][TYPE]or sum, max, min, avg, median, or stdduration. These values will not influenceRATIOSorROUTINEfeatures. - Any
[LEVEL]grouping is done within the elements of each classCLASSIC,STAGES, andUNIFIED. That is, we never combineCLASSICorSTAGEStypes to compute features whenLEVELS_AND_TYPES_COMBINING_ALLis True or when computingRATIOS.
PRICE provider¶
Available time segments
- Available for any time segments larger or equal to one day
File Sequence
- 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 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. |
Assumptions/Observations
- 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).
- Even though Fitbit provides 2 types of sleep episodes (
mainandnap), onlymainsleep episodes are considered. -
How do we assign sleep episodes to specific dates?
START_TIMEandLENGTHcontrol 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:
Relationship between sleep episodes and the given times ([START_TIME], [LENGTH])-
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
mainsleep 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 followingmainsleep 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.
-
-
Time related features represent the number of minutes between the start/end/midpoint of sleep episodes and the assigned day’s midnight.
-
All
mainsleep episodes are chunked within the requested 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 -