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Phone Data Quality

Phone Valid Sensed Bins

A valid bin is any period of BIN_SIZE minutes starting from midnight with at least 1 row from any phone sensor. PHONE_VALID_SENSED_BINS are used to compute PHONE_VALID_SENSED_DAYS, to resample fused location data and to compute some screen features.

!!! hint PHONE_VALID_SENSED_DAYS are an approximation to the time the phone was sensing data so add as many sensors as you have to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS]

Parameters description for PHONE_VALID_SENSED_BINS:

Key                              Description
[COMPUTE] Set to True to compute
[BIN_SIZE] Size of each bin in minutes
[PHONE_SENSORS] One or more sensor config keys (PHONE_MESSAGE) to be used to flag a bing as valid or not (whether or not a bin contains at least one row from any sensor)

!!! info "Possible values for [PHONE_VALID_SENSED_BINS][PHONE_SENSORS]" yaml PHONE_MESSAGES PHONE_CALLS PHONE_LOCATIONS PHONE_BLUETOOTH PHONE_ACTIVITY_RECOGNITION PHONE_BATTERY PHONE_SCREEN PHONE_LIGHT PHONE_ACCELEROMETER PHONE_APPLICATIONS_FOREGROUND PHONE_WIFI_VISIBLE PHONE_WIFI_CONNECTED PHONE_CONVERSATION

Phone Valid Sensed Days

On any given day, a phone could have sensed data only for a few minutes or for 24 hours. Features should considered more reliable the more hours the phone was logging data, for example, 10 calls logged on a day when only 1 hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded.

Therefore, we define a valid hour as those that contain a minimum number of valid bins (see above). We mark an hour as valid when contains at least MIN_VALID_BINS_PER_HOUR (out of 60min/BIN_SIZE bins). In turn, we mark a day as valid if it has at least MIN_VALID_HOURS_PER_DAY. You can use PHONE_VALID_SENSED_DAYS to manually discard days when not enough data was collected after your features are computed.

Parameters description for PHONE_VALID_SENSED_DAYS:

Key                                                Description
[COMPUTE] Set to True to compute
[MIN_VALID_BINS_PER_HOUR] An array of integer values, 6 by default. Minimum number of valid bins to mark an hour as valid
[MIN_VALID_HOURS_PER_DAY] An array of integer values, 16 by default. Minimum number of valid hours to mark a day as valid

WiFi

See WiFi Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/features.snakefile/wifi_features

::: {#wifi-parameters} WiFi Rule Parameters (wifi_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features Features to be computed, see table below

::: {#wifi-available-features} Available WiFi Features :::

Name Units Description

countscans devices Number of scanned WiFi access points during a day_segment, an access point can be detected multiple times over time and these appearances are counted separately uniquedevices devices Number of unique access point during a day_segment as identified by their hardware address countscansmostuniquedevice scans Number of scans of the most scanned access point during a day_segment across the whole monitoring period

Assumptions/Observations: Both phone platforms record the wifi networks a phone is connected to in sensor_wifi and those networks that are being broadcasted around a phone in wifi. However, iOS cannot record any broadcasting network due to API restrictions, therefore iOS wifi data only exists in sensor_wifi.

Accelerometer

See Accelerometer Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/features.snakefile/accelerometer_features

::: {#Accelerometer-parameters} Accelerometer Rule Parameters (accelerometer_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features Features to be computed, see table below

::: {#accelerometer-available-features} Available Accelerometer Features :::

Name Units Description

maxmagnitude m/s^2^ The maximum magnitude of acceleration (\|acceleration\| = \sqrt{x^2 + y^2 + z^2}). minmagnitude m/s^2^ The minimum magnitude of acceleration. avgmagnitude m/s^2^ The average magnitude of acceleration. medianmagnitude m/s^2^ The median magnitude of acceleration. stdmagnitude m/s^2^ The standard deviation of acceleration. sumduration minutes Total duration of all exertional or non-exertional activity episodes. maxduration minutes Longest duration of any exertional or non-exertional activity episode. minduration minutes Shortest duration of any exertional or non-exertional activity episode. avgduration minutes Average duration of any exertional or non-exertional activity episode. medianduration minutes Median duration of any exertional or non-exertional activity episode. stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes.

Assumptions/Observations:

Exertional activity episodes are based on this paper: Panda N, Solsky I, Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020;155(2):123--129. doi:10.1001/jamasurg.2019.4702

Applications Foreground

See Applications Foreground Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/preprocessing.snakefile/application_genres
  • Rule rules/features.snakefile/applications_foreground_features

::: {#applications-foreground-parameters} Applications Foreground Rule Parameters (applications_foreground_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night single_categories App categories to be included in the feature extraction computation. See APPLICATION_GENRES in this file to add new categories or use the catalogue we provide and read Assumtions and Observations <applications-foreground-observations>{.interpreted-text role="ref"} for more information. multiple_categories You can group multiple categories into meta categories, for example social: ["socialnetworks", "socialmediatools"]. single_apps Apps to be included in the feature extraction computation. Use their package name, for example, com.google.android.youtube or the reserved word top1global (the most used app by a participant over the whole monitoring study). excluded_categories App categories to be excluded in the feature extraction computation. See APPLICATION_GENRES in this file to add new categories or use the catalogue we provide and read Assumtions and Observations <applications-foreground-observations>{.interpreted-text role="ref"} for more information. excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: com.google.android.youtube features Features to be computed, see table below

::: {#applications-foreground-available-features} Available Applications Foreground Features :::

Name Units Description

count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app). timeoffirstuse minutes The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a day_segment. timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a day_segment. frequencyentropy nats The entropy of the used apps within a category during a day_segment (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app.

::: {#applications-foreground-observations} Assumptions/Observations: :::

Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta categories). For example, we can get features for Facebook, for Social Network Apps (including Facebook and others) or for a meta category called Social formed by Social Network and Social Media Tools categories.

Apps installed by default like YouTube are considered systems apps on some phones. We do an exact match to exclude apps where "genre" == EXCLUDED_CATEGORIES or "package_name" == EXCLUDED_APPS.

We provide three ways of classifying and app within a category (genre): a) by automatically scraping its official category from the Google Play Store, b) by using the catalogue created by Stachl et al. which we provide in RAPIDS (data/external/), or c) by manually creating a personalized catalogue.

The way you choose strategy a, b or c is by modifying APPLICATION_GENRES keys and values. Set CATALOGUE_SOURCE to FILE if you want to use a CSV file as catalogue (strategy b and c) or to GOOGLE if you want to scrape the genres from the Play Store (strategy a). By default CATALOGUE_FILE points to the catalogue created by Stachl et al. (strategy b) and you can change this path to your own catalogue that follows the same format (strategy c). In addition, set SCRAPE_MISSING_GENRES to true if you are using a FILE catalogue and you want to scrape from the Play Store any missing genres and UPDATE_CATALOGUE_FILE to true if you want to save those scrapped genres back into the FILE.

The genre catalogue we provide was shared as part of the Supplemental Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., ... Bühner, M. (2019, June 12). Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits. https://doi.org/10.31234/osf.io/ks4vd

Battery

See Battery Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/features.snakefile/battery_deltas
  • Rule rules/features.snakefile/battery_features

::: {#battery-parameters} Battery Rule Parameters (battery_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features Features to be computed, see table below

::: {#battery-available-features} Available Battery Features :::

Name Units Description

countdischarge episodes Number of discharging episodes. sumdurationdischarge minutes The total duration of all discharging episodes. countcharge episodes Number of battery charging episodes. sumdurationcharge minutes The total duration of all charging episodes. avgconsumptionrate episodes/minutes The average of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration maxconsumptionrate episodes/minutes The highest of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration

Assumptions/Observations:

For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client V2 does not have this problem.

Activity Recognition

See Activity Recognition Config Code

Available Epochs: daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/preprocessing.snakefile/unify_ios_android
  • Rule rules/features.snakefile/google_activity_recognition_deltas
  • Rule rules/features.snakefile/ios_activity_recognition_deltas
  • Rule rules/features.snakefile/activity_features

::: {#activity-recognition-parameters} Rule Parameters (activity_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features Features to be computed, see table below

::: {#activity-recognition-available-features} Available Activity Recognition Features :::

Name Units Description

count rows Number of episodes. mostcommonactivity activity_type The most common activity_type. If this feature is not unique the first activity_type of the set of most common activity_types is selected ordered by activity_type. countuniqueactivities activity_type Number of unique activity_type. durationstationary minutes The total duration of episodes of still and tilting (phone) activities. durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities durationvehicle minutes The total duration of episodes of on vehicle activity

Assumptions/Observations:

iOS Activity Recognition data labels are unified with Google Activity Recognition labels: "automotive" to "in_vehicle", "cycling" to "on_bicycle", "walking" and "running" to "on_foot", "stationary" to "still". In addition, iOS activity pairs formed by "stationary" and "automotive" labels (driving but stopped at a traffic light) are transformed to "automotive" only.

In AWARE, Activity Recognition data for Google (Android) and iOS are stored in two different database tables, RAPIDS (via Snakemake) automatically infers what platform each participant belongs to based on their participant file (data/external/) which in turn takes this information from the aware_device table (see optional_ar_input function in rules/features.snakefile).

The activties are mapped to activity_types as follows:

Activity Name Activity Type

in_vehicle 0 on_bicycle 1 on_foot 2 still 3 unknown 4 tilting 5 walking 7 running 8

Light

See Light Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android

Rule Chain:

  • Rule: rules/preprocessing.snakefile/download_dataset
  • Rule: rules/preprocessing.snakefile/readable_datetime
  • Rule: rules/features.snakefile/light_features

::: {#light-parameters} Light Rule Parameters (light_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features Features to be computed, see table below

::: {#light-available-features} Available Light Features :::

Name Units Description

count rows Number light sensor rows recorded. maxlux lux The maximum ambient luminance. minlux lux The minimum ambient luminance. avglux lux The average ambient luminance. medianlux lux The median ambient luminance. stdlux lux The standard deviation of ambient luminance.

Assumptions/Observations: N/A

Location (Barnett's) Features

Barnett's location features are based on the concept of flights and pauses. GPS coordinates are converted into a sequence of flights (straight line movements) and pauses (time spent stationary). Data is imputed before features are computed. See Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98--e112, https://doi.org/10.1093/biostatistics/kxy059. The code for these features was made open source by Ian Barnett (https://scholar.harvard.edu/ibarnett/software/gpsmobility).

See Location (Barnett's) Config Code

Available Day Segments (epochs) : only daily periods of EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at 00:00:00 and end at 23:59:59 on the same day)

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset (de duplication and sorting by timestamp)
  • Rule rules/preprocessing.snakefile/readable_datetime (add local date and time components, add local day segment)
  • Rule rules/preprocessing.snakefile/phone_sensed_bins (get the periods of time the phone was sensing data to resample over them)
  • Rule rules/preprocessing.snakefile/process_location_types (filter gps data or resample fused location, deletes (0,0) coordinates)
  • Rule rules/features.snakefile/locations_r_features (RAPIDS executes barnett_location_features from [`src/features/location/barnett/main.R]{.title-ref})
  • Rule rules/features.snakefile/join_features_from_providers (joins the location features of all python and r providers)

::: {#location-parameters} Location Rule Parameters (location_barnett_features): :::

Name Description

location_to_use Read the Observations section below. The specifies what type of location data will be use in the analysis. Possible options are ALL, GPS OR RESAMPLE_FUSED accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius specified. timezone The timezone used to calculate location. minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. features Features to be computed, see table below

::: {#location-available-features} Available Location Features :::

Description taken from Beiwe Summary Statistics.

Name Units Description

hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. disttravelled meters Total distance travelled over a day (flights). rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. maxdiam meters The maximum diameter is the largest distance between any two pauses. maxhomedist meters The maximum distance from home in meters. siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. avgflightlen meters Mean length of all flights. stdflightlen meters Standard deviation of the length of all flights. avgflightdur seconds Mean duration of all flights. stdflightdur probpause seconds The standard deviation of the duration of all flights. The fraction of a day spent in a pause (as opposed to a flight) siglocentropy circdnrtn wkenddayrtn nats Shannon's entropy measurement based on the proportion of time spent at each significant location visited during a day. A continuous metric quantifying a person's circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. Same as circdnrtn but computed separately for weekends and weekdays.

Assumptions/Observations:

Types of location data to use

Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set location_to_use to GPS, if you want to use all providers (not recommended due to the difference in accuracy) set location_to_use to ALL, if your Aware client was configured to use fused location only or want to focus only on this provider, set location_to_use to RESAMPLE_FUSED. RESAMPLE_FUSED takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by phone_sensed_bins (see Phone valid sensed days <phone-valid-sensed-days>{.interpreted-text role="ref"}), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one.

There are two parameters associated with resampling fused location in the LOCATIONS section of the config.yaml file. RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know.

Barnett's et al features

These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See this paper for more information: https://doi.org/10.1093/biostatistics/kxy059.

In RAPIDS we only expose two parameters for these features (timezone and accuracy). If you wish to change others you can do so in src/features/location/barnett/library/MobilityFeatures.R

Significant Locations

Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of https://doi.org/10.1093/biostatistics/kxy059.

The Circadian Calculation

For a detailed description of how this is calculated, see Canzian, L., & Musolesi, M. (2015, September). Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure was followed using 30-min increments as a bin size. Taken from Beiwe Summary Statistics.

Location (Doryab's) Features

Doryab's location features are based on this paper: Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394

See Location (Doryab's) Config Code

Available Day Segments (epochs): any of EVERY_DAY_FREQUENCY, EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset (de duplication and sorting by timestamp)
  • Rule rules/preprocessing.snakefile/readable_datetime (add local date and time components, add local day segment)
  • Rule rules/preprocessing.snakefile/phone_sensed_bins (get the periods of time the phone was sensing data to resample over them)
  • Rule rules/preprocessing.snakefile/process_location_types (filter gps data or resample fused location, deletes (0,0) coordinates)
  • Rule rules/features.snakefile/locations_python_features (RAPIDS executes doryab_location_features from [`src/features/location/doryab/main.py]{.title-ref})
  • Rule rules/features.snakefile/join_features_from_providers (joins the location features of all python and r providers)

::: {#location-doryab-parameters} Location Rule Parameters (location_doryab_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night location_to_use Read the Observations section below. The specifies what type of location data will be use in the analysis. Possible options are ALL, GPS OR RESAMPLE_FUSED. features Features to be computed, see table below. threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. sampling_frequency Expected time difference between any two location rows in minutes. If set to '0', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations.

::: {#location-doryab-available-features} Available Location Features :::

Name Units Description

locationvariance loglocationvariance meters^2 The sum of the variances of the latitude and longitude columns. Log of the sum of the variances of the latitude and longitude columns. totaldistance meters Total distance travelled in a day_segment using the haversine formula. averagespeed km/hr Average speed in a day_segment considering only the instances labeled as Moving. varspeed circadianmovement km/hr Speed variance in a day_segment considering only the instances labeled as Moving. "It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle." (Doryab et. al. 2019) numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place. numberlocationtransitions transitions Number of movements between any two clusters in a day_segment. radiusgyration meters Quantifies the area covered by a participant timeattop1location minutes Time spent at the most significant location. timeattop2location minutes Time spent at the 2nd most significant location. timeattop3location movingtostaticratio outlierstimepercent minutes Time spent at the 3rd most significant location. Ratio between the number of rows labeled Moving versus Static Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a day_segment. maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location). minlengthstayatclusters minutes Minimum time spent in a cluster (significant location). meanlengthstayatclusters minutes Average time spent in a cluster (significant location). stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location). locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location).

Assumptions/Observations:

Types of location data to use

Aware Android and iOS clients can collect location coordinates through the phone's GPS, the network cellular towers around the phone or Google's fused location API. If you want to use only the GPS provider set location_to_use to GPS, if you want to use all providers (not recommended due to the difference in accuracy) set location_to_use to ALL, if your Aware client was configured to use fused location only or want to focus only on this provider, set location_to_use to RESAMPLE_FUSED. RESAMPLE_FUSED takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by phone_sensed_bins (see Phone valid sensed days <phone-valid-sensed-days>{.interpreted-text role="ref"}), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one.

There are two parameters associated with resampling fused location in the LOCATIONS section of the config.yaml file. RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION (in minutes, default 720 or 12 hours) makes that the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know.

Significant Locations Identified

Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection.

Circadian Movement Calculation

"Circadian movement (Saeb et al. 2015) is calculated using the Lomb-Scargle method" (Doryab et. al. 2019)

Screen

See Screen Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/preprocessing.snakefile/unify_ios_android
  • Rule rules/features.snakefile/screen_deltas
  • Rule rules/features.snakefile/screen_features

::: {#screen-parameters} Screen Rule Parameters (screen_features): :::

Name Description

day_segment The particular day_segments that will be analyzed. The available options are daily, morning, afternoon, evening, night reference_hour_first_use The reference point from which firstuseafter is to be computed, default is midnight ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. features_deltas Features to be computed, see table below episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off)

::: {#screen-episodes-available-features} Available Screen Episodes Features :::

Name Units Description

sumduration minutes Total duration of all unlock episodes. maxduration minutes Longest duration of any unlock episode. minduration minutes Shortest duration of any unlock episode. avgduration minutes Average duration of all unlock episodes. stdduration minutes Standard deviation duration of all unlock episodes. countepisode episodes Number of all unlock episodes episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data. firstuseafter minutes Minutes until the first unlock episode.

Assumptions/Observations:

In Android, lock events can happen right after an off event, after a few seconds of an off event, or never happen depending on the phone's settings, therefore, an unlock episode is defined as the time between an unlock and a off event. In iOS, on and off events do not exist, so an unlock episode is defined as the time between an unlock and a lock event.

Events in iOS are recorded reliably albeit some duplicated lock events within milliseconds from each other, so we only keep consecutive unlock/lock pairs. In Android you cand find multiple consecutive unlock or lock events, so we only keep consecutive unlock/off pairs. In our experiments these cases are less than 10% of the screen events collected and this happens because ACTION_SCREEN_OFF and ACTION_SCREEN_ON are "sent when the device becomes non-interactive which may have nothing to do with the screen turning off". In addition to unlock/off episodes, in Android it is possible to measure the time spent on the lock screen before an unlock event as well as the total screen time (i.e. ON to OFF) but these are not implemented at the moment.

To unify the screen processing and use the same code in RAPIDS, we replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as mentioned above this is still computing unlock to lock episodes.

Conversation

See Conversation Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms: Android and iOS

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/readable_datetime
  • Rule rules/features.snakefile/conversation_features

::: {#conversation-parameters} Conversation Rule Parameters (conversation_features): :::

Name Description

day_segment The particular day_segments that will be analyzed. The available options are daily, morning, afternoon, evening, night recordingMinutes Minutes the plugin was recording audio (default 1 min) pausedMinutes Minutes the plugin was NOT recording audio (default 3 min) features Features to be computed, see table below

::: {#conversation-available-features} Available Conversation Features :::

Name Units Description

minutessilence minutes Minutes labeled as silence minutesnoise minutes Minutes labeled as noise minutesvoice minutes Minutes labeled as voice minutesunknown minutes Minutes labeled as unknown sumconversationduration minutes Total duration of all conversations maxconversationduration minutes Longest duration of all conversations minconversationduration minutes Shortest duration of all conversations avgconversationduration minutes Average duration of all conversations sdconversationduration minutes Standard Deviation of the duration of all conversations timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected noisesumenergy L2-norm Sum of all energy values when inference is noise noiseavgenergy L2-norm Average of all energy values when inference is noise noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise noiseminenergy L2-norm Minimum of all energy values when inference is noise noisemaxenergy L2-norm Maximum of all energy values when inference is noise voicesumenergy L2-norm Sum of all energy values when inference is voice voiceavgenergy L2-norm Average of all energy values when inference is voice voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice voiceminenergy L2-norm Minimum of all energy values when inference is voice voicemaxenergy silencesensedfraction noisesensedfraction voicesensedfraction unknownsensedfraction silenceexpectedfraction noiseexpectedfraction voiceexpectedfraction unknownexpectedfraction L2-norm Maximum of all energy values when inference is voice Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes)

Assumptions/Observations: N/A

Fitbit: Sleep

See Fitbit: Sleep Config Code

Available Epochs (day_segment) : daily

Available Platforms:: Fitbit

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/fitbit_with_datetime
  • Rule rules/features.snakefile/fitbit_sleep_features

::: {#fitbit-sleep-parameters} Fitbit: Sleep Rule Parameters (fitbit_sleep_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. For this sensor only daily is used. sleep_types The types of sleep provided by Fitbit: main, nap, all. daily_features_from_summary_data The sleep features that can be computed based on Fitbit's summary data. See Available Fitbit: Sleep Features <fitbit-sleep-available-features>{.interpreted-text role="ref"} Table below

::: {#fitbit-sleep-available-features} Available Fitbit: Sleep Features :::

Name Units Description

sumdurationtofallasleep minutes Time it took the user to fall asleep for sleep_type during day_segment. sumdurationawake minutes Time the user was awake but still in bed for sleep_type during day_segment. sumdurationasleep minutes Sleep duration for sleep_type during day_segment. sumdurationafterwakeup minutes Time the user stayed in bed after waking up for sleep_type during day_segment. sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for sleep_type during day_segment. avgefficiency scores Sleep efficiency average for sleep_type during day_segment. countepisode episodes Number of sleep episodes for sleep_type during day_segment.

Assumptions/Observations:

Only features from summary data are available at the momement.

The [fitbit_with_datetime]{.title-ref} rule will extract Summary data ([fitbit_sleep_summary_with_datetime.csv]{.title-ref}) and Intraday data ([fitbit_sleep_intraday_with_datetime.csv]{.title-ref}). There are two versions of Fitbit's sleep API (version 1 and version 1.2), and each provides raw sleep data in a different format:

  • Sleep level. In v1, sleep level is an integer with three possible values (1, 2, 3) while in v1.2 is a string. We convert integer levels to strings, asleep, restless or awake respectively.
  • Count summaries. For Summary data, v1 contains count_awake, duration_awake, count_awakenings, count_restless, and duration_restless fields for every sleep record while v1.2 does not.
  • Types of sleep records. v1.2 has two types of sleep records: classic and stages. The classic type contains three sleep levels: awake, restless and asleep. The stages type contains four sleep levels: wake, deep, light, and rem. Sleep records from v1 will have the same sleep levels as [v1.2]{.title-ref} classic type; therefore we set their type to classic.
  • Unified level of sleep. For intraday data, we unify sleep levels of each sleep record with a column named unified_level. Based on this Fitbit forum post , we merge levels into two categories:
    • For the classic type unified_level is one of {0, 1} where 0 means awake and groups awake + restless, while 1 means asleep and groups asleep.
    • For the stages type, unified_level is one of {0, 1} where 0 means awake and groups wake while 1 means asleep and groups deep + light + rem.
  • Short Data. In v1.2, records of type stages contain shortData in addition to data. We merge both to extract intraday data.
    • data contains sleep stages and any wake periods > 3 minutes (180 seconds).
    • shortData contains short wake periods representing physiological awakenings that are <= 3 minutes (180 seconds).
  • The following columns of Summary data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: efficiency, minutes_after_wakeup, minutes_asleep, minutes_awake, minutes_to_fall_asleep, minutes_in_bed, is_main_sleep and type
  • The following columns of Intraday data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: original_level, is_main_sleep and type. We compute unified_level as explained above.

These are examples of intraday and summary data:

  • Intraday data (at 30-second intervals for stages type or 60-second intervals for classic type)

device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment

did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening

  • Summary data

device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment

did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning

Fitbit: Heart Rate

See Fitbit: Heart Rate Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms:: Fitbit

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/fitbit_with_datetime
  • Rule rules/features.snakefile/fitbit_heartrate_features

::: {#fitbit-heart-rate-parameters} Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features The heartrate features that can be computed. See Available Fitbit: Heart Rate Features <fitbit-heart-rate-available-features>{.interpreted-text role="ref"} Table below

::: {#fitbit-heart-rate-available-features} Available Fitbit: Heart Rate Features :::

Name Units Description

restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for daily epoch. calories cals Calories burned during heartrate_zone for daily epoch. maxhr beats/mins The maximum heart rate during day_segment epoch. minhr beats/mins The minimum heart rate during day_segment epoch. avghr beats/mins The average heart rate during day_segment epoch. medianhr beats/mins The median of heart rate during day_segment epoch. modehr beats/mins The mode of heart rate during day_segment epoch. stdhr beats/mins The standard deviation of heart rate during day_segment epoch. diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during day_segment epoch. diffminmodehr beats/mins The difference between the mode and minimum heart rate during day_segment epoch. entropyhr nats Shannon's entropy measurement based on heart rate during day_segment epoch. minutesonZONE minutes Number of minutes the user's heartrate fell within each heartrate_zone during day_segment epoch.

Assumptions/Observations:

There are four heart rate zones: out_of_range, fat_burn, cardio, and peak. Please refer to Fitbit documentation for more information about the way they are computed.

Calories' accuracy depends on the users' Fitbit profile (weight, height, etc.).

Fitbit: Steps

See Fitbit: Steps Config Code

Available Epochs (day_segment) : daily, morning, afternoon, evening, night

Available Platforms:: Fitbit

Snakemake rule chain:

  • Rule rules/preprocessing.snakefile/download_dataset
  • Rule rules/preprocessing.snakefile/fitbit_with_datetime
  • Rule rules/features.snakefile/fitbit_step_features

::: {#fitbit-steps-parameters} Fitbit: Steps Rule Parameters (fitbit_step_features): :::

Name Description

day_segment The particular day_segment that will be analyzed. The available options are daily, morning, afternoon, evening, night features The features that can be computed. See Available Fitbit: Steps Features <fitbit-steps-available-features>{.interpreted-text role="ref"} Table below threshold_active_bout Every minute with Fitbit step data wil be labelled as sedentary if its step count is below this threshold, otherwise, active. include_zero_step_rows Whether or not to include day segments with a 0 step count exclude_sleep Whether or not to exclude step rows that happen during sleep exclude_sleep_type If exclude_sleep is True, then you can choose between FIXED or FITBIT_BASED. FIXED will exclude all step rows that happen between a start and end time (see below). FITBIT_BASED will exclude step rows that happen during main sleep segments as measured by the Fitbit device (config[SLEEP][DB_TABLE] should be a valid table in your database, it usually is the same table that contains your STEP data) exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if exclude_sleep is True and exclude_sleep_type is FIXED exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if exclude_sleep is True and exclude_sleep_type is FIXED

::: {#fitbit-steps-available-features} Available Fitbit: Steps Features :::

Name Units Description

sumallsteps steps The total step count during day_segment epoch. maxallsteps steps The maximum step count during day_segment epoch. minallsteps steps The minimum step count during day_segment epoch. avgallsteps steps The average step count during day_segment epoch. stdallsteps steps The standard deviation of step count during day_segment epoch. countepisodesedentarybout bouts Number of sedentary bouts during day_segment epoch. sumdurationsedentarybout minutes Total duration of all sedentary bouts during day_segment epoch. maxdurationsedentarybout minutes The maximum duration of any sedentary bout during day_segment epoch. mindurationsedentarybout minutes The minimum duration of any sedentary bout during day_segment epoch. avgdurationsedentarybout minutes The average duration of sedentary bouts during day_segment epoch. stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during day_segment epoch. countepisodeactivebout bouts Number of active bouts during day_segment epoch. sumdurationactivebout minutes Total duration of all active bouts during day_segment epoch. maxdurationactivebout minutes The maximum duration of any active bout during day_segment epoch. mindurationactivebout minutes The minimum duration of any active bout during day_segment epoch. avgdurationactivebout minutes The average duration of active bouts during day_segment epoch. stddurationactivebout minutes The standard deviation of the duration of active bouts during day_segment epoch.

Assumptions/Observations:

Active and sedentary bouts. If the step count per minute is smaller than THRESHOLD_ACTIVE_BOUT (default value is 10), that minute is labelled as sedentary, otherwise, is labelled as active. Active and sedentary bouts are periods of consecutive minutes labelled as active or sedentary.

validsensedminutes feature is not available for Step sensor as we cannot determine the valid minutes based on the raw Fitbit step data.