07. GLOBEM

Data Information

Data Group

The four years of datasets are named as

• INS-W_1 (2018) - 155 participants
• INS-W_2 (2019) - 218 participants
• INS-W_3 (2020) - 137 participants
• INS-W_4 (2021) - 195 participants

Data Type

Each datasets consist of three parts:

1. Feature Data,
2. Survey Data,
   a. Pre/Post Surveys: collected at the start/end of the study
   b. EMA Surveys: collectedly regualrly during the study
3. Participant Info Data

Behaivor Feature Data

• Source: a mobile phone and a wearable fitness tracker
• Period: 24×7.
• Feature data types
  • Location
  • PhoneUsage
  • Call
  • Bluetooth
  • PhysicalActivity
  • Sleep

Participant Info Data

• smartphone platform
• demographics (e.g., age, gender, racical group)

Data Structure

Each dataset is a folder and has its unique name, and every dataset has three folders:

• SurveyData: a list of files containing participants' survey responses, including pre/post long surveys and weekly short EMA surveys.
• FeatureData: behavior feature vectors from all data types, using RAPIDS as the feature extraction tool.
• ParticipantInfoData: some additional information about participants, e.g., device platform (iOS or Android).

. root of a dataset folder
├── SurveyData
│   ├── dep_weekly.csv
│   ├── dep_endterm.csv
│   ├── pre.csv
│   ├── post.csv
│   └── ema.csv
├── FeatureData
│   ├── rapids.csv
│   ├── location.csv
│   ├── screen.csv
│   ├── call.csv
│   ├── bluetooth.csv
│   ├── steps.csv
│   ├── sleep.csv
│   └── wifi.csv
└── ParticipantsInfoData
    └── platform.csv

Survey Data

File Instructions

The SurveyData folder contains five files, all indexed by pid and date:

• pre.csv: The file contains all questionnaires that participants filled in right before the start of the data collection study (thus pre-study).
• post.csv: The file contains all questionnaires that participants filled in right after the end of the data collection study (thus post-study).
• ema.csv: The file contains all EMA surveys that participants filled in during the study. Some EMAs were delivered on Wednesdays, while some were delivered on Sundays.

Our current benchmark takes depression detection as the main task. Thus we also prepare the following two files. We envision future work can be extended to other modeling targets as well.

• dep_weekly.csv: The specific file for depression labels (column dep) combining post and EMA surveys.
• dep_endterm.csv: The specific file for depression labels (column dep) only in post surveys. Some prior depression detection tasks focus on end-of-term depression prediction.

Pre/Post Survey

Personality

Name	Full Name	Available Datasets	Description
BFI10	The Big-Five Inventory-10	INS-1, INS-2, INS-3, INS-4	A 10-item scale measuring the Big Five personality traits Extroversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness. The higher the score, the greater the tendency of the corresponding personality.

Physical well-being

Name	Full Name	Available Datasets	Description
CHIPS	Cohen-Hoberman Inventory of Physical Symptoms	INS-1, INS-2, INS-3, INS-4	A 33-item scale measuring the perceived burden from physical symptoms, and resulting psychological effect during the past 2 weeks. Higher values indicate more perceived burden from physical symptoms.

Mental well-being

Name	Full Name	Available Datasets	Description
UCLA	Short-form UCLA Loneliness Scale	INS-1, INS-2, INS-3, INS-4	A 10-item scale measuring one's subjective feelings of loneliness as well as social isolation. Items 2, 6, 10, 11, 13, 14, 16, 18, 19, and 20 of the original scale are included in the short form. Higher values indicate more subjective loneliness.
SocialFit	Sense of Social and Academic Fit Scale	INS-1, INS-2, INS-3, INS-4	A 17-item scale measuring the sense of social and academic fit of students at the institution where this study was conducted. Higher values indicate higher feelings of belongings.
2-Way SSS	2-Way SocialSupport Scale	INS-1, INS-2, INS-3, INS-4	A 21-item scale measuring social supports from four aspects (a) giving emotional support, (b) giving instrumental support, (c) receiving emotional support, and (d) receiving instrumental support. Higher values indicate more social support.
PSS	Perceived Stress Scale	INS-1, INS-2, INS-3, INS-4	A 14-item scale used to assess stress levels during the last month. Note that Year 1 used the 10-item version. Higher values indicate more perceived stress.
ERQ	Emotion Regulation Questionnaire	INS-1, INS-2, INS-3, INS-4	A 10-item scale assessing individual differences in the habitual use of two emotion regulation strategies: (a) cognitive reappraisal and (b) expressive suppression. Higher scores indicate more habitual use of reappraisal/suppression.
BRS	Brief Resilience Scale	INS-1, INS-2, INS-3, INS-4	A 6-item scale assessing the ability to bounce back or recover from stress. Higher scores indicate more resilient from stress.
STAI	State-Trait Anxiety Inventory for Adults	INS-1, INS-2, INS-3, INS-4	A 20-item scale measuring State-Trait anxiety. Year 1 used the State version, while other years used the Trait version. Higher values indicate higher anxiety.
CSE-D	Center for Epidemiologic Studies Depression Scale Cole version	INS-1, INS-2, INS-3, INS-4	A 10-item scale measuring current level of depressive symptomatology, with emphasis on the affective component, depressed mood. Year 2 used the 9-item version. Higher scores indicate more depressive symptoms.
BDI2	Beck Depression Inventory-II	INS-1, INS-2, INS-3, INS-4	A 21-item detect depressive symptoms. Higher values indicate more depressive symptoms. 0-13: minimal to none, 14-19: mild, 20-28: moderate and 26-63: severe.
MAAS	Mindful Attention Awareness Scale	INS-1, INS-2, INS-3, INS-4	A 15-item scale assessing a core characteristic of mindfulness. Year 1 used a 7-item version, while other years used the full version. Higher values indicate higher mindfulness.
Brief-COPE	Brief Coping Orientationto Problems Experienced	INS-2, INS-3, INS-4	A 28-item scale measuring (a) adaptive and (b) maladaptive ways to cope with a stressful life event. Higher values indicate more effective/ineffective ways to cope with a stressful life event.
GQ	Gratitude Questionnaire	INS-2, INS-3, INS-4	A 6-item scale assessing individual differences in the proneness to experience gratitude in daily life. Higher scores indicate a greater tendency to experience gratitude.
FSPWB	Flourishing Scale & Psychological Well-Being Scale	INS-2, INS-3, INS-4	An 8-item scale measuring the psychological well-being. Higher scores indicate a person with "more psychological resources and mental strengths".

social justice

Name	Full Name	Available Datasets	Description
EDS	Everyday Discrimination Scale	INS-2, INS-3, INS-4	A 9-item scale assessing everyday discrimination. Higher values indicate more frequent experience of discrimination.
CEDH	Chronic Work Discrimination and Harassment	INS-2, INS-3, INS-4	A 12-item scale assessing experiences of discrimination in educational settings. Higher values indicate more frequent experience of discrimination in the work environment.

substance usage

Name	Full Name	Available Datasets	Description
B-YAACQ	The Brief Young Adult Alcohol Consequences Questionnaire (optional)	INS-2, INS-3, INS-4	A 24-item scale measuring the alcohol problem severity continuum in college students. Higher values indicates more severe alcohol problems.

Info

PS: Due to the design iteration, some questionnaires are not available in all studies. Moreover, some questionnaires have different versions across years. We clarify them using column names. For example, INS-W_2 only has CESD_9items_POST, while others have CESD_10items_POST. CESD_9items_POST is also calculated in other datasets to make the modeling target comparable across datasets.

EMA Surveys

Weekly Ecological Momentary Assessment (EMA) surveys during the study to collect in-the-moment self-report data. They mainly focus on capturing participants' recent sense of their mental health.

Name	Full Name	Available Datasets	Description
PHQ-4	Patient Health Questionnaire 4	INS-2, INS-3, INS-4	A 4-item scale assessing (a) mental health, (b) anxiety, and (c) depression. Higher values indicate higher risk of mental health, anxiety, and depression.
PSS-4	Perceived Stress Scale 4	INS-2, INS-3, INS-4	A 4-item scale assessing stress levels during the last month. Higher values indicates more perceived stress.
PANAS	Positive and Negative Affect Schedule	INS-2, INS-3, INS-4	A 10-item scale measuring the level of (a) positive and (b) negative affects. Higher values indicates larger extent.

Feature Data

File Instructions

• rapids.csv: The complete feature file that contains all features.
• location.csv: The feature file that contains all Location features.
• screen.csv: The feature file that contains all PhoneUsage features.
• call.csv: The feature file that contains all Call features.
• bluetooth.csv: The feature file that contains all Bluetooth features.
• steps.csv: The feature file that contains all PhysicalActivity features.
• sleep.csv: The feature file that contains all Sleep features.
• wifi.csv: The feature file that contains all WiFi features. Note that this feature type is not used by any existing algorithms and often has a high data missing rate.

Processing

• Time segment

  variable	desc
  morning	(6 am - 12 pm, calculated daily)
  afternoon	(12 pm - 6 pm, calculated daily)
  evening	(6 pm - 12 am, calculated daily)
  night	(12 am - 6 am, calculated daily)
  allday	(24 hrs from 12 am to 11:59 pm, calculated daily)
  7-day history	(calculated daily)
  14-day history	(calculated daily)
  weekdays	(calculated once per week on Friday)
  weekend	(calculated once per week on Sunday)

• Numeric value

  variable	desc
  normalized	subtracted by each participant's median and divided by the 5-95 quantile range
  discretized	low/medium/high split by 33/66 quantile of each participant's feature value

Name format

All features follow a consistent naming format: [feature_type]:[feature_name][version]:[time_segment]

• feature_type: It corresponds to the six data types.
  • location - f_loc,
  • screen - f_screen,
  • call - f_call,
  • bluetooth - f_blue,
  • steps - f_steps,
  • sleep - f_slp.
• feature_name: The name of the feature provided by RAPIDS, i.e., the second column of the following figure, plus some additional information. A typical format is [SensorType]_[CodeProvider]_[featurename]. Please refer to RAPIDS's naming format for more details.
• version: It has three versions:
  1. nothing, just empty "";
  2. normalized, _norm;
  3. discretized, _dis.
• time_segment: It corresponds to the specific time segment.
  • morning - morning,
  • afternoon - afternoon,
  • evening - evening,
  • night - night,
  • allday - allday,
  • 7-day history - 7dhist,
  • 14-day history - 14dhist,
  • weekday - weekday,
  • weekend - weekend.

Example
A participant's sumdurationunlock normalized feature in mornings is
f_loc:phone_screen_rapids_sumdurationunlock_norm:morning.

Features

Location

Feature Name	Unit	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 traveled 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	seconds	The standard deviation of the duration of all flights.
probpause	-	The fraction of a day spent in a pause (as opposed to a flight).
siglocentropy	nats	Shannon’s entropy measurement is based on the proportion of time spent at each significant location visited during a day.
circdnrtn	-	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.
wkenddayrtn	-	Same as circdnrtn but computed separately for weekends and weekdays.
locationvariance	meters2	The sum of the variances of the latitude and longitude columns.
loglocationvariance	-	Log of the sum of the variances of the latitude and longitude columns.
totaldistance	meters	Total distance traveled in a time segment using the haversine formula.
avgspeed	km/hr	Average speed in a time segment considering only the instances labeled as Moving. This feature is 0 when the participant is stationary during a time segment.
varspeed	km/hr	Speed variance in a time segment considering only the instances labeled as Moving. This feature is 0 when the participant is stationary during a time segment.
numberofsignificantplaces	places	Number of significant locations visited. It is calculated using the DBSCAN/OPTICS clustering algorithm which takes in EPS and MIN_SAMPLES as parameters to identify clusters. Each cluster is a significant place.
numberlocationtransitions	transitions	Number of movements between any two clusters in a time 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	minutes	Time spent at the 3rd most significant location.
movingtostaticratio	-	Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labeled as stationary if its speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine.
outlierstimepercent	-	Ratio between the time spent in non-significant clusters divided by the time spent in all clusters (stationary time. Only stationary samples are clustered). A higher value represents more time spent in non-significant clusters.
maxlengthstayatclusters	minutes	Maximum time spent in a cluster (significant location).
minlengthstayatclusters	minutes	Minimum time spent in a cluster (significant location).
avglengthstayatclusters	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 is 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 is higher the more rows belong to a cluster (i.e., the more time a participant spent at a significant location).
timeathome	minutes	Time spent at home.
timeat[PLACE]	minutes	Time spent at [PLACE], which can be living, exercise, study, greens.

Phone Usage

Feature Name	Unit	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.
firstuseafter	minutes	Minutes until the first unlock episode.
sumduration[PLACE]	minutes	Total duration of all unlock episodes. [PLACE] can be living, exercise, study, greens. Same below.
maxduration[PLACE]	minutes	Longest duration of any unlock episode.
minduration[PLACE]	minutes	Shortest duration of any unlock episode.
avgduration[PLACE]	minutes	Average duration of all unlock episodes.
stdduration[PLACE]	minutes	Standard deviation duration of all unlock episodes.
countepisode[PLACE]	episodes	Number of all unlock episodes.
firstuseafter[PLACE]	minutes	Minutes until the first unlock episode.

Call

Feature Name	Unit	Description
count	calls	Number of calls of a particular call_type (incoming/outgoing) occurred during a particular time_segment.
distinctcontacts	contacts	Number of distinct contacts that are associated with a particular call_type for a particular time_segment.
meanduration	seconds	The mean duration of all calls of a particular call_type during a particular time_segment.
sumduration	seconds	The sum of the duration of all calls of a particular call_type during a particular time_segment.
minduration	seconds	The duration of the shortest call of a particular call_type during a particular time_segment.
maxduration	seconds	The duration of the longest call of a particular call_type during a particular time_segment.
stdduration	seconds	The standard deviation of the duration of all the calls of a particular call_type during a particular time_segment.
modeduration	seconds	The mode of the duration of all the calls of a particular call_type during a particular time_segment.
entropyduration	nats	The estimate of the Shannon entropy for the the duration of all the calls of a particular call_type during a particular time_segment.
timefirstcall	minutes	The time in minutes between 12:00am (midnight) and the first call of call_type.
timelastcall	minutes	The time in minutes between 12:00am (midnight) and the last call of call_type.
countmostfrequentcontact	calls	The number of calls of a particular call_type during a particular time_segment of the most frequent contact throughout the monitored period.

Bluetooth

Feature Name	Unit	Description
countscans	scans	Number of scans (rows) from the devices sensed during a time segment instance. The more scans a bluetooth device has the longer it remained within range of the participant’s phone.
uniquedevices	devices	Number of unique bluetooth devices sensed during a time segment instance as identified by their hardware addresses.
meanscans	scans	Mean of the scans of every sensed device within each time segment instance.
stdscans	scans	Standard deviation of the scans of every sensed device within each time segment instance.
countscansmostfrequentdevicewithinsegments	scans	Number of scans of the most sensed device within each time segment instance.
countscansleastfrequentdevicewithinsegments	scans	Number of scans of the least sensed device within each time segment instance.
countscansmostfrequentdeviceacrosssegments	scans	Number of scans of the most sensed device across time segment instances of the same type.
countscansleastfrequentdeviceacrosssegments	scans	Number of scans of the least sensed device across time segment instances of the same type per device.
countscansmostfrequentdeviceacrossdataset	scans	Number of scans of the most sensed device across the entire dataset of every participant.
countscansleastfrequentdeviceacrossdataset	scans	Number of scans of the least sensed device across the entire dataset of every participant.

Wi-Fi

Feature Name	Unit	Description
countscans	devices	Number of scanned WiFi access points connected during a time_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 time_segment as identified by their hardware address.
countscansmostuniquedevice	scans	Number of scans of the most scanned access point during a time_segment across the whole monitoring period.

Physical Activity

Feature Name	Unit	Description
maxsumsteps	steps	The maximum daily step count during a time segment.
minsumsteps	steps	The minimum daily step count during a time segment.
avgsumsteps	steps	The average daily step count during a time segment.
mediansumsteps	steps	The median of daily step count during a time segment.
stdsumsteps	steps	The standard deviation of daily step count during a time segment.
sumsteps	steps	The total step count during a time segment.
maxsteps	steps	The maximum step count during a time segment.
minsteps	steps	The minimum step count during a time segment.
avgsteps	steps	The average step count during a time segment.
stdsteps	steps	The standard deviation of step count during a time segment.
countepisodesedentarybout	bouts	Number of sedentary bouts during a time segment.
sumdurationsedentarybout	minutes	Total duration of all sedentary bouts during a time segment.
maxdurationsedentarybout	minutes	The maximum duration of any sedentary bout during a time segment.
mindurationsedentarybout	minutes	The minimum duration of any sedentary bout during a time segment.
avgdurationsedentarybout	minutes	The average duration of sedentary bouts during a time segment.
stddurationsedentarybout	minutes	The standard deviation of the duration of sedentary bouts during a time segment.
countepisodeactivebout	bouts	Number of active bouts during a time segment.
sumdurationactivebout	minutes	Total duration of all active bouts during a time segment.
maxdurationactivebout	minutes	The maximum duration of any active bout during a time segment.
mindurationactivebout	minutes	The minimum duration of any active bout during a time segment.
avgdurationactivebout	minutes	The average duration of active bouts during a time segment.
stddurationactivebout	minutes	The standard deviation of the duration of active bouts during a time segment.

Sleep

We leverage sleep-related features from RAPIDS-Fitbit-Sleep, including high-level summary features (total duration of being asleep or in bed), and low-level features about the statistics (count, mean, max, min) of episodes of being asleep, restless, and awake during the sleep.

Feature Name	Unit	Description
countepisode[LEVEL][TYPE]	episodes	Number of [LEVEL][TYPE] sleep episodes. [LEVEL] is one of awake and asleep and [TYPE] is one of main, nap, and all. Same below.
sumduration[LEVEL][TYPE]	minutes	Total duration of all [LEVEL][TYPE] sleep episodes.
maxduration[LEVEL][TYPE]	minutes	Longest duration of any [LEVEL][TYPE] sleep episode.
minduration[LEVEL][TYPE]	minutes	Shortest duration of any [LEVEL][TYPE] sleep episode.
avgduration[LEVEL][TYPE]	minutes	Average duration of all [LEVEL][TYPE] sleep episodes.
medianduration[LEVEL][TYPE]	minutes	Median duration of all [LEVEL][TYPE] sleep episodes.
stdduration[LEVEL][TYPE]	minutes	Standard deviation duration of all [LEVEL][TYPE] sleep episodes.
firstwaketimeTYPE	minutes	First wake time for a certain sleep type during a time segment. Wake time is number of minutes after midnight of a sleep episode’s end time.
lastwaketimeTYPE	minutes	Last wake time for a certain sleep type during a time segment. Wake time is number of minutes after midnight of a sleep episode’s end time.
firstbedtimeTYPE	minutes	First bedtime for a certain sleep type during a time segment. Bedtime is number of minutes after midnight of a sleep episode’s start time.
lastbedtimeTYPE	minutes	Last bedtime for a certain sleep type during a time segment. Bedtime is number of minutes after midnight of a sleep episode’s start time.
countepisodeTYPE	episodes	Number of sleep episodes for a certain sleep type during a time segment.
avgefficiencyTYPE	scores	Average sleep efficiency for a certain sleep type during a time segment.
sumdurationafterwakeupTYPE	minutes	Total duration the user stayed in bed after waking up for a certain sleep type during a time segment.
sumdurationasleepTYPE	minutes	Total sleep duration for a certain sleep type during a time segment.
sumdurationawakeTYPE	minutes	Total duration the user stayed awake but still in bed for a certain sleep type during a time segment.
sumdurationtofallasleepTYPE	minutes	Total duration the user spent to fall asleep for a certain sleep type during a time segment.
sumdurationinbedTYPE	minutes	Total duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment.
avgdurationafterwakeupTYPE	minutes	Average duration the user stayed in bed after waking up for a certain sleep type during a time segment.
avgdurationasleepTYPE	minutes	Average sleep duration for a certain sleep type during a time segment.
avgdurationawakeTYPE	minutes	Average duration the user stayed awake but still in bed for a certain sleep type during a time segment.
avgdurationtofallasleepTYPE	minutes	Average duration the user spent to fall asleep for a certain sleep type during a time segment.
avgdurationinbedTYPE	minutes	Average duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment.

Participant Info Data

The ParticipantInfoData folder contains files with additional information about participants.

• platform.csv: The file contains each participant's major smartphone platform (iOS or Android), indexed by pid
• demographics.csv: Due to privacy concerns, demographic data (age, gender, racial group, etc.) are only available for special requests. Please reach out to us (uw-exp@uw.edu) with a clear research plan with demographic data.

Reference

1. BLOBEM