Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset

Sina Labbaf; Mahyar Abbasian; Brenda Nguyen; Matthew Lucero; Maryam Sabah Ahmed; Asal Yunusova; Alexander Rivera; Ramesh Jain; Jessica L Borelli; Nikil Dutt; Amir M Rahmani

doi:10.1016/j.dib.2024.110228

. 2024 Mar 4;54:110228. doi: 10.1016/j.dib.2024.110228

Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset

Sina Labbaf ^a,^⁎, Mahyar Abbasian ^a, Brenda Nguyen ^b, Matthew Lucero ^b, Maryam Sabah Ahmed ^b, Asal Yunusova ^b, Alexander Rivera ^b, Ramesh Jain ^a,^c, Jessica L Borelli ^b, Nikil Dutt ^a,^c, Amir M Rahmani ^a,^c,^d

PMCID: PMC11637128 PMID: 39669644

Abstract

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and physical activity (step, active calories, type of activity). Ecological momentary assessments were collected from participants on daily and weekly bases containing their Positive and Negative Affect Schedule (PANAS) questionnaire and their emotional responses to COVID-19 and their health. Subjective data was also collected through monthly surveys containing standard mood and mental health surveys such as Beck Depression Inventory II (BDI-II), Brief Symptom Inventory (BSI), GAD-7, Inclusion of Other in the Self Scale (IOS-Partner), Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM), Experiences in Close Relationships Scale Short Form (ECR-S), UCLA Three-Item Loneliness Scale (ULS), Multidimensional Scale of Perceived Social Support (MSPSS), Investment Model Scale (IMS), Conflict Management Scale (CMS), etc in addition to their response to important events and COVID-19. This dataset can be used to study emotions, mood, physical activity, and lifestyle of young adults through longitudinal subjective and objective measures. This dataset also contains valuable data regarding adjustment of lifestyle and emotions during the events of 2020 and 2021 including COVID-19 discovery and lockdown, Black Life Matter movement, 2020 US presidential elections, etc. On average, participants engaged in the EMA collection study at a rate of 86% (SD=10, MIN=65, MAX=99). Smartwatch usage saw an average participation rate of 51% (SD=20, MIN=16, MAX=88), while engagement with the Oura ring averaged at 85% (SD=12, MIN=60, MAX=99).

Keywords: Raw wearable physiological signals, Photoplethysmography (PPG) waveforms, Heart rate and heart rate variability, Self-reported real-life mental health data, Longitudinal sleep and physical activity, Ecological momentary assessments (EMAs), Inertial measurement unit waveform, Personal responses to 2020 events

Specifications Table

Subject	Psychology
Specific subject area	mobile health monitoring
Data format	Raw, Analyzed, Questionnaires
Type of data	Table
Data collection	The data are collected from wearable wristband smartwatch (Samsung Galaxy Gear Sport), smart ring (Oura Ring), Android questionnaire mobile application (Personicle Questions), Android life-logging mobile application (Personicle), Questionnaire surveys (Qualtrics). The data is collected from full-time students from the University of California, Irvine aged 18-22 years, who own an Android smartphone as their primary phone that is compatible with study devices and applications. Participants are ineligible if they are parents, married, returning to school after a period of ≥3 years, or unable to speak/write English fluently.
Data source location	Institution: University of California, Irvine City: Irvine State: California Country: United States of America
Data accessibility	Repository name: Dryad Data identification number: 10.7280/D1WH6T Direct URL to data: https://doi.org/10.7280/D1WH6T [2]
Related research article	[1] A. Yunusova, J. Lai, A.P. Rivera, S. Hu, S. Labbaf, A.M. Rahmani, N. Dutt, R.C. Jain, J.L. Borelli, 2021. Assessing the mental health of emerging adults through a mental health app: Protocol for a prospective pilot study, JMIR Res. Protoc. 10 e25775. https://doi.org/10.2196/25775.

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1. Value of the Data

•
Provides longitudinal raw data in everyday settings capturing everyday activities and lifestyle
•
Covering different dimensions of participants lifestyle such as sleep, activity, emotions, mental health, physical health, and personal events
•
It is a unique dataset that contains data from before, during, and after COVID-19 lockdown, black lives matter, 2020 presidential election, and other events of 2020 and 2021 from the same people

2. Data Description

2.1. Population information

Table 1 shows the demographic distribution of the participants’ population. To preserve the privacy of the participants the demographic data of each participant will only be available upon request.

Table 1.

Demographics of the study population.

Demographic	Distribution	Number
Ethnicity	Asian	13
	Hispanic or Latino	4
	White	3
	Other	1
Gender	Female	14
	Male	6
	Other	1
Birth year	1999 and before	6
	2000	7
	2001 and after	8
College start year	2016 and before	5
	2017	9
	2018 and after	8

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2.2. Participant's data

At the root of the dataset directory tree each participant has their own folder. These folders are names from “par_1” to “par_21” representing each of the participant ids. Each participant's folder contains all the data collected from that participant in its subfolders. In each participant's folder there are four subfolders for different modalities of the study. These modalities are a) EMAs: These are the questionnaires that were collected throughout the study by the Personicle Questionnaire mobile application. b) Oura: These are the data collected from Oura ring. c) Samsung: Samsung data were collected from Samsung Galaxy Gear Sport smartwatch. d) Assessments: These data were collected by the study coordinators 6 times throughout the study asking participants about their demographic information, standard questionnaires, and their participation in the study. Also another final questionnaire about their emotional responses toward important events of 2019 and 2020 and personal events in their life during their participation in the study. e) Personicle: Lifelogging and location data collected by Personicle Android app. Table 2 shows the different modalities of these subfolders.

Table 2.

Dataset subfolders organization.

Subfolder name	Collected by	Description
ema	Personicle Questionnaires App	This folder contains all the responses to weekly and daily ecological momentary assessments (EMAs) sent to participants' mobile phones. Participants were notified and reminded to fill these out by mobile push notifications.
oura	Oura ring v1	This folder contains all the sensor recordings from participants' Oura ring. The ring was used to monitor sleep and activity.
samsung	Samsung Galaxy Gear Sport watch	This folder contains all the data collected by Samsung Gear sport smartwatch. This folder provides some raw sensor reading from the watch as well as some processed information.
assessment	Qualtrics	This folder contains all the responses to the assessments captured within Qualtrics. These assessments were taken at 0 weeks, 4 weeks, and 16 weeks of the study.
personicle	Personicle Mobile App	This folder contains data from Personicle mobile application. Personicle was used to collect life-logging and behavioural data from participants.

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2.3. Dataset subfolders

2.3.1. EMA

EMA data folders contain two comma separated vector files “daily.csv” and “weekly.csv”. “daily.csv” represents the questionnaires that participants were supposed to respond to everyday. and “weekly.csv” is the questionnaire they responded to at the end of each week on Sundays. These EMAs were collected by Personicle Questions mobile application. The content of each of these files are described in Tables 3 and 4.

Table 3.

Ema/daily.csv.

Column name	Description	Range	Type of variable
submission_timestamp	Epoch timestamp of the submission time of the survey	-	Timestamp (milliseconds)
start_timestamp	Epoch timestamp of the time that participant started to fill out the survey	-	Timestamp (milliseconds)
Active	Answer to “Indicate the extent you have felt this way today: Active” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0 - 100	Number
Afraid	Answer to “Indicate the extent you have felt this way today: Afraid” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Alert	Answer to “Indicate the extent you have felt this way today: Alert” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Ashamed	Answer to “Indicate the extent you have felt this way today: Ashamed” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Attentive	Answer to “Indicate the extent you have felt this way today: Attentive” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Determined	Answer to “Indicate the extent you have felt this way today: Determined” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Distressed	Answer to “Indicate the extent you have felt this way today: Distressed” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Enthusiastic	Answer to “Indicate the extent you have felt this way today: Enthusiastic” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Excited	Answer to “Indicate the extent you have felt this way today: Enthusiastic” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Guilty	Answer to “Indicate the extent you have felt this way today: Guilty” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Hostile	Answer to “Indicate the extent you have felt this way today: Hostile” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Inspired	Answer to “Indicate the extent you have felt this way today: Inspired” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Interested	Answer to “Indicate the extent you have felt this way today: Interested” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Irritable	Answer to “Indicate the extent you have felt this way today: Irritable” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Jittery	Answer to “Indicate the extent you have felt this way today: Jittery” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Nervous	Answer to “Indicate the extent you have felt this way today: Nervous” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Proud	Answer to “Indicate the extent you have felt this way today: Proud” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Scared	Answer to “Indicate the extent you have felt this way today: Scared” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Strong	Answer to “Indicate the extent you have felt this way today: Strong” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Upset	Answer to “Indicate the extent you have felt this way today: Upset” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Covid_worried	Answer to “How worried were you about contracting COVID-19 today?” Indicated by: • Not worried at all • Extremely worried	0-100	Number (Slider)
daily_feeling	Answer to “Can you please take a few minutes to tell us in greater depth how you are feeling today, and what contributed to how you're feeling?” This field is only available upon request to protect participants’ privacy.	-	Text
health_worried	Answer to “Please rate how worried you felt about your health today?” Indicated by: • Not worried at all • Extremely worried	0-100	Number (Slider)
*_last_modified	Epoch timestamp of the last time that the participant has modified the value of this component in their app. If “undefined” it means that the participant didn't interact with this component.	-	Timestamp (milliseconds)

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daily.csv content.

Table 4.

Ema/weekly.csv.

Column name	Description	Range	Type of variable
submission_timestamp	Epoch timestamp of the submission time of the survey	-	Timestamp (milliseconds)
start_timestamp	Epoch timestamp of the time that participant started to fill out the survey	-	Timestamp (milliseconds)
Active	Answer to “Indicate the extent you have felt this way today: Active” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0 - 100	Number
Afraid	Answer to “Indicate the extent you have felt this way today: Afraid” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Alert	Answer to “Indicate the extent you have felt this way today: Alert” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Ashamed	Answer to “Indicate the extent you have felt this way today: Ashamed” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Attentive	Answer to “Indicate the extent you have felt this way today: Attentive” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Determined	Answer to “Indicate the extent you have felt this way today: Determined” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Distressed	Answer to “Indicate the extent you have felt this way today: Distressed” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Enthusiastic	Answer to “Indicate the extent you have felt this way today: Enthusiastic” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Excited	Answer to “Indicate the extent you have felt this way today: Excited” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Guilty	Answer to “Indicate the extent you have felt this way today: Guilty” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Hostile	Answer to “Indicate the extent you have felt this way today: Hostile” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number
Inspired	Answer to “Indicate the extent you have felt this way today: Inspired” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Interested	Answer to “Indicate the extent you have felt this way today: Interested” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Irritable	Answer to “Indicate the extent you have felt this way today: Irritable” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Jittery	Answer to “Indicate the extent you have felt this way today: Jittery” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Nervous	Answer to “Indicate the extent you have felt this way today: Nervous” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Proud	Answer to “Indicate the extent you have felt this way today: Proud” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Scared	Answer to “Indicate the extent you have felt this way today: Scared” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Strong	Answer to “Indicate the extent you have felt this way today: Strong” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
Upset	Answer to “Indicate the extent you have felt this way today: Upset” Indicated by: • Very slightly • A little • Moderately • Quite a bit • Extremely	0-100	Number (Slider)
week_felt	Answer to “Please rate how you felt about your week.” Indicated by: • 0: Completely Negative • 100: Completely Positive	0-100	Number (Slider)
Covid_worried	Answer to “How worried were you about contracting COVID-19 today?” Indicated by: • 0: Not worried at all • 100: Extremely worried	0-100	Number (Slider)
week_points	Answer to “Please write about your high points and low points this week. Please try to be as detailed as possible.” This field is only available upon request to protect participants’ privacy.	-	Text
*_last_modified	Epoch timestamp of the last time that the participant has modified the value of this component in their app. If “undefined” it means that the participant didn't interact with this component.	-	Timestamp (milliseconds)

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weekly.csv content.

2.3.2. Oura

This subfolder represents the data collected through Oura ring v1. Oura provides data in three modalities: activity, sleep, and readiness. Activity data is available throughout day and night and shows how active the participants were throughout the day and on average each day represented in Tables 5 and 6. It also contains heart rate and heart rate variability of participants during their sleep presentated by Table 7. The other modality is readiness which is a set of different scores that Oura calculates based on the collected data to show the overall wellbeing of participants during each day described in Table 8. The sleep data is generally only available for night time and represent the quality of sleep and different sleep stages at Tables 9 and 10 The descriptions for these fields have been recited from Oura API V1 documentation [3].

Table 5.

Oura/activity_level.csv.

Column name	Description	Range	Type
timestamp	Epoch timestamp of this data row	-	Timestamp (milliseconds)
activity_level	The numeric level of activity with: • 0: Non-wear • 1: Rest (MET level below 1.05) • 2: Inactive (MET level between 1.05 and 2) • 3: Low intensity activity (MET level between 2 and age/gender dependent limit) • 4: Medium intensity activity • 5: High intensity activity	0-5	Number
activity_class	The actual level of activity either: • non-wear • rest • inactive • low • medium • high	-	Text

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activity_level.csv.

Table 6.

Oura/activity.csv.

Column name	Description	Range	Type
date	The day for this row of data	-	Text (YYYY-MM-DD)
score	An estimation on how well recent physical activity has matched participant's needs. It is calculated as a weighted average of activity score contributors that represent one aspect of suitability of the activity each. (score = score_stay_active0.15 + score_move_every_hour0.1 + score_meet_daily_targets0.25 + score_training_frequency0.1 + score_training_volume0.15 + score_recovery_time0.25)	1-100 (0=miss)	Number
score_stay_active	An indication of how well the participant has managed to avoid inactivity (sitting or standing still) during the last 24 hours. The more inactivity, the lower contributor value. The contributor value is 100 when inactive time during the past 24 hours is below 5 hours. The contributor value is above 95 when inactive time during the past 24 hours is below 7 hours.	1-100 (0=miss)	Number
score_move_every_hour	An indication of how well the participant has managed to avoid long periods of inactivity (sitting or standing still) during the last 24 hours. The score includes a number of continuous inactive periods of 60 minutes or more (excluding sleeping). The more long inactive periods, the lower the value. The score is 100 when no continuous inactive periods of 60 minutes or more have been registered. The contributor value is above 95 when at most one continuous inactive period of 60 minutes or more has been registered.	1-100 (0=miss)	Number
score_meet_daily_targets	An indication of how often the participant has reached their daily activity target during seven last days (100 = six or seven times, 95 = five times).	1-100 (0=miss)	Number
score_training_frequency	An indication of how regularly the participant has had physical exercise during the last seven days. The value is 100 when the participant has got more than 100 minutes of medium or high intensity activity on at least four days during the past seven days. The score value is 95 when the user has got more than 100 minutes of medium or high intensity activity on at least three days during the past seven days.	1-100 (0=miss)	Number
score_training_volume	An indication of how much physical exercise the participant has got during the last seven days. The value is 100 when thes sum of weekly MET minutes is over 2000. The value is 95 when the sum of weekly MET minutes is over 750. There is a weighting function so that the effect of each day gradually disappears.	1-100 (0=miss)	Number
score_recovery_time	An indication of the recovery time during the last seven days. The value is 100 when: 1. The participant has got at least two recovery days during the past 7 days. 2. No more than two days elapsed after the latest recovery day. The value is 95 when: 1. The participant has got at least one recovery day during the past 7 days. 2. No more than three days elapsed after the latest recovery day. Here a day is considered as a recovery day when the amount of high intensity activity did not exceed 100 MET minutes and the amount of medium intensity activity did not exceed 200 MET minutes. The exact limits will be age and gender dependent.	1-100 (0=miss)	Number
cal_active	Energy consumption caused by the physical activity of the day.	-	Number (kcal)
cal_total	Total energy consumption during the day including Basal Metabolic Rate	-	Number (kcal)
daily_movement	Daily physical activity expressed as the amount of walking in meters that is needed to get the same amount of activity	-	Number (meters)
inactivity_alerts	Number of continuous inactive periods of 60 minutes or more during the day.		Number
steps	Total number of steps registered during the day.		Number
non_wear	Number of minutes during the day when the participant was not wearing the ring. Can be used as a proxy for data accuracy, i.e. how well the measured physical activity represents actual total activity.	0-1440	Number (minutes)
rest	Number of minutes during the day spent resting i.e. sleeping or lying down (average MET level of the minute is below 1.05).	0-1440	Number (minutes)
inactive	Number of inactive minutes (sitting or standing still, average MET level of the minute between 1.05 and 2) during the day.	0-1440	Number (minutes)
low	Number of minutes during the day with low intensity activity (e.g. household work, average MET level of the minute between 2 and age dependent limit).	0-1440	Number (minutes)
medium	Number of minutes during the day with medium intensity activity (e.g. walking). The upper and lower MET level limits for medium intensity activity depend on participant's age and gender.	0-1440	Number (minutes)
high	Number of minutes during the day with high intensity activity (e.g. running). The lower MET level limit for high intensity activity depends on the participant's age and gender.	0-1440	Number (minutes)
average_met	Average MET level during the whole day.	-	Number (MET)
met_min_inactive	Total MET minutes accumulated during inactive minutes of the day.	-	Number (MET)
met_min_low	Total MET minutes accumulated during low intensity activity minutes of the day.	-	Number (MET)
met_min_medium	Total MET minutes accumulated during medium intensity activity minutes of the day.	-	Number (MET)
met_min_high	Total MET minutes accumulated during high intensity activity minutes of the day.	-	Number (MET)
day_start_timestamp	Start of the day that this row of data is representing.	-	Timestamp (milliseconds)
day_end_timestamp	End of the day that this row of data is representing.	-	Timestamp (milliseconds)

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activity.csv.

Table 7.

Oura/heart_rate.csv.

Column name	Description	Range	Type (unit)
timestamp	Epoch timestamp of this data row.	-	Timestamp (milliseconds)
heart_rate	Average heart rate for each 5 minutes of the sleep period.	-	Number (beats/min)
heart_rmssd	The average root mean square of successive heartbeat interval differences for each 5 minutes of the sleep period.	-	Number (milliseconds)

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heart_rate.csv.

Table 8.

Oura/readiness.csv.

Column name	Description	Range	Type (unit)
date	The day for this row of data.	-	Text (YYYY-MM-DD)
score	Oura readiness score.	1-100 (0=miss)	Number
score_activity_balance	Measurement of how the participant's activity level over the past days is affecting their readiness to perform. A 100 indicates that they've been active, but kept from training at their maximum capacity. This has boosted their recovery and helped build up their energy levels.	1-100 (0=miss)
score_hrv_balance	Indicator of how close the heart rate variability is to the optimum of participant's ideal heart rate variability during their sleep.	1-100 (0=miss)
score_previous_day	When 100%, it shows a good balance of active time and rest. An exceptionally high amount of inactivity or activity leads to a drop in Readiness Score.	1-100 (0=miss)
score_previous_night	Oura previous night score.	1-100 (0=miss)
score_recovery_index	Measures how long it takes for resting heart rate to stabilize during the night. A sign of very good recovery is that resting heart rate stabilizes during the first half of the night, at least 6 hours before wake up time, leaving body time to recover for the next day.	1-100 (0=miss)
score_resting_hr	Indicator of how close the resting heart rate is to the optimum of participant's ideal resting heart rate during their sleep	1-100 (0=miss)
score_sleep_balance	Shows if the sleep over the past two weeks is in balance with participant's needs. Typically adults need 7-9 hours of sleep a night to stay healthy, alert, and to perform at their best both mentally and physically.	1-100 (0=miss)
score_temperature	An indicator of sleep temperature balance. If the temperature rapidly changes during the sleep or shows abnormal values this score will decrease.	1-100 (0=miss)

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readiness.csv.

Table 9.

Oura/sleep_hypnogram.csv.

Column name	Description	Range	Type
timestamp	Epoch timestamp of this data row.	-	Timestamp
hypnogram_level	The numeric level of sleep hypnogram: • 1 = deep (N3) sleep • 2 = light (N1 or N2) sleep • 3 = REM sleep • 4 = awake	1-4	Number
hypnogram_class	The actual class of sleep either: • deep • light • REM • awake	-	Text

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sleep_hypnogram.csv.

Table 10.

Oura/sleep.csv.

Column name	Description	Range	Type
date	One day prior to the date when the sleep period ended. This is one day before the date that is shown in Oura apps.	-	Text (YYYY-MM-DD)
score	Overall sleep quality during the sleep period. It is calculated as a weighted average of sleep score contributors that represent one aspect of sleep quality each. The sleep score contributor values are also available as separate parameters. (scope = 0.1score_alignment + 0.1score_deep + 0.15score_disturbances + 0.1score_efficiency + 0.1score_latency + 0.1score_rem + 0.35*score_total)	1-100
score_alignment	Circadian alignment's contribution for sleep score. Sleep midpoint time between 12PM and 3AM gives the highest score. The more the midpoint time deviates from that range, the lower the score.	1-100
score_deep	Represents deep (N3) sleep time's contribution for sleep quality. The value depends on the age of the participant - the younger, the more sleep is needed for good score.	1-100
score_disturbances	Represents sleep disturbances' contribution for sleep quality. Three separate measurements are used to calculate this contributor value: 1. Wake-up count - the more wake-ups, the lower the score. 2. Got-up count - the more get-ups, the lower the score. 3. Restless sleep (sleep.restless) - the more motion detected during sleep, the lower the score. Each of these three values has weight 0.05 in sleep score calculation.	1-100
score_efficiency	Represents sleep efficiency's contribution for sleep quality. The higher efficiency, the higher the score.	1-100
score_latency	Represents sleep onset latency's contribution for sleep quality. A latency of about 15 minutes gives the best score. Latency longer than that may indicate problems falling asleep, whereas a very short latency may be a sign of sleep debt.	1-100
score_rem	Represents REM sleep time's contribution for sleep quality. The value depends on the age of the user - the younger, the more sleep REM is needed for good score.	1-100
score_total	Represents total sleep time's contribution for sleep quality. The value depends on the age of the user - the younger, the more sleep is needed for a good score.	1-100
duration	Total duration of the sleep period.	-	Number (seconds)
awake	Total amount of awake time registered during the sleep period.	-	Number (seconds)
light	Total amount of light (N1 or N2) sleep registered during the sleep period.	-	Number (seconds)
rem	Total amount of REM sleep registered during the sleep period.	-	Number (seconds)
deep	Total amount of deep (N3) sleep registered during the sleep period.	-	Number (seconds)
total	Total amount of sleep registered during the sleep period (total = rem + light + deep).	-	Number (seconds)
onset_latency	Detected latency from getting into bed to the beginning of the first five minutes of persistent sleep.	-	Number (seconds)
midpoint_time	The time in seconds from the start of sleep to the midpoint of sleep. The midpoint ignores awake periods.	-	Number (seconds)
efficiency	Sleep efficiency is the percentage of the sleep period spent asleep (100 * total / duration).	0-100	Number
restless	Restlessness of the sleep time, i.e. percentage of sleep time when the user was moving.	0-100	Number
hr_average	The average heart rate registered during the sleep period.	-	Number (beats / minute)
hr_lowest	The lowest heart rate (5 minutes sliding average) registered during the sleep period.	-	Number (beats / minute)
rmssd	The average HRV calculated with the rMSSD method.	-	Number (milliseconds)
breath_average	Average respiratory rate.	-	Number (breaths per minute)
bedtime_start_midnight_delta	Difference between bedtime start and local midnight.	-	Number (seconds)
bedtime_end_midnight_delta	Difference between bedtime end and local midnight.	-	Number (seconds)
temperature_delta	Skin temperature deviation from the long-term temperature average.	-	Number (Celsius)
bedtime_start_timestamp	Epoch timestamp of the start of the sleep.	-	Timestamp (milliseconds)
bedtime_end_timestamp	Epoch timestamp of the end of the sleep.	-	Timestamp (milliseconds)

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sleep.csv.

Metabolic Equivalent (MET) value referred to in this document is a relative measurement of physical activity intensity. The Energy expenditure of a person while resting is 1 and the physical activity intensity of X MET means the person was expending X times more the energy that their resting state.

2.3.3. Samsung

These data were collected using Samsung Galaxy Gear Sport smartwatch. Data files “imu.csv”, “ppg.csv”, and “pressure.csv” are raw data collected using direct sensor reading from the watch. and “pedometer.csv”, and “awake_times.csv” are processed data collected from the watch. All of the data collected directly from Samsung are presented in Table 11, Table 12, Table 13, Table 14, Table 15.

Table 11.

Samsung/awake_times.csv.

Column name	Description	Unit
timestamp_start	Timestamp indicating the start of an awake period.	Timestamp (milliseconds)
timestamp_end	Timestamp indicating the end of an awake period.	Timestamp (milliseconds)
state	Always “awake”	Text

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awake_times.csv.

Table 12.

Samsung/imu.csv.

Column name	Description	Range	Unit
timestamp	The timestamp of this row of data reading.	-	Timestamp (milliseconds)
accx	Linear acceleration towards the X axis.	-19.6-19.6	Number $(m / s^{2})$
accy	Linear acceleration towards the Y axis.	-19.6-19.6	Number $(m / s^{2})$
accz	Linear acceleration towards the Z axis.	-19.6-19.6	Number $(m / s^{2})$
gyrx	Angular velocity around the X axis.	-573-573	Number $(^{\circ} / s)$
gyry	Angular velocity around the Y axis.	-573-573	Number $(^{\circ} / s)$
gyrz	Angular velocity around the Z axis.	-573-573	Number $(^{\circ} / s)$

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imu.csv.

Table 13.

Samsung/pedometer.csv.

Column name	Description	Unit
timestamp	The timestamp of this row of data reading.	Timestamp (milliseconds)
num_total_steps	Total number of steps since the last reboot of the device to time indicated by timestamp.	Number
num_total_walking_steps	Total number of steps walking since the last reboot of the device to time indicated by timestamp.	Number
num_total_running_steps	Total number of steps running since the last reboot of the device to time indicated by timestamp.	Number
move_distance_meter	Total distance travelled by foot since the last reboot of the device to time indicated by timestamp.	Number (meters)
cal_burn_kcal	Total calories burnt since the last reboot of the device to time indicated by timestamp.	Number (kcal)
last_speed_kmh	Last speed of the device before the timestamp.	Number (km/h)
last_step_freq	Last stepping frequency of the device before timestamp.	Number (steps/second)
last_state_level	Last state of the device before the timestamp between -1 to 2. • -1: unknown state • 0: stop state • 1: walking state • 2: running state	Number
last_state_class	The class of the last_state_level. Either “unknown”, “stop”, “walking”, or “running”.	Text

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pedometer.csv.

Table 14.

Samsung/ppg.csv.

Column name	Description	Range	Unit
timestamp	The timestamp of this row of data reading.	-	Timestamp (milliseconds)
ppg	The heart rate monitor (HRM) LED green sensor measures the amount of green light that is reflected back from a person's blood vessel.	0-4194304	Number
hr	Real-time heart rate extracted from ppg signal.	0-240	Number (beats/minute)

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ppg.csv.

Table 15.

Samsung/pressure.csv.

Column name	Description	Range	Unit
timestamp	The timestamp of this row of data reading	-	Timestamp (milliseconds)
pressure	The atmospheric pressure in the device's surrounding environment.	260-1260	Number (hectopascals)

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pressure.csv

In addition to the raw data and processed data collected from the Samsung watch, this subfolder contains two files that are post processed features extracted from the ppg signal by Khatibi et al. [4]. These features are in two files named “hrv_1min.csv” (for one minute time window features) and “hrv_5min.csv” (for 5 minute time window features) presented in Table 16.

Table 16.

Samsung/hrv_1min.csv and samsung/hrv_5min.csv.

Column name	Type of HRV Feature	Description	Range	Type of variable
timestamp	-	Starting time of the data segment	-	Timestamp
hrv_meannn	Time Domain	Mean of normal-to-normal interval (NN)	-	Number (milliseconds)
hrv_sdnn	Time Domain	The standard deviation (SD) of the NN	-	Number (milliseconds)
hrv_rmssd	Time Domain	Root mean square of successive NN interval differences	-	Number (milliseconds)
hrv_sdsd	Time Domain	SD of the successive differences between NN	-	Number (milliseconds)
hrv_cvnn	Time Domain	The standard deviation of the NN intervals (hrv_sdnn) divided by the mean of the NN intervals (hrv_meannn)	-	Number (%)
hrv_cvsd	Time Domain	hrv_sdnn divided by hrv_meannn	-	Number (%)
hrv_mediannn	Time Domain	Median of NN	-	Number (milliseconds)
hrv_madnn	Time Domain	Median absolute deviation of NN	-	Number (milliseconds)
hrv_mcvnn	Time Domain	Median absolute deviation of NN (hrv_madnn) divided by Median of NN (hrv_mediannn)	-	Number (milliseconds)
hrv_iqrnn	Time Domain	Interquartile range of NN	-	Number (milliseconds)
hrv_prc80nn	Time Domain	The 80th percentile of the NN intervals	-	Number (milliseconds)
hrv_pnn50	Time Domain	Percentage of successive NN that differ by more than 50ms	-	Number (%)
hrv_pnn20	Time Domain	Percentage of successive NN that differ by more than 20ms	-	Number (%)
hrv_minnn	Time Domain	The minimum of the NN intervals	-	Number (milliseconds)
hrv_maxnn	Time Domain	The maximum of the NN intervals	-	Number (milliseconds)
hrv_tinn	Time Domain	Baseline width of the NN distribution obtained by triangular interpolation	-	Number
hrv_hti	Time Domain	Total number of NN divided by the height of the NN histogram	-	Number
hrv_ulf	Time Domain	The spectral power of ultra low frequencies (.0033 Hz).	-	Number (ms²)
hrv_vlf	Frequency Domain	The spectral power of very low frequencies (.0033 to .04 Hz)	-	Number (ms²)
hrv_lf	Frequency Domain	The spectral power of low frequencies (by default, .04 to .15 Hz)	-	Number (ms²)
hrv_hf	Frequency Domain	The spectral power of high frequencies (by default, .15 to .4 Hz)	-	Number (ms²)
hrv_vhf	Frequency Domain	Absolute power of the very-high-frequency band (0.4–0.5Hz)	-	Number (ms²)
hrv_lfhf	Frequency Domain	The ratio obtained by dividing the low frequency power by the high frequency power	-	Number
hrv_lfn	Frequency Domain	The normalized low frequency, low-frequency power divided by the total power	-	Number
hrv_hfn	Frequency Domain	The normalized high frequency, high-frequency power divided by the total power	-	Number
hrv_lnhf	Frequency Domain	The log transformed hrv_hf	-	Number (ms²)
hrv_sd1	Non-linear Metric	Index of short-term NN fluctuation, the semi-short axis of the fitted ellipse in the Poincaré plot	-	Number
hrv_sd2	Non-linear Metric	Index of long-term NN fluctuation, the semi-long axis of the fitted ellipse in the Poincaré plot	-	Number
hrv_sd1sd2	Non-linear Metric	Ratio of SD1-to-SD2	-	Number
hrv_s	Non-linear Metric	Area of the fitted ellipse in the Poincaré plot	-	Number
hr	-	Heart rate	-	Number (beats/minute)

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hrv_1min.csv and hrv_5min.csv.

In a typical PPG signal, the Normal-to-Normal interval (NN) pertains to the duration between successive peaks (Fig. 1). The heart rate variability (HRV) analyses the time differences between these peaks in different ways and domains presented in the next table. The list of the features and their description has been taken from Ma et al. research [5].

2.3.4. Assessment

This directory contains two comma separated vector files “events.csv” and “surveys.csv” respectively presented in Tables 17 and 18. The surveys.csv contains the accumulation of all the surveys that were asked from the participant through different stages of the study. in the events.csv we asked the participants what they thought about different events that have happened during their participation in the study and how these events affected them. These surveys were collected in person before the COVID-19 lockdown and then online through Qualtrics software. events.csv.

Table 18.

Assessment/surveys.csv.

Column name	Description	Range	Unit
start_timestamp	The timestamp that the participant has started filling out the survey.	-	Timestamp (milliseconds)
end_timestamp	The timestamp that the participant has finished filling out the survey.	-	Timestamp (milliseconds)
duration_sec	The time it took the participant to finish the survey.	-	Number (seconds)
academicevent_1	Answer to “Please rate how impactful each academic event was to you - Winter Quarter 2020.” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_2	Answer to “Please rate how impactful each academic event was to you - Winter Quarter 2020 Finals” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_3	Answer to “Please rate how impactful each academic event was to you - Spring Break” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_4	Answer to “Please rate how impactful each academic event was to you - Spring Quarter 2020” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_5	Answer to “Please rate how impactful each academic event was to you - Spring Quarter 2020 Finals” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_6	Answer to “Please rate how impactful each academic event was to you - Summer Session I” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_7	Answer to “Please rate how impactful each academic event was to you - Summer Session I Finals” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_8	Answer to “Please rate how impactful each academic event was to you - Summer Session II Finals” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_9	Answer to “Please rate how impactful each academic event was to you - Summer Session II” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_10	Answer to “Please rate how impactful each academic event was to you - Summer 2020 Break” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_11	Answer to “Please rate how impactful each academic event was to you - Fall Quarter 2020” Choices: • (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_12	Answer to “Please rate how impactful each academic event was to you - Fall Quarter 2020 Finals” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
academicevent_13	Answer to “Please rate how impactful each academic event was to you - Winter Break 2020-2021” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_1	Answer to “Please rate how impactful each event was to you - Joe Biden wins Presidency” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_2	Answer to “Please rate how impactful each event was to you - First California Quarantine Order” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_3	Answer to “Please rate how impactful each event was to you - Black Lives Matter protests” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_4	Answer to “Please rate how impactful each event was to you - Northern California Wildfires” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_5	Answer to “Please rate how impactful each event was to you - Transition to Online Exams and Remote Learning” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_6	Answer to “Please rate how impactful each event was to you - Original Coronavirus WHO Announcement” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_7	Answer to “Please rate how impactful each event was to you - Capitol Riots” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_8	Answer to “Please rate how impactful each event was to you - Death of George Floyd” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_9	Answer to “Please rate how impactful each event was to you - Death of Ruth Bader-Ginsburg” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_10	Answer to “Please rate how impactful each event was to you - Donald Trump Impeached for the First Time” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_11	Answer to “Please rate how impactful each event was to you - Death of Breonna Taylor” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_12	Answer to “Please rate how impactful each event was to you - Impact of COVID on Student Financial Aid” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_13	Answer to “Please rate how impactful each event was to you - First COVID-19 Vaccine Rollout” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_14	Answer to “Please rate how impactful each event was to you - Kobe Bryant Helicopter Crash/Death” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_15	Answer to “Please rate how impactful each event was to you - Supreme Court Rules LGBTQ Employees are Protected by Civil Rights Employment Statutes” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_16	Answer to “Please rate how impactful each event was to you - Trump Impeached for Second Time” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_17	Answer to “Please rate how impactful each event was to you - First stimulus check” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful)	1-7	Number
event_18	Answer to “Please rate how impactful each event was to you - Jacob Blake Shooting” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_19	Answer to “Please rate how impactful each event was to you - President Trump Tests Positive for COVID” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
event_20	Answer to “Please rate how impactful each event was to you - Supreme Court Rules Against Ending DACA” Choices: • 1 (Not at all impactful) • 2 • 3 • 4 (Moderately Impactful) • 5 • 6 • 7 (Very Impactful) This field is only available upon request to protect participants’ privacy.	1-7	Number
gear_1	Answer to “What features of the Gear Sport watch did you like?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_2	Answer to “What features did you find irritating? What features would need to be changed or added in order for it not to be irritating?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_3	Answer to “Did you find the Gear Sport watch helpful? Why or why not?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_4	Answer to “How do you feel about how the data/information is presented back to you?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_5	Answer to “What questions do you have about your data that the Gear Sport watch doesn't answer?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_6	Answer to “What do you think is missing from the Gear Sport watch, if anything?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_7	Answer to “Do you believe the measurements displayed on the application accurately reflect reality? If not, please elaborate on these inaccuracies.” This field is only available upon request to protect participants’ privacy.	–	Text
gear_8_1	Answer to “How likely would you be to adopt the watch if you had the option to use it? - 1” This field is only available upon request to protect participants’ privacy.	–	Text
gear_9	Answer to “What features would need to be changed or added in order for you to adopt it, if any?” This field is only available upon request to protect participants’ privacy.	–	Text
gear_10	Answer to “If a friend who was interested in the Gear Sport watch came and asked you about it, what would you tell them?” This field is only available upon request to protect participants’ privacy.	–	Text
general_1	Answer to “What features of the overall study did you like?” This field is only available upon request to protect participants’ privacy.	–	Text
general_2	Answer to “What aspects did you find irritating? Are there things/features you would want to change? What would this look like?” This field is only available upon request to protect participants’ privacy.	–	Text
general_3	Answer to “Do you believe the Personicle system as a whole (daily emotion surveys, smart devices) could be used as a successful intervention to promote mental health? Please describe your reasoning.” This field is only available upon request to protect participants’ privacy.	–	Text
general_4	Answer to “Since joining the study have you noticed the Personicle system as a whole to have an impact on your life? If so, please describe in detail.” This field is only available upon request to protect participants’ privacy.	–	Text
general_5	Answer to “Did you ever/ do you currently have any concerns regarding the privacy and security of the data being collected by any of the applications? If so, please describe in detail.” This field is only available upon request to protect participants’ privacy.	–	Text
international	Answer to “Are you an international student?” Choices: • Yes • No This field is only available upon request to protect participants’ privacy.	One of: • Yes • No	Text
oura_1	Answer to “What features of the Oura Ring did you like?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_2	Answer to “What features did you find irritating? What features would need to be changed or added in order for it not to be irritating?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_3	Answer to “Did you find the Oura Ring helpful? Why or why not?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_4	Answer to “How do you feel about how the data/information is presented back to you?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_5	Answer to “What questions do you have about your data that the Oura Ring doesn't answer?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_6	Answer to “What do you think is missing from the Oura Ring, if anything?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_7	Answer to “Do you believe the measurements displayed on the application accurately reflect reality? If not, please elaborate on these inaccuracies.” This field is only available upon request to protect participants’ privacy.	–	Text
oura_8_1	Answer to “How likely would you be to adopt the ring if you had the option to use it? - 1” This field is only available upon request to protect participants’ privacy.	–	Text
oura_9	Answer to “What features would need to be changed or added in order for you to adopt it, if any?” This field is only available upon request to protect participants’ privacy.	–	Text
oura_10	Answer to “If a friend who was interested in the Oura Ring came and asked you about it, what would you tell them?” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_5	Answer to “What questions do you have about your data that the Personicle app doesn't answer?” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_6	Answer to “What do you think is missing from Personicle, if anything?” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_7	Answer to “Do you believe the measurements displayed on the application accurately reflect reality? If not, please elaborate on these inaccuracies.” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_8_1	Answer to “How likely would you be to adopt the application if you had the option to use it? - 1” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_9	Answer to “What features would need to be changed or added in order for you to adopt it, if any?” This field is only available upon request to protect participants’ privacy.	–	Text
personicle_app_10	Answer to “If a friend who was interested in Personicle came and asked you about it, what would you tell them?” This field is only available upon request to protect participants’ privacy.	–	Text
resources_1	Answer to “Have you utilized any of the following resources since the beginning of your enrollment in the study? - Selected Choice” Choices: • Psychotherapy • Psychotropic Medication (e.g. SSRI) • Support Group • Religious/spiritual supports (e.g., retreats, bible study, group meditation, etc.) • Other This field is only available upon request to protect participants’ privacy.	One of: • Psychotherapy • Psychotropic Medication (e.g. SSRI) • Support Group • Religious/spiritual supports (e.g., retreats, bible study, group meditation, etc.) • Other	Text
resources_1_5_text	Answer to “Have you utilized any of the following resources since the beginning of your enrollment in the study? - Selected “Other”” This field is only available upon request to protect participants’ privacy.	–	Text
resources_2	Answer to “Have you had as much access to resources to support your mental health as you would like during your enrollment in the study?” Choices: • Yes • No • N/A	One of: • Yes • No • N/A	Text
resources_3	Answer to “Have you utilized UCI's Wellness, Health, & Counseling Services?” Choices: Yes No This field is only available upon request to protect participants’ privacy.	One of: • Yes • No	Text
vote	Answer to “Did you vote in the 2020 United States Presidential Election?” Choices: • Yes • No	One of: • Yes • No	Text

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Table 17.

Assessment/events.csv.

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surveys.csv

2.3.5. Personicle

The data is collected using Personicle mobile application [6]. It records life logs of participants and converts them into event segments including location and activity information of participants. Segments indicate the 5 minutes interval of event changes. If two consecutive events have the same label, they will be stored as one with the start time of the earliest happening event. As soon as the segment label changes, a new event segment is created. The data collected via Personicle mobile application is presentated in Table 19.

Table 19.

Personicle/personicle.csv.

Column name	Description	Range	Type of variable
timestamp	Starting Epoch timestamp of the data segment.	-	Timestamp
activity_level	Average score of the physical activity set $C = \sum_{i = 0}^{4} \frac{i \times {(P h y s i c a l A c t i v i t y)}_{i}}{k}$ We have 5 types of activities and they are scored from 0 to 4. Then activity level can be calculated based on the average of the frequency of each activity in five minute time frames. [6]	-	Number
activity_name	Name of the current activity.	-	Text
sub_activity_name	Name of the current Sub-activity.	-	Text
activity_type	Activity type extracted from Google activity detector. Valid values: • IN_VEHICLE • ON_BICYCLE • ON_FOOT • RUNNING • STILL • TILTING • UNKNOWN • WALKING	-	Text
activity_previous_event	The name of the previous event which is the previous segment activity_name of the same day.	-	Text
venue_name	The name of the venue the user is currently in which is extracted from google location service. This field is only available upon request to protect participants’ privacy.	-	Text
venue_name_arrival	The name of the destination venue extracted from google location service. This field is only available upon request to protect participants’ privacy.	-	Text
venue_name_departure	The name of the departure venue extracted from google location service. This field is only available upon request to protect participants’ privacy.	-	Text
venue_type	The type of the venu extracted from Google location service.	-	Text
time_band	List of timebands the current segment is in. • 0: 00:00 - 03:59 • 1: 04:00 - 07:59 • 2: 08:00 - 11:59 • 3: 12:00 - 15:59 • 4: 16:00 - 19:59 • 5: 20:00 - 23:59 example: [1,2]	-	Text
major_activity	The name of the major activity done in the current segment based on extracted activity type from Google activity detector. Valid values: • IN_VEHICLE • ON_BICYCLE • ON_FOOT • RUNNING • STILL • TILTING • UNKNOWN • WALKING	-	Text
activity_duration	Indicates the qualitative activity duration: • very short • short • long • very long	-	Text
time_window	The 5 minutes-based indexing. Each 5 minutes is 1 index and one day has 288 5 minutes.	-	Text
step_count	Aggregated step count from the start of the day till the current segment timestamp.	-	Number

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personicle.csv.

2.4. Code

There is going to be a “ifh_affect.py” file alongside the dataset folders. This file does not contain any data but it can facilitate loading and transforming the data in Python.

This file provides two classes DataLoader and DataTransform. The DataLoader class can take the address of the dataset root or zipfile and load different modalities for each participant or for multiple of them. The DataTransform function provides functions for converting the timestamps to human readable times.

3. Experimental Design, Materials and Methods

3.1. Experiment design and setup

This study was designed to create a longitudinal dataset of physiological and emotional assessments for emerging adults. This dataset can be used for studying affect and correlations of mental health, affect, physiology, sleep, and activity of emerging adults.

However, as COVID-19 pandemic and lockdown started during the study, the purpose of this study was shifted toward studying the effects of the lockdown on participants' life and mental health. To adapt to the new conditions, the study design, recruitment materials, and questionnaires were updated as the study was ongoing.

This study collected objective ubiquitous data from wearable devices and life-logger apps and combined them with subjective ecological momentary assessments (EMAs) and surveys to create a spectrum of physiological and mental profile for each participant over time. To achieve this objective, this study was required to collect different modalities of data. First, the participants needed to be objectively monitored for their sleep and activity. While many wearable devices can provide accurate measurements on sleep and activity, collecting raw data can be useful in extracting additional features and creating opportunities for future research. Second, the participants' mood and mental state needed to be monitored by subjectively asking them about their mood and mental state.

In order to collect this dataset, it was necessary to build an Internet platform that can ubiquitously monitor the participants over time, and provide tools for the study coordinator to monitor the progress and data collection remotely. To this end, ZotCare [7] was utilised. ZotCare is an online mHealth service that can be used to collect, store, and analyse the data on different levels of technology stack. Services used in this study include the collection service for collecting objective data from participants' wearable devices and collecting subjective EMAs through mobile app. The collected data then was processed by ZotCare's cloud server and stored in a database. Besides that, ZotCare's dashboard was utilised in this study to monitor the collected data and ensure participants' collaboration in data collection.

To collect the subjective EMAs, ZotCare's frontend app was customised in this study and was published as Personicle Questions in Google Play. In this application participants would answer daily EMA questionnaires and weekly ones. Both EMAs contain questionnaires that focus on participants’ emotions and their worries about their health in regards to COVID-19. The weekly EMAs additionally contains participants’ written responses about the highs and lows of the previous week. The emotion questions stem from an established measurement, the Positive and Negative Affect Scale (PANAS) [6], while others are brief self-developed, single-item questions that help get an understanding of participants’ overall emotions and worries as a result of the largely-impactful event at the time, the COVID-19 pandemic. The participants could answer the daily questionnaire from 7:50 PM to 3:00 AM PST time. Every participant was notified at 8PM every night to respond to this questionnaire. If the participant did not respond to the EMA, two follow up reminders would be sent to them at 10PM and 11:59 PM. The weekly EMA was open for participants from 7:50 AM to 8 PM PST time every Sunday with the first reminder at 8 AM and two follow up reminders at 10 AM and noon in case they failed to respond to the EMA before the reminder.

In order to collect raw objective data of the study, ZotCare Tizen wearable app was used to collect the data directly from the participant's watch. This app was set to collect raw data from sensors such as ppg, accelerometer, gyroscope with frequency of 20Hz, and pressure with frequency of 10Hz every 2 hours for 12 minutes. Besides the raw sensors, the samsung watch also collected data for pedometer and awake times. The pedometer data is supposed to contain step counts and more details of the participants' stepping pace. The frequency of pedometer data is random since it depends on the frequency of the steps taken and its detection by the watch. The awake times data shows the times that the watch detects that the participant is awake.

In this experiment, Oura ring was used to monitor participants' activity and sleep. Oura is one of the most accurate commercially available wearable devices for sleep monitoring and its battery life is higher than smartwatches. The data collected by Oura is stored in Oura's servers. ZotCare can use Open Authentication to collect these data from Oura and make them available to researchers on daily bases.

The Personicle Android mobile application was used to collect participants' data, which collected and processed the Google location, Google place, mobile device-specific measures like Calendar, and Ambient Light to identify points of interest and predict the activities performed by the user on a daily basis. Datapoints Personicle, as a mobile application, collected the phone data and sent it to the server for further processing, which, in turn, created the following data points: Lifelog: This was the raw data collected on every 5-minute interval from the phone. Point of interest (POI): This was the derived location/place of a user based on the state of the user (still/motion), Google location, and place API. Home and Work locations were also set as POI after collecting the data for 2 days from the date of app installation. Segment: Segments, in general, were the aggregated/filtered results from lifelog and POI between the occurrences of events. Changes in the state (still/motion) of a person or events like shopping or sleep were considered as events. We are providing access to Segments in this dataset.

The last data collection modality was subjective assessments. These assessments were collected upon participant's enrollment, their exit, and every month of their participation in the study. Also another assessment was collected from participants about the events that have happened during their enrollment in the study and their effect on their mental state and emotions.These assessments were developed using Qualtrics software. Each assessment was a combination of different standard and self-developed questionnaires that is represented in the Table 20.

Table 20.

Sources of all the assessment questionnaires.

Measure	Description	Source
Technology Acceptance Model Measurement (Modified)	Modified to assess a user's acceptance of the Personicle application/device. The constructs include perceived ease of use, perceived usefulness, attitude, behavioural intention, self-efficacy, subjective norm, and system accessibility.	[9]
Acceptability of Intervention Measure (AIM-Modified), Intervention Appropriateness Measure (IAM- Modified), and Feasibility of Intervention Measure (FIM-Modified)	Assesses the implementation outcomes of the Personicle devices and application in terms of acceptability, appropriateness and feasibility.	[10]
Beck Depression Inventory II (BDI-II)	Assesses an individual's depression severity ranging from mild to severe.	[11]
Positive and Negative Affect Schedule (PANAS-SF)	Measures an individual's positive and negative emotions.	[8]
Brief Symptom Inventory (BSI)	Evaluates current or past level of symptomatology, intensity of symptoms, and number of reported symptoms. The questionnaire covers nine dimensions of symptoms including somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoia, and psychosis.	[12]
Experiences in Close Relationships Scale Short Form (ECR-S)	Assesses attachment avoidance and attachment anxiety.	[13]
UCLA Three-Item Loneliness Scale (ULS)	Assesses loneliness.	[14]
Multidimensional Scale of Perceived Social Support (MSPSS)	Assesses social support from family, friends, and significant other.	[15]
Investment Model Scale (IMS)	Measures four constructs (commitment level, satisfaction level, quality of alternatives, an investment size).	[16]
Inclusion of Other in the Self Scale (IOS-Partner)	Measures how close the respondent feels with another person (romantic partner).	[17]
Conflict Management Scale (CMS)	Assesses conflict management in romantic relationships.	[18]
GAD-7	A brief scale that is used to help identify cases of Generalized Anxiety Disorder.	[19]
Social Isolation	Assesses social isolation in relation to the COVID-19 pandemic.	Manuscript in progress
Racial Profiling/Bias	Measures how the respondent interprets interactions with others based on race after the COVID-19 pandemic.	Manuscript in progress
Health Exposure	Measures the degree of exposure to the coronavirus.	Manuscript in progress
Health Mindset	Measures an individual's opinion on the effect size of changes in one's physical health.	Manuscript in progress
Health Behavioral Changes & Health Impact	Assesses behavioral changes relating to health in conjunction to the COVID-19 pandemic.	Manuscript in progress
Health Anxiety	Measures how stressed and/or anxious an individual is over their health regarding the COVID-19 pandemic.	Manuscript in progress
Health Perceived Control	Measures an individual's perceived control over their health in relation to the COVID-19 pandemic.	Manuscript in progress
Health Perceived Threat	Assesses the degree that the respondent believes the Coronavirus has or will impact their health.	Manuscript in progress
Belief in Conspiracy Theories	A brief scale assessing an individual's belief in conspiracy theories in relation to the COVID-19 pandemic.	Manuscript in progress
Political Orientation	A single question assessing the degree of political orientation from liberal to conservative.	Manuscript in progress
Emotion Regulation	Measures how respondents manage emotions in relation to the COVID-19 pandemic and the events surrounding it.	Manuscript in progress
Brief COPE	Measures efforts of managing stressful events in relation to the COVID-19 pandemic.	[20]

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3.2. Recruitment and Enrollment

Participants were recruited through UCI faculty and through posted flyers around UCI campus. Participants were between the ages of 18-22, enrolled at the time as UCI students, were able to fluently speak and write in English, used an Android operating system of 6.0 or higher on their primary phone (to be compatible with wearable devices), was not a parent, was not married, did not come back to school after more than three years, and was not diagnosed with or met criterias for depression. These criterias were screened for via a phone call after participants expressed interest in participating via email.

If interested participants were eligible to participate, they were then scheduled for an enrollment session. Participants visited Dr. Borelli's THRIVE lab at University of California, Irvine to complete the pre-assessment. During this enrollment session, participants completed a battery in which they were asked specific questions that re-confirmed eligibility for the study. Additionally, we collected select demographic information from them (i.e., age, year in school, gender, ethnicity). The research assistant provided information about the study and reviewed the consent form. Once the participant consented, the participant then completed psychological assessments (e.g., BDI-II, PANAS, BSI). If during the pre-assessment, the participant happened to score high on the BDI-II (i.e., a score that indicates moderate depression) or if the participant indicated suicidality, the PI, Dr. Borelli who is a liscensed clinical psychologist, contacted the participant to perform additional psychological assessments on suicide, depression, and social support screening before deciding if the participant can continue in the study or should be withdrawn. In case the participant needed additional assistance, health referrals and resources were provided to them and they were compensated in a prorated way.

If the participants were selected for the study, they had to go through an enrollment session. During the enrollment session, the research team helped set up the wearable smartwatch and smartring, download all necessary applications onto the participants’ phone (Personicle, Personicle Questions, Oura, Galaxy Wearable), and review device care and study expectations. After the session, participants would then receive an email with $30 of compensation in the form of an Amazon gift-code.

The majority of the participants were recruited during the COVID-19 pandemic. To address the social distancing orders, we modified our procedures so that the enrollment process could be completed 100% remotely. Participants would be asked to measure their ring at home (this was done using a measuring tape) to get an estimation of their ring size for the Oura ring. Participants were then mailed a shipping package, which contained the monitoring devices (watch and Oura ring). In this package, they were also given a prepaid postage stamp and an address label for them to use when returning their devices. Participants were given instructions for how to use the devices during a remote pre-assessment visit with a research member, which was accomplished via Zoom. During this remote session, the research team would guide the participants through the same procedures as an in-person visit and participants were compensated the same amount after the session.

3.3. Data collection

Participation was originally set for 3 months, but participants had the option to continue for an additional 3 months afterwards, up to 3 more times for a maximum of 12 months of total participation. Participants completed a followup assessment at the end of the initial 3-month study period, and completed additional follow ups after each 3-month period if they opted in. Our research team would monitor for incoming data daily and would send reminders to participants to complete surveys if 2-3 were incomplete in a row. If watch or ring data was not submitted regularly, our research team would email the participant a reminder to wear the device and would follow-up with troubleshooting methods if necessary. Participants were compensated $15 per week via email in the form of an Amazon gift-code.

3.4. Exit

At the end of the 12 week period, participants were emailed a Qualtrics link with their ID# for them to complete their exit assessment which took approximately 30 minutes. The exit assessment consisted of the same questions as the baseline assessment in addition to event-mining questions that asked participants to rate how large events impacted their life (i.e.,the impact of the first COVID-19 vaccine rollout). Participants were then given instructions to delete all the phone applications that were downloaded during the start of the study (Personicle, Personicle Questions, Oura, Galaxy Wearable).

All participants who were enrolled in the study between January 2020 and March 2020 were invited to an extension of the current study. At the end of the initial 12-week period, participants completed their exit survey (30 minutes). After completion of the survey, the research coordinator would then review the participant's scores on the BDI-II and follow the same protocol for screening and professional assistance as during the enrollment session. Participants were then given the opportunity to electronically sign a new consent form that asked them to consent to be involved in a continuation of the study (this occurred via DocuSign). If participants agreed to participate in the study continuation, they then continued wearing the devices and completing the surveys for an extended period of 8 weeks. These 8 weeks would have the same on-going data collection, daily surveys, and weekly surveys. At the end of the extended 8 weeks, participants were provided with a Qualtrics link for their second and final exit survey. Participants were then given instructions to delete all the phone applications that were downloaded during the start of the study (Personicle, Personicle Questions, Oura, Galaxy Wearable) and how to delete the watch's MAC address off of their OIT account. They were then asked to return the devices and chargers by using the packaging, postage stamp, and address label that was provided to them when they first received the devices. Participants were then compensated $50 via email in the form of Amazon gift-codes. Participants who decide to continue participation into the study's extension would receive an additional $50 after the second exit assessment was completed.

Limitations

IoT device missing data: Working with wearable devices in longitudinal study can cause many missing data. Data collection might occasionally be overlooked due to unintentional oversights from participants or unforeseen technical issues.. Participants often forget to wear their devices, charge them, keep their bluetooth and Internet connection on, and also sometimes they might restart the devices or log out of their accounts by mistake and cause data loss. Besides that, we faced some technical challenges collecting data from participants. Modern operating systems such as Android and Tizen shut down applications that users do not interact with. This feature affected our Tizen application and the Personicle application since the apps were only background monitoring and users did not interact with. Another type of technical issues came from our services being down from time to time since the services used in this study were in the development stage.

Ethics Statement

This study was approved by the institutional review board at the University of California, Irvine (approval number: 2019-5153).

CRediT authorship contribution statement

Sina Labbaf: Methodology, Software, Formal analysis, Data curation, Writing – original draft. Mahyar Abbasian: Writing – original draft. Brenda Nguyen: Data curation, Writing – original draft. Matthew Lucero: Writing – original draft. Maryam Sabah Ahmed: Writing – original draft. Asal Yunusova: Project administration. Alexander Rivera: Project administration. Ramesh Jain: Supervision. Jessica L. Borelli: Supervision, Writing – review & editing. Nikil Dutt: Supervision, Writing – review & editing. Amir M. Rahmani: Supervision, Writing – review & editing.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability

Physiological and Emotional Assessment of College Students using Wearable and Mobile Devices during the 2020 COVID-19 Lockdown: An Intensive, Longitudinal Dataset (Original data) (Dryad)

References

1.Yunusova A., Lai J., Rivera A.P., Hu S., Labbaf S., Rahmani A.M., Dutt N., Jain R.C., Borelli J.L. Assessing the mental health of emerging adults through a mental health app: protocol for a prospective pilot study. JMIR Res. Protoc. 2021;10:e25775. doi: 10.2196/25775. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Labbaf S., et al. Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive. Longitudinal Dataset, Dryad, Dataset. 2023 doi: 10.7280/D1WH6T. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Ōura Health Oy. “Getting started with the Oura API V1.” Oura Cloud, https://cloud.ouraring.com/docs/. Accessed 26 July 2023.
4.E. Khatibi, M. Abbasian, I. Azimi, S. Labbaf, M. Feli, J. Borelli, N. Dutt, A.M. Rahmani, 2023. Impact of COVID-19 pandemic on sleep including HRV and physical activity as mediators: a causal ML approach, MedRxiv. 2023.06.08.23291008. 10.1101/2023.06.08.23291008. [DOI]
5.Ma C., Xu H., Yan M., Huang J., Yan W., Lan K., Wang J., Zhang Z. Longitudinal changes and recovery in heart rate variability of young healthy subjects when exposure to a hypobaric hypoxic environment. Front. Physiol. 2022;12 doi: 10.3389/FPHYS.2021.688921/BIBTEX. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Oh H., Jalali L., Jain R. Proc. - IEEE Int. Conf. Multimed. Expo. IEEE Computer Society; 2015. An intelligent notification system using context from real-time personal activity monitoring. [DOI] [Google Scholar]
7.S. Labbaf, M. Abbasian, I. Azimi, N. Dutt, A.M. Rahmani, (2023) ZotCare: a flexible, personalizable, and affordable mHealth service provider. https://arxiv.org/abs/2307.01905v1 (accessed July 12, 2023). [DOI] [PMC free article] [PubMed]
8.Watson D., Clark L.A., Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Personal. Soc. Psychol. 1988;54(6):1063. doi: 10.1037//0022-3514.54.6.1063. [DOI] [PubMed] [Google Scholar]
9.Park S.Y. An analysis of the technology acceptance model in understanding university students' behavioural intention to use e-Learning. Educ. Technol. Soc. 2009;12(3):150–162. [Google Scholar]
10.Weiner B.J., Lewis C.C., Stanick C., Powell B.J., Dorsey C.N., Clary A.S., et al. Psychometric assessment of three newly developed implementation outcome measures. Implement. Sci. 2017;12(1):108. doi: 10.1186/s13012-017-0635-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Beck A.T., Steer R.A., Brown G.K. 2nd ed. The Psychological Corporation; New York, NY: 1996. Beck Depression Inventory Manual. [Google Scholar]
12.L.R. Derogatis, M. Fitzpatrick, The SCL-90-R, the Brief Symptom Inventory (BSI), and the BSI 18. In M. E. Maruish (Ed.), The Use of Psychological Testing for Treatment Planning and Outcomes Assessment: Instruments for Adults (2004) 1-41.
13.Brennan K., Clark C., Shaver P.R. In: Attachment Theory and Close Relationships. Simpson J.A., editor. Guilford Press; New York: 1998. Self-report measurement of adult attachment: An integrative overview; pp. 46–76. [Google Scholar]
14.Hughes M.E., Waite L.J., Hawkley L.C., Cacioppo J.T. A short scale for measuring loneliness in large surveys: Results From two population-based studies. Research on going. 2004;26(6):655–672. doi: 10.1177/0164027504268574. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Zimet G.D., Dahlem N.W., Zimet. G.K. Farley S.G. The multidimensional scale of perceived social support. J. Pers. Assess. 1988;52:30–41. doi: 10.1080/00223891.1990.9674095. [DOI] [PubMed] [Google Scholar]
16.Rusbult C.E., Martz J.M., Agnew C.R. The Investment Model Scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size. Pers. Relatsh. 1998;5(4):357–391. [Google Scholar]
17.Aron A., Aron E.N., Smollan D. Inclusion of other in the self scale and the structure of interpersonal closeness. J. Pers. Soc. Psychol. 1992;63:596–612. [Google Scholar]
18.Mannarini S., Balottin L., Munari C., Gatta M. Assessing conflict management in the couple: the definition of a latent dimension. Family J. 2017;25(1):13–22. [Google Scholar]
19.Spitzer R.L., Kroenke K., Williams J.B., Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 2006;166(10):1092–1097. doi: 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
20.Carver C.S. You want to measure coping but your protocol'too long: consider the brief cope. Int. J. Behav. Med. 1997;4(1):92–100. doi: 10.1207/s15327558ijbm0401_6. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Physiological and Emotional Assessment of College Students using Wearable and Mobile Devices during the 2020 COVID-19 Lockdown: An Intensive, Longitudinal Dataset (Original data) (Dryad)

[bib0001] 1.Yunusova A., Lai J., Rivera A.P., Hu S., Labbaf S., Rahmani A.M., Dutt N., Jain R.C., Borelli J.L. Assessing the mental health of emerging adults through a mental health app: protocol for a prospective pilot study. JMIR Res. Protoc. 2021;10:e25775. doi: 10.2196/25775. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib0002] 2.Labbaf S., et al. Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive. Longitudinal Dataset, Dryad, Dataset. 2023 doi: 10.7280/D1WH6T. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib0003] 3.Ōura Health Oy. “Getting started with the Oura API V1.” Oura Cloud, https://cloud.ouraring.com/docs/. Accessed 26 July 2023.

[bib0004] 4.E. Khatibi, M. Abbasian, I. Azimi, S. Labbaf, M. Feli, J. Borelli, N. Dutt, A.M. Rahmani, 2023. Impact of COVID-19 pandemic on sleep including HRV and physical activity as mediators: a causal ML approach, MedRxiv. 2023.06.08.23291008. 10.1101/2023.06.08.23291008. [DOI]

[bib0005] 5.Ma C., Xu H., Yan M., Huang J., Yan W., Lan K., Wang J., Zhang Z. Longitudinal changes and recovery in heart rate variability of young healthy subjects when exposure to a hypobaric hypoxic environment. Front. Physiol. 2022;12 doi: 10.3389/FPHYS.2021.688921/BIBTEX. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib0006] 6.Oh H., Jalali L., Jain R. Proc. - IEEE Int. Conf. Multimed. Expo. IEEE Computer Society; 2015. An intelligent notification system using context from real-time personal activity monitoring. [DOI] [Google Scholar]

[bib0007] 7.S. Labbaf, M. Abbasian, I. Azimi, N. Dutt, A.M. Rahmani, (2023) ZotCare: a flexible, personalizable, and affordable mHealth service provider. https://arxiv.org/abs/2307.01905v1 (accessed July 12, 2023). [DOI] [PMC free article] [PubMed]

[bib0008] 8.Watson D., Clark L.A., Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Personal. Soc. Psychol. 1988;54(6):1063. doi: 10.1037//0022-3514.54.6.1063. [DOI] [PubMed] [Google Scholar]

[bib0009] 9.Park S.Y. An analysis of the technology acceptance model in understanding university students' behavioural intention to use e-Learning. Educ. Technol. Soc. 2009;12(3):150–162. [Google Scholar]

[bib0010] 10.Weiner B.J., Lewis C.C., Stanick C., Powell B.J., Dorsey C.N., Clary A.S., et al. Psychometric assessment of three newly developed implementation outcome measures. Implement. Sci. 2017;12(1):108. doi: 10.1186/s13012-017-0635-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib0011] 11.Beck A.T., Steer R.A., Brown G.K. 2nd ed. The Psychological Corporation; New York, NY: 1996. Beck Depression Inventory Manual. [Google Scholar]

[bib0012] 12.L.R. Derogatis, M. Fitzpatrick, The SCL-90-R, the Brief Symptom Inventory (BSI), and the BSI 18. In M. E. Maruish (Ed.), The Use of Psychological Testing for Treatment Planning and Outcomes Assessment: Instruments for Adults (2004) 1-41.

[bib0013] 13.Brennan K., Clark C., Shaver P.R. In: Attachment Theory and Close Relationships. Simpson J.A., editor. Guilford Press; New York: 1998. Self-report measurement of adult attachment: An integrative overview; pp. 46–76. [Google Scholar]

[bib0014] 14.Hughes M.E., Waite L.J., Hawkley L.C., Cacioppo J.T. A short scale for measuring loneliness in large surveys: Results From two population-based studies. Research on going. 2004;26(6):655–672. doi: 10.1177/0164027504268574. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib0015] 15.Zimet G.D., Dahlem N.W., Zimet. G.K. Farley S.G. The multidimensional scale of perceived social support. J. Pers. Assess. 1988;52:30–41. doi: 10.1080/00223891.1990.9674095. [DOI] [PubMed] [Google Scholar]

[bib0016] 16.Rusbult C.E., Martz J.M., Agnew C.R. The Investment Model Scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size. Pers. Relatsh. 1998;5(4):357–391. [Google Scholar]

[bib0017] 17.Aron A., Aron E.N., Smollan D. Inclusion of other in the self scale and the structure of interpersonal closeness. J. Pers. Soc. Psychol. 1992;63:596–612. [Google Scholar]

[bib0018] 18.Mannarini S., Balottin L., Munari C., Gatta M. Assessing conflict management in the couple: the definition of a latent dimension. Family J. 2017;25(1):13–22. [Google Scholar]

[bib0019] 19.Spitzer R.L., Kroenke K., Williams J.B., Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 2006;166(10):1092–1097. doi: 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]

[bib0020] 20.Carver C.S. You want to measure coping but your protocol'too long: consider the brief cope. Int. J. Behav. Med. 1997;4(1):92–100. doi: 10.1207/s15327558ijbm0401_6. [DOI] [PubMed] [Google Scholar]

PERMALINK

Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset

Sina Labbaf

Mahyar Abbasian

Brenda Nguyen

Matthew Lucero

Maryam Sabah Ahmed

Asal Yunusova

Alexander Rivera

Ramesh Jain

Jessica L Borelli

Nikil Dutt

Amir M Rahmani

Abstract

1. Value of the Data

2. Data Description

2.1. Population information

Table 1.

2.2. Participant's data

Table 2.

2.3. Dataset subfolders

2.3.1. EMA

Table 3.

Table 4.

2.3.2. Oura

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

2.3.3. Samsung

Table 11.

Table 12.

Table 13.

Table 14.

Table 15.

Table 16.

Fig. 1.

2.3.4. Assessment

Table 18.

Table 17.

2.3.5. Personicle

Table 19.

2.4. Code

3. Experimental Design, Materials and Methods

3.1. Experiment design and setup

Table 20.

3.2. Recruitment and Enrollment

3.3. Data collection

3.4. Exit

Limitations

Ethics Statement

CRediT authorship contribution statement

Acknowledgements

Declaration of Competing Interest

Data Availability

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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