LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit <path to file fitbit.bson>

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema <path to file sema.bson>

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys <path to file surveys.bson>

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
    _id: <ObjectId>
    id (or user_id): <ObjectId>
    type: <String>
    data: <Object>
}

Each document consists of four fields: id (also found as user_id in sema and survey collections), type, and data. The _id field is the MongoDB-defined primary key and can be ignored. The id field refers to a user-specific ID used to uniquely identify each user across all collections. The type field refers to the specific data type within the collection, e.g., steps, heart rate, calories, etc. The data field contains the actual information about the document e.g., steps count for a specific timestamp for the steps type, in the form of an embedded object. The contents of the data object are type-dependent, meaning that the fields within the data object are different between different types of data. As mentioned previously, all times are stored in local time, and user IDs are common across different collections. For more information on the available data types, see the related publication.

Surveys Encoding

BREQ2

Why do you engage in exercise?

PANAS

Indicate the extent you have felt this way over the past week 

Personality

How Accurately Can You Describe Yourself?

STAI

Indicate how you feel right now

TTM

Do you engage in regular physical activity according to the definition above? How frequently did each event or experience occur in the past month?

Files Description

Scored Surveys: CSV files containing scored versions of BREQ-2, IPIP, PANAS, STAI, and TTM surveys in scored_surveys folder

Fitbit & EMA Data (daily granularity): csv_rais_anonymized/daily_fitbit_sema_df_unprocessed.csv

Fitbit & EMA Data (hourly granularity): csv_rais_anonymized/hourly_fitbit_sema_df_unprocessed.csv

MongoDB Dumps (compressed for practicality): mongo_rais_anonymized/fitbit.bson for Fitbit data, mongo_rais_anonymized/sema for EMA data, and mongo_rais_anonymized/surveys for raw surveys data. 

Code Availability

Data Anonymization: https://github.com/syfantid/RAIS-Anonymization

Exploratory Data Analysis: https://github.com/kcristinaa/LifeSnaps-EDA

Related Publications

Sofia Yfantidou, Christina Karagianni, Stefanos Efstathiou, Athena Vakali, Joao Palotti, Dimitrios Panteleimon Giakatos, Thomas Marchioro, Andrei Kazlouski, Elena Ferrari, and Sarunas Girdzijauskas, 2022, LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild (Submitted for peer-review).

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813162. The content of this paper reflects only the authors' view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. First and foremost, the authors would like to thank the participants of the LifeSnaps study who agreed to share their data for scientific advancement. The authors would like to further thank the web developers, T. Valk and S. Karamanidis, for their contribution to the project, all past and present RAIS fellows for their help with participants' recruitment, G. Pallis and M. Christodoulaki for their support with the ethics committee application, and B. Carminati for her feedback on data anonymization and privacy considerations.