Evidence map›Paper›PMID 36050329›Full record

ArticleScientific data2022

TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit.

Joanna C Yau, Benjamin Girault, Tiantian Feng, Karel Mundnich, Amrutha Nadarajan, Brandon M Booth, Emilio Ferrara, Kristina Lerman, Eric Hsieh, Shrikanth Narayanan

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
5.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Joanna C YauSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA. joannay@usc.edu.ORCID 0000-0002-2879-4856
Benjamin GiraultSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Tiantian FengSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Karel MundnichSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Amrutha NadarajanSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0001-6993-829X
Brandon M BoothSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0002-5780-8882
Emilio FerraraInformation Sciences Institute (USC), Marina del Rey, CA, USA.
Kristina LermanInformation Sciences Institute (USC), Marina del Rey, CA, USA.ORCID 0000-0002-5071-0575
Eric HsiehKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Shrikanth NarayananSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
University of Southern California · USMarina Del Rey Hospital · US

Funding

ODNI | Intelligence Advanced Research Projects Activity (IARPA) 2017-17042800005
6 · The paper itself

Abstract

The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a demanding environment, as residents worked in the ICU for three weeks. Residents wore a Fitbit, a Bluetooth-based proximity sensor, and an audio-feature recorder. They completed daily surveys and interviews at the beginning and end of their rotation. In addition, we collected data from environmental sensors (i.e., Internet-of-Things Bluetooth data hubs) and obtained hospital records (e.g., patient census) and residents' job evaluations. This data set may be may be of interest to researchers interested in workplace stress, group dynamics, social support, the physical and psychological effects of witnessing patient deaths, predicting survey data from sensors, and privacy-aware and privacy-preserving machine learning. Notably, a small subset of the data was collected during the first wave of the COVID-19 pandemic.

Indexed as

Internship and ResidencyOccupational StressCOVID-19HumansIntensive Care UnitsPandemics

Identifiers

PMID36050329
PMCPMC9436730
OpenAlexW4294052978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.