Evidence map›Paper›PMID 37960394›Full record

ArticleSensors (Basel, Switzerland)2023

Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators.

Mohamad Awada, Burcin Becerik-Gerber, Gale Lucas, Shawn C Roll

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

2 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 1 institution in 1 country.

Mohamad AwadaDepartment of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0002-1302-7988
Burcin Becerik-GerberDepartment of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Gale LucasUSC Institute for Creative Technologies, University of Southern California, Los Angeles, CA 90089, USA.
Shawn C RollChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0002-4202-396X
University of Southern California · US

Funding

Southern California Education and Research Center T42OH008412 · OH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JONES, RACHAEL MARY · 2005 to 2025
$28.1M
NIOSH CDC HHS T42 OH008412
6 · The paper itself

Abstract

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R

Indexed as

Wearable Electronic DevicesHumansMachine LearningSoftwareUpper ExtremityWristbehavioral featuresdistresseustressmoodphysiological featuresproductivitypsychological statestress

Identifiers

PMID37960394
PMCPMC10647707
OpenAlexW4387940362

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.