Does Today's Workload Predict Tomorrow's Stress, Fatigue, and Other Strain States? Exploring Directionality in Daily Dynamics
Peer Reviewed
-
2026/06/16
File Language:
English
Details
-
Journal Article:Ergonomics
-
Personal Author:
-
Description:We examined whether whole day workload predicts strain (e.g. stress, fatigue, impaired cognition) experienced on the next day, and/or whether strain predicts next-day workload. Data were analysed (using dynamic structural equation models) from 196 adults with type 1 diabetes (T1D) who completed two weeks of 5 to 6 daily ecological momentary assessment (EMA) surveys. Workload was assessed with a version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) adapted for the whole day context. Higher overall workload during the day predicted a shorter sleep duration that night, as well as increased fatigue and slower perceptual speed the next day. In turn, increased stress, fatigue, and shorter sleep duration on one day predicted a higher perceived workload the following day. Among adults with T1D, strain and workload appear to reinforce each other across days, suggesting a potential feedback loop that may escalate if recovery is insufficient. Practitioner Summary: Measures of workload are widely used, and study results contribute to understanding and interpretation of workload. Specifically, results suggested that workload measures could also be affected by workload and strain on the days prior, and that both work and non-work workload are important to consider. Description provided by NIOSH
-
Subjects:
-
Keywords:
-
ISSN:0014-0139
-
Document Type:
-
Funding:
-
Genre:
-
Place as Subject:
-
CIO:
-
Topic:
-
Location:
-
Pages in Document:16 pdf pages
-
NIOSHTIC Number:nn:20071814
-
Citation:Ergonomics 2026 Jun; :Epub ahead of print
-
Contact Point Address:Raymond Hernandez, USC Center for Economic & Social Research, 635 Downey Way, VPD 405, Los Angeles, CA 90089-3332, USA
-
Email:hern939@usc.edu
-
Federal Fiscal Year:2026
-
NORA Priority Area:
-
Performing Organization:University of Southern California
-
Peer Reviewed:True
-
Start Date:2024/09/01
-
End Date:2027/08/31
-
Download URL:
-
File Type:
-
Collection(s):
-
Main Document Checksum:urn:sha-512:ad4e2df1bdc0d758d19318b4b623f4ceca8e3b7cc61512188a82b806cf8db079e13dd761afb703f6d0c18d05da33e87e90be589e6fab37f722d81ebb5d9515a4
File Language:
English
CDC STACKS serves as an archival repository of CDC-published products including
scientific findings, journal articles, guidelines, recommendations, or other public health information authored or
co-authored by CDC or funded partners.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
You May Also Like