McGarrigle, William J, Furst, Jacob, Jason, Leonard A · Journal of health psychology · 2024 · DOI
Researchers tested whether a symptom questionnaire called the DSQ-SF can accurately identify and distinguish between Long COVID, ME/CFS, and healthy people. Using computer algorithms to analyze the results, they found the questionnaire works well at telling these groups apart and identified which specific symptoms are most helpful for telling Long COVID and ME/CFS apart from each other and from normal health.
Accurate and efficient screening tools are essential for improving diagnosis and treatment of both ME/CFS and Long COVID. This research validates the DSQ-SF as a reliable instrument for distinguishing these overlapping conditions, which could accelerate clinical recognition and enable more targeted therapeutic approaches for affected patients.
This study does not prove that the DSQ-SF is superior to other diagnostic instruments or that it should replace medical evaluation and clinical assessment. The study also does not establish the DSQ-SF's ability to predict disease progression, treatment response, or long-term outcomes, nor does it determine whether identified symptom differences reflect underlying biological distinctions between Long COVID and ME/CFS.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
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Primary citation
McGarrigle, William J, Furst, Jacob, & Jason, Leonard A (2024). Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach.. Journal of health psychology. https://doi.org/10.1177/13591053231223882
BibTeX
@article{mecfsatlas-mcgarrigle-2024-psychometric-evaluation,
author = {McGarrigle, William J and Furst, Jacob and Jason, Leonard A},
title = {Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach.},
journal = {Journal of health psychology},
year = {2024},
doi = {10.1177/13591053231223882},
note = {PubMed: 38282368},
url = {https://www.mecfsatlas.com/evidence/mcgarrigle-2024-psychometric-evaluation},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/mcgarrigle-2024-psychometric-evaluation
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