{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T10:34:37Z","timestamp":1777804477720,"version":"3.51.4"},"posted":{"date-parts":[[2026,4,27]]},"group-title":"Primary Care Research","reference-count":36,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,4,27]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Survivors of severe COVID-19 commonly experience post-intensive care syndrome (PICS), which includes depression and fatigue. Fatigue is far more common and may inflate depression severity given overlapping symptoms. We sought to disentangle fatigue from depression in PICS.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We conducted a cross-sectional analysis of the RAFT COVID study, a national multicenter longitudinal cohort of severe prolonged COVID-19 survivors. We included participants who completed validated surveys at 1-year from hospitalization for depression (PHQ-9) and fatigue (FACIT-Fatigue). We described correlation of FACIT-fatigue with the PHQ9, and separately with PHQ-2 and PHQ-7, which both omit the two items we hypothesized are influenced by fatigue\u2014tiredness and sleeping. Using a MIMIC model, we performed differential item functioning to evaluate the impact of fatigue on depression directly through these two questions and indirectly with the latent depression construct. We then compared PHQ-7 to PHQ-9 scores by fatigue status.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Among 82 participants, 61.0% reported fatigue (reverse-scored FACIT-Fatigue \u22659), and 15.9% moderately severe depression (PHQ-9 \u226510). FACIT-fatigue was strongly correlated with PHQ-9 (r=.87, p&lt;.001), but less so for PHQ-2 (r=.76, p&lt;.001) and PHQ-7 (\n                    <jats:italic>r=<\/jats:italic>\n                    .82, p&lt;.001). The MIMIC model identified significant direct effects on tiredness (\u03bb=.89, p&lt;.001) and sleep (\u03bb=.52, p&lt;.001). Among fatigued participants, the rescaled PHQ-7 was lower than the PHQ-9 (median of 4.5, IQR 1.50-9.75, vs 7, IQR 4-9.75).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Fatigue significantly inflated depression symptoms in severe COVID-19 survivors through tiredness and sleeping PHQ-9 items. PHQ-2 may better screen for true depressive symptoms in PICS, minimizing the risk of misdiagnosis and overtreatment.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>PLAIN ENGLISH SUMMARY<\/jats:title>\n                  <jats:p>Survivors of severe COVID-19 illness commonly experience post-intensive care syndrome (PICS), which includes depression and fatigue. Fatigue is far more common and may inflate depression severity given overlapping symptoms. We sought to disentangle fatigue from depression in PICS. We found that the presence of fatigue inflated depression severity through symptoms of tiredness and difficulty sleeping, which are two of the nine items of a commonly used depression screening tool, known as the Patient Health Questionnaire-9 (PHQ-9). Depression screening tools that omit these two items, such as the PHQ-2, may better screen for depressive symptoms in PICS, minimizing the risk of overestimating depression symptoms and potentially misdiagnosis.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.04.24.26351694","type":"posted-content","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T22:13:02Z","timestamp":1777327982000},"source":"Crossref","is-referenced-by-count":0,"title":["Disentangling Fatigue from Depression among Survivors of Severe COVID-19"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2526-6089","authenticated-orcid":false,"given":"Juan R.","family":"Cabrera","sequence":"first","affiliation":[{"name":"School of Public Health, University of California, Berkeley, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Pham","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W. John","family":"Boscardin","sequence":"additional","affiliation":[{"name":"Division of Geriatrics, UCSF, San Francisco, CA"},{"name":"Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anil N.","family":"Makam","sequence":"additional","affiliation":[{"name":"Division of Hospital Medicine, UCSF at San Francisco General Hospital, San Francisco, CA"},{"name":"UCSF Philip R. Lee Institute for Health Policy Studies, San Francisco, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026042913400641000_2026.04.24.26351694v1.1","unstructured":"CDC. COVID Data Tracker. Centers for Disease Control and Prevention. March 28, 2020. Accessed May 26, 2023. https:\/\/covid.cdc.gov\/covid-data-tracker"},{"key":"2026042913400641000_2026.04.24.26351694v1.2","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-020-03240-7"},{"key":"2026042913400641000_2026.04.24.26351694v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.10.014"},{"key":"2026042913400641000_2026.04.24.26351694v1.4","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa022450"},{"key":"2026042913400641000_2026.04.24.26351694v1.5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1011802"},{"key":"2026042913400641000_2026.04.24.26351694v1.6","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(14)70051-7"},{"key":"2026042913400641000_2026.04.24.26351694v1.7","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201103-0503OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.8","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.f1532"},{"key":"2026042913400641000_2026.04.24.26351694v1.9","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.200406-763OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.10","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1301372"},{"key":"2026042913400641000_2026.04.24.26351694v1.11","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201111-2025OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.12","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000006258"},{"key":"2026042913400641000_2026.04.24.26351694v1.13","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-021-03794-0"},{"key":"2026042913400641000_2026.04.24.26351694v1.14","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.202110-2335OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.15","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202104-520OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.16","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.3331"},{"key":"2026042913400641000_2026.04.24.26351694v1.17","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2021-218064"},{"key":"2026042913400641000_2026.04.24.26351694v1.18","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202207-630OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.19","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.0040"},{"key":"2026042913400641000_2026.04.24.26351694v1.20","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.202206-1145OC"},{"key":"2026042913400641000_2026.04.24.26351694v1.21","doi-asserted-by":"publisher","DOI":"10.1046\/j.1525-1497.2001.016009606.x"},{"key":"2026042913400641000_2026.04.24.26351694v1.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.03.059"},{"key":"2026042913400641000_2026.04.24.26351694v1.23","doi-asserted-by":"publisher","DOI":"10.1007\/s10865-014-9574-5"},{"key":"2026042913400641000_2026.04.24.26351694v1.24","doi-asserted-by":"publisher","DOI":"10.1007\/s11136-017-1601-x"},{"key":"2026042913400641000_2026.04.24.26351694v1.25","doi-asserted-by":"publisher","DOI":"10.5603\/ARM.2017.0054"},{"key":"2026042913400641000_2026.04.24.26351694v1.26","unstructured":"Long-Term Care Hospital (LTCH) Continuity Assessment Record and Evaluation (CARE) Data Set (LCDS) & LTCH Quality Reporting Program (QRP) Manual | CMS. Accessed May 31, 2022. https:\/\/www.cms.gov\/Medicare\/Quality-Initiatives-Patient-Assessment-Instruments\/LTCH-Quality-Reporting\/LTCH-CARE-Data-Set-and-LTCH-QRP-Manual"},{"key":"2026042913400641000_2026.04.24.26351694v1.27","unstructured":"FACIT-Fatigue. FACIT Group. Accessed September 20, 2022. https:\/\/www.facit.org\/measures\/FACIT-Fatigue"},{"key":"2026042913400641000_2026.04.24.26351694v1.28","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.10245"},{"key":"2026042913400641000_2026.04.24.26351694v1.29","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.6504"},{"key":"2026042913400641000_2026.04.24.26351694v1.30","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. Published online 2024. https:\/\/www.r-project.org\/"},{"issue":"1","key":"2026042913400641000_2026.04.24.26351694v1.31","first-page":"53","article-title":"Structural Equation Modelling: Guidelines for Determining Model Fit","volume":"6","year":"2008","journal-title":"Electronic Journal of Business Research Methods"},{"key":"2026042913400641000_2026.04.24.26351694v1.32","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6243"},{"key":"2026042913400641000_2026.04.24.26351694v1.33","doi-asserted-by":"publisher","DOI":"10.1187\/cbe.20-01-0016"},{"key":"2026042913400641000_2026.04.24.26351694v1.34","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-018-1055-2"},{"key":"2026042913400641000_2026.04.24.26351694v1.35","unstructured":"Taasoobshirazi G , Wang S . THE PERFORMANCE OF THE SRMR, RMSEA, CFI, AND TLI: AN EXAMINATION OF SAMPLE SIZE, PATH SIZE, AND DEGREES OF FREEDOM."},{"key":"2026042913400641000_2026.04.24.26351694v1.36","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-009-1396-5"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.04.24.26351694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:40:16Z","timestamp":1777495216000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.04.24.26351694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,27]]},"references-count":36,"URL":"https:\/\/doi.org\/10.64898\/2026.04.24.26351694","relation":{},"subject":[],"published":{"date-parts":[[2026,4,27]]},"subtype":"preprint"}}