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Record W4412069755 · doi:10.1093/ptj/pzaf091

Trajectories of Physical Disabilities Over 6 Months in Patients With Long COVID

2025· article· en· W4412069755 on OpenAlexafffundabout
Imane Salmam, François Desmeules, Kadija Perreault, Imane Zahouani, Simon Beaulieu‐Bonneau, Alexandre Campeau‐Lecours, Jean‐Sébastien Paquette, Simon Deslauriers, Jean Tittley, Gilles Drouin, Krista L. Best, Jean‐Sébastien Roy

Bibliographic record

VenuePhysical Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsCentre Intégré de Santé et Services Sociaux de Chaudière-AppalacheCegep regional de LanaudiereMcGill University Health CentreCentre for Interdisciplinary Research in RehabilitationHôpital Maisonneuve-RosemontCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalUniversité LavalCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersCanadian Institutes of Health ResearchRéseau Provincial de Recherche en Adaptation-Réadaptation
KeywordsPhysical therapyMedicineQuality of life (healthcare)RehabilitationCoronavirus disease 2019 (COVID-19)Prospective cohort studyLongitudinal studyCohort studyDisease

Abstract

fetched live from OpenAlex

IMPORTANCE: Understanding the long-term impact of long COVID on physical function and health-related quality of life (HRQoL) is essential to guide clinical care and rehabilitation. OBJECTIVE: The objective of this study was to compare physical capacity over time among adults in 3 groups: those without COVID-19 (control group [CG]), those who recovered from COVID without persistent symptoms (short COVID group [SCG]), and those with long COVID (long COVID group [LCG]). A secondary objective was to identify baseline predictors of 6-month HRQoL in the LCG. DESIGN: This study was a prospective longitudinal cohort study. SETTING: In-laboratory assessments were conducted at baseline, 3 months, and 6 months, at either the Center for Interdisciplinary Research in Rehabilitation and Social Integration (Quebec City) or the Orthopedic Clinical Research Unit (Montreal). PARTICIPANTS: A total of 360 age- and sex-matched adults (n = 120 per group), including individuals without a history of COVID-19 (CG), those with short COVID (symptom resolution within 4 weeks, SCG), and those with persistent symptoms ≥12 weeks (LCG) participated in the study. INTERVENTION/EXPOSURE: Participants were categorized based on their COVID-19 history and symptom duration and no intervention or exposure was applied. MAIN OUTCOMES AND MEASURES: Self-reported outcomes measuring HRQoL, comorbidities, sleep quality, pain, and fatigue, along with objective measures such as grip strength, Short Physical Performance Battery (SPPB), 6-min walk test (6MWT), and perceived exertion (Modified Borg Scale) during the 6MWT, were collected. Daily averages for resting heart rate, step count, and minutes of intensive activity were recorded over 7 days using a fitness tracker watch. Generalized estimating equations were used for longitudinal comparisons, and recursive partitioning analysis for predicting HRQoL factors. RESULTS: Significant time × group interactions were observed for HRQoL, sleep quality, pain, fatigue, SPPB, and 6MWT. Although the LCG showed significant improvements across these outcomes, only the reduction in fatigue reached a clinically meaningful level, whereas the other groups remained stable. A group effect was detected for all outcomes, except for heart rate and minutes of intensive activity, with the LCG consistently showing lower scores across all follow-ups. Recursive partitioning analysis identified 2 baseline predictors of HRQoL at 6 months in the LCG: self-reported fatigue and daily step count. CONCLUSIONS AND RELEVANCE: These findings highlight the persistent impairments in adults with long COVID and emphasize early HRQoL predictor identification to anticipate long-term needs and adjust treatment plans accordingly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.305
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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