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Record W3094139621 · doi:10.1080/09638288.2020.1833090

Unravelling perceived fatigue and activity pacing in maintaining a physically active lifestyle after stroke rehabilitation: a longitudinal cohort study

2020· article· en· W3094139621 on OpenAlexaff
Bregje L. Seves, Trynke Hoekstra, Femke Hoekstra, Florentina J. Hettinga, Rienk Dekker, L.H.V. van der Woude

Bibliographic record

VenueDisability and Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRehabilitationStroke (engine)Physical therapyMedicinePhysical medicine and rehabilitationLogistic regressionCohortQuality of life (healthcare)Longitudinal studyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSES: To identify fatigue trajectories during/after stroke rehabilitation, to determine characteristics associated with trajectory membership before discharge and to investigate how these trajectories and activity pacing are associated with sustained physical activity after rehabilitation. METHODS: = 206) were followed from 3-6 weeks before discharge (T0) to 14 (T1), 33 (T2) and 52 (T3) weeks after discharge from rehabilitation in the ReSpAct study. Latent Class analysis was used to identify trajectories of perceived fatigue. Binomial multivariable logistic regression analyses were performed to determine characteristics associated with trajectory membership (T0). Multilevel regression analyses were used to investigate how perceived fatigue and activity pacing were associated with self-reported physical activity (T0-T3). RESULTS: = 2). Compared with the high fatigue trajectory, people in the low fatigue trajectory were more likely to report higher levels of health-related quality of life (HR-QoL) (OR = 3.07, 95%CI = 1.51-6.26) and physical activity (OR = 1.93, 95%CI = 1.07-3.47). Sustained high levels of physical activity after rehabilitation were significantly associated with low perceived fatigue and high perceived risk of overactivity. CONCLUSIONS: Three fatigue trajectories after stroke rehabilitation were identified. High levels of HR-QoL and physical activity before discharge identified people in the low fatigue trajectory. A physically active lifestyle after rehabilitation was associated with low perceived fatigue and perceived risk of overactivity.IMPLICATIONS FOR REHABILITATIONSince almost 80% of people after stroke in this study perceived severe fatigue up to 1 year after stroke rehabilitation, activities focusing on the management of fatigue symptoms should be integrated in general stroke rehabilitation.In clinical practice, low levels of health-related quality of life and low levels of self-reported physical activity before discharge from stroke rehabilitation should be considered by rehabilitation professionals (e.g., physicians, physiotherapists, and physical activity counsellors) since these characteristics can predict chronic perceived fatigue up to 1 year after stroke rehabilitation.A physical activity counselling programme delivered during and after stroke rehabilitation may be improved by incorporating tailored advice regarding the management of fatigue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2020
Admission routes1
Has abstractyes

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