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Record W6922127521 · doi:10.11575/prism/49618

Feasibility of a Yoga Prehabilitation Intervention for Individuals with Acute Leukemia Undergoing an Allogeneic Hematopoietic Stem Cell Transplant

2025· other· en· W6922127521 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPrehabilitationIntervention (counseling)PsychosocialPsychological interventionQuality of life (healthcare)Activity trackerAdverse effectThematic analysisHematopoietic stem cell transplantation

Abstract

fetched live from OpenAlex

Purpose: To (1) determine the feasibility of delivering a pre-transplant yoga intervention for allogeneic hematopoietic stem cell transplant (alloHSCT) recipients, and (2) explore the potential change of individuals’ cancer-related fatigue (CRF), yoga self-efficacy, and health-related quality of life (HRQL). Background: Exercise interventions delivered pre-alloHSCT have shown a reduction in physical and psychosocial comorbidities, however assessment of feasibility of these prehabilitation programs highlights challenges due to patients’ limited capacity to engage in physical movement. Yoga has been used across cancer populations and is highlighted as an accessible means of physical movement and management of CRF. Methods: This case series used an explanatory sequential mixed-methods study design. The 4–6-week intervention of low-intensity yoga included 2 brief (<30 minute) sessions weekly; one offered synchronous with an instructor, and one asynchronous through a pre-recorded video. Adult patients with acute leukemia (n=6), planned for alloHSCT in Calgary, AB, were recruited. The study assessed the feasibility of the intervention via tracking of enrollment proportion, measurement completion, adherence, participant satisfaction, and adverse events. Additionally, the study explored the potential changes in CRF, yoga self-efficacy, and overall HRQL via questionnaires pre/post intervention and visual analog scales assessing fatigue and yoga self-efficacy pre/post individual yoga session. Post-intervention qualitative semi-structured interviews were conducted to further understand participants perceptions of the acceptability of the yoga intervention. Interviews were analyzed using conventional content analysis. Results: Challenges in feasibility metrics were seen across referral, retention, and intervention delivery mode (asynchronous vs synchronous). Three categories emerged from interviews: Enrollment Motivators, Perceived Value of Yoga Prehabilitation, and Barriers to Engagement and Suggestions for Change. Despite challenges in feasibility, participants reported high satisfaction and consistently reported improvements in fatigue and yoga self-efficacy from pre-to-post-yoga session. Conclusion: The pre-transplant period is intensive with many logistical barriers to introducing a prehabilitative intervention. Although it was not feasible in this setting primarily due to low recruitment, findings from this study provide important insights into navigating future symptom management work for the alloHSCT population during an often-prolonged pre-transplant phase. Given participants found it beneficial, continued investigation into yoga as both a movement-based and psychosocial symptom management strategy during the pre-transplant phase is warranted.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.330
Teacher spread0.292 · 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 designNon-randomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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