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Feasibility of a virtual hybrid resistance and balance training program for older patients with cancer and its preliminary effects on lower body strength and balance.

2021· article· en· W3168593142 on OpenAlexaffabout
Schroder Sattar, Corrie Effa, Joni Nedeljak, Kristen R. Haase, Shabbir M.H. Alibhai, Shawn Kuster, Eitan Amir, Diane Campbell, Margaret L. McNeely

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of British ColumbiaUniversity of SaskatchewanUniversity of AlbertaUniversity of Regina
Fundersnot available
KeywordsMedicinePhysical therapyPopulationCancerBalance (ability)Strength trainingInternal medicine

Abstract

fetched live from OpenAlex

12002 Background: Falls are a major issue among older patients with cancer and can lead to interruption in cancer treatment. Ample evidence shows resistance and balance training can prevent falls in older adults; however, there is a paucity of evidence regarding exercise on fall prevention in the older cancer population, who often have unique risk factors for falls. Given the new reality of the COVID-19 pandemic, minimizing group gatherings and its associated risks is imperative for older patients, who are a vulnerable population. This study sought to investigate the feasibility of an 8-week, virtual exercise program and its preliminary effects on lower body strength and balance in community-dwelling cancer patients. Methods: Study participants were recruited for this pretest-posttest intervention study using consecutive sampling over a one-year period from the Cross Cancer Institute in Edmonton, Alberta. The intervention entailed leg muscle strengthening and balance training exercises that progressed in difficulty as outlined by the Otago program, and involved a virtual component (facilitated live by a certified exercise physiologist via Zoom meeting platform once a week) and independent at-home training component (twice a week). Lower body strength and balance were assessed using the 5-times chair-stand and the 4-stage balance test, respectively, and were analyzed using the Wilcoxon Signed Rank test. Results: Twenty-seven older patients (mean age 70.1, range 65-76) participated. The most common cancer sites were breast (48%) and prostate (41%). One participant withdrew due to personal reasons unrelated to the program. The remaining 26 participants completed the intervention. Attendance rate for the virtual component was 97.6% and independent component 84.7%. Participants perceived the program as rewarding and enjoyable (100%), felt this program prepared them to exercise on their own (92%), were confident to continue exercising on their own (81%), and would recommend the program to other patients (100%). At baseline, 33% (n = 9) ≥1 fall over the past 6 months. A statistically significant improvement in lower body strength was detected post-intervention ( p =.001), whereas no difference was detected in balance ( p =.059). Conclusions: This virtual, hybrid resistance and balance training program was feasible, overwhelmingly accepted by our older participants, and appeared effective in improving lower body strength. Findings from this study may have potential to inform design of a larger, randomized multi-site study.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.043
GPT teacher head0.416
Teacher spread0.373 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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