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Record W4312102936 · doi:10.1093/geroni/igac059.269

FEASIBILITY OF REMOTELY SUPERVISED ONLINE CHAIR YOGA INTERVENTION FOR OLDER ADULTS WITH DEMENTIA

2022· article· en· W4312102936 on OpenAlexaff
Juyoung Park, Marlysa Sullivan, Keri J. Heilman, Jayshree Surage, Hannah Levine, Lillian Hung, María Ortega

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDementiaIntervention (counseling)LonelinessMedicinePhysical therapyPopulationPhysical medicine and rehabilitationGerontologyNursingPsychiatry

Abstract

fetched live from OpenAlex

Abstract This study assessed the feasibility of a remotely supervised online chair yoga (CY) intervention for older adults with dementia, exploring preliminary effects of CY on chronic pain, mobility, risk of falling, sleep disturbance, autonomic reactivity, cardiac rhythms (using IOM2 biofeedback device), and loneliness in this population. Using a one-group pretest/posttest design, a home-based CY intervention was delivered remotely to a group of 10 older adults with dementia who were socially isolated due to COVID-19. The online intervention was conducted twice weekly in 60-minute sessions for 8 weeks; data were collected virtually at baseline, mid-intervention, and post-intervention. Results indicated that online CY is a feasible approach for managing physical and psychological symptoms in older adults with dementia, based on retention (70%) and adherence (87.5%) with no injuries or other adverse events during the intervention. Senior-friendly videoconferencing should be available so that more older adults can gain access to the online intervention

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.417
Teacher spread0.332 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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