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Record W2023084814 · doi:10.1258/1357633054893346

A community-based exercise programme for older persons with knee pain using telemedicine

2005· article· en· W2023084814 on OpenAlexaboutno aff
Yatwa Wong, Elsie Hui, Jean Woo

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2005
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACPhysical therapyMedicineOsteoarthritisTelemedicineBalance (ability)VideoconferencingElderly peoplePhysical medicine and rehabilitationGerontologyHealth careMultimediaAlternative medicine

Abstract

fetched live from OpenAlex

We explored the feasibility and efficacy of an exercise programme for elderly people with knee pain conducted via videoconferencing. Twenty-two community-dwelling subjects aged 60 years or above with knee pain were recruited from two community centres in Hong Kong. A 12-week exercise programme, including strengthening and balance training, was given via videoconferencing to subjects at both centres, in conjunction with a home-based exercise programme. The outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), quadriceps muscle strength, Berg's Balance Scale (BBS) and subjects' degree of acceptance of videoconferencing. Twenty subjects completed the 12-week programme and significant improvements occurred in all domains of the WOMAC score (P <0.003). There was a 44% and a 13% increase in quadriceps muscle strength (P <0.001) and BBS (P <0.001), respectively. Over 80% of the elderly subjects who joined the programme agreed or strongly agreed about all aspects of using videoconferencing. Most of them felt that the system was user-friendly and convenient. Videoconferencing appears to be a useful method of delivering a resistance-training programme for community-dwelling elderly persons with knee pain.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.346
Teacher spread0.298 · 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.

Study designOther design
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

Citations69
Published2005
Admission routes1
Has abstractyes

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