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Online Unsupervised Tai Chi Intervention for Knee Pain and Function in People With Knee Osteoarthritis

2025· article· en· W4415590856 on OpenAlexaboutno aff
Shiyi Julia Zhu, Rana S. Hinman, Rachel K Nelligan, Peixuan Li, Anurika De Silva, Jenny Harrison, Alexander J. Kimp, Kim L. Bennell

Bibliographic record

VenueJAMA Internal Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisKnee painIntervention (counseling)Knee JointMEDLINE

Abstract

fetched live from OpenAlex

Importance: Tai chi is a type of exercise recommended for knee osteoarthritis, but access to in-person tai chi can be limited. Objective: To evaluate the effects of an unsupervised multimodal online tai chi intervention on knee pain and function for people with knee osteoarthritis. Design, Setting, and Participants: The RETREAT study was a 2-group superiority randomized clinical trial enrolling participants who met clinical criteria for knee osteoarthritis in Australian communities from August 2023 and November 2024. Interventions: Participants in the control group received access to a purpose-built website containing information about osteoarthritis and exercise benefits. Participants in the intervention group received the My Joint Tai Chi intervention comprising access to the same website plus tai chi information, a 12-week unsupervised video-based Yang-style tai chi program, and encouragement to use an app to facilitate program adherence. Main Outcomes and Measures: Changes in knee pain during walking (Numeric Rating Scale; range 0-10 with higher scores indicating greater pain) and difficulty with physical function (Western Ontario and McMaster Universities Osteoarthritis Index; range 0-68 with higher scores indicating greater dysfunction) during 12 weeks. Secondary outcomes included another knee pain measure, sport and recreation function, quality of life, physical and mental well-being, fear of movement, self-efficacy, balance confidence, positive activated affect, sleep quality, global improvement, and oral medication use. Results: Of 2106 patients screened, 178 met inclusion criteria and were randomized, 89 (mean [SD] age, 61.0 [8.7] years; 66 female [74%] and 23 [26%] male participants) to the control group and 89 (mean [SD] age, 62.1 [7.3] years; 59 [66%] female and 30 male [34%] participants) to the tai chi intervention. Of the total, 170 (96%) completed both of the primary outcomes at 12 weeks. The tai chi group reported greater improvements in knee pain (control, -1.3; tai chi, -2.7; mean difference, -1.4 [95% CI, -2.1 to -0.7] units; P < .001) and function (control, -6.9; tai chi, -12.0; mean difference, -5.6 [95% CI, -9.0 to -2.3] units; P < .001) compared to the control group. More participants in the tai chi than in the control group achieved a minimal clinically important difference in pain (73% vs 47%; risk difference, 0.3; 95% CI, 0.1 to 0.4; P < .001) and function (72% vs 52%; risk difference, 0.2; 95% CI, 0.1 to 0.3; P = .007). Between-group differences for most secondary outcomes favored tai chi, including another knee pain measure, sport and recreation function, quality of life, physical and mental well-being, global improvement, pain self-efficacy, and balance confidence. No associated serious adverse events were reported. Conclusions and Relevance: This randomized clinical trial found that this unsupervised multimodal online tai chi intervention improved knee pain and function compared with the control at 12 weeks. This free-to-access web-based intervention offers an effective, safe, accessible, and scalable option for guideline-recommended osteoarthritis exercise. Trial Registration: ANZCTR Identifier: ACTRN12623000780651.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2025
Admission routes1
Has abstractyes

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