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Record W2123105881 · doi:10.5014/ajot.2010.09198

Effects of a Tailored Activity Pacing Intervention on Pain and Fatigue for Adults With Osteoarthritis

2010· article· en· W2123105881 on OpenAlexaboutno aff
Susan L. Murphy, Angela J. Woodiwiss, Dylan M. Smith, Qian Dong, Jessica F. Koliba

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research ResourcesNational Institute on Aging
KeywordsMedicinePhysical therapyOsteoarthritisIntervention (counseling)Randomized controlled trialPhysical activityPhysical medicine and rehabilitationInternal medicineAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined whether tailored activity pacing intervention was more effective at reducing pain and fatigue than general activity pacing intervention. METHOD: Adults with knee or hip osteoarthritis (N = 32) stratified by age and gender were randomized to receive either tailored or general pacing intervention. Participants wore an accelerometer for 5 days that measured physical activity and allowed for repeated symptom assessment. Physical activity and symptom data were used to tailor activity pacing instruction. Outcomes at 10-week follow-up were pain (Western Ontario and McMaster Universities Osteoarthritis Index) and fatigue (Brief Fatigue Inventory). RESULTS: Compared with general intervention, the tailored group had less fatigue interference (p = .02) and trended toward decreased fatigue severity (p = .09) at 10-wk follow-up. No group differences were found in pain reduction. CONCLUSION: Tailoring instruction on the basis of recent symptoms and physical activity may be a more effective symptom management approach than general instruction given the positive effects on fatigue.

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 score0.869
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.016
GPT teacher head0.302
Teacher spread0.287 · 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

Citations102
Published2010
Admission routes1
Has abstractyes

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