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Record W4391014520 · doi:10.1016/j.arrct.2023.100316

Feasibility of a Self-directed Upper Extremity Training Program to Promote Actual Arm Use for Individuals Living in the Community With Chronic Stroke

2024· article· en· W4391014520 on OpenAlexaboutno aff
Grace Kim, Amanda Gahlot, Camile Magsombol, Margaret Waskiewicz, Nettie Capasso, Stephen Van Lew, Hayejin Kim, Avinash Parnandi, Victoria Vaughan Dickson, Yael Goverover

Bibliographic record

VenueArchives of Rehabilitation Research and Clinical Translation · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersAmerican Occupational Therapy Foundation
KeywordsChronic strokePhysical medicine and rehabilitationStroke (engine)Physical therapyActivities of daily livingTraining (meteorology)PsychologyMedicineRehabilitationEngineering

Abstract

fetched live from OpenAlex

Objective To determine the feasibility of a self-directed training protocol to promote actual arm use in everyday life. The secondary aim was to explore the initial efficacy on upper extremity (UE) outcome measures. Design Feasibility study using multiple methods. Setting Home and outpatient research lab. Participants Fifteen adults (6 women, 9 men, mean age=53.08 years) with chronic stroke living in the community. There was wide range of UE functional levels, ranging from dependent stabilizer (limited function) to functional assist (high function). Intervention Use My Arm-Remote protocol. Phase 1 consisted of clinician training on motivational interviewing (MI). Phase 2 consisted of MI sessions with participants to determine participant generated goals, training activities, and training schedules. Phase 3 consisted of UE task-oriented training (60 minutes/day, 5 days/week, for 4 weeks). Participants received daily surveys through an app to monitor arm training behavior and weekly virtual check-ins with clinicians to problem-solve challenges and adjust treatment plans. Outcome Measures Primary outcome measures were feasibility domains after intervention, measured by quantitative study data and qualitative semi-structured interviews. Secondary outcomes included the Canadian Occupational Performance Measure (COPM), Motor Activity Log (MAL), Fugl-Meyer Assessment (FMA), and accelerometry-based duration of use metric measured at baseline, discharge, and 4-week follow-up. Results The UMA-R was feasible in the following domains: recruitment rate, retention rate, intervention acceptance, intervention delivery, adherence frequency, and safety. Adherence to duration of daily practice did not meet our criteria. Improvements in UE outcomes were achieved at discharge and maintained at follow-up as measured by COPM-Performance subscale (F[1.42, 19.83]=17.72, P <.001) and COPM-Satisfaction subscale (F[2, 28]=14.73, P <.001), MAL (F[1.31, 18.30]=12.05, P <.01) and the FMA (F[2, 28]=16.62, P <.001). Conclusion The UMA-R was feasible and safe to implement for individuals living in the community with chronic stroke. Adherence duration was identified as area of refinement. Participants demonstrated improvements in standardized UE outcomes to support initial efficacy of the UMA-R. Shared decision-making and behavior change frameworks can support the implementation of UE self-directed rehabilitation. Our results warrant the refinement and further testing of the UMA-R.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.176
GPT teacher head0.465
Teacher spread0.289 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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