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Record W4414978958 · doi:10.32396/usurj.v10i2.875

Exploring the utility of resistance training for congenital mirror movement disorder

2025· article· en· W4414978958 on OpenAlexaffvenue
Nisarg Shiroya, Layla Gould, Gary Hunter, Jonathan P. Farthing

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIsometric exerciseMirroringResistance trainingHand strengthGrip strengthRepeated measures designElectromyographyMuscle contraction

Abstract

fetched live from OpenAlex

College of Kinesiology Research Theme: Human Performance Introduction: Congenital mirror movement (CMM) disorder is a very rare condition (<1 in a million) where voluntary limb movements trigger involuntary mirroring in the opposite limb. This study examined whether four weeks of unilateral resistance training, followed by 10 days of detraining, affects mirroring activity and cross-education effects (i.e., strength gain in the untrained opposite limb) in individuals with CMM. Methods: Two right-handed participants (both male; age 18 and 47) reporting CMM completed four weeks of maximal unilateral isometric grip training of the left hand, three times per week using a grip trainer (Digiflex), followed by 10 days of detraining. Training sessions increased from two to five sets of eight maximal repetitions per session. Participants completed testing for grip strength and muscle activation at baseline, mid-training, post-training, and after detraining. Testing included three brief 3-second maximal voluntary contractions (MVC) and a 1-minute MVC contraction of each hand. MVC force (kg) was measured using Jamar and Biopac grip dynamometers, while simultaneous EMG recordings captured muscle activity from the flexor carpi radialis, extensor carpi radialis, and flexor digitorum superficialis of both arms. Results: Due to the low sample size in this study (on account of the rare condition), only descriptive results are reported. After left-hand training, left grip strength increased in the first participant by ~12% and remained ~6% above baseline after detraining. Strength in the untrained right arm improved ~17% and increased to ~23% above baseline after detraining, indicative of cross-education. The second participant showed a modest increase of ~6% by the end of detraining and no cross-education. Both participants showed substantial mirroring force and EMG activity in the opposite limb during 1-minute MVC contractions of either hand. Mirroring force in the right hand during a 1-minute left MVC was ~20% MVC for both participants at baseline and decreased by almost half at the end of detraining. Mirroring force in the left hand during 1-minute right MVC was ~40% MVC and ~20% MVC for each participant, respectively, and decreased by almost half after detraining. EMG activation of the right hand during the left 1-minute MVC ranged from 67% to 82% MVC at baseline, with little change after training, and then increased after detraining. EMG activation of the left hand during the right 1-minute MVC was in the range of 80% MVC and tended to increase post-training, but reverted to baseline or lower after detraining. Conclusion: In two participants, unilateral grip training reduced mirroring force and improved strength, with signs of cross-education. However, muscle activation responses varied, and detraining effects were inconsistent. Further research, with larger samples and a longer intervention period, is needed to understand individual differences and long-term benefits for CMM. Acknowledgements: A special thank you to the participants with CMM who dedicated a considerable amount of time to training and testing for this study and to fellow students in Dr. Farthing’s lab.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.147
GPT teacher head0.332
Teacher spread0.185 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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