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Record W4410035671 · doi:10.1007/s40279-025-02225-2

Task Specificity of Dynamic Resistance Training and Its Transferability to Non-trained Isometric Muscle Strength: A Systematic Review with Meta-analysis

2025· review· en· W4410035671 on OpenAlexaff
Atle Hole Sæterbakken, Nicolay Stien, Gøran Paulsen, David G. Behm, Vidar Andersen, Tom Erik Jorung Solstad, Olaf Prieske

Bibliographic record

VenueSports Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIsometric exercisePhysical medicine and rehabilitationMedicineSports medicineStrength trainingPhysical therapyMuscle hypertrophyPhysical strengthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Resistance training (RT) specificity has been confirmed for movement patterns (e.g., multi-joint or single joint), movement velocities, ranges of motion, and contraction types (e.g., dynamic vs isometric). However, a systematic analysis of the effects of dynamic mass-loaded (e.g., isoinertial) RT on dynamic versus isometric strength tests is lacking. OBJECTIVE: We aimed to examine the specific effects of dynamic RT on dynamic (isoinertial) versus isometric muscle strength, including possible moderating factors (e.g., training length, single joint and multi-joint, upper body and lower body, RT status) and mechanisms (e.g., hypertrophy, muscle activation). METHODS: A systematic literature search was conducted in MEDLINE (EBSCO), Web of Science, and Scopus up to March 2024. The included interventions contained at least ten training sessions, both dynamic and isometric muscle strength assessments before and after the training period, and healthy participants aged 16-60 years (encompassing untrained and trained individuals). Advanced RT approaches, such as electrical stimulation, isokinetic training, velocity-based training, and blood flow restriction training, were excluded. Within-subject, weighted standardized mean differences (SMDs) of the pre-intervention to post-intervention tests were calculated for both dynamic and isometric muscle strength measures using a random-effects model. Univariate sub-group analyses of RT status, intervention length, complexity (i.e., single-joint or multi-joint exercises), and body segments (i.e., upper and lower body) were independently computed. Random-effects meta-regressions were computed to examine if dynamic RT effects on dynamic and isometric muscle strength are predicted by RT effects on muscle hypertrophy or muscle activity. RESULTS: Overall, 43 studies with 1660 participants across 72 different RT interventions were eligible for inclusion. The overall effect on dynamic strength was significant and moderate magnitude (SMD = 0.98, 95% confidence interval 0.91-1.06, p < 0.001), whereas the transfer to non-trained isometric strength measures was significant but small (SMD = 0.42, 95% confidence interval 0.35-0.49, p < 0.001). Sub-analyses demonstrated moderate-to-large task-specific effects (range SMD; 95% confidence interval 0.75-1.30) of conducting dynamic RT and only small-to-medium effects (range SMD; 0.29-0.70) of the transferability of muscle strength to the non-trained isometric contraction form. Muscle hypertrophy and activity changes did not significantly predict dynamic RT effects on dynamic and isometric muscle strength (p ≥ 0.222). CONCLUSIONS: Our findings demonstrated task specificity of dynamic RT, as dynamic strength increased with a two-fold larger effect size than non-trained isometric muscle strength. Medium-to-large effects were observed for the dynamic strength improvements in the different sub-group analyses with small-to-medium effects in the isometric improvements. The limited transferability of dynamic (task-specific) strength to non-trained isometric contractions suggests that these two strength outcomes represent different neuromuscular domains.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0280.004
Bibliometrics0.0020.007
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 head0.330
Teacher spread0.282 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2025
Admission routes1
Has abstractyes

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