ArticleJournal of sport rehabilitation2022
Immersive Real-Time Biofeedback Optimized With Enhanced Expectancies Improves Motor Learning: A Feasibility Study.
Article in Journal of sport rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.
- Virtual reality-based interventions targeting sports injury-related risk factors: a systematic review of biomechanical, neuromuscular, and functional outcomes.Frontiers in sports and active living · 2026Pooled it
- Biofeedback Modalities Targeting Kyphosis and Lordosis: A Critical Appraisal Topic.BioMed research international · 2026Pooled it
- Either Autonomy Support or Enhanced Expectancies Delivered Via Virtual-Reality Benefits Frontal-Plane Single-Leg Squatting Kinematics.Perceptual and motor skills · 2024Article
- The effects of sports-related concussion history on female adolescent brain activity and connectivity for bilateral lower extremity knee motor control.Psychophysiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
contextAn Optimizing Performance through Intrinsic Motivation and Attention for Learning theory-based motor learning intervention delivering autonomy support and enhanced expectancies (EE) shows promise for reducing cognitive-motor dual-task costs, or the relative difference in primary task performance when completed with and without a secondary cognitive task, that facilitate adaptive injury-resistant movement response. The current pilot study sought to determine the effectiveness of an autonomy support versus an EE-enhanced virtual reality motor learning intervention to reduce dual-task costs during single-leg balance.
designWithin-subjects 3 × 3 trial.
methodsTwenty-one male and 24 female participants, between the ages of 18 and 30 years, with no history of concussion, vertigo, lower-extremity surgery, or lower-extremity injuries the previous 6 months, were recruited for training sessions on consecutive days. Training consisted of 5 × 8 single-leg squats on each leg, during which all participants mimicked an avatar through virtual reality goggles. The autonomy support group chose an avatar color, and the EE group received positive kinematic biofeedback. Baseline, immediate, and delayed retention testing consisted of single-leg balancing under single- and dual-task conditions. Mixed-model analysis of variances compared dual-task costs for center of pressure velocity and SD between groups on each limb.
resultsOn the right side, dual-task costs for anterior-posterior center of pressure mean and SD were reduced in the EE group (mean Δ = -51.40, Cohen d = 0.80 and SD Δ = -66.00%, Cohen d = 0.88) compared with the control group (mean Δ = -22.09, Cohen d = 0.33 and SD Δ = -36.10%, Cohen d = 0.68) from baseline to immediate retention.
conclusionsThese findings indicate that EE strategies that can be easily implemented in a clinic or sport setting may be superior to task-irrelevant AS approaches for influencing injury-resistant movement adaptations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.