Can the Functional Movement Screen™ be used to capture changes in spine and knee motion control following 12 weeks of training?

Frost, David M.; Beach, Tyson A.C.; Campbell, Troy; Callaghan, Jack P.; McGill, Stuart M.

doi:10.1016/j.ptsp.2016.06.003

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…This study firstly designed different emotion and tension level evoking experiments under adverse states and collected six physiological signal parameters such as heart rate, respiration rate, skin impedance, blood oxygen saturation, pulse rate, and blood pressure under specific emotional states of multiple subjects in real time. Through preprocessing and feature extraction of these physiological data, combined with the results of the experimental subjective experience questionnaire, the PSO-kNN algorithm was used to select and classify the features of the experimental sample data and finally to establish the emotion recognition model under the adverse state [ 20 ].…”

Section: Research Methods For Identifying Emotions In the Adverse Statesmentioning

confidence: 99%

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

Han¹

2022

Computational Intelligence and Neuroscience

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It is well known that we, as human beings, are prone to a variety of undesirable emotions such as excitement, boredom, and fear, all of which are induced by varying degrees of negative states. In this paper, we designed an emotion-evoking experiment to induce calm, excited, bored, and fearful emotions, as well as low, moderate, and high levels of tension. Based on the six physiological signals such as heart rate and respiration rate of the subjects in these emotion states, feature extraction was performed after removing the baseline preprocessing, combined with particle swarm optimisation algorithm for feature selection, and the k-nearest neighbour algorithm was used to classify the different emotion and tension levels in the undesirable states. By comparing the results of several sets of experiments, we found that with baseline removal and particle swarm feature selection optimisation, our experimental results using k-nearest neighbour classification showed a significant improvement in recognition accuracy compared to the traditional k-nearest neighbour algorithm, which indicates that the proposed method has better recognition results.

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Section: Research Methods For Identifying Emotions In the Adverse Statesmentioning

confidence: 99%

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

Han¹

2022

Computational Intelligence and Neuroscience

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“…El Funtional Movement Screen (FMS) es una prueba de movimiento funcional, que permite identificar alteraciones en patrones de movimiento funcionales. (Frost et al 2015;Frost et al 2017). El FMS es utilizado constantemente en el ámbito deportivo para la prevención de lesiones (Kraus et al 2014); sin embargo, cuenta con ciertas limitaciones, ya que en algunas alteraciones posturales de la columna no es la herramienta más pertinente para evaluar movilidad, por lo cual, no debe ser el único método de evaluación para la prevención de lesiones deportivas, sino se debería usar como método complementario a otro tipo de evaluaciones, como los exámenes posturales.…”

Section: Introductionunclassified

Evaluación fisioterapéutica precompetitiva en deportistas del InderSantander

Corso-Amado,

Torres-Jaimes,

García-González

et al. 2023

Rev. Digit. Act. Fis. Deport. (En línea)

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Introducción: el riesgo de sufrir una lesión deportiva depende del tipo de disciplina que se practica y entre las que se pueden presentar son las fracturas, las luxaciones, las contusiones, las heridas y las conmociones. Objetivo: evaluar la postura estática y algunas pruebas de la batería “Functional Movement Screen”, en los deportistas profesionales del Instituto de Deporte y Recreación de Santander. Materiales y métodos: se realizó un estudio transversal descriptivo en deportistas en Bucaramanga, acerca de la prevalencia de alteraciones posturales en diferentes disciplinas deportivas. Usando la batería “Functional Movement Screen”, se evaluó la estabilidad CORE, mediante las pruebas de paso de valla y sentadilla profunda, movilidad de hombro, a través de la prueba Back Scratch y flexibilidad de la musculatura posterior del muslo, por medio de la prueba Sit and reach. Adicionalmente se realizaron evaluaciones posturales en los deportistas. Resultados y discusión: se logró determinar los factores de riesgo claves por deporte. Se encontró una alta prevalencia de alteraciones posturales en la columna vertebral, con diferencias significativas en su incidencia, según la edad y el deporte practicado. La alteración postural más prevalente fue la hiperlordosis y la zona corporal; con más alteraciones es la columna vertebral y raquis. Los deportistas mayores de 18 años presentan mayores alteraciones posturales. Conclusiones: la mayoría de los participantes obtuvo el mayor puntaje posible en algunas de las pruebas funcionales de la batería aplicada. Se encontraron diferencias posturales entre mayores y menores de 18 años y diferencias posturales y en el desempeño de las pruebas funcionales por disciplina deportiva.

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“…(2015c) reported that the effectiveness of FMS™ training requires the consideration of various factors, such as the number and type of participants, the scoring method (paper or video), the feedback provided during the test, and supervision by a coach. FMS™ may not be a viable tool to assess movement behaviors regardless of whether it is graded qualitatively using composite or task scores or quantitatively via kinematic analyses ( Frost et al., 2017 ; Cornell, 2016 ) and the effect of functional correction training on firefighters after FMS™ was unclear.…”

Section: Introductionmentioning

confidence: 99%

Effects of functional correction training on injury risk of athletes: a systematic review and meta-analysis

Chen

Chun-he

Chen

et al. 2021

PeerJ

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Background We explored functional correction training using the Functional Movement Screen (FMS™) tool. We also analyzed the effects of training on the injuries of athletes in a systematic review and meta-analysis of non-randomized clinical trials. Methodology We collected twenty-four articles from PubMed, CENTRAL, Scopus, ProQuest, Web of Science, EBSCOhost, SPORTDiscus, Embase, WanFang, and CNKI that were published between January 1997 to September 2020. Articles were selected based on the following inclusion criteria: randomized and non-randomized controlled trials, studies with functional correction training screened by FMS™ as the independent variable, and studies with injury risk to the athlete as the dependent variable. Data conditions included the sample size, mean, standard deviation, total FMS™ scores, number of injuries, and asymmetry movement patterns after interventions in the experimental and control groups. Exclusion criteria included: conference abstracts, cross-sectional studies, articles with retrospective study design. Results Twelve non-randomized trials were included in the meta-analysis. The injury risk ratio of athletes after functional correction training was 0.39 RR (95 CI [1.50–1.93]; Z = 15.53; P < 0.0001; I2 = 2.6%), indicating an improvement of athletes functional patterns. Conclusion Grade B evidence indicates that functional correction training based on FMS™ may improve the functional patterns of athletes and Grade D evidence indicates that it may reduce the risk of sports injury. However, the true effect is likely to be different from the estimate of the effect. Therefore, further studies are needed to explore the influence of functional correction training on the injury risks of athletes. Protocol registration: CRD42019145287.

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Can the Functional Movement Screen™ be used to capture changes in spine and knee motion control following 12 weeks of training?

Cited by 17 publications

References 17 publications

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

Evaluación fisioterapéutica precompetitiva en deportistas del InderSantander

Effects of functional correction training on injury risk of athletes: a systematic review and meta-analysis

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