Profiling Movement and Gait Quality Characteristics in Pre-School Children

Constructing a Talent Identification Index System and Evaluation Model for Cross-Country Skiers

A talent identification index system for male and female cross-country skiers in four age groups (11-12 years old, 13-14 years old, 15-16 years old, and 17-18 years old) was established. The system comprises five body shape indexes ( i =5): Leg-to-Body Ratio (LBR), body fat percentage, maturity status, spreaded brachia index, and upper extremity length. The physiological function indexes ( i =2) are VO_2max and haemoglobin mass (Hb). The psychological indexes ( i =5) cover reaction time, perception speed, a quality-of-will scale, an attention test, and operational thinking. The physical fitness indexes ( i =11) comprise upper limb explosiveness, vertical jump, 3000-metre run, orthostatic forward flexion, closed-eyes single-leg stand, standing long jump, 20-metre sprint, pull-ups (males), flexed arm hang (females), hexagon jump, and a Functional Movement Screen (FMS) test. The athletic performance indexes ( i =3) comprise on-snow time trials for 1.2 km, 5 km, and 10 km. The talent identification evaluation model was created using automated evaluation software. The talent identification index system and evaluation standard table for cross-country skiers passed the P₆₀ shortlist and P₉₀ elite boundaries established using the percentile method. Thus, the results of this test profile verify that the evaluative model is objectively effective.

Assessing the Internal Reliability and Construct Validity of the General Movement Competence Assessment for Children

Validated assessment tools for movement competence typically involve the isolation and reproduction of specific movement forms, which arguably neglects individuals’ ability to combine and adapt movements to overcome constraints within a dynamic environment. A new movement assessment tool, the General Movement Competence Assessment (GMCA), was developed for this study using Microsoft Kinect. Movement competence of 83 children (36 boys and 47 girls), aged 8–10 years (9.06 ± 0.75 years) was measured using the GMCA. An exploratory approach was undertaken to examine the internal consistency reliability (McDonald’s omega coefficient) and factorial structure of the GMCA for the study sample. Factorial structure was determined using exploratory factor analysis by principal component analysis with varimax rotation. For the sample data, reliability for the GMCA games were acceptable (ω = 0.53–0.89) and indicated that combinations of movement attributes were measured by GMCA games. Factorial analysis extracted four movement constructs accounting for 71.31% of variance. Dexterity was tentatively identified as a new independent construct alongside currently accepted movement constructs (i.e., locomotion, object-control, stability). While further development of the GMCA is still required, initial results are encouraging in view of an objective and theoretically informed approach to assess general movement competence in children.

Physical activity, motor competence and movement and gait quality: A principal component analysis

OBJECTIVE

While novel analytical methods have been used to examine movement behaviours, to date, no studies have examined whether a frequency-based measure, such a spectral purity, is useful in explaining key facets of human movement. The aim of this study was to investigate movement and gait quality, physical activity and motor competence using principal component analysis.

METHODS

Sixty-five children (38 boys, 4.3 ± 0.7y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI; 16.2 ± 1.9 kg∙m²) took part in this study. Measures included accelerometer-derived physical activity and movement quality (spectral purity), motor competence (Movement Assessment Battery for Children 2nd edition; MABC2), height, weight and waist circumference. All data were subjected to a principal component analysis, and the internal consistency of resultant components were assessed using Cronbach's alpha.

RESULTS

Two principal components, with excellent internal consistency (Cronbach α >0.9) were found; the 1st principal component, termed "movement component", contained spectral purity, traffic light MABC2 score, fine motor% and gross motor% (α = 0.93); the 2nd principal component, termed "anthropometric component", contained weight, BMI, BMI% and body fat% (α = 0.91).

CONCLUSION

The results of the present study demonstrate that accelerometric analyses can be used to assess motor competence in an automated manner, and that spectral purity is a meaningful, indicative, metric related to children's movement quality.

Physical activity characterization: does one site fit all?

BACKGROUND

It is evident that a growing number of studies advocate a wrist-worn accelerometer for the assessment of patterns of physical activity a priori, yet the veracity of this site rather than any other body-mounted location for its accuracy in classifying activity is hitherto unexplored.

OBJECTIVE

The objective of this review was to identify the relative accuracy with which physical activities can be classified according to accelerometer site and analytical technique.

METHODS

A search of electronic databases was conducted using Web of Science, PubMed and Google Scholar. This review included studies written in the English language, published between database inception and December 2017, which characterized physical activities using a single accelerometer and reported the accuracy of the technique.

RESULTS

A total of 118 articles were initially retrieved. After duplicates were removed and the remaining articles screened, 32 full-text articles were reviewed, resulting in the inclusion of 19 articles that met the eligibility criteria.

CONCLUSION

There is no 'one site fits all' approach to the selection of accelerometer site location or analytical technique. Research design and focus should always inform the most suitable location of attachment, and should be driven by the type of activity being characterized.