2020
DOI: 10.1186/s12966-020-00929-4
|View full text |Cite
|
Sign up to set email alerts
|

Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time?

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
30
0
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
8
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 39 publications
(31 citation statements)
references
References 23 publications
0
30
0
1
Order By: Relevance
“…FMS proficiency was measured using the TGMD-2, a widely-used and validated process-oriented assessment tool with published norms for both object control and locomotor movement skills. In addition, PA was measured objectively using a wearable sensor and the PA outcomes were derived from raw acceleration signal using state-of-the-art machine learning data processing methods [ 27 ]. The application machine learning methods allows researchers to monitor not only the intensity of physical activity, but also the quality of movement behaviors.…”
Section: Discussionmentioning
confidence: 99%
“…FMS proficiency was measured using the TGMD-2, a widely-used and validated process-oriented assessment tool with published norms for both object control and locomotor movement skills. In addition, PA was measured objectively using a wearable sensor and the PA outcomes were derived from raw acceleration signal using state-of-the-art machine learning data processing methods [ 27 ]. The application machine learning methods allows researchers to monitor not only the intensity of physical activity, but also the quality of movement behaviors.…”
Section: Discussionmentioning
confidence: 99%
“…FMS pro ciency was measured using the TGMD-2, a widely-used and validated process-oriented assessment tool with published norms for both object control and locomotor movement skills. In addition, PA was measured objectively using a wearable sensor and the PA outcomes were derived using state-of-the-art machine learning data processing methods [27]. The application machine learning methods allows researchers to monitor not only the intensity of physical activity, but also the quality of movement behaviors.…”
Section: Resultsmentioning
confidence: 99%
“…Because this study focused mainly on the volume and intensity of the behaviours, it remains unclear whether the observed associations with psychosocial health are moderated or mediated by the other aspects of the behaviours (e.g., types or contexts of PA, content of screen time). Lastly, we acknowledge that the use of intensity cut-points, while validated, may have resulted in some misclassification of movement behaviours as they do not account for upper limb movements during sedentary or stationary light-intensity activities [ 70 ].…”
Section: Discussionmentioning
confidence: 99%