Evidence map›Paper›PMID 36616649›Full record

ReviewSensors (Basel, Switzerland)2022

Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling-A Review.

Vera G de Vette, DirkJan H E J Veeger, Marit P van Dijk

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Vera G de VetteDepartment of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.
DirkJan H E J VeegerDepartment of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.ORCID 0000-0003-0292-6520
Marit P van DijkDepartment of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.

Funding

Netherlands Organisation for Health Research and Development 546003002
6 · The paper itself

Abstract

More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done.

Indexed as

SportsWearable Electronic DevicesAthletesBicyclingBiomechanical PhenomenaHumansMechanical Phenomenacyclic sportsIMUinertial measurement unitmechanical powerpower meterpower outputwearable sensorswearable technology

Identifiers

PMID36616649
PMCPMC9823913

What OpenQuestion holds

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Registered trials

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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.