Evidence map›Paper›PMID 36146263›Full record

ReviewSensors (Basel, Switzerland)2022

Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation.

Swathikan Chidambaram, Yathukulan Maheswaran, Kian Patel, Viknesh Sounderajah, Daniel A Hashimoto, Kenneth Patrick Seastedt, Alison H McGregor, Sheraz R Markar, Ara Darzi

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 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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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

9 authors.

Swathikan ChidambaramDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.
Yathukulan MaheswaranDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.
Kian PatelDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.ORCID 0000-0002-9271-3424
Viknesh SounderajahDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.ORCID 0000-0002-4595-8402
Daniel A HashimotoDepartment of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0003-4725-3104
Kenneth Patrick SeastedtDepartment of Surgery, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.ORCID 0000-0002-6345-8217
Alison H McGregorMusculoskeletal Laboratory, Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, White City Campus, London W12 OBZ, UK.ORCID 0000-0003-4672-332X
Sheraz R MarkarDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.
Ara DarziDepartment of Surgery & Cancer, Imperial College London, St. Mary's Hospital, London W2 1NY, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.

Indexed as

Athletic PerformanceSports MedicineWearable Electronic DevicesArtificial IntelligenceAthletesHumansartificial intelligencesensorssports medicinewearables

Identifiers

PMID36146263
PMCPMC9502817

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.