Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial

Abstract Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and improve bodyweight squat form in adult pa...

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Autores principales:	Alessandro Luna, Lorenzo Casertano, Jean Timmerberg, Margaret O’Neil, Jason Machowsky, Cheng-Shiun Leu, Jianghui Lin, Zhiqian Fang, William Douglas, Sunil Agrawal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89aeee58bea047ab8053de580c7f5f51
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spelling	oai:doaj.org-article:89aeee58bea047ab8053de580c7f5f512021-12-02T18:33:55ZArtificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial10.1038/s41598-021-97343-y2045-2322https://doaj.org/article/89aeee58bea047ab8053de580c7f5f512021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97343-yhttps://doaj.org/toc/2045-2322Abstract Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and improve bodyweight squat form in adult participants when compared to a physical therapist (PT). Participants randomized to AI group (n = 15) performed 3 squat sets: 10 unassisted control squats, 10 squats with performance feedback from AI, and 10 additional unassisted test squats. Participants randomized to PT group (n = 15) also performed 3 identical sets, but instead received performance feedback from PT. AI group intervention did not differ from PT group (log ratio of two odds ratios = − 0.462, 95% confidence interval (CI) (− 1.394, 0.471), p = 0.332). AI ability to identify a correct squat generated sensitivity 0.840 (95% CI (0.753, 0.901)), specificity 0.276 (95% CI (0.191, 0.382)), PPV 0.549 (95% CI (0.423, 0.669)), NPV 0.623 (95% CI (0.436, 0.780)), and accuracy 0.565 95% CI (0.477, 0.649)). There was no statistically significant association between group allocation and improved squat performance. Current AI had satisfactory ability to identify correct squat form and limited ability to identify incorrect squat form, which reduced diagnostic capabilities. Trial Registration NCT04624594, 12/11/2020, retrospectively registered.Alessandro LunaLorenzo CasertanoJean TimmerbergMargaret O’NeilJason MachowskyCheng-Shiun LeuJianghui LinZhiqian FangWilliam DouglasSunil AgrawalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alessandro Luna Lorenzo Casertano Jean Timmerberg Margaret O’Neil Jason Machowsky Cheng-Shiun Leu Jianghui Lin Zhiqian Fang William Douglas Sunil Agrawal Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
description	Abstract Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and improve bodyweight squat form in adult participants when compared to a physical therapist (PT). Participants randomized to AI group (n = 15) performed 3 squat sets: 10 unassisted control squats, 10 squats with performance feedback from AI, and 10 additional unassisted test squats. Participants randomized to PT group (n = 15) also performed 3 identical sets, but instead received performance feedback from PT. AI group intervention did not differ from PT group (log ratio of two odds ratios = − 0.462, 95% confidence interval (CI) (− 1.394, 0.471), p = 0.332). AI ability to identify a correct squat generated sensitivity 0.840 (95% CI (0.753, 0.901)), specificity 0.276 (95% CI (0.191, 0.382)), PPV 0.549 (95% CI (0.423, 0.669)), NPV 0.623 (95% CI (0.436, 0.780)), and accuracy 0.565 95% CI (0.477, 0.649)). There was no statistically significant association between group allocation and improved squat performance. Current AI had satisfactory ability to identify correct squat form and limited ability to identify incorrect squat form, which reduced diagnostic capabilities. Trial Registration NCT04624594, 12/11/2020, retrospectively registered.
format	article
author	Alessandro Luna Lorenzo Casertano Jean Timmerberg Margaret O’Neil Jason Machowsky Cheng-Shiun Leu Jianghui Lin Zhiqian Fang William Douglas Sunil Agrawal
author_facet	Alessandro Luna Lorenzo Casertano Jean Timmerberg Margaret O’Neil Jason Machowsky Cheng-Shiun Leu Jianghui Lin Zhiqian Fang William Douglas Sunil Agrawal
author_sort	Alessandro Luna
title	Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
title_short	Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
title_full	Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
title_fullStr	Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
title_full_unstemmed	Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
title_sort	artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89aeee58bea047ab8053de580c7f5f51
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Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial

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