Evidence map›Paper›PMID 40372459›Full record

ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2025

Video-estimated peak jump power using deep learning is associated with sarcopenia and low physical performance in adults.

Sang Wouk Cho, Sung Joon Cho, Eun-Young Park, Na-Rae Park, Sookyeong Han, Yumie Rhee, Namki Hong

Abstract read
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In one paragraph

Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sang Wouk ChoDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Sung Joon ChoDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Eun-Young ParkDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Na-Rae ParkDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Sookyeong HanDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Yumie RheeDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
Namki HongDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea. nkhong84@yuhs.ac.ORCID http://orcid.org/0000-0002-8246-1956

Funding

Ministry of Science and ICT, South Korea 2022R1C1C1006807Seokchunnanum Foundation SCY2104P
6 · The paper itself

Abstract

Video-estimated peak jump power (vJP) using deep learning showed strong agreement with ground truth jump power (gJP). vJP was associated with sarcopenia, age, and muscle parameters in adults, with providing a proof-of-concept that markerless monitoring of peak jump power could be feasible in daily life space.

objectivesLow peak countermovement jump power measured by ground force plate (GFP) is associated with sarcopenia, impaired physical function, and elevated risk of fracture in older adults. GFP is available at research setting yet, limiting its clinical applicability. Video-based estimation of peak jump power could enhance clinical applicability of jump power measurement over research setting.

methodsData were collected prospectively in osteoporosis clinic of Severance Hospital, Korea, between March and August 2022. Individuals performed three jump attempts on GFP (ground truth, gJP) under video recording, along with measurement of handgrip strength (HGS), 5-time chair rise (CRT) test, and appendicular lean mass (ALM). Open source deep learning pose estimation and machine learning algorithms were used to estimate video-estimated peak jump power (vJP) in 80% train set. Sarcopenia was defined by Korean Working Group for Sarcopenia 2023 definition.

resultsA total of 658 jump motion data from 220 patients (mean age 62 years; 77% women; sarcopenia 19%) were analyzed. In test set (20% hold-out set), average difference between predicted and actual jump power was 0.27 W/kg (95% limit of agreement - 5.01 to + 5.54 W/kg; correlation coefficient 0.93). vJP detected gJP-defined low jump power with 81.8% sensitivity and 91.3% specificity. vJP showed a steep decline across age like gJP, with modest to strong correlation with HGS and CRT. Eight landmarks (both shoulders, hip, knee joints, and ears) were the most contributing features to vJP estimation. vJP was associated with the presence of sarcopenia (unadjusted and adjusted, - 3.95 and - 2.30 W/kg), HGS (- 3.69 and - 1.96 W/kg per 1 SD decrement), and CRT performance (- 2.79 and - 1.87 W/kg per 1 SD decrement in log-CRT) similar to that of gJP.

conclusionvJP was associated with sarcopenia, age, and muscle parameters in adults, with good agreement with ground truth jump power.

Indexed as

Deep LearningPhysical Functional PerformanceSarcopeniaAgedAged, 80 and overExercise TestFemaleHand StrengthHumansMaleMiddle AgedMuscle StrengthProspective StudiesVideo RecordingDeep learningMonitoringMotion analysisPose estimationSarcopenia

Identifiers

PMID40372459

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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.