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Development and Cross-Validation of Non-exercise–based Prediction Equations for Estimating Cardiorespiratory Fitness in Korean College Students
Korean J Sports Med 2022;40:39-48
Published online March 1, 2022;  https://doi.org/10.5763/kjsm.2022.40.1.39
© 2022 The Korean Society of Sports Medicine.

Inhwan Lee1, Kwonseok Han1, Munku Song1,2, Hyunsik Kang1

1College of Sports Science, Sungkyunkwan University, Suwon, 2Samsung Training Center, Yongin, Korea
Correspondence to: Hyunsik Kang
College of Sports Science, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea
Tel: 竊82-31-299-6911, Fax: 竊82-31-299-6941, E-mail: hkang@skku.edu
This study was supported by a National Research Foundation grant funded by the Korean government (NRF-2019R1I1A1A01043771).
Received November 8, 2021; Revised November 18, 2021; Accepted November 22, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
Purpose: Non-exercise-based estimation of cardiorespiratory fitness (eCRF) is not available for Korean young adults. This study was to develop an eCRF regression equation and to validate its accuracy in Korean college students.
Methods: Subjects were undergraduate students (n=1,319; female, 219) who participated in the assessment of physical fitness and risk factors at our institute. Using a random sampling method, 70% of the subjects were selected and used to develop prediction equations for estimating CRF, and 30% of the subjects were used to verify the accuracy of the equations for CRF. Body mass index (BMI), percent body fat, waist circumference (WC), physical activity, smoking, and resting heart rate were measured as covariates. CRF was assessed as minute volume of maximal oxygen consumption (VO2max) with a graded exercise test. Prediction equations for CRF were derived using stepwise linear regressions. The differences between measured and estimated VO2max values were verified by using paired t-test and Bland-Altman plots.
Results: The coefficients of determination (R2) of BMI, % body fat, and WC-based regression models were 0.502, 0.514, and 0.518, respectively. The standard errors of estimate for BMI, % body fat, and WC regression models were 5.55, 5.48, and 5.46, respectively. In the validation study, no significant differences between estimated and measured VO2max values were found in BMI (p=0.971), % body fat (p=0.877), and WC (p=0.817)-based regression models.
Conclusion: The current findings of the study suggest that CRF can be estimated from non-exercise healthrelated parameters with an acceptable accuracy in Korean college students.
Keywords : Cardiorespiratory fitness, Physical activity, Prediction, Accuracy, Young adults
꽌 濡

嫄닿컯 泥대젰쓽 빑떖쟻 援ъ꽦슂냼씤 떖룓泥대젰(cardiorespiratory fitness) 떊泥댄솢룞 諛 슫룞쓣 옣湲곌컙뿉 嫄몄퀜 吏냽쟻쑝濡 닔뻾븷 닔 엳뒗 뒫젰쑝濡 젙쓽릺硫1, 룓, 떖옣, 삁愿쑝濡 씠뼱吏뒗 샇씉?닚솚怨꾩쓽 궛냼슫諛 뒫젰怨 留덉씠삤湲濡쒕퉰, 誘명넗肄섎뱶由ъ븘, 紐⑥꽭삁愿 遺꾪룷 벑 洹쇨낏寃⑷퀎쓽 湲곕뒫쟻 긽깭뿉 뵲씪 寃곗젙맂떎2. 삉븳 떖룓泥대젰 쑀쟾쟻 슂씤怨 썑泥쒖쟻 슂씤뿉 뵲씪 李⑥씠媛 엳뒗 寃껋쑝濡 븣젮졇 엳쑝硫3,4, 洹쒖튃쟻씤 以?怨좉컯룄쓽 떊泥댄솢룞 諛 슫룞 떖룓泥대젰쓣 利앹쭊븯뒗 媛옣 슚怨쇱쟻씤 닔떒쑝濡 븣젮졇 엳떎5.

떖룓泥대젰쓣 利앹쭊븯硫 꽦씤湲 留뚯꽦吏덊솚쓣 삁諛⑺븯뿬 嫄닿컯 愿젴 궣쓽 吏 利앹쭊뿉 湲띿젙쟻씤 슚怨쇰 쑀룄븷 肉먮쭔 븘땲씪 以묐뀈 씠썑 떎뼇븳 썝씤쑝濡 씤븳 議곌린궗留앷낵룄 諛젒븳 뿰愿꽦씠 엳뒗 寃껋쑝濡 蹂닿퀬릺怨 엳떎6-9. 떖룓泥대젰쓣 媛옣 媛앷쟻쑝濡 痢≪젙븯뒗 諛⑸쾿 떎뿕떎 슫룞遺븯寃궗(graded exercise test)瑜 넻빐 理쒕 궛냼꽠痍⑤웾(volume of maximal oxygen consumption, VO2max)쓣 痢≪젙븯뒗 寃껋쑝濡 븣젮졇 엳떎10. 洹몃윭굹 슫룞遺븯寃궗 諛⑸쾿쓣 씠슜븳 떖룓泥대젰 痢≪젙 怨좉쓽 옣鍮꾩 닕젴맂 쟾臾멸瑜 븘슂濡 븯뿬 怨좊퉬슜 臾몄젣媛 諛쒖깮븷 肉먮쭔 븘땲씪 痢≪젙뿉 빐 留롮 떆媛꾩씠 냼슂맂떎뒗 떒젏씠 遺媛곷릺뼱 蹂댄렪쟻쑝濡 닔뻾븯湲곗뿉뒗 젣븳씠 엳떎怨 蹂닿퀬릺怨 엳떎11. 씠뿉 援쇅 꽑뻾뿰援ъ뿉꽌뒗 떖룓泥대젰 痢≪젙쓽 뿬윭 媛吏 젣븳젏쓣 蹂댁셿븯怨 렪쓽꽦쓣 솗蹂댄븯怨좎옄 슫룞쓣 븯吏 븡怨 떖룓泥대젰쓣 뙆븙븷 닔 엳뒗 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 媛쒕컻븯뿬 궗슜븯怨 엳떎12.

鍮꾩슫룞꽦 異붿젙 떖룓泥대젰(estimated cardiorespiratory fitness) 쁽옣뿉꽌 鍮꾧탳쟻 媛꾨떒븯寃 뼸쓣 닔 엳뒗 蹂닔瑜 씠슜븯뿬 理쒕 궛냼꽠痍⑤웾쓣 異붿젙븯뒗 諛⑸쾿씠硫13, 痢≪젙맂 떖룓泥대젰뿉 鍮꾪빐 빟媛꾩쓽 異붿젙삤李⑤뒗 엳吏留 洹 렪쓽꽦쑝濡 씤빐 슫룞遺븯寃궗媛 遺덇븳 떒쐞 뿭븰議곗궗뿉꽌 쑀슜븯寃 궗슜릺怨 엳떎14. 떎濡濡, 誘멸뎅 援誘쇨굔媛뺤쁺뼇議곗궗 뿰援ъ뿉꽌 鍮꾩슫룞꽦 떖룓泥대젰 닔以씠 1 MET (metabolic equivalent task) 利앷븷 븣留덈떎 떖삁愿吏덊솚쑝濡 씤븳 궗留 쐞뿕 빟 20%–30%뵫 媛먯냼븯뒗 寃껋쑝濡 蹂닿퀬븳 諛 엳쑝硫15, 쁺援 嫄닿컯議곗궗 뿰援ъ뿉꽌룄 鍮꾩슫룞꽦 떖룓泥대젰씠 利앷븷닔濡 紐⑤뱺 썝씤 諛 떖삁愿吏덊솚쑝濡 씤븳 궗留 쐞뿕 쑀쓽븯寃 궙븘吏꾨떎怨 蹂닿퀬븳 諛 엳떎16. 씠泥섎읆 援쇅 꽑뻾뿰援ъ뿉꽌뒗 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 媛쒕컻븯怨 떒쐞 뿭븰議곗궗뿉 洹쇨굅븯뿬 二쇱슂吏덊솚 諛 궗留앹뿉 븳 떖룓泥대젰쓽 뿭븷쓣 寃利앺븯뒗 떆룄媛 吏냽쟻쑝濡 씠猷⑥뼱吏怨 엳떎. 洹몃윭굹 援궡쓽 寃쎌슦 떒쐞 뿭븰議곗궗뿉 湲곕컲븯뿬 二쇱슂 留뚯꽦吏덊솚뿉 븳 떖룓泥대젰쓽 뿭븷쓣 寃利앺븳 뿰援ш 留ㅼ슦 誘명씉븷 肉먮쭔 븘땲씪 슦由щ굹씪 꽦씤쓣 긽쑝濡 떦꽦씠 솗蹂대맂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 媛쒕컻 뿰援 삉븳 쟾臾댄븳 떎젙씠떎.

援쇅 硫뷀遺꾩꽍 뿰援ъ뿉꽌 鍮꾩슫룞꽦 떖룓泥대젰쓣 異붿젙븯뒗 뜲 엳뼱 굹씠, 꽦蹂, 떊泥댄솢룞, 븞젙 떆 떖諛뺤닔, 씉뿰, 떊泥닿뎄꽦쓣 異붿젙떇뿉 솢슜 鍮덈룄媛 媛옣 넂 蹂닔濡 蹂닿퀬븯怨 엳쑝硫17,18, 떊泥닿뎄꽦쓽 寃쎌슦 긽솴뿉 뵲瑜 떖룓泥대젰 異붿젙쓽 렪쓽꽦쓣 怨좊젮븯뿬 떖삁愿吏덊솚怨 뿰愿꽦씠 넂 蹂닔씤 泥댁쭏웾吏닔, 뿀由щ몮젅, 泥댁諛⑸쪧쓣 媛곴컖 룷븿븳 異붿젙떇쓣 젣떆븯怨 엳떎19. 씠 愿젴븯뿬, Jackson 벑20怨 Baynard 벑21쓽 뿰援ъ뿉꽌룄 泥댁쭏웾吏닔, 泥댁諛⑸쪧, 뿀由щ몮젅 벑 媛 떊泥닿뎄꽦뿉 뵲瑜 鍮꾩슫룞꽦 異붿젙 떖룓泥대젰 痢≪젙 떖룓泥대젰뿉 븳 삁痢〓젰씠 異⑸텇엳 솗蹂대릺뿀쓬 臾쇰줎, 떎뼇븳 삎깭쓽 異붿젙떇 젣떆瑜 넻빐 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙뿉 븳 렪쓽꽦쓣 젣怨듯븷 닔 엳떎怨 蹂닿퀬븳 諛 엳떎. 洹몃윭굹 씠윭븳 異붿젙떇쓣 씤醫, 뿰졊, 썑泥쒖쟻 슂씤 벑쓣 怨좊젮븯吏 븡怨 쟻슜븷 寃쎌슦 떎젣 떖룓泥대젰뿉 빐 궙 젙솗룄 諛 겙 삤瑜섍 諛쒖깮븷 닔 엳쓣 肉먮쭔 븘땲씪 嫄닿컯 愿젴 슂씤怨쇱쓽 뿰愿꽦뿉 븳 뿰援ъ뿉꽌룄 렪뼢맂 寃곌낵瑜 珥덈옒븷 닔 엳떎怨 븣젮졇 엳떎17. 떎젣, 슦由щ굹씪 끂씤떎깭議곗궗 옄猷뚮 솢슜븯뿬 誘멸뎅 꽦씤뿉寃 寃利앸맂 異붿젙떇쓣 넻빐 떖룓泥대젰쓣 궛異쒗븳 뮘 紐⑤뱺 썝씤쓽 궗留앷낵 뿰愿꽦쓣 寃利앺븳 援궡 뿰援ъ뿉꽌룄, 異붿젙떇쓽 씤醫 諛 뿰졊 李⑥씠濡 씤빐 寃곌낵뿉 삤李④ 諛쒖깮븯쓣 닔룄 엳떎怨 蹂닿퀬븳 諛 엳떎22. 씠뿉 援쇅 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 援궡 긽옄뿉寃 쟻슜븷 寃쎌슦, 슦由щ굹씪 꽦씤쓽 떖룓泥대젰 異붿젙쓣 씪諛섑솕븯湲곗뿉 젣븳쟻씠嫄곕굹 렪뼢맂 寃곌낵瑜 珥덈옒븷 닔 엳湲곗뿉 슦由щ굹씪 꽦씤쓽 媛 떊泥닿뎄꽦 蹂닔瑜 怨좊젮븳 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 媛쒕컻 뿰援ш 븘슂븯떎怨 뙋떒븯떎.

뵲씪꽌 蹂 뿰援ъ쓽 紐⑹쟻 꽭遺쟻씤 떊泥닿뎄꽦 蹂닔瑜 怨좊젮븯뿬 痢≪젙 諛 異붿젙 떖룓泥대젰쓽 援먯감-떦솕 寃利앹쓣 넻빐 슦由щ굹씪 젇 꽦씤쓽 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 媛쒕컻븯뒗 뜲 엳떎.

뿰援 諛⑸쾿

1. 뿰援щ긽

蹂 뿰援ъ쓽 긽 뿰援щぉ쟻 諛 諛⑸쾿뿉 븳 꽕紐낆쓣 뱽怨 옄諛쒖쟻씤 李몄뿬 쓽궗瑜 諛앺엺 꽦洹좉븰援 옱븰깮 1,319紐(20–42꽭)쓣 긽쑝濡 떎떆븯쑝硫, 떎뿕 吏꾪뻾怨쇱젙뿉꽌 痢≪젙쓣 嫄곕븯嫄곕굹 꽕臾몄“궗媛 늻씫맂 157紐(슫룞遺븯寃궗 73紐, 뿀由щ몮젅 6紐, 떊泥댄솢룞 53紐, 嫄닿컯 愿젴 꽕臾 23紐, 留뚯꽦吏덊솚 2紐)쓣 젣쇅븯떎. 씠썑 옄猷 遺꾩꽍 긽쑝濡 꽑젙맂 1,162紐낆뿉 빐 씪李⑥쟻쑝濡 꽦蹂꾩쓣 遺꾪븷븳 뮘, 떒닚 臾댁옉쐞 몴蹂몄텛異(simple random sampling)쓣 넻빐 70.1%씤 815紐(궓꽦 596紐, 뿬꽦 219紐)쓣 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 媛쒕컻 긽옄濡 遺꾨쪟븯쑝硫, 굹癒몄 29.9%씤 347紐(궓꽦 255紐, 뿬꽦 92紐)쓣 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 援먯감-떦룄 寃利 긽옄濡 遺꾨쪟븯떎.

蹂 뿰援щ뒗 꽦洹좉븰援 湲곌쑄由ъ떖쓽쐞썝쉶쓽 듅씤쓣 諛쏆븘 吏꾪뻾븯쑝硫(SKKU 2019-10-019-001), 긽옄 듅꽦 Table 1뿉 젣떆븳 諛붿 媛숇떎.

Table 1 . Characteristics of the study subjects

CharacteristicTotalDerivationCross-validation p-value
No. of subjects1,162815347
Female sex311 (26.8)219 (26.9)92 (26.5)0.900
Age (yr)23.7±2.223.7±2.223.8±2.20.660
Height (cm)171.2±8.0171.2±8.0171.1±8.30.958
Weight (kg)66.1±12.165.8±11.967.0±12.30.104
Body mass index (kg/m2)22.4±3.122.3±3.122.7±3.00.031*
Body fat (%)21.0±5.620.7±5.621.6±5.50.011*
WC (cm)80.2±8.279.8±8.281.0±8.30.029*
Lean mass (kg)52.0±9.151.9±9.052.3±9.20.483
RHR (beat/min)76.1±11.676.4±11.775.3±11.30.121
VO2max (mL/kg/min)43.5±7.943.5±7.843.3±7.90.739
Physical activity (MET/wk)1,308.7±1,137.31,284.2±1,128.41,366.0±1,157.70.262
Physical inactive827 (71.2)581 (71.3)246 (70.9)0.892
Smoking286 (24.6)207 (25.4)79 (22.8)0.242

Values are presented as number only, number (%), or mean±standard deviation.

WC: waist circumference, RHR: resting heart rate, VO2max: volume of maximal oxygen consumption, MET: metabolic equivalent.

*p<0.05.



2. 痢≪젙빆紐 諛 遺꾩꽍諛⑸쾿

1) 떊泥닿뎄꽦 諛 븞젙 떆 떖諛뺤닔

떊옣 옄룞 떊옣怨(DS-102; Jenix, Seoul, Korea)瑜 넻빐 痢≪젙븯쑝硫, 泥댁쨷, 泥댁쭏웾吏닔, 泥댁諛⑸쪧 벑 쟾諛섏쟻씤 떊泥닿뎄꽦 湲덉냽씠 룷븿릺吏 븡 렪븞븳 샆쓣 엯 긽깭뿉꽌 X-scan 떊泥닿뎄꽦 痢≪젙湲곌린(Jawon Medical, Seoul, Korea)瑜 씠슜븯뿬 痢≪젙븯떎. 삉븳 뿀由щ몮젅뒗 씤泥댁륫젙 以꾩옄瑜 씠슜븯뿬 옣怨⑤뒫 긽遺 뒔怨 븯떒遺쓽 以묎컙 吏젏쓣 cm 떒쐞濡 2쉶 痢≪젙븯뿬 룊洹좉컪쓣 궗슜븯쑝硫, 븞젙 떆 떖諛뺤닔뒗 긽옄媛 쓽옄뿉꽌 븠 긽깭뿉꽌 理쒖냼 10遺 씠긽 쑕떇쓣 痍⑦븯寃 븳 뮘 옄룞삁븬怨(FT-500R; Jawon Medical)瑜 씠슜븯뿬 쇊履 긽셿쓽 븞젙 떆 떖諛뺤닔瑜 2쉶 痢≪젙븯뿬 룊洹좉컪쓣 궗슜븯떎.

2) 떊泥댄솢룞 諛 씉뿰

떊泥댄솢룞 꽕臾몄쓣 넻빐 쓽룄쟻쑝濡 떎떆븯뒗 떊泥댄솢룞 諛 슫룞쓽 吏냽湲곌컙, 鍮덈룄, 醫낅쪟, 떆媛꾩쓣 議곗궗븯쑝硫, 理쒖냼 3媛쒖썡 씠긽 二 1쉶 洹쒖튃쟻쑝濡 떎떆븳 떊泥댄솢룞 諛 슫룞뿉 븳븯뿬 꽑뻾뿰援ъ뿉꽌 젣떆븳 媛 꽭遺빆紐⑸퀎 媛뺣룄瑜 쟻슜븯뿬 蹂솚븯떎. 씠뿉 醫낅쪟뿉 臾닿븯寃 4 MET 씠긽쓽 떊泥댄솢룞쓣 以?怨좉컯룄 떊泥댄솢룞쑝濡 젙쓽븯쑝硫, 二쇰떦 以?怨좉컯룄 떊泥댄솢룞 600 MET 씠긽뿉 빐떦븷 寃쎌슦 솢룞(active), 洹몃젃吏 븡쓣 寃쎌슦 鍮꾪솢룞(inactive)쑝濡 遺꾨쪟븯떎23,24. 삉븳 씉뿰 쁽옱 씉뿰 以묒씤 寃쎌슦 씉뿰(smoking), 怨쇨굅 씉뿰옄 삉뒗 寃쏀뿕씠 뾾뒗 寃쎌슦瑜 鍮꾪씉뿰(non-smoking)쑝濡 遺꾨쪟븯떎20.

3) 슫룞遺븯寃궗

긽옄뱾쓽 遺꾨떦 理쒕 궛냼꽠痍⑤웾쓣 痢≪젙븯湲 쐞빐 듃젅뱶諛(Medtrack ST65; Quinton, Seattle, WA, USA)怨 샇씉 媛뒪 遺꾩꽍湲(True-One; Quinton)瑜 씠슜븯뿬 理쒕 슫룞遺븯寃궗瑜 떎떆븯쑝硫, 寃궗 떆 蹂듭옣 媛踰쇱슫 슫룞蹂듦낵 슫룞솕瑜 李⑹슜븯룄濡 븯떎. 슫룞遺븯寃궗 봽濡쒗넗肄쒖 嫄닿컯븳 꽦씤뿉꽌 蹂댄렪쟻쑝濡 궗슜릺怨 엳뒗 Bruce 봽濡쒗넗肄쒓낵 닔젙삎 Bruce 봽濡쒗넗肄쒖쓣 궓 媛곴컖 쟻슜븯떎25. 삉븳 슫룞遺븯寃궗뿉꽌 理쒕 뒫젰 룄떖쓽 湲곗 뿬遺뒗 ‘(1) 뿰졊뿉 洹쇨굅븳 理쒕 떖諛뺤닔(220–굹씠)뿉 룄떖븳 寃쎌슦, (2) 샇씉援먰솚쑉(respiratory exchange ratio) 1.15 씠긽씤 寃쎌슦, (3) 옄媛곸쟻 슫룞媛뺣룄(rating perceived exertion)媛 17 씠긽씤 寃쎌슦, (4) 슫룞 媛뺣룄媛 利앷븯뿬룄 VO2 媛믪씠 긽듅릺吏 븡뒗 寃쎌슦’쓽 4媛 빆紐 以묒뿉꽌 2媛 씠긽쓣 異⑹”떆궎뒗 寃쎌슦 삉뒗 긽옄쓽 옄諛쒖쟻 以묐떒 쓽궗媛 엳뒗 寃쎌슦濡 꽕젙븯떎26.

4) 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙 蹂닔

蹂 뿰援ъ뿉꽌쓽 異붿젙 蹂닔瑜 寃곗젙븯湲 쐞빐 援쇅 鍮꾩슫룞꽦 떖룓泥대젰 꽑뻾뿰援 以 媛옣 蹂댄렪쟻쑝濡 솢슜릺怨 엳뒗 Jurca 벑27怨 Jackson 벑20쓽 異붿젙떇쓣 醫낇빀쟻쑝濡 怨좊젮븯뿬 굹씠, 꽦蹂, 떊泥댄솢룞, 븞젙 떆 떖諛뺤닔, 씉뿰, 떊泥닿뎄꽦(泥댁쭏웾吏닔, 泥댁諛⑸쪧, 뿀由щ몮젅)쓣 룷븿븯떎.

3. 옄猷 泥섎━ 諛⑸쾿

蹂 뿰援ъ쓽 紐⑤뱺 뿰냽삎 옄猷뚮뒗 룊洹좉낵 몴以렪李(mean±standard deviation [SD])濡 몴湲고븯쑝硫, 踰붿<삎 옄猷뚮뒗 媛 吏묐떒蹂 鍮꾩쑉(%)濡 몴湲고븯떎. 異붿젙떇 媛쒕컻 긽옄뿉 洹쇨굅븯뿬 떊泥닿뎄꽦蹂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 룄異쒗븯湲 쐞빐 꽑삎 쉶洹遺꾩꽍(linear regression analysis)쓽 떒怨꾩쟻 諛⑸쾿(stepwise)쓣 씠슜븯쑝硫, 痢≪젙 諛 異붿젙 떖룓泥대젰쓽 뿰愿꽦쓣 뙆븙븯湲 쐞빐 Pearson 긽愿遺꾩꽍쓣 떎떆븯떎. 삉븳 援먯감-떦룄 寃利 긽옄뿉 洹쇨굅븯뿬 痢≪젙 떖룓泥대젰怨 異붿젙 떖룓泥대젰쓽 李⑥씠瑜 寃利앺븯湲 쐞빐 쓳몴蹂 t-test瑜 떎떆븯쑝硫, Bland-Altman 遺꾩꽍쓣 씠슜븯뿬 븵꽑 떒怨꾩뿉꽌쓽 異붿젙떇뿉 洹쇨굅븳 異붿젙 떖룓泥대젰怨 痢≪젙 떖룓泥대젰쓽 씪移섎룄瑜 –1.96 SD濡쒕꽣 1.96 SD 踰붿쐞뿉꽌 솗씤븯떎. 紐⑤뱺 媛꽕 寃젙쓣 쐞븳 넻怨꾩쟻 쑀쓽닔以 α=0.05濡 꽕젙븯쑝硫, Bland-Altman 遺꾩꽍 MedCalc (version 14.8.1)瑜, 굹癒몄 遺꾩꽍 IBM SPSS-PC (version 23.0; IBM Corp., Armonk, NY, USA)瑜 씠슜븯떎.

寃 怨

1. 泥댁쭏웾吏닔뿉 洹쇨굅븳 떖룓泥대젰 異붿젙쓣 쐞븳 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍

Table 2뒗 異붿젙떇 媛쒕컻 긽옄쓽 痢≪젙 떖룓泥대젰뿉 빐 泥댁쭏웾吏닔 諛 痢≪젙 蹂닔뿉 洹쇨굅븯뿬 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍쓣 떎떆븳 寃곌낵씠떎. 洹 寃곌낵, 꽦蹂(β: 12.912, p<0.001), 泥댁쭏웾吏닔(β: –0.673, p<0.001), 떊泥댄솢룞(β: 3.866, p<0.001), 굹씠(β: –0.387, p<0.001), 븞젙 떆 떖諛뺤닔(β: –0.049, p=0.005), 씉뿰(β: –1.376, p=0.004)씠 痢≪젙 떖룓泥대젰뿉 븳 룆由 삁痢≪씤옄濡 굹궗떎. 삉븳 쉶洹 紐⑦삎쓽 꽕紐낅젰 50.2%, 異붿젙 몴以삤李⑤뒗 5.55씤 寃껋쑝濡 굹궗쑝硫, Durbin-Watson 吏닔뒗 1.750쑝濡 媛 蹂닔 媛 꽌濡 룆由쎌쟻씤 寃껋쑝濡 굹궗떎. 씠뿉 蹂 紐⑦삎쓣 넻빐 룄異쒕맂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 떎쓬怨 媛숇떎.

Table 2 . Stepwise multiple regression analysis for estimation of VO2max from BMI in derivation subjects (n=815)

VariableUnstandardized coefficientstp-valueToleranceVIFR2SEE

βSE
Constant61.2192.92220.949<0.0010.5025.55
Sex12.9120.53124.328<0.0010.6841.463
BMI?0.6730.070?9.676<0.0010.8331.201
Physical activity3.8660.4458.695<0.0010.9351.069
Age?0.3870.098?3.945<0.0010.7791.284
RHR?0.0490.017?2.8400.0050.9371.067
Smoking?1.3760.478?2.8790.0040.8751.143
Durbin-Watson, 1.750; F=135.648, p<0.001

VO2max: volume of maximal oxygen consumption, BMI: body mass index, SE: standard error, VIF: variance inflation factors, SEE: standard error of estimate, RHR: resting heart rate.



VO2max (mL/kg/min):

61.219竊12.912 (sex [male=1, female=0])–0.673 (body mass index)竊3.866 (physical activity [active=1, inactive=0])–0.387 (age)–0.049 (resting heart rate)–1.376 (smoking [no=0, yes=1])

2. 泥댁諛⑸쪧뿉 洹쇨굅븳 떖룓泥대젰 異붿젙쓣 쐞븳 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍

Table 3 異붿젙떇 媛쒕컻 긽옄쓽 痢≪젙 떖룓泥대젰뿉 빐 泥댁諛⑸쪧 諛 痢≪젙 蹂닔뿉 洹쇨굅븯뿬 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍쓣 떎떆븳 寃곌낵씠떎. 洹 寃곌낵, 꽦蹂(β: 9.031, p<0.001), 泥댁諛⑸쪧(β: –0.411, p<0.001), 떊泥댄솢룞(β: 3.513, p<0.001), 굹씠(β: –0.308, p=0.002), 븞젙 떆 떖諛뺤닔(β: –0.048, p=0.004), 씉뿰(β: –1.389, p=0.003)씠 痢≪젙 떖룓泥대젰뿉 븳 룆由 삁痢≪씤옄濡 굹궗떎. 삉븳 쉶洹 紐⑦삎쓽 꽕紐낅젰 51.4%, 異붿젙 몴以삤李⑤뒗 5.48씤 寃껋쑝濡 굹궗쑝硫, Durbin-Watson 吏닔뒗 1.722濡 媛 蹂닔 媛 꽌濡 룆由쎌쟻씤 寃껋쑝濡 굹궗떎. 씠뿉 蹂 紐⑦삎쓣 넻빐 룄異쒕맂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 떎쓬怨 媛숇떎.

Table 3 . Stepwise multiple regression analysis for estimation of VO2max from WC in derivation subjects (n=815)

VariableUnstandardized coefficientstp-valueToleranceVIFR2SEE

βSE
Constant55.7802.68320.793<0.0010.5145.48
Sex9.0310.54816.491<0.0010.6261.597
% Body fat?0.4110.038?10.789<0.0010.8141.228
Physical activity3.5130.4417.975<0.0010.9291.077
Age?0.3080.098?3.1520.0020.7641.309
RHR?0.0480.017?2.8520.0040.9381.066
Smoking?1.3890.472?2.9450.0030.8751.143
Durbin-Watson, 1.722 F=142.477, p<0.001

VO2max: volume of maximal oxygen consumption, WC: waist circumference, SE: standard error, VIF: variance inflation factors, SEE: standard error of estimate, RHR: resting heart rate.



VO2max (mL/kg/min):

55.780竊9.031 (sex [male=1, female=0])–0.411 (% body fat)竊3.513 (physical activity [active=1, inactive=0])–0.308 (age)–0.048 (resting heart rate)–1.389 (smoking [no=0, yes=1])

3. 뿀由щ몮젅뿉 洹쇨굅븳 떖룓泥대젰 異붿젙쓣 쐞븳 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍

Table 4뒗 異붿젙떇 媛쒕컻 긽옄쓽 痢≪젙 떖룓泥대젰뿉 빐 뿀由щ몮젅 諛 痢≪젙 蹂닔뿉 洹쇨굅븯뿬 떒怨꾩쟻 꽑삎 쉶洹遺꾩꽍쓣 떎떆븳 寃곌낵씠떎. 洹 寃곌낵, 꽦蹂(β: 13.161, p<0.001), 뿀由щ몮젅(β: –0.287, p<0.001), 떊泥댄솢룞(β: 3.522, p<0.001), 굹씠(β: –0.356, p<0.001), 븞젙 떆 떖諛뺤닔(β: –0.047, p=0.006), 씉뿰(β: –1.373, p=0.004)씠 痢≪젙 떖룓泥대젰뿉 븳 룆由 삁痢≪씤옄濡 굹궗떎. 삉븳 쉶洹 紐⑦삎쓽 꽕紐낅젰 51.8%, 異붿젙 몴以삤李⑤뒗 5.46씤 寃껋쑝濡 굹궗쑝硫, Durbin-Watson 吏닔뒗 1.772濡 媛 蹂닔 媛 꽌濡 룆由쎌쟻씤 寃껋쑝濡 굹궗떎. 씠뿉 蹂 紐⑦삎쓣 넻빐 룄異쒕맂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 떎쓬怨 媛숇떎.

Table 4 . Stepwise multiple regression analysis for estimation of VO2max from % body fat in derivation subjects (n=815)

VariableUnstandardized coefficientstp-valueToleranceVIFR2SEE

βSE
Constant68.1263.10421.949<0.0010.5185.46
Sex13.1610.52325.163<0.0010.6801.470
WC?0.2870.026?11.170<0.0010.8261.211
Physical activity3.5220.4388.033<0.0010.9301.076
Age?0.3560.097?3.689<0.0010.7761.289
RHR?0.0470.017?2.7640.0060.9391.065
Smoking?1.3730.470?2.9240.0040.8751.143
Durbin-Watson, 1.772 F=144.983, p<0.001

VO2max: volume of maximal oxygen consumption, SE: standard error, VIF: variance inflation factors, SEE: standard error of estimate, RHR: resting heart rate.



VO2max (mL/kg/min):

68.126竊13.161 (sex [male=1, female=0])–0.287 (waist circumference)竊3.522 (physical activity [active=1, inactive=0])–0.356 (age)–0.047 (resting heart rate)–1.373 (smoking [no=0, yes=1])

4. 異붿젙떇 媛쒕컻 긽옄쓽 痢≪젙 諛 異붿젙 떖룓泥대젰쓽 긽愿愿怨

Table 5뒗 異붿젙떇 媛쒕컻 긽옄쓽 痢≪젙 諛 異붿젙 떖룓泥대젰쓽 긽愿愿怨꾨 궛異쒗븳 寃곌낵씠떎. 洹 寃곌낵, 痢≪젙쓣 넻븳 떖룓泥대젰怨 泥댁쭏웾吏닔 紐⑦삎(r=0.708, p<0.001), 泥댁諛⑸쪧 紐⑦삎(r=0.717, p<0.001), 뿀由щ몮젅 紐⑦삎(r=0.720, p<0.001)뿉 湲곕컲븯뿬 異붿젙븳 떖룓泥대젰 쑀쓽븳 뼇쓽 긽愿愿怨꾧 엳뒗 寃껋쑝濡 굹궗떎.

Table 5 . Correlation analysis of measured and estimated VO2max in derivation subjects (n=815)

Equation modelrp-value
BMI model0.708<0.001
% Body fat model0.717<0.001
WC model0.720<0.001

VO2max: volume of maximal oxygen consumption, BMI: body mass index, WC: waist circumference.



5. 援먯감-떦룄 寃利 긽옄쓽 痢≪젙 諛 異붿젙 떖룓泥대젰 鍮꾧탳

Table 6뒗 援먯감-떦룄 寃利 긽옄뿉꽌 븵꽑 떒怨꾩쓽 異붿젙떇쓣 씠슜븳 異붿젙 떖룓泥대젰怨 痢≪젙 떖룓泥대젰쓣 鍮꾧탳븳 寃곌낵씠떎. 洹 寃곌낵, 痢≪젙 떖룓泥대젰 泥댁쭏웾吏닔 紐⑦삎(p=0.971), 泥댁諛⑸쪧 紐⑦삎(p=0.877), 뿀由щ몮젅 紐⑦삎(p=0.817)뿉 湲곕컲븳 異붿젙 떖룓泥대젰怨 쑀쓽븳 李⑥씠媛 뾾뒗 寃껋쑝濡 굹궗떎. 삉븳 痢≪젙 떖룓泥대젰怨 異붿젙 떖룓泥대젰쓽 룊洹 諛 몴以렪李⑥쓽 李⑥씠瑜 鍮꾧탳븳 寃곌낵 泥댁쭏웾吏닔 紐⑦삎 0.01±5.90 mL/kg/min, 泥댁諛⑸쪧 紐⑦삎 0.05±5.78 mL/kg/min, 뿀由щ몮젅 紐⑦삎 0.07±5.97 mL/kg/min씤 寃껋쑝濡 굹궗떎.

Table 6 . Analysis for comparison of measured and estimated VO2max in cross-validation subjects (n=347)

Equation modelVO2max (mL/kg/min)Difference (mL/kg/min)rtp-value
Measured VO2max43.34±7.92
BMI model43.32±5.740.01±5.900.6700.0370.971
% Body fat model43.29±5.750.05±5.780.6850.1550.877
WC model43.26±5.810.07±5.970.6600.2320.817

VO2max: volume of maximal oxygen consumption, BMI: body mass index, WC: waist circumference.


怨 李

蹂 뿰援щ뒗 슦由щ굹씪 젇 꽦씤쓣 긽쑝濡 떊泥닿뎄꽦 蹂닔瑜 怨좊젮븯뿬 痢≪젙 떖룓泥대젰怨 異붿젙 떖룓泥대젰쓽 援먯감-떦룄 寃利앹쓣 넻빐 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 媛쒕컻븯뒗 寃껋쓣 二쇱슂 紐⑹쟻쑝濡 븯떎. 씠뿉 異붿젙떇 媛쒕컻 긽옄瑜 넻빐 泥댁쭏웾吏닔, 泥댁諛⑸쪧, 뿀由щ몮젅뿉 洹쇨굅븳 3媛吏 異붿젙떇 紐⑦삎쓣 룄異쒗븯쑝硫, 紐⑤뱺 紐⑦삎뿉꽌쓽 異붿젙 떖룓泥대젰 痢≪젙 떖룓泥대젰怨 쑀쓽븳 닔以쓽 긽愿愿怨꾧 엳뒗 寃껋쑝濡 굹궗떎. 삉븳 援먯감-떦룄 寃利 긽뿉꽌 異붿젙 떖룓泥대젰怨 痢≪젙 떖룓泥대젰쓽 李⑥씠 寃利 寃곌낵, 쑀쓽븳 李⑥씠媛 뾾뒗 寃껋쑝濡 굹궗떎.

1. 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 媛쒕컻

留뚯꽦吏덊솚 諛 議곌린궗留앹뿉 븳 떖룓泥대젰쓽 湲띿젙쟻씤 뿭븷뿉 븳 뿰援ш 삤옖 湲곌컙 吏꾪뻾릺怨 엳뒗 媛슫뜲, 떖룓泥대젰쓣 媛옣 媛앷쟻쑝濡 痢≪젙븯뒗 諛⑸쾿 슫룞遺븯寃궗濡 븣젮졇 엳떎10. 洹몃윭굹 슫룞遺븯寃궗뒗 떎뼇븳 臾몄젣濡 씤빐 떒쐞 뿭븰議곗궗뿉 쟻슜븯湲곗뿉 븳怨꾧 엳뒗뜲, 씠윭븳 젣븳젏쓣 蹂댁셿븯怨 렪쓽꽦쓣 솗蹂댄븯怨좎옄 援쇅 뿰援ъ뿉꽌뒗 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓽 媛쒕컻쓣 넻빐 떒쐞 뿭븰議곗궗뿉꽌 궗슜븯怨 엳떎13.

諛섎㈃, 슦由щ굹씪쓽 寃쎌슦 嫄닿컯吏몴뿉 븳 떖룓泥대젰쓽 湲띿젙쟻씤 뿭븷뿉 빐 옒 븣젮졇 엳쓬뿉룄 遺덇뎄븯怨 떦꽦씠 寃利앸맂 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇씠 쟾臾댄븳 떎젙씠떎. 씠뿉 蹂 뿰援ъ뿉꽌뒗 援쇅쓽 異붿젙떇 媛슫뜲 媛옣 蹂댄렪쟻쑝濡 솢슜릺怨 엳뒗 꽑뻾뿰援ъ쓽 異붿젙 蹂닔씤 굹씠, 꽦蹂, 븞젙 떆 떖諛뺤닔, 떊泥댄솢룞, 씉뿰怨 떊泥닿뎄꽦뿉 洹쇨굅븯뿬 3媛吏 紐⑦삎쓽 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓣 룄異쒗븯떎. 씠뿉 泥댁쭏웾吏닔 紐⑦삎 꽕紐낅젰 50.2%, 몴以 異붿젙삤李 5.55, 泥댁諛⑸쪧 紐⑦삎 꽕紐낅젰 51.4%, 몴以 異붿젙삤李 5.48, 뿀由щ몮젅 紐⑦삎 꽕紐낅젰 51.8%, 몴以 異붿젙삤李 5.46쑝濡 굹궗쑝硫, 媛 쉶洹떇쓣 넻빐 룄異쒕맂 異붿젙 떖룓泥대젰怨 痢≪젙 떖룓泥대젰 泥댁쭏웾吏닔 紐⑦삎(r=0.708), 泥댁諛⑸쪧 紐⑦삎(r=0.717), 뿀由щ몮젅 紐⑦삎(r=0.720)뿉꽌 쑀쓽븳 닔以쓽 긽愿愿怨꾧 엳뒗 寃껋쑝濡 굹궗떎(Fig. 1).

Fig. 1. Scatter plots for correlation between measured and estimated volume of maximal oxygen consumption (VO2max). (A) Body mass index model, (B) % body fat model, and (C) waist circumference model using the derivation subjects.

씠윭븳 蹂 뿰援ъ쓽 寃곌낵뒗 굹씠, 뿀由щ몮젅, 떊泥댄솢룞, 븞젙 떆 떖諛뺤닔뿉 湲곕컲븳 異붿젙떇쓽 꽕紐낅젰씠 56%–61%, 異붿젙 몴以삤李④ 5.14–5.70쑝濡 굹궗떎怨 蹂닿퀬븳 Nes 벑28쓽 뿰援ъ, 굹씠, 꽦蹂, 떊泥닿뎄꽦뿉 湲곕컲븳 異붿젙떇쓽 꽕紐낅젰씠 64%–67%, 몴以 異붿젙삤李④ 4.72–4.90쑝濡 굹궗떎怨 蹂닿퀬븳 Wier 벑19쓽 뿰援ъ뿉 鍮꾪빐 꽕紐낅젰 떎냼 궙 寃껋쑝濡 굹궗쑝굹, 몴以 異붿젙삤李⑤뒗 쑀궗븳 닔以씠떎. 鍮꾩슫룞꽦 떖룓泥대젰 紐⑦삎뿉꽌 떊泥댄솢룞 蹂닔媛 李⑥븯뒗 꽕紐낅젰씠 以묒슂븳뜲, 꽑뻾뿰援ш 떊泥댄솢룞쓣 媛앷솕맂 꽕臾몄쓣 넻빐 議곗궗븳 諛섎㈃ 蹂 뿰援ъ쓽 寃쎌슦 二쇨쟻씤 꽕臾몄쑝濡 씤빐 떊泥댄솢룞 닔以쓽 媛앷솕맂 議곗궗媛 遺議깊뻽뜕 젏씠 꽑뻾뿰援ъ 蹂 뿰援ъ쓽 꽕紐낅젰 李⑥씠瑜 珥덈옒뻽떎怨 빐꽍맂떎17. 삉븳 떎닔쓽 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 媛쒕컻怨 愿젴븳 꽑뻾뿰援ъ뿉꽌 궓꽦怨 뿬꽦쓽 꽦蹂 遺꾪룷媛 쑀궗뻽뜕 諛섎㈃, 蹂 뿰援ъ쓽 꽦蹂 遺꾪룷뒗 궓꽦뿉寃 렪以묐맂 遺꾪룷媛 굹궃 寃껊룄 븯굹쓽 썝씤씪 寃껋씠떎. 洹몃윭굹 蹂 뿰援ъ뿉꽌 룄異쒕맂 媛 紐⑦삎쓽 꽕紐낅젰, 몴以 異붿젙삤李, 떎以 怨듭꽑꽦 吏닔瑜 怨좊젮븯쓣 븣, 떖룓泥대젰쓣 異붿젙븯湲곗뿉뒗 뿀슜 媛뒫븳 닔以씤 寃껋쑝濡 깮媛곷맂떎.

2. 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 援먯감-떦룄 寃利

蹂 뿰援ъ뿉꽌뒗 쟾泥 긽옄 以 빟 30%瑜 떦룄 寃利 긽옄濡 遺꾨쪟븯뿬 븵꽑 떒怨꾩뿉꽌 룄異쒕릺뿀뜕 3媛吏 紐⑦삎쓽 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓽 援먯감-떦룄瑜 寃利앺븯떎. 씠뿉 떦룄 寃利 긽옄쓽 痢≪젙 떖룓泥대젰怨 異붿젙 떖룓泥대젰쓽 李⑥씠뒗 泥댁쭏웾吏닔 紐⑦삎 0.01±5.90 mL/kg/min, 泥댁諛⑸쪧 紐⑦삎 0.05±5.78 mL/kg/min, 뿀由щ몮젅 紐⑦삎 0.07±5.97 mL/kg/min쑝濡 굹궗쑝硫, 痢≪젙 떖룓泥대젰怨 쑀쓽븳 룊洹 李⑥씠媛 뾾뒗 寃껋쑝濡 굹궗떎. 삉븳 異붿젙 떖룓泥대젰 痢≪젙 떖룓泥대젰怨 泥댁쭏웾吏닔 紐⑦삎 r=0.670, 泥댁諛⑸쪧 紐⑦삎 r=0.685, 뿀由щ몮젅 紐⑦삎 r=0.660쓽 긽愿꽦씠 엳뒗 寃껋쑝濡 굹궗쑝硫, 씪移섎룄 寃利앹뿉꽌 95% 닔以쓽 뿀슜 븳怨(limit of agreement, LoA) 媛믪 泥댁쭏웾吏닔 紐⑦삎뿉꽌 –11.6–11.5, 泥댁諛⑸쪧 紐⑦삎뿉꽌 –11.4–11.3, 뿀由щ몮젅 紐⑦삎뿉꽌 –11.8–11.6 닔以쑝濡 굹궗떎(Fig. 2).

Fig. 2. Bland-Altman plots for comparing measured and estimated volume of maximal oxygen consumption (VO2max). (A) Body mass index model, (B) % body fat model, and (C) waist circumference model using the cross-validation set. SD: standard deviation.

씠윭븳 寃곌낵뒗 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓽 援먯감-떦룄 寃利앷낵 愿젴븳 몢 꽑뻾뿰援ъ 쑀궗븳 닔以쓽 寃곌낵濡 Schembre Riebe29뒗 誘멸뎅 븰깮뿉꽌 異붿젙 떖룓泥대젰 痢≪젙 떖룓泥대젰怨 0.606 닔以쓽 긽愿꽦쓣 蹂댁怨 –0.40 (95% LoA, –10.90–5.45)쓽 룊洹 李⑥씠媛 굹궗떎怨 蹂닿퀬븯怨, Sloan 벑30 븘떆븘 꽦씤뿉꽌 異붿젙 떖룓泥대젰 痢≪젙 떖룓泥대젰怨 0.61–0.77 닔以쓽 긽愿꽦쓣 蹂댁怨 궓꽦 –1.05 (95% LoA, –8.51–6.40), 뿬꽦 0.95 (95% LoA, –4.90–6.81)쓽 룊洹 李⑥씠媛 굹궗떎怨 蹂닿퀬븯떎. 씠뿉 떊泥닿뎄꽦뿉 洹쇨굅븳 蹂 뿰援ъ쓽 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 異붿젙移섏쓽 룊洹 李⑥씠 諛 뿀슜 븳怨꾨 怨좊젮븷 븣 援먯감 寃利 긽옄瑜 넻빐 異⑸텇븳 닔以쓽 떦꽦씠 솗蹂대맂 寃껋쑝濡 깮媛곷릺硫, 異뷀썑 떖룓泥대젰 痢≪젙씠 젣븳쟻씤 떒쐞 뿭븰議곗궗뿉꽌 媛쒖씤쓽 嫄닿컯 긽깭뿉 븳 떖룓泥대젰쓽 뿭븷쓣 寃利앺븯뒗 뜲뿉 쑀슜븯寃 궗슜븷 닔 엳쓣 寃껋쑝濡 깮媛곹븳떎.

洹몃윭굹 蹂 뿰援щ뒗 떎쓬怨 媛숈 紐 媛吏 젣븳젏쓣 媛吏꾨떎. 泥レ㎏, 蹂 뿰援ъ쓽 긽옄뒗 20 30쓽 젇 꽦씤뿉 援븳릺뿀湲곗뿉 異뷀썑 뿰援ъ뿉꽌뒗 뿰졊쓽 踰붿쐞瑜 솗븯뿬 媛 뿰졊쓽 듅吏뺤쓣 怨좊젮븳 異붿젙떇 媛쒕컻 뿰援ш 븘슂븷 寃껋씠떎. 몮吏, 궓꽦쓽 鍮꾩쑉씠 넂븘 떎냼 렪뼢맂 寃곌낵媛 굹궗쓣 닔 엳湲곗뿉 異뷀썑 뿰援ъ뿉꽌뒗 꽦蹂꾩뿉 뵲瑜 異붿젙떇쓣 룄異쒗븯嫄곕굹 쑀궗븳 꽦鍮꾩뿉 븳 異붿젙떇쓣 媛쒕컻븷 븘슂媛 엳떎. 뀑吏, 蹂 뿰援щ뒗 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇쓽 二쇱슂 蹂닔씤 떊泥댄솢룞쓣 媛앷솕맂 꽕臾몄媛 븘땶 二쇨쟻씤 꽕臾몄쓣 넻빐 議곗궗븯뒗뜲, 異뷀썑 떊泥댄솢룞쓽 媛앷쟻씤 議곗궗瑜 넻븳 뿰援ш 븘슂븷 寃껋쑝濡 깮媛곹븳떎. 꽬吏, 異붿젙떇 媛쒕컻 떒怨꾩뿉꽌 異붿젙떇쓽 옱寃궗 떊猶곕룄媛 寃利앸릺吏 븡븯湲곗뿉 異뷀썑 뿰援ъ뿉꽌뒗 蹂대떎 泥닿퀎쟻씤 諛⑸쾿쓣 넻빐 異붿젙떇쓣 룄異쒗빐빞 븷 寃껋씠떎.

蹂 뿰援ъ쓽 寃곌낵瑜 醫낇빀빐蹂대㈃, 슦由щ굹씪 젇 꽦씤쓽 떊泥닿뎄꽦뿉 뵲瑜 鍮꾩슫룞꽦 떖룓泥대젰 異붿젙떇 떊猶고븷 젙룄쓽 떦꽦씠 솗蹂대릺뿀쑝硫, 蹂 뿰援ъ쓽 異붿젙떇쓣 넻빐 떖룓泥대젰쓽 痢≪젙씠 젣븳쟻씤 떒쐞 뿭븰議곗궗뿉꽌 떖룓泥대젰 닔以쓣 媛꾩젒쟻쑝濡 뙆븙븯怨, 굹븘媛 嫄닿컯 긽깭뿉 븳 떖룓泥대젰쓽 뿭븷쓣 寃利앺븯뒗 뜲뿉 쑀슜븳 洹쇨굅옄猷뚭 맆 寃껋쑝濡 뙋떒븳떎.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: IL, KH, HK. Methodology: IL, KH, MS, HK. Writing–original draft: IL, MS, HK. Writing–review & editing: All authors.

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