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Rasvaprosenttimittauksen sisällyttäminen VO2max:yn “non-exercise” -arviointiin on todettu tuottavan luotettavia tuloksia. Tutkielman tavoitteena oli arvioida erilaisten rasvaprosentti (rasva%) -mittausten vaikutusta VO2max:yn arviointiin ilman hapenottokyvyn testausta. GE Lunar Prodigy DXA:n, InBody 720:n ja Huawei AH100:n rasva%-mittausten yhteneväisyyttä ja ekvivalenssia testattiin ja näiden eri laitteiden tuottamien rasva%:ien yhteyksiä maksimaalisen hapenottokyvyn kanssa arvioitiin Pearsonin korrelaatiokertoimilla. Lisäksi luotiin ennustemallit maksimihapenottokyvylle askeltavaa backward poistomenetelmää käyttäen jokaista käytettyä kehonkoostumusmittaria kohden. Tutkimukseen rekrytoitiin terveitä ja vaihtelevan kuntoisia miehiä ja naisia (ikä 18-54, n=278). Tutkimus oli poikkileikkausasetelmalla toteutettu ja mittaukset suoritettiin maantieteellisesti kahdessa paikassa: Jyväskylän Yliopiston ja KIHU:n liikunta- ja terveyslaboratoriossa Jyväskylässä sekä Shanghai Jiao Tong -yliopiston liikuntalaboratoriossa Minhangissa. Tutkimuksen analyysit tehtiin kuudessa eri analyysiryhmässä riippuen siitä, mihin mittauksiin tutkittavat olivat osallistuneet (GE Lunar Prodigy & InBody 720 n=146, GE Lunar Prodigy & Huawei AH100 n=95, InBody 720 & Huawei AH100 n=130, VO2max & GE Lunar Prodigy n=146, VO2max & InBody 720 n=278, VO2max & Huawei AH100 n=125).
Huawei AH100 yliarvioi rasva%:ia DXA:aan verrattuna 2,5% (95% LOA -8,7-13,6, MAPE 15,37) ja havaittiin negatiivinen proportionaalinen harha mittausten välillä (p<0.001). Vastaavasti InBody 720 aliarvioi rasva%:n GE Lunar Prodigy DXA:iin nähden -3,8% (95% LOA -10,3-2,7, MAPE 16,75) ja jälleen proportionaalinen harha havaittiin (p<0.001). Huawei AH100 ja InBody 720 erosivat systemaattisesti toisistaan 4,6% (95% LOA -6,7-15,9, MAPE 25,69) eikä proportionaalista harhaa havaittu (p=0.634). Pearsonin korrelaatio VO2max:yn ja GE Lunar Prodigy-rasva%:n välillä oli -0,81 (p<0.001), VO2max:yn ja InBody 720-rasva%:n välillä vastaavasti -0,62 (p<0.001) ja VO2max:yn ja Huawei AH100-rasva%:n välilllä oli -0,60 (p<0.001). Kolme luotua lopullista VO2max:yn ennustemallia luotiin askeltavalla backward poistomenetelmällä ja mallit selittivät VO2max:yn vaihtelusta 71%, 45% ja 40%, ja selkeästi eri analyysiryhmät ja eri laitteilla suoritetut kehonkoostumusmittaukset vaikuttivat malleihin. Ensimmäiseen malliin (SEE 3,72) ennustemuuttujiksi valikoituivat GE Lunar Prodigy DXA:n rasva% (p<0.001), BMI (p<0.001) ja ikä (p<0.001). Toiseen malliin (SEE 5,69) vastaavasti InBody 720:n rasva% (p<0.001), rasvaton pehmytkudosmassa (LBM) (p<0.001), BMI (p<0.001) ja ikä (p<0.001). Kolmanteen malliin (SEE 5,94) taas Huawei AH100:n rasva% (p=0.001), ikä (p=0.009) ja sukupuoli (p=0.044). Ensimmäinen malli osoittautui tarkimmaksi ja kolmas malli heikoimmaksi. Näissä kolmesssa ennustemallissa VO2max:n vaihtelun selittäjiksi osoittautuivat siis rasva%:n lisäksi BMI, ikä, LBM ja sukupuoli.
Eri laitteiden rasva%:n arvioinnit olivat toisistaan eroavia ja tilastollisesti mittaukset eivät olleet ekvivalentteja. Tämän vuoksi olisi suositeltavaa tulkita BIA-mittareiden antamia tuloksia varauksella. Toisekseen laitteiden kehonkoostumuksen arvioinnit ja analyysiryhmien erot vaikuttivat maksimaalisen hapenottokyvyn ja rasva% korrelaatioihin. Kehonkoostumuksen arviointimenetelmien ja analyysiryhmien erot vaikuttivat paljon myös luotujen ennustemallien lopullisiin ennustemuuttujiin ja mallien tarkkuuteen. Tutkielman tuloksista voidaan todeta, että tarkasti arvioitu kehonkoostumus voi parantaa maksimihapenottokyvyn ennustettavuutta. Toisaalta ”non-exercise” -menetelmiin liittyy myös muita epätarkkuutta aiheuttavia tekijöitä, joita tulee myös huomioida.
Estimated fat% in non-exercise estimations of VO2max can yield reliable predictions. The aim of the thesis was to assess the influence of different assessments of body fat percentage (fat%) on the estimation of VO2max. The level of agreement and equivalence of the assessed fat% were tested between different devices: GE Lunar Prodigy DXA, InBody 720, and Huawei AH100. Their associations with VO2max were evaluated by Pearson correlation coefficients and prediction models on VO2max were created by stepwise backward elimination method regarding each body composition assessment device. Healthy men and women in diverse range in age (18-54), fitness, and body composition were recruited (n=278). The study was cross-sectional, and the data was collected at two centres: the sports laboratories of the University of Jyväskylä and the Research Institute for Olympic Sports in Jyväskylä and Shanghai Jiao Tong University in Minhang. The analyses conducted in six separate groups depending on the completed measurements by the subjects (GE Lunar Prodigy & InBody 720 n=146, GE Lunar Prodigy & Huawei AH100 n=95, InBody 720 & Huawei AH100 n=130, VO2max & GE Lunar Prodigy n=146, VO2max & InBody 720 n=278, VO2max & Huawei AH100 n=125).
Huawei AH100 overestimated fat% compared to GE Lunar Prodigy by 2.5% (95% LOA -8.7-13.6, MAPE 15.37) and a negative proportional bias was found (p<0.001). The mean bias of InBody 720 to GE Lunar Prodigy was -3.8% (95% LOA -10.3-2.7, MAPE 16.75) and a negative proportional bias was found as well (p<0.001). Huawei AH100 and InBody 720 were systematically different from each other (4.6%, 95% LOA -6.7-15.9, MAPE 25.69) with no proportional bias (p=0.634). Pearson correlation between VO2max and GE Lunar Prodigy-estimated fat% was -0.81 (p<0.001), between VO2max and InBody 720-estimated fat% -0.62 (p<0.001), and between VO2max and Huawei AH100-estimated fat% -0.60 (p<0.001). When using Lunar Prodigy-estimated fat% to predict VO2max, the final predictors in the model were fat% (p<0.001), BMI (p<0.001), and age (p<0.001) which explained 71% (SEE 3.72) of the variance in VO2max, indicating that, in addition to fat%, BMI and age contributed to the VO2max variance. When using fat% assessed by InBody 720 to predict VO2max, the final predictors in the model were fat% (p<0.001), lean body mass (LBM) (p<0.001), BMI (p<0.001), and age (p<0.001) which explained 45% (SEE 5.69) of the variance of VO2max, showing that LBM, BMI and age are contributed to the VO2max variance. When using fat% assessed by Huawei AH100, the final predictors in the model (SEE 5.94) were fat% (p=0.001), age (p=0.009), and gender (p=0.044) which explained 40% (SEE 5.94) of the variance of VO2max indicating age and also gender contributed to the VO2max variance. The first model turned out to be the most accurate and the third model the least accurate model to predict VO2max.
The estimations of fat% between DXA GE Lunar Prodigy, InBody 720, and Huawei AH100 differed from each other. Because of the lack of statistical equivalence, it would be recommended to interpret the estimations of BIA devices with caution. Second, the use of different body composition assessment methods and study groups notably affected the inverse association between VO2max and fat%. Due to these two affecting factors, the prediction models differed greatly from each other. From these results, it can be stated that the accurate assessment of body composition may lead to better predictions on VO2max, although there are also other factors to be considered when estimating VO2max without exercise.
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[{"key": "dc.contributor.author", "value": "Hyttinen, Konsta", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2023-03-16T07:15:02Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2023-03-16T07:15:02Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2020", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/86027", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Rasvaprosenttimittauksen sis\u00e4llytt\u00e4minen VO2max:yn \u201cnon-exercise\u201d -arviointiin on todettu tuottavan luotettavia tuloksia. Tutkielman tavoitteena oli arvioida erilaisten rasvaprosentti (rasva%) -mittausten vaikutusta VO2max:yn arviointiin ilman hapenottokyvyn testausta. GE Lunar Prodigy DXA:n, InBody 720:n ja Huawei AH100:n rasva%-mittausten yhtenev\u00e4isyytt\u00e4 ja ekvivalenssia testattiin ja n\u00e4iden eri laitteiden tuottamien rasva%:ien yhteyksi\u00e4 maksimaalisen hapenottokyvyn kanssa arvioitiin Pearsonin korrelaatiokertoimilla. Lis\u00e4ksi luotiin ennustemallit maksimihapenottokyvylle askeltavaa backward poistomenetelm\u00e4\u00e4 k\u00e4ytt\u00e4en jokaista k\u00e4ytetty\u00e4 kehonkoostumusmittaria kohden. Tutkimukseen rekrytoitiin terveit\u00e4 ja vaihtelevan kuntoisia miehi\u00e4 ja naisia (ik\u00e4 18-54, n=278). Tutkimus oli poikkileikkausasetelmalla toteutettu ja mittaukset suoritettiin maantieteellisesti kahdessa paikassa: Jyv\u00e4skyl\u00e4n Yliopiston ja KIHU:n liikunta- ja terveyslaboratoriossa Jyv\u00e4skyl\u00e4ss\u00e4 sek\u00e4 Shanghai Jiao Tong -yliopiston liikuntalaboratoriossa Minhangissa. Tutkimuksen analyysit tehtiin kuudessa eri analyysiryhm\u00e4ss\u00e4 riippuen siit\u00e4, mihin mittauksiin tutkittavat olivat osallistuneet (GE Lunar Prodigy & InBody 720 n=146, GE Lunar Prodigy & Huawei AH100 n=95, InBody 720 & Huawei AH100 n=130, VO2max & GE Lunar Prodigy n=146, VO2max & InBody 720 n=278, VO2max & Huawei AH100 n=125).\r\nHuawei AH100 yliarvioi rasva%:ia DXA:aan verrattuna 2,5% (95% LOA -8,7-13,6, MAPE 15,37) ja havaittiin negatiivinen proportionaalinen harha mittausten v\u00e4lill\u00e4 (p<0.001). Vastaavasti InBody 720 aliarvioi rasva%:n GE Lunar Prodigy DXA:iin n\u00e4hden -3,8% (95% LOA -10,3-2,7, MAPE 16,75) ja j\u00e4lleen proportionaalinen harha havaittiin (p<0.001). Huawei AH100 ja InBody 720 erosivat systemaattisesti toisistaan 4,6% (95% LOA -6,7-15,9, MAPE 25,69) eik\u00e4 proportionaalista harhaa havaittu (p=0.634). Pearsonin korrelaatio VO2max:yn ja GE Lunar Prodigy-rasva%:n v\u00e4lill\u00e4 oli -0,81 (p<0.001), VO2max:yn ja InBody 720-rasva%:n v\u00e4lill\u00e4 vastaavasti -0,62 (p<0.001) ja VO2max:yn ja Huawei AH100-rasva%:n v\u00e4lilll\u00e4 oli -0,60 (p<0.001). Kolme luotua lopullista VO2max:yn ennustemallia luotiin askeltavalla backward poistomenetelm\u00e4ll\u00e4 ja mallit selittiv\u00e4t VO2max:yn vaihtelusta 71%, 45% ja 40%, ja selke\u00e4sti eri analyysiryhm\u00e4t ja eri laitteilla suoritetut kehonkoostumusmittaukset vaikuttivat malleihin. Ensimm\u00e4iseen malliin (SEE 3,72) ennustemuuttujiksi valikoituivat GE Lunar Prodigy DXA:n rasva% (p<0.001), BMI (p<0.001) ja ik\u00e4 (p<0.001). Toiseen malliin (SEE 5,69) vastaavasti InBody 720:n rasva% (p<0.001), rasvaton pehmytkudosmassa (LBM) (p<0.001), BMI (p<0.001) ja ik\u00e4 (p<0.001). Kolmanteen malliin (SEE 5,94) taas Huawei AH100:n rasva% (p=0.001), ik\u00e4 (p=0.009) ja sukupuoli (p=0.044). Ensimm\u00e4inen malli osoittautui tarkimmaksi ja kolmas malli heikoimmaksi. N\u00e4iss\u00e4 kolmesssa ennustemallissa VO2max:n vaihtelun selitt\u00e4jiksi osoittautuivat siis rasva%:n lis\u00e4ksi BMI, ik\u00e4, LBM ja sukupuoli.\r\nEri laitteiden rasva%:n arvioinnit olivat toisistaan eroavia ja tilastollisesti mittaukset eiv\u00e4t olleet ekvivalentteja. T\u00e4m\u00e4n vuoksi olisi suositeltavaa tulkita BIA-mittareiden antamia tuloksia varauksella. Toisekseen laitteiden kehonkoostumuksen arvioinnit ja analyysiryhmien erot vaikuttivat maksimaalisen hapenottokyvyn ja rasva% korrelaatioihin. Kehonkoostumuksen arviointimenetelmien ja analyysiryhmien erot vaikuttivat paljon my\u00f6s luotujen ennustemallien lopullisiin ennustemuuttujiin ja mallien tarkkuuteen. Tutkielman tuloksista voidaan todeta, ett\u00e4 tarkasti arvioitu kehonkoostumus voi parantaa maksimihapenottokyvyn ennustettavuutta. Toisaalta \u201dnon-exercise\u201d -menetelmiin liittyy my\u00f6s muita ep\u00e4tarkkuutta aiheuttavia tekij\u00f6it\u00e4, joita tulee my\u00f6s huomioida.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Estimated fat% in non-exercise estimations of VO2max can yield reliable predictions. The aim of the thesis was to assess the influence of different assessments of body fat percentage (fat%) on the estimation of VO2max. The level of agreement and equivalence of the assessed fat% were tested between different devices: GE Lunar Prodigy DXA, InBody 720, and Huawei AH100. Their associations with VO2max were evaluated by Pearson correlation coefficients and prediction models on VO2max were created by stepwise backward elimination method regarding each body composition assessment device. Healthy men and women in diverse range in age (18-54), fitness, and body composition were recruited (n=278). The study was cross-sectional, and the data was collected at two centres: the sports laboratories of the University of Jyv\u00e4skyl\u00e4 and the Research Institute for Olympic Sports in Jyv\u00e4skyl\u00e4 and Shanghai Jiao Tong University in Minhang. The analyses conducted in six separate groups depending on the completed measurements by the subjects (GE Lunar Prodigy & InBody 720 n=146, GE Lunar Prodigy & Huawei AH100 n=95, InBody 720 & Huawei AH100 n=130, VO2max & GE Lunar Prodigy n=146, VO2max & InBody 720 n=278, VO2max & Huawei AH100 n=125).\r\nHuawei AH100 overestimated fat% compared to GE Lunar Prodigy by 2.5% (95% LOA -8.7-13.6, MAPE 15.37) and a negative proportional bias was found (p<0.001). The mean bias of InBody 720 to GE Lunar Prodigy was -3.8% (95% LOA -10.3-2.7, MAPE 16.75) and a negative proportional bias was found as well (p<0.001). Huawei AH100 and InBody 720 were systematically different from each other (4.6%, 95% LOA -6.7-15.9, MAPE 25.69) with no proportional bias (p=0.634). Pearson correlation between VO2max and GE Lunar Prodigy-estimated fat% was -0.81 (p<0.001), between VO2max and InBody 720-estimated fat% -0.62 (p<0.001), and between VO2max and Huawei AH100-estimated fat% -0.60 (p<0.001). When using Lunar Prodigy-estimated fat% to predict VO2max, the final predictors in the model were fat% (p<0.001), BMI (p<0.001), and age (p<0.001) which explained 71% (SEE 3.72) of the variance in VO2max, indicating that, in addition to fat%, BMI and age contributed to the VO2max variance. When using fat% assessed by InBody 720 to predict VO2max, the final predictors in the model were fat% (p<0.001), lean body mass (LBM) (p<0.001), BMI (p<0.001), and age (p<0.001) which explained 45% (SEE 5.69) of the variance of VO2max, showing that LBM, BMI and age are contributed to the VO2max variance. When using fat% assessed by Huawei AH100, the final predictors in the model (SEE 5.94) were fat% (p=0.001), age (p=0.009), and gender (p=0.044) which explained 40% (SEE 5.94) of the variance of VO2max indicating age and also gender contributed to the VO2max variance. The first model turned out to be the most accurate and the third model the least accurate model to predict VO2max.\r\nThe estimations of fat% between DXA GE Lunar Prodigy, InBody 720, and Huawei AH100 differed from each other. Because of the lack of statistical equivalence, it would be recommended to interpret the estimations of BIA devices with caution. Second, the use of different body composition assessment methods and study groups notably affected the inverse association between VO2max and fat%. Due to these two affecting factors, the prediction models differed greatly from each other. From these results, it can be stated that the accurate assessment of body composition may lead to better predictions on VO2max, although there are also other factors to be considered when estimating VO2max without exercise.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2023-03-16T07:15:01Z\r\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2023-03-16T07:15:02Z (GMT). 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