Accuracy of BMI correction using multiple reports in children
Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children f...
Ausführliche Beschreibung
Autor*in: |
Ghosh-Dastidar, Madhumita (Bonnie) [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2016 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s). 2016 |
---|
Übergeordnetes Werk: |
Enthalten in: BMC Obesity - London : BioMed Central, 2014, 3(2016), 1 vom: 13. Sept. |
---|---|
Übergeordnetes Werk: |
volume:3 ; year:2016 ; number:1 ; day:13 ; month:09 |
Links: |
---|
DOI / URN: |
10.1186/s40608-016-0117-1 |
---|
Katalog-ID: |
SPR036745111 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | SPR036745111 | ||
003 | DE-627 | ||
005 | 20230519223900.0 | ||
007 | cr uuu---uuuuu | ||
008 | 201007s2016 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s40608-016-0117-1 |2 doi | |
035 | |a (DE-627)SPR036745111 | ||
035 | |a (SPR)s40608-016-0117-1-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Ghosh-Dastidar, Madhumita (Bonnie) |e verfasserin |4 aut | |
245 | 1 | 0 | |a Accuracy of BMI correction using multiple reports in children |
264 | 1 | |c 2016 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © The Author(s). 2016 | ||
520 | |a Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. | ||
650 | 4 | |a BMI |7 (dpeaa)DE-He213 | |
650 | 4 | |a Linear correction |7 (dpeaa)DE-He213 | |
650 | 4 | |a Height |7 (dpeaa)DE-He213 | |
650 | 4 | |a Weight |7 (dpeaa)DE-He213 | |
650 | 4 | |a Measurement error |7 (dpeaa)DE-He213 | |
650 | 4 | |a Obesity |7 (dpeaa)DE-He213 | |
650 | 4 | |a Children |7 (dpeaa)DE-He213 | |
700 | 1 | |a Haas, Ann C |4 aut | |
700 | 1 | |a Nicosia, Nancy |4 aut | |
700 | 1 | |a Datar, Ashlesha |4 aut | |
773 | 0 | 8 | |i Enthalten in |t BMC Obesity |d London : BioMed Central, 2014 |g 3(2016), 1 vom: 13. Sept. |w (DE-627)779400887 |w (DE-600)2758687-X |x 2052-9538 |7 nnns |
773 | 1 | 8 | |g volume:3 |g year:2016 |g number:1 |g day:13 |g month:09 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s40608-016-0117-1 |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_SPRINGER | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2446 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 3 |j 2016 |e 1 |b 13 |c 09 |
author_variant |
m b g d mbg mbgd a c h ac ach n n nn a d ad |
---|---|
matchkey_str |
article:20529538:2016----::cuayfmcretouigutpee |
hierarchy_sort_str |
2016 |
publishDate |
2016 |
allfields |
10.1186/s40608-016-0117-1 doi (DE-627)SPR036745111 (SPR)s40608-016-0117-1-e DE-627 ger DE-627 rakwb eng Ghosh-Dastidar, Madhumita (Bonnie) verfasserin aut Accuracy of BMI correction using multiple reports in children 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 Haas, Ann C aut Nicosia, Nancy aut Datar, Ashlesha aut Enthalten in BMC Obesity London : BioMed Central, 2014 3(2016), 1 vom: 13. Sept. (DE-627)779400887 (DE-600)2758687-X 2052-9538 nnns volume:3 year:2016 number:1 day:13 month:09 https://dx.doi.org/10.1186/s40608-016-0117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2016 1 13 09 |
spelling |
10.1186/s40608-016-0117-1 doi (DE-627)SPR036745111 (SPR)s40608-016-0117-1-e DE-627 ger DE-627 rakwb eng Ghosh-Dastidar, Madhumita (Bonnie) verfasserin aut Accuracy of BMI correction using multiple reports in children 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 Haas, Ann C aut Nicosia, Nancy aut Datar, Ashlesha aut Enthalten in BMC Obesity London : BioMed Central, 2014 3(2016), 1 vom: 13. Sept. (DE-627)779400887 (DE-600)2758687-X 2052-9538 nnns volume:3 year:2016 number:1 day:13 month:09 https://dx.doi.org/10.1186/s40608-016-0117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2016 1 13 09 |
allfields_unstemmed |
10.1186/s40608-016-0117-1 doi (DE-627)SPR036745111 (SPR)s40608-016-0117-1-e DE-627 ger DE-627 rakwb eng Ghosh-Dastidar, Madhumita (Bonnie) verfasserin aut Accuracy of BMI correction using multiple reports in children 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 Haas, Ann C aut Nicosia, Nancy aut Datar, Ashlesha aut Enthalten in BMC Obesity London : BioMed Central, 2014 3(2016), 1 vom: 13. Sept. (DE-627)779400887 (DE-600)2758687-X 2052-9538 nnns volume:3 year:2016 number:1 day:13 month:09 https://dx.doi.org/10.1186/s40608-016-0117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2016 1 13 09 |
allfieldsGer |
10.1186/s40608-016-0117-1 doi (DE-627)SPR036745111 (SPR)s40608-016-0117-1-e DE-627 ger DE-627 rakwb eng Ghosh-Dastidar, Madhumita (Bonnie) verfasserin aut Accuracy of BMI correction using multiple reports in children 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 Haas, Ann C aut Nicosia, Nancy aut Datar, Ashlesha aut Enthalten in BMC Obesity London : BioMed Central, 2014 3(2016), 1 vom: 13. Sept. (DE-627)779400887 (DE-600)2758687-X 2052-9538 nnns volume:3 year:2016 number:1 day:13 month:09 https://dx.doi.org/10.1186/s40608-016-0117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2016 1 13 09 |
allfieldsSound |
10.1186/s40608-016-0117-1 doi (DE-627)SPR036745111 (SPR)s40608-016-0117-1-e DE-627 ger DE-627 rakwb eng Ghosh-Dastidar, Madhumita (Bonnie) verfasserin aut Accuracy of BMI correction using multiple reports in children 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 Haas, Ann C aut Nicosia, Nancy aut Datar, Ashlesha aut Enthalten in BMC Obesity London : BioMed Central, 2014 3(2016), 1 vom: 13. Sept. (DE-627)779400887 (DE-600)2758687-X 2052-9538 nnns volume:3 year:2016 number:1 day:13 month:09 https://dx.doi.org/10.1186/s40608-016-0117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2016 1 13 09 |
language |
English |
source |
Enthalten in BMC Obesity 3(2016), 1 vom: 13. Sept. volume:3 year:2016 number:1 day:13 month:09 |
sourceStr |
Enthalten in BMC Obesity 3(2016), 1 vom: 13. Sept. volume:3 year:2016 number:1 day:13 month:09 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
BMI Linear correction Height Weight Measurement error Obesity Children |
isfreeaccess_bool |
true |
container_title |
BMC Obesity |
authorswithroles_txt_mv |
Ghosh-Dastidar, Madhumita (Bonnie) @@aut@@ Haas, Ann C @@aut@@ Nicosia, Nancy @@aut@@ Datar, Ashlesha @@aut@@ |
publishDateDaySort_date |
2016-09-13T00:00:00Z |
hierarchy_top_id |
779400887 |
id |
SPR036745111 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR036745111</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519223900.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s40608-016-0117-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR036745111</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40608-016-0117-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ghosh-Dastidar, Madhumita (Bonnie)</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Accuracy of BMI correction using multiple reports in children</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s). 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BMI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Linear correction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Height</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weight</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Measurement error</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Obesity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Children</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haas, Ann C</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicosia, Nancy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Datar, Ashlesha</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC Obesity</subfield><subfield code="d">London : BioMed Central, 2014</subfield><subfield code="g">3(2016), 1 vom: 13. Sept.</subfield><subfield code="w">(DE-627)779400887</subfield><subfield code="w">(DE-600)2758687-X</subfield><subfield code="x">2052-9538</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">day:13</subfield><subfield code="g">month:09</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s40608-016-0117-1</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="b">13</subfield><subfield code="c">09</subfield></datafield></record></collection>
|
author |
Ghosh-Dastidar, Madhumita (Bonnie) |
spellingShingle |
Ghosh-Dastidar, Madhumita (Bonnie) misc BMI misc Linear correction misc Height misc Weight misc Measurement error misc Obesity misc Children Accuracy of BMI correction using multiple reports in children |
authorStr |
Ghosh-Dastidar, Madhumita (Bonnie) |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)779400887 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
2052-9538 |
topic_title |
Accuracy of BMI correction using multiple reports in children BMI (dpeaa)DE-He213 Linear correction (dpeaa)DE-He213 Height (dpeaa)DE-He213 Weight (dpeaa)DE-He213 Measurement error (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Children (dpeaa)DE-He213 |
topic |
misc BMI misc Linear correction misc Height misc Weight misc Measurement error misc Obesity misc Children |
topic_unstemmed |
misc BMI misc Linear correction misc Height misc Weight misc Measurement error misc Obesity misc Children |
topic_browse |
misc BMI misc Linear correction misc Height misc Weight misc Measurement error misc Obesity misc Children |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
BMC Obesity |
hierarchy_parent_id |
779400887 |
hierarchy_top_title |
BMC Obesity |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)779400887 (DE-600)2758687-X |
title |
Accuracy of BMI correction using multiple reports in children |
ctrlnum |
(DE-627)SPR036745111 (SPR)s40608-016-0117-1-e |
title_full |
Accuracy of BMI correction using multiple reports in children |
author_sort |
Ghosh-Dastidar, Madhumita (Bonnie) |
journal |
BMC Obesity |
journalStr |
BMC Obesity |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2016 |
contenttype_str_mv |
txt |
author_browse |
Ghosh-Dastidar, Madhumita (Bonnie) Haas, Ann C Nicosia, Nancy Datar, Ashlesha |
container_volume |
3 |
format_se |
Elektronische Aufsätze |
author-letter |
Ghosh-Dastidar, Madhumita (Bonnie) |
doi_str_mv |
10.1186/s40608-016-0117-1 |
title_sort |
accuracy of bmi correction using multiple reports in children |
title_auth |
Accuracy of BMI correction using multiple reports in children |
abstract |
Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. © The Author(s). 2016 |
abstractGer |
Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. © The Author(s). 2016 |
abstract_unstemmed |
Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports. © The Author(s). 2016 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
Accuracy of BMI correction using multiple reports in children |
url |
https://dx.doi.org/10.1186/s40608-016-0117-1 |
remote_bool |
true |
author2 |
Haas, Ann C Nicosia, Nancy Datar, Ashlesha |
author2Str |
Haas, Ann C Nicosia, Nancy Datar, Ashlesha |
ppnlink |
779400887 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s40608-016-0117-1 |
up_date |
2024-07-03T19:24:38.991Z |
_version_ |
1803587083558715392 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR036745111</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519223900.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s40608-016-0117-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR036745111</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40608-016-0117-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ghosh-Dastidar, Madhumita (Bonnie)</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Accuracy of BMI correction using multiple reports in children</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s). 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI. Methods Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers’ Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators. Results Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted $ R^{2} $ of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models. Conclusion Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children’s height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BMI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Linear correction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Height</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weight</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Measurement error</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Obesity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Children</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haas, Ann C</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicosia, Nancy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Datar, Ashlesha</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC Obesity</subfield><subfield code="d">London : BioMed Central, 2014</subfield><subfield code="g">3(2016), 1 vom: 13. Sept.</subfield><subfield code="w">(DE-627)779400887</subfield><subfield code="w">(DE-600)2758687-X</subfield><subfield code="x">2052-9538</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">day:13</subfield><subfield code="g">month:09</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s40608-016-0117-1</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="b">13</subfield><subfield code="c">09</subfield></datafield></record></collection>
|
score |
7.401531 |