## Abstract

### Background

### Objective

### Design

### Participants and setting

### Main outcome measures

### Statistical analyses performed

### Results

*P*=0.098, Cohen’s

*d*

_{z}=0.21) and –0.98%±3.65% (

*P*=0.079, Cohen’s

*d*

_{z}=0.27), respectively. The final equations were split into three height categories from which the sex-specific prediction charts were generated.

### Conclusions

## Keywords

**Research Question:**Can body fat percentage of healthy Asian Chinese adults be predicted using simple measurements and a visual chart?

**Key Findings:**Simplified sex-specific equations were developed to predict body fat percentage among Chinese adults living in Singapore. Graphics-based charts were created from the prediction equations to facilitate easy assessment of adiposity by the general public. Validation analysis revealed nonsignificant prediction bias in body fat percentage of 0.84%±3.94% (

*P*= 0.098) in women and –0.98%±3.65% (

*P*=0.079) in men using the new prediction equations.

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## Materials and Methods

### Study Design and Participants

### Anthropometric Measurements

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### Statistical Analysis

*R*

^{2}

*R*

^{2}

*R*and smaller SEE were indications of better model performance. A single model was then selected for further simplification (Step 2). The simplified model was defined to be one that had at least two anthropometric variables that could be easily obtained.

Step | Models and independent variables | Predicted R^{2} | AIC | SEE | Adjusted R^{2} | Mean absolute percentage error | Prediction bias (%±standard deviation) |
---|---|---|---|---|---|---|---|

1 | Model 1: Age, body mass index | 0.54 | 713.5 | 3.8 | — | — | — |

Model 2: Age, waist-to-height ratio, weight | 0.57 | 699.8 | 3.7 | — | — | — | |

Model 3: A circumference, height, weight | 0.57 | 695.8 | 3.6 | 0.58 | 9.2 | 0.73±3.81 | |

2 | Model 3a: Age, waist circumference, height | 0.55 | — | 3.7 | 0.56 | 9.5 | 0.94±3.87 |

Model 3b: waist circumference, height | 0.54 | — | 3.8 | 0.54 | 9.8 | 0.84±3.94 | |

Liu model: Body mass index, waist circumference, age, waist circumference^{2} | — | — | — | — | 18.0 | –6.24±3.94 |

*R*

^{2}, adjusted

*R*

^{2}, standard error of estimate (SEE), and Akaike information criterion (AIC) were obtained using the model building dataset (n=269).

*i*represents the individuals in the sample;

*n*is the total number of people in the sample under consideration. The smaller the value of mean absolute percentage error, the more accurate the model estimates.

*R*

^{2}, lowest AIC, and SEE. This model was further simplified in Step 2.

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^{2}.

*P*<0.001.

Step | Models and independent variables | Predicted R^{2} | AIC | SEE | Adjusted R^{2} | Mean absolute percentage error | Prediction bias (%)±standard deviation |
---|---|---|---|---|---|---|---|

1 | Model 1: Age, body mass index | 0.49 | 478.0 | 4.0 | — | — | — |

Model 2: Waist-to-height ratio | 0.61 | 431.1 | 3.5 | — | — | — | |

Model 3: age, waist circumference, height | 0.62 | 428.5 | 3.5 | 0.63 | 12.4 | –1.0±3.66 | |

2 | Model 3a: Waist circumference, height | 0.62 | — | 3.5 | 0.63 | 12.3 | –0.98±3.65 |

Liu model: Body mass index, waist circumference, waist circumference^{2} | — | — | — | — | 22.5 | –5.94±3.82 |

*R*

^{2}, adjusted

*R*

^{2}, standard error of estimate (SEE), and Akaike information criterion (AIC) were obtained using the model building dataset (n=170).

*i*represents the individuals in the sample;

*n*is the total number of people in the sample under consideration. The smaller the value of mean absolute percentage error, the more accurate the model estimates.

*R*

^{2}, lowest AIC and SEE. This model was further simplified in Step 2.

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^{2}

_{.}

*P*<0.001.

*i*represents the individuals in the sample and

*n*is the total number of people in the sample under consideration.

*t*test and deviation from the line of identity (a test for the slope=1 and the intercept=0). For the deviation from the line of identity assessment, the predicted values were used as the dependent variable and the actual %BF measured with DEXA was used as the independent variable. A statistically significant result would indicate there was fixed or proportional bias in the predictive model. To indicate the size of the standardized difference, Cohen’s

*d*

_{z}for the effect size of a paired sample

*t*test was also reported. The accuracy and performance of the new chosen simplified prediction equations were also cross-validated in an independent validation dataset as well as compared with the prediction equations developed by Liu and colleagues.

## Results

*P*>0.05). Model development was performed separately for each sex using this dataset. Model 3 was chosen to be further reduced (Table 1 and Table 2) because it had the highest predicted

*R*and lowest SEE for both sexes compared with models 1 and 2.

Characteristic^{a}There was no significant difference in the participant characteristics between the model-building and validation dataset. Comparison was made using Mann-Whitney test. Comparison of the proportion of married individuals in the model building dataset and validation dataset was done using two-sample test for equality of proportions. | Model-Building Dataset | |
---|---|---|

Women (n=269) | Men (n=170) | |

←mean±standard deviation (range)→ | ||

Age (y) | 38.0±14.5 (21.0-68.6) | 38.4±14.5 (21.0-69.2) |

Height (cm) | 159.5±5.8 (144-175) | 171.2±5.8 (157-188) |

Weight (kg) | 55.0±9.2 (34.5-88.3) | 68.5±9.9 (46.0-111.0) |

Waist circumference (cm) | 69.7±7.8 (52.8-99.1) | 79.1±8.6 (62.1-109.9) |

Body fat from DEXA (%) | 34.8±5.6 (21.9-52.7) | 24.2±5.7 (11.8-37.2) |

Body mass index | 21.6±3.3 (16.2-33.2) | 23.4±3.2 (16.3-37.5) |

←n (%)→ | ||

Married | 87 (32.3) | 75 (44.1) |

Validation Dataset | ||

Women (n=62) | Men (n=45) | |

←mean±standard deviation (range)→ | ||

Age (y) | 40.6±14.6 (21-74) | 37.8±15.4 (22.0-74.0) |

Height (cm) | 160.3±5.2 (148-177) | 172.7±7.4 (157-185) |

Weight (kg) | 57.1±11.8 (39-104) | 70.7±10.7 (51.6-94.5) |

Waist circumference (cm) | 72.2±9.0 (55-103) | 81.2±7.7 (68.0-98.5) |

Body fat from DEXA (%) | 35.1±6.1 (23-49) | 26.1±5.6 (14.8-38.1) |

Body mass index | 22.1±3.9 (16-38) | 23.6±2.7 (19.0-30.6) |

←n (%)→ | ||

Married | 24 (38.7) | 14 (31.1) |

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Sex | Model | |
---|---|---|

Liu and colleagues | New Simplified | |

Estimated intercept of line of identity (95% CI) | ||

Female | 10.75 (6.81-14.69) | 16.12 (11.73-20.51) |

Estimated slope of line of identity (95% CI) | ||

0.52 (0.41-0.63) | 0.57 (0.44-0.69) | |

Estimated intercept of line of identity (95% CI) | ||

Male | 8.49 (5.17-11.80) | 11.56 (7.92-15.20) |

Estimated slope of line of identity (95% CI) | ||

0.45 (0.32-0.57) | 0.52 (0.38-0.66) |

*P*<0.001.

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Sex | Model | Mean absolute percentage error | Prediction bias (%) |
---|---|---|---|

mean±standard deviation | |||

Female (n=61) | Model: Waist circumference, height categories | 9.87 | 0.90±3.99 |

Model: Waist circumference, height | 9.59 | 0.79±3.90 | |

Male (n=45) | Model: Waist circumference, height categories | 12.34 | –0.98±3.65 |

Model: Waist circumference, height | 12.31 | –0.99±3.65 |

*i*represents the individuals in the sample;

*n*is the total number of people in the sample under consideration. The smaller the value of mean absolute percentage error, the better it is.

## Discussion

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^{, }but skinfold measurements are difficult to obtain without the proper training and equipment. In contrast, the use of simpler anthropometric measurements, such as height, WC, and weight, do not require specialized equipment and the %BF can be more easily calculated.

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*P*<0.001for both sexes). Evaluation of validity using the line of identity method showed that there was a statistically significant nonzero intercept and significant slope that is not equal to 1 in both the newly developed simplified equations and the equations developed by Liu and colleagues. The results indicate that there is no one-to-one relationship between the predicted %BF values and the actual observed values as desired. The significant deviations from zero and one indicated potential bias in the prediction models.

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*P*=0.098, Cohen’s

*d*

_{z}=0.21 and for men

*P*=0.079, Cohen’s

*d*

_{z}=0.27). This modest bias of 1% using the newly developed model can be compared with the large significant (

*P*<0.001 in both sexes) bias of about 6% in both sexes (women: –6.24%±3.94% and men: –5.94%±3.82%) from the Liu and colleagues

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## Conclusions

## Acknowledgements

### Author Contributions

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## Biography

## Article Info

### Publication History

### Footnotes

**STATEMENT OF POTENTIAL CONFLICT OF INTEREST** No potential conflict of interest was reported by the authors.

**FUNDING/SUPPORT** The research reported in this article was funded by the Singapore Agency for Science, Technology and Research (grant no. SPF/003 ).

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