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Lee and Lee: Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women
Research Article

Korean Journal of Community Nutrition 2018; 23(4): 277-288.

Published online: 31 August 2018

DOI: https://doi.org/10.5720/kjcn.2018.23.4.277

Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women

Hye-Jin Lee, Kyung-Hea Lee

Department of Food and Nutrition, Changwon National University, Gyeongnam, Korea.

Corresponding author: Kyung-Hea Lee. Department of Food and Nutrition, Changwon National University, 20 Changwondaehakro, Uichang-gu, Changwon, Gyeongnam, 51140, Korea. Tel: (055) 213-3514, Fax: (055) 281-7480, khl@changwon.ac.kr

Received 20 July 2018       Revised 7 August 2018       Accepted 7 August 2018

Copyright © 2018 The Korean Society of Community Nutrition

The Korean Society of Community Nutrition

(open-access):

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives

This study was performed to verify the validity and judgment criteria setting of a health status assessment tool based on dietary patterns for middle-aged women.

Methods

A total of 474 middle-aged women who visited the Comprehensive Medical Examination Center at Hanmaeum Hospital in Changwon were enrolled (IRB 2013-0005). The validity was verified using clinical indicators for the diagnosis of metabolic syndrome (MS), and it was used to set the criteria for the tool. A logistic regression analysis was performed for validation. The area under-receiver operation (AUC), sensitivity, specificity, and Youden Index were calculated through ROC curve analysis. Statistical analysis was performed by SPSS 21, and p value <0.05 was considered to be statistically significant.

Results

The mean score of the group with no MS (73.3 points) was significantly higher compared to the group with MS (65.7 points) (p<0.001). An analysis of the association between the tool scores and risk of MS showed a 0.15-fold reduction in the risk of MS every time the tool's score increased by one point. As the result of the ROC curve analysis, the assessment reference point was set to 71 points, indicating 77.0% sensitivity and 61.0% specificity. Risk of MS was significantly higher in the group with a score of less than 71.0 than a group with more than 71 points (OR=5.28, p<0.001).

Conclusions

This study was the first attempt to develop a health status assessment tool based on the dietary patterns for middle-aged women, and this tool has proven its usefulness as an MS assessment tool through the application of middle-aged women in the field of health screening.

Keywords: health status assessment tool, dietary patterns, middle-aged women, women's health, metabolic syndrome

Figures and Tables

Fig. 1

Receiver-operating characteristic (ROC) curves for assessment tool score

kjcn-23-277-g001
Table 1

General informations about subjects with or without metabolic syndrome

kjcn-23-277-i001

1) Values are mean ± SE, 2) by t-test, 3) N (%), 4) by χ2-test.

Table 2

Anthropometric and serum biochemical characteristics of subjects by the metabolic syndrome

kjcn-23-277-i002

1) by t-test, 2) Values are mean ± SE.

Table 3

Scoring according of the assessment tool questions by metabolic syndrome

kjcn-23-277-i003

1) by t-test, 2) Values are mean ± SE, 3) Total score: sum of 20 questions.

Table 4

Results of logistic regression analysis

kjcn-23-277-i004

1) Model 1: Unadjusted model, 2) Model 2: Adjustment for age, education level, monthly income, 3) CI: Confidence Interval.

Table 5

ROC1) curve and cutoff points of assessment tool

kjcn-23-277-i005

1) ROC: Receiver-Operating Characteristic, 2) PPV: Positive Predictive Value, 3) NPV: Negative Predictive Value.

Table 6

Forecasting check of assessment tool cut-off point

kjcn-23-277-i006

1) CI: Confidence Interval, 2) by logistic regression analysis.

Acknowledgments

This research was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number. 2011-0013053).

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ORCID iDs

Kyung-Hea Lee
https://orcid.org/0000-0003-0444-4824

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