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Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women
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Research Article
Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women
Hye-Jin Lee, Kyung-Hea Leeorcid
Korean Journal of Community Nutrition 2018;23(4):277-288.
DOI: https://doi.org/10.5720/kjcn.2018.23.4.277
Published online: August 31, 2018

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: July 20, 2018   • Revised: August 7, 2018   • Accepted: August 7, 2018

Copyright © 2018 The Korean Society of Community Nutrition

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.

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  • 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.
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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Fig. 1

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

kjcn-23-277-g001.jpg
Table 1

General informations about subjects with or without metabolic syndrome

kjcn-23-277-i001.jpg

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.jpg

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.jpg

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.jpg

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.jpg

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.jpg

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

Figure & Data

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    • Association of milk and dairy product consumption with the incidence of cardio-cerebrovascular disease incidence in middle-aged and older Korean adults: a 16-year follow-up of the Korean Genome and Epidemiology Study
      Yeseung Jeong, Kyung Won Lee, Hyekyeong Kim, Yuri Kim
      Nutrition Research and Practice.2023; 17(6): 1225.     CrossRef

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    Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women
    Image
    Fig. 1 Receiver-operating characteristic (ROC) curves for assessment tool score
    Field Application and Evaluation of Health Status Assessment Tool based on Dietary Patterns for Middle-Aged Women

    General informations about subjects with or without metabolic syndrome

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

    Anthropometric and serum biochemical characteristics of subjects by the metabolic syndrome

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

    Scoring according of the assessment tool questions by metabolic syndrome

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

    Results of logistic regression analysis

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

    ROC1) curve and cutoff points of assessment tool

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

    Forecasting check of assessment tool cut-off point

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

    Table 1 General informations about subjects with or without metabolic syndrome

    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

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

    Table 3 Scoring according of the assessment tool questions by metabolic syndrome

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

    Table 4 Results of logistic regression analysis

    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

    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

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


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