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Published on in Vol 4 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38852, first published .
Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study

Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study

Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study

Authors of this article:

Abderrahmen Masmoudi1 Author Orcid Image ;   Amine Zouari1 Author Orcid Image ;   Ahmed Bouzid1 Author Orcid Image ;   Kais Fourati1 Author Orcid Image ;   Soulaimen Baklouti1 Author Orcid Image ;   Mohamed Ben Amar1 Author Orcid Image ;   Salah Boujelben1 Author Orcid Image
Article Authors Cited by (4) Tweetations (5) Metrics

    Surgery Department, Habib Bourguiba University Hospital, Sfax, Tunisia

    Corresponding Author:

    Abderrahmen Masmoudi, MD


    Related ArticlesPreprint (JMIR Preprints) https://preprints.jmir.org/preprint/38852
    Peer-Review Report by Mohammed Shahriar Arefin (Reviewer K) https://med.jmirx.org/2023/1/e54012
    Peer-Review Report by William A Barletta (Reviewer L) https://med.jmirx.org/2023/1/e54045
    Peer-Review Report by Zied Hadrich (Reviewer R) https://med.jmirx.org/2023/1/e54011
    Authors' Response to Peer-Review Reports https://med.jmirx.org/2023/1/e53817

    Background: Despite the existing evidence that waist circumference (WC) provides independent and additive information to BMI when predicting morbidity and mortality, this measurement is not routinely obtained in clinical practice. Using computed tomography (CT) scan images, mobile health (mHealth) has the potential to make this abdominal obesity parameter easily available even in retrospective studies.

    Objective: This study aimed to develop a mobile app as a tool for facilitating the measurement of WC based on a cross-sectional CT image.

    Methods: The development process included three stages: determination of the principles of WC measurement from CT images, app prototype design, and validation. We performed a preliminary validity study in which we compared WC measurements obtained both by the conventional method using a tape measurement in a standing position and by the mobile app using the last abdominal CT slice not showing the iliac bone. Pearson correlation, student t tests, and Q-Q and Bland-Altman plots were used for statistical analysis. Moreover, to perform a diagnostic test evaluation, we also analyzed the accuracy of the app in detecting abdominal obesity.

    Results: We developed a prototype of the app Measure It, which is capable of estimating WC from a single cross-sectional CT image. We used an estimation based on an ellipse formula adjusted to the gender of the patient. The validity study included 20 patients (10 men and 10 women). There was a good correlation between both measurements (Pearson R=0.906). The student t test showed no significant differences between the two measurements (P=.98). Both the Q-Q dispersion plot and Bland-Altman analysis graphs showed good overlap with some dispersion of extreme values. The diagnostic test evaluation showed an accuracy of 83% when using the mobile app to detect abdominal obesity.

    Conclusions: This app is a simple and accessible mHealth tool to routinely measure WC as a valuable obesity indicator in clinical and research practice. A usability and validity evaluation among medical teams will be the next step before its use in clinical trials and multicentric studies.

    JMIRx Med 2023;4:e38852

    doi:10.2196/38852

    Keywords

    waist circumference; computed tomography; abdominal CT; mobile health; health apps; CT; CT scan; CT image; mobile app; app; application; waist; body; body mass; BMI; morbidity; mortality; clinical; tool; prototype; design; obesity; abdominal; usability; validity; medical 


    Obesity is a major public health problem worldwide, and the reliance on BMI measurements alone has proven insufficient to help assess obesity-related health risks in patients [1]. Waist circumference (WC) is a simple method to evaluate abdominal adiposity that is easy to standardize. It is also an independent cardiovascular risk factor, with a higher predicting value than BMI [2,3]. However, this measurement is not routinely used in clinical practice.

    Recently, a computed tomography (CT) scan estimation became a valid measure of standing WC [4,5]. This method is truly valuable in retrospective studies, where it can be difficult to obtain such measurements. Moreover, conventional WC assessment using a measurement tape can be challenging in patients with intellectual or motor disabilities. However, for a radiologist, this method may require time and training. Therefore, despite its widespread availability and limited cost, using CT images to assess WC is not routinely included in clinical and research practice.

    Accordingly, the major aim of this study was to develop a mobile app to overcome these barriers and help clinicians routinely assess WC whenever a CT scan is available.


    Overview

    The development process involved three stages: determination of the principles of WC measurement from CT images, prototype design, and validation of the developed product.

    As validated by Ciudin et al [6], the abdominal perimeter was estimated using the formula of the perimeter of an ellipse (Figure 1). In this previous study, there was a good correlation between conventional standing WC measurement and ellipse-estimated WC, with a Pearson test of 0.987 and a mean error of 0.4 cm.

    We applied the same formula:

    “a” being the anterior-posterior diameter and “b” being the transverse diameter. Afterward, we performed WC measurement on 10 healthy candidates using both the conventional tape method and the ellipse formula. We then used a simple linear regression analysis to adjust the final WC formula to the gender of the patient.

    After confirming the app requirements (ellipse formula, required measurements, final formula applied to gender and the needed parameters, and organization of the steps required by the physician to ameliorate the user experience), we initiated the design and development of the app. After preparing the app prototype, we performed a preliminary validity study including 20 patients selected retrospectively based on the existence of a previous WC measurement and CT scan images in their file. We compared the conventional WC measurement (cWC) method to the mobile app–based WC measurement (mWC) method based on CT scan images.

    The first measurement was done using a measuring tape placed horizontally around the patient’s abdomen just above the iliac crest as recommended by the National Institutes of Health National Institutes of Health National Institutes of Health guidelines [7]. It was done in a standing position, at the end of a normal expiration. The second measurement was performed with the mobile app. Using the camera of the phone, the app employused the last slice of the CT scan image, on the last slice, from cranial to caudal, not showing the iliac bone.

    Measurements were expressed as mean ± (SD)standard deviation and range. Data were collected and analyszed using SPSS 20 software (SPSS Inc., Chicago, IL, USAIBM Corp). Abdominal obesity was defined by waist circumferenceWC measurements of >102 cm (~40 inches) and >88 cm (~35 inches) for men and women, respectively [8]. Student t test, Pearson correlation, Q-Q plot, and the Bland-Altman analysis were used. P values <inferior to 0.05 were considered statistically significant from a statistical point of view. In order tTo perform a diagnostic test evaluation, we also analyszed the mWC accuracy in detecting abdominal obesity.

    At the end of the design stage, the app was demonstrated in several team meetings, which led to further modifications.

    ‎
    Figure 1. Evaluation of the waist circumference on a computed tomography image using an ellipse perimeter formula. (a) Anterior-posterior diameter, (b) transverse diameter, and (c) ellipse perimeter.

    Patient and Public Involvement

    The patients were not involved in setting the research question or the outcome measures, designing or implementing the study, or reporting or disseminating the research. Additionally, the public did not participate in the design, implementation, reporting, or dissemination plans of this study.

    Ethical Considerations

    Personal data have been respected. This study was approved by the ethics committee at Habib Bourguiba University Hospital in Sfax (Ref CE-03-2022).


    Following the design principles and requirements, the prototype of the app was developed and named Measure It. The flow of the app was designed to be simple and productive to ensure quality interaction between the app and the visitor (Figure 2). A demo video was provided in the app to facilitate its use.

    ‎
    Figure 2. Illustration of the flow of the app. WC: waist circumference.

    The preliminary validation study included 20 patients. It included 10 men and 10 women. The mean age was 54 (SD 17) years. The mean BMI was 26 (SD 4; women: mean 27.8, SD 2.7; men: mean 24.2, SD 4.4). The mean cWC was 93.7 (SD 12.6, range 68-122; women: mean 95.1, SD 11.9; men: mean 92.3, SD 13.8) cm. The most common reason why patients (n=10) underwent an abdominal CT scan was for biliary stone disease.

    The comparison between cWC and mWC showed a good correlation (Pearson R=0.90). The student t test showed no significant difference between the two measurements (P=.98). We also compared the two measurements using a Q-Q dispersion and a Bland-Altman plot. The analysis graphs can be found in Figures 3 and 4. The Q-Q plot showed good overlap with some dispersion of extreme values. The Bland-Altman analysis showed a mean difference of 0.03 cm (95% CI –2.46 to 2.53) between the two measurements ().

    ‎
    Figure 3. Q-Q plot of estimated WC versus measured WC. WC: waist circumference.
    ‎
    Figure 4. Bland-Altman plot of the differences between the standing and app-estimated waist circumferences.

    We have also performed a diagnostic test evaluation regarding the accuracy of the mWC in detecting abdominal obesity. Results were adjusted to the prevalence of abdominal obesity (59%) [9]. The analysis showed an accuracy of 83% when using mWC to detect abdominal obesity (Table 1).

    Table 1. Diagnostic test evaluation for mobile app waist circumference.
    StatisticValue (95% CI)
    Sensitivity (%)72.73 (39.03-93.98)
    Specificity (%)100.00 (66.37-100.00)
    Positive predictive valuea (%)100.00
    Negative predictive valuea (%)71.82 (49.26-86.99)
    Accuracya (%)83.91 (60.82-96.28)

    aThese values are dependent on abdominal obesity prevalence [9].


    Principal Findings

    Guidelines for the management of obesity from several professional societies recognize the importance of measuring WC in the context of risk stratification for future cardiometabolic morbidity and mortality [3,10-13]. Moreover, WC is gaining significant importance among surgeons since abdominal obesity has a growing value in preoperative risk assessment for morbidity and mortality in different surgeries [14-19].

    We developed a prototype of the mobile app Measure It to accurately estimate WC using CT scan images. The app was developed based on a validated method [5,6] measuring WC using CT scan cross-sectional images. To our knowledge, this is the first mobile app that helps physicians estimate WC. The app was designed to be a simple and accessible tool with the purpose of routinely including this valuable obesity parameter in clinical and research practice. One of the most valuable advantages of our app is its usability in retrospective studies. WC measurements mostly do not exist in patient observations. However, CT scan slides or images are often available. Moreover, the simplicity of the app may reduce the time required for physicians to assess WC [20]. Conventional tape measuring is sometimes not possible, particularly for patients who are disabled. Additionally, for a radiologist, the conventional CT scan method requires training and can be more or less time-consuming. Eventually, being simple, accessible, and reproducible, the app may reduce the technology barriers for nontech physicians since smartphones are commonly available even in low- and middle-income countries [21].

    As a screening tool for abdominal obesity, this attribute may be beneficial, especially in retrospective studies. With an accuracy of 83% compared to the conventional method, the mobile app method is reliable. However, the accuracy of WC measurement may be altered in some cases. This may be due to a measurement error in the conventional method or to particular body shapes and extreme values of WC.

    One major problem with currently available mobile health apps is that few are established with strong research evidence [22,23]. Measure It is developed based on a strong statistical analysis, even though it needs to be validated in a prospective study.

    The main limitation of this study is the small sample size used to validate the app. We consider this validity study as a preliminary validation that needs to be confirmed. Therefore, we would expect our app to be ready for clinical use to a certain degree.

    Another limitation is that the small screen on the smartphone makes it difficult to precisely set reference scales and perform measurements on a CT scan image. However, the zooming functionality makes the app’s accuracy very sufficient. To overcome this problem, we plan to develop a second prototype app with artificial intelligence technology to automatically detect the reference scale and make the essential measurements on the image without the user interfering.

    Conclusions

    In this study, we developed a prototype of a mobile app for estimating WC for physicians. Being simple, available, and reproducible, this app has the potential to positively affect the quality of data in future research. Usability and validity evaluation among medical teams will be the next step before its use in clinical trials and multicentric studies.

    Data Availability

    The data that support the findings of this study are available from the corresponding author upon reasonable request.

    Authors' Contributions

    AM and AZ conceived the idea for the app and the document, and contributed to the writing and editing of the manuscript. MBA reviewed and edited the statistical analysis and the results in the manuscript. AB and KF reviewed and evaluated the app. MBA and S Boujelben reviewed the article before submission. All authors read and approved the final manuscript.

    Conflicts of Interest

    None declared.

    1. Ross R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR working group on visceral obesity. Nat Rev Endocrinol. 2020 Mar;16(3):177-189 [CrossRef] [Medline]
    2. Alberti K, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med. 2006 May;23(5):469-480 [CrossRef] [Medline]
    3. Janssen I, Katzmarzyk PT, Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med. 2002 Oct 14;162(18):2074-2079 [CrossRef] [Medline]
    4. Waninge A, Ligthart KAM, Kramer J, Hoeve S, van der Schans CP, Haisma HH. Measuring waist circumference in disabled adults. Res Dev Disabil. May-Jun 2010;31(3):839-847 [CrossRef] [Medline]
    5. Gomez-Perez SL, Haus JM, Sheean P, et al. Measuring abdominal circumference and skeletal muscle from a single cross-sectional computed tomography image: a step-by-step guide for clinicians using National Institutes of Health ImageJ. JPEN J Parenter Enteral Nutr. 2016 Mar;40(3):308-318 [CrossRef] [Medline]
    6. Ciudin A, Salvador R, Budoy A, et al. Measurement of waist circumference for retrospective studies - prospective validation of use of CT images to assess abdominal circumference. Endocrinol Nutr. 2014 Mar;61(3):147-152 [CrossRef] [Medline]
    7. The practical guide identification, evaluation, and treatment of overweight and obesity in adults. National Heart, Lung, and Blood Institute. Oct 2000. URL: www.nhlbi.nih.gov/files/docs/guidelines/prctgd_c.pdf [accessed 2022-01-20]
    8. Waist circumference and waist-hip ratio: report of a WHO expert consultation: Geneva, 8-11 December 2008. World Health Organization. May 16, 2011. URL: www.who.int/publications-detail-redirect/9789241501491 [accessed 2022-01-26]
    9. Wang Y, Beydoun MA, Min J, Xue H, Kaminsky LA, Cheskin LJ. Has the prevalence of overweight, obesity and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol. 2020 Jun 1;49(3):810-823 [CrossRef] [Medline]
    10. Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2010 Dec 21;122(25):2748-2764 [CrossRef] [Medline]
    11. Obesity: preventing and managing the global epidemic: report of a WHO consultation. World Health Organization. 2000. URL: https://apps.who.int/iris/handle/10665/42330 [accessed 2022-01-20]
    12. Garvey WT, Mechanick JI, Brett EM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract. 2016 Jul;22 Suppl 3:1-203 [CrossRef] [Medline]
    13. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014 Jun 24;129(25 Suppl 2):S102-S138 [CrossRef] [Medline]
    14. Verduin WM, Warps AL, van den Helder R, Doodeman HJ, Houdijk APJ, Influences of Fat and Muscle in Colorectal Surgery Collaborative. Visceral fat and anastomotic leakage after colon cancer resection. Dis Colon Rectum. 2021 Feb 1;64(2):163-170 [CrossRef] [Medline]
    15. Pecorelli N, Carrara G, De Cobelli F, et al. Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery. Br J Surg. 2016 Mar;103(4):434-442 [CrossRef] [Medline]
    16. Balentine CJ, Robinson CN, Marshall CR, et al. Waist circumference predicts increased complications in rectal cancer surgery. J Gastrointest Surg. 2010 Nov;14(11):1669-1679 [CrossRef] [Medline]
    17. Doyle SL, Mongan AM, Donohoe CL, et al. Impact of visceral obesity and metabolic syndrome on the postoperative immune, inflammatory, and endocrine response following surgery for esophageal adenocarcinoma. Dis Esophagus. 2017 Jun 1;30(6):1-11 [CrossRef] [Medline]
    18. Shadyab AH, Li W, Eaton CB, LaCroix AZ. General and abdominal obesity as risk factors for late-life mobility limitation after total knee or hip replacement for osteoarthritis among women. Arthritis Care Res (Hoboken). 2018 Jul;70(7):1030-1038 [CrossRef] [Medline]
    19. Chen WZ, Chen XD, Ma LL, et al. Impact of visceral obesity and sarcopenia on short-term outcomes after colorectal cancer surgery. Dig Dis Sci. 2018 Jun;63(6):1620-1630 [CrossRef] [Medline]
    20. Zhao J, Freeman B, Li M. Can mobile phone apps influence people’s health behavior change? An evidence review. J Med Internet Res. 2016 Oct 31;18(11):e287 [CrossRef] [Medline]
    21. Barton AJ. The regulation of mobile health applications. BMC Med. 2012 May 8;10:46 [CrossRef] [Medline]
    22. Liang X, You M, Wen C, et al. Self-administration of complex decongestive therapy facilitated by the mobile application WeChat improves lymphedema and quality of life in breast cancer survivors: an observational study. Ann Transl Med. 2022 Feb;10(3):146 [CrossRef] [Medline]
    23. Ginossar T, Shah SFA, West AJ, et al. Content, usability, and utilization of plain language in breast cancer mobile phone apps: a systematic analysis. JMIR Mhealth Uhealth. 2017 Mar 13;5(3):e20 [CrossRef] [Medline]

    ‎
    CT: computed tomography
    cWC: conventional waist circumference measurement
    mHealth: mobile health
    mWC: mobile app–based waist circumference measurement
    WC: waist circumference

    Edited by Edward Meinert; submitted 19.04.22; peer-reviewed by Mohammed Shahriar Arefin, William A Barletta, Zied Hadrich; final revised version received 27.09.23; accepted 11.10.23; published 13.12.23

    Copyright

    © Abderrahmen Masmoudi, Amine Zouari, Ahmed Bouzid, Kais Fourati, Soulaimen Baklouti, Mohamed Ben Amar, Salah Boujelben. Originally published in JMIRx Med (https://med.jmirx.org), 13.12.2023.

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